Language and Wealth Management

DISSERTATION of the University of St.Gallen,

School of Management, Economics, Law, Social Sciences

and International Affairs to obtain the title of

Doctor of Philosophy in Management

submitted by

Maximilian Schellen

from

Germany

Approved on the application of

Prof. Dr. Karl Frauendorfer

and

Prof. Robert Gutsche, PhD

Dissertation no. 4852

Difo-Druck GmbH, Untersiemau 2019

The University of St.Gallen, School of Management, Economics, Law, Social Sci-ences and International Affairs hereby consents to the printing of the present dis-sertation, without hereby expressing any opinion on the views herein expressed.

St.Gallen, October 23, 2018

The President:

Prof. Dr. Thomas Bieger

Acknowledgements I want to thank Prof. Dr. Karl Frauendorfer for the unique opportunity to write my dissertation under his supervision. After my studies at the University of St.Gallen he encouraged me to undertake the endeavor of a PhD program despite my full-time engagement at a leading financial services firm. Without his trust and contin-ued support, this passion project could never have materialized. I furthermore thank Prof. Robert Gutsche, PhD, for his efforts as co-supervisor of my thesis.

I am grateful to my family for the support over the past years and particularly ap-preciate the sacrifice of my fiancée Manuela, who (almost always) accepted when I spent evenings, weekends, and holidays on my research. I dedicate this book to her.

Zurich, December 2018

Maximilian Schellen

IV

Table of Content Acknowledgements .............................................................................................. III

Table of Content ...................................................................................................IV

List of Figures ..................................................................................................... VII

List of Tables ........................................................................................................IX

Abstract ..................................................................................................................X

Zusammenfassung ...............................................................................................XI

1 Introduction ...................................................................................................... 1

1.1 General introduction ................................................................................ 1

1.2 Research strategy and structure ............................................................. 5

2 Theoretical concept ......................................................................................... 6

2.1 Linguistic relativity .................................................................................... 7

2.1.1 Historical review ................................................................................ 7

2.1.2 Variations of the linguistic relativity hypothesis ............................... 8

2.2 Culture and language ............................................................................ 11

2.2.1 Culture and the cultural dimensions by Hofstede.......................... 12

2.2.2 The complex relationship of culture and language ....................... 13

2.3 Linguistic relativity in economic research ............................................. 16

2.3.1 Application of linguistic relativity for decision theory ..................... 16

2.3.2 Contributions along linguistic characteristics ................................. 19

2.3.3 Challenges for economic research and a solution proposal ......... 21

2.4 Future-time reference as linguistic characteristic ................................. 23

2.4.1 The linguistic background of future-time reference ....................... 23

2.4.2 Review of Keith Chen’s 2013 paper .............................................. 26

2.5 Applications of future-time reference in research ................................. 30

2.5.1 Origins of language variation and economic impact ..................... 32

2.5.2 Saving propensity in households ................................................... 33

2.5.3 Corporate decision-making ............................................................ 34

2.5.4 Future-oriented behavior and public policy ................................... 35

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2.5.5 Intertemporal choice ....................................................................... 37

2.6 Behavioral economics as neighboring field to psychology .................. 40

3 Analytical focus ............................................................................................. 42

3.1 Risk propensity and influencing factors ................................................ 42

3.2 Global private investors on upper wealth bands .................................. 45

3.2.1 (Ultra) High Net-Worth Individuals Globally ................................... 46

3.2.2 Mobility, migration, and the exposure to language environments 48

3.3 Advisors and their impact on decision-makers ..................................... 51

3.3.1 The role of advisors in economic research .................................... 51

3.3.2 Psychological mechanisms in client-advisor relationships ........... 53

4 Development of hypotheses and expectations ............................................ 57

4.1 Design of hypotheses ............................................................................ 57

4.2 Hypothesis 1 – FTR of individual investors .......................................... 58

4.3 Hypothesis 2 – FTR of advisors ............................................................ 60

4.4 Hypothesis 3 – Migration of individual investors .................................. 61

4.5 Expectations on potentially confounding factors .................................. 62

5 Empirical context ........................................................................................... 67

5.1 Data set and background ...................................................................... 67

5.2 Dependent variable ................................................................................ 68

5.3 Main factor variables .............................................................................. 70

5.4 Control variables .................................................................................... 72

6 ANCOVA model ............................................................................................ 78

6.1 Model setup ............................................................................................ 78

6.2 Model validity ......................................................................................... 79

6.3 Overall results ........................................................................................ 81

6.4 Covariates .............................................................................................. 84

6.5 Main factors ............................................................................................ 87

6.6 Two-way interactions – introduction of the significance box ................ 89

6.6.1 FTR * Migration ............................................................................... 90

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6.6.2 FTR * RM FTR ................................................................................ 91

6.6.3 Migration * RM FTR ........................................................................ 92

6.7 Three-way interaction ............................................................................ 93

6.7.1 FTR view ......................................................................................... 96

6.7.2 RM FTR view .................................................................................. 97

6.7.3 Migration view ................................................................................. 98

6.8 The storyboard view .............................................................................. 99

6.9 Robustness checks .............................................................................. 101

6.9.1 ANOVA model without covariates ................................................ 102

6.9.2 ANCOVA model with additional interaction terms ....................... 104

6.9.3 Summary from robustness checks............................................... 108

7 Discussion ................................................................................................... 109

7.1 Hypothesis 1 – FTR of individual investors ........................................ 110

7.2 Hypothesis 2 – FTR of advisors .......................................................... 113

7.3 Hypothesis 3 – Migration of individual investors ................................ 115

7.4 Expectations on potentially confounding factors ................................ 119

8 Application in practice ................................................................................. 125

9 Conclusion and outlook .............................................................................. 129

9.1 Theoretical contribution ....................................................................... 129

9.2 Limitations and outlook ........................................................................ 131

9.3 Conclusion ........................................................................................... 133

References............................................................................................................XI

Appendix ........................................................................................................... XXV

Main factor tables .......................................................................................... XXV

Two-way interaction tables ....................................................................... XXVIII

Three-way interaction tables: FTR * Migration * RM FTR....................... XXXIV

Correlations of covariates and dependent variable ............................... XXXVIII

Robustness check: extended ANCOVA .................................................. XXXIX

Curriculum Vitae ................................................................................................. XL

VII

List of Figures Figure 2.1: Classification of LRH approaches (Wolff & Holmes, 2011) .................. 9

Figure 2.2: Language, culture, and behavior (Mavisakalyan & Weber, 2017) ...... 14

Figure 2.3: Language and contemporary behavior (Galor et al., 2016) ................ 33

Figure 2.4: Hyperbolic time discounting (Thoma & Tytus, 2017) .......................... 39

Figure 2.5: Time discounting with Weibull function (Thoma & Tytus, 2017) ......... 40

Figure 3.1: Top of the Wealth Pyramid 2017 (Shorrocks et al., 2017) .................. 46

Figure 3.2: Identity change triggered by migration (own illustration)..................... 49

Figure 5.1: Overview of variables ........................................................................... 68

Figure 5.2: Overview of MSCI indices (MSCI, 2017) ............................................. 73

Figure 6.1: ANCOVA model setup .......................................................................... 78

Figure 6.2: Groups and covariates in ANCOVA (Miller & Chapman, 2001) ......... 80

Figure 6.3: FTR (main factor) pairwise plot and significance box ......................... 88

Figure 6.4: RM FTR (main factor) pairwise plot and significance box................... 88

Figure 6.5: Migration (main factor) pairwise plot and significance box ................. 89

Figure 6.6: FTR * Migration (interaction) pairwise plot and significance box ........ 91

Figure 6.7: FTR * RM FTR (interaction) pairwise plot and significance box ......... 92

Figure 6.8: Migration * RM FTR (interaction) pairwise plot and significance box . 93

Figure 6.9: Three-way interaction pairwise plots in full view ................................. 94

Figure 6.10: Three-way interaction significance box with full view ........................ 95

Figure 6.11: Three-way interaction pairwise plot with FTR view ........................... 96

Figure 6.12: Three-way interaction significance box with FTR view ..................... 96

Figure 6.13: Three-way interaction pairwise plot with RM FTR view .................... 97

Figure 6.14: Three-way interaction significance box with RM FTR view .............. 97

Figure 6.15: Three-way interaction pairwise plot with Migration view ................... 98

Figure 6.16: Three-way interaction significance box with Migration view ............. 99

Figure 6.17: Storyboard of all factorial significance boxes along main factors ... 100

Figure 6.18: Robustness check storyboard of ANOVA ........................................ 103

List of Figures

VIII

Figure 6.19: Robustness check storyboard of the extended ANCOVA .............. 107

Figure 7.1: Particular case for advice discounting in three-way interaction ........ 118

Figure 8.1: Typology of wealth management clients ............................................ 126

IX

List of Tables Table 2.1: Literature on FTR application in economic context .............................. 31

Table 6.1: Test of between-subjects effects ........................................................... 82

Table 6.2: Parameter estimates including bootstrap .............................................. 83

Table 6.3: ANOVA (robustness check) test of between subject effects .............. 102

Table 6.4: ANCOVA (robustness check) test of between subject effects ........... 105

Table 6.5: ANCOVA (robustness check) parameter estimates ........................... 106

Table 7.1: Summary of ANCOVA model results along hypotheses .................... 109

Appendix 1: Main factor tables (FTR) .................................................................. XXV

Appendix 2: Main factor tables (Migration) .........................................................XXVI

Appendix 3: Main factor tables (RM FTR) .........................................................XXVII

Appendix 4: Two-way interaction tables (FTR * Migration)............................. XXVIII

Appendix 5: Two-way interaction tables (FTR * RM FTR) .................................. XXX

Appendix 6: Two-way interaction tables (Migration * RM FTR) ........................XXXII

Appendix 7: Three-way interaction tables (FTR * Migration * RM FTR ......... XXXIV

Appendix 8: Bootstrapped Pearson's correlations ........................................ XXXVIII

Appendix 9: ANCOVA (robustness check) parameter estimates ................... XXXIX

X

Abstract Recent empirical research suggests a connection between grammatical structures of languages and their speakers’ behavior. Native speakers of languages that re-quire the use of future tense when relating to the future, so called strong future-time reference (FTR) languages, exhibit less propensity to engage in future-ori-ented activities. I apply these findings to the wealth management industry, showing that similar language effects can be found for the risk-taking behavior of wealthy investors. Drawing upon findings in psychology on linguistic relativity and advice-taking, I identify three language environments to impact investment decisions: the investor’s mother tongue, the mother tongue of the financial advisor, and the lan-guage of the investor’s country of domicile. I analyze a unique sample of more than 20,000 investor portfolios provided by one globally operating bank using a factorial 2x2x2 ANCOVA. The factorial design allows to not only understand main effects but also interactions, which I interpret with a novel illustration approach: the signif-icance box. I find significant effects from investor language and advisor language. However, the direction contradicts expectations derived from previous research: studies have shown speakers of strong future-time reference languages to engage less in future-oriented behavior. I, on the other hand, show that these speakers take less risk when investing, and not more, as one may assume. Confirming lan-guage effects in context of financial risk-taking contributes to the broad psychology literature on linguistic relativity and to the growing body of research applying this theory in economics. Based on empirical findings, I furthermore propose a typology of banking clients for practitioners to improve compliance, quality, and effective-ness of investment services.

XI

Zusammenfassung Neuere Studien zeigen eine Verbindung zwischen grammatikalischen Strukturen von Sprachen und dem Verhalten ihrer Sprecher. Dabei zeigen Muttersprachler, die in ihrer Sprache gezwungen sind, die Zukunft im Futur zu beschreiben, weniger Neigung zu zukunftsorientiertem Verhalten. Solche Sprachen werden von Linguis-ten als Sprachen mit starkem Zukunftsbezug beschrieben. Ich wende die Unter-scheidung zwischen starkem und schwachem linguistischen Zukunftsbezug in mei-ner Studie auf die Bankbranche an. Dort kann für wohlhabende Bankkunden die Existenz von Spracheffekten im Risikoverhalten privater Investitionsentscheidun-gen bestätigt werden. Theoretische Grundlage dieser Erkenntnis sind die psycho-logischen Felder der linguistischen Relativität und der Beratung. Im Allgemeinen kommt ein Investor mit Sprache auf drei Ebenen in Kontakt: Seine Muttersprache, die Muttersprache seines Bankberaters und die Sprache seines Domizillandes. Der Datensatz einer global tätigen Bank mit mehr als 20.000 einzelnen Portfoliowerten erlaubt mir die Anwendung einer 2x2x2 faktoriellen ANCOVA. Mithilfe dieser Me-thode kann ich nicht bloss die Haupteffekte der drei Sprachebenen untersuchen, sondern auch die zugehörigen Interaktionen interpretieren. Mit der Significance Box führe ich zu diesem Zweck eine neuartige Darstellungsmethode ein. Die Ein-flüsse von Investorensprache und Beratersprache sind jeweils signifikant für das Risikoverhalten von Investoren. Die Richtung allerdings widerspricht den Erwartun-gen, die man aus früheren Studien ableiten könnte. Wer einen starken Zukunfts-bezug in der Sprache aufweist, nimmt weniger Risiko in seinem Portfolio – gleiches gilt für die Beratersprache. Das widerspricht früheren Erkenntnissen in der Litera-tur, wonach Sprecher solcher Sprachen zu weniger zukunftsorientiertem Verhalten neigen. Meine Erkenntnisse leisten damit sowohl einen Beitrag zur breiten psycho-logischen Literatur im Feld linguistischer Relativität, als auch zu einem neueren und wachsenden Fundus von Studien, die linguistische Relativität in ökonomischen Zusammenhängen anwenden. Darüber hinaus kann die Typologie von Bankkun-den, die ich aus meinen empirischen Erkenntnissen ableite, Praktikern in der Ban-kenbranche dazu dienen, ihre Leistungen gezielter und im Rahmen regulatorischer Vorgaben besser zu erbringen.

1

1 Introduction 1.1 General introduction

Language matters – language matters more than individual investors might as-sume. Scholars in psychology provide experimental evidence that differences in languages may explain differences in color recognition (Winawer et al., 2007) or spatial orientation (Boroditsky, 2011). Linguistic differences in the use of grammat-ical gender my even predict the strength of gender roles and labor market partici-pation of women (Gay, Hicks, Santacreu-Vasut, & Shoham, 2017). More interesting for private investors, however, is the finding that speakers of languages using the future tense are more future-oriented and thus show a higher propensity to save (Chen, 2013). This study is an attempt to broaden the focus of individual future-oriented economic behavior beyond saving propensity. Findings show not only that language characteristics matter for private investors, but they may even serve as indicators for risk-taking in financial decision-making.

Literature on the impact of language on thought and behavior builds on a long tradition across linguistics, psychology, and philosophy (Scholz, Pelletier, & Pullum, 2016). The hypothesis of linguistic relativity, also known as the Sapir-Whorf hypoth-esis dates back to the early 20th century and has since then let to an intense debate on the question whether and how language is related to thought. While in the early days, it was argued that language and thought are the same and that anything that cannot be spoken cannot be thought, the emergence of cognitive sciences led to profound criticism of these extreme claims (Lucy, 1997). Weaker forms of the orig-inal hypothesis gained much momentum since the mid 1990s, producing experi-mental evidence in psychology like the examples stated above. Only more recently, Chen (2013) applied the theoretical concept of linguistic relativity to explain eco-nomic outcomes in large cross-country data sets. Until then, the hypothesis was only tested in experiments and did not make its way into economic research. As a distinguishing characteristic between languages, Chen used the future-time refer-ence of languages: those with a strong reference force their speakers to use the future tense when talking about the future, as in ‘tomorrow it will be cold’. Lan-guages with a weak reference are those that either do not know a future tense like Mandarin speakers or they do not force their speakers to use the future tense, let-ting them use the present tense instead (as in German you may say ‘morgen ist es kalt’). Through this, Chen argues, does the future feel less distant for the weak future-time reference speakers and more distant for those with a strong reference.

Introduction

2

He shows empirically that the weak reference speakers act more future-oriented, i.e. they save more and live healthier. Since his contribution there has been rapid growth in the literature applying linguistic relativity in economic context. Studies on future-time reference largely confirm his findings in applying the concept to different analytical focus areas connected to future-related behavior.

However, to date this literature has not yet attended to the analytical focus of risk behavior in financial investments. Literature in psychology assumes that risk-taking equally qualifies as future-related behavior (Zimbardo & Boyd, 2005), so assump-tions on the propensity of future-related behavior should also hold for risk behavior. In the context of economic literature on future-time reference this link is not yet fully specified. Furthermore, such a language effect may not only be connected to the mother tongue of an individual but more broadly to the language environments he faces. In the tradition of Chen (2013), authors find in the context of corporate deci-sion-making that not only the future-time reference of the company language has an impact but also the linguistic background of the CEO and the connection of the company with countries with other languages (Liang, Marquis, Renneboog, & Sun, 2014). With regards to the theoretical foundation of the linguistic relativity hypothe-sis, the intuitive critique of the concept remains that any findings may simply be related to the underlying culture of individuals and not to language per se. The existing literature deals differently with relationship between culture and language. Only few scholars make a clear statement on this relationship or propose a sound theoretical foundation on their assumptions (Imai, Kanero, & Masuda, 2016; Mavisakalyan & Weber, 2017). Neglecting this basis provides grounds for doubt whether there is truly a language effect, or whether it merely displays a proxy for more deeply rooted cultural traits.

Thus, in this study I propose applying the theory on linguistic relativity and the grammatical characteristic of future-time reference on the novel analytical focus of risk behavior in financial investments. Investment decisions, i.e. choice of assets in an investment portfolio, are not performed in isolation but typically within a banking relationship. Typically, banks not only offer execution services but provide financial advice to support their clients in their financial decision-making. Based on the as-sumption of a language affecting behavior, I not only assume that the language of the investors impacts such joint decision but also the language of the financial ad-visor. These two constitute the major linguistic environments that may both affect portfolio choices. In addition, I assume that private banking clients on the upper

Introduction

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wealth levels are through their financial independence able to freely choose in which country they want to live. For those that live in another country, this means that they face another culture and potentially another language. This is the third language environment that I propose as relevant for the analytical focus of financial risk behavior. Concretely, I address the following three research questions:

1) Does the linguistic future-time reference of individual investors affect their risk propensity, and how does such an investor language effect impact the risk-taking on portfolio level?

2) Is the investment decision affected by the linguistic future-time reference of the relationship manager, and how does such an advisor language effect im-pact the risk-taking of individual investors?

3) Does living abroad influence investors, and if so, what is the impact of such a migration effect for their risk-taking?

In addition, and as a theoretical basis, I address the question how language and culture are related and whether such a relationship allows to assume language effects that are independent from cultural effects on cognition.

I test my hypotheses on a comprehensive single-source data set1 containing more than 20,000 individual portfolios. The data set contains portfolios from more than two dozen countries ranging from Europe, Asia, the Middle East, and Latin America. I apply a three-factor ANCOVA model with the main factors of investor future-time reference, advisor future-time reference, and migration status to the de-pendent variable of 12-months portfolio volatility. The full factorial model allows not only to evaluate main factor effects but also all two- and three-way interactions, while controlling for covariates of market volatilities, cultural dimensions, and indi-cators of individual portfolio structure. To interpret interaction effects, I propose a novel illustration method: the significance box. It illustrates the post-hoc test of pair-wise comparisons in a comprehensive manner and simplifies interpretation.

1 This data set is provided by a large internationally operating bank under strict conditions of

adherence to legal and compliance regulations on data confidentiality. The data set only contains a sub set of the bank’s full client base. This study does not allow do draw any conclusion on the client structure of the bank, risk levels of any specific countries or any client identifying data.

Introduction

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I find that all three language environments impact the risk-taking of private in-vestors. The investor language effect shows that weak future-time reference speak-ers display higher levels of portfolio volatility, and by interpretation, this indicates that these investors show a higher risk propensity. The advisor language effect acts in a similar direction: clients advised by weak future-time reference speakers show higher portfolio volatilities. These two main effects are significant and show the same tendency in a two-way interaction. This provides a clear directional indication on portfolio volatility when both parties have the same future language character-istic. Constellations of mixed future-time reference pairings between client and re-lationship manager lead to more moderate portfolio volatilities. Especially for these constellation, the migration effect comes into play through moderating the overall language effect. In tendency, the investor language effect is stronger than the ad-visor language effect if investors still live in their country of origin. This moderated language effect supports two insights from the psychology literature on advice-tak-ing (Cialdini & Goldstein, 2004; Tost, Gino, & Larrick, 2012): first, clients are more likely to follow advice if they have characteristics that are similar to their advisors’ (two weak reference speakers show higher, two strong reference speakers show lower, and mixed constellations show moderate volatilities). Second, if decision-makers feel more confident and powerful they tend to discount the advice of their advisors (for those in the comfort of living in their home country, their own direc-tional language effect acts in tendency more strongly than that of their advisors).

While finding an overall language effect is in line with previous literature, the direction of the effect contradicts expectations. Psychology typically assumes that risk behavior is negatively related to future orientation (Zimbardo & Boyd, 2005). My findings either show that the holistic assumption that future-time reference im-pacts future-oriented behavior may not hold for the specific analytical focus of in-vestment risk behavior or it may unveil that the direction depends on more charac-teristics of the sample. Concretely, future research may show if my findings also hold beyond the specific scope of private banking clients on the upper echelons of the wealth pyramid.

This study is an attempt to contribute to the growing literature on the linguistic relativity hypothesis in the context of economic outcomes. Finding an overall lan-guage effect supports the claim of this hypothesis in general and confirms the spe-cific claim that the future-time reference of individuals impacts cognition, and thereby, behavior. Proposing a clear argument on the triangular relationship

Introduction

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between language, culture, and cognition contributes to the debate if language can be viewed indecently from culture. Establishing that there are both language effects and cultural effects allows me to empirically test and to theoretically solidify the linguistic relativity hypothesis in general. Furthermore, by introducing multiple lan-guage environments, I present an attempt to broaden the argument for linguistic effects from previous research. Addressing the counter-intuitive findings in this study, further research may be able to analyze further nuances within the effect of languages on future-oriented behavior.

1.2 Research strategy and structure

Before empirically testing my hypothesis, I rely on a three-step approach for my research strategy. First, I introduce the theoretical concept. This contains the body of literature to which I attempt to contribute with my findings. At the same time, this theory provides the perspective or theoretical tool of analysis for my study. Con-cretely, this part introduces the background of the linguistic relativity hypothesis, addresses the controversial relationship of language and culture, and it describes the specific concept of future-time reference. At the same time, I review the related existing body of literature on economic outcomes. This part lays the theoretical foundation of my research strategy and illustrates challenges and gaps in this field. Second, I present the analytical focus, providing background on the phenomenon I intend to analyze. This includes a discussion of risk propensity and other factors of influence, a description of global private investors on upper wealth bands and a review of the client-advisor relationship from economic and psychological perspec-tives. Combining the first two steps on theory leads to the development of hypoth-eses and expectations. In a third step, I provide further details on the empirical context, i.e. the concrete data set on which I test these hypotheses. I then use a three-factor ANCOVA model to show test results and provide a broader discussion along the previously stated hypotheses. Based on that I propose a typology of wealth management clients and discuss how this model may pose implications in practice. Closing remarks then include more details on potential theoretical contri-butions and limitations of this study.

6

2 Theoretical concept The linguistic relativity hypothesis (LRH) in its broadest form assumes that lan-guage is not only a tool of communication, but it stands in direct relationship with thought. More specifically, language is expected to impact cognition and, thereby, also behavior. This basic assumption and the corresponding literature are the core foundation of this study. To answer the three research questions stated in the in-troduction, I propose a theoretical line of argumentation that allows to apply the LRH concept to the specific analytical focus of investment risk behavior in the con-text of private banking. In a first step I illustrate the basic claim of the LRH and discuss how language and culture are two closely related but separate factors to impact cognition. I demonstrate through reviewing prior research that LRH findings from psychology can also be applied to understand economic outcomes. Different language characteristics provide evidence for specific LRH effects in this context. One of the most frequently applied linguistic characteristic is that of future-time ref-erence (FTR), i.e. how a language is grammatically marking future-events. Eco-nomic literature on FTR was initiated through a seminal contribution by Keith Chen in 2013. His finding that FTR is influencing the likelihood for future-oriented behav-ior provided grounds for a growing body of literature on the subject, largely confirm-ing his proposal. Reviewing these contributions shows that, so far, the analytical focus of financial risk behavior as a form of future-related behavior has not yet been addressed. Having established the theoretical foundation, I introduce this analytical focus in more detail and develop a set of hypotheses, which are then empirically tested.

The structure of this line of argumentation is reflected throughout the chapters on the theoretical context. Chapter 2.1 provides the historic background of the LRH, introduces different variations of the LRH and summarizes the scholarly debate on the validity of the theory. Chapter 2.2 describes culture and cultural effects as an important neighbor of language when it comes to influencing cognition and behav-ior. Chapter 2.3 reviews previous literature applying the LRH in the context of eco-nomic outcomes and underlines challenges for economic research. Chapter 2.4 then focuses on FTR as the key linguistic concept for this study and reviews the paper by Chen (2013) in more detail. All literature on FTR following this seminal contribution is reviewed throughout chapter 2.5. This literature review on economic FTR research is to my knowledge the first attempt to illustrate the breadth of this growing body of research. In order to put LRH research in economic context into

Theoretical concept

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perspective with other theories to explain patterns in decision-making, chapter 2.6 briefly shows structural similarities with the literature of behavioral economics.

2.1 Linguistic relativity

2.1.1 Historical review

Today there are 7,097 living languages recorded, which show similarities but also vast differences (Simons & Fennig, 2018). All these languages, their form, the cir-cumstances of their use, and the overall role for human life constitute the field of linguistics (Robins, 2014). The hypothesis of linguistic relativity (LRH), or the Sapir-Whorf hypothesis, dates back to the early 20th century and American linguists Ed-ward Sapir (1884-1339) and Benjamin Lee Whorf (1897-1941) (See Hussein, 2012; Lucy, 2015; Whorf, 1956). The classic view since Aristotle suggested that all hu-mans think alike, and language only translates already-formed thought into com-munication. However, the diversity of languages, especially with regards to gram-mar, gave rise to the idea that language can affect thought by forcing speakers to constantly think about certain aspects – be it the location in time for English speak-ers, social relations in Mandarin, or a constant marking the source of knowledge for speakers of Aymara (See Leavitt, 2014). In a broad form, the linguistic relativity hypothesis (LRH) consists of two claims: on the one hand, the linguistic claim that languages show differences in important aspects. On the other hand, the psycho-logical claim that such differences impact how their speakers perceive and concep-tualize the world around them (Baghramian & Carter, 2017). While Whorf himself did not state this particular hypothesis as such, he suggested that “the world is presented in a kaleidoscope flux of impressions which has to be organized by our minds – and this means largely by the linguistic systems of our minds” (Whorf, 1956: p. 213).

Starting in the 1950’s and lasting until the 1990’s, the linguistic relativity hypoth-esis grew unpopular due to the rise of cognitive sciences and for reasons that were either empirical or conceptually-theoretical. Mavisakalyan and Weber (2017) sum-marize that the psychological claim of the LRH was disputed because it either lacked empirical foundation or empirical studies were based on poor experimental design. Also, the LRH remained vague without a standardized and precise formu-lation (Pinker, 2003). Since the mid 1990’s, however, scholars have been address-ing these weaknesses, which gave rise to a revival of the theory, lasting to the

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present day (e.g. Boroditsky, Schmidt, & Phillips, 2003; Casasanto, 2016; Lucy, 2015).

2.1.2 Variations of the linguistic relativity hypothesis

To capture the broad spectrum of theorization regarding the LRH, Scholz et al. (2016) classify different approaches along two dimensions. They distinguish spe-cific and general views, as well as weak and strong ones. Specific versions of the LRH present claims that certain linguistic features impact certain cognitive features. Such a specific version would be for example Roberson and Hanley (2010) pro-posing that the recognition of colors differs for speakers of different languages, de-pending on their languages’ definition of colors. In contrast, general versions of the LRH claim that select linguistic features impact select aspects of thought, without making precise statements on which ones. Falsifying specific claims does not au-tomatically falsify the general claim. However, confirming a specific claim automat-ically conforms the general claim. Weak versions of the LRH state that languages have influence on thought in ways that are systematic and non-trivial, leading to interesting and regular patterns. Strong versions, on the other hand, are determin-istic in nature: thought is impossible without linguistic structures and thoughts are bound by language (Mavisakalyan & Weber, 2017). Frank, Everett, Fedorenko, and Gibson (2008) show for example that a tribe in the Amazon region that does not know any linguistic expression for exact numbers is not able to distinguish between exact quantities. Overall, critics of the LRH tend to dismiss weak claims on the account of being banal and strong claims as rather implausible. The latter deter-ministic approaches imply that languages limit our space of thought, making some thoughts inaccessible. Hence, only learning new languages would hold the key to these areas. This strong notion is challenged not only by linguists but also by phi-losophers, so Scholz et al. (2016) propose a defensible compromise across disci-plines: “language could affect certain aspects of our cognitive functioning without making certain thoughts unthinkable for us” (p. 44).

Differentiating only between strong and weak versions of the LRH over-simplifies the broad variety of approaches that scholars represent today. Instead, Wolff and Holmes (2011) summarize the field of linguistic relativity in a classification of

Theoretical concept

9

assumptions (see Figure 2.1).2 Language as a language of thought is the extreme assumption that humans think in their language and that they are thereby com-pletely restricted by their language. Early scholars like Müller (1887) or Watson (1913) proposed this more extreme position in the philosophical tradition of Plato and Kant. However, it remains theoretical and not realistic as, for example, babies and non-human primates can think even without the capability of language.

Figure 2.1: Classification of LRH approaches (Wolff & Holmes, 2011)

Linguistic determinism separates language from a system of conceptualization (thought), but still suggests that language directly influences thought. This hypoth-esis describes early Whorfianism. In this view, language as determining factor is that strong that it may even recalibrate capabilities of perception and conception (Bowerman & Levinson, 2001). Contemporary scholars reject this tight relationship between language and thought, as cognitive science studies do not support such a claim. Between language, thought, and reality, they find the strongest connection

2 The following descriptions and examples follow the line of thought presented by Wolff and

Homes (2011).

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between thought and reality, and not, as proposed by linguistic determinism, be-tween language and thought (e.g. Smith, Colunga, & Yoshida, 2003).

Thinking before speaking structurally differentiates between the thought we per-form when preparing to speak and the thought that remains in our head. Experi-ments show that speakers of different languages show the same eye movements when simply watching a motion picture and different eye movement based on their languages when they were asked to verbally refer to the film afterwards (Gennari, Sloman, Malt, & Fitch, 2002; Papafragou, Hulbert, & Trueswell, 2008). Differences in pre-speech thinking are also expected with regards to the specification of tense: English speakers need to think about precise timing of events before speaking, whereas Mandarin speakers do not because their language does not know gram-matical tense (Boroditsky, 2001).

Thinking with language as a class of approaches suggests that linguistic and non-linguistic processes take place at the same time, and not consecutively. Within that proposal, language as meddler represents the understanding that language offers standard categories that help the thinking process. When faced with a cog-nitive task, speakers who can apply more profound categories from their languages are hence able to react faster and more accurately. In the case of color studies, speakers of languages with more color definitions are faster in seeing color differ-ences than speakers of languages with less color definitions. More concretely, Rus-sian defines separate names for light and dark blue in contrast to English, which only knows blue as color, suggesting this effect (Winawer et al., 2007). Language as augmenter as another subcategory sees language as an additional toolbox to the conceptual thought process, combining linguistic and nonlinguistic representa-tion for more powerful results. While in the previous category language interfered randomly, this category defines language as improving thought. One example is the definition of exact numbers, which some (tribal) languages do not know. In line with that argument, evidence shows that speakers of such languages fail to repro-duce exact quantities in experiments (Gordon, 2010).

Thinking after language as a separate class of approaches is not focused on an individual act but on the more long-term influence of language. This means that speaking a language leaves an imprint on cognition, even in nonlinguistic context. Language as spotlight describes how grammatical features, like the use of gram-matical gender or the use of tenses, makes speakers and listeners constantly aware about these differences. Boroditsky et al. (2003) for example show that

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languages differentiating between grammatical gender also show subtle differ-ences in the semantics of nouns. The effect was even stable for speakers of such languages if the experiments were conducted in a foreign language not differenti-ating between grammatical genders. Wolff and Holmes (2011) finally suggest lan-guage as inducer with language influencing the thought process when language is not involved in a given situation. They base this most abstract class on their own research of simulating motion in static scenes (See Holmes & Wolff, 2010). How-ever, the experimental design in their study is not primarily focused on the role of language, and the theoretical distinction between the latter two classes is less strong than for the others. Hence, it is more stringent to reduce the classification for thinking after language by one node. A similar argument could be brought for the classes below thinking with language, although their distinction, remains more solid.

In summary, the linguistic relativity hypothesis comes in different gravity levels across the psychology literature. The different claims differ in their assumptions on how language and thought are related. The original and most criticized hypothesis is that language equals thought or at least sets strict boundaries on what is thinka-ble and what is not. On the opposite side of the spectrum, scholars argue that lan-guage shapes thought long-term, and even if language is not involved in activities. Simply put, speaking a language shapes the mindset. On the middle of the spec-trum, we find those approaches that understand language more as a structural tool or magnifying glass for cognition. Given that these claims appear as the least fun-damental and restrict their assumptions on select aspects, they remain the least contested across the literature.

2.2 Culture and language

When investigating the impact of language, the question rises whether language is not simply an expression of culture, or at least just an incremental part of culture. Such a pre-conception on the relationship between culture and language is a chal-lenge that scholars need to address when assuming linguistic relativity. However, addressing this relationship is more complex than it appears on first sight. First, defining culture is less obvious than defining language. Depending on the research domain, there is a broad variety of concepts, only some of which are applicable in economic research. The most common one that does allow this feature is the framework of cultural dimensions by Geert Hofstede, which is discussed in more

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detail in chapter 2.2.1 and later in chapter 5.4. Having assumed a definition of cul-ture allows to discuss the triangle between culture, language, and cognition. If and how these three components are related characterizes different standpoints that either provide the necessary foundation for the linguistic relativity hypothesis or provides grounds for critique.

