The role of media reported weather shocks on mutual fund capital …1444635/FULLTEXT01.pdf ·...

The role of media reported weather shocks on mutual

fund capital flow:

A comparison of socially responsible- and conventional funds

Tefera, Bizuayehu Tsegaye

2020-06-05

Department of Business Administration

Master’s Program in Finance

Master’s thesis in business administration III, 30 Credits, Term 2020

Supervisor: Phd. Jörgen Hellström

https://www.google.com/url?sa=i&url=http%3A%2F%2Fwww.diva-portal.org%2Fsmash%2Fget%2Fdiva2%3A1335378%2FFULLTEXT01.pdf&psig=AOvVaw1fSwL24qJKSBhNhVIHUyow&ust=1585246543645000&source=images&cd=vfe&ved=0CAIQjRxqFwoTCPjv-t6dtugCFQAAAAAdAAAAABAE

Abstract

Identifying factors that affect the flow of mutual fund capital and between mutual fund types

has the potential, among others, to relief fund management and investors from unnecessary

administrative costs. This study investigated the role media reported weather shocks have

on socially responsible and conventional mutual trust funds’ capital flow. The study also has

compared the magnitude of influence media reported weather shocks has on capital flow

between socially responsible- and convectional mutual trust funds. It gives conclusion after

empirically studying all accessible socially responsible mutual trust funds with relevant

accessible financial data, originated, and actively traded in the Swedish financial market

with the Swedish currency (Kronor) as well as taking conventional mutual trust funds with

similar maturity. And, the study result shows that media reported weather shocks has

statistically significant role in the flow of capital, on both socially responsible- and

conventional mutual funds in Sweden. It also shows that there is no significant difference in

the role media reported weather shocks play between the two fund types. The result is

concurrent with Hirshleifer & Shumway (2003)’s study which indicate that weather

affecting investors mood and behavior. The result is interesting as it implicates to the

psychological and emotional factors playing a significant role in affecting the flow of

investment capital in general, in contrast to the rational economic behavior characterized by

fund return and risk performance.

Key words:

Capital flow, Weather shocks, Socially responsible investments

Acknowledgement

This study is supervised by Jörgen Hellström and from the start of this thesis project Jörgen

has provided valuable information and guidance in areas of relevant related studies on the

topic. Without his guidance the study process would have been more challenging.

Fortunately, being advised to adjust the study’s scope as well as the domain together with

information on different Swedish institutions related to the financial sector, has been

valuable learning process. For his help, I thank Jörgen Hellström.

Constructive criticisms and discussion points during the work in progress seminars of the

project has also been valuable inputs to this study, for this I thank all fellow students who

participated and put forward their ideas.

Contents

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

1.1 Problem background ................................................................................................ 2

1.1.1 The Swedish investment cultural environment ................................................ 3

1.1.2 Socially responsible investment challenges ..................................................... 4

1.2 Research gap and research question ........................................................................ 5

1.3 The purpose of the study ......................................................................................... 7

1.4 Delimitation ............................................................................................................. 8

2 Scientific Methodology................................................................................................... 9

2.1 Fundamental Assumptions ...................................................................................... 9

2.1.1 Ontological stances ................................................................................................ 9

2.1.2 Epistemological stances ....................................................................................... 10

2.1.3 Paradigms of a system of beliefs and practices ................................................... 11

2.1.4 Axiology .............................................................................................................. 12

2.2 Research Approach ................................................................................................ 12

2.3 Method ................................................................................................................... 13

2.4 Secondary data and literature source ..................................................................... 14

2.5 Research strategy and design ................................................................................ 14

3 Literature review ........................................................................................................... 17

3.1 Theoretical point of departure .................................................................................... 24

3.1.1 Classical economic theories and past performance dependency of

investments ................................................................................................................... 24

3.1.2 Behavioral finance and factors shaping SR Investment desire ...................... 26

3.1.2 Investors orientation under high and low risk financial market ..................... 30

3.2 Hypothesis formation ............................................................................................ 32

4. Practical method ............................................................................................................... 34

4.1 Financial data collection ............................................................................................. 34

4.1.1 Financial data input ........................................................................................ 35

4.2 Weather data ............................................................................................................... 48

4.2.1 Weather data input, refined search results ..................................................... 51

4.3 Emphatical representation of variables and relationships .......................................... 53

4.4 Regression analysis result interpretation tools ...................................................... 56

4.5 Data input control before the analysis ........................................................................ 58

5. Data analysis and result .................................................................................................... 60

5.1 Initial multiple regression result ............................................................................ 60

5.1.1 Robustness test on the initial regression result .............................................. 64

5.2 Regression limiting extreme outliers (value greater than 2 and less than -2) ....... 65

5.2.1 Robustness test of results found limiting extreme outliers ............................ 68

5.3 Result ..................................................................................................................... 69

6. Conclusion ....................................................................................................................... 71

6.1 Recommendation for future related study ............................................................. 71

6.2 The studies contribution and implication .............................................................. 72

6.2.1 Theoretical contribution ................................................................................. 72

6.2.2 Practical contribution ..................................................................................... 72

6.2.3 Societal implication ........................................................................................ 72

6.3 The studies limitations ........................................................................................... 73

6.4 Quality criteria in quantitative study ..................................................................... 74

6.4.1 Reliability ....................................................................................................... 74

6.4.2 Validity ........................................................................................................... 74

6.4.3 Generalizability .............................................................................................. 74

List of tables

Table 1. Number of socially responsible funds that are eligible and included in the study,

selected samples, as well as those that are excluded from the study. .................................. 35

Table 2. Total number of mutual fund samples. .................................................................. 36

Table 3. Socially responsible funds that have ten and more years of maturity. ................... 37

Table 4. Number of socially responsible funds that have ten- and more years of maturity,

under the management of their respective fund managing institution. ................................ 37

Table 5. Socially responsible funds that have eight to two and a quarter year of maturity. 39

Table 6. Number of socially responsible funds that have eight to two and a quarter year of

maturity, under the management of their respective fund managing institution.................. 39

Table 7. Socially responsible funds that have two and less than two year of maturity. ...... 41

Table 8. Number of socially responsible funds that have two and less than two year of


Table 9. Conventional funds that have ten- and more than ten year of maturity. ................ 42

Table 10. Number of conventional funds that have ten- and more years of maturity, under

the management of their respective fund managing institution. .......................................... 43

Table 11. Conventional funds that have nine to two and a quarter year of maturity. .......... 44

Table 12. Number of conventional funds that have nine to two and a quarter year of


Table 13. Conventional funds that have two and less than two year of maturity. ............... 46

Table 14. Number of conventional funds that have two and less than two year of maturity,


Table 15. Preliminary review with the search word ‘’Exteremet väder’’, sources and

number of reports found. ...................................................................................................... 49

Table 16. Preliminarily review with the search word ‘’SMHI, klass 3 värningar’’, sources

and the number of reports found. ......................................................................................... 49

Table 17. Preliminarily review with the search word ‘’Skogsbränder’’, sources and number

of reports found. ................................................................................................................... 50

Table 18. Total observations from each of the fund types under study. .............................. 58

Table 19. Total observations for each of the variables under study..................................... 58

Table 20. Multicollinearity test of the variables including dummy variables ..................... 59

Table 21. Multicollinearity test of the variables excluding dummy variables ..................... 59

Table 22. Initial multiple regression result .......................................................................... 60

Table 23. Breusch-pagan heteroskedasticity test on the initial regression result ................. 62

Table 24. White’s heteroskedasticity test n the initial regression result .............................. 63

Table 25. Robustness test on the initial regression result .................................................... 64

Table 26. Regression result limiting extreme outliers ......................................................... 65

Table 27. Breusch-pagan heteroskedasticity test on the regression result limiting extreme

outliers .................................................................................................................................. 67

Table 28. White’s heteroskedasticity test limiting extreme outliers .................................... 67

Table 29. Robustness test of results found limiting extreme outliers .................................. 68

List of figures

Figure 1. The study design depicting data collection process. ............................................. 15

Figure 2. The study design depicting the process taken to reach on the study’s result and

conclusion. ........................................................................................................................... 16

Figure 3. Percentage of socially responsible funds under study and that are excluded. ...... 36

Figure 4. Percentage of SRIF and conventional funds in the total mutual funds sample size.

.............................................................................................................................................. 36

Figure 5. Percentage of socially responsible funds that have ten- and more years of


Figure 6. Percentage of socially responsible funds that have eight to two and a quarter year

of maturity, under the management of their respective fund managing institution. ............ 40

Figure 7. Percentage of socially responsible funds that have two and less than two year of


Figure 8. Percentage of conventional funds that have ten- and more years of maturity,


Figure 9. Percentage of conventional funds that have nine to two and a quarter year of


Figure 10. Percentage of conventional funds that have two and less than two year of


List of appendices

Appendix 1. SRIF convenient population ............................................................................ 80

Appendix 2. SRIF excluded from the convenient sample size due to lack of full data ....... 82

Appendix 3. A histogram of capital flow observation distribution without excluding

extreme outliers. ................................................................................................................... 82

Appendix 4. A histogram of capital flow observation distribution excluding extreme

outliers. ................................................................................................................................. 83

Appendix 5. A histogram of price index observation distribution. ...................................... 83

Appendix 6. A histogram of historical beta observation distribution. ................................. 84

Appendix 7. A histogram of market risk level (OMXS30, S.D) observation distribution. . 84

Appendix 8. A histogram of weather shocks observation distribution. ............................... 85

Appendix 9. Scatter plots of capital flow ............................................................................. 85

Appendix 10. A scatter plot of price index .......................................................................... 86

Appendix 11. A scatter plot of historical beta ..................................................................... 86

Appendix 12. A scatter plot of market risk level (OMXS30, S.D) ..................................... 87

Appendix 13. A plot of capital flow to each explanatory variable. ..................................... 87

Key words and Acronyms

CAPM: CAPM is an abbreviation or acronym for Capital Asset Pricing Model

Climate: Climate is a long term and sessional meteorological state

Class 3 warning: ‘’Refers to very extreme weather which can mean severe danger or

hazard to the public and substantially big disturbance of key public

infrastructures’’ translated form SMHI cited in Svenska Dagbladet,

September 20, 2018,)

Capital flow: Capital flow is, in this study, the change in total net asset excluding

return. It is an inference for the net capital that either flows in- or out

of mutual trust funds.

ESG: ESG is an abbreviation or acronym for Environmental, Social and

Governance.

Extreme Weather: ‘’Extreme weather is a weather event such as snow, rain, drought,

flood, or storm that is rare for the place where it occurs’’

(Encyclopedia, n.d,).

Mutual trust funds: Refers to capitals that are managed on the behalf of the investor by

the fund management. The net return on investment paid back to the

investor without holding for further future reinvestment

(Investopedia, n.d.)

SR: SR is an abbreviation or acronym for socially responsible

SRI: SRI is an abbreviation or acronym socially responsible investment

SRIF: SRIF is an abbreviation or acronym socially responsible investment

fund. All funds with the name sustainable, sustainability,

sustainability fund, green, ethic, ethical as well as climate and

environment and environment friendly are regarded as socially

responsible funds in this study.

Weather: Weather is a short term usually hours or days meteorological state.

Weather Shock: Weather shock is extreme weather incident that is not sessional in

nature and happens unexpectedly. In this study it is inferred from

weather incidents occurred after a class 3 warning is issued by the

Swedish Metrological and Hydrological Institution. In addition, big

forest fires are taken as an inference for weather shock related to high

temperature.

1

1 Introduction

Like other business operations, the success of mutual trust fund management is increasingly

dependent on cost reduction and administrative efficiency Bollen (2007). For this reason,

financial service providers relay on the identification of investment risks and risk factors that

may result in significant costs. Today’s financial institutions are highly dependent on

identifying current issues that are popular in their product market to assess treats and

opportunities. Identifiable production and consumption pattern in their domain market offer

financial institutions an understanding of their customers behavior and better adapt to new

challenges that might potentially come out to be costly.

Extreme weather events, on the other hand, are one of the risks identified by World

Economic Forum’s global risk report as the challenge’s humankind faces (Global Risks,

2015, cited in Van der Vegt, Essens, Wahlström & George, 2015, p. 1). Dependency on

fossil fuels as energy source and the exhaustive land use accelerates the greenhouse effect

(Howard-Grenville, Buckle, Hoskins & George, 2014, p. 3). Such activities contribute to the

increase in greenhouse gasses for example carbon dioxide and methane, which can prevent

more and more heat energy coming from the sun leaving earth (Howard-Grenville et al.,

2014, p. 3). This results in a rise in global temperature and variability in natural climate

causing weather shocks (Howard-Grenville et al., 2014, p. 4). One of the objectives the

Global sustainable development goal for 2030 identified by the UN (UN, nd,) is to prevent

the impact of greenhouse effect through, among others, the control of heat absorbing gasses

from being released into the atmosphere. The sustainable development goal setting and the

subsequent regulative measures taken by nations, including Sweden, shapes the alignment

of business goals with social responsibility objectives around the globe (Herremans &

Nazari, 2016, p. 104). In addition, such goal setting and subsequent informative actions taken

have substantial impact in creating awareness among ordinary members of a society about

the challenged faced by climate change, extreme weather, and related incidents.

Currently, financial institutions attempt to incorporate climate and weather change concerns

as social responsibilities in their operations. Those efforts are prevalent in their product

offerings and risk assessment approaches. For example, socially responsible funds having

its own share in the market for investment funds in different themes, while climate and

weather conditions being part of such teams. Their risk assessments have also broadened in

its’ scope by including social, environmental and governance (ESG) factors in addition to

economic risks. Given, the current popularity and dominance of climate and weather-related

opinions globally, identifying the role media reported weather shocks has on the flow of

capital between mutual trust fund types is relevant from administrative and investment cost

efficiency perspective. For business, therefore, besides regulatory factors, the implication of

environmental phenomenon such as weather shocks to organizational performance could be

significant. This is due to the reason that the norms, customs and expectations, regarding

responsible approach to the natural environment, adhered by investors and financial product

consumers is highly influential in aligning the business strategies of fund managements’ to

those values (Herremans & Nazari, 2016, p. 104; EY, 2014, cited in Bradford, Earp,

Showalter & Williams, 2017, p. 86).

2

1.1 Problem background

‘’SRI builds on the idea that there are good and bad parts of today’s corporate sector.’’

(Cowton & Sandbery, 2012, P. 144). And, it is, generally assumed as a mechanism by which

capital owner and investors do good to the society through the market system (Boettke &

Sautet, 2011, p.1). However, the notion of characterizing an act or social act as good or bad

is a source of debate by itself (O’Neil & Pienta, 1994, p. 71). O’Neil & Pienta (1994) assert

how challenging it might be determining ‘’what are the good and right things to do’’ and

argues that this limits individuals’ motive and courage to act according to their judgment on

what is good and right. Perhaps the significant emphasis O’Neil & Pienta (1994) given in

this regard is the difference in implication between ‘’what is good and band’’ and ‘’ what is

right and wrong’’. ‘’Good and Band’’ referring to taking or not taking advantages to oneself

and centers around self-interest (O’Neil & Pienta, 1994, p. 74). While ‘’Right and Wrong’’

referring to respecting and not respecting others when taking advantages in one’s favor and

is considerate about others interest (O’Neil & Pienta, 1994, p. 74). This is referred as

orientation and by drawing similar analogy, investors orientation towards others is expressed

through the integration of Environmental, Social and Governance (ESG) factors when taking

economic advantages. Despite the generality of where the characterization of good and bad

as well as wright and wrong can be applied, ever, the main focuses of the growing interest

in socially responsible investment rests on environmental concern, especially climate change

(Eurosif, 2016). And, the subsequent determination of what is right and wrong for the

environment seem not to be a challenge with the magnitude that O’Neil & Pienta pointed.

Rather, the dominance of the climate and weather-related concerns in the mainstream media

should be considered informative on what is good and right to do for the natural

environment. It may, accordingly, be considered such awareness creation efforts has helped

the ever-growing prevalence of socially responsible investment alternatives in the financial

market.

Contrary to the observation of a growing interest to meet the demand for investments with

an objective of satisfying not only ‘’good’’ or financial desire but also doing the ‘’right

thing’’ and participating in the environmental issue, there is escapism about how social

responsibility is conceptualized in an organizational setting as well as how it is defined and

measured among other academics. Which is giving way to the debate on its consensually

undefined purposes. For example, Sampson considers it as economically irrational concept

that cannot be used to capitalize savings and refers it a as a topic belonging to a ‘’lunatic

fringe’’ (Sampson, 2000, cited in McLachlan & Gardner, 2004, P. 11), while Jones et al.,

2008, refers any financial performance difference between conventional and SR or morally

valuable funds as ‘’sacrifice’ (Jones et al., 2008, P. 183) and a number of others calls it as

‘’an act of whitewashed’’ or enthusiastically publicized topic for the purpose of gaining

unearned reputation (Higgins & Walker, 2012, Castello & Lozano, 2011, Deegan, 2002,

Hooghiemstra, 2000, Cho et al., 2010, cited in Hahn & Lulfs, 2014, P. 402). However, such

an argument does not give a base to conclude that investment funds does not serve as a

source of other kind of utility other than risk and return preference of investors. Those,

arguments are the typical reflection of ‘’economic trade-offs’’ that prior studies made by

Renneboog, et al. (2008), Lopez-Arceiz, et al. (2018), Renneboog, et al. (2011), Nofsinger

&Varma (2014) and Tippet &Leung (2001) based on.