2.2.1 Culture and the cultural dimensions by Hofstede

While the definition of language is rather intuitive, the meaning of culture has been subject to an intense debate among scholars in anthropology and psychology. Some define cultures through external artifacts, others focus on internal psycho-logical aspects (Prinz, 2016). For this study, I follow an internal psychological view. This allows to theoretically connect culture with concepts of language and thought. Guiso, Sapienza, and Zingales (2006), as representatives of such approach, define culture broadly as those beliefs and values that are socially transmitted from gen-eration to generation within groups. The goal of definitions like that is to define culture on a more theoretical level in categories that remain almost philosophical in nature. That is, to distinguish between culture and non-culture. While scholars in psychology or anthropology oftentimes define culture for an individual or a social group, their definitions do not provide the opportunity to analyze intercultural differ-ences on a broader scale. Geert Hofstede, with the goal to understand differences, defined culture as “the collective programming of the mind which distinguishes the members of one human group from another” (Hofstede, 1980: p. 25). Based on more than 100,000 survey data points across employees of IBM globally he pro-posed a framework with cultural dimensions, which has been further refined over the past 40 years (Hofstede, Hofstede, & Minkov, 2010). In the most recent devel-opment of the framework Hofstede (2011: p. 8) describes the following six dimen-sions to characterize cultures and their differences:

1. Power Distance, related to the different solutions to the basic problem of human inequality;

2. Uncertainty Avoidance, related to the level of stress in a society in the face of an unknown future;

3. Individualism versus Collectivism, related to the integration of individuals into primary groups;

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4. Masculinity versus Femininity, related to the division of emotional roles be-tween women and men;

5. Long-term versus Short-term Orientation, related to the choice of focus for people’s efforts: the future or the present and past;

6. Indulgence versus Restraint, related to the gratification versus control of basic human desires related to enjoying life.

Kirkman, Lowe, and Gibson (2006) show in a comprehensive review that the Hofstede approach is the most commonly used in empirical studies across psychol-ogy and business research. Over the past decades, the approach has proven to be helpful for both practitioners and researchers. When reviewing contributions that apply the LRH across the following chapters, I note how the scholars refer to culture in general, and whether and how they used the Hofstede dimensions for their stud-ies. These observations are in line with findings by Kirkman and colleagues: across 180 articles and chapters applying the Hofstede dimensions, they find that not all scholars make use of all six dimensions but tend to isolate the individualism dimen-sion in their studies. As for the studies discussed in later chapters, scholars tend to pick those cultural dimensions that they hypothize to have an impact for their model. As echoed by Hofstede (2011), these studies do not capture the full scope of culture and may miss important insights. Given the complex relationship between culture and language, not controlling for a broad concept of culture creates challenges. Both are discussed over the course of the following chapters.

2.2.2 The complex relationship of culture and language

Language and culture have a complex relationship, especially in the context of the linguistic relatively hypothesis. While scholars like Levinson (2003) see language more as a result of cultural tradition, other scholars suspect a more complex inter-action with more independence between the two (Galor, Özak, & Sarid, 2016; Tabellini, 2008). Mavisakalyan and Weber (2017) show these two approaches in in a simplified manner in Figure 2.2. Part (a), the simple picture, refers to language as manifestation of culture. In this view language does not impact cognition or even behavior. Part (b), the complex picture, draws a more interactive relationship. Cul-ture still affects language, but it recognizes that, in a dynamic view, language can conversely also shape culture. Galor et al. (2016) argue for example that once cul-tural aspects manifest in language, they remain more rigid and less vulnerable to

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changes in culture. This implies that, from a non-static view point, language and culture develop more independently and with their own characteristic dynamics. With this assumption made, they assume that both, culture and language, impact cognition and, ultimately, behavior. The linguistic relativity hypothesis fits into this picture by theorizing on the specific relationship between language and cognition. For both cases, the assumption holds that any influence of culture or language are mediated through the individual’s cognitive system (Mavisakalyan & Weber, 2017).

Figure 2.2: Language, culture, and behavior (Mavisakalyan & Weber, 2017)

The exact relationship between culture, language, and thought is a core topic to understand human cognition (Imai & Masuda, 2013). However, across different dis-ciplines there are discrepancies in defining these concepts and their roles. Scholars attending to the linguistic relativity hypothesis, mostly in the tradition of cognitive psychology, tend to neglect the consideration that specific linguistic characteristics are rooted in cultural systems, or how cognition through language interacts with cognition through cultural styles of thinking (Imai et al., 2016). In their understand-ing, culture as knowledge collection is mostly what differentiates human cognition from animal cognition (e.g. Tomasello, 2001). Scholars in cultural psychology, on the other hand, take the opposite approach, disregarding language and its potential role for cognition (e.g. Oishi, 2014). To them, language is a means of transport for culture. Communication between these conflicting disciplines remains scarce (Imai et al., 2016). However, some studies do combine both approaches and deliberately include aspects of language as well as aspects of culture. Comparing test subjects speaking German, Mandarin, and Japanese allows to see differences: while Man-darin speakers and Japanese speakers share certain cultural traits related to thought process, their language differs. In contrast to German speakers, both

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language and culture are different. Applying this setup, studies on conceptual rela-tions to organize objects (Saalbach & Imai, 2012) and verb learning in children (Imai et al., 2008) suggest there are cognitive biases that are culture specific. However, linguistic traits can take precedence and dominate cultural influences (Imai et al., 2016).

Research on the interlink between language and culture shows that the two can work together to shape human cognition. From an evolutionary perspective, values and behavior that are specific to cultures stem from adaptation to the respective environments, for example agricultural characteristics (Talhelm et al., 2014). The development of language, and with that linguistic characteristics, is shaped by the culture of their speakers (Tamariz & Kirby, 2016). In the opposite direction, lan-guage may reinforce cultural traits and biases for cognition (Senzaki, Masuda, & Ishii, 2014). This line of argumentation is confirmed by Galor et al. (2016). They show empirically that the co-evolution of culture and language are rooted in geo-graphical origins: pre-industrial characteristics in geography triggered the develop-ment of cultural traits, which then manifested in linguistic characteristics. They show this in particular for linguistic structures of future-time reference, use of grammatical gender, and grammatical politeness distinctions. Arguing that language served as means to enforce cultural traits, they find that relevant linguistic characteristics re-mained so persistent over time that they survived advancements in technology and social development, leading to cultural changes. This behavior constitutes a rela-tionship between culture and language that is historically dependent, but rather in-dependent for contemporary analysis3.

In recent years Farzad Sharifian pioneered the development of cultural linguis-tics, a sub-discipline with a distinct focus on the relationship between culture, lan-guage and thought (Sharifian, 2014). Similar to the scholars described before, who advocate a complex relationship between culture and language, Sharifian (2017a) rejects the simplistic view that language is a direct and complete proxy for culture. Instead, languages may serve as archives of cultural conceptualizations that are not necessarily active anymore. In contrast to historic views of the linguistic relativ-ity hypothesis, cultural linguistics do not assume that cultural traits or cultural cog-nition are homogeneously distributed among speakers of the same language (Sharifian, 2017b). This more dynamic view on culture, language, and their

3 For a detailed discussion of analysis by Galor et al. (2016) on future-time reference histori-

cally and contemporarily, see chapter 2.4.1.

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interaction can help interpreting differences in cognition and behavior within lan-guage communities.

In summary, concepts of culture and language and how they interact with cogni-tion are applied differently across the literature. While some argue that one entails or represents the other, scholars from different disciplines provide evidence that they both influence human cognition. The historic perspective provides a stringent line of argumentation that scholars share in application of the LRH: geography and thereby macroeconomic factors shaped cultures in pre-industrial times. Languages adjusted to these cultures. However, while cultures developed fast since the indus-trialization, languages evolved considerably slower than their culture counterparts. Today, this co-evolution results in the situation that languages are historically re-lated to cultures, but they remain to some extend independent from culture. This partial independence is the foundation of linguistic relativity.

2.3 Linguistic relativity in economic research

For decades, the linguistic relativity hypothesis has been an intensely discussed topic in psychology and many of the respective sub-disciplines. Only more recently, culture and also language as determinants of economic outcomes gained momen-tum (Galor et al., 2016; Guiso et al., 2006). To establish a connection between language characteristics and economic outcomes, economic scholars refer to eco-nomic theories, especially decision theory. They motivate their studies by showing that language characteristics impact different components of decision-making. Concretely, scholars find that economic outcomes may be affected by the use of pronouns, grammatical gender, mood, and tense. However, including the linguistic relativity hypothesis from psychology also means that scholars inherited a crucial challenge: addressing the complex relationship between culture, language, and cognition.

2.3.1 Application of linguistic relativity for decision theory

Based on the discussions in linguistics and psychology, economic researchers fol-low the claim that linguistic differences (influenced by cultural and social traits) can impact decision-making and behavior. However, economic studies show differ-ences in both, design and analytical focus. While studies in psychology mostly rely on smaller samples in experimental designs, economic studies typically use larger

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sample sizes, investigating the impact between linguistic characteristics and eco-nomic or social outcomes (Mavisakalyan & Weber, 2017). Without the notion of linguistic relativity, language in economic research is mostly viewed as a positive means of communication (Lazear, 1999). In this regard, language is found to be a positive skill increasing human capital (See for example Chiswick & Miller, 2015; Di Paolo & Tansel, 2015). On an aggregate level, language can be a positive factor for bilateral trading (Fidrmuc & Fidrmuc, 2016). Hence, the focus of studies in eco-nomics pre-LRH is mostly not the differences among languages but advantages of possessing specific language skills.

Mavisakalyan and Weber (2017) provide an overview of economic studies that rely on the foundations in psychology and linguistics to investigate the impact of linguistic features on economic outcomes4. For that, they put the studies into per-spective with basic decision theory: “an agent chooses among a set of actions which have various outcomes depending on different states of the world; the agent has varying credence in different states, represented by a probability function, and desires different outcomes to varying extent, measured by a utility function” (p. 5). Linguistic characteristics in this context may therefore impact decision-making in either of four ways: (1) shaping the probability function, (2) shaping the utility func-tion, (3) impacting the grain in which actions, states, and outcomes partition the possibility space, or (4) impacting the salience of actions, states, and outcomes. All four of these possible ways of how language may impact the decision-making pro-cess from economic point of view are discussed below, putting them into perspec-tive to findings from chapter 2.1 on the linguistic relativity hypothesis in general.

To affect the probability function, languages would need to impact how and whether information is available to its speakers (Tversky & Kahneman, 1974). One example is how languages specify special terms: some languages only know ab-solute terms like east and west, and no relative descriptions like left and right (Majid, Bowerman, Kita, Haun, & Levinson, 2004). As a result, language can trigger what information is more readily available than others. This implies that, language can conversely limit cognition. Recalling the previous sections, this claim remains disputed. While some studies might find isolated effects that support this claim, using this route to explain broader economic effects does not appear promising. In a similar direction but less absolute is the idea that linguistic characteristics may impact the grain for agents to partition the possibility space. This angle does not

4 The following description summarizes the review of Mavisakalyan and Weber (2017).

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suggest that some information is available and other is not. Instead, an impact would stem from the ability of a speaker to classify based on linguistic characteris-tics. Bradley (2017) suggests in his book on decision theory that not all agents partition the world in a similar fashion. The more an agent can draw from a broad range of conceptualizations, the more detailed he is able to distinguish between different actions, states, and outcomes. A radical example would be the already discussed contribution of Frank et al. (2008) that not all languages know the con-cept of precise numbers and quantities. Speakers of such languages would parti-tion their decision space differently from others. Findings by Roberson and Hanley (2010) on color distinctions or those on viewing spatial differences (Majid et al., 2004) would be appropriate examples. Recalling the classification of approaches by Wolff and Holmes (2011) from chapter 2.1, we can classify such a partitioning effect as “language as meddler” or “language as augmenter” perspectives. For both it is assumed that thought and language take place at the same time, and language supports thought as a toolbox of conceptualizations. From an economic research perspective, this would be applied for the decision-making process.

The claim that language characteristics may influence the preferences and hence the utility function of agents appears intuitive and also finds application in economic research. Most prominently for this study, Chen (2013) suggests that the use of future-tense in a language triggers how their speakers discount future re-wards for present investments. Similarly, Mavisakalyan (2015) finds that grammat-ical gender in languages influences the preferences in employment: in languages with gender distinctions men are more likely to be hired than women. In another study, Kovacic, Costantini, and Bernhofer (2016) suggest that subjunctive mood in languages impacts preferences of risk-taking in investment decisions. Following the classification of Wolff and Holmes (2011), such a preference effect corresponds to the view of “language as inducer”, where even without using language in a given situation, linguistic characteristics prime certain conceptualizations manifested in a set of preferences.

Another potential way of how language may impact the economic decision-mak-ing process is through salience of certain actions, states, or outcomes. In contrast to the partition effect discussed before, a salience effect might occur if speakers of multiple languages partition the possibility space in a similar manner, but one lan-guage highlights certain elements more than others. Languages differentiating be-tween different grammatical genders (Gay, Hicks, Santacreu-Vasut, & Shoham,

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2015) may make speakers of such languages more aware of differences, poten-tially even leading to more segregated roles in society. Languages with grammati-cal tense may drive speakers to think differently about time because they are con-stantly forced to put events into perspective while other languages do not require such thought (Boroditsky, 2001). A salience effect corresponds to the classificatory group viewing “language as spotlight” (Wolff & Holmes, 2011). This effect can also be isolated from an availability effect (to the probability function) or a preference effect (to the utility function). Agents that are not perfectly rational may not only base their decision-making on what information is available or what outcomes they prefer but based on what information is most prominent to them.

2.3.2 Contributions along linguistic characteristics

A growing body of economic research is attending to linguistic characteristics in analysis of economic or social outcomes. More concretely, these studies typically focus on either of the following features: the use of personal pronouns, grammatical gender, grammatical mood, or the use of tense. Major contributions in these fields are discussed here (see Mavisakalyan and Weber (2017) for further review).

Languages differ in their use of pronouns. A first concrete characteristic is the ability of a language to drop personal pronouns. Languages like English require their speakers to use personal pronouns as in I am dancing. Other languages like Spanish do not require the use of the personal pronoun, making estoy cantando equally correct as yo estoy cantando (see Dryer, 2013). A second characteristic is whether a language knows more than one pronoun for the singular second-person. English, as an example, only knows you to address another personal directly, whereas German knows a distinction between du and sie. The former is the infor-mal address, whereas the latter is the polite form (Helmbrecht, 2013). Studies by Kashima and Kashima (1998), and Davis and Abdurazokzoda (2016) analyze the impact of both characteristics on social outcomes. They suggest that languages allowing a drop of pronouns hint at less individualistic cultures. A similar effect of politeness forms is less strong, but sill suggesting that languages with polite dis-tinctions hint to cultures with larger power distance. In comparison to other lan-guage features, the use of pronouns is mostly analyzed for its correlation to culture. Davis and Williamson (2016), however, make the connection from pronoun drop to individualism in culture which, in turn, is suggested to impact the amount of regu-lation and policy-making in a country.

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Languages also differ in their use of grammatical gender: while some languages like Finish distinguish no gender whatsoever, other languages distinguish between two (French), three (German), or even more than five (Corbett, 2013). Among other gender characteristics in languages, the number of genders in a language can sug-gest economic and social outcomes. Fundamentally, such impact is found for the correlation with women’s rights (Amy, Sarah, Lindsey, & Zsombor, 2017), for fe-male participation in the labor market, hours and weeks worked, and for attitudes qualifying as gender-discriminatory (Gay et al., 2017; Mavisakalyan, 2015). Once in the workforce, Santacreu-Vasut, Shenkar, and Shoham (2014) find an impact of grammatical gender marking on ratios of women in senior management and boards. Others find an impact on staffing in multinational companies and the likeli-hood of cross-country mergers and acquisitions (Bazel-Shoham, Lee, Rivera, & Shoham, 2017). In private life, Hicks, Santacreu-Vasut, and Shoham (2015) find an impact on the division in household labor. Mavisakalyan and Weber (2017) review further studies in this particular area, including impacts on health, attitudes, wage gaps, and quotas. In summary, we see a growing body of literature investigating the use of gender in languages and gender roles in society. Economic analyses and contributions in political and social sciences go hand in hand for this linguistic characteristic, which is less the case for other linguistic markers.

Languages differ in their way to distinguish between fact and potential, probable, unrealistic, and future states of the world. The linguistic concept of mood in this context goes beyond a simplistic difference between realis and irrealis (Quer, 2009). In a first effort to shed light on the different ways languages deal with situa-tions of uncertainty, Rothstein and Thieroff (2010) provide an overview of issue for European Languages. Based on this contribution, Kovacic et al. (2016) further de-velop the mood classification of languages to show that the more a language makes use of non-indicative (factual) moods the higher the risk aversion of its speakers. In contrast to use of gender and pronouns, there is to date no comprehensive cross-linguistic overview of how languages deal with uncertainty. Hence, Kovacic et al. (2016) define six specific context situations and count in how many of these a lan-guage does not use the indicative but other moods like imperative, the conditional, the subjunctive, or the conjunctive. This more experimental approach shows prom-ising results to understand the relationship between language and risk propensity. To my knowledge, this study is to date the only one applying the feature of mood in an economic context.

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Another linguistic feature receiving increasing attention in economic research is a language’s use of tense. Tense is a grammatical feature of languages, marking a location in time. Aspect, on the other hand, is “grammaticalisation of expression of internal temporal constituency” (Comrie, 1985). Languages differ widely in the application of these linguistic concepts (See Dahl & Velupillai, 2013a). While there are concepts of tense to describe past, present, and future, economic analyses in the context of linguistic relativity focus on the future tense. In some languages (like French) the use of the future tense is obligatory when referring to the future. Other languages are less strict in this requirement (like German), where especially the spoken language allows to use the present tense instead of the future tense. A third group of languages (like Mandarin) do not even know a distinction of tense, and all markings are performed through adverbs like “tomorrow” (See Dahl, 2000; Thieroff, 2000). In his seminal contribution Chen (2013) presents the linguistic concept of future-time reference (FTR) as a classificatory framework to distinguish future tense usage for inter-linguistic research. FTR is the focal linguistic characteristic of this study. As such, chapter 2.4 provides an in-depth introduction on the back-ground and concept, which is beyond the brief description of other linguistic mark-ers in this overview chapter. In the subsequent chapter 2.5 I provide a detailed review of economic studies that emerged since Chen’s 2013 contribution.

The application of the linguistic relativity hypothesis in economic research overall is a novel trend for analysis on the level of individuals, of firms and in cross-country comparison. This growing body of literature is mostly attending to the characteris-tics of tense and gender, finding evidence for language impact on future-related behavior and manifestation of gender roles, respectively. In the economic context, the novel and early approach to link grammatical mood to economic decision-mak-ing appears promising, but it requires still considerable ground work to further this angle – from the linguistics angle and from applications in economics.

2.3.3 Challenges for economic research and a solution proposal

Applying the linguistic relativity hypothesis in economic research, especially in cross-country studies, requires attention to a set of challenges. As briefly hinted to in the previous section, any large-scale analysis can only be based on high quality data inputs on different linguistic features. The World Atlas of Language Structures (WALS) as a comprehensive collection of language features on a global basis re-mains a key source of classification. However, as the editors of this continuously

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evolving data base note in their introduction, the collection of language character-istics is far from complete (Comrie, Dryer, Gil, & Haspelmath, 2013). For each char-acteristic, the maps of differences include multiple hundred languages. This covers those that are most common, but it still does not provide a complete picture in light of some 7,000 languages in the world. As most economic literature focuses on large populations, the WALS remains a solid source across languages. Nonetheless, there are also restrictions with regards to the covered characteristics. As discussed in the previous chapter on the example of mood (Kovacic et al., 2016), some more complex features are yet to be comprehensively included. Having one competent source of linguistic differences like the WALS supports the growing economic liter-ature on the subject, without forcing scholars to collect their own linguistic data.

The biggest challenge for economic research in this field remains the complex relationship between culture and language. As already introduced in chapter 2.2, it can be assumed that culture and language are neither completely independent nor exactly the same. Galor et al. (2016) for example show with the historic co-evolution of culture and language that there are common factors of influence in the past, but that, from there onwards, cultures as value and belief systems developed faster and more diversely than language structures. Still, some studies assume that lan-guage serves to some extent as proxy for culture. In their critique of Chen (2013), Roberts, Winters, and Chen (2015) claim that inter-linguistic relatedness needs to be controlled for in cross-country models. They argue that languages are concep-tually very close to culture. Over time, traits in one culture may be dependent of other cultures because they affected one another through geographical closeness or historic developments like migration or colonial influence. This issue of cultures being the result of common descent or borrowing of certain traits is historically called ‘Galton’s Problem’ (Dean Keith, 1975). When comparing cultures, controls for their similarity are required to address potential issues of autocorrelation (Roberts & Winters, 2013). Translated into the situation of cross-linguistic studies, Roberts et al. (2015) suggest to include controls for language family, linguistic dis-tance, and country effects. As a result, these technical factors on language relat-edness would account for the (historic) similarities of underlying cultures. Again, this shows the extent to which these scholars assume that language is a strong proxy for culture. If we follow a more complex understanding on the relationship between culture and language, and we accept the criticism that the robustness of results can be better achieved when controlling for cultural similarities, why not do exactly that? Instead of controlling for linguistic relatedness, I propose to control

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directly for similarities in culture. Applying for example the six-dimensions frame-work by Hofstede et al. (2010) allows to describe cultures in some granularity. In contrast, only controlling for a similar linguistic ancestor ignores subtle differences in how cultures and languages have co-evolved historically. In a globalized world with fast means of communication and transportation, tracing cultural dependen-cies with historical factors alone may underestimate relationships between cultures that a more granular dimensional model of culture can capture. All in all, applying a concept from psychology in economic research can provide fascinating insights, but with that, economic scholars also inherit some challenges from the original field. Not addressing the triangular relationship between culture, language, and cognition provides grounds for profound criticism undermining the validity of their findings.

2.4 Future-time reference as linguistic characteristic

One way or another, humans always deal with the concept of time. This means not necessarily the concrete concept of a clock or calendars, but more broadly the dis-tinction of what is now in this very moment, what was before and what will be after. Speaking about these three abstract points in time is supported by the linguistic feature of tense. This concept allows to distinguish between the past, the present, and the future. It even allows to bring more granularity on the time frame and put specific points in time into perspective of one another (Comrie, 1985). The linguistic concept of future-time reference allows to make distinctions on how different lan-guages refer to the future. Keith Chen introduced this particular characteristic of languages for the application in economics. This seminal contribution on savings and future-related behavior in general initiated further efforts to understand the im-pact of FTR on a broad range of levels like corporate behavior, policy preferences, and overall intertemporal choice. This chapter introduces the linguistic characteris-tics of FTR in general and the original paper by Keith Chen. Chapter 2.5 then builds on these insights to provide a more detailed review on the existing literature in this novel field of research.

2.4.1 The linguistic background of future-time reference

To understand the differences of how languages deal with the future, their future-time reference (FTR), is one particular focus topic in linguistics. The future, in con-trast to the past, can never be factual. In practical terms, we do not have a memory

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of the future. Statements about the future can be seen as intentions, predictions, or formulation of schedules. Intentions for the future are statements where the speaker or the active subject is in control of their respective actions, for example ‘I will take the train’. Predictions, on the other hand, are statements about the future where the speaker has no control on the outcome, for example ‘It will be cloudy’. The case of schedules is somewhere in between the two because it is man-made but still not under the control of an individual. The example ‘The train departs at 4pm’ also shows that this instance of the future is not marked with a future tense in English, which is common in most languages. For the cases of intention and pre-diction the future-time reference may be different, also across languages (Dahl, 2000).

Not all languages provide a distinct marking for future events. For example, lan-guages like Mandarin or Finnish show “no systematic marking of future-time refer-ence” (Dahl, 2000: p. 325). Other languages grammaticalize FTR either with an inflection or in periphrasis (Dahl & Velupillai, 2013b). To illustrate the difference between the two, take French and English as examples and statements on the temperature today and tomorrow (Dahl & Velupillai, 2013a):

Il fait froid aujourd’hui. Il fera froid demain.

(it do.pres.3sg cold today) (it do.fut.3sg cold tomorrow)

‘It is cold today.’ ‘It will be cold tomorrow.’

French uses an inflectional marker, meaning that there is a separate verb form for the future tense. English uses a periphrastic marker, meaning that the auxiliary ‘will’ is combined with the verb’s infinitive. Finnish or Mandarin speakers would not gram-matically mark their FTR, but only use adverbs like ‘today’ and ‘tomorrow’ to illus-trate the difference in time.

So far, we have seen that there are different situations when to refer to the future and that some language mark their FTR grammatically, while others do not. How-ever, for those that are able to mark their FTR, some always require their speakers to use that marker, while others leave the use more optional. It is a tendency that inflectional markers are obligatory for a speaker of a language, whereas periphras-tic markers may or may not be obligatory. If a language knows both, then the latter is more frequent in spoken language and the inflection is more frequent in written

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language (Bybee, Perkins, & Pagliuca, 1994). As classification between languages and their FTR, we find a three-step approach in the literature. First, in his analysis of European languages Dahl (2000) describes a futureless area where “the obliga-tory use in (main clause) prediction-based contexts as a main criterion for gram-maticalization” (p. 325) is not fulfilled. Hence, all other languages that require FTR marking in his scope are non-futureless. In a second step, Thieroff (2000) adds a note of precision to the same scope, when he argues that ‘futureless’ is too absolute as a term because most of these languages still know some devices to mark their FTR, even if their use is optional. He therefore proposes to refer to them as weakly grammaicalized (p.289) instead. In a third step, Chen (2013) follows Thieroff’s ter-minology, adjusting it slightly to call these languages weak FTR languages. In con-sequence, he calls all non-weak FTR languages strong FTR languages (p. 691). The following example from Dahl and Velupillai (2013a) illustrates the difference:

(1) French (2) French

Il fait froid aujourd’hui. Il fera froid demain.

it do.pres.3sg cold today it do.fut.3sg cold tomorrow

‘It is cold today.’ ‘It will be cold tomorrow.’

(3) Finnish (4) Finnish

Tänään on kylmää. Huomenna on kylmää.

today is cold.part tomorrow is cold.part

‘It is cold today.’ ‘It will be cold tomorrow.’

Regarding the languages in scope, Chen (2013) expands also to languages that were not covered by Dahl and Thieroff while applying the same criteria. To find data points for these additional languages in situations of non-intentional predictions, he collects online weather forecast texts and classifies them accordingly. For further robustness, results are cross-checked with other cross-linguistic and language-specific linguistic studies. In application of this broadened dataset, Chen proposes the linguistic savings hypothesis discussed in the following chapter.

Stro

ng F

TR

Wea

k FT

R

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2.4.2 Review of Keith Chen’s 2013 paper

In his seminal paper The Effect of Language on Economic Behavior: Evidence from Savings Rates, Health Behaviors, and Retirement Assets5 economist Keith Chen pioneered the use of FTR as linguistic characteristic in economic research. In the tradition of the linguistic relativity hypothesis, he formulates a linguistic-savings hy-pothesis, suggesting that weak FTR speakers save more than strong FTR speak-ers. Chen proposes two mechanisms to theorize his hypothesis: (1) FTR biases beliefs, and (2) FTR distinctions produces more precise beliefs. Both suggest the same direction: weak FTR speakers are more likely to save than strong FTR speak-ers.

Recalling the four possible economic implications of linguistic relativity from chapter 2.3.1, the assumption that FTR as linguistic characteristic biases beliefs falls into the category shaping the utility function (see Mavisakalyan & Weber, 2017). In the overall linguistic classification by Wolff and Holmes (2011), introduced in chapter 2.1.2, such an effect corresponds with the approach of language as in-ducer. For that, Chen argues that there should be a certain analogy between the reference to the past and the reference to the future. If speakers use the present tense when referring to the past to make stories feel more vivid (Schiffrin, 1981) and less distant (Dancygier & Sweetser, 2009), a similar relationship should hold when referring to the future. To translate this into an economic mechanism, less perceived distance to the future of weak FTR speakers, could reduce their discount rates of future returns when compared to strong FTR speakers. Lower discount rates for weak FTR speakers, he proposes, would hence be more likely to save today to receive rewards in the future.

As a second mechanism, Chen proposes that FTR may produce more precise beliefs on the timing of rewards in the future. In the economic categories of chapter 2.3.1 this corresponds to the salience of certain decision elements, which repre-sents the psychological category of language as spotlight from chapter 2.1.2. For that, he draws parallels to the psychological experiments of color (e.g. Kay & Regier, 2006). If speakers of languages with more distinct concepts of color show to some extent a faster and more precise ability to distinguish color, a similar effect could be triggered by FTR, he argues. More precise beliefs on the exact timing of

5 Chen, M. K. 2013. The effect of language on economic behavior: Evidence from savings

rates, health behaviors, and retirement assets. The American Economic Review, 103(2): 690-731.

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the future will lead strong FTR speakers to find saving less attractive then weak FTR speakers. With the concept of discounting future rewards being a function of time that is strictly convex, uncertainty about the exact timing in the case of weak FTR speakers increases attractiveness to save.

Chen’s contribution is based on previous research on the linguistic relativity hy-pothesis with its various roots in psychology. However, he only explicitly mentions this rich theoretical foundation during two sentences (p.719) after having presented all results. This is followed by a short paragraph on discussions in the field. The paper does not contain a statement on the overall relations between language, cognition, and behavior. From a logical perspective, he therefore misses the link between language and behavior and jumps straight into a correlation analysis be-tween the two. As discussed throughout chapter 2.1, there is a long-standing de-bate on this relationship. Still, when developing his hypothesis that FTR may (1) bias beliefs or (2) produce more precise beliefs, no reference is made to the general idea of linguistic relativity. The first mechanism is developed solely on the basis of an analogy to the linguistic concept of ‘historical present’. Chen assumes from the description of linguistics (that stories told in the present tense feel differently from those told in the past tense) that this has an analogy to the future, and there, it directly affects decision-making. Referring to psychology studies in the category of ‘language as inducer’ (like for example Holmes & Wolff, 2010) would have strength-ened his argument. For the second mechanism (more precise beliefs for weak FTR speakers), Chen makes reference to studies on color cognition that are an im-portant pillar in the linguistic relativity literature. With that, the second argument is more substantiated than the first.

Chen tests his linguistic savings hypothesis through cross-country regressions and also within-country regressions distinguishing households with weak and strong FTR. For the latter, similar and comparable households are in scope that live in a multilingual country with both strong and weak FTR speakers. The data is based on the linguistic dataset described in the previous chapter and survey data of the World Value Survey (WVSA, 2009). He finds that weak FTR speakers are more likely to save and accumulated more wealth for retirement. In attempt to broaden his hypothesis, he also tests for health behavior. Although his theoretical argumentation is only limited to saving propensity, as described above, he lever-ages his results to formulate an even broader statement beyond the original hy-pothesis (p.691):

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“[S]peakers of weak-FTR languages (with little to no grammati-cal distinction between the present and the future) appear more

future-oriented in numerous monetary and non-monetary behaviors”

With the ‘non-monetary behaviors’ he is referring to his results that weak FTR live healthier (less smoking, more physical activity, less obese, more use of condoms) than strong FTR speakers. These findings are stable after controlling for the survey questions on individual appreciation of saving behavior.6 Separately, but more closely related to his original hypothesis, Chen also finds in a cross-country analy-sis that countries speaking weak FTR languages show higher aggregate savings rates than countries with strong FTR languages. This tendency holds equally true across developed nations that are members of the OECD7 and developing nations as per World Bank data.

The relationship between language and culture is another weakness in Chen’s argumentation. Throughout the development of his argumentation and hypothesis building, there is no explicit assumption how culture and language are related. In-stead, Chen refers to culture only in the context of “cultural attitudes toward sav-ings” (p.692) as control variables. Concretely, this means that household answers from the World Value Survey to the following questions are included (p. 722):

§ Is saving important as a quality that children should learn at home? § Can most people be trusted? § How important is family?

Chen suggests: “To a limited extent, this allows me to investigate whether language acts as a marker of deep cultural values that drive savings, or whether language itself has a direct effect on savings behavior” (p. 701). He finds that answers to the first question (teaching savings to children) are significant and not correlated to the also significant FTR marker. “This suggests that the channel through which lan-guage affects the propensity to save is largely independent of the saving as a self-reported value” (p. 708). “That is to say, while both language and cultural values appear to drive savings behavior, these measured effects do not appear to interact with each other in a way you would expect if they were both markers of some com-mon causal factor” (p.721). This line of argumentation implies the following

6 Other controls for the cross-country analyses are trust, importance of family and country-

specific controls like legal origin and macro-economic data. 7 Organisation for Economic Co-operation and Development.

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assumptions that is not theoretically backed throughout the paper: language and culture are expected to be independent to some extent. Still, the paper uses three particular questions on beliefs from the WVS that Chen expects are somewhat re-lated to savings propensity. This cannot be a full representation of overall culture. Chapter 2.2 has shown that conceptualizing culture in a comprehensive manner is challenging. Yet, concepts like the cultural dimensions by Hofstede are widely ap-plied in economic research (Kirkman et al., 2006). Chen is not the only scholar neglecting a discussion on language on culture in general. Roberts et al. (2015) criticize Chen (2013) for not controlling for inter-linguistic relatedness. As already briefly discussed in chapter 2.3.3, they argue that languages are also representa-tions of underlying culture. Common descent and borrowing of certain cultural traits makes contemporary cultures not independent from one-another. They further ar-gue that this cultural dependence in cross-country studies needs to be controlled and suggest controlling for relatedness of languages (e.g. language families). With this proposal they follow the same pattern as Chen himself regarding a weak theo-retical foundation of the language-culture relationship. Including a broader set of cultural dimensions would have made Chen’s argument more robust and would have addressed some concern of his critics. As per my proposal from chapter 2.3.3, this would have allowed to control for culture directly instead of a linguistic proxy of sorts. However, retesting Chen’s hypothesis with the proposed linguistic-related-ness controls has shown that findings are mostly robust (Roberts et al., 2015). Hence, the overall findings are still valid but future research needs to address this issue to prove robustness beyond this and similar criticism.