3

1.1.1 The Swedish investment cultural environment

Generally, one of the two major reason for describing the current over-all state of socially

responsible investment is a growing concern for the environment (Cowton & Sandbery 2012,

p. 144). This growing concern for the environment has begun to be institutionalized and one

example is the European Union’s the zero greenhouse gasses emissions goal by the year

2050 (Euroif,2016). More so, in 2015 at the Paris agreement, the major actors in the global

carbon emission has also reach an agreement to collaborate in an effort towards solving the

challenge faced by the world climate change (UN, n.d,). The development trend of the SRI

in the Nordic countries, specifically Denmark, Norway and Sweden, however, seems to

coincide with the occurrence of socially irresponsible behavior by corporations and

exemplary ‘'incidents seem to have acted as a catalyst for the development of responsible

investing’’ (Bengtsson, 2008b; Kreander, 2001, cited in Scholtens & Sievänen, 2013, P.609).

However, if those incidents include also extreme weather and weather shocks, among others,

is not identified.

As member of the European Union, Sweden has adopted EU’s zero greenhouse gasses

emissions goal by the year 2050 (Eurosif, 2016) and has adopted the 17 Global sustainable

development goal initiatives set by the UN (UN, nd). However, despite the political initiative

and the relatively long history of SRI market, Sweden has no legal framework that govern

SRI market (Eurosif, 2016). And, the development of SRI in Sweden and the Nordic

countries in general, might be shaped by institutions and non-governmental organizations

(Bengtsson, 2008b, cited in (Scholtens & Sievänen, 2013, P.609). Thus, it is common for

large institutional investors to have a guiding principle or policy related to investment

activity in a socially responsible manner (Eurosif, 2016). ‘’SRI in Sweden already started in

the 1960s, when the first ethical fund was established and it can be regarded as a pioneer in

providing ethical private investor funds’’ (Scholtens & Sievänen, 2013, P.609). A probable

explanatory reason given for the trend of leniency towards dependency on guiding principles

rather than legal framework in Sweden might, therefore, be that significant number of

investors and investment service providers in Sweden has for more than a decade been

engaged in socially responsible investment market in the country (Eurosif, 2016) and this

might have given the authorities a sense of trust and low risk exposure in relation to socially

irresponsible investments.

In addition, Sweden have low assertiveness and is less masculine, or more ‘’feminine’’ in

comparison to its neighbors Finland and Denmark, this might be associated with a growing

development of socially responsible investment options since socially responsible

investments have more feminine values (Scholtens & Sievänen, 2013, P.612). Therefore,

those socio-cultural characteristics may have influence on Swedish investors orientation in

their investment decision making. Although, based on a study from 2007 and 2009, most

socially responsible investments in Sweden use one or two socially responsible criteria, that

is values or norms, to include or exclude funds to their portfolio (Scholtens & Sievänen,

2013, P.609).

4

1.1.2 Socially responsible investment challenges

There are critical challenges that faces SRI in comparison to conventional mutual funds. For

example, one of the most prevalent is the non-existence of universally agreed up on criteria

for the evaluation of what is socially responsible act or not (Cowton & Sandbery, 2012, P.

144). Which contributes to a greater extent for the lack of performance measurement- and

indicators, as well as for the marking or cataloguing of investments as socially responsible

(SR) with a well identified and defining behavior. This is attributed to the existence of large

variety of social responsibility that depends on the geographical location and cultural settings

of the funds. Social responsibility reporting currently is usually performed without an

underling accounting system that continuously records and updates information; therefore,

only limited assurance can be given (Fagerström, et al., 2017, P. 46). Which constitutes

social responsibility reports lack of comparison mechanism that can assess performance over

time and among companies (Fagerström, et al. 2017, P. 46). The nonattendance of systematic

approach to prepare social responsibility performance reports, might have caused institutions

to be motivated to project a positive image of their efforts regarding social responsibility

(Fagerström, et al., 2017, P. 46). Therefore, one of the challenges is lack of performance

indicators which makes it challenging for management control and makes assessment and

follow ups on the development trend of SR not feasible. This prevents progressive

improvements from taking place in areas, for example, product development, technological

innovations that could support easy accessibility and availability of a wide variety of SRI

opportunities. The second challenge is the absence of commonly used identification criteria.

The terms ethical investment, socially responsible investments sustainable, sustainability,

sustainability fund, green, ethic, ethical as well as climate and environment and environment

friendly are used interchangeably. Although, depending on the location some of the terms

may persist (Cowton & Sandbery, 2012, P. 142). With the identification of some funds with

those terms might lead to the assumption that fund that are not labeled with those names, or

conventional funds, as ‘’antisocial or irresponsible’’ (Cowton & Sandbery, 2012, P. 142).

The third main challenge is related to the existence of a large variety of social

responsibilities. Which makes how investors react to factors affecting non-financial

performance or social responsibility performance, currently, a widely debated topic (Cowton

& Sandbery, 2012, P. 14). In both the financial and non-financial motive, the central issue

is behavioral and on how people react to external factors. Although, they differ in the variety

and articulation of the motives that initiates investors reaction to each category. While, on

the other hand, understanding the rational economic motive of investors may be eased by

having reliable information on the financial performance of the mutual trust funds.

Explanation based on Capital Asset Pricing Model (CAPM) on how investors react to factors

affecting financial performance is well documented and have reached the tipping point

where it is possible to observe and monitor it instantly. On the other hand, in the non-

financial factors, the range of variables are wide and not yet have uniformly identified and

used terms. This tells us that collective behavior associated with social responsibility still is

likely one of the most complex elements of the human nature and the reality in the business

filed. Last but not list, is the possibility of applying investment selection or screening criteria

that SRI uses can as well be used in the selection process of conventional investments

(Cowton & Sandbery, 2012, P. 145).

5

1.2 Research gap and research question

Contrarily to conventional investment, SRI funds are not expected to be sensitive to past

financial performance. This infers that investment in socially responsible funds grow

steadily rather than showing a sudden fluctuation regardless of financial performance, thus,

this growth of investments in SRF to be consistent and unaffected by past as well as expected

future financial performance (Renneboog et al., 2008, p. 1725). Therefore, the potential

impact of incidents such as weather shocks on such an assumed stable SRI Capital flow is

not addressed and documented yet. Most of those previous studies on SRI, thus, focused on

testing if the concept of low magnitude of sensitivity to past financial performance

dependency for socially Responsible funds, in contrary to Conventional funds, holds true or

not. The studies identified and that compared SRIF and Conventional funds, has taken the

quality criteria of conventional investments, i.e. return, and thus portfolio theory and CAPM

model as a base for defining the behavior of conventional fund as past-performance

dependent. Those studies, on the other hand, considered SRI funds have additional quality

criteria, i.e. ESG, and thus is not guided by portfolio theory and CAPM model. Rather, SRIF

should show a consistent growth regardless of past performance.

Examples of such studies are Renneboog, et al. (2008)’s past-performance dependence

concept as well as Nofsinger &Varma (2014)’s concept of the impact the general market

condition has on SRI and conventional fund performance which indicates the effect the

general market condition could have on capital flow. Those are also some of the studies

which are the starting reference or what constitutes the theoretical framework for this study.

Although, since socially responsible investments have additional screening criteria’s, which

are related to environmental, social and governance (ESG) values, classical economic

variables alone may not be complete enough to describe how SRIF behaves in a certain

financial market. Addressing this gap, Lopez-Arceiz, et al. (2018)’s and Renneboog,

(2011)’s study on the impact cultural environment’s has on the socially responsible

investment funds, are the studies that investigated both fund types taking variable other than

classical economic variable as an explanatory variables or a factor affecting how SRIF

behaves in comparison to conventional fund. Those studies are in line with Bollen (2007)’s

study which is based on the possibility for both financial and non-financial utilities affecting

investment funds. Additional variety of non-neoclassical economic variables as explanation

for the behavior of socially responsible investments is Tippet &Leung (2001, p. 51)’s study,

which shows that gender-based difference in utility preference. Gender the share of each

gender in the investors pool is one potential factor affecting how capital flows in and out of

a fund.

Generally, most of related previous studies has focused on financial performance and risk

tolerance of SR investments in comparison to conventional funds (Bollen, 2007, 684 and

Cowton & Sandbery, 2012, P. 149), rather than the impact of SR related incidents,

particularly weather shocks, on fund movement. A closely related study to the topic is

conducted by Hirshleifer & Shumway (2003) where they empirically studied the relation

between sunshine and the stock market. Their study’s focus is on weather related ‘’moods’’

rather than ’shocks’’ in relation to the stock market performance in general rather than the

capital flow of socially responsible funds. Therefore, observing the standard indicators on

6

the financial market and study the impact of weather-related incidents on how capital flows

in and out of socially responsible investment funds would hardly lead to success.

Recently, attempts to bring and place such non-financial performance in the platform, where

financial performances are publicly evaluated, are being made to make investments more

sensitive to incidents related to social responsibility as well. Currently, such an effort mostly

known with the term ‘’ESG integration’’ (Willem, 2016). However, since the development

of ‘’ESG integration’’ is in its infancy, studies on investments sensitivity to different

dimensions or variables of social responsibilities need to be encouraged. climate and weather

related socially responsible investment is barely studied (Riikka Sievänen, 2013, p.207).

Accordingly, the relation between extreme weather and capital flow is not yet empirically

well-established or the studies are limited to support informed financial decision making for

both business and individual investors (Cowton & Sandbery, 2012, P. 142).

Whether weather shock motivates investors to choose moving in to -and out their capital of

investment fund type is the most relevant information for decision making. As Bollen (2007,

685,) addressed it, managing capital flow (capital in-and out flow) might be costly business

activity and understanding how capital flows in relation to weather shocks can be considered

as a key administrative efficiency measure. For investors, on the other hand, rational

economic- and emotional human behavior that weather shocks can be associated with could

be relevant in satisfying their investment needs. Consequently, it is relevant to investigate,

whether weathers shocks create the desire to participate in solving environmental problem,

thus, have utility.

The growing offer in socially responsible teamed investment choices by financial service

providers may indicate the existence of a growing demand for SRIF (Bollen, 2007, 683).

Hence, the relevance lays on understanding capital owner investment decision making

behavior, which is choose either to- or not to invest in- and withdraw from business activities

that does not take into consideration social responsibility or to selectively invest in business

that are engaged in socially responsible business activities. The relationship between weather

shocks and Capital flow is, however, not yet empirically well-established or the studies are

limited to support informed financial decision making for both business and individual

investors (Cowton & Sandbery (2012). P. 142). Accordingly, studying the relationship

between extreme weather, in this case weather shocks and investment found movements

could full fill an important role in practical business and organizational environment as well

as for the academic filed. For this reason, it is desirable to contribute towards the

standardization of SRI and strengthen the possibility of doing so despite its heterogeneity

(Sandbery & et al., 2009, p. 519). This study attempts to address this gab by doing a

quantitative study on the sensitivity of funds to media reported weather shocks. Hence, the

research question is formulated as follow.

‘’ What is the role of media reported weather shocks on mutual funds capital

flow?’’

7

1.3 The purpose of the study

Capital flow is, in this study, the words used to describe the periodical change in the amount

of total net asset (TNA) a fund management has in comparison to the presiding period. And,

it can be affected and be dependent on multiple factors such as the financial performance,

which are return and risk level of the fund, the general market condition, the preference and

desire of market participant as well as the dominate social values in a cultural environment.

This study, further, attempts to identify whether one additional factor, which is weather

shocks, can also affect the in and out flow of socially responsible mutual fund.

The purpose of the study, therefore, is to understand whether media reported weathers shocks

create the desire to participate in solving environmental problem. In attempt to answer the

question, investigating the relationship between weather shocks and SRI- as well as

conventional funds’ capital flow is the convenient approach. Accordingly, capital flow is

inferred from a change in the fund’s total net asset for a period since its volume is affected

by the coming in- and going out of a capital. Weather related incidents that are associated to

class three warning are categorized as weather shock in this study. Thus, the purpose is to

observe whether there is a relationship between weather and capital flow of SR investments.

It will also investigate whether such nature of relationship of SR mutual funds with weather

shocks is similar for conventional mutual funds as well. To achieve this goal, three main

variables, the market conditions and risk as well as past financial performance of the funds,

will be studied simultaneously. Although, there is the possibility that capital flow can be

influenced by a variety of other factors than weather shocks, there is also the possibility that

its response to weather shock could be selective.

Thus, without discrediting factors affecting individual financial decision making, the study

will assess the simultaneous socially responsible decision-making behavior of groups

through a dummy or proxy variable, which is the Capital flow or change in total net asset

value for a period in association with media reported weather shocks. While doing so,

consideration will be made regarding the context of the market ‘’Cultural environment, i.e.

‘’The Nordic-cultural environment’’ which (Lopez-Arceiz, F.Jose, et al.,2018)

conceptualized as a background that shapes behavioral aspects of SR investment and

whether the general market is in a high or low risk condition (Sliwinski & Lobza, 2017, p.

660).

8

1.4 Delimitation

The scope of the study is limited to a few areas. For example, inclusivity of available study

population, time frame and the variables chosen. The scope of the study is based on an

already identified factors that can affect the in-and out flow of capital from and into a mutual

fund. For example, financial performance, the general market condition, the preference, and

desire of market participant as well as the dominant social values in a cultural environment.

This study, further, attempts to identify whether one additional factor, which is weather

shocks, can also affect the in and out flow of mutual funds’ capital. Therefore, it is delimited

on studding the relationship between media reported weather shocks and capital flow. It also

is limited to the type of relation weather shocks has with capital flow, not other implication

it may have. For example, in relation to productivity and fatalities that weather shocks may

cause. Subsequently, only the inclusion and exclusion investment strategies can be assumed

while observing a change in the volume of funds under the management of fund managing

institution.

The funds will, also, be studied in relation to only one dimension out of the multiple

dimensions of social responsibility, that is weather shock which is under the umbrella of

environmental stewardship. Though it considers cultural environment in the interpretation

of the study result, it will not attempt to study the effect of individual demographic variables

and educational background in mutual fund capital flow. In relation to the study population,

the scope of the study is limited to analyzing all accessible socially responsible investment

funds that meet the criteria for the study. Thus, including exhaustively all SRI funds that

might be available is not the study’s objective. The period under study is, in addition, limited

to a maximum of ten years with the intent to match the availability of both financial as well

as media reported extreme weather incidents.

Due to resource and time constraints, the role of product marketing and investment screening

approach applied by fund managements will, also, not be included, though those factors

could be considered to have the potential to influence investment decision, thereby, fund

movement.

9

2 Scientific Methodology

This part of the study covers various concepts, assumptions, and theoretical bases of

conducting a scientific study. It first coves philosophical terminologies and explains it to

motivate the authors view and express the standpoints taken.

2.1 Fundamental Assumptions

The most common and widely discussed scientific study related assumptions in literatures

are assumption on the nature of truth or reality which is identified as ontology in this study

and the nature of knowledge which is identified as epistemology in this study as well as the

nature of values which is also identified as axiology in this study. Those are discussed as

follows.

2.1.1 Ontological stances

Ontological explanations help explain the nature of truth and reality and help the

characterization of the metaphysical paradigms. Morgan (2007, p. 50) refers to the nature of

reality or truth in terms of paradigms that represent different worldviews. Ontology,

therefore, gives distinctively identifiable characteristics of the topic under consideration

depending on the worldview assumed. Long (2000, p. 190), similarly, defines ontology as

an assumption which is utilized in categorizing the characteristics or nature of ‘’social

reality’’ or truth. By which he refers to two questions about the nature of reality on a topic.

The first is, if reality exists independent of individuals, which is objective and constant. Such

a claim to the nature of reality is represented by the positivist paradigm (Morgan, 2007, p.

49). Positivism is the term initially used to refer an optimistic attitude and belief that research

questions can be answered with quantitative method thus indicating the dominance of a

realistic worldview (Morgan, 2007, p. 49, 56 & 63). Scott (2005, p. 635) refers this world

view as a model that describes how the world works with certainty or ‘’naïve realism’’.

Or rather, the second, if the nature of social reality is dependent on individuals and subjected

to cognitive ability, and therefore, subjective and variable (Long, 2000, p. 190). Paradigm

of subjective world view was initiated as a counter to the positivist and associated to a claim

that the nature of reality is characterized by subject dependability (Morgan, 2007, p. 49).

Subjectivism is the term initially used to indicate that research questions can be answered

with qualitative method (Morgan, 2007, p. 49, 56 & 63). Scott (2005, p. 635) also refers it

as ‘’radical relativism’’. He described it as a reality that is only confined to an individual and

not shared with others.

Nevertheless, classification’s as objective and subjective is based on the two opposing and

extreme sides or as Morgan (2007, p. 49 & 59) refers it Metaphysical paradigm. Thus, there

is a possibility for the nature of truth/social reality to be classified in between as well, where

it can take the form of pragmatic paradigm (Morgan, 2007, p. 49 & 70). Similarly, Long

10

(2000) also discusses four possible variants of assumptions, in addition to the two extreme

types of nature of truth in four different metaphors. The first, and the one which is relevant

for this study of such, metaphors is ‘’The social world as an organism’’ which he describes

it as objective and operates according to the natural law, yet it reacts and adapts to its

surrounding (Long, 2000, p. 192). Thus, there exist causal relationships that can be studied

even though those causal relationships are subject to gradual change rather than being

constant or static (Long, 200, p. 192). In addition, Scott (2005, p. 635) have discussed

alternative world views, which he refers ‘’critical realism’’. Critical realism assumes a world

where, even though constricting a model certainly representative is not possible, its existence

is shared by others, rather than being confined to an individual only (Scott, 2005, p. 635).

The study believes there is reality in the variables created, which are the dependent and

independent, and those variables are related to social behavior and it is possible to study each

of those variables independent of the other. It also is possible to study how they behave when

those variables come together. Therefore, this studies philosophy on the nature of truth or

the nature of social reality is that even though the variables are considered to be studied are

subjected to be affected by different factors it is believed that they still can be identified

independently and causal relationships between those variables can be studied. In addition,

a commonly shared worldview determines the things that are socially responsible or morally

right in a social group (Morgan, 2007, p. 50). But a social group is not always purely

homogeneous in worldviews thus what is considered socially responsible and morally

justifiable could differ for some individuals. Therefore, though there exist objectively

observable pattern in social reality the study requires a critical examination of exceptional

view of social responsibility and morality. For this reason, this study will exclude cultural

environment from empirical study rather will be dispensed with qualitative narration. Which

is similar to ‘’The social world as an organism’’ metaphor of Long. Thus, the nature of truth

about the study topic is ‘’critical realism’’.