In summary, I find three weaknesses in Chen’s contribution. First, the theoretical development of his hypothesis does not leverage the literature on linguistic relativ-ity, although that would help his argument. Second, he only theorizes about savings propensity but then analyzes future-oriented behavior on a broader scale. Although he does find that the non-monetary aspects are in line with his original linguistic savings hypothesis, the contribution would have been even stronger with proper theory development. Third, he is not precise in stating his assumptions on the re-lationship between language and culture. Instead, he limits culture to a few sup-posedly related survey questions, but does not control for a more comprehensive concept of culture. This could have preempted some of the later critique on the paper. Despite this critique, the 2013 paper by Chen has been widely successful in accelerating research on the overall domain of linguistic relativity (for an overview see Mavisakalyan & Weber, 2017). In particular, the paper contributed to the further

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development of the FTR characteristic and its application in economic research. The following chapter provides an overview of recent contributions in this domain.

2.5 Applications of future-time reference in research

Since Chen’s paper in 2013 the application of FTR as linguistic characteristic in the context of linguistic relativity has seen continuous growth. Scholars not only at-tended to his propositions on savings behavior but also expanded into other related fields. Table 2.1 provides an overview of the studies that, to the best of my knowledge, comprehend the major contributions in this new field.

As discussed in the following sections, these studies differ with respect to the analytical context, i.e. the phenomena of interest, the unit(s) of analysis, the scope, and design of studies. Furthermore, the studies are rooted in different concepts of language, cognition, and culture. Nonetheless, apart from one experimental study (Thoma & Tytus, 2017), they all find that in at least one form, weak FTR speakers are more likely to display future-oriented behavior. This is in line with the original proposal by Chen (2013).

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Table 2.1: Literature on FTR application in economic context

Author(s) Analytical focus Unit(s) of analysis Scope Design

Chen (2013) Household saving, re-tirement wealth, health

Households, countries

Global Cross country, within country

Chen et al. (2017) Corporate saving Firms Global Cross country, within country

Fasan et al. (2016) Corporate earnings management

Firms Global Cross country

Galor et al. (2016) Origins of language dif-ferences and impact on contemporary behavior in education

Individuals Global and USA

Cross regional, within country

Guin (2016) Household saving Households Switzerland Within country

Kim and Filimonau (2017)

Environmental attitudes in tourists

Individuals Korea, China Cross country

Kim et al. (2017) Corporate earnings management

Firms Global and USA

Cross country, within country

Liang et al. (2014) Corporate Social Re-sponsibility

Firms Global Cross country

Masella et al. (2017) Household saving for 2nd generation immi-grants

Households Germany, UK Separate within country

Mavisakalyan et al. (2018)

Environmental behavior Individuals, countries

Global Cross country

Pérez and Tavits (2017)

Support for policies with temporal component

Individuals Bilingual speakers of Russian and Estonian, Global

Experiment, cross country

Sutter et al. (2015) Intertemporal choice Individuals Children in bi-lingual South Tyrol

Experiment

Thoma and Tytus (2017)

Intertemporal choice Individuals China, Ger-many, Den-mark, Colum-bia, USA

Experiment

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2.5.1 Origins of language variation and economic impact

In chapter 2.2.2 we already discussed the complex relationship between culture and language, where some psychology scholars view language as the mere proxy for underlying culture (e.g. Oishi, 2014), while the literature on linguistic relativity argues that, to some extent at least, language impacts cognition directly (Imai et al., 2016; Mavisakalyan & Weber, 2017). Galor and Özak (2016) address the ques-tion on the relationship between culture and language in an historic context and in application of the FTR of languages. They propose that agricultural features were one root cause for the development of the future tense in languages. Differences in these features hence explain inter-linguistic diversity in FTR. Analyzing pre-in-dustrial characteristics of regions that conduced higher yields on crop returns shows that pre-1500AD potential crop return is negatively associated with the de-velopment of a future tense. Contemporary languages are daughter languages of proto-languages8, which developed diversity in grammar. The authors find the ten-dency of these daughter languages to have a weak FTR if potential crop return was high in the ancestral homeland of their proto-languages. The underlying mecha-nism, the authors suggest, is the co-evolution of culture and language. Over the long run, natural selection between different language structures would have fa-vored those that best reflect the cultural traits that are dominant among speakers. Language as important means of cultural transmission and coordination faces net-work effects because any unilateral deviations diminish its efficiency. Hence, the authors propose that languages in their development must be more persistent than related cultures. While individual deviation from culture is possible, the adoption of individual linguistic variations would require the whole society of speakers to adjust. This relationship between culture and language over time and space is the core of the author’s co-evolution assumption (see Figure 2.3 for the general case). As a result for contemporary analysis, we can expect languages to be historically rooted in cultures, but they cannot be complete representation of one-another because of their different speeds of adjustment. This must be particularly true for the faster developing era after 1500AD. Based on the historic analysis on the origins of the FTR characteristic, they deduce that weak FTR language is associated with long-term orientation as cultural trait. Applying this assumption to the US today, they find that second generation immigrants with weak FTR parental language are more likely to attend college, i.e. invest in their future (see Figure 2.3 for the special case).

8 Concept in historical linguistics to describe the early ancestral languages across regions.

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Figure 2.3: Language and contemporary behavior (Galor et al., 2016)

2.5.2 Saving propensity in households

Chen (2013) has laid the foundation for the growing economic literature on the FTR characteristic. With that, he established the linguistic saving hypothesis for house-holds globally, but also within multilingual countries. While he does not discuss the relationship between culture and language in particular (see chapter 2.4.2), two recent studies on the same phenomenon show interesting patterns in this regard. Guin (2016) shows differences in saving propensity between language groups in Switzerland. Concretely, he confirms Chen’s finding, stating that German speakers (with weak FTR) are more likely to save than French speakers (with strong FTR). His argument is primarily based on cultural differences and he makes use of the FTR characteristic to distinguish two cultural regions within the country with the proxy of language. The second study by Masella, Paule-Paludkiewicz, and Fuchs-Schündeln (2017) follows a slightly different approach, which allows to distinguish between the effect of culture and the effect of language. The authors also conduct a within country analysis, but for second generation immigrants to Germany and the UK. The main theoretical background of the study is not linguistic characteris-tics per se, but the cultural dimensions of the ancestral homeland. For that, they introduce the cultural dimensions by Hofstede et al. (2010). Based on the cultural dimensions alone, they do not find general support for the assumption that the cul-tural long-term orientation of their parents’ home country affects the saving propen-sity of second generation immigrants. This changes if the component of language is introduced: the cultural long-term orientation of their parental home country be-comes impactful if they also speak the language of their parents. If they do not speak the language of their parental home country, the cultural dimensions of this country do not seem to influence saving propensity. Although the study does not

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distinguish between weak and strong FTR, it contributes to the argument that lan-guage is a channel to transmit values and beliefs that is somewhat independent of culture. In particular, the findings suggest that language has the ability to activate patterns that would otherwise not impact our behavior.

2.5.3 Corporate decision-making

While most studies covered in the overview of Table 2.1 refer to the behavior of individuals, a few scholars also find that similar effects also hold for the corporate level. In close analogy to the linguistic saving hypothesis for individuals, Chen, Cronqvist, Ni, and Zhang (2017) analyze the tendency of firms to hold higher amounts of precautionary cash. They find that companies with headquarters in weak FTR countries show higher cash holdings than those domiciled in strong FTR countries. To underline the robustness of their findings, they show that Hong Kong based companies have increased the tendency to hold cash after the 1997 hando-ver from the UK to China. They argue that the Chinese languages (weak FTR) have become increasingly important for business since this time. Additionally, they show that after the 2008 financial crises corporate cash holdings globally have risen more in weak FTR countries compared to strong FTR countries. The study hence sup-ports the original linguistic saving hypothesis also on corporate level. The study makes no claim on the language-culture relationship but includes dimensions by Hofstede to control for cultural differences.

Other studies on corporate level refer to a broader concept of future-oriented behavior. Concretely, Fasan, Gotti, Kang, and Liu (2016) suggest that firms in weak FTR countries are less likely to engage in earnings management activities to meet short-term benchmarks for earnings. In turn, this means that companies in strong FTR countries show a higher tendency for either real activities or accrual earnings management. Such activities have the impact to boost current earnings at the ex-pense of other periods, either through deliberately timing revenue streams directly or through moving their impact across periods by means of accruals. This is relating to future-oriented behavior as artificially moving rewards to the present comes at a cost in the future. The authors argue that strong FTR speakers, with their more distanced relationship to the future, feel less pressure from the future and therefore engage more in short-term behavior. Kim, Kim, and Zhou (2017) confirm this be-havior in their cross-country analysis. In addition, they propose that the CEO plays a crucial role for this phenomenon. They test for US companies and the linguistic

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exposure of their CEOs: if CEOs were born in weak FTR countries the respective companies are less likely to engage in earnings management. Both studies make no explicit statement on the culture-language relationship but control for Hofstede dimensions. They confirm the claim that an FTR effect on future-related behavior can also be found at the corporate level, proxied through their headquarters. Fur-thermore, they find that the individual level also comes into play in the context of corporate decision-making. This suggests that multiple language environments, i.e. a mother tongue of a decision-maker and the company language, may interact in their effects.

Liang et al. (2014) further develop this line of thought in their study on corporate social responsibility (CSR). In line with expectations on broader future-related ac-tivities, they find that companies in countries with weak FTR languages show better performance in their CSR activities than companies in strong FTR countries. How-ever, company language is not absolute in this effect: if companies are headquar-tered in countries that are highly globalized, if the company itself is acting more internationally or the company CEO is internationally experienced, the overall effect is moderated. Liang and colleagues also make a statement on the culture-language relationship: “[L]anguages do not merely express thoughts that are rooted in cul-ture; the structures within language also shape the very thoughts that people wish to express” (p. 3). As the other three corporate studies, the Hofstede dimensions are used to control for cultural factors.

The contribution of scholars regarding FTR on the corporate level shows that language does not only have effects for rather isolated and personal decision-mak-ing, but also in more complex situations as corporate decision processes. FTR ef-fects on different levels with different language environments appear to interact with one-another if more parties or institutions are involved in the decision-making pro-cess. While these parties and institutions are not yet comprehensively investigated, the studies provide early evidence for interaction effects.

2.5.4 Future-oriented behavior and public policy

Future-oriented behavior comes in different forms, one of which is the attitude to-wards the environment. Pro-environmental behavior, especially if it comes as a re-striction or cost today for a non-negative reward in the future, is studied by Kim and Filimonau (2017). In particular, they investigate if tourists show more pro-environ-mental behavior, depending on their mother tongue. Comparing Korean and

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Mandarin speakers, they find that the Mandarin speakers with a weak FTR lan-guage show more pro-environmental behavior than strong FTR Korean speakers. At the same time, the authors make clear reference to the relationship between language and culture: on the one hand, they state that language can represent cultural realities (Moutinho, 1987) and a medium for communication, it also influ-ences cognitive processes (Harley, 2014).

The willingness to pro-environmental behavior in general and independently of tourism is at the core of the study by Mavisakalyan, Tarverdi, and Weber (2018). In a broad cross-country analysis, they confirm the tendency stated above: weak FTR speakers are more likely than strong FTR speakers to engage in pro-environmental activities – even at a cost. Their main argument on the underlying mechanism al-ready provides insight on how they view language in relation to culture: “Future tense may be a marker for cultural factors, or future tense may directly affect speaker’s cognition and behavior (or both). Both the cultural and the linguistic-cog-nitive channel of influence rely on the phenomenon of temporal discounting” (p.5 of article). They suggest that weak FTR speakers apply lower discount rates than strong FTR speakers. This is in line with the argument of Chen (2013) (for more detailed discussion on the explanatory power of this economic mechanism, see the following section and the 2017 paper by Thoma and Tytus). However, Mavisa-kalyan and colleagues apply this rationale not only on individuals, but also on coun-try level. They find that countries with a majority of weak FTR speakers show more stringent policies on environmental issues than countries with a majority of strong FTR speakers.

Pérez and Tavits (2017) discuss the implication of an FTR effect on policy-mak-ing in more depths. They claim that speakers of weak FTR languages are more likely to support policies that are future-oriented. This holds, they say, not only for environmental policies but can even be broadened to political reforms in general. After all, market reforms towards free trade, reforms of social systems, or even democratization may come at investment costs today but provide rewards in the future. They prove their argument with two separate designs. First, they show that bilingual speakers of Estonian (weak FTR) and Russian (strong FTR) show differ-ent opinions towards a pro-environmental policy, depending on the interview lan-guage. If the interview was conducted in Estonian, they were more likely to be in favor of the policy than those interviewed in Russian. They authors found no lin-guistic effect if interviewing the same subjects on a non-future-related policy.

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Second, they show a similar effect in a cross-country study quite similar to that by Mavisakalyan and colleagues. All three described studies on future-oriented be-havior and public policy suggest that a linguistic effect may have impact on society beyond the individual and beyond aggregate saving propensity. If more research on the subject confirms this tendency, then politicians and media may want to play closer attention to the language of their audience.

2.5.5 Intertemporal choice

Two groups of scholars address the question of intertemporal choice in purely ex-perimental designs. First, Sutter, Angerer, Rützler, and Lergetporer (2015) investi-gate the inter-temporal choices of primary school children in the bilingual region of South Tyrol, Italy. They find that children from German-speaking households (weak FTR) are more patient than children from Italian-speaking households (strong FTR). In households were both languages were frequently spoken, children showed patience levels that were in-between these two extremes. This tendency of FTR impact and moderation also holds for households were other languages than the two are spoken (effect along FTR categories).

The other purely experimental study by Thoma and Tytus (2017) is interesting in two aspects. First, the authors challenge the dichotomous classification of FTR be-tween weak and strong, by stating that this approach oversimplifies the complex differences in how languages apply their future-referencing. Instead, they take speakers of five languages and order them by FTR strength: Chinese with the weakest FTR, then German, Danish, Spanish, and English with the strongest FTR. The second and even more insightful aspect is the result of the study: they find the opposite direction for the FTR effect compared to all other studies presented here. Concretely, their results show a general future-preference across all subjects which magnifies with FTR-strength. As a result, the weakest FTR speakers show less future-oriented behavior and the strongest FTR speakers show the highest. The authors do not suggest that their findings falsify prior contributions, but they provide a range of potential explanations why their subjects behaved differently. First, they might encounter a selection bias as individuals signing up for an experiment on “time perception” show generally more interest in the future and potentially even a general future-bias. Second, their main sub-study addressed students who are, with respect to future-oriented behavior, not necessarily representative of the over-all population (they show future-oriented behavior already by investing time and

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effort in education). Third and most theoretically intriguing, they suggest subjects may display social desirability bias (Schwarz, 1999) and/or a hypothetical-bias (Loomis, 2011). That means that subjects might have answered strategically in dis-guise of their true preferences to fulfill social desirability or they might have decided differently because it was not their own money that was at stake. Concretely, the authors speculate that strong FTR speakers are less future-oriented, potentially even know about this, and now take the opportunity of the experiment to overcom-pensate this tendency. While the experimental design by Thoma and Tytus cannot control for either of these affects comprehensively, this experimental contradiction provides opportunity for further research.

While the original results by Chen were based on real-life past intertemporal choice behavior, the experiment by Thoma and Tytus (2017) was purely hypothet-ical. The theoretical explanation how biases may be the reason why their results differ can be illustrated with the concept of intertemporal discounting.9 Chen (2013) provides two mechanisms to develop his hypothesis: first, “linguistic precisions lead to more precise beliefs” (p. 697) and second, “obligatory distinctions bias beliefs” (p.695). Broadly speaking, strong FTR speakers have a more precise understand-ing of timing and generally a more distant relationship to the future. Both mecha-nisms are illustrated in Figure 2.4. Theory on time discounting assumes hyperbolic discounting (Zauberman, Kim, Malkoc, & Bettman, 2009). The left side shows at a given point in time for a reward, strong FTR speakers (orange) have a narrower belief about this point in time than the weak FTR speakers (blue). Given the shape of the discounting function, this increases the perceived award for weak FTR speakers.

On the right side, we see the mechanism that the future seems less distant for weak FTR speakers (orange). Reading the graph this time not bottom-up, but from left to right, this leads to the same effect as the graph on the left: the perceived reward for the weak FTR speaker is higher than that for the strong FTR speaker. Both these mechanisms are in line with the hypothesis by Chen and the other stud-ies discussed here. In that form, the model does not explain why Thoma and Tytus find the opposite effect.

9 The following line of argumentation follows Thoma and Tytus (2017).

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Figure 2.4: Hyperbolic time discounting (Thoma & Tytus, 2017)

To incorporate their reasoning of potential biases of their experiment, they suggest that discounting function may not be hyperbolic but rather invertedly S-shaped like the generalized Weibull function (Takeuchi, 2011). This function (see Figure 2.5) assumes a future-bias for future rewards that are closer to the present moment, and a present-bias for future rewards that are more distant to the present moment. Intuitively speaking, waiting for a short while is attractive, waiting for longer not so much. The different shape of the discounting function alone does not yet explain the difference in outcomes. It only explains why Thoma and Tytus find a general future-bias in their results: apparently, the distance to the present day was not large enough to switch the slope from concave to convex. To explain an opposite effect of FTR, the biases discussed before would need to trigger an overcompensation of the subjects. As can be seen graphically, the more such bias effects would hit a spot on the curve that is almost linear, the bigger the chances for a switch in direc-tions for the FTR effect.

The experimental design studies show that the effect of FTR may be less obvi-ous, depending on study design. In particular, it is quite likely that real-life decisions and what we report to do hypothetically or in the future differs with regards to our time preferences. To capture this, further research needs to address this particular contradiction. To date, Thoma and Tytus are the only study in FTR research that find weak FTR speakers to be less future-oriented. This also shows that scholars in the domain of FTR must consciously choose between hypothetical and inten-tional preferences on the one hand, and real-life and past decision on the other.

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Figure 2.5: Time discounting with Weibull function (Thoma & Tytus, 2017)

2.6 Behavioral economics as neighboring field to psychology

The goal of behavioral economics is not to contradict neoclassical theory but to provide additional explanatory power through psychological foundations. Research in the field is usually either on judgement or on choice. The former addresses the processes individuals follow to estimate probabilities, whereas research on choice focuses on the selection among actions, based on their judgements and prefer-ences (Camerer & Loewenstein, 2004). Today the field for behavioral economics is broad, including multiple interdisciplinary extensions. The introduction of pro-spect theory as an explanation for individuals’ behavior, contradicting rational choice, marks the starting point for the field (Kahneman & Tversky, 1979). The ‘dual-system theory’ suggests that humans are governed by one system of rational thought and one system of intuition, automatism, experience, and unconsciousness (Kahneman, 2011). Based on experiments, theories in behavioral economics have defined a large number of biases, explaining typical behavioral patterns that

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contradict the concept of individuals as constant rational decision-makers (Camerer & Loewenstein, 2004).

On the social dimensions, scholars of behavioral economics assume that indi-viduals do not make choices in isolation or purely for their own interests. Social environments may include components of trust (Fehr, 2009) or honesty (Bohnet, Greig, Herrmann, & Zeckhauser, 2008). In a recent publication, Falk et al. (2015) show that preferences of individuals are not homogeneously distributed across countries, but preferences vary globally. Originally, the field of behavioral econom-ics did not emphasize why certain actors displayed certain biases more than others (See for example Aggarwal & Goodell, 2014). While some scholars even consider the genetic influence on behavioral biases (See Cronqvist & Siegel, 2014), diversity in cultural influences as trigger for behavioral biases gains momentum. On cross-country level Kanagaretnam, Lobo, Wang, and Whalen (2015), among others, show that behavioral biases of investors may be triggered by differences in norms and values in societies.

The influence of cultural aspects is only a small part of the overall literature of behavioral economics. However, I include this perspective because it can be seen as a mirror image to the efforts of linguistic relativity. While linguistic relativists start with the assumption that language has an impact on thought, they look to find evi-dence that language ultimately also influences decision-making. Behavioral econ-omists, on the other hand, usually start with decision-making that cannot be ex-plained by a simple rational-choice assumption. They look to find explanations in psychology, which recently also includes cultural aspects. As a result, the two fields can be seen as mirror images of one another with respect to this specific topic.

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3 Analytical focus Having introduced the theoretical background of linguistic relativity in general and having discussed the literature on FTR in particular, this chapter aims at describing the phenomenon of interest in this study. Most basically, this study aims to explain variation in risk-taking for private investors. Chapter 3.1 therefore briefly describes risk, risk-taking, and risk propensity, as well as potential factors of influence. It es-tablishes the basic assumption that risk propensity is a major driver for portfolio risk and, as a result, portfolio risk may serve as proxy for underlying risk propensity. Chapter 3.2 provides background on the particular scope of this study by describing individuals on the upper wealth band. Given the financial means of these individu-als, they are highly mobile. In contrast to previous studies, I expect that exposure to other culture and language environments interacts with the proposed language effect, so concepts of acculturation and the foreign language are introduced. Chap-ter 3.3 concentrates on another particularity of this study: each individual in scope also has a relationship manager to advise him on financial decisions. Hence, I pro-vide a brief introduction into the economic and psychological literature on financial advice.

3.1 Risk propensity and influencing factors

Investors differ in their investment choices. Investment portfolios are characterized by the choice of different asset classes, the share between these different asset classes and the individual investments within these asset classes. Each asset pro-vides a certain relationship between risk and returns. Investors know that these two key components of investments generally follow a positive relationship, so investing in riskier assets allows for higher potential returns (Yao, Hanna, & Lindamood, 2004). Individuals choose between different products for reasons that can be soci-oeconomic, demographic, behavioral, or related to their personal risk attitudes. All these factors may change over time, also related to macro-economic factors (West & Worthington, 2012). For example, investors may be motivated by recent returns in their decision (Clark-Murphy, Gerrans, & Speelman, 2009) or they may be driven by regret (Grable, Lytton, & O'Neill, 2004). This suggest that in an environment of rising prices, we can see that risk tolerance for momentum investors rise because they feel they might regret not having invested and thereby miss out on prices rising even further. The same herd behavior might occur for falling prices when investors

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sell in fear of regret about losses. Individual investors differ with regards to their individual risk attitudes, ranging from risk-seeking to risk averse behavior. The con-cepts of absolute and relative risk aversion were pioneered by Pratt (1964) and Arrow (1964), with the relative risk aversion concept as a key component of the expected utility framework in modern portfolio theory (Wang & Hanna, 2007). From the advisory perspective, the concept of risk tolerance is more common. Barsky, Juster, Kimball, and Shapiro (1997) understand this as the inverse of risk aversion, and hence, also an indicator of risk attitude.

Risk attitudes (and as the mirror image also risk tolerance) of individuals are influenced by a variety of factors, as various scholars claim. For example the level of education, age, gender, race, marital status, having young children, financial as-sets in relation to income, household income, non-financial assets, self-employ-ment status, health status, home ownership, or expectance of inheritance are sug-gested to impact risk attitudes and ownership of risky assets (see for example Gutter & Fontes, 2006; Karlsson & Nordén, 2007; Yao et al., 2004). As West and Worthington (2012) show in their review, size of overall wealth does not provide a simple indication: while individuals on low wealth levels appear to be less risk averse because they feel they do not have much to lose, high levels of wealth may also decrease risk aversion because they can afford additional financial risk. As the authors suggest, it is the middle of the wealth band that is most risk averse.

The challenge with concepts of risk attitude, risk tolerance, or risk propensity is that there is no simple objective measure available. As for other preferences rooted in psychology (see the discussion on long term orientation in previous chapters), there are different strategies to proxy the attitudes of an individual. Hanna, Gutter, and Fan (2001: 53) state that “there are at least four methods of measuring risk tolerance: asking about investment choices, asking a combination of investment and subjective questions, accessing actual behavior and asking hypothetical ques-tions with carefully specified scenarios”. The first two methods relate to question-naires where individuals are asked about their risk attitudes concretely with a self-assessment on risk-taking in general and, potentially, more subjective survey ques-tions (Yao et al., 2004). The fourth method of hypothetical scenarios are somewhat similar to the self-assessment approach and remind of experiments in psychology (for example Barsky et al., 1997). The hypothetical assessment brings the ad-vantage that, based on the concrete design, the results are very much linked to the underlying economic theory and they are more independent from the individuals’

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current situation or limitations in information (Hanna et al., 2001). As a result, these methods provide detailed data that allow advisors in practice to advise according to the stated preferences and it allows researchers to investigate influential factors as described above. However, there is reason to doubt that these stated prefer-ences are automatically good predictors for actual investment behavior (Merkle & Weber, 2014). If we recall the findings by Loomis (2011) and Schwarz (1999) from chapter 2.5.5, such methods may struggle with a hypothetical bias or social desir-ability bias. One might imagine that a risk averse client for example does not want to appear too timid in from of his relationship manager. Also, if asked directly, indi-viduals may not know what their preferences are or they have a different perception of risk than the concept that is underlying the survey approach (see for example Veld & Veld-Merkoulova, 2008).

Apart from the three survey-based or hypothetical methods reviewed by Hanna et al. (2001), they mention the forth one: assessing actual behavior through portfo-lio risk or share of risky assets in a portfolio. This raises the question, how the survey approach and the actual behavior are related to one another. Merkle and Weber (2014) address this question in a longitudinal study between 2008 and 2010. In intervals of 3 months, they record the individuals’ demographics, their prefer-ences, and beliefs. Comparing these with the portfolio performance shows that the portfolio risk is positively related to risk tolerance and return expectations. A nega-tive relationship is found for expectations on market risk, age, and wealth. The study design by Merkle and Weber also allows to draw conclusions from the dy-namic relationship between the factors and portfolio risk. They find that preferences and beliefs are best incorporated when reviewing the long-term volatility in portfo-lios. In turn, short-term volatility is not well reflecting preferences and beliefs. Find-ings are in line with the previous study by Dorn and Huberman (2005) who suggest self-reported risk attitude as strong predictor of portfolio volatility over a longer pe-riod. Combining actual behavior and self-assessments over a longer time period also mitigates the potential caveats for questionnaires described before.

Across literature on predictors of investment risk, portfolio volatility is common as a measure for risk-taking (for further examples see Calvet, Campbell, & Sodini, 2007; Hoffmann, Post, & Pennings, 2013). Scholars agree across their studies that risk attitudes and beliefs are reflected in the portfolio risk. That is first of all good news for banking clients, who trust that their stated preferences are implemented in their investment behavior. It is also a positive sign for banks as regulatory

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scrutiny on banks to assess risk tolerance of their clients to ensure suitability of products has become more intense during the last decade. It also confirms claims by classic portfolio theory. After all, Dorn and Huberman (2005) state that according to the mean-variance framework, “the portfolio’s aggregate volatility is the only measure of risk an investor should be concerned with” (p. 461).

The brief review of investment risk and strategies to assess overall risk propen-sity shows that the two aspects are closely related. Scholars seem to agree on the notion that risk propensity is represented in portfolio risk. In this study I investigate if language, through its impact on cognition, also affects observed portfolio risk. Based on the insight that risk propensity is reflected in portfolio risk as a major factor of influence, I make the assumption that, reversely, portfolio risk may serve as a proxy for overall risk propensity. More concretely, I suggest that the observed portfolio volatility represents a certain risk propensity of its beneficial owner, where lower volatility represents lower risk propensity, and higher volatility represents higher risk propensity (more details on the development of hypotheses in chapter 4).

3.2 Global private investors on upper wealth bands

As will be described in chapter 5.1 for the overall empirical context, this study differs from previous studies with regards to the scope of individuals. While Chen (2013) and other colleagues derive their data from the World Value Survey (WVSA, 2009), which has the ambition to be representative for the people in each of the included countries, the demographic for this study only represents a unique sub-set of the overall population. Individuals in this study are also internationally diversified, but they are all clients of a large international wealth manager and they are all in pos-session of wealth that qualifies them mostly for the top of the global wealth pyramid. This specification produces certain impacts that are discussed throughout this chapter. First, their needs and related service levels by banks are likely to differ from the broader population. Second, higher levels of wealth allow for mobility be-tween domicile countries. Living abroad comes with exposure to other cultures and languages, which is expected to reflect on the cognition and ultimately the behavior of individuals.

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3.2.1 (Ultra) High Net-Worth Individuals globally

The distribution of wealth differs across countries and within countries. According to the Credit Suisse Wealth Report 2017, 70.1% of adults belong to the bottom of the wealth pyramid with assets below USD 10,000. The mid-range between USD 10,000 and 100,000 accounts for another 21.3%. The high wealth bands of more than USD 100,000 covers 8.6% or 427m adults worldwide (Shorrocks, Davies, & Lluberas, 2017). Those at the top of pyramid are referred to as High Net-Worth Individuals (HNWI: USD 1-50m) or even Ultra-High Net-Worth Individuals (UHNWI: USD 50m+). While the exact classification boundaries are not uniform across the wealth management industry, these (U)HNWI are the core target group of private banking institutions worldwide. As such, they are the population in scope for this study. Figure 3.1 further distinguishes the two segments.

Figure 3.1: Top of the Wealth Pyramid 2017 (Shorrocks et al., 2017)

(U)NWI clients differ in their needs from the majority of retail or affluent custom-ers (below USD 1m in wealth). The banks’ offering for these lower segments typi-cally includes payment and cash services, savings solutions, basic investment so-lutions, mortgages, or consumer credit. Services are, apart from the wealth levels, mostly determined by the private lifecycle of individuals. The needs of (U)NWI ex-pand beyond those basic banking needs from different perspectives. On a global

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basis, most of the upper echelon clients are self-made, which creates a large group of individuals who are first-generation entrepreneurs (Wealth-X, 2017). As a result, the (U)HNWI base also requires corporate and investment banking capabilities on a global scale. A large group of first-generation entrepreneurs also creates the need for support in wealth transfer to the next generation and overall wealth planning. On a longer time-horizon across generations, a family estate requires structuring and planning to ensure the preservation of wealth. On the lending side, (U)HNWI look not only for plain-vanilla mortgages but complex financing solutions for ships, planes, art, or other acquisitions. On the asset side, banks offer their sophisticated and wealthy clients typically three service models for investments. These three models differ in who makes individual investment decisions and in the role of the advisors. One extreme case is a discretionary mandate, where the client mandates the bank to make individual investment decisions on his behalf and according to his objectives and investment strategy. The client can receive detailed reporting on the positioning and performance of the portfolio, but the decision-making in the day to day portfolio management remains with the bank (Cao, Fischli, & Rieger, 2017). The other extreme is the classic advice model that PWC describes as ad-hoc ad-visory (Strategy&, 2016). The client is in full control of all investment decisions and asks for financial advice on an ad-hoc basis. This model is not standardized and while some clients may use this service-level for execution-only (i.e. the client in-structs the bank to make an investment without being advised), others have estab-lished a close advisory relationship with their bank. The exact level of service de-pends on the client and his relationship manager. An advisory mandate as the third service model, is somewhat in between these two extremes. As for the ad-hoc ad-vice in a classic sense, the client remains the ultimate decision maker. However, the service of advice is typically more institutionalized, and the amount of advice differs less between client relationships. Similar to the discretionary mandates, cli-ents develop a strategy and investment objectives with their advisor (Cao et al., 2017). Based on the research capabilities of the bank, the advisor then suggests investment opportunities that are in line with client objectives and the bank’s house view. The amount of interactions, reporting and involvement of further specialists are agreed upon when entering the mandate.

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3.2.2 Mobility, migration, and the exposure to language environments

Individuals on the top of the wealth pyramid have the means to be mobile, to not only cross borders without many limitations and their means allow them to almost freely decide on where in the world they want to live. This distinguishes them from many people from the lower echelons, who may lack these abilities and who may have different motives for migration. While technically both qualify under the con-cept of migration and certain aspects apply to all individuals across wealth levels, the upper echelons remain somewhat unique. Hence, describing potential effects of living abroad must consider the background of the individuals. Belonging to the upper wealth bands comes with different challenges than the other extreme of forced migration. While many contributions around acculturation focus on migration of refugees, asylum seekers, or economic migrants, this study restricts the scope of migrants to well-educated expatriates and global citizens. This group of immi-grants typically has access to social, economic, and public services and belong to the upper-middle or upper class strata of the host society (Adams & van de Vijver, 2015).

The theory review in the previous chapters introduced the triangle relationship between language, culture, and cognition. When individuals move to another coun-try, they might face differences in culture, language, attitudes, and behavior in con-trast to their country of origin. The phenomena resulting from cultures getting in direct contact with one another are referred to as acculturation. This term is more precise than the otherwise often used concept of assimilation because not only the culture of the migrant changes as a result of the intercultural contact, but so does the culture of the host (Sam & Berry, 2006). For this thesis and in application to individual investors, I focus on the individual psychological impact, rather than the cultural or group level (see also Ryder & Dere, 2010). Individual changes can range from simple behavioral shifts to negative impacts like acculturative stress (Sam & Berry, 2010).

While it is intuitive to speak about cultures when referring to larger groups or societies, this study is focused on individuals. An individual may diverge from the overall culture of his home society. Such tendency, can be expected when moving abroad. To understand individual differences and mechanisms, I follow the concept of identity as formulated by Adams and van de Vijver (2015: 324): “Identity is what makes an individual both distinct from and similar to others”. Identity as a concept of manifested acculturation can be described in three dimensions: personal identity,

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social identity, and relational identity (Vignoles, Schwartz, & Luyckx, 2011). Living abroad may result in a change of identity, which is influenced by the home and the host cultures. The degree of identity change can vary, as research on the network perspective of expatriates and the social and relational dimensions show (Mao & Shen, 2015). Figure 3.2 illustrates the different types of identity change:

Figure 3.2: Identity change triggered by migration (own illustration)

Affirmation is, according to Mao and Shen, the strengthening of the home culture identity when abroad. Contact with the host culture triggers a search of own identity and thereby augments the home culture even more (See also Kohonen, 2008). Reactions can be reflexive and sense-making in their extreme form (Sussman, 2011). In the context of this study, this suggest that influences from home culture on cognition may be elevated for an individual living abroad. Assimilation is the opposite effect. Expatriates adopt the host culture to an extent that reduces their home country identity (See also Cox, 2004). This effect would mean that the home culture influence cognition only to a small extent. Instead, the host culture is influ-ential for the cognition of an individual living abroad. On the middle ground, inte-gration describes the situation, when expatriates develop a bi-cultural identity by adopting host culture beliefs and values while keeping their home country identity (See also Osland & Osland, 2005). This suggests a moderating effect between home and host culture on cognition. A stark contrast to these three types of identity change between home and host influences is the state of desintegration. This is reached when expatriates ascend to a multicultural view on the world. Any deeply rooted connection to either a host or home culture is collapsed and a global identity is formed (See also Erez & Gati, 2004). In the context of this study, the status of disintegration can be interpreted as an extreme moderation effect, where not only the home and host culture are influential but multiple third cultures as well. Which

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of these typical reactions to exposure to other cultures emerges, depends on the circumstances and the personal characteristics of the individuals in question.

The effects of acculturation and different types of resulting identity touch upon the cultural part of the triangle relationship between culture, language, and cogni-tion. However, living abroad often also comes with exposure to another language environment. Speaking a foreign language affects cognition and influences deci-sion-making. Scholars in cognitive psychology find that the use of foreign language in experiments affects the choice of subjects regarding, morality, inference, and risk (Hayakawa, Costa, Foucart, & Keysar, 2016). Keysar, Hayakawa, and An (2012) find that using a foreign language reduces biases in decision-making. For the par-ticular case of risk, they find when speaking a foreign language subjects are less risk averse and more willing to accept hypothetical and real bets with non-negative expected value. The authors argue that this foreign-language effect is due to re-duced emotional resonance. In a foreign language, speakers are less emotional and more rational in their decision-making. Costa, Foucart, Arnon, Aparici, and Apesteguia (2014) confirm these findings and add that individuals are more con-sistent in their risk preferences when using a foreign language. On the root cause of this foreign-language effect they provide evidence that reduced emotion may be most important factor: for decision problems that are less emotional in nature, the effect appears to be smaller. The foreign-language effect also holds for experi-ments that do not simulate decisions but test risk perception (Hadjichristidis, Geipel, & Savadori, 2015). Judgement of risk and benefits from activities, environmental issues, substances or technologies are less subject to framing effects when pre-sented in a foreign language. Furthermore, individuals show a higher tendency for utilitarian moral choices in a foreign language. Gao, Zika, Rogers, and Thierry (2015) show that the foreign-language effect also impacts repeated risk-reward judgements. The ‘hot hand’ fallacy shows that individuals have the bias to overes-timate the autocorrelation of repeated outcomes. In the context of gambling this means that they believe in a winning streak after positive outcomes, in the context of financial markets this may translate into investors keeping long positions in bull markets for too long. Gao and colleagues show that this tendency is attenuated if speaking in a foreign language rather than mother tongue.

In summary, it can be assumed that living in a different country affects the cul-tural identity of an individual. If he is also exposed to a foreign language environ-ment a foreign-language effect is assumed to reduce potential decision biases. In

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line with the concept of culture, language, and cognition as a triangle, both effects may affect cognition to different extents. Together, they may form a migration ef-fect.

3.3 Advisors and their impact on decision-makers

This study analyses the investment behavior of banking clients. One reason why individuals choose a bank to make investments over a pure online tool may be that the bank will provide advice on their decisions. The situation that individuals do not make decisions in isolation but with an expert advisor is particular, also in contrast to previous studies. To understand how the exchange between client and relation-ship manager influences investment decisions, the economic literature attends to the roles advisors play and the performance impact advisors may have. In addition, scholars in psychology provide insight on the questions what advisors individuals prefer and what factors trigger if they actually follow the advice presented to them.

3.3.1 The role of advisors in economic research

A major service a bank provides in the space of wealth management is offering advice to clients. Cruciani (2017: p. 70) defines the role of advisors in the context of financial decision-making as follows:

Advisors provide guidance in financial investment decisions, in order to support individuals in making the right choices and ful-filling their financial expectations and goals. The basic role of an advisor is to improve the quality of the financial decisions taken;

for example protecting capital and increasing returns.

There is, however, no consensus among scholars if the advice from banks or inde-pendent advisors has positive or negative impact on the portfolio of individual in-vestors. While von Gaudecker (2015) finds advisors to contribute positively to per-formance, scholars like Hackethal, Haliassos, and Jappelli (2012) find the exact opposite. Others like Kramer (2012) cannot find any positive or negative impact overall. Apart from the focus on the performance of portfolios, the literature in this field of finance addresses the impact of financial literacy and the mitigation of be-havioral biases (Hoechle, Ruenzi, Schaub, & Schmid, 2016). Hence, financial ad-visors are expected to assume roles that extend beyond the basic portfolio

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optimization. Following Collins (2012), Cruciani (2017: p. 73) formulates five distinct tasks for advisors10:

1. Information provision 2. Financial education 3. Addressing cognitive biases 4. Addressing emotional biases 5. Mediation in collective decision-making

The most basic service of advisors is providing information. Gathering of relevant information for decision-making is not only time- but also cost-intensive. Advisors reduce this cost and provide clients with a sorting of relevant information and pre-senting suitable alternative opportunities (Collins, 2010). Advisors are, in contrast to many of their clients, financial experts so they can provide some financial edu-cation if need be. Financial literacy is not only helpful for making better investment decisions, but it is also correlated with participation in financial markets in general (van Rooij, Lusardi, & Alessie, 2011). However, studies find that not all investors are as likely to seek professional financial advice: Bachmann and Hens (2015) ar-gue that advise-seeking comes with higher levels of financial literacy and that those who could benefit most from professional advice are less likely to ask for it. This tendency is one reason for regulation regarding suitability of products. For more educated clients, financial advice can be seen as complimentary to their own knowledge (Collins, 2012). Calcagno and Monticone (2015) underline this tendency and add that advisors are also more likely to share superior information with expe-rienced and educated clients. Furthermore, advisors address cognitive biases in clients. Biases in the sense of Kahneman and Tversky (1979) describe non-rational behavior under risk. Advisors in their capacity as experts are trained to know and detect heuristic behavior that might lead investors to biased decisions. Shapira and Venezia (2001) for example find in their study that professional advisors are less prone to the disposition effect. The disposition effect is the tendency to sell stock where prices have risen and keep stock where prices have decreased. The authors state that the positive influence of more rational advisors is also visible when ana-lyzing client portfolios: managed accounts show less disposition effect that inde-pendent ones. Emotions can influence decision-making by either directly affecting cognition or interacting with cognitive processes (Cruciani, 2017). Addressing

10 The description of the five roles follows the line of argumentation by Collins (2012) and

Cruciani (2017).

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emotional bias is somewhat related to the previous role. In an attempt to support financial advisors in practice, Pompian and Longo (2005: p. 59) suggest to “adapt to emotional biases and moderate cognitive biases”. While cognitive biases can be corrected to a considerable extend by a professional and well-informed advisor, adjusting emotional biases remains more complicated and depends very much on the individual relationship (Pompian, 2012). Statman (2002) goes a step further and suggests that financial advisors should consider themselves as ‘financial physi-cians’ because their clients come to them with aspirations, fears, stress, and other difficulties. To address this, they should follow similar procedure as physicians: “asking, listening, diagnosing, educating, and treating” (p. 5). The last role of medi-ation goes into a similar direction. Oftentimes, advisors are the impartial third party in case of conflict in collective-investment exercises (Cruciani, 2017). Such situa-tions may arise if multiple individuals make a joint investment in a club deal, but more often it is the case of multiple beneficial owners within a family. Different fam-ily members may have conflicting (emotional) convictions and except the mediation of an advisor if he is both trusted and skilled.

In summary, the economic literature defines an advisor to be good if he improves the investment decisions of his clients. This might be related to concrete returns, but also extends to a broad range of support. In his role to address deviations from rational decisions, he may be able to address biases from emotions but potentially also those stemming from language or culture. To better understand such mecha-nisms, psychology provides a body of literature on advice-taking and advice-giving.

3.3.2 Psychological mechanisms in client-advisor relationships

The economic literature on financial advice, as briefly described above, is con-cerned with the tasks of an advisor and, more importantly, if advised investment portfolios perform better than those that are not advised. However, this study is not concerned with portfolio performance per se but only focusing on the volatility of portfolios and how it is influenced by language characteristics. Furthermore, the distinction of interest is not a binary choice between having an advisor or not. We are interested in how the individual characteristics of both investor and advisor in-teract, and ultimately impact investment decisions. The fields of psychology and organizational behavior address exactly these issues for advisory situations in gen-eral, and not financial advice in particular. Such situations are referred to as Judge-Advisor Systems (JAS). According to Sniezek and Buckley (1995: p. 159), advisors

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“formulate judgements or recommend alternatives and communicate these to the person in the role of the judge”. In this literature, the banking client would be the judge, as he makes the ultimate decision. Scholars contribute to these JAS mostly with experiments. They show under which circumstances advice is followed (advice utilization) or not (advice discounting), and how the characteristics of individuals may influence this choice (Bonaccio & Dalal, 2006).

The majority of JAS research focuses on the advice-taking of the judge (or bank-ing client in this case) and only a minority on the behavior of the advisor. Jonas and Frey (2003) show in their study on giving advice that advisors act differently in their role as advisors, compared to the decision they would make for themselves. Advi-sors recommend options that they think most clients prefer, even if such advice contradicts their own preferences (Kray, 2000). When having a decision space be-tween different choices, advisors and judges differ in how to compare them. Con-cretely, advisors prefer the choice providing the best outcome on the attribute that is most important. This specific weighting of attributes is more specific to advisors, as personal decision-makers seem to review them more evenly (Bonaccio & Dalal, 2006). Furthermore, in their capacity as advisors, individuals are relatively more concerned about the accuracy of their recommendation. For their own decision, the same individuals are less concerned about accuracy of different alternatives (Jonas & Frey, 2003). However, giving advice is not always the same. In some cases, an advisor is making a recommendation and the judge/client makes the decision. In other cases, the advisor is even asked to make the decision on behalf of the client. For these situations, advisors show an increased tendency to prefer information that supports their position (Jonas, Schulz-Hardt, & Frey, 2005). Direct accounta-bility for outcomes seems to make it more important to convincingly justify a deci-sion than elaborating on all alternatives. On the other hand, the situation where one person makes decisions on behalf of another also suggests a reduction in the bias through loss aversion (Polman, 2012). This does, however, not mean that individ-uals choose per se more risky options for others – it only shows that they display less loss aversion as a bias.

From the client perspective, the choice of advisors shows two major patterns in the literature. In studies where clients could decide on their advisors, those advisors were preferred that offered some unique information that would complement the knowledge of the clients, and not the advisors with whom the advisee shared infor-mation (Van Swol & Ludutsky, 2007). This tendency is remarkable as in the ‘small

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groups’ literature in psychology, where the JAS research is rooted, usually shared information is preferred over unique information (Bonaccio & Dalal, 2006). Hence, individuals prefer advisors that have information that are not similar to their own. However, similarity still makes a difference when choosing advisors: when it comes to the characteristics or personality of advisors, Gino, Shang, and Croson (2009) suggest that clients prefer advisors that are most similar to themselves. Individuals like others better if they are similar to themselves, and more inclined to comply with a request or advice if they like the advisor (Cialdini & Goldstein, 2004). Similarities in this regard do not need to be profound. Having similarities in values, attitudes or opinions is just as impactful as surface-level similarities in demographics (Phillips, 2003; Phillips & Loyd, 2006). This tendency of choosing similar advisors is particu-larly strong if the client has to make a decision for himself and he will be faced with the consequences. In cases were individuals decide for somebody else, they do prefer an advisor that is dissimilar to themselves (Gino et al., 2009). In summary, if decision-makers decide at their own risk, they prefer advisors with similar charac-teristics to their own, and who possess information that are not similar to their own. If deciding not on their own behalf, they prefer advisors with characteristics dissim-ilar to their own in an attempt to justify the decision.

Choosing an advisor does not imply that the judge will accept all advice from the advisor to the full extent. Scholars have found several factors that increase the likelihood of advice being taken. Gino (2008) finds that, generally, advise is taken more often if it is not for free but comes at a cost. Hence, individuals consider value and price to be related. Regarding the characteristics of the advisors, the judges are more likely to accept advice if the advisor is experienced (Feng & MacGeorge, 2006) or he appears confident (van Swol & Sniezek, 2005). However, also the per-sonal characteristic of the advised individual influence the accepting of advice, for example the task experience in a given situation (Dalal & Bonaccio, 2010). For discrete choices, studies show that judges are influenced by advisors and they de-viate from their original estimate. Yet, on average they only move about 30% in the direction towards the advisor’s estimate, shows Yaniv (2004). Apart from arguing that anchor effects may drive judges to ignore advice, another explanation remains that they feel superior to the advisor and thereby more accurate and important (Soll & Mannes, 2011). This is confirmed by Tost et al. (2012), who show that individuals with higher levels of power over others are more confident and thereby less likely to take advice. Independent on the control over other but just related to the self-

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image of individuals, Kausel, Culbertson, Leiva, Slaughter, and Jackson (2015) suggest that people with narcissistic tendencies are also less likely to follow advice.

The literature on the psychological effects of advisory situations suggest a set of typical behaviors for both advisors and their clients. Independent of the expected roles of an advisor discussed in the previous chapter, personal characteristics of both parties trigger what advice is provided and if advice is accepted. Important for this study is the finding that similarity in characteristics may be important and the extend by which decisions are delegated from a client to his advisor.

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4 Development of hypotheses and expectations The research strategy in this study follows a three-step approach as introduced in chapter 1.2. The theoretical concept in chapter 2 provides an overview of theory to which I attempt to contribute. That includes the literature on the linguistic relativity hypothesis (LRH) in general, and application of future-time reference (FTR) in par-ticular. Both components are accompanied by the theoretical considerations on the triangular relationship between language, culture, and cognition. This overall theo-retical concept provides the lens through which I analyze the phenomenon of inter-est. Chapter 3 on the analytical focus then further describes this overall phenome-non of investors’ risk behavior in its three major components: the theory on risk behavior, background on the specific scope of (U)HNW investors and considera-tions on the client-advisor relationship. As a result of these two steps, I develop hypotheses and expectation throughout this chapter, concluding the theoretical part of this study. The third step follows later with chapter 5, where I provide details on the concrete empirical context. The specific data set allows to empirically test my hypotheses and expectations.

4.1 Design of hypotheses

Languages differ with regards to numerous characteristics. At the same time risk-taking and risk propensity differ between private investors. Research in linguistic relativity as applied in psychology and, as of late, in economics assumes that there is causal link between language characteristics and its speakers’ cognition and be-havior. While the link between the future-time reference of individuals and their fu-ture-oriented behavior has been established for a variety of phenomena, this study proposes causal link between future-time reference and the risk behavior of private investors. In contrast to previous studies across the FTR literature, I do not only consider the language of the decision-maker, but I distinguish between three differ-ent language environments to influence economic outcome. First and in line with previous literature, I expect that the FTR characteristic of the investor impacts his investment decisions. Second, in the particular case of a banking relationship, I expect that the linguistic characteristics of the advisor also impacts the decision-making of the investor. Third, I assume that those investors living abroad will also be affected in their cognition as a result from being exposed to another culture and potentially another language. By considering these different language environ-ments instead of the simple investor FTR, I follow FTR scholars applying the LRH

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in context of corporate decision-making (Kim et al., 2017; Liang et al., 2014). They find that in a company the language environment of headquarters country has im-pact but also the personal mother tongue of the CEO, as well as the extent to which the company and the CEO have (had) exposure to other countries. I apply their findings to the private banking environment as I expect that, also here, decisions are not made in isolation. As a result, I expect an investor language effect (hypoth-esis 1), an advisor language effect (hypothesis 2) and a migration effect (hypothe-sis 3).

Previous literature suggests that language characteristics cannot be the only in-fluencing factors to ultimately drive individual behavior. As a result, I control for market effects, home culture, and structural effects from the individual portfolios. To understand how these confounding factors potentially influence portfolio risk, I also derive a series of expectations based on theoretical considerations. Both, the hypotheses and expectations are later discussed in light of the empirical model.

4.2 Hypothesis 1 – FTR of individual investors

Research on FTR in the context of linguistic relativity has established that the re-quirement to mark the future by means of grammatical construction is causally con-nected to the future orientation of individuals, as well as to their future-oriented behavior. In particular, the evidence discussed across chapter 2.5 shows that FTR impacts saving propensity, health behavior, support of future-oriented policies and general intertemporal choice. So far, however, risk-taking and private investment behavior in this context have not been the analytical focus of FTR research. A first attempt in a similar direction is the contribution by Kovacic et al. (2016), who find that risk behavior is related to the grammatical characteristic of mood. Mood, as discussed in chapter 2.3.2, is closely related to grammatical tense as it provides means to express distance to the factual present. However, also given very limited availability of linguistic category data, their study remains an exception across LRH research. Instead of following their approach, I therefore focus on the FTR charac-teristic of languages and its broader body of literature. In order to make the con-nection between overall future-oriented behavior and risk-taking, I make reference to the conceptualization of time in psychology. In their broad literature review on the field, Zimbardo and Boyd (2005: 96) summarize:

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In addition to performing behaviors that can be predicted to be associated with positive future consequences, individuals high in future time perspective also avoid behaviors that are likely to be

associated with negative future consequences. For example, high future time perspective has been associated with lower risk-taking

[…].

If scholars in psychology find that risk-taking is not only associated with future ori-entation, but it is one form how such future orientation can manifest in behavior, then findings from FTR research should also relate to the phenomenon of risk-tak-ing. In order to allow for a statement that the linguistic characteristic of FTR impacts concrete investment behavior, I must make an assumption on how language and behavior are related. Following the findings from chapter 2.2 and the review of pre-vious studies thereafter, I assume that language impacts behavior not directly. In-stead, language impacts cognition, which conversely affects decision-making (as-sumption 1). This assumption is the precondition to allow for the application of find-ings in cognitive psychology. Furthermore, I must make an assumption on the re-lationship between language and culture and how they influence cognition. I follow the reasoning of Galor et al. (2016) for a triangular relationship between the three components (Mavisakalyan & Weber, 2017): although historically, culture, and lan-guage are related, their different dynamics have made them independent enough in their coevolution that we can distinguish between the effect that language has on cognition and the effect that culture has on cognition (assumption 2). The effect of culture is controlled for by cultural dimensions (see expectation 2). For the lan-guage effect on cognition and ultimately risk behavior in this study I propose:

Hypothesis 1a: The future-time reference of investors has an impact on the volatility in their portfolios.

FTR research on future-oriented behavior suggests that weak FTR speakers are more likely to engage in future-related activities than strong FTR speakers. Follow-ing again the review by Zimbardo and Boyd lets assume that weak FTR speakers display lower risk-taking than strong FTR speakers. The overall investor language effect of H1a thereby receives a directional component for risk-taking. Portfolio

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volatility as a measure for risk-taking can vary over time for the same investor, especially in the short term. Research on the influencing factors for risk-taking has shown that the risk propensity of investors is closely related to longer term portfolio risk (see chapter 3.1). From the causal relationship that risk propensity drives port-folio risk, I assume that, conversely, portfolio risk may serve as an indicator for underlying risk propensity (assumption 3). Hence, I propose:

Hypothesis 1b: Investors with a strong future-time reference display higher risk propensity than investors with a weak future-time reference

Discussing the risk propensity of investors instead of the risk-taking measure of portfolio volatility puts potential findings from testing H1b in analogy to previous FTR studies. In particular, this study is the attempt to propose a language effect on risk propensity in parallel to the already established literature on the language effect on saving propensity.

4.3 Hypothesis 2 – FTR of advisors

Advisors in the context of a banking relationship are human beings with a mother tongue just like their clients. In a first step, it is safe to assume that the profession of an individual is unlikely to completely override mechanisms of cognition. Hence, I expect that, on an individual level, advisors display the same language effect as their clients. Hypothesis 1 should therefore also hold for them, in a broader sense. However, analytical focus of this study is not the advisor as investor himself, but the question how the advisor in his role as advisor impacts the decision-making of his client. Hence, the decision of interest for the advisor is not making an investment but the activity to search information, sort information and translate a reduced set of information into a recommendation for his client (see Cruciani, 2017). If we now take the indication from prior FTR literature that did not necessarily assume the concrete case of financial decision-making, then the forming of recommendations may also be affected by such FTR effect. This alone does of course not automati-cally translate into portfolio activity. To make this connection it is necessary to de-ploy the psychology literature on advice-taking. Advice takers, bank clients in this particular case, do not automatically follow the recommendations of their advisors, and if they do, they might discount it. Yaniv (2004) for example shows this

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tendency. Still, even if recommendations may not be fully implemented, direction-ally, clients at least tend to move into the direction of the recommendation. In sum-mary, I expect that also advisors are affected by a language effect in their advising activity and given that clients have the tendency to at least follow the direction of their advisor, I propose:

Hypothesis 2a: The future-time reference of advisors has an impact on the portfolio volatility of their clients

With regards to the direction of the advisor language effect, I assume the same as for hypothesis 1. All things being equal and independent of the investors FTR characteristic, I expect that the FTR characteristic of the advisor affects the risk-taking of his client in the following way:

Hypothesis 2b: Investors with a strong future-time reference advisor display more risk-taking than investors with weak future-time reference advisors

4.4 Hypothesis 3 – Migration of individual investors

Private investors on the upper wealth bands have, as established in chapter 3.2 the distinct opportunity of mobility. In contrast to other forms of migration, mostly on the lower wealth bands, the impact on the identity as manifested acculturation is only addressed in the literature on expatriates for this population. Nonetheless, possible reactions to acculturation in form of identity change show a broad range of possibilities. Hence, from a cultural perspective it is not clearly established if and how living abroad may influence the particular aspect of risk-taking or risk propen-sity. However, regarding a pure foreign language effect, scholars like Hayakawa et al. (2016) state that using another language than individual mother tongue shows impact on risk-taking and, more generally, a reduction in decision biases. Although these studies do not refer to FTR in particular, they provide reason to expect that the exposure to a secondary language environment can alter the language impact on cognition. As a result, psychology literature shows that the cultural element of living abroad may have impact but can go either way, depending on the individual,

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whereas the linguistic element of living abroad may reduce biases. Overall, I pro-pose:

Hypothesis 3a: Migration of investors has an impact on the volatility in their portfolios.

Evidence in psychology suggests that there is an effect resulting from living abroad, but there is no clear direction for this effect. On first sight, it might be intui-tive to assume that those who are brave enough to leave the comfort zone of their own country may be per se more risk-seeking in nature. This, however is denied by scholars like Bonin, Constant, Tatsiramos, and Zimmermann (2009), who find that first generation immigration are indeed more risk averse. An effect which seems to diminish with the next generation. It is important to mention that Bonin et al. do not refer to migrants on the upper wealth levels. Hence, their findings cannot suffice to assume a directional effect in the context of wealth management.

The pure linguistic effect that any foreign language reduces bias hints to a mod-eration effect. A similar assumption can be derived from the FTR literature on cor-porate behavior. Liang et al. (2014) find that international exposure for a company and international experience for a CEO moderate the language effect from the home country FTR. I therefor also expect a moderation effect on the portfolio risk of investors. In line with assumption 3 on the relationship between risk-taking and risk propensity, I propose the following migration effect:

Hypothesis 3b: Living abroad moderates the language effect on risk propen-sity for investors.

4.5 Expectations on potentially confounding factors

Apart from the three main effects in this study, I expect a certain behavior of poten-tially confounding factors in the analysis. Risk-taking and risk propensity are, as discussed throughout chapter 3.1, affected by factors other than language. Demo-graphic factors are not included as a result of restrictions in the data set. This and other restricting factors for the concrete data set are discussed in chapter 5 on empirical context. Most importantly, the control factors for this study include market

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volatility, comprehensive cultural dimensions, and structural factors from the indi-vidual portfolios.

Portfolio volatility as dominant measure for risk-taking behavior is related to the volatility across the market. Different asset classes show differences in how their volatility levels typically differ with respect to one another. Choosing between dif-ferent assets classes and even between individual assets drives the portfolio vola-tility of investors. This portfolio choice is at the core of this study as this is the part of volatility in a portfolio that an investor can steer. In contrast, the investor is not able to steer market volatility. Merkle and Weber (2014: p. 376) state in this context that “[…] a large part of the changes in portfolio volatility will be passively caused by changes in market volatility […]”. The banking clients in this study can be as-sumed to have all similar access to global markets as they are all served by the same institution.11 Thus, I could control for this with a volatility measure on a global market index. Instead, I propose to control for the volatility of regional market indi-ces with distinction of development status for two reasons. First, finance scholars like Karlsson and Nordén (2007) find for investors a certain home bias, i.e. the tendency to not fully diversify their portfolios across geographies. Although Broer (2017) finds that increased wealth levels appear to have a negative relationship to home bias, a certain preference for the close markets seems to persist. Second, variation in returns typically differ when comparing developed markets with emerg-ing markets, with the tendency of higher market volatilities in non-developed mar-kets (Maharaj, Galagedera, & Dark, 2011). In acknowledgement of how influencing market volatilities are on portfolio volatilities and while distinguishing potential dif-ferences across regions, I propose:

Expectation 1: A higher volatility in the equity markets of an investor’s home region leads to higher volatility in his own portfolio

As a result of the review across chapters 2.2 and following, I introduced assump-tions 1 and 2 above. They summarize my understanding that language and culture both affect cognition (and thereby behavior), and although language and culture are historically connected, they are different enough to distinguish the effects

11 Certain limitations hold with regards to suitability and other regulation. These factors, how-

ever, are not assumed to attenuate the overall argument.

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between the two. Hypothesis 1 captures the expectations for this language effect. To test the related culture effect I follow most scholars in FTR research (see chapter 2.5) and numerous other studies across business and psychology (Kirkman et al., 2006) in applying the cultural dimensions framework by Hofstede et al. (2010). In this framework, national cultures are characterized by scores along six different dimensions of culture. While no model is able to completely capture culture in its complexity, and arguably the national level potentially ignores within-country differ-ences, this approach is a solid attempt to capture the diversity of cultures with im-portant nuances. I include controls for differences in culture for two reasons. First, as a result from assuming a language and a culture effect at the same time, I must control for the cultural effect. Second, including a set of cultural dimensions ad-dresses the critique by Roberts et al. (2015) on Chen (2013). They argue that lan-guages are not independent from one another because they are the result of un-derlying cultures, which show similarities because of borrowing certain cultural traits over time. The authors suggest controlling for language families as a potential solution. I argue that if cultural similarities are the root cause, it is the better solution to control for these cultural similarities with their nuances instead of a simplistic language family parameter.

The Hofstede dimensions of culture comprehend the following six dimensions: power distance, individualism, masculinity, uncertainty avoidance, long term orien-tation and indulgence (Hofstede et al., 2010). Culture is complex and on first sight, not all dimensions are intuitively related to risk-taking or risk propensity. Still, as Kirkman et al. (2006) highlight, it is a common weakness in applying the cultural dimensions if scholars only choose those dimensions in their study that they intui-tively expect to be related to their analytical focus. This can lead to missing out on further insights and provides ground for the critique that culture is not controlled comprehensively. Hence, I include the full set of dimensions in this study. However, I expect the two dimensions of uncertainty avoidance and indulgence to be most closely related to the phenomenon of portfolio volatilities. I expect those cultures with high levels of uncertainty avoidance to be less risk-seeking and therefore dis-play a lower portfolio volatility. Similarly, I expect that high levels of indulgence in a society to increase the likelihood of higher portfolio volatilities. For the other dimen-sions, I do not formulate expectations. In summary, I propose:

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Expectation 2: Two out of six cultural dimensions by Hofstede have an impact on the risk propensity of investors. High levels of uncertainty avoidance lead to low levels of portfolio volatility. High levels of indulgence lead to high levels of portfolio volatility.

Structurally, banking clients differ in their amount of assets under management (AuM) with the bank and portfolios differ with respect to their volumes. Both can serve as indicators of wealth size, although there are limitations. If a client has a certain AuM with the bank, it is safe to assume that he at least possesses that much wealth. Yet, a low level of AuM may either mean that the client has low wealth, but it can also simply mean that the rest of the wealth is not with this particular bank. A bank can only know for sure how assets are with them, they do not have perfect information on the overall wealth of their clients. With this limitation, it is at least safe to assume that high volumes correspond with high wealth levels. With regards to risk attitude, West and Worthington (2012) show in their review that the literature on the relationship between wealth and risk propensity is not conclusive on a simple directional tendency. From the perspective of biases and financial literacy, a ten-dency may be inferred from the opportunity of UHNWI to operate through family offices. If a client is represented by a group of financial experts at his personal disposal, a reduction in loss aversion may be a result. However, this does not tell anything on the risk propensity itself, only on being potentially less prone to biases. Still, one other behavioral bias may come into play in the context of portfolio risk: mental accounting (see Cruciani, 2017). In line with this it is conceivable that inves-tors hold multiple accounts on which they display different risk propensities. One smaller account may be considered as play money and a larger account serves the longer-term goal of wealth preservation. In summary, I expect at least a weak ten-dency that higher volumes in AuM and portfolio assets are related to lower volatili-ties:

Expectation 3: Higher levels of AuM and portfolio volume are leading to lower portfolio volatility

The share of cash on client level has no direct influence on the volatility of a client’s portfolio. After all, the cash is not invested in the portfolio. Holding larger

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amounts of cash can be due to a variety of reasons, the most obvious being that liquidity is required for consumption. Higher wealth levels can correspond to a more expensive lifestyle requiring more liquidity. Hence, absolute cash amounts must be reviewed in relation to the overall wealth levels. The purpose of this study is under-standing the risk propensity of investors and the variance of portfolio returns serves as proxy for risk propensity. The share of cash holding, not absolute cash amounts, can be understood as an alternative measure of risk propensity (See for example Barasinska, Schäfer, & Stephan, 2012). At the end of the day, I assume that an investor makes two major decision based on his risk propensity: how much of my assets do I keep in cash-equivalent form and how much risk am I willing to take on the rest that I invest? I therefore propose:

Expectation 4: Investors with high shares of cash in their overall assets dis-play lower portfolio volatilities

Portfolios differ in the service model that is agreed upon with the bank. Chapter 3.2.1 elaborates on the differences between a discretionary mandate, an advisory mandate and ad-hoc advisory. In the first case, the client delegates the concrete investment decisions to the bank, which on behalf of the client and following his strategy decides on portfolio allocations. In the other two instances, the client re-mains the ultimate decision-maker. A major difference remains the involvement of the relationship manager and the role of advice. In this classic banking setup of ad-hoc advice, the involvement of the advisor is not contractually agreed and can vary from relationship to relationship. In their study on the differences between advisory and discretionary mandates, Cao et al. (2017) find that volatilities of discretionary mandates are lower than those of advisory mandates. Hence, I propose:

Expectation 5: Investors with high shares of discretionary solutions in their overall assets display lower portfolio volatilities.

Expectation 6: Investors with high shares of advisory solutions in their overall assets display higher portfolio volatilities.

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5 Empirical context 5.1 Data set and background

I have the privilege to base all the analyses in this study on one single source of data as far as portfolio information, investor and advisor characteristics are con-cerned. A large internationally operating bank with strong wealth management ca-pabilities has produced this data set in their Management and Business Information Systems unit and granted me access for the purpose of this research. Complying with legal regulations and internal guidelines, all data has been strictly anonymized for analysis, excluding all client or employee identifying information. Furthermore, all results are reported on an aggregated level, preventing readers to draw conclu-sions about individuals or even single countries in scope. As the data set is only a sub set of the bank’s complete client base, none of the reported results provide complete information on the client structure or strategy of the bank. In addition, whenever relating to risk propensity in this study, I only refer to observed volatility of returns over a 12-months period (see chapter 5.2 for more details). This does not refer to the relationship between self-declared risk appetite and risk of chosen products. In addition, the data set does not include any information on returns them-selves as this study only focuses on observed volatilities. Risk alone is neither a positive or negative indicator for the quality of a portfolio and no reference is made to individual risk suitability rules. Results do not allow to draw conclusions about quality of services, adherence to regulation or client satisfaction at any stage.

The data set as per 30 April 2017 contains more than 21,000 observations on individual portfolio level. Investors from Europe, Asia, the Middle East and Latin America are included in the sample. Both, developed and emerging markets are represented across more than two dozen countries. Strong and weak FTR lan-guages can be found in all regions and across different levels of development. FTR, as a result, neither represent specific regions nor wealth levels. To prevent bias from small country samples, nationality and domicile countries with less than 150 portfolios remain out of scope and are not included in the 21,000 observations. Portfolios with more than 90% of assets held in cash and not invested are excluded. To account for cash holdings as a structural indicator for risk propensity, share of cash is included as covariate (see chapter 5.4).

When reviewing the results of analysis, it is crucial to understand the character-istics of wealth management clients in scope of this analysis. Any retail customers

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are excluded from analysis, leaving only high net-worth or ultra-high net-worth in-dividuals in scope. These global millionaires and billionaires display characteristics, needs, time perspectives, and risk preferences that differ from the average popu-lation in most countries. The motivation to invest is not only rooted in saving for future consumption or retirement capital, but the wealth preservation across gen-erations becomes crucial with elevated levels of wealth. I therefore assume that a lifecycle perspective of salaried employees between first job salary and saving for retirement does mostly not apply to the individuals in scope.

Figure 5.1 provides an overview of the variables used in this study. Each of the variables is discussed in depth throughout the following chapters.