2.1.2 Epistemological stances

Epistemology denotes the explanations of paradigms of knowledge base and knowledge

transfer. Long (200, p. 190) defines epistemology as an assumption which is utilized to

categorize the ‘’bases of knowledge’’ and the way in which this knowledge is shared to

others. Which he describes it in a similar way to how he states the nature of truth, which is

referring to the question of if knowledge can be shared independent of individuals beliefs

and experience leading to an objective and statically/theoretically reachable to all. or rather,

if it is dependent on individuals’ beliefs and experience leading to subjective and reachable

to others in non-constant, variable way. On the other hand, Morgan, 2007, p. 52, describes

it in terms of two stances of separate belief systems that affect the bases or nature of

knowledge and how it is transferred, i.e. realism and constructivism. Therefore, the

epistemological stances are dependent on the metaphysical paradigms worldviews assumed

or ontological assumptions (Morgan 2007, p. 64) But the classification based on objective

and subjective is based on the two opposing and extreme sides, thus, there is a possibility

for the categorization of knowledge base and the way in which knowledge can be shared can

11

be classified in between. The pragmatic paradigm is one such approach to address the issue

of knowledge base and knowledge transfer (Morgan, 2007, p. 65-70).

The knowledge about economic variables addressed in this study can be found and observed

objectively to all individuals without the interference of the values and personal experiences

of the researcher. This enables conducting quantitative study and empirical test. However,

knowledge about the behavioral and cultural environment explanations requires human

interaction with larger group of individuals to identify certain similar patterns of behavior.

Identifying such pattern is subject to the researcher’s cognitive ability and is variable from

individual to individual. Such knowledge base necessitates a qualitative study and narration.

Therefore, it is not feasible to claim any of the metaphysical stances in this study as those

assumptions does not reflect the characteristics of the knowledge base of the study. Despite

the stance taken, an attempt to study even the cultural environment could be made either

empirically assuming the positivist side of metaphysical stance or qualitatively assuming a

constructivism side of the metaphysical stance. The study rather, advocates for pragmatic

paradigm and claims to have a ‘’critical positivism’’ knowledge base and way of sharing

knowledge to others. This is because, even though the main variables of the study and the

relationship between them can be supported with evidence, a full understanding of the nature

of the topic and similarity on the degree of the understating of the topic among individuals

is not guaranteed (Scott, 2005, p. 635). Accordingly, ‘’ a pragmatic approach would redirect

our attention to investigating the factors that have the most impact on what we choose to

study and how we choose to do so’’ (Morgan, 2007, p. 70).

2.1.3 Paradigms of a system of beliefs and practices

A system of shared beliefs and practices relates to the choice of study topic and the research

approach to follow. It influences and shapes researcher’s way of selecting the research

question and the process as well as methods used to answer the question (Morgan,2007, p.

49). Similarly, shared beliefs among members of a specialty area establishes a consensus

about the relevance criteria on research questions (Morgan, 2007, p. 53). It dictates the

validity of the procedures or methods followed in the process of answering the research

question as well (Morgan, 2007, p. 53)

This study is in the process of being conducted by a student in the field of finance and in an

environment consisting of other students and researchers, thus the choice of the research

question is subjected to influences from the individual’s experiences of the academic

environment, study subjects and consultations with members of this academic community.

Subsequently, to identify the appropriate way of answering the research question, it is

important first to consider the preceding philosophical explanation of the nature of reality

and how this reality is created and shared by others.

12

2.1.4 Axiology

Axiology refers to a theory about value and help identify values that are in the favor of the

society’s wellbeing (Biedenbach & Jacobsson, 2016, p. 140). More specifically, it is the

concept used to refer the consideration given to ethical and moral corners in a social science

research study (Morgan, 2007, p. 69). Basing some of the ethical considerations identified

in Biedenbach & Jacobsson (2016), the proper use of material and physical value available

at the university such as the Eikon database and other outside websites for example

Morningstar need consideration. Information accessed through registering on Morningstar

may not be used without the consent of the organization. The Universities computer where

Eikon database can be used might be sought after by more than one student at a time.

Communicating with others on the use of the computer is appropriate. However, there is no

identified asset that its value could be affected in relation to the process of doing this study.

The study’s data impute is accessed from databases that are accessible for the public at large.

Therefore, concerns regarding damages on privacy, integrity, informed consent etc. are

limited. In addition, regarding concerns for social value, data input on the social values and

responsibilities related to the natural environment is planned to be from sources that are

available to the public. Hence, the use of those data is not considered to be a concern for

damage on social values. This applies also for political values, aesthetic value, religious

value. On the other hand, regarding intellectual value, the ethical concern in doing this study

is significantly related to the intellectual value since the main purpose of conducing such a

study is to contribute to the society and the intellectual community through the expansion of

the knowledgebase. Accordingly, the necessary procedures in doing a research study, from

identifying and naming sources of information, acknowledging contribution of previous

studies as well as verifiability of the study itself are responsibility being followed.

2.2 Research Approach

Research approach refers to methodological stance, and quantitative and qualitative

approaches are the two widely known research approaches (Morgan, 2007, p. 70). But

research approaches are usually identified with reasonings such as deductive and inductive

rather than quantitative and qualitative, consecutively. When addressing ontology or the

nature of reality in terms of metaphysical paradigm it simultaneously defines the

epistemological stance either as realism/positivism or constructivism and justify the use of

only either quantitative or qualitative research approach. However, since there is an

alternative world view to the metaphysical, there are also third alternative approaches. The

alternative is pragmatic approach, or mixed approach, which is identified with abductive

reasoning (Morgan, 2007, p. 71). Pragmatic or mixed approach uses both quantitative and

qualitative approaches simultaneously (Morgan, 2007, p. 70 & Baskarada & Koronios, 2018,

p. 3). And those approaches are shaped by the fundamental assumptions or paradigms the

researcher considers. Hence, the discussions in the fundamental assumption above helps take

stance on which research approach the study intends to follow. Guided by the research

question and the identified theoretical framework, which is discussed in the subsequent

chapter, the study will follow a deductive research approach.

13

2.3 Method

Method is about the connection between theory and data, as well as theory and practice, and

inference from data (Morgan, 2007, p. 71). It is concerned with the steps to follow in the

process of answering the research question rather than how the research is generally viewed.

Long (2000, p. 194) refers it as an approach to investigation or general type of investigation.

Thus, it guides which type of investigation is appropriate to which type of research based on

the generality and contextuality of the knowledge to be generated (Morgan, 2007, p. 71).

Accordioning, theory generation research is best if uses qualitative method, while if theory

testing uses quantitative method (Anderson, 1983; & Deshpande, 1983, cited in Long, 2000,

p.195). Hence, the use of one research approach for both theory testing and theory generation

can cause problems in reliability and validity (Long, 2000, p.195). Researchers who favor

the metaphysical paradigm advices that the research question should guide the research

method to be used (Morgan, 2007, p. 64). However, there is a contrast between the

requirement of metaphysical paradigm, which is to work only either in the realism or

constructivism view of the nature of reality (Morgan, 2007, p. 64).

Despite the contrast, and although this study does not advocate for metaphysical paradigm,

the research question will guide the choice of methods used. Subsequently, the research

question suggests and helps identify the factors that can affect the concepts in the study. The

research question to this study is ‘’What is the role of media reported weather shocks on

mutual funds capital flow?’’. The research question, thus, indicates a study of relationship

between weather shocks and found capital flow or causal relationship. This gives a clear

indication in to the independent and dependent variables, i.e. weather shock indicators and

socially responsible as well as conventional founds capital flow, consecutively. Model

examples of research can also be considered as paradigms as they provide perspective and

guidance in how to conduct research in a field of study (Morgan, 2007, p. 53 & 54), which

he also points that such model studies have the potential to give examples of studies that

used a combined methods, i.e. quantitative and qualitative.

This study has looked how, and which research methods other related studies used. Thus, it

would be possible to conduct, based on the findings of previous studies, a hypothesis test on

the variables identified quantitatively. In summary, the dependent or respondent variable is

a multi-factor dependent. Mutual funds capital flow may, however, be moderated or affected

by factors that are external to this studies scope. For example, cultural environment different

financial markets may have different effect on how mutual funds capital flow. Since, this

study is bound in only the Swedish cultural environment, it cannot be quantified and is prone

to subjective interpretation. But, considering it qualitatively in the result interpretation, may

contribute in understanding the financial market setting and the intensity of the capital flow.

Therefore, even though, it uses qualitative interpretation of the results reached through

quantitative method, the study is methodologically regarded as quantitative study.

14

2.4 Secondary data and literature source

The source of secondary data is Umeå University library. The library’s search engine was

used to find relevant studies on the topic. While, most of the literatures are scientific

publications, published and electronic books are also accessed to refer to the main theories

and concepts. It is collected in the process of reviewing the related studies that has been done

in the past. Thus, the collection process is the same as the process followed while reviewing

those studies. Which is using the search words such as socially responsible versus

conventional investments, socially responsible investment. Once, a few related articles have

been found, further search was made following the reference lists identified in those previous

studies. Some of the literatures are also reviewed based on the recommendation of the thesis

supervisor.

2.5 Research strategy and design

Identifying the research approach or the methodological stance guides the planning of the

research study. As address in previous part of the writing, the purpose, this study intends to

understand the relationship between weather shocks and investment capital flow. It also

intends to categorize the relationship into SR and conventional investment funds as well as

to make a comparison between the categories. However, capital flow is dependent on

multiple other variables. Those variables this study identifies are risk and return as well as

market condition. The consideration of variables other than weather shock, consequently,

strengthens the study result. The variables identified in this study, which is pointed in the

theoretical point of departure in the next chapter, have an already established theories.

Accordingly, a test to understand if those theories and findings of prior studies apply to the

relationship between weather shocks and the flow of capital will be made. And the findings

could add a refinement to the already existing studies on factors affecting the flow of capital

as well as a comparison of SRI and conventional investments. Therefore, although the study

is not using purely quantitative interpretation of the study result, the study’s strategy is to

follow a deductive reasoning type of research as discussed in the presiding section. The

stance the study takes is based on the argument that the use of a mixed approach in the

interpretation of the result should not dictate the method used to answer the research

question. Thus, the choice of research method should consider the identified variables and

the feasibility of reaching on reliable and valid answer to the research question and not on

how the findings are interpreted.

15

Figure 1. The study design depicting data collection process.

This is the initial stage for the practical study. Data for both socially responsible- and

conventional funds is retrieved in three batches. The first batch is funds with a maturity of

ten- and more years, the second is funds with a maturity of nine to two and a quarter years

and the third is funds with two- and less than two years of maturity.

16

Figure 2. The study design depicting the process taken to reach on the study’s result and

conclusion.

In the subsequent stage, the relationship of both fund types to their respective explanatory

variables as well as the result at the end.

17

3 Literature review

The flow of capital into- and out of a mutual investment fund and a mutual investment fund

type can be affected by a several factors. The neo-classical economic theories suggest that

return on invested capital and fund risk level plays a strong role in the flow of capital between

mutual fund types (Abbarno, 2001, & Lopez-Arceiz et al., 2018). Although, Bollen (2007)’s

‘’Mutual fund attributes and investor behavior’’ shares similar idea, it suggests for non-

rational economic or emotional human behavior to have a role in the flow of capital from-

and out of a mutual fund as well as between mutual fund types. Therefore, the flow of capital

can be affected and be dependent on multiple factors other than the funds return and risk

performance. For example, Renneboog et al. (2008) and Lopez-Arceiz et al. (2018) suggest

that the flow of capital between mutual fund types can be influenced by the cultural

environment of investors and the financial market’s location. While, Sliwinski & Lobza

(2017) and Nofsinger & Varma (2014) suggest that the flow of capital can be influenced not

only by the fund’s own return and risk performance but also the general markets

performance. Studies have also identified demographic variables as a potential explanatory

factor for the flow of capital. Tippet and Leung (2001), for example, suggest that difference

in age, gender and educational level could play a role in the flow of capital between

investment fund types, while Hellström, et al. (2020) point to the possibility of inherited

investment behaviors from parents by individual investors affecting the flow of capital.

The only litterateur identified studding directly mutual funds’ capital flow is Bollen (2007)’s

‘’Mutual fund attributes and investor behaviors’’. Which argues for the exitance of factors

that are related to human emotional behavior affecting the flow of mutual funds capital flow,

rather than only rational economic behavior. The rest of the studies identified have

investigated performance difference between socially responsible and conventional funds as

well as protentional expiations for differences in investment behaviors, rather than directly

investigating the flow of capital and factors affecting it. However, the basic concept from

the studies is that performance differences, both financial and non-financial, can affect the

flow of capital between mutual fund types. This study has reviewed those studies for the

purpose of further identification of whether one additional factor, which is weather shocks,

can also affect the in and out flow of capital in a fund type. The following part of the literature

review, thus, is categorized in accordance with those factors and the previous studies that

have focused on investigating them. The first part of the review discusses literatures

addressing different factors affecting capital flow. And, the last part will cover discussion

on the critical similarity and differences between those literatures.

18

One of the factors affecting Capital flow is individual’s cognitive ability which is interpreted

through the financial decision made. Which is according to Frydman & Camerer (2016, p.

661) one of the all the important decision’s individuals make. They point that cognitive

reasons, and low financial literacy which could also be categorized as a sub-element of

cognitive constraint, is the reason for inefficient financial decision making. Taking

inefficiency in decision making as a non-optimal balance between risk and utility derived,

both from financial and non-financial performance perspective, it relates more specifically

to neuropsychological factors besides cognitive ability. In this regard, part of a human brain

called anterior insula in interoceptio is associated with awareness of internal state of one’s

body, emotion, and assessment of risk, prompting safer investment decision making

(Frydman & Camerer, 2016, p. 666). Similarly, another brain part called VSt is associated

with ‘’Expected-reward signals’’, hence, to risk taking in investment (Frydman & Camerer,

2016, p. 666). The prevalence difference, of those two neuro-psychological attributes of

behavior, between individuals is explained by genetic differences among other things

(Frydman & Camerer, 2016, p. 666). A study that closely investigated, the influence of

psychological factors on economic variables is conducted by Hirshleifer & Shumway (2003)

where they empirically studied the relation between sunshine and the return on stock market.

Investors in a bad mood lean towards accessing unpleasant or negative substances out of

their encounter (Isen et, al, 1978, and Forgas & Bower, 1987, cited in Hirshleifer &

Shumway, 2003, p. 1012). Which their study result showed a strong relation between

sunshine and the return on stock market (Hirshleifer & Shumway, 2003, p.1028). What this

study differs from Hirshleifer & Shumway’s is that it focused ‘’shocks’’ rather than

‘’moods’’ related to weather and socially responsible funds rather than the stock market in

general. Which may play a role in showing a different behavior in relation to weather and

investment funds than what might be projected from their study.

Besides pure differences in psychological and cognitive ability, behaviors attributed to

demographic differences in terms of age, gender as well as individual-parental relationship

indicated by Hellström et.al. (2020) are the medium for the manifestation of the effect of the

surrounding environment. This surrounding environment in our context is the cultural

environment. The study is focused on the effect of groups behavior on fund movement,

attempts has been made to understand how cultural environment stimulate ‘’Expected-

reward signals’’ or consciousness to financial and non-financial risks. Thus, it can influence

the intensity of capital flow of a certain type of the fund. Accordingly, in the following part

the cultural environment and demographic variables will be discussed.

The other variable identified to affect capital flow is age and gender differences. The role of

age difference in investment decision making that is related to socially responsible

investment is contested among different studies. For example, Hayes (2001) states that

younger, aged between eighteen and twenty four years, prioritized non-financial

performance such as environmental stewardship and stakeholder orientation of corporates

than financial rewards compared to older investors (Hayes, 2001, cited in McLachlan &

Gardner, 2004, p. 12). Tippet & Leung (2001)’s study also supports Hayes (2001)’s

statement, affirming the probability for younger investors to be socially responsible investors

higher than for older (Tippet &Leung, 2001, p. 51). But (McLachlan & Gardner, 2004)’s

own study shows no difference in age between socially responsible and conventional funds

(McLachlan & Gardner, 2004, p. 12). On the other hand, (Tippet & Leung, 2001, p. 51)’s

19

study result indicated gender-based difference, where the probability of female’s to be

ethical investors is greater than that of for male, which concurs with Scholtens & Sievänen

(2013) argument stating that socially responsible investments have more feminine values

(Scholtens & Sievänen, 2013, p.612). Thus, in a society with more female investors it might

be possible to observe a thriving socially investment market with more capital moving in to

SRIF.

Cultural environment is also identified as a factor affecting how funds move. Cultural

environment is explained in relation to religious believes and customs and leading to shape

ethical values, moral guidance and shape the behavior of the mass population (Lopez-Arceiz

et al., 2018, p. 262, and Renneboog et al., 2008, p. 1725). Thus, capital may flow into socially

responsible investment funds in a location where cultural values are strong and financial

institutions are able to offer financial products tailored to those values. To date, in the

modern world, religious values are affecting the selection of funds, for example Pioneer

Fund, which was established in the late 1930’s (Rosenthal, 1995, p.45), used religious

traditions to screen investments (Renneboog et al., 2008, p. 1725), although the fund might

be associated with the study of differences among humans. Accordingly, Lopez-Arceiz et al.

(2018) tried to categorize the major cultural differences into two broad sets of cultural

environments based on two prominent religious ideologies in the western world, the catholic

and protestant. Lopez-Arceiz et al. (2018) discuss how the following two major cultural

environments could affect the significance of the differences within SRI funds as well as

with conventional funds.