Figure 5.1: Overview of variables

5.2 Dependent variable

The risk-taking and thereby the risk propensity of investors is the core variable of this study. It can be defined in multiple ways, hence requiring additional background on assumptions and details of measurement. For this study, risk is represented by the volatility of returns, which is a standard in wealth management and the typical way to communicate the risk of assets and portfolios to investors (Sachse, Jungermann, & Belting, 2012). The volatility of returns is based on historic

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performance and a backward-looking indicator. As such, it may represent risk, but not necessarily risk propensity of investors. In the setup of a bank-client relation-ship, the beneficial owner (BO) of a portfolio is the ultimate decision-maker. In a discretionary mandate, the BO defines strategy and risk appetite, leaving the indi-vidual investment decisions to achieve those goals to the bank. In other situations, the BO makes the final investment decision himself (see chapter 5.4 for more de-tails on the difference between discretionary and advisory solutions). We can there-fore assume that historic volatility in a portfolio is based on the decisions of the investor himself. One may argue that, while this displays the actual risk over time, it does not accurately reflect the risk propensity of the investor as the assets might not have performed exactly in line with his expectations; markets may have been adverse, and, after all, historic performance can never be a complete prediction for future results. This is accurate for the perspective of the individual investor but as this study does not discuss the relationship between historic results and the pre-investment expectations, it is not relevant. As the data set is based on the exact same period for all investors, they have access to the same global set of assets12 and they are all affected by the same economic factors, volatility of returns is as-sumed to be solid proxy for the investors’ underlying risk propensity in the aggre-gate. From a theoretical perspective, chapter 3.1 reviews evidence that risk pro-pensity is, in the longer term, well reflected in portfolio risk. The theoretical consid-erations also provide a clear argument for using portfolio volatility and not self-as-sessed risk propensity, although they are evidently closely related. Any self-as-sessment that a bank performs with a client is conducted through the means of language. Chapter 2.5 has shown that conducting surveys in different languages may lead to different results. This is in line with the overall expectation that lan-guage, through different mechanism, affects cognition. Evaluating the risk that an investor chose through his portfolio selection is therefore the better dependent var-iable as a proxy for risky behavior than survey approaches (see further aspects to this argument in chapter 3.1). Furthermore, as discussed in chapters 2.5.5 and 3.1, surveys in their more hypothetical nature may be subject to biases.

The volatility of returns in this study is computed as the variance of monthly port-folio returns over a period of 12 months. The period of 12 months was chosen be-cause it is the same period that is typically reported to investors and it is in line with

12 While due to legal restrictions in some countries and suitability rules for some investors some

products may not be available to some investors, it can be assumed that such restrictions are only an exception to the global availability of assets.

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findings by Merkle and Weber (2014) that risk propensity and portfolio risk are most closely related for longer time periods. Although investors can monitor the perfor-mance of their investments at any point in time, the standard period reported in portfolio reviews is the previous 12 months. This period also allows to neglect any intra-year seasonality that might be typical for certain asset classes within the port-folio (Bouman & Jacobsen, 2002).

5.3 Main factor variables

Future-time reference (FTR) is the first main factor of this study, allowing to ad-dress the research question: does the future-time reference of individual investors affect their risk propensity and how does such an investor language effect impact the risk-taking on portfolio level? FTR is a language characteristic that refers to the use of the future tense. As such, it is not only relevant for linguistics, but also to an increasing degree for economics (see chapter 2.4 for theoretical background and chapter 2.5 for recent examples of application in economic research).

Following the approach of Chen (2013) based on linguistic classification of Thieroff (2000), all languages display either a weak or strong future-time reference. A weak FTR means that a language does not force speakers to use a future tense when referring to the future, while strong FTR languages pose such a requirement. Languages of investors in scope of this study are classified into a dichotomous nominal variable of either strong FTR or weak FTR. The data set and information systems of the providing bank do, however, not contain complete information on the mother tongue of clients.13 For legal reasons however, banks are required to document the nationality of their customers. In absence of mother tongue infor-mation, the nationality of investors serves as proxy for their mother tongue. This proxy is reasonable for citizens of countries with only one official language spoken by a clear majority (e.g. German nationality as proxy for German mother tongue). In other countries, more than one official language is spoken and widely-used. I differentiate between two cases to address this complexity. In the case of Canada for example, where English and French are both common and the mother tongue of an individual cannot be differentiated, I find that both languages are strong FTR languages. For the purpose of this study, it can therefore be assumed that,

13 The mother tongue of a client is not business relevant for a bank. They typically store infor-

mation on the correspondence language of clients but that does not necessarily represent the mother tongue as many languages may not be available for correspondence.

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irrespective of the mother tongue of the investor, the FTR factor is strong. Belgium, on the other hand, is an example for the second type of country with multiple lan-guages. As the common languages do not belong to the same FTR category, I cannot deduce the mother tongue FTR of an individual and hence exclude all citi-zens of this country from the study. For all countries in the data set this manual analysis either leads to inclusion or exclusion due to missing information. This proxy approach is in line with procedure in Chen et al. (2017). Switzerland remains the only country in scope where, despite multiple FTR languages, a clear FTR identifi-cation can be performed. As for this particular country the correspondence lan-guage information is available, and it contains the three main languages, I use the correspondence language as a proxy for mother tongue and FTR.

The future-time reference of the advisor (RM FTR) is the second main factor of this study, allowing to address the research question: Is the investment decision affected by the future-time reference of the relationship manager and how does such an advisor language effect impact the risk-taking of individual investors? The theoretical concept of FTR described for the investor in this chapter analogously applies. As the mother tongue of employees is not recorded in the data source (similar to client mother tongue), the same proxy approach is applied as described above. The nationality of RMs is used as proxy on a case by case basis. In case of multiple languages per country, the same approach as for investor FTR either pro-duces a clear FTR classification or leads to exclusion of the country altogether. Switzerland, again, is an exception. Analogously to the correspondence language of clients, I use the application language of advisors as a proxy for their mother tongue if they are Swiss citizens. The application language is the language that employees themselves choose for their IT environment. As the three major lan-guages are available it is fair to assume that RMs chose their mother tongue in-stead of a foreign language.

Migration is the third main factor of this study, allowing to address the research question: does living abroad influence investors and what is the impact of such a migration effect for their risk-taking? In a globalized world it must be assumed that especially millionaires and billionaires as the target group of this study do not nec-essarily live in their country of origin (see chapter 3.2 for theoretical background). The dataset does not only include information about the nationality of investors (used to derive the FTR), but also their domicile. The dichotomous nominal variable of Migration in this study distinguishes between the following two cases: if the

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nationality and domicile country are the same, no migration is assumed. If the na-tionality and domicile country differ, then Migration is confirmed. The evaluation of this variable is static, and it is only assessed at one point in time (in April 2017). The data source does not allow for a historic perspective: if there was migration in the past but an individual is now domiciled in his country of origin, the Migration variable is set to ‘no’.

5.4 Control variables

Volatility of regional MSCI indices (MSCI vol) control for regional characteristics of investors’ home countries. When analyzing the risk that individuals are willing to take with their investments, controlling for regional volatilities serves two purposes. First, individuals from different countries tend to display different risk propensities, depending on country and individual characteristics. Typical risk-taking differs from country to country, depending on several factors, one of which being the develop-ment level of countries (Canale et al., 2018; Falk et al., 2015). Second, behavioral theory suggests that investors display a certain level of home bias, resulting in port-folio allocations dominated by assets with high proximity to their home country or even domestic assets (Karlsson & Nordén, 2007). Although this may be to some extend mitigated by higher wealth levels (Broer, 2017), the tendency must be con-trolled when comparing the influence of FTR languages. This allows to isolate the influence of FTR in the global data set.

In line with various scholars (See Jacobs, Müller, & Weber, 2014) I use indices produced by Morgan Stanley Capital International (MSCI) to analyze the equity market developments with respect to volatility. MSCI offers a broad variety of indi-ces on different aggregation levels. For this study, I used the regional indices with the country classification shown in Figure 5.2. These indices differentiate between regions, but also between development levels (developed markets, emerging mar-kets, frontier markets). That means that investors from Germany, France and the Netherlands are compared to the MSCI Developed Markets Europe & Middle East index, whereas individuals from Brazil and Mexico are compared to the MSCI Emerging Markets Americas index. Analogous to the portfolio-level volatilities (de-pendent variable), for each index the variance of monthly returns is computed dur-ing the same 12 months period as for the portfolios. I do not use individual country level stock market indices as they might differ in composition and definition. Some countries may be performance indices while others are price indices. The MSCI

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regional indices (price indices) are consistent across regions and thereby allow for better comparison. Furthermore, regional indices are better diversified than country indices and may therefore be a better proxy for general risk propensity in a region.

Figure 5.2: Overview of MSCI indices (MSCI, 2017)

Cultural dimensions (power distance, individualism, masculinity, uncer-tainty avoidance, long-term orientation, indulgence) according to Hofstede et al. (2010) are included as controls to address the discussion from chapter 2.3.3 on the relationship with between culture and language. Culture as a concept is very broad and it is not limited to the notion of risk propensity as main focus of this study. Aggarwal, Faccio, Guedhami, and Kwok (2016) summarize that various cultural dimensions have influence not only on the country and corporate level when mak-ing financial decisions, but culture also impacts the individual.

While there are several approaches to evaluate culture in cross-country compar-ison, the approach by Hofstede (1980) remains after some refinement the most widely used (Aggarwal et al., 2016). The following six dimension with definitions by Hofstede (2011) are included as control variables:

§ Power distance “The extent to which less powerful members of organi-zations and institutions (like the family) accept and ex-pect that power is distributed unequally” (item, p. 9)

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§ Individualism “The degree to which people in a society are integrated into groups. On the individualist side we find cultures in which the ties between individuals are loose: everyone is expected to look after him/herself […]. On the collec-tivist side we find cultures in which people from birth on-wards are integrated into strong, cohesive in-groups […] that continue protecting them in exchange for unques-tioning loyalty, and oppose other in-groups” (item, p.11)

§ Masculinity “Masculinity versus its opposite, femininity, again as so-cietal, not as an individual characteristic, refers to the distribution of values between the genders which is an-other fundamental issue for any society, to which a range of solutions can be found. […] The women in fem-inine countries have the same modest, caring values as the men; in the masculine countries they are somewhat assertive and competitive, but not as much as the men, so that these countries show a gap between men’s val-ues and women’s values” (item, p. 12)

§ Uncertainty avoidance

“Uncertainty avoidance is not the same as risk avoid-ance; it deals with a society’s tolerance for ambiguity. It indicates to what extent a culture programs its members to feel either uncomfortable or comfortable in structured situations. Unstructured situations are novel, unknown, surprising and different from usual” (item, p. 10)

§ Long-term orientation

“Values found [for societies with long-term orientation] were perseverance, thrift, ordering relationships by sta-tus, and having a sense of shame; values at the oppo-site, short-term pole were reciprocating social obliga-tions, respect for tradition, protecting one’s ‘face’, and personal steadiness and stability” (item, p. 13)

§ Indulgence “Indulgence stands for a society that allows relatively free gratification of basic and natural human desires re-lated to enjoying life and having fun. Restraint stands for

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a society that controls gratification of needs and regu-lates it by means of strict social norms” (item, p. 15)

The cultural dimensions framework by Hofstede et al. (2010) provides scale level results for each of the six dimensions. These country-level results are mapped to the nationality of the investors to describe the influence of their home culture. Using cultural controls from a national (home country) level is arguably a simplification, which cannot take into account the diversity of sub-cultures within a country. After all, the development of sub-cultures in contrast to languages (which are more per-sistent over time) constitutes the main argument for the triangular relationship be-tween language, culture and cognition (see argumentation in chapter 2.2.2). Yet, I use the Hofstede dimensions for two simple reasons: first, the simplified six-dimen-sional framework by Hofstede was developed to compare larger groups and to identify strong differences in cultures. In this study with a global scope, the goal is not to perfectly distinguish between the nuances of fragmented sub-cultures, but to see if the differences in broader cultural spaces impact individual investment deci-sions. Second, the level of abstraction that comes with using the Hofstede dimen-sions is similar to that of FTR. In absence of detailed survey data on the language history, I can only assume a mother tongue from the nationality of an investor. In-sofar, I use a level of abstraction for the language and the culture of an investor that is determinate by national borders and citizenship. While this cannot be a per-fect representation, it is however consistent, as language and culture are intro-duced to the model on a like-for-like basis.

Portfolio structure (AuM, portfolio volume, share of cash, share of discre-tionary solutions, share of advisory solutions) is introduced to control for more technical aspects driving volatilities. The amount of assets under management (AuM) represents the volume of client assets, including all advisory and discretion-ary assets, assets in trust instruments and assets with third parties but managed in private banking. It consists of the passive balance of all balance sheet accounts, fiduciary investments (deposits), and client safekeeping accounts. AuM are valued on the basis of end-of-month prices used in financial accounting to value client safekeeping accounts.14 AuM relate to the overall client relationship with the bank.

The portfolio volume as the second measure of volume is a measure of the positive assets in a portfolio, comprising depot holdings and related account

14 Definition according to the data providing bank.

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balances.15 The underlying assumption to include volumes at all is that wealth lev-els may impact the volatilities investigated in this study. Portfolio volume, as the name suggests, refers to the individual portfolio. One client can have multiple port-folios, while the amount of AuM is measured per client relationship. AuM can be understood as the wealth a client has with a bank, whereas the portfolio volume is a subset of that, referring only to the specific portfolio. Both volume measures are included as control variables in this study because they control for two different aspects: AuM is a proxy for the overall wealth of an individual16, while the portfolio volume is limited to the individual portfolio. This accounts for non-symmetrical cases where a client heavily uses one portfolio for transactions and keeps a smaller portfolio on the side.

In contrast to the volume indicators, asset mix indicators describe the structure of assets. Cash for this study is defined as the sum of cash and time deposit vol-ume17 and fiduciary deposits18 excluding volumes of Swiss 2nd and 3rd pillar ac-counts19. To derive the share of cash, it is divided by the amount of AuM. This indicator is based on the client relationship and not on the individual portfolio, sim-ilar to the AuM indicator.

The shares of discretionary and advisory solutions are very different from the share of cash indicator. While cash holdings serve as additional indicator for risk attitude, the amount of discretionary and advisory solutions refer to the way a client is served and priced in his investment activities. It defines the roles of client and advisor and is therefore not only an important structural factor for risk-taking, but it can also determine the penetration of RM advice.

Discretionary solutions include discretionary mandates, alternative investment instruments such as hedge fund discretionary or private equity discretionary and multi-asset class portfolio funds. The term discretionary mandate includes the eco-nomic and technical management of assets under a discretionary portfolio

15 Definition according to the data providing bank. 16 It can only be a proxy because clients on the upper wealth bands often work with more than

just one bank, among which wealth is typically split. 17 According to the data providing bank: the sum of money market and account deposits which

can be withdrawn without advance notice and money market and account deposits with fixed maturity date and all client balances from private and saving accounts.

18 According to the data providing bank: an investment placed overseas. The investment is made in the name of the bank but for the account and at the risk of the client.

19 The pillar 2 retirement capital is dedicated exclusively and irrevocably to occupational retire-ment provisions. The pillar 3 accounts specialize in personal pension provisions and tax-privileged capital accumulation.

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management agreement. The client’s contractual agreements with the bank require the latter to carry out all transactions considered appropriate as part of the normal portfolio management service.20 In a nutshell, discretionary solutions are all those circumstances where a client does not decide on each and every investment or transaction but mandates the bank to conduct it on his behalf, following a clearly stated agreement. The client still defines the strategy and risk-taking. The share of discretionary solutions is measured as the ratio between discretionary solutions volume and AuM. Choosing discretionary solutions may also be a sign for the in-vestors risk propensity. If a client is willing to relinquish more control, he might thereby show a lower risk propensity for a given level of agreed risk.

Advisory solutions on the other hand are a special form of the regular client servicing model, where the client decides on each and every transaction made. Advisory solutions are defined as the sum of advisory mandates with an advisory fee or an investment consultant involvement, as well as alternative investments such as hedge fund advisory and real estate advisory.21 An advisory mandate can be understood as institutionalized advisor-client relationship with contractual agree-ments on numbers of interactions with a dedicated RM, what kind of investment specialists are part of the relationship, and typically a pricing model with an advisory fee and reduced transaction fees. As a client you pay for the level of advice that suits your needs and not primarily for the transactions. In addition, there is a con-tractually agreed reporting and portfolio review cycle that increases transparency for the client and ensures the involvement of advisors.

The existence of advisory solutions does not mean that the classic form of ad-hoc advice or direct investments no longer exists. In this classic banking setup of ad-hoc advice, the involvement of the advisor is not contractually agreed and can vary from relationship to relationship. The residual of assets serviced in the classic approach can be computed as the residual from AuM, the amounts of cash, discre-tionary and advisory solutions. The asset quality and product access are not nec-essarily related to the type of service model. The latter, however, may have an impact on the amount of volatility in a portfolio. Delegating to an expect, being ad-vised by an expert and instructing an expert to execute may lead to three different investment decisions.

20 Definition according to the data providing bank. 21 Definition according to the data providing bank.

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6 ANCOVA model

Figure 6.1: ANCOVA model setup

6.1 Model setup

To test the hypotheses from chapter 4, I use a 2 x 2 x 2 ANCOVA design. Analysis of covariance, as an application of the general linear model, allows to compare the average value of the dependent variable across different classifications while con-trolling for other predictors. The dependent variable in the analysis is the 12-months portfolio volatility. The three main factors are the future-time reference of an inves-tor (FTR), the FTR of the advisor (RM FTR), and the information whether the in-vestor is living abroad (Migration). The ANCOVA is an F-test with the null hypoth-esis that there is no treatment effect, i.e. that the means across all groups are equal. Similar to the Analysis of Variance (ANOVA) the test compares the within-groups variation to the between-groups variation. Including covariates into the comparison helps reducing error variance within groups and reduces the influence of confounds (Tabachnick & Fidell, 2014). Four types of covariates are included in the model: first, volatility of regional MSCI indices control for region-specific risk levels and

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potential home-bias (regional market volatility). Second, cultural dimensions by Hofstede control for the influence of culture as opposed to language. Third, AuM and portfolio volume provide information on the wealth levels of investors (asset volumes). And fourth, share of cash provides an additional risk propensity measure, whereas share of discretionary and advisory solutions informs about the chosen service level (asset structure). Figure 6.1 summarizes the model setup.

Hypotheses 1a and 1b are tested based on the factor significance and pairwise comparisons of FTR and the related two-way and three-way interaction effects. In analogy, the factors RM FTR and Migration allow testing for hypotheses 2a / 2b and hypotheses 3a / 3b, respectively. The four covariates groups are included be-cause I expect them to influence the dependent variable as developed in chapter 4.5. Although not part of the main-hypotheses testing, discussing if the expecta-tions formulated in chapter 4.5 can be confirmed can provide interesting insights for the phenomenon of risk propensity in wealth management.

6.2 Model validity

ANCOVA requires the linear model assumptions of linearity, independence, nor-mality, and homoscedasticity. The data set contains a total 21,560 observations, which qualifies as a large sample. As the data does not follow a normal distribution a log transformation was performed on the dependent variable of 12-months port-folio volatility. The significantly large sample allows us to assume normality neglect-ing the exact sample data shape (Lumley, Diehr, Emerson, & Chen, 2002). Hence, the assumption of normality is not violated.

Regarding the assumption of homoscedasticity, comparing groups shows that variances are not equal, but differences are rather small (variance ratio of 1.5).22 For data sets with some level of heteroscedasticity Field and Wilcox (2017) suggest methods that are robust to potential violations of the homoscedasticity assumption. In this study I apply the method of bootstrapping, which estimates the properties of the sampling distribution from the sample data (Wright, London, & Field, 2011). The concept of bootstrap is applied to the parameter estimates (Table 6.2), to correla-tion coefficients and all pairwise comparisons (see appendix from p. XXV for a com-plete overview). The bootstrapped results for confidence intervals and significance

22 Levene’s test of homogeneity of variance is significant. However, using this test is debatable

if applied to significantly large samples (Zimmerman, 2004).

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levels are reported together with non-bootstrapped results. Comparison between the two shows that the initial model is robust to minor deviations from assumptions.

In contrast to a 2x2x2 ANOVA model with the same main factors, the model in this study includes a number of covariates to reduce error within groups. The re-sulting ANCOVA model is subject to the assumption of homogenous regression slopes. In the context of this study this would mean that the groups resulting from the main factors do not differ in the regression of the dependent variable (portfolio volatility) on the covariates (Miller & Chapman, 2001). Before discussing if this as-sumption holds and what potential consequences may emerge for the validity of the results, it is important to mind the design of this study. The ANCOVA method is commonly used in the context of experimental designs, where groups are formed randomly. This study, however, forms natural groups that are not the result of ma-nipulation but of naturally occurring characteristics (Frey, 2018): much like groups based on gender, as example, the groups in this study are based on information of nationality and domicile, all of which are pre-treatment characteristics. Frey (2018) suggests that the assumption of homogeneity of regression slopes can be more readily violated in such a quasi-experimental design than for randomized experi-mental designs. Furthermore, the ANCOVA model in this study is quite large with 2x2x2 main factors and a total of 12 covariates. Hence, it can be expected that some shared variance between groups and covariates occurs. To illustrate the con-sequence of potential overlaps between covariates and groups, see Figure 6.2 and the following line of argumentation by Miller and Chapman (2001).

Figure 6.2: Groups and covariates in ANCOVA (Miller & Chapman, 2001)

Figure 6.2 shows the simplified relationship between dependent variable, covariate and group membership with respect to their variance. The left-hand side shows the

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optimal situation for the application of the ANCOVA method: group and covariate do not overlap, so the expected result of the covariate in the model would be the same, irrespective of the group. Including the covariate thereby reduces variance of the dependent variable (area 4) that is not related to group membership. This area 4 embodies the difference between the ANCOVA model (where noise in area 4 is removed) and the simple ANOVA without including the covariate. In such cir-cumstance, including the covariate increases the power of the test. On the right-hand side, the quasi-experiment shows correlation between the covariate and the group membership (areas 2 and 5). Understanding ANCOVA as linear model where the covariate is entered in the model first and then the main factors and interactions, this means that not only area 4 is removed by the covariate but also areas 2 and 5. The latter embody the variance that is shared between the covariate and the group. As previously shown on the left, the ANCOVA model on the right would compare area 6 with areas 6+7 (Miller & Chapman, 2001). The larger the area 5 is in a given data set, the more problematic the inclusion of the covariate becomes. This can lead to problems for interpretation and, statistically, increase type II error, making the model more conservative. However, violating the assumption homogeneity of regression slopes does not automatically disqualify the model. In chapter 6.9 I dis-cuss two strategies to address non-parallel regressions: first, one may want to ex-clude the covariates from the model, reducing it to a simpler ANOVA. The second strategy, on the other hand, does not exclude covariates that correlate with groups but transforms this overlap into theoretical insights by including interaction terms between covariates and groups. Comparing the results between the original AN-COVA model, the reduced ANOVA without covariates and the more complex AN-COVA with additional interaction terms shows that the findings of the main model are stable. This shows that the model is robust to some violations of the homoge-neity of regression slopes assumption.23

6.3 Overall results

Table 6.1 summarizes the results of the factorial ANCOVA. The overall model is significant (F(19, 21540) = 148.980, p = <.0005***) with R2 = .116 (R2 adj. = .115). Main effects, interactions, and covariates are discussed separately in the following

23 For more details, see chapter 6.9. The robustness checks and the related discussion are

only included at a later stage as the following chapters introduce the illustration method of significance boxes, which is an important pre-condition to understand the model comparison later on.

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chapters, taking into account parameter estimates in Table 6.2 as well as pairwise comparisons (see appendix from p. XXV).

Table 6.1: Test of between-subjects effects

*** ** *

Significant at the 0.1 percent level Significant at the 1 percent level Significant at the 5 percent level

Type III sum of

squares dfMean

square F

Corrected Model 276.830 19 14.570 148.980 < 0.0005 ***Intercept 13.010 1 13.010 133.028 < 0.0005 ***

MSCI vol 4.387 1 4.387 44.856 < 0.0005 ***

Power distance 1.142 1 1.142 11.672 0.001 **Individualism 0.146 1 0.146 1.494 0.222Masculinity 0.385 1 0.385 3.932 0.047 * Uncertainty avoidance 4.116 1 4.116 42.089 < 0.0005 ***Long-term orientation 0.002 1 0.002 0.023 0.879Indulgence 0.772 1 0.772 7.892 0.005 **

AuM 0.000 1 0.000 0.004 0.953Portfolio volume 0.099 1 0.099 1.016 0.313Share of cash 134.479 1 134.479 1375.059 < 0.0005 ***Share of disc. solutions 175.174 1 175.174 1791.171 < 0.0005 ***Share of adv. solutions 3.184 1 3.184 32.560 < 0.0005 ***

FTR 0.476 1 0.476 4.863 0.027 *Migration 0.232 1 0.232 2.374 0.123RM FTR 0.643 1 0.643 6.573 0.010 *

FTR * Migration 1.019 1 1.019 10.418 0.001 **FTR * RM FTR 0.129 1 0.129 1.321 0.250Migration * RM FTR 1.004 1 1.004 10.267 0.001 **FTR * Migration * RM FTR 2.035 1 2.035 20.811 < 0.0005 ***

Error 2106.582 21540 0.098Total 10019.397 21560Corrected total 2383.412 21559R sqared = 0.116 (Adjusted R squared = 0.115)

Sig.

Table 6.2: Parameter estimates including bootstrap

B Std. Error t Lower Upper B Bias Std. Error Lower UpperIntercept 1.125 0.101 11.171 < 0.0005 *** 0.928 1.323 1.125 0.006 0.110 0.001 ** 0.912 1.341Regional MSCI volatility -0.243 0.036 -6.697 < 0.0005 *** -0.314 -0.172 -0.243 0.001 0.041 0.001 ** -0.318 -0.16Power distance 0.083 0.024 3.416 0.001 ** 0.035 0.131 0.083 < .0005 0.023 0.002 ** 0.037 0.128Individualism 0.031 0.025 1.222 0.222 -0.019 0.081 0.031 -0.001 0.029 0.274 -0.025 0.086Masculinity 0.028 0.014 1.983 0.047 * < .0005 0.055 0.028 < .0005 0.014 0.039 * < .0005 0.054Uncertainty avoidance -0.225 0.035 -6.488 < 0.0005 *** -0.293 -0.157 -0.225 -0.003 0.039 0.001 ** -0.305 -0.156Long-term orientation -0.004 0.023 -0.152 0.879 -0.05 0.042 -0.004 < .0005 0.027 0.872 -0.052 0.053Indulgence 0.044 0.015 2.809 0.005 ** 0.013 0.074 0.044 -0.001 0.018 0.014 * 0.008 0.077AuM 1.56E-11 2.63E-10 0.059 0.953 -4.99E-10 5.30E-10 1.56E-11 < .0005 3.89E-10 0.973 -7.72E-10 7.60E-10Portfolio volume 2.93E-10 2.91E-10 1.008 0.313 -2.77E-10 8.63E-10 2.93E-10 < .0005 4.87E-10 0.503 -4.61E-10 1.48E-09Share of cash -0.401 0.011 -37.082 < 0.0005 *** -0.422 -0.379 -0.401 < .0005 0.014 0.001 ** -0.430 -0.373Share of disc. solutions -0.245 0.006 -42.322 < 0.0005 *** -0.256 -0.233 -0.245 < .0005 0.005 0.001 ** -0.255 -0.234Share of adv. solutions -0.054 0.009 -5.706 < 0.0005 *** -0.072 -0.035 -0.054 < .0005 0.008 0.001 ** -0.069 -0.036FTR = Weak -0.033 0.022 -1.517 0.129 -0.076 0.01 -0.033 < .0005 0.023 0.153 -0.080 0.011Migration = No -0.026 0.011 -2.390 0.017 * -0.047 -0.005 -0.026 < .0005 0.012 0.034 * -0.050 -0.003RM FTR = Weak 0.020 0.017 1.173 0.241 -0.013 0.053 0.020 < .0005 0.019 0.321 -0.016 0.056FTR = Weak * Migration = Yes

0.133 0.028 4.703 < 0.0005 *** 0.077 0.188 0.133 < .0005 0.029 0.001 ** 0.078 0.19

FTR = Weak * RM FTR = Weak

0.058 0.027 2.117 0.034 * 0.004 0.112 0.058 < .0005 0.029 0.039 * 0.002 0.111

Migration = No * RM FTR = Weak

0.024 0.018 1.289 0.198 -0.012 0.059 0.024 0.001 0.020 0.266 -0.016 0.064

FTR = Weak * Migration = No * RM FTR = Weak

-0.155 0.34 -4.562 < 0.0005 *** -0.222 -0.088 -0.155 -0.001 0.340 0.001 ** -0.226 -0.087

With the three factors FTR, Migration and RM FTR being dichotomous variables, all other (interaction) parameters are not included in above table as values are 0Bootstrap results are based on 1000 bootstrap samples.*** Significant at the 0.1 percent level, ** Significant at the 1 percent level, *Significant at the 5 percent level.

Parameter estimates Bootstrap for parameter estimates95% Confidence Intvl. Sig.

(2-tailed)95% Confidence Intvl.

Sig.

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6.4 Covariates

The ANCOVA results for the covariates show their respective significance levels in Table 6.1. This overview does not include a bootstrap to correct for potential bias from non-perfect homoscedasticity. However, as the covariates in ANCOVA adjust the means of the main factors (and interactions) by way of linear regression, the significance of the covariates in the ANCOVA equals the significance of their pa-rameter estimates in Table 6.2, which are bootstrapped. Hence, I report the boot-strap results of the covariates separately to confirm the robustness of the ANCOVA results for the covariates. Apart from the significance of a covariate, the direction of its relationship with the dependent variable is useful for interpretation. I report two separate relationship levels: first, the sign of the regression coefficient, predict-ing the influence of the covariate on the dependent variable given the overall model. Second, the Pearson’s correlation based on 1000 bootstrap samples, showing the unconditional simple relationship between the covariate and the dependent varia-ble (see appendix p. XXXVIII). For most covariates, the two provide the same di-rection, in other cases they do not. Strong multicollinearity among the covariates as a potential reason for such behavior is not likely as the only correlation r > .8 can be found between the two volume parameters AuM and portfolio volume.

Regional MSCI volatility is highly significant (F(1, 21540) = 148.980, p = <.0005***, bootstrapped p = .001**). Regional MSCI volatility is negatively related to portfolio volatility with a negative regression coefficient (b = -.243), hence a lower volatility in the region of the individual’s home country predicts a higher volatility in his portfolio, and vice versa. This negative relationship is also confirmed when re-ferring to the bivariate correlation (r = -.066, p = <.0005***).

The six covariates representing Hofstede’s dimensions of culture show mixed results. Uncertainty avoidance is the most significant cultural covariate in the model (F(1,21540) = 42.089, p = <.0005***, bootstrapped p = .001**). The regres-sion coefficient (b = -.225) and the correlation coefficient with the dependent varia-ble (r = -.099, p = <.0005***) are both negative. Hence, individuals that originate from a culture, where uncertainty in life is accepted and people are comfortable with ambiguity, display higher levels of risk propensity.

Power distance is highly significant (F(1,21540) = 11.672, p = .001**, boot-strapped p = .002**), with a positive regression coefficient (b = .083). However, the correlation with portfolio volatility is negative (r = -.066, p = <.0005***). While the unconditional simple relationship between power distance is negative, in the overall

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model, the influence is positive. For this study it means that investors from cultures with more emphasized hierarchies and less equality of roles display higher levels of risk propensity.

Indulgence as a cultural dimension is highly significant (F(1,21540) = 7.892, p = .005**, bootstrapped p = .014*). With a positive regression coefficient (b = .044) and a positive correlation with the dependent variable (r = .034, p = <.0005***), the results suggest that individuals from cultures that are less constrained and more focus on leisure and personal gratification tend to take more risk than others.

Masculinity in culture is still significant (F(1,21540) = 3.932, p = .047*, boot-strapped p = .039*), although only at a low level. The regression coefficient (b = .028) is positive and the correlation coefficient (r = .012, p = .067) tends to be pos-itive, although not significantly. With some precaution, this hints to a situation where investors from tougher cultures with stronger separation of gender and admiration for the strong display higher levels of risk propensity.

Individualism as covariate is not significant (F (1,21540) = 1.494, p = .222, boot-strapped p = .274) and neither is long-term orientation (F(1,21540) = .023, p = .879, bootstrapped p = .872).

AuM and portfolio volume as size indicators are both clearly non-significant with p = .953 and p = .313, respectively. Although they display a strong correlation of r = .929 (p = <.0005***) amongst themselves, they were both kept in the model for theoretical considerations as they control for two different aspects of size: the client size and the individual portfolio size. Both do not have an influence in the model.

The three covariates relating to the structure of a client’s assets with the bank give meaningful insights when reviewed individually and also with regard to one another. The share of cash of a client explains how much of the AuM is not in-vested in any portfolios. As the risk for cash is lowest compared with other asset classes, it can serve as additional risk-taking indicator as discussed in chapter 5.4. The covariate is highly significant (F(1,21540) = 1375.059, p = <.0005***, boot-strapped p = .001**) with a negative regression coefficient (b = -.401) and a nega-tive correlation with the dependent variable (r = -.149, p = <.0005***). Hence, the larger the share of cash for a client is overall, the lower his risk propensity on port-folio level will be.

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The share of discretionary solutions refers to the amount of AuM that is ser-viced in a way that the client decides on the strategy overall, but the individual transactions are decided upon by the bank. The covariate is highly significant (F(1,21540) = 175.174, p = <.0005***, bootstrapped p = .001**) with a negative regression coefficient (b = -.245) and a negative correlation with the dependent variable (r = -.165, p = <.0005***). Therefore, the larger the share of assets is where a client mandates the bank to make investments on his behalf, the lower the vola-tility in the portfolio will be.

The share of advisory solutions refers to the amount of AuM where the client still makes all investment decisions but is served in an institutionalized way with strong advice through investment experts. The covariate is highly significant (F(1,21540) = 32.560, p = <.0005***, bootstrapped p = .001**). The regression co-efficient is negative (b = -.054), but the unconditional simple relationship between the share of advisory solutions and the portfolio volatility is positive (r = .037, p = <.0005***). To explain this change of signs, recall the relationship between AuM and the three structural covariates. AuM can either be cash, can be invested in discretionary solutions or advisory solutions, or it can be none of the above, which often means that the client makes direct investments with the bank as a partner for execution. The latter situation is not included as a covariate in the model, as it is the residual of the other three. As the parameter estimates in an ANCOVA repre-sent a multiple linear regression, this residual can be understood as the control group that is by default set to zero. The three regression coefficients of the struc-tural covariates predict the deviation from this latter case. An increased share of advisory solutions will reduce volatility compared to the non-structured execution-only service model (represented by the negative regression coefficient). However, the unconditional simple relationship with portfolio volatility is positive (represented by the correlation coefficient). Comparing all three results for shares of cash, dis-cretionary and advisory solutions must therefore mean the following: The highest level of portfolio volatility can be expected from the classic execution-only service model (“ad-hoc advisory” as described before). The advisory solution model still leads to high volatility in the portfolio, but lower than that in the classic model. Dis-cretionary mandates show clearly less volatility than both, advisory mandates and classic service model. This difference in effect size can also be observed when comparing the sum of squares between discretionary and advisory in Table 6.1: sums of squares for the share of advisory mandates are more than 50 times smaller than for the share of discretionary solutions. The smaller difference with the control

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group of the classic model becomes also obvious when referring to the difference in regression coefficients in Table 6.2: the coefficient for advisory solutions is more than 4 times smaller than that of discretionary solutions. Lastly, the share of cash shows the strongest deviation from the control group with by far the lowest levels of portfolio volatilities, following the same logic.

6.5 Main factors

The three main factors (1) investors’ future-time reference (FTR), (2) the future-time reference of their advisor (RM FTR), and (3) whether they are living abroad or in their country of origin (Migration), give a first indication if the related hypotheses hold. For each, I show the differences in means (post-hoc test) in a plot and pro-pose a significance box to demonstrate the significance in pairwise comparisons. Each area in the box stands for a certain case, so for the main effects we compare two areas (more for the interaction effects discussed later). On the border between the cases I show the direction of the differences in a round shape. A ‘+’ indicates that the mean for this area is higher than the one on the opposite side of the border, which in turn is marked with a ‘-‘. If this directional indicator is colored in green, the mean difference is significant at least at a 95% confidence level. P-values are re-ported in the bold figure on the side of the directional indicator. In addition, all pair-wise comparisons have been bootstrapped to account for potential bias and to un-derline the robustness of the analysis. The bootstrapped p-values are reported be-low the ANCOVA p-values. Full tables are reported for each of the main factors in the appendix (see pp. XXV).

The difference between weak and strong FTR for investors is significant at F(1,21540) = 4.863, p = .027* (see Table 6.1 for a full overview of factor signifi-cances). Figure 6.3 shows that weak FTR speakers display higher levels of portfolio volatility than strong FTR speakers. The significance box provides the same p-value as the overall factor (this is true for all main factors, but not the interactions effects later on). Bootstrap confirms both direction and significance level (p = 0.024*).

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Figure 6.3: FTR (main factor) pairwise plot and significance box

The second effect of RM FTR shows the strongest significance among the main effects (F(1,21540) = 6.573, p = .010*). Individuals who are advised by weak FTR RMs display higher levels of portfolio volatility than those advised by strong FTR RMs

Figure 6.4: RM FTR (main factor) pairwise plot and significance box

The influence of Migration on the portfolio volatilities is not significant (at least as far as the main factor is concerned). Although investors living abroad show a slightly higher volatility on average (see Figure 6.5), this difference is not significant

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(F(1,21540) = 2.374, p = .123). This is also confirmed by the bootstrap of the pair-wise comparison (p = .135).

Figure 6.5: Migration (main factor) pairwise plot and significance box

The main factor results, if significant, can give a strong directional tendency. However, as three out of four interaction effects are significant, these interactions are crucial to understand the directional influence of the three factors in more detail.

6.6 Two-way interactions – introduction of the significance box

Reporting of the interaction effects follows the same principle as the main effects. In a two-way interaction, we distinguish four different cases, each represented by their mean (post-hoc, corrected by the covariates). The plot on the left shows these means, identified by one dimension on the x-axis and the other dimension through the legend (triangle and circle as group symbols in the plot). The same four cases are represented in the significance box on the right. Each point on the left corre-sponds to area on the right. To simplify readability, the x-axis on the left corre-sponds to the x-axis on the right. The second dimension in the significance box is displayed on the y-axis. Results of the pairwise comparisons (see appendix from p. XXVIII for full tables) are reported as borders between the areas. Green border indicators represent a significant difference in means on a 95% confidence level, with exact p-values (incl. bootstrap) on the side.

Reported interactions with the means plot and the significance box read the fol-lowing way: the plot gives a first indication on how much the individual points differ

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with regards to the dependent variable, especially when comparing them pairwise. This alone does not yet give any indication on the significance of differences. Con-sulting the significance box now shows which areas significantly differ from others. Both factor dimensions have the same status (none is per se dependent on the other), so they condition one another. Taking Figure 6.6 as example we read hori-zontally with conditions vertically: the FTR of an individual does not matter if he lives abroad. If he still lives in his country of origin, then his portfolio will display a higher volatility if he is a weak FTR speaker. Furthermore, we can also read verti-cally with conditions on the horizontal axis: migration does only matter if the investor is a weak FTR speaker. In that case his portfolio volatility will be lower if he lives abroad.

Although factor significances from the overall ANCOVA (see Table 6.1) are re-ported in the text, the significance box can illustrate visually why there is signifi-cance for the overall factor. At least one of the areas requires all-significant borders (illustrated in green). This corresponds to at least one point in the plot with a mean significantly different from the rest of the points. For a two-way interaction to be significant, at least one dimension needs to display non-consistent directions (con-sistent direction would be that both ‘+’ show into the same direction). This corre-sponds to different slope directions in the plot. Using the significance box illustration for the illustration of three-way full factorial variance models, provides also the op-portunity to combine all the individual factor significance boxes into one compre-hensive overview. Figure 6.17 shows such a storyboard for all factors in this thesis. This illustration serves as the basis for the interpretation of the overall model. How-ever, first we discuss the results of the interaction effect individually.

6.6.1 FTR * Migration

The interaction effect between investor FTR and Migration overall is significant (F(1,21540) = 10.418, p = .001**). This can also be confirmed visually by the sig-nificance box in Figure 6.6: all boxes are either marked with two positive or two negative border indications and all borders of one area (weak FTR, no Migration) are significant.

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Figure 6.6: FTR * Migration (interaction) pairwise plot and significance box

The interaction confirms the influence of investor FTR under the condition the in-vestor still lives in his country of origin (p = <.0005***, bootstrapped p = .001**). In this case, the portfolio volatility will be higher for weak FTR speakers. If investors live abroad, their FTR does not have a significant impact. Hence, the interaction effect confirms the overall direction of the significant main effect with restrictions. The effect of Migration can be confirmed if the investor is a weak FTR speaker. In that case living abroad results in a lower portfolio volatility than living in the country of origin (p = .004**, bootstrapped p = .005**). For strong FTR speakers Migration does not matter.

6.6.2 FTR * RM FTR

The main effects of both, investor FTR and RM FTR, are significant (see chapter 6.5). They both report that weak FTR speakers, be it investors or advisors, increase the volatility of portfolios. The interaction effect between the two factors, however, is not significant (F(1,21540) = 1.321, p = .250). Visually this can be illustrated in the significance box in Figure 6.7: although there is one area with all significant borders (strong investor FTR, strong RM FTR), all border indicators per dimension follow the same direction.

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Figure 6.7: FTR * RM FTR (interaction) pairwise plot and significance box

Although the overall interaction is not significant, we can still see the tendencies from the pairwise comparisons. Investors with strong FTR display lower volatility levels if they are advised by strong FTR RMs (p = .033*, bootstrapped p = .034*). RMs with strong FTR are related to lower volatility if investors also speak with strong FTR (p = .001**, bootstrapped p = .002**). Overall, the interaction effect confirms the directions of both main effects. Furthermore, the volatility appears to be lowest, if both speak strong FTR. In case of mixed FTR pairings the interaction effect suggests a moderating tendency between the two main effects.

6.6.3 Migration * RM FTR

The interaction between Migration and RM FTR is significant (F(1,21540) = 10.267, p = .001**). This is also visually confirmed by the significance box, with one area only having significant borders and all border indicators per area being either strictly positive or negative.

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Figure 6.8: Migration * RM FTR (interaction) pairwise plot and significance box

The interaction confirms the influence of Migration under the condition that the ad-visor is strong FTR speaker (p = .004**, bootstrapped p = .003**). In this case living abroad is related to lower portfolio volatility. If the advisor is a weak FTR speaker, then Migration does not have a significant impact. The effect of RM FTR can be confirmed if the investor is living abroad. In that case clients with weak FTR advi-sors will find more volatility in their portfolios than with strong FTR advisors (p = <.0005***, bootstrapped p = .002**). For investors still living in their country of origin, the FTR of their advisor does not matter for their portfolio volatility. Hence, the interaction effect confirms the overall direction of the significant main effect with restrictions.

6.7 Three-way interaction

The three-way interaction between investor FTR, RM FTR and Migration is signifi-cant (F(1, 21540) = 20.811, p = <.0005***). Following the same reporting as the two-way interactions, we consult the plot of means and the significance box ap-proach to identify significant groups. As for this interaction a third visual dimension is required, one could show the one plot in three dimensions and show the signifi-cant box as cubes instead of squares. However, this would complicate matters for readability, so instead, I propose to cut the third dimension into two cases, which are each two-dimensional. Concretely, I distinguish between the two cases for the factor of Migration. The case of individuals who remained in the country of origin

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(Stayed) is shown on the left, the case of investors living abroad (Moved) is shown on the right.

The plots in Figure 6.9 show the means for all eight cases as individual points. Pairwise visual comparison of dotted line slopes shows opposite directions for the following pairs: first, in the case of ‘Stayed’ weak and strong FTR slopes differ in sign. Second, slopes of weak FTR speakers (triangles) differ between case ‘stayed’ and ‘moved’. Furthermore, the two points for strong RM FTR in the case ‘stayed’ appear distinctly different from the other points when comparing them pairwise along the three dimensions. This first indication visually confirms the significance of the overall interaction effect.

Figure 6.9: Three-way interaction pairwise plots in full view

Comparing individual points in the plot view may give first tendencies but be-comes increasingly complicated for three dimensions and, after all, they still do not tell anything about significance of differences. Figure 6.10 summarizes pairwise comparisons by way of two significance boxes. As with the plots, they distinguish between the two cases for the factor of Migration. Although they are illustrated two-dimensionally, we need to take a closer look at three instead of two significance borders. The new third dimension is illustrated with the round one-sign indication in the middle of each area. This additional border describes the pairwise compari-son between the two cases of Migration on the right and the left. For example, a

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negative sign in the top left corner of the left significance box describes the pairwise comparison with the same area in the significance box on the right.

Figure 6.10: Three-way interaction significance box with full view

Analyzing the border indicators for each of the squares, we find that there are two squares with strictly significant borders. First, the case of a weak FTR investor with a strong FTR advisor without Migration (top right area in the left box): portfolio volatility is higher if compared ceteris paribus to a strong FTR investor (p = <.0005***, bootstrapped p = .001**), to an investor that lives abroad (p = <.0005***, bootstrapped p = .001**) or to an advisor with weak FTR (p = .004**, bootstrapped p = .004**).

Second, the case of a strong FTR investor with a strong FTR advisor without Migration (bottom right area in the left box): portfolio volatility is lower if compared ceteris paribus to a weak FTR investor (p = <.0005***, bootstrapped p = .001**), to an investor that lives abroad (p = .017*, bootstrapped p = .034*) or to an advisor with weak FTR (p = <.0005***, bootstrapped p = .001**).

Apart from these two significant cases (which were also visible in the plots above), it is insightful to study all the significant pairwise comparisons. To simplify readability, I introduce in the following sub chapters a simplified view for each of the dimensions. These separate views will also be included in the before mentioned storyboard for the overall interpretation of the model results (see Figure 6.17).

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6.7.1 FTR view

The FTR view corresponds with reading the significance box in Figure 6.10 from left to right. For this view, the other two dimensions of RM FTR and Migration are combined into the x-axis, distinguishing 2x2 = 4 cases.

Figure 6.11: Three-way interaction pairwise plot with FTR view

Figure 6.12: Three-way interaction significance box with FTR view

The influence of investor FTR in the three-way interaction factor is only signifi-cant in case the investor is advised by a strong FTR RM and he does not live abroad (p = <.0005***, bootstrapped p = .001**). In this case, weak FTR speakers will find more volatility in their portfolio than strong FTR speakers. This tendency confirms

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once again the significant pairwise comparisons of the main effect and the two rel-evant two-way interactions. In all these cases, weak FTR speakers are related to higher portfolio volatilities than strong FTR speakers.

6.7.2 RM FTR view

The RM FTR view corresponds with reading the significance box in Figure 6.10 from top down. For this view, the other two dimensions of investor FTR and Migra-tion are combined into the x-axis, distinguishing 2x2 = 4 cases.

Figure 6.13: Three-way interaction pairwise plot with RM FTR view

Figure 6.14: Three-way interaction significance box with RM FTR view

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The influence of RM FTR in the three-way interaction factor is significant in three out of four cases. In two of these cases, the tendency of significant main factor and the two relevant interaction factors is confirmed: portfolios advised by a weak FTR RM display higher volatility if either the investor is a weak FTR speaker and he lives abroad (p = <.0005***, bootstrapped p = .002**) or if the investor is a strong FTR speaker and he lives still in his country of origin (p = <.0005***, bootstrapped p = .001**). The case of a weak FTR speaking investor not living abroad shows the opposite effect: if served by a weak FTR RM, portfolios show lower volatility (p = <.004**, bootstrapped p = .004**)

6.7.3 Migration view

The Migration view corresponds with reading the significance box in Figure 6.10 between the two separated cases for each square position. For this view, the other two dimensions of investor FTR and RM FTR are combined into the x-axis, distin-guishing 2x2 = 4 cases.

Figure 6.15: Three-way interaction pairwise plot with Migration view

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Figure 6.16: Three-way interaction significance box with Migration view

The influence of Migration in the three-way interaction factor is significant in three out of four cases. An interesting pattern arises when comparing the FTR of investor and advisor. For the two cases in which they share the same FTR, portfolios of individuals living abroad will show higher levels of volatility. This is true if they are both weak FTR speakers (p = .030*, bootstrapped p = .020*) or strong FTR speak-ers (p = .017*, bootstrapped p = .034*). For the one significant case where investor and advisor FTR differ, the opposite direction is true and investors living abroad tend to have less volatility in their portfolio (p = <.0005***, bootstrapped p = .001**). The changes in direction between the different cases is in line with the main effect not being significant and the two relevant two-way interactions showing separate directions themselves. Resulting from all the interactions, no clear stand-alone ten-dency for the influence of Migration can be identified. The influence depends on the other factors of investor and advisor FTR.

6.8 The storyboard view

Results of the ANCOVA models show a variety of significant main effects and in-teraction between the three factors of FTR, Migration and RM FTR. The illustration with significance boxes for pairwise comparisons allows to merge all these into one comprehensive storyboard that facilitates the interpretation along the hypotheses developed in chapter 4. In Figure 6.17 hypotheses 1a and 1b can be discussed by reading the FTR dimension in the top third from left two right. Analogous, the Mi-gration dimension in the mid third of the illustration address hypotheses 2a and 2b, whereas the FTR dimension in the bottom third allows for testing hypotheses 3a and 3b.

Figure 6.17: Storyboard of all factorial significance boxes along main factors

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6.9 Robustness checks

The ANCOVA method is commonly applied in the context of experimental designs with randomized assignment of groups. This study’s model, on the other hand, makes use of natural groups, which requires additional scrutiny in interpretation. Chapter 6.2 already provided a brief discussion on the validity of the model, high-lighting the ANCOVA-specific assumption of homogeneity of regression slopes. A 2x2x2 factorial model with 12 covariates is large enough that some correlation be-tween groups and covariates is likely to occur. However, the model was designed so that no covariate would be able to fully explain group memberships, or formu-lated the other way around, so that including a certain covariate would not render the model interpretation meaningless. A simple example of such a strong correla-tion between covariate and groups is presented by Miller and Chapman (2001): an ANCOVA model to understand the difference in basketball performance of 3rd and 4th graders becomes quite meaningless if age is introduced as covariate. Age is so closely related to being in either 3rd or 4th grade that it is likely to take all variance of grade.

To identify which combinations of groups and covariates would show significant interactions in violation of the homogeneity of regression slopes assumption, I ex-tended the original ANCOVA model and introduced all 84 interactions between the 7 (interaction) factors and 12 covariates. Following the suggestion of Miller and Chapman (2001), I would need to keep those interactions that were significant at a 5 percent level for additional interpretation, namely:

§ Migration * Regional MSCI volatility § Migration * Long-term orientation § Migration * Portfolio volume § Migration * Share of cash § Migration * Share of advisory solutions § FTR * share of cash § RM FTR * share of cash

The other alternative to address the heterogeneity of regression slopes for these cases would be to exclude the covariates and reduce the model to a simple 2x2x2 ANOVA. To underline the robustness of the original model, both strategies are dis-cussed in the following sections. It shows that for the testing of hypotheses, the original model is robust, as the main findings from chapters 6.5, 6.6 and 6.7 can also be confirmed with either of the two robustness tests.

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6.9.1 ANOVA model without covariates

Reducing the model by exclusion of all covariates creates a simple ANOVA that does not require the homogeneity of regression slopes. Table 6.3 shows the overall model with the same 2x2x2 factors as the original ANCOVA. As a result of exclud-ing the covariates the R2 adj. is only at 1.2%, whereas the original model represents 11.5%. As far as the consistency is concerned between the factors’ F-test results, we see that the same factors and interactions are significant as in the original model (only Migration as main factor and the interaction FTR * RM FTR are not signifi-cant).

Table 6.3: ANOVA (robustness check) test of between subject effects

*** ** *

Significant at the 0.1 percent level Significant at the 1 percent level Significant at the 5 percent level

Figure 6.18 shows the storyboard of the same ANOVA model. As introduced in the previous chapter, the storyboard summarizes all pairwise comparisons for main and interaction factors. Comparing now the ANOVA storyboard with the original ANCOVA storyboard (Figure 6.17) illustrates that the ANOVA method is less con-servative, identifying more significant borders (in green). It is important to note that all the significant borders identified by the ANCOVA model are also identified with

Type III sum of

squares dfMean

square F

Corrected Model 28.345 7 4.049 37.017 < 0.0005 ***Intercept 2064.811 1 2064.811 18876.119 < 0.0005 ***

FTR 2.321 1 2.321 21.219 < 0.0005 ***Migration 0.031 1 0.031 0.279 0.597RM FTR 1.063 1 1.063 9.716 0.002 **

FTR * Migration 2.063 1 2.063 18.863 < 0.0005 ***FTR * RM FTR 0.003 1 0.003 0.031 0.861Migration * RM FTR 1.500 1 1.500 13.715 < 0.0005 ***FTR * Migration * RM FTR 1.874 1 1.874 17.134 < 0.0005 ***

Error 2426.542 22183 0.109Total 10273.917 22191Corrected total 2454.887 22190R sqared = 0.012 (Adjusted R squared = 0.011)

Sig.

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the same directions in the ANOVA model. The only exception can be found in the interaction between Migration and RM FTR. In the original ANCOVA confirms the influence of Migration under the condition that the advisor is a strong FTR speaker (p = .004*, bootstrapped p = .003**). The ANOVA, however, is not significant at a 5 percent level for the same comparison (p = .057, bootstrapped p = .055). Yet, they both confirm the same direction and the ANOVA would be significant at a 6 percent level.

Figure 6.18: Robustness check storyboard of ANOVA

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6.9.2 ANCOVA model with additional interaction terms

Testing for the homogeneity of regression slopes assumption in a test ANCOVA with 84 interaction terms between factors and covariates highlighted seven inter-actions to be significant at a 5 percent level:

§ Migration * Regional MSCI volatility § Migration * Long-term orientation § Migration * Portfolio volume § Migration * Share of cash § Migration * Share of advisory solutions § FTR * share of cash § RM FTR * share of cash

Including these interactions into the original ANCOVA results in the model de-scribed in Table 6.4. As before with the ANOVA, this robustness ANCOVA confirms the same factors and interactions to be significant as in the original model’s F-tests (in addition, the extended ANCOVA also qualifies Migration as main factor to be significant). R2 adj. of the extended model is with 11.8% in line with the original model’s 11.5%.

Table 6.5 shows the extended model’s parameter estimates (for the full table including bootstrap, see Appendix 9). The objective of including the seven addi-tional interactions is to understand the difference in the affected groups in connec-tion with the covariates. Particularly interesting are those cases where belonging to either group changes the sign of the covariate impact on the dependent variable. The first of two such cases is that of Migration * MSCI vol, which is significant (F(1, 21540) = 9.09, p = .003**) and provides a negative regression coefficient (b = -.302) when the investor does not live abroad. Combined with the smaller and pos-itive regression coefficient of MSCI vol (b = .015), this means that the regional MSCI volatility has a small positive impact on investor volatilities if the investor lives abroad, and a larger negative impact if he lives in his home country. The second case with an impact on signs is that of Migration * Long-term orientation, which is significant (F(1, 21540) = 7.849, p = .005**) and provides a negative regression coefficient (b = -.183) when the investor does not live abroad. Combined with the smaller and positive regression coefficient of Long-term orientation (b = .148), this means that the cultural dimension of Long-term orientation has a small positive impact on investor volatilities if the investor lives abroad, and a smaller negative impact if he lives in his home country.

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Table 6.4: ANCOVA (robustness check) test of between subject effects

*** ** *

Significant at the 0.1 percent level Significant at the 1 percent level Significant at the 5 percent level

Type III sum of

squares dfMean

square F Sig.

Corrected Model 284.939 26 10.959 112.455 < 0.0005 ***Intercept 3.693 1 3.693 37.896 < 0.0005 ***

MSCI vol 0.609 1 0.609 6.252 0.012 *

Power distance 1.482 1 1.482 15.204 < 0.0005 ***Individualism 0.353 1 0.353 3.620 0.057Masculinity 0.461 1 0.461 4.732 0.030 * Uncertainty avoidance 3.887 1 3.887 39.886 < 0.0005 ***Long-term orientation 0.252 1 0.252 2.584 0.108Indulgence 1.446 1 1.446 14.834 < 0.0005 ***

AuM 0.002 1 0.002 0.025 0.874Portfolio volume 0.072 1 0.072 0.738 0.390Share of cash 65.478 1 65.478 671.886 < 0.0005 ***Share of disc. solutions 179.345 1 179.345 1840.303 < 0.0005 ***Share of adv. solutions 0.682 1 0.682 7.003 0.008 **

FTR 1.813 1 1.813 18.608 < 0.0005 ***Migration 1.088 1 1.088 11.159 0.001 **RM FTR 0.424 1 0.424 4.356 0.037 *

FTR * Migration 1.072 1 1.072 11.004 0.001 **FTR * RM FTR 0.162 1 0.162 1.664 0.197Migration * RM FTR 1.217 1 1.217 12.483 < 0.0005 ***FTR * Migration * RM FTR 2.193 1 2.193 22.502 < 0.0005 ***

Migration * MSCI vol 0.886 1 0.886 9.09 0.003 **Migration * Long-term orientation 0.765 1 0.765 7.849 0.005 **Migration * Portfolio volume 0.001 1 0.001 0.014 0.905Migration * Share of cash 1.431 1 1.431 14.681 < 0.0005 ***Migration * Share of adv. sol. 0.370 1 0.370 3.801 0.051FTR * Share of cash 3.917 1 3.917 40.194 < 0.0005 ***RM FTR * Share of cash 0.005 1 0.005 0.049 0.825

Error 2098.473 21533 0.097Total 10019.397 21560Corrected total 2383.412 21559R sqared = 0.120 (Adjusted R squared = 0.118)

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Table 6.5: ANCOVA (robustness check) parameter estimates

B Std. Error t Lower UpperIntercept 0.453 0.215 2.108 0.035 * 0.032 0.874Regional MSCI volatility 0.015 0.097 0.155 0.877 -0.176 0.206Power distance 0.095 0.024 3.899 < 0.0005 *** 0.047 0.143Individualism 0.049 0.026 1.903 0.057 -0.001 0.099Masculinity 0.030 0.014 2.175 0.030 * 0.003 0.057Uncertainty avoidance -0.219 0.035 -6.316 < 0.0005 *** -0.287 -0.151Long-term orientation 0.148 0.064 2.334 0.020 * 0.024 0.273Indulgence 0.060 0.016 3.852 < 0.0005 *** 0.030 0.091AuM -4.16E-11 2.63E-10 -0.159 0.874 -5.57E-10 4.73E-10Portfolio volume 2.84E-10 5.39E-10 0.527 0.598 -7.73E-10 1.34E-09Share of cash -0.256 0.028 -9.151 < 0.0005 *** -0.311 -0.201Share of disc. solutions -0.248 0.006 -42.899 < 0.0005 *** -0.26 -0.237Share of adv. solutions -0.010 0.025 -0.390 0.696 -0.059 0.039FTR = Weak -0.008 0.024 -0.347 0.728 -0.054 0.038Migration = No 0.647 0.204 3.181 0.001 ** 0.248 1.046RM FTR = Weak 0.021 0.018 1.177 0.239 -0.014 0.056FTR = Weak * Migration = No

0.143 0.029 4.895 < 0.0005 *** 0.086 0.200

FTR = Weak * RM FTR = Weak

0.059 0.027 2.147 0.032 * 0.005 0.112

Migration = No * RM FTR = Weak

0.021 0.018 1.160 0.246 -0.015 0.057

FTR = Weak * Migration = No * RM FTR = Weak

-0.162 0.034 -4.744 < 0.0005 *** -0.228 -0.095

Migration = No *Regional MSCI volatility

-0.302 0.100 -3.015 0.003 ** -0.498 -0.106

Migration = No *Long-term orientation

-0.183 0.065 -2.802 0.005 ** -0.311 -0.055

Migration = No *Portfolio volume

5.65E-11 4.71E-10 0.120 0.905 -8.66E-10 9.79E-10

Migration = No *Share of cash

-0.107 0.028 -3.832 < 0.0005 ** -0.162 -0.052

Migration = No *Share of adv. Solutions

-0.052 0.027 -1.950 0.051 -0.105 0.000

FTR = Weak *Share of cash

-0.164 0.026 -6.340 < 0.0005 ** -0.214 -0.113

RM FTR = Weak *Share of cash 0.005 0.025 0.221 0.825 -0.043 0.054

With the three factors FTR, Migration and RM FTR being dichotomous variables, all other (interaction) parameters are not included in above table as values are 0. *** Significant at the 0.1% level, ** Significant at the 1% level, *Significant at the 5% level.Bootstrap results are based on 1000 bootstrap samples

Parameter estimates95% Confidence Intvl.

Sig.

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Figure 6.19 shows the storyboard of the extended ANCOVA model. As for the ANOVA robustness check before, a comparison with the results of the original AN-COVA confirms the same significant pairwise comparisons with only one exception: the Migration view in the three-way interaction (middle row) of the original ANCOVA confirms the influence of Migration if the investor and his advisor are both weak FTR speakers (p = .030*, bootstrapped p = .020*). The extended ANCOVA, how-ever, is not significant at a 5 percent level for the same comparison (p = .085, boot-strapped p = .100). Yet, they both confirm the same direction and the extended ANCOVA would be significant at a 11 percent level.

Figure 6.19: Robustness check storyboard of the extended ANCOVA

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6.9.3 Summary from robustness checks

Including all possible interactions between factors and covariates confirmed that some interactions violate the homogeneity of regression slopes assumption, which is required for the general validity of ANCOVA. However, a robustness test of re-sults shows for the two possible mitigation strategies that those results are not af-fected that are crucial for the following discussion. Excluding the covariates from the original model as well as including additional interactions both result in the same storyboard results (with one minor exception each) as the original model. With fac-tor results remaining stable through the robustness checks, I continue to interpret the original model in the following chapter.

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7 Discussion Table 7.1 briefly summarizes the main findings for hypotheses and expectations developed throughout chapter 4. Each of the findings are discussed in detail in the following sub-chapters, putting the main findings in perspective to the existing liter-ature and in considerations of the patterns emerging from interactions of the three main factors. For consistent interpretation of pairwise comparisons, the following discussion makes repeated reference to the storyboard view (Figure 6.17 on page 100), introduced as a summary illustration of model results.

Table 7.1: Summary of ANCOVA model results along hypotheses

Hypotheses Main Findings

H1a P FTR of investors has a significant impact on portfolio volatilities

H1b O Weak FTR investors show higher risk propensity than strong FTR inves-tors

H2a P FTR of advisors has a significant impact on portfolio volatilities

H2b O Investors advised by weak FTR RMs show higher portfolio volatility than those advised by strong FTR RMs

H3a P Migration has an indirect impact on portfolio volatility if taking into ac-count investor and advisor FTR characteristics

H3b P If investor and advisor share the same FTR characteristics, then risk pro-pensity is higher for investors living abroad rather than in their home country. In mixed FTR constellations, living abroad shows a moderation effect for weak FTR investors: their portfolio volatility is higher if they do not live aboard

E1 O Higher volatility in an investor’s home region leads to lower volatility in his portfolio

E2 P Investors from societies with low levels of uncertainty avoidance or high levels of indulgence display higher portfolio volatilities. Also, more mas-culine cultures or higher levels of power distance in a society indicate more portfolio volatility

E3 O AuM levels and portfolio volumes do not have a significant impact on portfolio volatilities

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E4-6 P If more assets are held in cash, then the investor’s risk propensity and volatilities for invested assets are lower. For these invested assets, vol-atilities are lowest for discretionary solutions. Advisory solutions show more volatilities then discretionary solutions, but still less than those as-sets that are neither in advisory nor discretionary solutions, but served through ad-hoc advisory

7.1 Hypothesis 1 – FTR of individual investors

This study presents the attempt to link the FTR characteristic to risk behavior in the context of individual investors. While Kovacic et al. (2016) show an influence of grammatical mood on investment risk, the literature on FTR does, to date, not pro-vide any evidence for this analytical focus. I deduced my hypothesis on the direction of a potential language effect on general findings in psychology (see Zimbardo & Boyd, 2005) that suggest a negative relationship between future orientation and risk-taking. Combining this with evidence from the FTR literature that weak FTR speakers display more future-oriented behavior than strong FTR speakers resulted in the following hypotheses:

Hypothesis 1a: The future-time reference of investors has an impact on the volatility in their portfolios.

Hypothesis 1b: Investors with a strong future-time reference display higher risk propensity than investors with a weak future-time reference.

Results of the ANCOVA model with a significant main effect and two significant interaction effects confirm the existence of an overall investor language effect, and we can hence confirm H1a. The fact that the FTR of individuals has an impact their decision-making is in line with the literature on linguistic relativity (see chapter 2.1), and, more specifically, it confirms FTR as relevant linguistic marker to predict be-havior (see chapter 2.5). This study thereby contributes to the growing literature on the FTR application for psychological and economic research. It also confirms that specific examples for linguistic relativity not only hold true in experimental designs but also in large empirical studies. As such, this study is in line with the contribution of Chen (2013) and the broader economic literature since 2013.

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H1b is based on H1a by proposing not only the existence of an investor language effect but also its direction. With H1a confirmed, H1b can be tested. Figure 6.17 summarizes the model’s results for the H1b perspective in the upper third row. The significant main effect lets us conclude that weak FTR speakers find their portfolios to be more volatile than strong FTR speakers. The direction of the FTR factor is confirmed throughout all significant pairwise comparisons. This consistency allows to interpret the interactions as special cases of the main tendency. Confirmed also by the respective mean differences (see Appendix from p. XXV), we can therefore make additional statements on the directional investor language effect: The ten-dency of weak FTR speakers to find higher portfolio volatility is particularly strong if the investor does not live abroad or if the investor is served by a strong FTR RM. If both of these elements apply to an investor, this results in the strongest case of the investor language effect.

Testing H1b shows that there is indeed a strong directional tendency for the in-vestor language effect, but the direction is the exact opposite of what the hypothesis proposed. We must therefore reject H1b in the current form. The direction proposal of H1b was based on the assumption that FTR drives future-orientation (FTR re-search) and risk-taking is a form of future-oriented behavior. There are two possi-bilities to explain this variation: the characteristics of the sample population or the relationship between portfolio risk-taking and intertemporal choice as two forms of future-oriented behavior.

Potential explanation 1 – sample population. The underlying dataset of this study contains a sample of a very specific demographic globally. Only millionaires and billionaires with assets served by a globally operating wealth management firm are part of this scope (see chapter 3.2). Chen (2013) and other colleagues performing cross-country FTR studies, on the other hand, base their analysis on data from the World Values Survey. This survey samples the survey population with the goal to represent the overall population in each country (WVSA, 2009). The specific scope used for this study is not representative for the overall population in any of the countries in scope. Therefore, it is conceivable that the originally proposed direction still holds and only the specific demographic in this study displays a diverging pat-tern. This would assume that the direction of the investor language effect on FTR is not the same across all wealth levels, which would require additional research. In this study, and within the specific scope of wealthy individuals, however, we find that neither AuM and nor portfolio volumes have a significant impact on portfolio

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volatilities (see chapter 6.4 for the results of covariates). This, of cause, only con-firms that wealth levels have no significant impact within the upper wealth levels. Testing across the wealth pyramid may show different results. As briefly discussed in chapter 3.1, West and Worthington (2012) summarize that there is no simple relationship between wealth levels and risk attitudes as scholars come to conflicting conclusions. The authors assume instead that low and high levels of wealth may display less risk aversion than the mid-levels.

Potential explanation 2 – risk-taking and future-oriented behavior. The first ex-planation does not rule out that the findings in this study are not representative for a broader population and that risk-taking and future-oriented behavior are nega-tively related, as originally stated. However, if my findings also hold for a broader population of financial investors, then we need to revisit the relationship between risk-taking and future-oriented behavior. In their large-scale global preference study, Becker et al. (2018) find that patience and risk-taking are positively corre-lated. They base their findings on survey questions and do not consider observed risk-taking in a financial context. However, while they find that weak FTR speakers show more patience than strong FTR speakers, they do not find a significant influ-ence of the FTR marker on risk-taking. An alternative link between this study’s re-sults and the original proposal by Chen (2013) may be his suggestion that weak FTR speakers save more and attend more to their health. Yao, Gutter, and Hanna (2005) and Gutter and Fontes (2006) for example state that individuals with pre-cautionary savings display more risk tolerance. However, this can only be a weak theoretical link, as their findings are based on differences in financial behavior be-tween races in the US and they argue that saving is the pre-condition to have as-sets to invest in the first place. Arguably, that alone does not help explaining why (U)HNWI differ in their saving propensity. The second connection with health may be slightly stronger. Yao et al. (2004) find across households that those considering themselves in good health are more likely to take risks. While on lower wealth levels health risks and financial risk may be closely related, it is conceivable that being in good health may also influence cognition. Nevertheless, these alternatives remain a bit far-fetched. Only further research will be able to confirm if the relationship between future-oriented behavior and risk-taking.