Anglo-Saxon cultural environment: Lopez-Arceiz et al. (2018) argue that funds in Anglo-

Saxon cultural environment are likely to establish negative screening based on the specific

preference criteria of a certain group in the portfolio composition (Lopez-Arceiz et al., 2018,

p. 262). Even though their indication is related mainly to the context of social responsibility

factors, the probability of financial performance criteria to be one besides the many variant

of social responsibility (Lopez-Arceiz et al., 2018, p. 262).

Continental Europe cultural environment: They argue that in this cultural environment

commonly shared values and principles are nurtured through investment in social projects

and active engage from the investors side leading to a positive attitude wards social

development and acceptance of such business activities, or positive screening. Further, these

practices promoting and strengthen the values that are common in society (Lopez-Arceiz et

al., 2018, p. 262). A supporting statement is pointed by Cortez et al., 2009, indicating the

possibility of positive screening or social screening of European funds does not lead to

financial underperformance (Cortez et al., 2009, p. 573 & 581).

This cultural environment could be considered the force shaping the cognitive and

personality characteristics of any decision making, particularly financial decision making.

Similarly, the cultural environment has the potential to answer questions related to the

possible difference in behavior between SR and conventional investors, which is of most

importance for the finance sector, for the purpose of product pricing, customer relation etc.

(Renneboog et al., 2008, p. 1724). Hence, individuals subjective attribute such as ‘’moral

intensity’’ is under such influence, for example what one finds acceptable or the materiality

of departure from ethical or social responsibility by one fund (Tippet & Leung, 2001, p. 48)

could be judge against the shared values in that particular cultural environment. On the other

20

hand, there could be many other factors that influences what is acceptable level of departure

from ‘’moral or social responsibility’’ and what is not, in each cultural environment. In

support of this idea (Jones et al., 2008, p. 182) discuss the difficulty attributed to the

nonexistence of a pre-set all serving standard for the evaluation of socially responsible fund,

which opens the opportunity for different level of ‘’moral intensity’’ associated with each

investment fund (Jones et al., 2008, p. 182) in one cultural environment.

Culture and shared values develop through long period of time and may take several

generations before a new one establishes in a society. Hence, the commonly shared moral

values, social responsibility and ethical behavior is not expected to vary due to an occurrence

of incidents in a cultural environment. However, a cultural environment could determine the

intensity of decision taken in the society, whether it is ethical or socially responsible

behavior. Consideration of the cultural environment in the study of how funds move (capital

flows), therefore, gives a holistic understanding of the factors that are affecting investment

fund movement, especially the behavior of socially responsible investment funds.

The most widely recognized factor affecting mutual fund’s flow of capital is probability the

economic variables such as return and risk. The classical economic variables, such as risk

and return, are what most of the finance academic literatures are established around.

Accordingly, Milton Friedman’s theory of the role of business or profit maximization and

Markowitz’s theory of portfolio as well as the Capital Asset Pricing Model (CAPM) are the

most dominant while considering financial utility (Abbarno, 2001, & Lopez-Arceiz et al.,

2018). The goal of profit maximization concept is used for institutions for a similar phrase

used to refer to the behavior of value optimizing individual investor in Markowitz’s portfolio

theory.

Capital is expected to flow or move into investment funds with superior performance than

the competing funds based on the classical economic theory (Markowitz, 1991, p. 469 &

470,). The classification of comparison between SRI and Conventional funds in performance

scale of under-, over-performance and no significant difference, is generalized due to the

‘’historical roots, the market development, the regulatory background, and the effect of

investment screens/strategy used by SRI funds (Renneboog et al., 2008, p. 1724),

methodologies used, sample size, difference in analysis periods, return estimation modelling

framework and etc. differences (Jones et al., 2008, p. 181). However, while portfolio theory

and CAPM tell the sensitive of conventional funds to past performance, Bollen (2007)’s

study, on the other hand, shows that SRIF are more sensitive to past positive return and less

sensitive to past negative return performance. This is due to the conventional investor’s

rational economic behavior and they move their capital to SRIF when past performance is

positive in attempt to either maximize the value of their capital or to gain additional utility

from socially responsible investments (Bollen, 2007, p. 683 & 706).

In the firs category of findings from previous studies, which is SR underperforming against

conventional fund, is suggested by Chang and Witte (2010) who indicated that SRF have

lower financial performance, both in total return and risk adjusted return (Chang & Witte,

2010, p. 9, 13 &14). The finding is supported by Renneboog et al. (2008) and indicate SR

investors willingness to accept an inferior financial reward from their investment SR

business (Renneboog et al. (2008) p. (Renneboog et al., 2008, p. 1723 &1739). Jones et al

(2008, p.188)’s study result also indicates that significant difference in financial

21

performance, although they stated the nonexistence of indication on the statistical

significance, of SR, where SR underperforms, and conventional investments. Which they

consider the result to be contradicting previous studies that points to significant statistical

differences between the investments. The second category of performance evaluation is, SR

founds outperforming conventional founds in terms of profitability, are observed in (Lopez-

Arceiz et al., 2018, p.265)’s study. And, in the third category of performance evaluation, no

significant financial performance difference is supported by Cortez et al. (2009), who’s

empirical study, on European funds, result indicated that the performance of SR funds is

comparable to that of conventional funds (Cortez et al., 2009, 581 & 584). However, Lopez-

Arceiz et al. (2018)’s study also indicate that when introducing the cultural environment

factor of all the funds geographical location into the analysis, the result points to no

difference between the two fund types in general (Lopez-Arceiz et al., 2018, p.266).

Capital is expected to flow or move into investment funds with superior performance than

the competing funds based on the classical economic theory (Markowitz, 1991, p. 469 &

470,). According to Markowitz, however, the return performance of investments is not

guaranteed, and this creates uncertainty or risk. ‘’Classic portfolio theory states that the

specific risk of an investment will tend to decrease as the volume of financial assets held in

the portfolio increases (Markowitz, 1952,), it would be reasonable to expect the performance

of SRI funds to be lower than conventional funds‘’ (Lopez-Arceiz et al., 2018, p. 260). In

similar logical argument Tippet and Leung (2001)’s study result, in relation to modern

portfolio theory, indicates that socially responsible investors held smaller or less diversified

portfolios than conventional investors (Tippet and Leung, 2001, p. 53), implying the

exposure of SRI to higher risk than conventional funds. However, the findings of different

empirical studies neither confirm nor reject collectively the theory. For example, (Lopez-

Arceiz et al., 2018, p. 261)’s analysis of selected previous studies on the Risk and Return

performance of SRF and conventional funds indicates that those studies have found strong

differences between SRF and conventional funds when risk levels are identified and studied,

but missed to indicate which one scored highest and which one have the lowest risks

exposure. But, Lopez-Arceiz et al.’s own study indicates that SR funds shows lower

risk/volatility in comparison to conventional Founds (Lopez-Arceiz et al., 2018, p.265),

which is contrary to the expectation of classical portfolio theory. Bollen (2007)’s study result

pointing that SRIF are more sensitive to past positive return and less sensitive to past

negative return performance. Which might contribute SRIF to have less risk than

conventional due to the reason that less funs (capital) is moving out of SRIF when return

performance is inferior. This is due to the conventional investor’s rational economic

behavior and they move their capital to SRIF when past performance is positive in attempt

to either maximize the value of their capital or to gain additional utility from socially

responsible investments (Bollen, 2007, p. 683 & 706), while the majority of SRI investors

are reluctant to move out their capital from SRIF due to their orientation (values and loyalty).

In general, concurrent with modern portfolio theory, socially responsible investments

indicated to have less volatility than conventional investments (Bollen, 2007, and

Renneboog et al., 2005, cited in Renneboog et al., 2008, p. 1724). However, this is due to a

different reason than portfolio volume and diversity.

It is, however, may often not be feasible to access financial information instantly or in hours

or day-based timeframe. Thus, investors may tend to resort towards the use of past financial

22

performances to evaluate investment funds. Subsequently, investments with superior

performance than the competing funds in the past period may attract capital and their fund

volume may move upward. However, the degree of sensitivity to past performance between

different fund types, such as socially responsible funds and conventional funds, might be

argued. McLachlan & Gardner (2004)’s study indicated the lack of evidence in supporting

any argument on whether financial performance is more important to conventional investors

in comparison to socially responsible investors. (Sliwinski & Lobza, 2017, p. 660),

similarly, acknowledged the difficulty of giving such conclusion to their research reviews

on the financial performance comparison of SR and conventional funds, and in the study

they conducted, indicators of difference in utility derived from financial and non-financial

performance pointed not to be found. On the other hand, (Renneboog et al., 2008, p. 1739)

took a clear position indicating that SR investors give priority to non-financial utility derived

from their investment and are willing to accept inferior financial performance. Contrarily to

conventional investment, SRI are not expected to be sensitive and react to past financial

performance, inferring that investment in socially responsible funds to grow regard less of

financial performance and this growth of investment to be consistent and unaffected by past

as well as expected future financial performance (Renneboog et al., 2008, p. 1724-1725 &

1739). However, contrary to this Renneboog et al. (2008)’s claim, Cortez et al. (2009)

indicate that they found SRF to be sensitive to conventional indexes (or market indexes), in

comparison to social responsibility factors or values (Cortez et al., 2009, p. 573 & 579).

An additional to the above identified factors affecting fund movement, another variety of the

application of the neoclassical economic variables in on the general market. This is to assess

and observe how investors and investment funds behave in different state of the market. It

is indicated the possibility of the general market condition, i.e. market stability and crises,

affecting the financial performance of SRI and Conventional funds in different way

(Sliwinski & Lobza, 2017, p. 660). Nofsinger & Varma (2014)’s study points that the

possibility of socially responsible funds outperforming conventional funds during market

crisis or high market risk level and the reverse during stable market conditions (Nofsinger

& Varma, 2014, cited in Sliwinski & Lobza, 2017, p. 660). However, Sliwinski & Lobza

(2017)’s own study pints to the findings that shows the nonexistence of significant statistical

difference in the financial performance of socially responsible- and conventional investment

funds during either of the market condition, high and low risk condition (Sliwinski & Lobza,

2017, p. 667). Though not mentioned about actual fund movement, Frydman & Camerer

also pointed to the existence of a study that shows more attention being pied by investors to

their portfolio during rising market condition and low market volatility (Frydman & Camerer

,2016, p. 664).

The main argument made by several scholars in clouding Aslakes et al. (2003 cited in Lopez

et al., 2018, p.259), Lopez-Arceiz et al. (2018) and Renneboog et al. (2008) as well as the

rest of the studies comparing socially responsible- and conventional investments is that

socially responsible investment funds use both financial and social value criteria in the

selection of investments for either inclusion or exclusion in to their portfolios. Which posies

a critical attention to the trade-offs between the return sought after from an investment and

the environmental, social and governance aspects of investments. A more explicit statement

expressed by Renneboog et al. (2008) considers the utility derived from non-financial

variables of socially responsible investment as a counter to interest for financial performance

23

of a conventional investment (Renneboog et al., 2008, p. 1723). This trade-off further

proclaimed by Sampson (2000, cited in McLachlan & Gardner, 2004, p.11), however, only

with one side benefit. Sampson (2000) claims that ‘’Ethical investments belong to a lunatic

fringe and are not suitable savings deposit’’, to refer investing in a socially responsible funds

as a cost to incurred without a reward. Thus, referring investing in socially responsible funds

a trading the benefit of superior financial performance of conventional funds to either

nothing or a loss. And, Jones et al. (2008, p. 181) address the subject topic of trade-off

between socially responsible- and conventional funds amongst the existing literature.

Another related study is conducted by Riikka Sievänen (2013, p. 205)’s which indicates that

climate change is one of the socially responsible investment factors, besides human right,

that is not given emphasis buy funds claiming to have responsible investment strategy.

Investments in areas of alcohol, adult content cinematography etc. might have substantial

implication in the process of investment inclusion and execution to portfolios in comparison

to environmental topics. However, what is interesting with Riikka Sievänen (2013)’s study

is that the implicit impostor of trade-off between financial goal and sustainability goals

(Riikka Sievänen, 2013, p. 206). And, suggest that this unclarity of trade-off might be the

reason for why attention to the climate change and human right factors has not been

significant in socially responsible investment of pension funds (Riikka Sievänen, 2013,

p.207). Although, Riikka Sievänen (2013, p.207) acknowledging that previous studies do

not support financial performance difference between SR and conventional founds. In other

words, the benefits, other than financial performance, gained from investing in socially

responsible funds might not be well defined to pension funds, or other type of investments

for that matter, to motivate moving their capital into environment related socially responsible

investment funds.

24

3.1 Theoretical point of departure

Theories identified and used in the presiding different prior studies are the base for this study.

Those previous studies, however, have their base in economic and finance theories that are

well established, for example, Modern portfolio theory and the Capital Asset Pricing Model.

They are also based on the principle on how the modern-day market operates. Therefore, it

might be appropriate to account for concepts and principles related to the general market and

that have legitimate academic establishment. Those concepts and principles relevant to this

study are related to product demand, preference, and utility. In addition, it also investigates

the research question taking perspective that are related to behavioral finance. This is done

by including theories from the field of psychology study, for example pre-reflective and

narrative self-awareness.

The relevance of those theories and concepts in relation to this topic is on factors that

influence the flow of capital in to- and out of mutual funds. And, in this study, those factors

are categorized in to three major categories. Which is Classical economic theory, Behavioral

finance, and the general market condition. In the first part the constituents of classical

economic theory such as portfolio and CAPM are discussed in relation to the related prior

studies. The second part, Under the Behavioral finance category, mainly sociopsychological

and Neuropsychological factors are discussed in relation to the respective related previous

studies. In the third and last part a specific behavioral factor, which is investors orientation

in relation to the general financial market condition is discussed based on the respective prior

study.

Bollen (2007)’s study of ‘’Mutual fund attributes and investor behavior’’, which identifies

factors affecting the flow of capital between mutual fund types. Other prior related studies

that make up the theoretical framework for this study, and for factors affecting capital flow,

are research conducted by Hirshleifer and Shumway (2003)’s weather and market

performance, Renneboog, et.al (2008)’s past-performance dependence concept, Lopez-

Arceiz, et. al (2018)’s and Renneboog, et.al (2011)’s concept of the impact Cultural

Environment’s has on the socially responsible investment funds as well as Tippet and Leung

(2001)’s study result that indicated demographic-based difference in utility preference. And,

Nofsinger and Varma (2014)’s concept of the impact the general market condition has on

SRI and conventional fund performance. In the subsequent part those factor categories are

discussed in detail.

3.1.1 Classical economic theories and past performance dependency of investments

Bollen (2007) identified the return and risk economic variables a factor affecting the flow of

capital into- and out of a mutual fund as well as between mutual fund types. Past financial

performance of funds is the widely acknowledged explanation for investors behavior and the

flow of capital. Those classical economic theories are often linked to the modern portfolio

theory and CAPM which are fundamentally based on- and work under the Modern market

concept. In the market for financial products or investments, quality is expressed in terms of

return. However, the return each investment has in different time is not certain and this

25

uncertainty is the reason for why investors have a collection or a portfolio of different

investments, not more of one investment (Markowitz, 1991, p. 469 & 470). Consequently,

the rational investigation of investors behavior is centered on how investors make decision

when they are not certain about the future return of investments and portfolio theory

addressed this issue (Markowitz, 1991, p. 469).

The two currently know portfolio theories are Markowitz’s (1959) Bayesian-based Modern

Portfolio Theory and Frankfurter and Phillips (1995) Normative portfolio Theory (NPT)

(Nawrocki & Viole, 2014, p. 13). Portfolio theory, thus, is developed first by Markowitz

(Markowitz, 1991, p. 469). Markowitz’s theory suggests and gives a description of the

process that an investor who continuously is enhancing or optimizing its asset value might

undergo, in other words the behavior of value optimizing investor (Markowitz, 1991, p. 469).

On the other hand, the capital asset pricing model (CAPM) is developed by Sharpe and

Lintner and it is based on the portfolio theory developed by Markowitz (Markowitz, 1991,

p. 469). The central concept with the CAPM is the state of homogeneous value enhancing

behavior amongst investors (Markowitz, 1991, p. 469). Thus, CAPM explains the state of

all investors behaving according to Markowitz’s portfolio theory, which is economic

equilibrium (Markowitz, 1991, p. 469).

Bollen (2007, P. 686) discusses different assumption related to Capital flow and financial

performance, i.e. return, relationship. One such assumption is the efficient market hypothesis

where it is argued only fund management expenses should be considered when investing

since abnormal return is not feasible. The other is the representative heuristic of Kahneman

& Tversky (1982, cited in Bollen, 2007, P. 686), where it is assumed investors considers

recent performances. The third is rational learning (Brav & Heaton, 2002, cited in Bollen,

2007, P. 686) which contradicts the efficient market hypothesis and assumes a continues

effort to identify new ways of earing positive abnormal return. This study considers, rather,

the representative heuristic and investors considers the recent past price index performance

to infer for return performance. In addition, due to uncertainty in the return of some of the

investments, investors may prefer to move their capital in to different investment funds with

different level of return variability or risk level.

The prior studies identified and that compared SRIF and Conventional funds, such as

Renneboog et.al (2008) has taken the quality criteria of conventional investments, i.e. return,

and thus portfolio theory and CAPM model as a base for defining the behavior of

conventional fund as past-performance dependent. Those studies, on the other hand,

considered SRI funds having additional quality criteria, i.e. ESG, and thus is not guided by

portfolio theory and CAPM model. Rather, SRIF should show a consistent growth regardless

of past performance. This is line of argument is expressed by O’Neil & Pienta (1994, p. 73)

as investors ‘’orientation to others’’ when taking economic advantages, indicating the

exception of SRIF from the idea of all investors behaving according to Marlowitz’s portfolio

theory and CAPM economic/market equilibrium. However, Bollen (2007)’s study shows

that SRIF are more sensitive to past positive return and less sensitive to past negative return

performance. This is due to the conventional investor’s rational economic behavior and they

move their capital to SRIF when past performance is positive in attempt to either maximize

the value of their capital or to gain additional utility from socially responsible investments

(Bollen, 2007, p. 683 & 706).

26

This study, on the other hand, does not intend to investigate the comparative financial

performance of SRIF and conventional fund. Rather, it intends to study the relationship

between capital in-and out flow of investment funds through a change in total net asset

(TNA) with weather shocks. Capital flow, however, might be caused by past-financial

performance and risk preference of investors besides weather shocks. Therefore, to control

the study of capital flow and weather shock is not interfered by past return and risk levels,

their relation to the funds will also be under observation.

Therefore, the relationship between the capital flow of the funds and their return

performance will be observed to control if the change in capital flow is caused by the funds

return performance and not by weather shocks. The relationship between the risk level of

the funds and the change in capital flow will also be observed to control if capital flow is

caused by the funds risk level and not by weather shocks.

3.1.2 Behavioral finance and factors shaping SR Investment desire

Bollen (2007) indicated, in addition to the rational economic decision making, the possibility

for another category of factor affecting capital flow, for example, emotional human behavior.

Those factors relate to behavioral finance where different previous studies assess the

behavioral difference between cultural environments, demographic variable such as age,

gender, and family structure as well as individual’s cognitive ability, that are related to

investment choices between SRIF and conventional funds. Behavioral finance is an idea

derived from experimentations within the field of economics and psychology to establish

legitimate credibility and reliability for models used in both behavioral and empirical

analyses (Duxbury, 2015, p. 78). Duxbury’s following direct quotation is identified to help

better understand the topic.

‘’The ability to observe directly, control, and manipulate variables of theoretical

importance, are well suited to a study of behavioral finance. Many of the key variables of

interest in behavioral models are unobservable to researchers examining data from

naturally occurring financial markets, hence such empirical studies adopt proxies to capture

or measure the effect of many variables of theoretical importance. For example, in the

context of portfolio decisions, Baltussen and Post (2011) note that challenges arise when

analyzing real-life investment portfolios. An evaluation of the merit of an investor’s portfolio

decisions, requires knowledge of their risk preferences which assets they considered and

their expectations about them. Such data are largely unobservable, and so difficult to

measure or control, in studies of real-life data, but can be controlled and manipulated

directly in experimental studies. The ability to control allows experimenters to test the

impact of variables of interest on investor behavior and the function of financial markets

free from the confounding effects of other variables’’ (Duxbury, 2015, p. 79).

Behavioral finance provides the explanation for Bollen (2007)’s study which covers and

reflects up on the multiple sources of utility, investing for a factor that can affect Capital

flow in addition to economic performance or return. Manipulating, controlling, and

observing variables, on the other hand, is what Hirshleifer & Shumway (2003) and Lopez-

Arceiz et al. (2018) and Renneboog, L., Ter Horst, J., & Zhang, C. (2011) did. Hirshleifer

27

& Shumway (2003) used the variable ‘’Sunshine’’ to observe its effect on markets

performance while studying the relationship between mood and investment market. Lopez-

Arceiz et al. (2018) and Renneboog, et al. (2011), on the other hand, used the variable

‘’cultural environment’’ while studying its impact on investment decision making.

A choose made between alternative investment funds is a conscious decision making, which

could be affected by the investors state of consciousness. Two state of consciousness

identified in the psychological studies, pre-reflected self-awareness or simply referred as

state of experience and reflected self-awareness or narrated identity (Frie, 2011, p. 51), may

support the expiation of experience based decision making that could influence the flow of

capital. An insight into the state of mind, thus, may better explain the relationship between

capital flow and the natural environment as well as the sociocultural environment.

Narration and Sociocultural environment

Identities are given by the position or the role an individual has in a community, thus, an

individual’s image of oneself and others is constructed by the everyday actions taken that

are narrated through time (Frie, 2011, p.57 & 58). However, those action are framed in the

local culture, social structure, economic condition as well as the political situation (Frie,

2011, p. 57). Subsequently, an individual’s judgement is key in matching one’s action to the

surrounding social environment either for the purpose of fitting or improving the existing

condition. This might be supported by psychological studies indicating part of a human

brain called anterior insula in interoceptio being associated with awareness of internal state

of one’s body, emotion and assessment of risk, while another brain part called VSt is

associated with ‘’Expected-reward signals’’ (Frydman & Camerer, 2016, p. 666). Hence,

an individual’s risk awareness and expected-reward signals are involved in the narration

process.

Similarly, an individual’s awareness to natural environmental, such as climate and weather-

related, risks is thus subject to be shaped by the values held about the natural environment

in a community. This implies that the maturity of behavioral change associated with taking

decisions related to social responsibility and concern for environmental concern is gradual

and stable. Lopez-Arceiz et al. (2018)’s and Renneboog, et al. (2011)’s study has examined

the maturity of such behavioral change in SR in different cultural environment.

A closely related factor that can affect Capital flow is demographic, especially gender,

difference. Although, behaviors attributed to demographic differences in terms of age and

gender are the medium for the manifestation of the effect of the surrounding environment.

This surrounding environment in our context is the cultural environment. ‘’Contemporary

discussions of gender suggest that identity categories are fashioned through our involvement

with, and subjection to, everyday discursive practices. (…) Because our bodies exist for us

through the ways we talk, write, and perform about them, our identities are formed through

sets of repeated gender performances that are governed by the cultural norms of masculinity

and femininity that we see around us.’’ Frie, 2011, p. 57). In this regard the prominent

investigation is done by Tippet and Leung (2001) where their study result indicated that

female investors tend to be more ethical or the probability of higher capital inflow to SRIF

than Conventional fund if the majority of the investors in a financial market are females.

28

The study, however, does not consider cultural environment and demographic differences as

a variable that affects how capital flows in and out of an investment fund in a short period.

Rather, those are considered as a factor that can affect the long-term trend on the flow of

capital. In addition, the study is conducted in only the Nordic cultural environment, to be

articulate the Swedish cultural environment, and there is no other competing cultural

environment to be compared with in the study. For this reason, an already existing studies

on the Nordic cultural environment is intended to be based for the interpretation of the

study’s result.

Experience and the environment

Experiencing weather shocks is related to psychological and cognitive ability. ‘’From a

phenomenological perspective, the ability to self-identify depends upon an experiential, pre-

reflective self-awareness ‘’ (Frie, 2011, p. 51). This gives an insight in to individuals

preferred and valued experiences, although, experiences are short lived. Decisions in

experiential circumstances are sudden and exceptional than the decisions/actions taken in a

narrative or stable circumstance. Narratives are an act of selective information retention from

pre-reflections that takes place in a long and serious period of times, thus, those are

‘’reflective selections and organization of a life’’ (Frie, 2011, p. 51). Therefore, ‘’the

narrative captures less than an individual’s life, for not all of a life as pre-reflectively lived

can be fitted into a narrative’’ (Drummond, 2004, p. 119, quoted in Zahavi, 2005, p. 113,

cited in (Frie , 2011, p. 51).

Individual’s self-awareness is formed by a combination of the exposure to the surrounding

social environment and ones’ own interpretation of- and reaction to it (Martin, Sugarman &

Thompson, 2003, cited in (Frie, 2011, p. 58). Therefore, even in times of experiencing

sudden circumstances or incidents such as weather shocks, an individual’s pre-reflected self-

awareness is grounded on an already narrated or shaped self-awareness which is expressed

in the form of the individuals interpretation of- and the reaction to the incident (Frie, 2011,

p. 58). Thus, the environmental values an investor reflects while experiencing weather shock

is possible to be shaped by the sociocultural environment. Yet, still it is possible for those

values are formed by the individual’s own interpretation of- and reaction to the

environmental values in that sociocultural environment. Which forms the uncertainty that

this study looking an explanation for.

The idea of investors interpretation of- and reaction to environmental conditions, for

example weather affecting investors mood is what Hirshleifer & Shumway (2003)’s study

focusing on when they empirically studied the relation between sunshine and return on the

stock market. Investors in a bad mood lean towards accessing unpleasant or negative

substances out of their encounter (Isen et, al, 1978, and Forgas & Bower, 1987, cited in

Hirshleifer & Shumway, 2003, p. 1012). When it comes to weather shocks, however,

investors orientation might affect how investors interpret-and react to an experience of

weather shock. In such circumstances weather shocks might be considered as a triggering

factor for the utility of socially responsible investment options. To better explain the

relationship between experiencing weather shocks and investors behavior through an

inference such as the flow of capital, although it might be a broad topic, looking into the

29

Utility theory might be necessary. The reason is that moods and feelings can affect

preference (Hirshleifer & Shumway, 2003, p. 1013).

Utility theory is constructed based on decision making or the act of showing preference

between alternative actions (Fishburn, 1979, p. 1). Fishburn discusses about the connection

between preference and decision making, saying that decision making is guided or dictated

by the alternative that and individual has preference for. Hence, preference as the center

between value and decision making is the core idea Utility theory addresses. The theory is

dates to 1738 to Daniel Bernoulli’s essay (Nawrocki & Viole, 2014, p. 12). Bernoulli’s

original proposition started with challenging Huygens’s, Montmort and de Moivre’s idea

about the value of game, but the word product is used here on in this study, (Nawrocki &

Viole, 2014, p. 12). Their idea suggests to perceive or acknowledge the evaluator (in this

study the investor) and the value of the product as two separate entities, implying the

irrelevance of the subjectivity of the investor and that the investments value is ‘’an objective

quantity’’ (Nawrocki & Viole, 2014, p. 12). Bernoulli, on the other hand, believes that the

value of a product (Socially investment options in this studies case) is exposed to subjective

judgement which is affected by the individual’s values, past experiences and preferences in

general, thus considering the interdependence between the value of socially responsible

investment options and the investor (Nawrocki & Viole, 2014, p. 13). Nawrocki & Viole

(2014) summarizes Bernoulli’s hypotheses as ‘’The value of a good is not given by its price,

but by its utility’’ (Nawrocki & Viole, 2014, p. 13), which accretes the variability of the

value that socially responsibility investment option have depending on the investors

preference. For the investors, the value of SRIF might depend on the desire the investors

show when experiencing weather shocks. Therefore, SRIF might have a utility that is related

to weather shocks.

Investors judgment of SRI and conventional investments is ‘’based on subjective

interpretations that often vary with the mode’’ (Narens, 2016, p. 42). Accordingly, for this

study the ‘’Expected-Utility Theory’’ is relevant to consider. It is also expressed as

Subjective expected utility and it can be modified to include investors emotion, bias, and

other modes that can influence their decision-making judgment about the utilities of financial

products (Narens, 2016, p. 42). Hence, expected utility is where the uncertainty about capital

flow is expressed in terms of the probability that capital will flow in to SRIF if weather

shocks occur (Fishburn, 1979, p. 2).

This study is attempting to reach on an understanding if weather-related shocks, which is

related to stimulating neuropsychological activities, can cause the in- and out flow of capital

to- and from a socially responsible mutual fund. This is closely related to a study conducted

by Hirshleifer & Shumway (2003) where they empirically studied the relation between

sunshine and return on stock market. What this study differs from Hirshleifer & Shumway’s

is that it is focused on ‘’shocks’’ rather than ‘’moods’’ that might be pleasant or otherwise.

The investment types also differ, the focus of this study socially responsible mutual fund and

comparable conventional mutual funds rather than the stock market in general. Those

difference may play a role in showing a different behavior in relation to weather and the flow

of investment capitals than what might be possible to project from their study. Hence, it

considers that weather shocks as circumstances that creates experience or pre-reflected self-

awareness. A reaction to such an event materialized through the choose between mutual

30

investment funds should, therefore, be reflected on inflow and outflow of capital from

mutual socially responsible as well as conventional mutual funds. Accordingly, it will

attempt to look for effect, which is whether there is a change in the flow of capital after a

weather shock occurred.

3.1.2 Investors orientation under high and low risk financial market

The third category of factors that is identified as influencing the flow of capital is the general

market condition. Nofsinger &Varma (2014)’s and Sliwinski & Lobza (2017) argue that

since socially responsible funds are less sensitive to past financial performance, socially

responsible funds might generally exhibit less variability and risk to the general market

condition. Therefore, conventional investors might take the opportunity to move their funds

in to socially responsible mutual funds in an event of high market risk. Even though, this

suggestion is based on the rational economic behavior, the general market condition may not

be fully explainable by rational economic variables only.

SRIF’s less sensitivity to market risk is based on Individuals orientation which is explained

by O’Neil & Pienta (1994, p. 72) through the ‘’self-interest motive or the desire to improve’’

once quality of life. Which is in line with the principle on which market system operates, the

idea that an individual’s act is guided by his or her desires (Boettke & Sautet, 2011, p.1) and

thus leads for the demand for a product that can satisfy desires. Socially responsible

investment funds can be chosen to invest in by individuals who look after to satisfy their

desire of seeing the values and morals they adhere to being maintained, in addition to

economic benefits, and will take any opportunity freely in order to do so (Boettke & Sautet,

2011, p.1). It constitutes investors orientation towards others and is expressed through the

integration of Environmental, Social and Governance (ESG) factors when taking economic

advantages in the financial market. O’Neil & Pienta (1994)’s idea pointes to the affirmation

of the nonrecognition of others interest in the classical economic theory such as the portfolio

theory and the Capital Asset Pricing Models. They explain, the classical economic theory

rather considers, an independent individual self and self-interest (O’Neil & Pienta, 1994, p.

73). Thus, conventional investors might be considered as self-oriented when taking

economic advantages through investing. In other term, the behavior of conventional

investors and their investment might be described as ‘’Homo economicus’’ (Frazier, Haidt

& Kluver, 2014, p. 151) or that of rational economic actors. Therefore, conventional

investors are likely to move their capital in to socially responsible funds in times of high

market risk to optimize the value of their capital.

From product perspective, today’s market operates with the basic principle that all products

in the market has a purpose to fulfill for their respective customer, which is to satisfy their

desire (Bollen, 2007, 683) Socially responsible funds have also the purpose of meeting the

desire or demand of investors who are ‘others oriented’ or looking for investment

opportunities that ‘’integrate social and environmental criteria in financial investment

decisions’’ (Cowton & Sandber, 2012, p. 142). While, conventional funds, meet the demand

of investors’ who only considers economical risk and return factors in making decision to

include or exclude in their portfolio (Cowton & Sandber, 2012, p. 142). It might be radical

31

to label SRI and Conventional investment funds as other’s- and self-oriented, respectively.

However, generally, the market for SRI fund exists because it has developed to the level

where it becomes complex and offers a vast opportunity to satisfy investment desires

(Boettke & Sautet, 2011, p.1). Thus, it is a mechanism by which capital owner and investors

express their orientation and satisfy their desire of doing good for the society through the

market system, rather than doing it individually on their own (Boettke & Sautet, 2011, p.1).

Despite the expectation of SRIF’s not to be past performance dependent and thus to have

less risk level, what actually happens to investors orientation when the financial market is in

its worst state, that is with high risk, as well as when it is stable and in a moderate risk level,

is what Nofsinger & Varma (2014) and Sliwinski & Lobza (2017) studied.

Their focus is concerned on understanding the behavior of each of those funds (investors

orientation) in high risk as well as stable and moderate financial market. Which is if those

funds show lower or higher- risk and return level comparatively in each of the general market

condition. In another word, they have studied the difference in the magnitude of effect that

financial market being in low risk or high-risk state has on the SRIF and conventional fund.

This might be considered as a test of investors orientation through risk and return in relation

to the general market condition. Therefore, this study will not consider investigating the risk

and return related behavioral difference of SRIF and conventional mutual funds in relation

to the general market condition.

This study, however, is interested in understanding the capital in- and out flow related

behavioral difference of SRIF and conventional funds in relation to weather shocks. Thus, it

is interested in understanding how each of the fund types behave through their respective

change in the flow of capital when a weather shock occurs. On the other hand, since capital

flow can be affected by both the market condition, i.e. the financial markets risk level, and

weather shocks, it observes the general market condition in order to control if the flow of

capital can be explained by weather shocks and not by the general market condition.

Therefore, the relationship between the general market risk level and capital flow will be

observed to control if capital flow can also be explained by the risk level in the financial

market and not only by weather shocks.

32

3.2 Hypothesis formation

Individual’s self-awareness is not formed only by the exposure to the surrounding social

environment, but also with ones’ own interpretation of- and reaction to it (Martin, Sugarman

& Thompson, 2003, cited in (Frie, 2011, p. 58). This makes the identification of all the

environmental variables as well as establishing causal relation of individuals interpretations

and reactions to an incident difficult, if not impossible. Therefore, rather than attempting to

predict a reaction to a certain type of incident in such manner, it would be feasible to start

the observation from the incident. Then, following the actual reaction and infer on the

individuals state of mind when the incident occurred.

This study hypothesizes based on ‘’Expected utility theory’’; weather shock captures

investors state of mind, that is pre-reflected self-awareness or experience. Thus, an

observation of socially responsible mutual fund’s capital flow after weather shock should

prevail consistent result of showing a sudden change in the flow of capital inflow. It might

be noticed that, in the case of utilities related to financial performance or return, an individual

capital owner invests expecting the maximum return possible considering the risk taken,

which can be proven soon. While in the case of utilities related to weather shock, an

individual capital owner invests with the expectation to limit weather shocks from happening

in unidentified time in the future, or as a preventive measure. Therefore, this study

hypothesizes as follows.

Hypothesis 1. There is significant relation between weather shocks and SRI Fund

movement.