In summary, while the finding of this study that weak FTR speakers are in ten-dency more likely to take financial risk than strong FTR speakers maybe contradict

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expectations, it does not per se contradict previous findings in the FTR literature. For this particular analytical focus and empirical context, findings hold.

7.2 Hypothesis 2 – FTR of advisors

Independent of their professional role, I expect that advisors are prone to the same language effects as individual investors. While I expect the language effect for in-vestors to impact their investment decision, I expect the language effect for advi-sors to affect their activity of searching for relevant information and making ade-quate recommendations to their clients (see Cruciani, 2017). Although not all rec-ommendations are accepted by investors, and even if so, it is likely that there will be some discounting to it (see Yaniv, 2004), I expected an advisor language effect with the same direction as the investor language effect:

Hypothesis 2a: The future-time reference of advisors has an impact on the portfolio volatility of their clients.

Hypothesis 2b: Investors with a strong future-time reference advisor display more risk-taking than investors with weak future-time reference advisors.

Results of the ANCOVA model with a significant main effect and two significant interaction effects confirm the existence of an overall advisor language effect and we can hence confirm H2a. Having already confirmed an investor language effect based on significant main and interaction terms in chapter 7, allows to compare the two FTR effects side-by-side. In analogy to the influence of investor FTR, H2b pro-poses not only the existence of an advisor language effect but also its direction. This can be tested as we confirm H2a as pre-condition. Figure 6.17 summarizes the model’s results for the H2b perspective in the lower third row. The significant main effect suggests that weak FTR advisors find their client’s portfolios to be more volatile than strong FTR advisors. This is consistent with the findings for the inves-tor language effect. The direction of the FTR factor is confirmed across most sig-nificant pairwise comparisons. Following the two-way interactions, this directional effect is particularly true if either the investor’s FTR is strong or the investor is living abroad. These effects are also confirmed when consulting the three-way pairwise comparisons. However, a considerably different and opposite effect can be found

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if both of these characteristics (strong FTR investor or investor living abroad) are not given. A weak FTR investor living in his home country is the only significant case where the advisor language effect from the other cases is turned. This will be discussed in detail throughout the next chapter.

In summary, we must reject hypothesis 3b in its current form almost like we re-jected hypothesis 1b: while there seems to be an advisor language effect with an overall tendency, this tendency follows the opposite direction of the proposal in H2b. In addition, there is one specific case where this tendency does not hold true. Background of choosing the direction in hypothesis 3b was the same rationale as for hypothesis 1b (see chapter 7). Findings on the interaction of investor language and advisor language are consistent with findings in psychology, as discussed in chapter 3.3. On the psychological effects in the context of advice-taking and advice-giving, scholars find, among others, that decision-makers prefer advisors that are similar to them in characteristics (Phillips & Loyd, 2006). Also, it is likely that deci-sion-makers follow advice more if there is such similarity between themselves and their advisor (Cialdini & Goldstein, 2004). In this study we find that the FTR char-acteristics of both, advisors and investors both follow the same pattern for risk pro-pensity: weak FTR speakers show higher risk propensity than strong FTR speak-ers. This effect magnifies if both individuals display the same FTR characteristic. More interesting are the cases where the two do not follow the same FTR pattern. The two-way interaction suggests that in these cases the risk propensity of investor and advisor moderate one another, leading to portfolio volatility on mid-level be-tween the two extremes. This concurs with findings by Yaniv (2004) that advice is typically discounted by the decision-maker, i.e. he follows directionally the recom-mendation of the advisor but not fully. This suggests that client and relationship manager meet on a middle ground.

In summary, testing hypothesis 2 is quite consistent with findings for hypothesis 1: in the context of private banking there are an investor language effect and an advisor language effect, both in a sense that weak FTR is related to higher levels of portfolio risk. While the direction may be surprising, the interaction between both effects is in line with expectations: given the two strong main effects in the same direction, we can assume that portfolio volatility will be highest for two weak FTR speakers and lowest for two strong FTR speakers. Mixed FTR characteristics be-tween client and advisor show further interesting patterns that are discussed in re-lation to the moderator of Migration.

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Finding evidence for an advisor language effect contributes to the literature on linguistic relativity and FTR research in similar manner as the investor language effect discussed before. However, if we do not consider the impact of RM FTR on a stand-alone basis but together with the investor FTR impact, then a joint language effect with regards to risk propensity can be assumed: combining hypotheses 1a and 2a confirms that the FTR characteristics of the involved individuals have a sig-nificant impact on the risk-taking in the circumstance of investment decisions. Alt-hough the hypotheses 1b and 2b have to be rejected in the current form, the overall direction of this joint language effect is aligned between the two FTR factors (op-posite direction from the original hypotheses).

To a reduced degree the findings from the interactions can also be of interest for the advice literature. Although the aligned direction of single effects complicates interpretation in this regard, the special case discussed in this chapter may be a starting point for further research. Generally, this study adds to the advice literature due to the fact that I show a scenario for testing that is not based on closed exper-iments but on a large dataset.

On a last note, the influence of RM FTR on the risk propensity of investors un-derlined that the human interaction in wealth management matters. In times where digitization and the introduction of robo-advisors are on the rise, the findings in this study suggest that human advisors do not only transport standardized proposals. Their personal characteristics impact their clients, at least in this upper wealth sphere of private banking operations.

7.3 Hypothesis 3 – Migration of individual investors

Private investors on upper wealth levels are likely to face less constraints in their mobility than individuals on lower wealth level might face. Living abroad leads indi-viduals to face differences in culture and potentially also in languages. The litera-ture on acculturation shows how individuals cope with the cultural aspect, but there is no clear expectation on how risk behavior might be affected (see chapter 3.2.2). On the overall language side, however, scholars find a foreign language effect in psychology that leads to a reduction in biases (see for example Hayakawa et al., 2016). Recent studies on the FTR effect in corporate decision-making suggest that the exposure to different language environments moderates the FTR language

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effect (see Liang et al., 2014). Hence, Migration was expected to moderate lan-guage effects:

Hypothesis 3a: Migration of investors has an impact on the volatility in their portfolios.

Hypothesis 3b: Living abroad moderates the language effect on risk propen-sity for investors.

The ANCOVA model finds no significant main effect for Migration – in line with expectations. However, all interaction effects with Migration are significant. As a result, hypothesis 3a can be confirmed under the condition that the factors investor FTR and RM FTR need to be taken into account to find a significant migration ef-fect. Finding evidence for the fact that individuals are impacted by potential differ-ences between home country and host country is in line with the literature summa-rized in chapter 3.2. Again, it is important to highlight that the individuals in scope must not be confused with migrants from other wealth bands. If the individuals an-alyzed in this study move abroad, their challenges mostly concern cultural and lin-guistic differences, not financial distress. Irrespective of their country of origin they need to be considered as highly mobile on a global scale. Their higher wealth levels can mitigate many issues that individuals with very low wealth levels might face. Socially, these individuals can be assumed to have immediate access to mid and upper levels of society.

As we focus on the interpretation of the three-way interaction when we want to understand the migration effect, we need to better understand the two-way interac-tion between the two FTR factors. Following chapter 6.6.2 and the summary illus-tration in Figure 6.17, we find that both FTR types show more volatility for weak FTR speakers and that this effect magnifies when the two factors interact. That means that strong FTR investors with strong FTR RMs display the lowest volatility. Weak FTR investors with weak FTR RMs have at least the tendency to display the highest volatility. Mixed FTR forms result in more moderate volatility levels. H3b expects a moderation effect of Migration, not a main effect. Figure 6.17 summarizes the model’s results for the H3b perspective in the middle row. For a simpler

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interpretation we focus on the three-way effect.24 The first observation is that for the two cases, where client and RM share the same FTR characteristic, living abroad is related to more portfolio risk than staying at home. Hence, if investor and RM already both have a higher risk propensity (weak FTR), then the ones living abroad have the highest risk propensity. On the other hand, if investor and advisor already share a lower risk propensity (strong FTR speakers), then the ones still living in their home country have the lowest risk propensity. This represents the intuitive idea that those living abroad might be more risk-taking (although there is no prior empirical evidence for this finding as discussed in chapter 4.4).

The second observation is that there is the opposite tendency if the FTR char-acteristic differs between the two: more volatility is found in portfolios of investors still living in their home country. To better understand this pattern and potentially explain this migration effect, recall the summary on the psychology of advice-taking in chapter 3.3.2. Concretely, psychology scholars suggest that not only the similar-ity between the two individuals impacts the degree of accepting advice, also the individual characteristics come into play. This study cannot account for the experi-ence or confidence levels of advisors, which, as examples, increase the likelihood for a decision-maker to follow advice (Feng & MacGeorge, 2006; van Swol & Sniezek, 2005). However, a bit more can be inferred from the characteristics of individual investors, i.e. characteristics that might emerge from living abroad or in the original home country. From the discussion on the impact of Migration, we could assume that individuals without Migration show elevated levels of confidence as they live in a zone of comfort. In a similar direction, Tost et al. (2012) argue that the self-perception of power leads to more confidence and hence to decreased likeli-hood of following advice. This line of argumentation helps understanding the spe-cial case of mixed FTR constellations.

Figure 7.1 shows again this three-way interaction as significance box in the com-prehensive visualization. Focus on the left significance box that shows the case for investors still living in their home country: the top left corner (highlighted in red) describes the particular case of strong FTR investors advised by a strong FTR RM.

24 We can focus on the three-way interaction for two technical reasons: first, in contrast to

investor FTR and RM FTR there is no significant main effect for Migration. Second, there are two significant borders for the two-way interactions. Both, i.e. the case for weak FTR inves-tors and strong FTR advisors, follow the same direction and suggest that those who stayed in their home country show more portfolio volatility. This exact case is, with the same direc-tion and highly significant, also to be found in the three-way interaction. Hence, we can focus on the three-way interaction only as it contains all relevant and significant relationships.

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As can be seen by the very top green border indicator, this area relates to a higher portfolio volatility than the area to the left, which means that home-staying weak FTR investors take more risk if advised by strong FTR RM compared to a weak FTR RM.

Figure 7.1: Particular case for advice discounting in three-way interaction

This contradicts the otherwise significant main effect of advisor language. Tech-nically speaking, we can say that for this particular case, introducing the dimension of Migration turns around the advisor language effect. Furthermore, and this is im-portant to consider, this is the only area on the left-hand side showing a ‘+‘ in the middle boarder indicator. This means that, for this area (weak FTR investor, strong FTR RM), staying in the country of origin is related to higher portfolio risk, whereas for all other constellations, Migration increases risk-taking. Taking the argument from psychology scholars, I suggest that because weak FTR speakers feel ex-tremely confident if they live in their country of origin they overpower the influence of their advisor. If the advisor is a strong FTR speaker, these weak FTR speakers discount the advice to a point that their language effect is more dominant. For the other mixed constellation of strong FTR investors at home with a weak FTR RM, the effect of Migration is not significant, which does at least not contradict this in-terpretation.

In summary, both investor language effect and advisor language effect follow the consistent direction that weak FTR is related to higher portfolio volatility. In

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constellation of the same FTR this effect magnifies, in constellations of mixed FTR there is a tendency for in-between levels of volatility. These mixed FTR cases are influenced by the moderating effect of Migration, where those at home show the tendency to be particularly confident in their conviction and by discounting advice, lead to mid-level volatilities.

7.4 Expectations on potentially confounding factors

Language effects as described in the previous chapter show interesting patterns when reviewing the portfolio volatility of individual investors. However, it is safe to assume that language alone cannot explain variations in risk behavior. As previ-ously mentioned, restrictions from the data set do not allow to control for further demographic factors, which poses a limitation to this study design (see chapter 9.2). Based on available indicators, I expect that regional market volatilities, cultural dimensions, and structural characteristics on portfolios are important factors to con-sider.

Volatility of regional MSCI indices (MSCI vol) is a covariate in the ANCOVA model that is supposed to control for a series of aspects (see chapters 4.5 and 5.4). first, scholars suggest that market volatilities passively cause changes in individual portfolios volatilities (Merkle & Weber, 2014). Second, literature suggests that indi-vidual preferences show differences across regions (see Becker et al., 2018). Third, markets with different levels of development show, in tendency, different volatility levels (Maharaj et al., 2011). Lastly, studies show a certain home bias in their in-vestments that prevent global diversification and boost investments in higher prox-imity to home markets (Karlsson & Nordén, 2007). In summary of these findings, I expected:

Expectation 1: A higher volatility in the equity markets of an investor’s home region leads to higher volatility in his own portfolio

The regional MSCI volatility as covariate is significant in the ANCOVA model. How-ever, reviewing the simple correlation and the regression slope shows that the op-posite direction of E1 is true: higher volatility in the equity markets of an investor’s

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home region leads to a lower volatility in his own portfolio. The robustness check in chapter 6.9.2 shows that this is particularly true for those investors not living abroad. Hence, expectation 1 cannot be confirmed in the current form. The result suggests that portfolios are in tendency well diversified. This hints into the direction of Broer (2017) who suggests that the tendency for home bias decreases with higher levels of wealth.

Cultural dimensions (power distance, individualism, masculinity, uncer-tainty avoidance, long term orientation, indulgence) according to Hofstede (Hofstede et al., 2010) are introduced as covariates to control for the relationship between language and culture. At the core of this study is the assumption that both language and culture impact cognition and thereby behavior. Culture and language are insofar to be considered independent as they both directly impact cognition. Historically, cultures are influential for the evolution of languages, but as cultures developed faster and languages are more persistent, I do not expect language to simply be a proxy for underlying cultures. Applying this basic triangular relationship between culture, language, and cognition requires to comprehensively control for culture when investigating a language effect. Hence, all six cultural dimensions by Hofstede et al. (2010) are included in the ANCOVA model as covariates. While the six dimensions aim at classifying national cultures comprehensively, this study is only focused on the impacts on risk propensity. As risk propensity in investment decisions is strongly related to future-oriented behavior, I expect that the three di-mensions of power distance, individualism and masculinity do not impact individual investment decisions. Also, as risk-taking per-se can equally be applied to long term and short-term perspectives, I also expect that long-term orientation does not impact risk propensity, either. This leaves the two dimensions of uncertainty avoid-ance and indulgence. The former connects intuitively to the issue of risk-taking, so the more a national culture is averse to ambiguity, the less risk-taking I expect. The latter refers to a societal openness for gratifications and liberal social norms, so I expect that higher levels of indulgence in a national culture leads to increased risk propensity. In summary, I propose:

Expectation 2: Two out of six cultural dimensions by Hofstede have an impact on the risk propensity of investors. High levels of uncertainty avoidance lead

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to low levels of portfolio volatility. High levels of indulgence lead to high levels of portfolio volatility.

The ANCOVA model finds a strongly significant negative influence of the uncer-tainty avoidance dimension. Also, the covariate of indulgence is strongly significant, suggesting a positive influence on portfolio volatility. Hence, we can confirm the expectation 2 in that regard. However, not only these two dimensions are signifi-cant, but power distance and, to a lesser extent, masculinity are significant. While the latter appears to have a clearly positive correlation with volatility. Power dis-tance shows a more interesting pattern: while the simple unconditional correlation is negative, the overall model suggests a positive influence. Interpreting all these results in light of the definitions by Hofstede et al. (2010) provides the following insights: Investors from societies that are less comfortable with uncertainty and ambiguity are also more risk averse when it comes to investment decisions. Indi-viduals are willing to take more risk if they stem from countries that are more liberal societies promoting personal gratification. On the other hand, liberal societies with regards to equal gender role models and participation produce investors that take less risk in their investments. If isolating the effect (by reviewing the simple uncon-ditional correlation with portfolio volatility), then societies with strong hierarchies produce more risk averse investors. However, if combining all influencing factors from the model, then we find that investors from hierarchical societies invest riskier. The dimension of power distance may also serve as an interesting factor when further investigating the relationship between clients and their advisors. This could be taken into account for further research, when focusing more on the subject of advice-taking and the impact of advisors in wealth management.

Portfolio structure (AuM, portfolio volume) with regards to volumes are intro-duced to control for differences in wealth levels. West and Worthington (2012) sug-gest in their review that wealth and risk propensity do not follow a linear relation-ship. Instead low and high wealth levels show less risk aversion than the middle of the wealth spectrum. Furthermore, there is the structural relationship between AuM and portfolio volumes: while for one client relationship there is only one compre-hensive measure for AuM as a measure of overall asset size, one client may pos-sess multiple portfolios. It is therefore possible that in a multiple-portfolio scenario, clients display mental accounting bias (Cruciani, 2017) between these portfolios.

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Therefore, I control for both AuM and portfolio volumes as indicators to account for potential effects by size of wealth. In summary, I propose:

Expectation 3: Higher levels of AuM and portfolio volume are leading to lower portfolio volatility

The ANCOVA model finds that both, AuM and portfolio volumes do not have an influence on the volatility of portfolios. The expectation 3 must be rejected. For the sake of detail, I may add that the aspect of portfolio volume is less non-significant than that of AuM. Of course, this nuance cannot be a robust finding. We can, how-ever, conclude from this overall result that the assumed differences in investment styles across wealth levels in this study do not hold. At least within the demographic of millionaires and billionaires more precise differences in wealth do not seem to matter. Recalling the results of the overall language effect from the previous chap-ter, this finding might be misleading if one were to apply this to broader de-mographics. It is conceivable that for this particular study, variations in wealth have no influence on investment behavior. However, wealth levels may one explaining factor why the language effect in this study follows a direction that contradicts ex-pectations.

Portfolio structure with share of cash, share of discretionary solutions, share of advisory solutions as covariates follows a different rationale than for asset volumes. The share of cash, measured as percentage of AuM, describes the amount of assets that are not invested in portfolios. This portion represents assets that are not volatile. Although these assets are not directly influencing the volatility for the assets that are invested in portfolios, it can serve as an alternative measure of risk propensity. I assume that an investor keeping large amounts of assets away from volatile portfolios will also invest in a more risk averse manner, hence I pro-pose:

Expectation 4: Investors with high shares of cash in their overall assets dis-play lower portfolio volatilities

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The share of cash is a significant predictor for portfolio volatilities: the more cash a client holds compared to other invested assets, the lower the volatility of such in-vested assets is. This clearly confirms expectation 4 and our introductory assump-tion that cash holdings are a valid predictor for risk propensity. On a side note, this finding together with the fact that portfolio volumes are not significant may be inter-preted as a hint that the investors in this study do not display much mental account-ing bias.

Assets that are not held in cash are invested with either of the following three service levels: they can be invested in discretionary solutions, where the client del-egates individual investment decisions to the bank. They can be invested in advi-sory solutions, where the client remains the ultimate decision maker but is closely accompanied by advisors and investment experts. Or, and that is the residual op-tion, assets can be invested by a client in a classic approach, oftentimes without close advice and the bank mostly as partner for execution. The residual last option of ad-hoc advisory can differ very much among clients as there is very little stand-ardization in service levels. In summary, the asset structure between these options can serve as indicator for the relationship between client and bank. I expect the lowest volatility to occur for discretionary solutions as professional portfolio man-agers can be expected to manage portfolios in a stable manner over time. The highest volatilities I expect for the residual, i.e. the classic approach with less insti-tutionalized advice services. In these cases, the investor is oftentimes on his own and also non-strategic stock picking is possible. With advisory solutions, I expect that volatilities will be somewhat between the two, but closer to the classic approach as clients remain the ultimate decision-makers. In summary, I propose:

Expectation 5: Investors with high shares of discretionary solutions in their overall assets display lower portfolio volatilities

Expectation 6: Investors with high shares of advisory solutions in their overall assets display higher portfolio volatilities

The share of discretionary assets has not only a strongly significant influence on portfolio, but also comes with a large effect size. The more discretionary solutions an investor holds, the lower are the volatilities in his portfolio. Hence, we can clearly

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confirm expectation 5. This is in line with the assumption that somebody who is risk-seeking and constantly looking for opportunities is less likely to invest in a dis-cretionary mandate. Trusting a bank to follow a defined investment strategy in a calm and structured manner correlates well with more risk averse investors and results in lower volatilities in their portfolios.

The influence of advisory solutions on portfolio volatility is more complicated. While we find a positive unconditional simple relationship between advisory solu-tions and portfolio volatility, the overall ANCOVA model suggests a negative rela-tionship. To make a judgement on the direction of this effect, we must recall that a multiple linear regression (which the ANCOVA is for the covariates) compares per-centage shares to one type that is the base case. For this model, the base case is the classic ad-hoc advisory model. All other assets are either in cash, invested in discretionary solutions or advisory solutions. In summary, we can interpret the model results for share of advisory solutions the following way: compared to cash or discretionary solutions, the investment in advisory solutions results in higher vol-atilities as the client is the ultimate decision-maker. However, the service level of advisory solutions still reduces volatilities compared to the classic servicing model, where the client advisor relationship is not contractually defined. The overall rela-tionship between the volatility differences across service model are in line with ex-pectations.

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8 Application in practice The present study is a first attempt to consider the influence of language in the context of wealth management. In the concrete context of portfolio volatilities, it confirms language effects from clients and advisors, partially moderated by the mi-gration background of individual investors. If either an investor or a relationship manager has a mother tongue that does not force to use the future tense when referring to the future (weak FTR characteristic), then there is the tendency that portfolios will show higher levels of volatility. If combining both effects, then we can expect high volatilities if both are weak FTR speakers and low volatilities if both are strong FTR speakers. Mixed constellation result in the two main effects moderating one another. For the resulting portfolio volatilities in such constellations, it is possi-ble that the migration background of the client moderates the two opposing lan-guage effects. Concretely, if weak FTR investors are still living in their country of origin, they are more likely to ignore the language-driven recommendations of their strong FTR RMs and display higher and not moderate volatility levels. For strong FTR investors still living in their home countries, this migration effect is not as strong. All these findings are controlled for the influence of regional market volatili-ties, differences in culture and structural effects from portfolio characteristics. Based on these findings, I propose a typology of wealth management clients that shows the impact of the investor language effect, the advisor language effect, and the migration effect on portfolio volatilities. Figure 8.1 shows this typology, starting with the individual client on top and three likely portfolio volatility levels as a result on the bottom. On the first level, the language of the client distinguishes between weak FTR and strong FTR. This describes the most primal of the three levels be-cause the mother tongue of a clients is set and not a result of decision-making. In this first step a choice between investor FTR characteristics illustrates the main investor language effect: weak FTR speakers are more likely to display a higher risk propensity, translating into higher portfolio volatilities. Strong FTR speakers are more likely to display a lower risk propensity, translating into lower portfolio volatil-ities. From these nodes, the second level introduces the second main effect of the advisor language. Like the investors before, advisors are either characterized by a weak or a strong FTR characteristic in their mother tongue. The expected main effect is consistent with that of the clients: weak FTR is more likely to lead to higher volatilities and strong FTR to lower volatilities. However, the impact of the RM can never occur in isolation, as the client is always in some way or another responsible for the investment strategy in his portfolio. Hence, the results from the second layer

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incorporate the interaction between investor and advisor language effects. This leads to the expectation that portfolios with two weak FTR effects result in a higher volatility and portfolios with two strong FTR effects result in lower volatilities. Both mixed constellations of weak and strong FTR characteristics are expected to lead to more moderate volatilities. Already at this stage, we can refer to psychological effects of advice-taking: clients prefer advisors that are similar to them, even in superficial characteristics. Language as one such characteristic can create the feel-ing of closeness and trust, which leads clients to prefer advisors with similar char-acteristics and a higher likelihood of following their advice. In the mixed constella-tion we can interpret that clients are more likely to discount advice, leading to re-sults somewhere in the middle-ground between the client’s initial preferences and the advisor’s recommendations.

Figure 8.1: Typology of wealth management clients

With the third level, psychological mechanisms become even more important to understand special effects. On this level, the typology distinguishes between those

Client

Higher volatility

Lower volatility

Weak FTR Strong FTR

Higher volatility

Moderate volatility

Moderate volatility

Lower volatility

Weak FTR Strong FTR Weak FTR Strong FTR

Higher volatility

Moderate volatility

Lower volatility

Abroad Home Abroad HomeWhere does

the client

live?

Language of

advisor?

Language of

client?

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clients that still live in their country of origin and those who live abroad. The findings in this study rely on the distinction between nationality and domicile country. It has, of course, to be acknowledged that individual situations may be quite more compli-cated than that, but for the sake of this simple typology, we focus on the distinction between nationality and domicile country. Research shows that living abroad may affect the identity of an individual from a cultural perspective and it may lead to cognitive effects from a language perspective. In contrast to the two language ef-fects on the previous levels, there is not one dominant direction on how a migration effect might work. Instead, it can be assumed that it moderates the language effects from the previous levels. Concretely, we see that, if both, the client and the RM, belong to the same FTR family, then living abroad is related to more risk-taking. This appears intuitive, although there is no scientific basis for this assumption from previous research. More interesting are the mixed constellations, where client and RM have different language backgrounds. If the client is a strong FTR speaker and the RM has a weak FTR background, then migration does not seem to have an effect on the previous level result: the portfolio volatilities remain moderate, in ten-dency. However, for the case of weak FTR investors and strong FTR RMs, we see that the moderate volatility expectation from the previous level only remains if the client is living abroad. If still living in his home country, then higher volatilities are expected. This can be interpreted in the context of the psychology of advice-taking. As seen before, clients are likely to discount advice if their original preferences and recommendations by advisors differ. This is what led to the moderate volatility lev-els on the second level. However, if clients show very strong confidence than this advice discounting is elevated. I expect that weak FTR investors that live in their country of origin remained in their comfort zone which makes them feel more con-fident. As a result, their original investor language effect dominates, and portfolio volatilities are expected to be higher.

For banks and wealth managers serving (U)HNWI clients the typology described above may prove helpful along three topics of application: product suitability, trans-parency in the advisory process and providing preferred products to their clients. For all three topics it is important to note that volatility in portfolios or portfolio risk are neither purely positive or negative. First, volatility describes the changes in re-turns, which can either describe downward, but also upward movements. In a purely positive scenario of assets only rising in value, also portfolio volatilities would rise. This shows that volatilities should not be judged on a stand-alone basis but in

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relation to returns, to suitability of certain risky assets for the individual client, and the client’s strategy with expectations on risk and returns.

The first area of potential application in practice is the regulatory requirement for suitability of products. Suitability governs what products a bank is allowed to pro-vide to a client. For example, if the client has neither previous experience in highly-risky structured products with high levels of leverage nor the financial literacy to fully comprehend the risks, then the bank is not allowed to sell such product. To ensure that regulations on suitability are adhered to for individual clients, compli-ance units review and monitor portfolios for potential breaches. Applying the typol-ogy of banking clients as proposed above could be used to run sample reviews on those clients that due to language effects might be more prone to take excessive risks in their portfolios. This can help protecting clients and ensure compliance of the wealth manager.

The second area for potential application could be the advisory process. When initially onboarding, but also on a regular basis, wealth manager need to collect information from the client that includes their willingness to take risk, their preferred investment strategy and other preference regarding asset classes, markets and so forth. During the advisory process, when the relationship manager discusses all these aspects with the clients, one clear goal is to create transparency for the client and potentially even provide some financial education. In this context, advisors dis-cuss potential impacts of behavioral biases and illustrate how they can be miti-gated. The language effects from this study show some structural analogy to such biases. If advisors educate their clients that language and cultural aspects may influence their preferences, the clients can make an informed decision.

The last area of potential application comes into play once a client is onboarded, suitability boundaries are set, and the advisory process is completed: providing the client with relevant products and investment strategies. In addition to the regulatory restrictions and self-assessment information on preferences, the typology pre-sented above can be a source of inspiration for the relationship manager on what products to present to the client. Already today, the broad product offering typically also includes dedicated categories like investment themes, regional trends, and more. Sorting by risk propensity tendency may therefore add to these categories, which all have the goal to provide the client with the best products for their prefer-ences and needs.

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9 Conclusion and outlook

9.1 Theoretical contribution

The debate on the linguistic relativity hypothesis and its validity started several dec-ades ago and has produced a variety of LRH versions between specific and general views, as well as weak and strong ones (Scholz et al., 2016). This study contributes to the debate in the tradition of weak versions of the LRH that assume that language is not equal to thought or deterministic for thought but affecting cognition (Wolff & Holmes, 2011). By providing evidence for linguistic effects in application of the fu-ture-time reference characteristic, I confirm the proposal of previous research on specific claims of the LRH.

Still in the context of the overall LRH, I address the challenge of positioning LRH effects vis-à-vis effects of culture. Scholars in psychology show some tendency to see language either as a mere representation of culture and a means of transport-ing culture, or, in some cases for cognitive psychology, neglect the origins of lan-guages in cultural traits (Imai & Masuda, 2013). This study contributes to the LRH literature by developing a line of argumentation that allows distinguishing cultural effects from linguistic effects on cognition. By that I further support the theoretical foundation of specific LRH effects. Concretely, I follow the concept of a triangular relationship between language, culture, and cognition, where language and culture individually influence cognition and thereby behavior (Mavisakalyan & Weber, 2017). Within this triangle I establish that, in the long run, language is influenced by culture and in the short run, language is able to sustain or even activate influ-ences from cultural traits. The argument is based on the historic co-evolution be-tween culture and language, with languages being more persistent over time than cultures (Galor & Özak, 2016). These individual dynamics in evolution provide the theoretical foundation to propose effects of languages on cognition in the first place. As part of my literature review on LRH applications in economic contexts in general and with respect to FTR in particular, I review how scholars position their contribu-tions in contrast to culture. I find that this issue is not always explicitly addressed providing grounds for critique. One particular critique for the application of LRH in studies with large cross-country data sets is that scholars do not control for the relatedness of languages (Roberts et al., 2015). Instead of introducing language family controls, I propose an alternative solution of considering cross-country dif-ferences of cultural dimensions in a comprehensive manner. In line with my argu-ment on the independence of linguistic and cultural effects on cognition, I argue

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that a comprehensive set of cultural controls not only provides an alternative to language family indicators, but they also provide a first source for interpretation on cultural effects.

This study is largely based in psychological literature on cognition to provide the necessary theoretical foundation. However, regarding the analytical focus and study design I follow the growing literature in economics that applies variations in linguistic characteristics to explain economic, social or political outcomes (see Mavisakalyan & Weber, 2017). All these studies refer to the seminal contribution of Chen (2013), who introduced the linguistic marker of future-time reference to eco-nomic research. Finding that weak FTR speakers show an increased tendency for future-related tendency initiated further economic research on FTR and other lin-guistic characteristics. In this tradition I introduce a new analytical focus: private banking clients on a global scale. With that, I contribute to the growing literature on FTR by broadening the scope of behavioral areas that qualify as part of future-related behavior. In addition, I further develop the argument of the FTR literature that the FTR characteristic of individuals can impact their cognition. In analogy to scholars that apply FTR on corporate behavior (Liang et al., 2014), I make the as-sumption that not only mother tongue may produce FTR effects, but that there are multiple language environments to consider. Concretely, I distinguish between the mother tongue of an individual and the language of his country of domicile. In the context of banking relationships, I establish that there is not only an investor lan-guage effect and a moderating migration effect, but also an advisor language effect. These three language environments interact, which provides interesting insights on how FTR effects can be complex outside of controlled experiments. Irrespective of individual effects, this finding contributes to the LRH literature as future research may want to consider multiple language environments instead of only one.

Risk behavior in context of (U)HNWI private banking clients is a novel analytical focus and very specific empirical context for the existing FTR literature. Findings contradict expectations I developed based on existing evidence on future-related behavior. Based on that, weak FTR investors are supposed to show more future-related behavior. Hence, I expected that they were to display lower levels of risk-taking. Only, this study suggests the opposite effect – not only for investors, but also for their advisors. As I do not apply exactly the same analytical focus as pre-vious studies, my findings do not contradict their findings. However, the findings are surprising as similar arguments in the existing literature would expect the

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opposite effect. As such, this finding contributes to the literature on FTR and overall future-related behavior by providing an evidence for nuance in general future-re-lated behavior. This provides opportunity for future research to find if this directional difference occurs because of the very specific empirical context, or if the analytical focus of risk behavior indeed displays a different dynamic for FTR characteristics.

To a lesser extend this study may also be reviewed in the literature on financial risk behavior (West & Worthington, 2012). Similar to incorporating the effects of certain demographic factors, language characteristics like FTR may be worth con-sidering as well. Another recent example apart from the FTR characteristic may be grammatical mood (Kovacic et al., 2016). Furthermore, and also to a lesser extent, this study may be viewed as a more practical application of the psychological ad-vice literature (Bonaccio & Dalal, 2006). While this study remains quite different in its design and scope, it may serve as a practical example for advice discounting based on facts of similarity.

9.2 Limitations and outlook

This study is facing limitations in the areas of the analytical focus and the empirical context. As discussed in chapter 9.1, this study contributes mostly to the economic literature on FTR. Chen (2013) and colleagues following his approach have pro-posed that FTR impacts general future-oriented behavior across different concrete examples, ranging from household savings, health behavior, investment in educa-tion, environmental attitudes, corporate earnings management, corporate social re-sponsibility, and support of future-related policies. This study is an attempt to add private banking investment risk behavior to the examples of general future-oriented behavior. However, while most studies for the individual level base their findings on survey data that aim to be representative of the different countries in scope, this study is restricted to a very specific scope of individuals. (U)HNWI are in many regards not representative for the wider population in their respective countries of origin. Findings in this study can therefore only claim limited generalizability. While it is likely that insights from this study are representative for the client base of inter-nationally operating wealth managers, comparing it to previous findings in FTR lit-erature is only possible to a limited extent. However, this does not disqualify my findings per se but to fully establish investment risk behavior as analytical focus in parallel to other examples of future-related behavior requires further research.