This is based on the study of Hirshleifer & Shumway (2003) which associates stock markets

performance to investors mood affected by weather condition. In this study, however, it more

specifically based on weather shocks affecting investors capital placement into socially

responsible mutual funds.

Hypothesis 2. There is no significant difference between the capital flow and

weather shock relationship of socially responsible funds and that of conventional

funds.

This could be explained by either the neoclassical economic theories, portfolio and CAPM,

and the behavior of rational economic actor that is guided only by return performance and

risk level and values such weather shock should not affect how capital moves into investment

funds. Or, even though, the investors drive utilities other than economic or in addition to it,

they narrate their weather shock experiences rather than directly reacting to them.

33

Hypothesis 3. The capital flow and weather shock relationship are stronger for

socially responsible funds than that of the conventional funds.

This based on Bollen (2007), Lopez-Arceiz et al. (2018) and Renneboog, et al. (2011)’s idea

of multi-utility factor, where socially responsible investors derive additional utilities from

investing in social causes than only financial rewards. Thus, if weather shock occurs

conventional investors might likely be affected in their investment decision and that they

might place their capital in SR mutual funds as long as they have similar financial

performance as conventional mutual funds.

34

4. Practical method

Practical method, or study technique, in research context refers to the mechanisms or

investigative tools used in sampling, data collection and data analysis (Long, 2000, p.195).

The tools and the process of investigation are presented in subsequent sections.

4.1 Financial data collection

The two categories of sampling techniques are probability and non-probability sampling

(Bryman and Bell, 2015, p. 190 & 200). Non-probability sampling has also two sub-

categories, convenience, and quota sampling (Bryman and Bell, 2015, p. 200 – 2005). A

convenient sampling approach was followed to include financial data in the study. However,

filtering criterion on the samples was applied to identify those funds that have similar

lifespan or can be categorized into a certain period and that have the necessary data needed

to conduct the study. The common filtering criteria for both socially responsible and

conventional funds are categorization as a unit trust, originated and actively traded in the

Swedish financial market with the Swedish currency, Swedish Krona.

Initially attempts were made to find a complete list of an already identified socially

responsible investment funds in Sweden. This was done by sending email to the Swedish

investment fund association, Morningstar, and the Swedish statistics institution or Statistics

Sweden. However, it was reached on an understanding that there exists no such list for

socially responsible investment funds at the time. Mutual funds that can be categorized as

socially responsible, therefore, first identified from financial service providers websites such

as ‘’Morningstar’’ and ‘’Avanza’’. Based on the lists a search for the actual data were made

on the Eikon database. To increase the number of mutual funds to be included in the study

and to assure that all SRIF’s accessible are included further search on Eikon database were

made with the search words ‘’hallbar’’, ‘’hallbarhet’’, ‘’hallbarhetsfond’’, ‘’Gron’’, Etisk’’,

‘’Etiska’’ as well as ‘’Climat’’ and ‘’Miljo’’. The Swedish spellings ‘’ä, å and ö’’ are replace

with the normal Latin spellings ‘’a, a and o’’ respectively to make the search words

recognizable by the database. And those words translate in to sustainable, sustainability,

sustainability fund, green, ethic, ethical as well as climate and environment, respectively. A

total of 128 socially responsible funds was identified through the search. Although, there

were no socially responsible funds found with the search work ‘’Climat’’ that is originated

from Sweden and there were also no socially responsible funds found with the search work

‘’Miljo’’. However, when applying filtering criteria, that is on funds that are active, that have

Sweden as their market and registration country and using the Swedish currency or Swedish

krona, only 67 socially responsible funds were available. All the 67 SRIF were downloaded

and send via e-mail, which then were downloaded to a computer for further analysis. First,

financial data for socially responsible funds were retrieved in three batches. Funds with

maturity of ten- and more years grouped as a ten-year, which is funds with starting date in

2010 and backward. Followed by funds with start date between 2011 to 2017 and finally,

funds with start date from 2018 onwards. Financial data types retrieved are total net asset,

35

total return index, historical beta, and price index, for each of the fund categories accessed

from 2010-01-31 to 2020-02-28 for the ten-year category for each of the funds.

Matching conventional mutual funds are filtered based on name and number of SRIF in each

year categories. When conventional funds that are under the same fund management as the

matching socially responsible investment fund are not available, available conventional

funds from other fund management that are identified in similar maturity to that of the

socially responsible investment funds are used as a replacement. The market index used as

a benchmark to infer the general market condition or the market risk level is OMXS30. The

price index data which allowed the inference of the market risk is also retrieved from Eikon

database.

4.1.1 Financial data input

A total of 127 mutual funds is under this study, of which 60 are socially responsible funds

and the rest 67 are conventional funds that resembles the maturity period of the identified

socially responsible funds. Initially, 671 socially responsible funds that are categorized as a

unit trust, originated, and actively traded in the Swedish financial market with the Swedish

currency (Krona) were identified. However, seven of the socially responsible funds

identified lack full financial data that is relevant to the study. Therefore, those are excluded.

Table 1. Number of socially responsible funds that are eligible and included in the study,

selected samples, as well as those that are excluded from the study.

A total of 67 socially responsible funds that meet the filtering criteria identified, of which

six are excluded due to lack of full data.

1 Note: It is possible for the total number of mutual funds that can be categorized socially responsible and

available in the Swedish financial market to be more than 67. This study identified only those funds that meet

the filtering criteria and the search approach discussed. For other future studies with a different purpose and

scope, an extensive search might be necessary.

Table 1. SRIF sample selection

Under study 60

Excluded due to lack of full data 7

Total 67

36

Figure 3. Percentage of socially responsible funds under study and that are excluded.

Figure 3 shows the percentage of socially responsible funds that are under study as well as

that are excluded from the study due to lack of full data out of the total 67 socially responsible

fund identified population. 89% of the 67 socially responsible funds are under study and

10.47 % of the funds are excluded due to lack of full data.

Table 2. Total number of mutual fund samples.

A total of 127 mutual trust fund are included in the study of which 60 of the funds are in

socially responsible category, while the rest of 67 are conventional fund category.

Figure 4. Percentage of SRIF and conventional funds in the total mutual funds sample

size.

Of the total 127 mutual funds under study, 47% are from the socially responsible category

and the rest 53% are conventional funds.

47%53%

Figure 4. Total sample mutual funds

SRIF 60

Conventional 67

Table 2. Total sample mutual funds

SRIF 60

Conventional 67

Total 127

89.55%

10.45%

Figure 3. SRIF sample selection

Under Study 60

Excluded due to lack of full data 7

37

Table 3. Socially responsible funds that have ten and more years of maturity.

Table 4. Number of socially responsible funds that have ten- and more years of maturity,

under the management of their respective fund managing institution.

The seventeen socially responsible funds that have ten- and more years of maturity are

managed by seven fund managements. While Swedbank manages four of the SRIFs,Catella

and SEB manages two each. Similarly, Handelsbanken, Lannebo and Lansforsakringar

manages one of the socially responsible funds each. Ohman, on the other hand, manages six

of the seventeen funds with a ten- and more years of maturity.

Table 3. SRIF Ten and more years of maturity

Number Name Symbol Start Date

1 LANSFORSAKRINGAR GLOBAL HALLBAR A 88730C 1990-11-27

2 OHMAN GLOBAL HALLBAR A 88732H 1998-12-21

3 OHMAN ETISK INDEX EUROPA 88734N 1999-10-18

4 OHMAN ETISK INDEX JAPAN 88734Q 1999-10-18

5 OHMAN ETISK INDEX PACIFIC 88734T 1999-10-18

6 OHMAN ETISK INDEX USA A 88734L 1999-10-18

7 OHMAN ETISK INDEX SVERIGE A 88749H 2005-08-24

8 BANCO FUNDS ETISK SVERIGE 411567 2006-08-25

9 HANDELSBANKEN HALLBAR ENERGI (A1 SEK) 9146VZ 2008-02-29

10 CATELLA SVERIGE AKTIV HALLBARHET 70033M 2010-08-31

11 CATELLA SVERIGE HALLBARHET BETA A 70033P 2010-08-31

12 SEB HALLBARHETSFOND GLOBAL 70126X 2010-09-10

13 SEB HALLBARHETSFOND SVERIGE INDEX UTD 70129K 2010-09-10

14 SWEDBANK ROBUR KPA ETISK AKTIEFOND 70147C 2010-09-14

15 SWEDBANK ROBUR KPA ETISK BLAND 2 70147E 2010-09-14

16 SWEDBANK ROBUR KPA ETISK RANTEFOND 70147F 2010-09-14

17 LANNEBO SVERIGE HALLBAR B 93109C 2010-10-01

Table 4. SRIF with Ten and more years of maturity:

Number of funds per Fund management

SWEDBANK (including Banco) 4

CATELLA 2

HANDELSBANKEN 1

LANNEBO 1

LANSFORSAKRINGAR 1

OHMAN 6

SEB 2

Total 17

38

Figure 5. Percentage of socially responsible funds that have ten- and more years of

maturity, under the management of their respective fund managing institution.

35% percent of the seventeen socially responsible funds that have ten- and more years of

maturity are under the management of Ohman, 23% are under the management of Swedbank,

12% are under the management of Catella, 12% are under the management of SEB. While,

Handelsbanken, Lannebo and Lansforsakrinar each manages 6% of the seventeen socially

responsible funds that have ten- and more years of maturity.

23%

12%

6%6%6%

35%

12%

Figure 5. SRIF with Ten and more years of maturity

SWEDBANK (including Banco) 4

CATELLA 2

HANDELSBANKEN 1

LANNEBO 1

LANSFORSAKRINGAR 1

OHMAN 6

SEB 2

39

Table 5. Socially responsible funds that have eight to two and a quarter year of maturity.

Table 6. Number of socially responsible funds that have eight to two and a quarter year of


Table 5. SRIF Eight to two and a quarter years maturity


1 OHMAN FTOF.HALLBAR A 88772J 2012-02-01

2 OHMAN ETISK EMERGING MARKETS 88777D 2012-07-17

3 OHMAN FRN HALLBAR A 88782H 2012-12-14

4 CATELLA SVERIGE HALLBARHET BETA B 88782P 2013-01-08

5 SEB HALLBARHETSFOND GLOBAL UTD 88785D 2013-02-28

6 OHMAN FTOF.HALLBAR B 89259U 2013-03-21

7 OHMAN FRN HALLBAR B 89263J 2013-05-15

8 OHMAN SVERIGE HALLBAR A 91246L 2013-08-19

9 HALLBAR FORETAGSOBLIGATION 8737G3 2013-12-02

10 OHMAN ETISK INDEX USA B 8747W9 2013-12-12

11 OHMAN ETISK INDEX SVERIGE B 8747VH 2014-01-09

12 OHMAN SVERIGE HALLBAR B 9302GF 2015-01-02

13 SPP GRON OBLIGATIONSFOND A 9366K8 2015-03-02

14 OHMAN RANTEFOND KOMPASS HALLBAR A 94462E 2015-04-13

15 LANSFORSAKRINGAR GLOBAL HALLBAR B 2606EA 2016-09-16

16 SPP GRON OBLIGATIONSFOND B 9077YZ 2017-04-25

17 OHMAN ETISK EMERGING MARKETS B 9077QQ 2017-05-05

18 OHMAN GLOBAL HALLBAR B 9101TF 2017-05-19

19 OHMAN SVERIGE MARKNAD HALLBAR A 91101P 2017-06-20

20 OHMAN GLOBAL MARKNAD HALLBAR A 9181XC 2017-10-11

21 OHMAN GLOBAL MARKNAD HALLBAR B 9181XA 2017-10-11

22 OHMAN GRON OBLIGATIONSFOND A 9174DW 2017-10-12

23 OHMAN SVERIGE MARKNAD HALLBAR B 92179Z 2017-11-14

24 SKANDIA SVERIGE HALLBAR 9275CU 2017-12-14

25 OHMAN FRN HALLBAR C 9314MU 2017-12-15

Table 6. SRIF Eight to two and a quarter year maturity:


CATELLA 1

LANSFORSAKRINGAR 2

OHMAN 18

SEB 1

SKANDIA 1

SPP 2

Total 25

40

The twenty-five socially responsible funds with eight to two and a quarter year of maturity,

are managed by six fund managements. Ohman manages most of the funds, 18.

Lansforsakringar and SPP manages two of the funds each. While, Catella, SEB and Skandia

manages one fund each.

Figure 6. Percentage of socially responsible funds that have eight to two and a quarter

year of maturity, under the management of their respective fund managing institution.

72 % the twenty-five socially responsible funds with eight to two and a quarter year of

maturity are under the management of Ohman, 8 % are under the management of

Lansforsakringar and similarly 8 % are under the management of SPP. While Catella, SEB

and Skandia each manages 4% of the twenty-five socially responsible funds with eight to

two and a quarter year of maturity.

4% 8%

72%

4%4%

8%

Figure 6. SRIF Eight to two and a quarter years maturity

CATELLA 1

LANSFORSAKRINGAR 2

OHMAN 18

SEB 1

SKANDIA 1

SPP 2

41

Table 7. Socially responsible funds that have two and less than two year of maturity.

Table 8. Number of socially responsible funds that have two and less than two year of


The eighteen socially responsible funds that are included in the three-year category are

managed by four three managements. SEB manages most of the funds, 13 SRIF’s. While,

Handelsbanken and Ohman manages one and four funds, respectively.

Table 7. SRIF Two and less than two years of maturity


1 OHMAN GRON OBLIGATIONSFOND B 9298D0 2018-02-26

2 SEB HALLBAR FAKTOR EMERGING MARKETS 9388AK 2018-11-19

3 SEB HALLBAR FAKTOR EMERGING MARKETS UTD 9388AT 2018-11-19

4 SEB HALLBAR FAKTOR GLOBAL 9388AL 2018-11-19

5 SEB HALLBAR FAKTOR GLOBAL UTD 9388AV 2018-11-19

6 HANDELSBANKEN HALLBAR GLBL OBLIG (A1 SEK) 9442EY 2019-02-20

7 SEB HALLBARHETSFOND GLOBAL C 9435VX 2019-02-25

8 SEB FOTF.HALLBAR 94645W 2019-03-22

9 SEB FOTF.HALLBAR INST 94646Y 2019-03-22

10 SEB FRN FOND HALLBAR A 9442ZP 2019-04-12

11 SEB FRN FOND HALLBAR B UTD 9442ZL 2019-04-12

12 SEB FRN FOND HALLBAR C 9442ZQ 2019-04-12

13 SEB FRN FOND HALLBAR D 9442ZM 2019-04-12

14 SEB FRN FOND HALLBAR E 9442ZN 2019-04-12

15 OHMAN RANTEFOND KOMPASS HALLBAR B 9449VV 2019-05-17

16 SEB FRN FOND HALLBAR F 9512A9 2019-09-05

17 OHMAN GLOBAL SMABOLAG HALLBAR A 9534KN 2019-12-10

18 OHMAN GLOBAL SMABOLAG HALLBAR B 9534KM 2019-12-10

Table 8. SRIF with Two and less than two years of maturity:


HANDELSBANKEN 1

OHMAN 4

SEB 13

Total 18

42

Figure 7. Percentage of socially responsible funds that have two and less than two year of


72% of the eighteen socially responsible funds with two and less than two year of maturity

are under the management of SEB. While, 22 % are under the management of Ohman, the

rest 6% are under the management of Handelsbanken.

Table 9. Conventional funds that have ten- and more than ten year of maturity.

6%

22%

72%

Figure 7. SRIF with Two and less than two years of

maturity

HANDELSBANKEN 1

OHMAN 4

SEB 13

Table 9. Conventional Ten and more years of maturity


1 CATELLA AVKASTNINGS 70033D 01-02-99

2 CATELLA BALANSERAD 70033E 31-08-10

3 LANNEBO FONDER LIKVIDITET 70115H 09-09-10

4 LANSFORSAKRINGAR SVERIGE INDEXNARA 88759D 17-11-08

5 OHMAN FORETAGSOBLIGATION FOND A 88742J 11-11-03

6 OHMAN GLOBAL GROWTH 88729L 12-04-96

7 OHMAN KORT RANTA A 88727L 29-06-95

8 OHMAN OBLIGATIONSFOND A 88727F 29-06-95

9 OHMAN SMABOLAGSFOND A 27969W 04-11-03

10 SEB TOTAL POTENTIAL 88742L 05-01-04

11 SWEDBANK ROBUR ACCESS SVERIGE 99295U 24-04-07

12 SWEDBANK ROBUR ACTION 70147M 13-09-10

13 SWEDBANK ROBUR KAPITALINVEST 70146W 14-09-10

14 SWEDBANK ROBUR SVERIGEFOND 70154U 16-09-96

15 HANDELSBANKEN SVERIGE SELEKTIV (A1) 9145RV 06-06-05

16 SEB ASTMGMT.SVERIGE SMABOLAG CHANCE RISK 70129H 18-04-95

17 OHMAN REALRANTEFOND A 70027E 31-08-10

43

Table 10. Number of conventional funds that have ten- and more years of maturity, under

the management of their respective fund managing institution.

Table 10. Conventional Ten and more years of maturity:


CATELLA 2

HANDELSBANKEN 1

LANNEBO 1

LANSFORSAKRINGAR 1

OHMAN 6

SEB 2

SWEDBANK 4

Total 17

The seventeen conventional funds with ten- and more years of maturity are managed by

seven fund managements. While, Ohman and Swedbank manage six and four funds,

respectively, Catella and SEB manage two funds each. Handelsbanken, Lannebo and

Lansforsakringar manages the rest, one each.

Figure 8. Percentage of conventional funds that have ten- and more years of maturity,


35% of the ten-year category conventional funds are under the management of Ohman, 23%

are under the management of Swedbank, 12% are under the management of Catella, 12%

are under the management of SEB, 6% are under the management of Handelsbanken, the

other 6% are under the management of Lannebo and the rest of 6% is managed by

Lansforsakringar.

12%

6%

6%

6%

35%

12%

23%

Figure 8. Conventional Ten and more years of maturity

CATELLA 2

HANDELSBANKEN 1

LANNEBO 1

LANSFORSAKRINGAR 1

OHMAN 6

SEB 2

SWEDBANK 4

44

Table 11. Conventional funds that have nine to two and a quarter year of maturity.

Table 12. Number of conventional funds that have nine to two and a quarter year of


Table 11. Conventional Funds Nine to two and a quarter years maturity


1 SEB ASSET MANAGEMENT SVERIGE SMABOLAGS 74961F 15-02-11

2 SKANDIA NORDEN 88775L 27-04-12

3 SEB DYNAMISK AKTIEFOND UTD 88784X 28-02-13

4 SEB LIKVIDITETSFOND SEK UTD 88785J 28-02-13

5 SEB OBLIGATIONSFOND SEK UTD 88784V 28-02-13

6 SKANDIA SVERIGE EXPONERING 88785N 12-03-13

7 OHMAN REALRANTEFOND B 89260P 21-03-13

8 OHMAN SMABOLAGSFOND B 89260K 21-03-13

9 LANSFORSAKRINGAR GLOBAL INDEXNARA 89223V 11-06-13

10 SKANDIA GLOBAL EXPONERING A 91419P 28-08-13

11 SKANDIA TILLVAXTMARKNADSFOND 8681RT 20-11-13

12 OHMAN FORETAGSOBLIGATION FOND B 8748YE 12-12-13

13 OHMAN INDEX NORDAMERIKA MSCI NORTH AMERICA B 8748N0 12-12-13

14 CATELLA CREDIT OPPORTUNITY 9217PK 24-11-14

15 LANSFORSAKRINGAR BEKVAM FOND TILLVAXT 9453JG 14-04-15

16 SPP MIX 100 99310X 15-09-15

17 SPP MIX 20 99310Z 15-09-15

18 OHMAN OBLIGATIONSFOND B 8748FM 26-11-15

19 SEB DYNAMISK AKTIEFOND INST 7026FA 21-03-16

20 LANSFORSAKRINGAR SPARMAL 2050 7256RU 15-04-16

21 OHMAN OBLIGATIONSFOND SEK A 2563YL 30-08-16

22 LANSFORSAKRINGAR MULTISTRATEGI 2599QL 15-09-16

23 LANSFORSAKRINGAR SVERIGE AKTIV B 2606XA 16-09-16

24 SPP SVERIGE PLUS A 26212Z 26-09-16

25 OHMAN KORT RANTA B 9101R6 18-05-17

26 OHMAN SVERIGE FOKUS B 9105HG 31-05-17

Table 12. Nine to two and a quarter years maturity:


CATELLA 1

LANSFORSAKRINGAR 5

OHMAN 8

SEB 5

SKANDIA 4

SPP 3

Total 26

45

The twenty-six conventional funds with nine to two and a quarter year of maturity are

managed by six fund managements. Eight of the funds are managed by Ohman.

Lansforsakringar and SEB manages five funds each, while Skandia and SPP manages four

and three funds, respectively. The rest one fund is managed by Catella.

Figure 9. Percentage of conventional funds that have nine to two and a quarter year of


31 % percent of the twenty-six conventional funds with nine to two and a quarter year of

maturity are under the management of Ohman, Lansforsakrinagar and SEB manages 19% of

the funds each. While Skandia and SPP manages 15% and 12 % of the funds, respectively,

the rest 4% is managed by Catella.

4%19%

31%19%

15%

12%

Figure 9. Nine to two and a quarter years maturity

CATELLA 1

LANSFORSAKRINGAR 5

OHMAN 8

SEB 5

SKANDIA 4

SPP 3

46

Table 13. Conventional funds that have two and less than two year of maturity.

Table 14. Number of conventional funds that have two and less than two year of maturity,


The twenty-four conventional funds with two and less than two years of maturity are

managed by six fund managements. Nine of the funds are managed by Swedbank. SPP and

Table 13. Conventional Funds with Two and less than two years of maturity


1 SWEDBANK ROBUR FORETAGSOBLIGATION MIX 9264MP 15-01-18

2 SWEDBANK ROBUR GLOBAL IMPACT 9317LA 29-05-18

3 SWEDBANK ROBUR SELECTION 25 9383CH 22-11-18

4 SWEDBANK ROBUR SELECTION 50 9383AX 22-11-18

5 SWEDBANK ROBUR SMALL CAP EM MARKETS A 9442TH 18-02-19

6 SWEDBANK ROBUR FORBUNDSFOND 9462DZ 12-07-19

7 SWEDBANK ROBUR ACCES EDGE EM MARKETS 9528Q0 11-11-19

8 SPP GLOBAL SOLUTIONS B 93186F 01-03-18

9 SPP EUROPA PLUS A 93186H 19-03-18

10 SPP EUROPA PLUS B 93186K 19-03-18

11 SPP GLBL FORETAGSOBLIGATION PLS A 93186U 14-05-18

12 SPP GLBL FORETAGSOBLIGATION PLS B 93186W 14-05-18

13 SPP FRN FORETAGSOBLIGATION C 9336FF 12-09-18

14 LANNEBO NORDIC EQUITES A 9311M3 14-06-18

15 OHMAN NAVIGATOR A 9313N9 25-04-18

16 OHMAN NAVIGATOR B 9313PA 25-04-18

17 OHMAN SMABOLAGSFOND C 9327HE 26-06-18

18 SEB NANO CAP A 9388EN 30-11-18

19 SEB NANO CAP B 9388EP 30-11-18

20 SEB NANO CAP C 9388EQ 30-11-18

21 SEB SVERIGEFOND SMABOLAG D 9434ZE 25-02-19

22 SEB STIFTELSEFOND BALANSERAD B 9514FG 13-09-19

23 SWEDBANK ROBUR SELECTION 75 9383AY 22-11-18

24 SWEDBANK ROBUR FORBUNDSRANTEFONDEN 9462D1 12-07-19

Table 14. Two & less than two years of maturity:


LANNEBO 1

OHMAN 3

SEB 5

SPP 6

SWEDBANK 9

Total 24

47

SEB manages six and five, respectively. While Ohman manages three of the funds, the rest

one fund is managed by Lannebo.

Figure 10. Percentage of conventional funds that have two and less than two year of


38 % percent of the twenty-four conventional funds with two and less than two year of

maturity are under the management of Swedbank, SPP and SEB manages 25% and 21% of

the funds, respectively. While Ohman manages 12% of the funds, the rest of 4% is managed

by Lannebo.

4%12%

21%

25%

38%

Figure 10. Two & less than two years of maturity

LANNEBO 1

OHMAN 3

SEB 5

SPP 6

SWEDBANK 9

48

4.2 Weather data

Convenient sampling was used for weather related incidents as well. However, filtering

criterion on the samples was applied to identify those that are related to extreme weather.

This study considers weather incidents associated with class three warnings as extreme

weather and forest fires that are big and covers wide area. In addition, extreme weather

events happened elsewhere in the globe were originally intended to be considered for

motivation if those should be considered as extreme weather events that can capture the

attention of the Swedish society. However, during the study- and the weather shock related

incidents review process it comes to light that there are several incidents and selectively

considering those incidents outside of Sweden may increase the subjectivity and the

researcher’s interference. For this reason, weather shocks that occurred outside of Sweden

are not included in this study.

A search for media reported extreme weather events was made. For this purpose, the media

archive in the ‘’Retriever’’ database available at Umeå Universality’s’ library used. To

narrow the search to the most relevant information search word such as ‘’Exteremt väder’’,

‘’SMHI’’, ‘’SMHI, klass 3 varninagar’’, which are the Swedish words for extreme weather,

Swedish Metrological and Hydrological Institute, class 3 warning. A total of 4031 posts are

preliminary reviewed, which constitutes reposts of same event and informative post on the

different level of weather-related warnings. Guided search was possible through identifying

the media with the highest number of posts, both in a printed and web based report or article

archive’s available by well-known medias such as ‘’Expressen’’, ‘’Svenskadag bladet’’,

‘’TT Nyhetsbyrån’’ etc. The relevant posts were then selected and send to an e-mail as a

news tips, which then downloaded on a word document for secondary review and screening.

During the secondary review relevant weather incidents, which are incidents reported with

extreme weather and incidents that occurred after a class 3 warning were issued, are recorded

in an excel file. The process was repeated until the same incident is repeated in three media

and the process of identifying a new extreme- and related to class 3 warning weather

incidents is stopped once the findings are similar with what has been identified.

To identify weather shocks associated with extreme hot temperature a search with the word

‘’Skogsbränder’’ was made. The reviewing and filtering process is the same as in the

preceding. However, there is no high temperature related incident preceded by a class 3

warning issue. The alternative taken, was to identify forest fires that occurred in several part

of the country and that are big. In this category, two incidents are identified.

49

Weather related media reports; preliminarily reviewed

Table 15. Preliminary review with the search word ‘’Exteremet väder’’, sources and

number of reports found.

A total of 1269 media posts are preliminarily reviewed with the search word ‘’Exteremet

väder’’, of which 201 are from Svensk Dagbladet, 208 from Aftonbladet, 236 from

Expressen, 159 from Helsingbors Dagbladet and 465 from TT nyhetsbyrå.

Table 16. Preliminarily review with the search word ‘’SMHI, klass 3 värningar’’, sources

and the number of reports found.

Table 15. Exteremet Väder Year Svensk

Dagbladet

Aftonbladet Expressen Helsingbors

Dagblad

TT

nyhetsbyrå

Total

2020 11 3 4 1 25 44

2019 28 35 37 15 56 171

2018 19 35 17 35 63 169

2017 12 21 17 12 63 125

2016 13 7 15 11 42 88

2015 22 12 24 18 42 118

2014 22 18 43 20 34 137

2013 8 14 18 22 25 87

2012 19 16 21 10 25 91

2011 25 13 18 10 51 117

2010 22 34 22 5 39 122

Total 201 208 236 159 465 1269

Table 16. SMHI, klass 3 värningar Year Aftonbladet Brås

Tidningen

Västerbottens-

Kuriren

TT

nyhetsbyrå

Total

2020 11 9 0 26 46

2019 23 11 0 31 65

2018 39 12 18 35 104

2017 47 10 17 38 112

2016 22 5 21 35 83

2015 28 10 21 36 95

2014 34 33 24 26 117

2013 58 62 53 37 210

2012 32 28 17 50 127

2011 26 28 21 34 109

2010 20 8 7 17 52

Total 340 216 199 365 1120

50

A total of 1120 posts are preliminary review with the search words ‘’ SMHI, klass 3

värningar’’. Of which 340 are from Aftonbladet, 216 from Brås Tidningen, 199 from

Västerbottens-Kuriren and 365 from TT nyhetsbyrå.

Table 17. Preliminarily review with the search word ‘’Skogsbränder’’, sources and

number of reports found.

A total of 1642 posts are preliminarily reviewed with the search word ‘’ Skogsbränder’’. Of

which 192 are from Dagens Nyheter and 189 posts are from Svenska Dagbladet. For both

medias, the search was limited specifying to look for incidents happened only in Sweden by

typing the Swedish equivalent word for Sweden ‘’Sverige’’. The rest 1261 posts without

limiting the search, which is most of the posts with the search word, is from TT nyhetsbyrå.

Table 17. Skogsbränder Years Dagens Nyheter Svenska Dagbladet TT nyhetsbyrå Total

Sverige Sverige All

2020 11 7 28 46

2019 42 48 293 383

2018 86 75 367 528

2017 7 8 146 161

2016 11 10 97 118

2015 7 11 84 102

2014 12 15 62 89

2013 3 2 62 67

2012 1 2 34 37

2011 2 6 38 46

2010 10 5 50 65

Total 192 189 1261 1642

51

4.2.1 Weather data input, refined search results

2020,

February Weather incident related to extreme floods after a class three warning in Nissan

and Lagan. In most part of Southern Sweden class 3 warning issued. (Aftonbladet, 2020,

Borås tidngen, 2020, TT nyhetsbyrå, 2020, and Västerbottens-kuriren, 2020,).

2018,

July, Big forest fires all over the country. It occurred in 44 different areas in Sweden,

including Torslanda-Hisingen in Götebory, Gävleborg, Dalarna, Jämtland & Västernorrland

regions.

May, Flood preceded by a class three warning in wide part of Sweden, especially in Norr-

and Västerbotten districts including Boden, Luleå Piteå and Happaranda as well as Dalarna

district (Aftonbladet, 2018, Borås tidngen, 2018, TT nyhetsbyrå, 2018, and Västerbottens-

kuriren, 2018,).

2015,

December, Extreme flood after heavy rainfall and the storms named ‘’Gorm and Helga’’ in

wider part of Sweden including Norrköping, Nyköping, Götebory, Uddevalla. Which caused

rail traffic to be cancelled and heavy rainfall creating risk for high water flood in Western

Götaland (Aftonbladet, 2015, Borås tidngen, 2015, TT nyhetsbyrå, 2015, and Västerbottens-

kuriren, 2015,).

2014,

August, Extremely high flood after skyfall and heavy rainfall related to the storm named ‘’

Oskar’’ in western part of Swede including Åled, Senna, Nissan, Halmstad, Bohus region

and western Götaland, which caused evacuations (Aftonbladet, 2014, Borås tidngen, 2014,

TT nyhetsbyrå, 2014, and Västerbottens-kuriren, 2014,).

July, What is referred as ‘’the biggest forest fire in Sweden in modern time’’ at the time, in

Västmanland, Sala, Gammelby, Ängelsberg & Västervåla areas as well as in Norrland.

2013,

December, Skåne & Halland, Storm as well as winds with tornedo strength due to the storm

''Ivar Sven'' over Blekinge which caused the cancellation of rail and flight travel. It also

caused 10,000's of households to be without electric power and trees to fall on roads. In

addition, heavy good transport cars have been pushed to roll on the road in Ängelholm and

Malmö. Furthermore, in Östersund, central Norrland, electric power loss for tens of

52

thousands of households and roads closed (Aftonbladet, 2013, Borås tidngen, 2013, TT

nyhetsbyrå, 2013, and Västerbottens-kuriren, 2013,).

October, Southern & Swest Sweden - including Kronobergs district, Western Jönköpings

district, Sjuhäradsbygden, Göta älv in west Götaland & Skånde district, which caused around

80,000 households to be without electric power & trees fallen due to the storm named ''Judas

Simone''. Which also caused passenger traffics to be stopped. In addition, high food was

experienced in Kattegatt & Bohusl district & Halland (Aftonbladet, 2013, Borås

tidngen,2013, TT nyhetsbyrå, 2013, and Västerbottens-kuriren, 2013,).

2012

July, Småland - Jönköping, Kalmar district & Tierp in Uppland, extremely high flood.

Jönköping and Kalmar district in Småland region. Roads disrupted and rail traffic stopped

due to high flood (Aftonbladet,2012, Borås tidngen,2012, TT nyhetsbyrå, 2012, and

Västerbottens-kuriren,2012,).

2011

December, High flood in Ätran - Falkenberg & Svenljunga, West cost - flood and in

Norrland as well as slippery roads. Generally extreme weather over the whole country.

(Aftonbladet,2011, Borås tidngen,2011, TT nyhetsbyrå,2011, and Västerbottens-

kuriren,2011,).

November, Southern & West Sweden - Skåne & Halland, 50,000 without electric power &

tree fallen due to a storm. Halland and Skåne; 80,000 households without el. Power, and four

individuals died in Norwey due to the extreme weather. (Aftonbladet, 2011, Borås tidngen,

2011, TT nyhetsbyrå, 2011, and Västerbottens-kuriren ,2011,).

53

4.3 Emphatical representation of variables and relationships

Fund capital flow

Bollen (2007) used the rational learning explanation for capital flow and fund return

relationship and used the following estimation for capital flow (fund flow).

𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑓𝑙𝑜𝑤𝑖,𝑡 = 𝑇𝑁𝐴𝑡 − 𝑇𝑁𝐴𝑡−1(1 + 𝑅𝑡) (Bollen, 2007, p. 694)

This study, however, follows the Kahneman and Tversky (1982)’s representative heuristic

explanation for Capital flow and return relationship and choose to observe price index

performance of the funds to control if the capital flow is affected by recent return

performance. This is with the assumption that a profit maximizing investor will continuedly

monitor the performance of funds and allocate capital to those funds that performs best.

Therefore, the capital flow is inferred as the following estimation.

𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑓𝑙𝑜𝑤𝑖,𝑡 = (𝑇𝑁𝐴𝑖,𝑡 − 𝑅𝑖,𝑡) − (𝑇𝑁𝐴𝑖,𝑡−1 − 𝑅𝑖,𝑡−1),

Which is indexed as (𝑇𝑁𝐴𝑖,𝑡−𝑅𝑖,𝑡)−(𝑇𝑁𝐴𝑖,𝑡−1− 𝑅𝑖,𝑡−1)

(𝑇𝑁𝐴𝑖,𝑡−1− 𝑅𝑖,𝑡−1),

Where, 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝑓𝑙𝑜𝑤𝑖,𝑡= is the in or out flow of capital from a mutual fund,

𝑇𝑁𝐴 = Fund volume (Total Net Asset),

i = refers to the individual mutual fund under observation,

t = base period,

t-1 = prior period, R = return.

Return performance

Bollen (2007, p. 694) inferred return from net asset value per share and distributions paid

out per share (dividend). Bollen’s return inference represented as

𝑅𝑖,𝑡 = (𝑁𝐴𝑉𝑖,𝑡−𝑁𝐴𝑉𝑖,𝑡−1+ 𝐷𝑖,𝑡 )

𝑁𝐴𝑉𝑖,𝑡−1 (Bollen, 2007, p. 694).

54

Where, R = return per share,

NAV = net asset value per share,

D = dividend per share,

t = observation period,

t-1 = prior period.