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Limitations for the empirical context are based either on the availability of data or on the adherence to legal restrictions. One limitation based on the pure availa-bility of data, is the identification of mother tongue. The data source does not con-tain any information on the mother tongue of clients, as this is neither a business-related nor regulatory-relevant piece of information. I mitigate this issue by using nationality as proxy for countries where it is clear what FTR characteristic is to be found for the far majority of their citizens. For the case of Switzerland, I can use the self-chosen correspondence language of clients as a proxy. While I expect that this solution does not pose a large quality issue, it would be more precise to use real information on mother tongue instead. This would allow to also attend to more spe-cific cases like bilingual speakers or to countries that had to be excluded with the proxy method because they do not display a clear FTR characteristic. Another lim-itation for the empirical context is the availability of more detailed demographic in-formation. As the source of data has the primarily purpose to contain information that is relevant for conducting business, detailed demographics are not available. Previous research shows that levels of education, health status, gender, race, reli-gion and others impact risk attitudes (e.g. Gutter & Fontes, 2006; Karlsson & Nordén, 2007; West & Worthington, 2012). Controlling for such demographic fac-tors may allow to further support the validity of findings. The last limitation for the empirical context relates more to proper documentation. Due to legal and compli-ance restrictions I am not able to share more details on descriptive data in this study. Concretely, I cannot disclose the number of clients in scope for individual countries. Also, I cannot conduct any within-country analyses as with a single source of data, this would reveal too much about the client structure in given coun-try. Future research may overcome these issues if they do not only use data from one wealth manager but combine the client base of multiple banks. This way, nei-ther the analysis itself nor the findings could disclose any potentially sensitive in-formation.

Further research may not only address the limitations addressed above, but also expand the direction of this study. First, the approach to broaden the understanding of FTR by introducing the idea of different language environments already provides additional nuance. In the context of applying linguistic relativity in economic re-search, it could prove insightful to not only consider FTR as a characteristic but in the context of risk behavior also the concept of grammatical mood. Kovacic et al. (2016) already made a first attempt to combine different characteristics in parallel for the analysis of survey results. Combining multiple language environments with

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multiple characteristics may be able to further support the claim of the general lin-guistic relativity hypothesis.

9.3 Conclusion

In the context of private banking for (U)HNWI clients on a global scale, language can serve as predictor for portfolio risk and, by further interpretation, as predictor for the risk propensity of investors. Investors speaking a language with a weak fu-ture-time reference show in tendency more portfolio volatility than those with strong FTR. Assuming that observed risk-taking is closely related to risk propensity, this means that weak FTR investors show a higher risk propensity than strong FTR speakers. Relationship managers advising these clients show a similar cognitive tendency. Through providing advice they thereby also influence the portfolio risk of their clients. In interaction of the two, I find that weak FTR investors advised by weak FTR RMs show higher volatilities and strong FTR investors advised by strong FTR RMs show lower volatility. Mixed FTR constellations result in more moderate levels of volatility. All these findings are robust after controlling for market volatili-ties, cross-country differences in cultural dimensions, indicators for investor wealth levels and differences in service models.

Clear distinctions between effects of language and effects of culture on human cognition provide the theoretical foundation to assume that language is not only an indicator for cultural traits but produces effects of its own. Based on this, the psy-chology literature on the linguistic relativity hypothesis can be applied also to differ-ent economic outcomes. This study contributes to the growing economic literature on FTR by expanding into a novel analytical focus and in establishing language effects not only for the mother tongue of an individual but for multiple language environments at the same time. The specific context of private banking clients pro-duces findings for risk behavior that contradict what might be expected from the existing literature on neighboring examples of future-oriented behavior. This pro-vides opportunity for future research to show more details on FTR effects: either the established FTR argument on future orientation does not apply to investment risk behavior as it does for savings or health behavior, or scholars might consider that FTR effects depend additional factors. Concretely, future research can assess if the findings of this study hold also beyond the scope of wealthy private investors.

XI

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XXV

Appendix

Main factor tables

Appendix 1: Main factor tables (FTR)

Notes: Covariates appearing in the model are evaluated at the following values: MSCI vol = 1.112, Power distance = 1.654, Individualism = 1.742, Masculinity = 1.743, Uncertainty avoidance = 1.853, Long-term orientation = 1.722, Indulgence= 1.671, AuM = 3670708.08, Portfolio volume = 2530569.53, Share of cash = 0.172, Share of disc. solutions = 0.336, Share of advisory solutions = .073

*** ** *

Significant at the 0.1 percent level Significant at the 1 percent level Significant at the 5 percent level

FTR Mean Std. Error Lower Upper Bias Std. Error Lower UpperWeak 0.617 0.008 0.601 0.633 < 0.0005 0.008 0.602 0.633Strong 0.594 0.005 0.584 0.604 < 0.0005 0.006 0.582 0.605Bootstrap results based on 1000 bootstrap samples

EstimatesBootstrap for mean

95% Confidence I. 95% Confidence I.

(I) FTR (J) FTR Std. Error Lower UpperWeak Strong 0.023 0.011 0.027 * 0.003 0.044Strong Weak -0.023 0.011 0.027 * -0.044 -0.003Adjustment for multiple comparisons: Least Significant Difference

Pairwise comparisonDifference

(I-J)95% Confidence I.

Sig.

(I) FTR (J) FTR Bias Std. Error Lower UpperWeak Strong 0.023 < 0.0005 0.011 0.024 * 0.002 0.045Strong Weak -0.023 < 0.0005 0.011 0.024 * -0.045 -0.002Bootstrap results based on 1000 bootstrap samples

Bootstrap for pairwise comparisonDifference

(I-J)Sig.

(2-tailed)95% Confidence I.

Appendix

XXVI

Appendix 2: Main factor tables (Migration)

Notes: Covariates appearing in the model are evaluated at the following values: MSCI vol = 1.112, Power distance = 1.654, Individualism = 1.742, Masculinity = 1.743, Uncertainty avoidance = 1.853, Long-term orientation = 1.722, Indulgence= 1.671, AuM = 3670708.08, Portfolio volume = 2530569.53, Share of cash = 0.172, Share of disc. solutions = 0.336, Share of advisory solutions = .073

*** ** *

Significant at the 0.1 percent level Significant at the 1 percent level Significant at the 5 percent level

Migration Mean Std. Error Lower Upper Bias Std. Error Lower UpperNo 0.612 0.005 0.602 0.622 < 0.0005 0.005 0.603 0.622Yes 0.599 0.007 0.585 0.613 < 0.0005 0.008 0.584 0.613Bootstrap results based on 1000 bootstrap samples

EstimatesBootstrap for mean

95% Confidence I. 95% Confidence I.

(I) Migration (J) Migration Std. Error Lower UpperNo Yes 0.013 0.009 0.123 -0.004 0.030Yes No -0.013 0.009 0.123 -0.030 0.004Adjustment for multiple comparisons: Least Significant Difference

Pairwise comparisonDifference

(I-J)95% Confidence I.

Sig.

(I) Migration (J) Migration Bias Std. Error Lower UpperNo Yes 0.013 < 0.0005 0.009 0.135 -0.004 0.032Yes No -0.013 < 0.0005 0.009 0.135 -0.032 0.004Bootstrap results based on 1000 bootstrap samples

Bootstrap for pairwise comparisonDifference

(I-J)Sig.

(2-tailed)95% Confidence I.

Appendix

XXVII

Appendix 3: Main factor tables (RM FTR)

Notes: Covariates appearing in the model are evaluated at the following values: MSCI vol = 1.112, Power distance = 1.654, Individualism = 1.742, Masculinity = 1.743, Uncertainty avoidance = 1.853, Long-term orientation = 1.722, Indulgence= 1.671, AuM = 3670708.08, Portfolio volume = 2530569.53, Share of cash = 0.172, Share of disc. solutions = 0.336, Share of advisory solutions = .073

*** ** *

Significant at the 0.1 percent level Significant at the 1 percent level Significant at the 5 percent level

RM FTR Mean Std. Error Lower Upper Bias Std. Error Lower UpperWeak 0.616 0.005 0.607 0.626 < 0.0005 0.005 0.606 0.627Strong 0.595 0.007 0.581 0.608 < 0.0005 0.007 0.581 0.608Bootstrap results based on 1000 bootstrap samples

EstimatesBootstrap for mean

95% Confidence I. 95% Confidence I.

(I) RM FTR (J) RM FTR Std. Error Lower UpperWeak Strong 0.022 0.008 0.010 * 0.005 0.038Strong Weak -0.022 0.008 0.010 * -0.038 -0.005Adjustment for multiple comparisons: Least Significant Difference

Pairwise comparisonDifference

(I-J)95% Confidence I.

Sig.

(I) RM FTR (J) RM FTR Bias Std. Error Lower UpperWeak Strong 0.022 < 0.0005 0.009 0.019 * 0.003 0.040Strong Weak -0.022 < 0.0005 0.009 0.019 * -0.040 -0.003Bootstrap results based on 1000 bootstrap samples

Bootstrap for pairwise comparisonDifference

(I-J)Sig.

(2-tailed)95% Confidence I.

Two-way interaction tables

Appendix 4: Two-way interaction tables (FTR * Migration)

FTR Migration Mean Std. Error Lower Upper Bias Std. Error Lower UpperWeak No 0.637 0.010 0.618 0.657 < 0.0005 0.010 0.618 0.658

Yes 0.597 0.011 0.574 0.619 < 0.0005 0.011 0.574 0.620Strong No 0.587 0.005 0.577 0.596 < 0.0005 0.005 0.577 0.597

Yes 0.601 0.009 0.584 0.618 < 0.0005 0.011 0.580 0.621Bootstrap results based on 1000 bootstrap samples

EstimatesBootstrap for mean

95% Confidence I. 95% Confidence Interval

FTR (I) Migration (J) Migration Std. Error Lower UpperWeak No Yes 0.041 0.014 0.004 ** 0.013 0.069

Yes No -0.041 0.014 0.004 ** -0.069 -0.013Strong No Yes -0.014 0.010 0.138 -0.033 0.005

Yes No 0.014 0.010 0.138 -0.005 0.033Adjustment for multiple comparisons: Least Significant Difference

Pairwise comparison FTR - MigrationDifference

(I-J)95% Confidence Interval

Sig. (2-tailed)

FTR (I) Migration (J) Migration Bias Std. Error Lower UpperWeak No Yes 0.041 < 0.0005 0.014 0.005 ** 0.013 0.069

Yes No -0.041 < 0.0005 0.014 0.005 ** -0.069 -0.013Strong No Yes -0.14 0.001 0.011 0.202 -0.035 0.010

Yes No 0.14 -0.001 0.011 0.202 -0.010 0.035Bootstrap results based on 1000 bootstrap samples

Bootstrap for pairwise comparison FTR - MigrationDifference

(I-J)95% Confidence Interval

Sig. (2-tailed)

Notes: Covariates appearing in the model are evaluated at the following values: MSCI vol = 1.112, Power distance = 1.654, Individualism = 1.742, Masculinity = 1.743, Uncertainty avoidance = 1.853, Long-term orientation = 1.722, Indulgence= 1.671, AuM = 3670708.08, Portfolio volume = 2530569.53, Share of cash = 0.172, Share of disc. solutions = 0.336, Share of advisory solutions = .073

*** ** *

Significant at the 0.1 percent level Significant at the 1 percent level Significant at the 5 percent level

Migration (I) FTR (J) Std. Error Lower UpperNo Weak Strong 0.051 0.012 < 0.0005 *** 0.027 0.074

Strong Weak -0.051 0.012 < 0.0005 *** -0.074 -0.027Yes Weak Strong -0.004 0.015 0.775 -0.033 0.025

Strong Weak 0.004 0.015 0.775 -0.025 0.033Adjustment for multiple comparisons: Least Significant Difference

Pairwise comparison Migration - FTRDifference

(I-J)95% Confidence Interval

Sig. (2-tailed)

Migration (I) FTR (J) Bias Std. Error Lower UpperNo Weak Strong 0.051 < 0.0005 0.013 0.001 ** 0.027 0.075

Strong Weak -0.051 < 0.0005 0.013 0.001 ** -0.075 -0.027Yes Weak Strong -0.004 < 0.0005 0.016 0.795 -0.036 0.028

Strong Weak 0.004 < 0.0005 0.016 0.795 -0.028 0.036Bootstrap results based on 1000 bootstrap samples

Bootstrap for pairwise comparison Migration - FTRDifference

(I-J)95% Confidence Interval

Sig. (2-tailed)

Appendix 5: Two-way interaction tables (FTR * RM FTR)

FTR RM FTR Mean Std. Error Lower Upper Bias Std. Error Lower UpperWeak Weak 0.623 0.007 0.609 0.637 < 0.0005 0.007 0.609 0.637

Strong 0.611 0.013 0.585 0.638 < 0.0005 0.014 0.584 0.638Strong Weak 0.610 0.008 0.594 0.625 < 0.0005 0.009 0.591 0.627

Strong 0.578 0.006 0.566 0.590 < 0.0005 0.007 0.565 0.591Bootstrap results based on 1000 bootstrap samples

EstimatesBootstrap for mean

95% Confidence I. 95% Confidence Interval

FTR (I) RM FTR (J) RM FTR Std. Error Lower UpperWeak Weak Strong 0.012 0.014 0.406 -0.016 0.040

Strong Weak -0.012 0.014 0.406 -0.040 0.016Strong Weak Strong 0.031 0.009 0.001 ** 0.014 0.049

Strong Weak -0.031 0.009 0.001 ** -0.049 -0.014Adjustment for multiple comparisons: Least Significant Difference

Pairwise comparison FTR - RM FTRDifference

(I-J)95% Confidence Interval

Sig. (2-tailed)

FTR (I) RM FTR (J) RM FTR Bias Std. Error Lower UpperWeak Weak Strong 0.012 < 0.0005 0.015 0.416 -0.018 0.040

Strong Weak -0.012 < 0.0005 0.015 0.416 -0.040 0.018Strong Weak Strong 0.031 < 0.0005 0.010 0.002 ** 0.011 0.051

Strong Weak -0.031 < 0.0005 0.010 0.002 ** -0.051 -0.011Bootstrap results based on 1000 bootstrap samples

Bootstrap for pairwise comparison FTR - RM FTRDifference

(I-J)95% Confidence Interval

Sig. (2-tailed)

Notes: Covariates appearing in the model are evaluated at the following values: MSCI vol = 1.112, Power distance = 1.654, Individualism = 1.742, Masculinity = 1.743, Uncertainty avoidance = 1.853, Long-term orientation = 1.722, Indulgence= 1.671, AuM = 3670708.08, Portfolio volume = 2530569.53, Share of cash = 0.172, Share of disc. solutions = 0.336, Share of advisory solutions = .073

*** ** *

Significant at the 0.1 percent level Significant at the 1 percent level Significant at the 5 percent level

RM FTR (I) FTR (J) Std. Error Lower UpperWeak Weak Strong 0.013 0.011 0.237 -0.009 0.036

Strong Weak -0.013 0.011 0.237 -0.036 0.009Strong Weak Strong 0.033 0.015 0.033 * 0.003 0.063

Strong Weak -0.033 0.015 0.033 * -0.063 -0.003Adjustment for multiple comparisons: Least Significant Difference

Pairwise comparison RM FTR - FTRDifference

(I-J)95% Confidence Interval

Sig. (2-tailed)

RM FTR (I) FTR (J) Bias Std. Error Lower UpperWeak Weak Strong 0.013 < 0.0005 0.012 0.286 -0.011 0.038

Strong Weak -0.013 < 0.0005 0.012 0.286 -0.038 0.011Strong Weak Strong 0.033 < 0.0005 0.016 0.034 * 0.003 0.064

Strong Weak -0.033 < 0.0005 0.016 0.034 * -0.064 -0.003Bootstrap results based on 1000 bootstrap samples

Bootstrap for pairwise comparison RM FTR - FTRDifference

(I-J)95% Confidence Interval

Sig. (2-tailed)

Appendix 6: Two-way interaction tables (Migration * RM FTR)

Migration RM FTR Mean Std. Error Lower Upper Bias Std. Error Lower UpperNo Weak 0.609 0.003 0.603 0.616 < 0.0005 0.004 0.603 0.617

Strong 0.615 0.009 0.597 0.633 < 0.0005 0.009 0.596 0.633Yes Weak 0.623 0.009 0.606 0.641 < 0.0005 0.010 0.602 0.643

Strong 0.574 0.011 0.554 0.595 < 0.0005 0.011 0.552 0.595Bootstrap results based on 1000 bootstrap samples

Bootstrap for mean95% Confidence I. 95% Confidence Interval

Estimates

RM FTR (I) Migration (J) Std. Error Lower UpperWeak No Yes -0.014 0.010 0.151 -0.032 0.005

Yes No 0.014 0.010 0.151 -0.005 0.032Strong No Yes 0.040 0.014 0.004 ** 0.013 0.068

Yes No -0.040 0.014 0.004 ** -0.068 -0.013Adjustment for multiple comparisons: Least Significant Difference

Pairwise comparison RM FTR - MigrationDifference

(I-J)95% Confidence Interval

Sig. (2-tailed)

RM FTR (I) Migration (J) Bias Std. Error Lower UpperWeak No Yes -0.014 < 0.0005 0.011 0.193 -0.034 0.009

Yes No 0.014 < 0.0005 0.011 0.193 -0.009 0.034Strong No Yes 0.040 < 0.0005 0.014 0.003 ** 0.014 0.068

Yes No -0.040 < 0.0005 0.014 0.003 ** -0.068 -0.014Bootstrap results based on 1000 bootstrap samples

Bootstrap for pairwise comparison RM FTR - MigrationDifference

(I-J)95% Confidence Interval

Sig. (2-tailed)

Notes: Covariates appearing in the model are evaluated at the following values: MSCI vol = 1.112, Power distance = 1.654, Individualism = 1.742, Masculinity = 1.743, Uncertainty avoidance = 1.853, Long-term orientation = 1.722, Indulgence= 1.671, AuM = 3670708.08, Portfolio volume = 2530569.53, Share of cash = 0.172, Share of disc. solutions = 0.336, Share of advisory solutions = .073

*** ** *

Significant at the 0.1 percent level Significant at the 1 percent level Significant at the 5 percent level

Migration (I) RM FTR (J) RM FTR Std. Error Lower UpperNo Weak Strong -0.005 0.010 0.591 -0.025 0.014

Strong Weak 0.005 0.010 0.591 -0.014 0.025Yes Weak Strong 0.049 0.014 < 0.0005 *** 0.022 0.076

Strong Weak -0.049 0.014 < 0.0005 *** -0.076 -0.022Adjustment for multiple comparisons: Least Significant Difference

Pairwise comparison Migration - RM FTRDifference

(I-J)95% Confidence Interval

Sig. (2-tailed)

Migration (I) RM FTR (J) RM FTR Bias Std. Error Lower UpperNo Weak Strong -0.005 < 0.0005 0.010 0.590 -0.025 0.014

Strong Weak 0.005 < 0.0005 0.010 0.590 -0.014 0.025Yes Weak Strong 0.049 < 0.0005 0.015 0.002 ** 0.020 0.077

Strong Weak -0.049 < 0.0005 0.015 0.002 ** -0.077 -0.020Bootstrap results based on 1000 bootstrap samples

Bootstrap for pairwise comparison Migration - RM FTRDifference

(I-J)95% Confidence Interval

Sig. (2-tailed)

Three-way interaction tables: FTR * Migration * RM FTR

Appendix 7: Three-way interaction tables (FTR * Migration * RM FTR

Notes: Covariates appearing in the model are evaluated at the following values: MSCI vol = 1.112, Power distance = 1.654, Individualism = 1.742, Masculinity = 1.743, Uncertainty avoidance = 1.853, Long-term orientation = 1.722, Indulgence= 1.671, AuM = 3670708.08, Portfolio volume = 2530569.53, Share of cash = 0.172, Share of disc. solutions = 0.336, Share of advisory solutions = .073

FTR Migration RM FTR Mean Std. Error Lower Upper Bias Std. Error Lower UpperWeak No Weak 0.611 0.005 0.600 0.621 < 0.0005 0.005 0.599 0.621

Strong 0.664 0.019 0.628 0.701 < 0.0005 0.018 0.630 0.702Yes Weak 0.635 0.012 0.613 0.658 < 0.0005 0.011 0.613 0.658

Strong 0.558 0.019 0.521 0.595 < 0.0005 0.019 0.521 0.593Strong No Weak 0.608 0.007 0.595 0.622 < 0.0005 0.007 0.595 0.622

Strong 0.565 0.005 0.555 0.575 < 0.0005 0.006 0.554 0.577Yes Weak 0.611 0.014 0.584 0.638 < 0.0005 0.017 0.577 0.642

Strong 0.591 0.010 0.571 0.611 < 0.0005 0.011 0.569 0.613Bootstrap results based on 1000 bootstrap samples

EstimatesBootstrap for mean

95% Confidence Interval 95% Confidence Interval

*** ** *

Significant at the 0.1 percent level Significant at the 1 percent level Significant at the 5 percent level

FTR Migration (I) RM FTR (J) RM FTR Std. Error Sig. (2-tailed) Lower UpperWeak No Weak Strong -0.054 0.190 0.004 ** -0.091 -0.017

Strong Weak 0.054 0.190 0.004 ** 0.017 0.091Yes Weak Strong 0.078 0.022 < 0.0005 *** 0.035 0.120

Strong Weak -0.078 0.022 < 0.0005 *** -0.120 -0.035Strong No Weak Strong 0.043 0.007 < 0.0005 *** 0.029 0.058

Strong Weak -0.043 0.007 < 0.0005 *** -0.058 -0.029Yes Weak Strong 0.020 0.017 0.241 -0.013 0.053

Strong Weak -0.020 0.017 0.241 -0.053 0.013Adjustment for multiple comparisons: Least Significant Difference

Pairwise comparison FTR - Migration - RM FTRDifference

(I-J)95% Confidence Interval

FTR Migration (I) RM FTR (J) RM FTR Bias Std. Error Lower UpperWeak No Weak Strong -0.054 -0.001 0.018 0.004 ** -0.090 -0.020

Strong Weak 0.054 0.001 0.018 0.004 ** 0.020 0.090Yes Weak Strong 0.078 < 0.0005 0.022 0.002 ** 0.036 0.120

Strong Weak -0.078 < 0.0005 0.022 0.002 ** -0.120 -0.036Strong No Weak Strong 0.043 < 0.0005 0.008 0.001 ** 0.028 0.059

Strong Weak -0.043 < 0.0005 0.008 0.001 ** -0.059 -0.028Yes Weak Strong 0.02 < 0.0005 0.019 0.321 -0.016 0.056

Strong Weak -0.02 < 0.0005 0.019 0.321 -0.056 0.016Bootstrap results based on 1000 bootstrap samples

Bootstrap for pairwise comparison FTR - Migration - RM FTRDifference

(I-J)95% Confidence Interval

Sig. (2-tailed)

*** ** *

Significant at the 0.1 percent level Significant at the 1 percent level Significant at the 5 percent level

FTR RM FTR (I) Migration (J) Migration Std. Error Sig. (2-tailed) Lower UpperWeak Weak No Yes -0.025 0.012 0.030 * -0.048 -0.002

Yes No 0.025 0.012 0.030 * 0.002 0.048Strong No Yes 0.107 0.026 < 0.0005 *** 0.055 0.158

Yes No -0.107 0.026 < 0.0005 *** -0.158 -0.055Strong Weak No Yes -0.002 0.015 0.878 -0.032 0.027

Yes No 0.002 0.015 0.878 -0.027 0.032Strong No Yes -0.026 0.011 0.017 * -0.047 -0.005

Yes No 0.026 0.011 0.017 * 0.005 0.047Adjustment for multiple comparisons: Least Significant Difference

Pairwise comparison FTR - RM FTR - MigrationDifference

(I-J)95% Confidence Interval

FTR RM FTR (I) Migration (J) Migration Bias Std. Error Lower UpperWeak Weak No Yes -0.025 < 0.0005 0.011 0.020 * -0.046 -0.004

Yes No 0.025 < 0.0005 0.011 0.020 * 0.004 0.046Strong No Yes 0.107 < 0.0005 0.025 0.001 ** 0.058 0.157

Yes No -0.107 < 0.0005 0.025 0.001 ** -0.157 -0.058Strong Weak No Yes -0.002 0.001 0.018 0.903 -0.036 0.036

Yes No 0.002 -0.001 0.018 0.903 -0.036 0.036Strong No Yes -0.026 < 0.0005 0.012 0.034 * -0.050 -0.003

Yes No 0.026 < 0.0005 0.012 0.034 * 0.003 0.050Bootstrap results based on 1000 bootstrap samples

Bootstrap for pairwise comparison FTR - RM FTR - MigrationDifference

(I-J)95% Confidence Interval

Sig. (2-tailed)

*** ** *

Significant at the 0.1 percent level Significant at the 1 percent level Significant at the 5 percent level

Migration RM FTR (I) FTR (J) FTR Std. Error Sig. (2-tailed) Lower UpperNo Weak Weak Strong 0.002 0.010 0.835 -0.018 0.022

Strong Weak -0.002 0.010 0.835 -0.022 0.018Strong Weak Strong 0.099 0.020 < 0.0005 *** 0.060 0.138

Strong Weak -0.099 0.020 < 0.0005 *** -0.138 -0.060Yes Weak Weak Strong 0.025 0.018 0.179 -0.011 0.061

Strong Weak -0.025 0.018 0.179 -0.061 0.011Strong Weak Strong -0.033 0.022 0.129 -0.076 0.010

Strong Weak 0.033 0.022 0.129 -0.010 0.076Adjustment for multiple comparisons: Least Significant Difference

Pairwise comparison Migration - RM FTR - FTRDifference

(I-J)95% Confidence Interval

Migration RM FTR (I) FTR (J) FTR Bias Std. Error Lower UpperNo Weak Weak Strong 0.002 -0.001 0.010 0.855 -0.018 0.023

Strong Weak -0.002 0.001 0.010 0.855 -0.023 0.018Strong Weak Strong 0.099 < 0.0005 0.020 0.001 ** 0.061 0.139

Strong Weak -0.099 < 0.0005 0.020 0.001 ** -0.139 -0.061Yes Weak Weak Strong 0.025 < 0.0005 0.020 0.230 -0.014 0.064

Strong Weak -0.025 < 0.0005 0.020 0.230 -0.064 0.014Strong Weak Strong -0.033 < 0.0005 0.023 0.153 -0.080 0.011

Strong Weak 0.033 < 0.0005 0.023 0.153 -0.011 0.080Bootstrap results based on 1000 bootstrap samples

Bootstrap for pairwise comparison Migration - RM FTR - FTRDifference

(I-J)95% Confidence Interval

Sig. (2-tailed)

Correlations of covariates and dependent variable

Appendix 8: Bootstrapped Pearson's correlations

Bootstrap results based on 1000 bootstrap samples. *** significant at the 0.1 percent level, ** significant at the 1 percent level, * significant at the 5 percent level

Portfolio vol 12m 1 -.066 *** -.061 *** .081 *** .012 -.099 *** .073 *** .034 *** .032 *** .032 *** -.149 *** -.165 *** .037 ***

MSCI vol 12m -.066 *** 1 .421 *** -.659 *** .007 .439 *** -.764 *** .304 *** -.022 ** -.013 * .059 *** -.222 *** -.035 ***

Power distance -.061 *** .421 *** 1 -.543 *** -.258 *** .563 *** -.461 *** -.167 *** -.002 .001 .087 *** -.120 *** -.071 ***

INDIV .081 *** -.659 *** -.543 *** 1 .063 *** -.582 *** .724 *** .003 -.001 -.002 -.107 *** .188 *** .036 ***

MAS .012 .007 -.258 *** .063 *** 1 .053 *** .118 *** -.002 .006 .006 .012 .008 .035 ***

UA -.099 *** .439 *** .563 *** -.582 *** .053 *** 1 -.400 *** -.221 *** -.045 *** -.025 *** .046 *** -.097 *** -.036 ***

LTO .073 *** -.764 *** -.461 *** .724 *** .118 *** -.400 *** 1 -.012 .015 * .009 -.088 *** .218 *** .026 ***

INDU .034 *** .304 *** -.167 *** .003 -.002 -.221 *** -.012 1 .020 ** .015 * -.007 -.082 *** .019 **

AuM .032 *** -.022 ** -.002 -.001 .006 -.045 *** .015 * .020 ** 1 .929 *** .019 ** -.049 *** .018 **

Portfolio volume .032 *** -.013 * .001 -.002 .006 -.025 *** .009 .015 * .929 *** 1 .005 -.046 *** .015 *

Share cash -.149 *** .059 *** .087 *** -.107 *** .012 .046 *** -.088 *** -.007 .019 ** .005 1 -.353 *** -.069 ***

Share disc. -.165 *** -.222 *** -.120 *** .188 *** .008 -.097 *** .218 *** -.082 *** -.049 *** -.046 *** -.353 *** 1 -.177 ***

Share adv. .037 *** -.035 *** -.071 *** .036 *** .035 *** -.036 *** .026 *** .019 ** .018 ** .015 * -.069 *** -.177 *** 1

Portfolio vol 12m

MSCI vol 12m

Power distance INDIV MAS UA

Share adv.LTO INDU AuM

Portfolio volume

Share cash

Share disc.

Robustness check: extended ANCOVA

Appendix 9: ANCOVA (robustness check) parameter estimates

B Std. Error t Lower Upper B Bias Std. Error Lower UpperIntercept 0.453 0.215 2.108 0.035 * 0.032 0.874 0.453 -0.020 0.307 0.131 -0.194 1.043Regional MSCI volatility 0.015 0.097 0.155 0.877 -0.176 0.206 0.015 0.008 0.122 0.904 -0.223 0.27Power distance 0.095 0.024 3.899 < 0.0005 *** 0.047 0.143 0.095 < 0.0005 0.024 0.002 ** 0.051 0.140Individualism 0.049 0.026 1.903 0.057 -0.001 0.099 0.049 < 0.0005 0.028 0.079 -0.007 0.103Masculinity 0.030 0.014 2.175 0.030 * 0.003 0.057 0.030 < 0.0005 0.014 0.025 * 0.004 0.058Uncertainty avoidance -0.219 0.035 -6.316 < 0.0005 *** -0.287 -0.151 -0.219 < 0.0005 0.040 0.001 ** -0.299 -0.138Long-term orientation 0.148 0.064 2.334 0.020 * 0.024 0.273 0.148 0.006 0.099 0.149 -0.043 0.347Indulgence 0.060 0.016 3.852 < 0.0005 *** 0.030 0.091 0.060 < 0.0005 0.018 0.001 ** 0.028 0.097AuM -4.16E-11 2.63E-10 -0.159 0.874 -5.57E-10 4.73E-10 -4.16E-11 < 0.0005 3.89E-10 0.892 -8.24E-10 7.37E-10Portfolio volume 2.84E-10 5.39E-10 0.527 0.598 -7.73E-10 1.34E-09 2.84E-10 < 0.0005 5.61E-10 0.559 -8.48E-10 1.38E-09Share of cash -0.256 0.028 -9.151 < 0.0005 *** -0.311 -0.201 -0.256 0.001 0.036 0.001 ** -0.328 -0.188Share of disc. solutions -0.248 0.006 -42.899 < 0.0005 *** -0.26 -0.237 -0.248 0.001 0.005 0.001 ** -0.259 -0.238Share of adv. solutions -0.010 0.025 -0.390 0.696 -0.059 0.039 -0.010 0.002 0.022 0.648 -0.049 0.036FTR = Weak -0.008 0.024 -0.347 0.728 -0.054 0.038 -0.008 -0.002 0.024 0.734 -0.058 0.037Migration = No 0.647 0.204 3.181 0.001 ** 0.248 1.046 0.647 0.022 0.294 0.031 * 0.098 1.280RM FTR = Weak 0.021 0.018 1.177 0.239 -0.014 0.056 0.021 0.001 0.020 0.280 -0.018 0.059FTR = Weak * Migration = No

0.143 0.029 4.895 < 0.0005 *** 0.086 0.200 0.143 0.002 0.031 0.001 ** 0.084 0.203

FTR = Weak * RM FTR = Weak

0.059 0.027 2.147 0.032 * 0.005 0.112 0.059 < 0.0005 0.028 0.033 * 0.005 0.114

Migration = No * RM FTR = Weak

0.021 0.018 1.160 0.246 -0.015 0.057 0.021 < 0.0005 0.020 0.298 -0.017 0.059

FTR = Weak * Migration = No * RM FTR = Weak

-0.162 0.034 -4.744 < 0.0005 *** -0.228 -0.095 -0.162 -0.001 0.034 0.001 ** -0.232 -0.096

Migration = No *Regional MSCI volatility

-0.302 0.100 -3.015 0.003 ** -0.498 -0.106 -0.302 -0.009 0.125 0.023 * -0.569 -0.066

Migration = No *Long-term orientation

-0.183 0.065 -2.802 0.005 ** -0.311 -0.055 -0.183 -0.007 0.101 0.077 -0.401 0.002

Migration = No *Portfolio volume

5.65E-11 4.71E-10 0.120 0.905 -8.66E-10 9.79E-10 5.65E-08 < 0.0005 4.78E-10 0.875 -5.34E-10 1.31E-09

Migration = No *Share of cash

-0.107 0.028 -3.832 < 0.0005 ** -0.162 -0.052 -0.107 -0.001 0.036 0.001 ** -0.177 -0.035

Migration = No *Share of adv. Solutions

-0.052 0.027 -1.950 0.051 -0.105 0.000 -0.052 -0.002 0.024 0.026 * -0.102 -0.010

FTR = Weak *Share of cash

-0.164 0.026 -6.340 < 0.0005 ** -0.214 -0.113 -0.164 0.001 0.033 0.001 ** -0.232 -0.102

RM FTR = Weak *Share of cash 0.005 0.025 0.221 0.825 -0.043 0.054 0.005 < 0.0005 0.032 0.860 -0.058 0.065

With the three factors FTR, Migration and RM FTR being dichotomous variables, all other (interaction) parameters are not included in above table as values are 0.

Parameter estimates Bootstrap for parameter estimates95% Confidence Intvl. Sig.

(2-tailed)95% Confidence Intvl.

Sig.

XL

Curriculum Vitae

Personal Information

Name: Maximilian Schellen

Date of birth: 09 December 1989

Place of birth: Arnsberg, Germany

Nationality: German

Education

2015 – 2018 University of St. Gallen (HSG) Ph.D. in Management (Strategy & Management track)

2012 – 2015 HEC Paris and University of St. Gallen (HSG) CEMS Master’s in International Management (CEMS MIM)

2011 – 2015 University of St. Gallen (HSG) Master of Arts in Banking & Finance (M.A. HSG)

2008 – 2011 University of St. Gallen (HSG) Bachelor of Arts in Economics (B.A. HSG)

Professional Experience

2014 – Financial industry, Zürich Strategy consulting and senior management support