In this study, return is inferred based on Kahneman and Tversky (1982)’s representative

heuristic explanation and with the assumption that a profit maximizing investor will

continuedly monitor the performance of funds and allocate capital to those funds that

performs best.

𝑅𝑒𝑡𝑢𝑟𝑛𝑖,𝑡 = 𝑃𝐼𝑖,𝑡 − 𝑃𝐼𝑖,𝑡−1

Which is indexed as; - 𝑃𝐼𝑖,𝑡−𝑃𝐼𝑖,𝑡−1

𝑃𝐼𝑖,𝑡−1

Where, 𝛥𝑃𝐼𝑡 = Change in prince index (change in price index is an inference

for return performance, however this inference is not used to estimate

the funds’ total currency amount of return that is deducted from the

total net asset to observe the net capital flow )

PI = Prince index,

t = base period,

t-1 = prior period.

Funds Risk performance

Risk performance is inferred based on Markowitz’s (1952) portfolio theory, where an

investor with uncertainty about return performance will continuedly enhance its portfolio

composition to adjust the portfolio risk to that of the market. Therefore, this study took the

historical beta of the funds, which shows the funds degree of sensitivity to the market risk.

𝐹𝑢𝑛𝑑 𝑟𝑖𝑠𝑘𝑡 = 𝐻𝑖𝑠𝑡𝑜𝑟𝑖𝑐𝑎𝑙 𝑏𝑒𝑡𝑎 𝑜𝑓 𝑡ℎ𝑒 𝑓𝑢𝑛𝑑𝑡

Where, Fund beta tells the funds risk level at period t in relation to the risk level of the market

in which the fund is traded.

55

Market Risk level

The market risk is inferred from the price variability of each months OMXS30 price index

separately. And it is calculated as the following.

𝑀𝑎𝑟𝑘𝑒𝑡 𝑟𝑖𝑠𝑘 𝑙𝑒𝑣𝑒𝑙𝑡 = (√∑(𝑅𝑖−𝜇)2

𝑁)

𝑡

Where, 𝑀𝑎𝑟𝑘𝑒𝑡 𝑟𝑖𝑠𝑘 𝑙𝑒𝑣𝑒𝑙𝑡 = the markets risk level at period t,

∑= the sum of,

Ri = return,

𝜇 = Mean,

N = total number of observations,

t = base/observation period.

Weather shocks

Months with identified weather shock are labeled or represented with the numerical number

‘’1’’ while months with no identified weather shocks are labeled with ‘’0’’ in excel

document to make the analysis possible.

Capital flow and explanatory variables relationship

Often, to understand the relationship between three or more variables or observation types,

multiple regression is used. In this study, four multiple linear regressions were performed

using the software Stata16 which is provided by Umeå university. Additional linear

regression was not conducted to observe the relationship between the independent,

moderator variables, to control for multicollinearity. The first two multiple regression

analysis were conducted without any modification to the total observation under the study.

While the subsequent two multiple regressions were conducted excluding extreme outliers

in the capital flow observation.

Additional linear regression for the purpose of comparing the difference and similarity of

relationship between media reported weather shock and mutual fund types capital flow, were

not conducted. Rather, this analysis was performed using representative variables, or dummy

variables, in each of the four multiple regression analysis conducted. The capital flow and

explanatory variables relationship is empirically represented as follows.

56

𝐹𝑢𝑛𝑑 𝑓𝑙𝑜𝑤𝑖,𝑡 = 𝛽0 + 𝛽1𝑑𝑊𝑆𝑡−1 + 𝛽2𝐹𝑢𝑛𝑑 𝑚𝑎𝑟𝑘𝑒𝑡 𝑟𝑒𝑡𝑢𝑟𝑛𝑖,𝑡−1

+ 𝛽3𝑀𝑎𝑟𝑘𝑒𝑡 𝑖𝑛𝑑𝑒𝑥 𝑠. 𝑑.𝑡−1+ 𝛽4𝐹𝑢𝑛𝑑 𝑏𝑒𝑡𝑎𝑖,𝑡−1 + 𝛽5𝑑𝐹𝑢𝑛𝑑𝑡𝑦𝑝𝑒𝑖

+ 𝛽6𝑑𝐹𝑢𝑛𝑑𝑡𝑦𝑝𝑒𝑖 × 𝑑𝑊𝑆𝑡−1 + 𝜀𝑖,𝑡

Where 𝛽0 = constant or the intercept,

𝛽1 = correlation coefficient of weather shocks,

𝛽2= correlation coefficient of the funds’ return on the financial market,

𝛽3= correlation coefficient of the market index’s risk level,

𝛽4= correlation coefficient of the funds risk level (inferred form historical

beta),

𝛽5 = correlation coefficient of the fund type,

𝛽6= correlation coefficient of interactive variable fund type-weather shocks

WS = weather shocks,

s.d = standard deviation,

d=dummy variable.

4.4 Regression analysis result interpretation tools

Multiple R tells what extent of the respondent variable is affected by the multiple

explanatory variables. Although it is displayed in decimal numbers, it usually expressed in

percentage (Walkenbach, 2016, p. 873). The result table displayed in stata16 does not show

multiple result. It shows, however, R-squared. Which tells the predictability of respondent

variable and explanatory variable relationship based on the observed trend (Walkenbach,

2016, p. 873). Although it also is displayed in decimal numbers, it usually expressed in

percentage. Adjusted R-squared, similarly, tells the predictability of respondent variable and

explanatory variable relationship (Walkenbach, 2016, p. 873). Although it also is displayed

in decimal numbers, it usually expressed in percentage and is always less than the value of

R-squared.

Standard error tells the average distance of the resulting value of the reaggregation from the

straight line that can be drown passing through the displayed, which tells the amount that

differs from the prediction (Walkenbach, 2016, p. 874).

Significance F, or probability F, tells the possibility of unexpended or unpredictable value

occurring from the overall regression analysis result. Although it also is displayed in decimal

numbers, it usually expressed in percentage. Statistical significance is the level of risk that

one prepares to take by inferring that there is a relationship between two variables in the

population from which the sample was taken when in fact no such relationship exists

(Bryman, 2012, p. 348). In this study, the significance F value, that is for the statistical

57

regression to be accepted as significant, is <= 0.05. Which depends on the confidence level

that is intended to be achieved (See the subsequent part).

Confidence level tells the level of certainty that the expected value occurring (Bryman, 2012,

p. 348). Although it also is displayed in decimal numbers, it usually expressed in percentage.

In this study, the confidence level needed to be achieved is 95%.

Coefficients tells the degree of relationship between the respondent and explanatory variable

(Bryman, 2012, p. 349). It is displayed in decimal numbers and a multiplication of its value

with the amount of change in the explanatory variable shows the amount to changes in the

respondent variable. In this study, the coefficient value accepted as significant is the absolute

value that is greater than 0.5 or > /0.5/. However, given that there is a standard error in the

analysis that is different than 1, an adjusted correlation coefficient those errors are

considered to evaluate the significance of the relationship. This can be observed from the t-

value. t-value tells if there is substantial or statistically significant difference between the

mean value of the respondent and explanatory variables (Walkenbach, 2016, p. 872). In

multiple regression, its value is equal to the value that can be found when dividing the

coefficient between two variables with the standard error of the relationship between those

variables. In this study, the error adjusted coefficient value or t-value accepted as significant

is the absolute value that is greater than 0.5 or > /0.5/.

P-Value tells the possibility of unexpended or unpredictable value occurring from inferring

on the existence of relationship between two variables, i.e. respondent and the explanatory

variable. This is an indicator of Statistical significance between two variable’s and is the

level of risk that one prepares to take by inferring that there is a relationship between two

variables in the population from which the sample was taken when in fact no such

relationship exists (Bryman, 2012, p. 348). In this study, the P-value, that is for the relation

between two variables to be accepted as significant, is <= 0.05. Which depends on the

confidence level that is intended to be achieved (See the previous part for the confidence

level assumed).

In summary, the statistical significance level for this study is t-value > /0.5/ and a p-value of

<= 0.05. The results felling to meet both of those criterions are considered as statistically

insignificant results.

58

4.5 Data input control before the analysis

To control the quality of the data input and have a full understanding of the regression

analysis, two procedures were outlined in advance. First, the data analysis result

interpretation mechanisms were identified and, then, a review of the data input as well as the

evaluation of the result using different statistical tools. Data inputs were evaluated using a

summary statistic and correlation. The summary statistics shows the total number of

observations for each main variable, their means, standard deviations, and the minimum as

well as maximum value. Doing so has helped identify missing values and the adjustment of

any typing and recording errors.

Table 18. Total observations from each of the fund types under study.

The percentage of socially responsible mutual fund observations are almost 47%, while it is

around 53 for conventional funds. The summary statistics on the observations of both fund

types corresponds to their respective data input percentage.

Table 19. Total observations for each of the variables under study

Table 18. Total observations fund types

Table 19. Total observations for each of the variables under study

59

The total observation for each of the variables are equivalent, 7, 247 observations each.

Which confirmed that there are no missing data. Extreme outlying values can also be

observed from the minimum- and maximum values, where outliers of capital flow and

historical beta can easily be detected.

By assessing the correlation between the variables, multicollinearity was identified. Which

refers to two or more independent variables which are highly correlated, in this case >0.6,

and both explain the respondent or dependent variable which is capital flow.

Table 20. Multicollinearity test of the variables including dummy variables

The variables Return and price index are highly correlated with a value of 0.989. The

correlation between the variables dSRIF_dWeather and weather is also statistically

significant with a value of 0.6751. While the variable return is excluded from the regression

analysis, the variables weather and dSRIF_dWeather are kept for the reason weather is the

main variables the study is aiming to understand if it can correlate with capital flow, while

the latter has the purpose of helping identify the difference in correlation between SRIF and

conventional funds. The following is the correlation excluding the dummy variables. There

is no statistically significant correlation between the variables except between return and

price index.

Table 21. Multicollinearity test of the variables excluding dummy variables

Table 20. Multicollinearity test of the variables including dummy

Table 21. Multicollinearity test of the variables excluding dummy

60

5. Data analysis and result

This part of the study discusses multiple regression analysis performed in four stages which

are categorized in two parts, initial multiple regression result and regressions limiting

extreme outliers. The additional regression in each of those states are performed as a

robustness test.

5.1 Initial multiple regression result

The initial multiple regression is performed without any adjustment on the distribution of

the respondent variable (capital flow), and it is shown in the following table, table 23.

Table 22. Initial multiple regression result

Overall, the initial multiple regression shows none of the explanatory variables, that are

historical beta, price index, the market risk level (OMXS30’s) and weather shocks, affect

the funds capital flow. The predictability of this result is also almost 0 % as can be seen from

R-squared and gets even lower in when adjusted. The overall multifactor regression cannot

be accepted with 95% confidence level as the significance F value is 0.9311, which is

exceeding the significance criteria value <= 0.05.

On the other hand, when looking the individual explanatory variables relationship with

capital flow, it is well over 357 % correlation with price index with standard error of 14.936.

Both of which are extremely high. However, the p-value at 0.811 rejects the result from

being accepted with 95% certainty. The correlation of capital flow with market risk level,

Table 22. Initial multiple regression result

61

OMXS30’s standard deviation, is extremely high with a value of 6438,979% and standard

error of almost 110.555. The p-value of capital flow and market risk relationship, at 0.560,

also rejects the result. The correlation of capital flow with fund historical beta is around

21.01% and the standard error around 0.411. The p-value of capital flow and fund historical

beta relationship at 0.609 rejects the result as well. The correlation between capital flow and

media reported weather shocks is -5.732% with a standard error of 2.183. The p-value of

capital flow and media reported weather shocks, at 0.979, rejects the correlation result. The

correlation between capital flow and fund types (represent as dSRIF) is around 104.309 %

with a standard error of around 0.949. The P-value of capital flow and fund types, at 0.272,

rejects the result. And, the correlation of capital flow of socially responsible fund with

weather shocks (dSRIF_dweather) is around 103.1 % with a standard error of around 3.14.

The P-value of socially responsible funds’ capital flow with weather shocks, at 0.743, rejects

the result. On the other hand, the correlation coefficient value of the relationship between

the respondent and each of the explanatory variables can be better explained by the t-value.

The t-value provides a correlation value adjusted for the standard error observed in the

relationship, which can be observed by dividing the correlation coefficient value of each of

the relationships by the respective standard error value. Accordingly, the t-value of capital

flow and historical beta, market risk (OMXS30) and fund types (dSRIF) is significant with

a value of greater than /0.5/, that is / 0.51/, /0.58/ and /- 1.10/ consecutively. However, the

regression result of capital flow with all the explanatory variables cannot be accepted with

95% confidence level since the P-value of each relationships are > 0.05.

In addition, the initial regression result was assessed using heteroskedasticity tests.

Heteroskedasticity refers to a difference in the error level of a regression analysis between

variables. There are two ways of observing if there is heteroskedasticity in the regression

analysis. Either it can be looked at through the histogram of a variable or by using a

command in the Stata, either the White or Breusch-Pagan heteroskedasticity test (Williams,

2020, p. 4). Whether heteroskedasticity exists or not can be assessed by directly looking at

Chi2 result of Breusch-Pagan test, higher chi2 number should indicate heteroskedasticity

(Williams, 2020, p. 4). Breusch-Pagan test, however, is not useful to test for

heteroskedasticity that are not linear and non-normal distribution (Williams, 2020, p. 5). On

the other hand, White’s general heteroskedasticity, offers flexibility to make similar test for

non-linear and non-normal distributions (Williams, 2020, p. 4). Here under, the result for

heteroskedasticity by using the syntax command in Stata16 can be seen. (See Appendix 9 to

12 for an observation of the scatter plots for the variables capital flow, price index, historical

beta and market risk level (OMXS30’s S.D)).

62

Table 23. Breusch-pagan heteroskedasticity test on the initial regression result

For the variable Capital flow, the Breusch-Pagan heteroskedasticity statistical test result of

73311.72 is with 1 degree of freedom as can be observed from chi2(1). For the variables,

Price index, H.Beta, OMXS30 (S.D), weather, dSRIF and dSRIF_dweather, the Breusch-

Pagan heteroskedasticity statistical test result of 7325.27 is with 6 degree of freedom as can

be observed from chi2(6). And, the nolle hypothesis referrers to the variability of the errors

is the same throughout the data. With a significantly large number of chi2 and the probability

result of chi2 statistics, which is 0.000 in both cases, the nolle hypothesis can be rejected.

The data in this case has the problem of heteroskedasticity, or the variability of the standard

errors is not the same throughout the data at list for one observation. The variability of the

standard errors, in this case, either increases or decreases from one observation to the other.

Table 23. Breusch-pagan heteroskedasticity test on the initial regression result

63

Table 24. White’s heteroskedasticity test n the initial regression result

Using White’s general heteroskedasticity test, the result shows relatively lower statistical

number of Chi2, with 21 degree of freedom, in comparison to Breusch-Pagan

heteroskedasticity test. However, the P-value, or probability result of chi2 statistics, is

statistically insignificant with a value of 0.9950. The nolle hypothesis may not be rejected

with 95% confidence level. The data in this case has not exhibited the problem of

heteroskedasticity, or the variability of the standard errors can’t not be determined as ‘’not

the same throughout the data’’.

To resolve the issue of heteroskedasticity, the there are two, that is specifying the data input

and using ‘’Robust standard errors’’ during the regression. viable approaches as

recommended by Williams (2020, p. 6-8). For this study, both approaches are followed.

Table 24. White’s heteroskedasticity test:

on the initial regression result

64

5.1.1 Robustness test on the initial regression result

Robustness test is used when the data collected contains extreme outliers, which can mislead

the analysis and give unrealistic result (Morgenthaler,2011,). The effect should prevail on

the result indicators, i.e. correlation coefficient, standard error, t-value, p-value etc. (See

Appendix 3 in the appendix for an observation of the distribution of capital flow observation

with the extreme outliers).

Table 25. Robustness test on the initial regression result

Similarly, the initial robust multiple regression shows that none of the explanatory variables,

that are historical beta, price index, the market risk level (OMXS30’s) and weather shocks,

affect the funds capital flow. The predictability of this result is also almost 0 % as can be

seen from R-squared. The overall multifactor regression cannot be accepted with 95%

confidence level as the significance F value is still similar with a value of 0.9187, which is

exceeding the significance criteria value <= 0.05.

When looking the individual explanatory variables relationship with capital flow, here also

it is well over 357 % correlation with price index with standard error of around 4.875, which

significantly less than the preceding result but. Both of which are still extremely high.

However, the p-value at 0.464 rejects the result from being accepted with 95% certainty.

The correlation of capital flow with market risk level, OMXS30’s standard deviation, is

extremely high with a value of 6438,979% and standard error of around 66.821, which is

still extremely high. The p-value of capital flow and market risk relationship, at 0.335, also

rejects the result. The correlation of capital flow with fund historical beta is around 21.01%

and the standard error around 0.281. The p-value of capital flow and fund historical beta

relationship, at 0.454, rejects the result as well. The correlation between capital flow and

media reported weather shocks is around -5.732% with a standard error of around 0.068,

Table 25. Robustness test on the initial regression result

65

which is less than the preceding result. The p-value of capital flow and media reported

weather shocks, at 0.402, rejects the correlation result. The correlation between capital flow

and fund types (represent as dSRIF) is around 104.309 % with a standard error of around

1.085. The P-value of capital flow and fund types, at 0.336, rejects the result. And, the

correlation of capital flow of socially responsible fund with weather shocks

(dSRIF_dweather) is around 103.1 % with a standard error of around 1.061. The P-value of

socially responsible funds’ capital flow with weather shocks, at 0.331, rejects the result. On

the other hand, the t-value of the relationship of capital flow with all the explanatory

variables is significant with a value of greater than /0.5/, that is / 0.73/, /0.75/, /0.96/, /-

0.84/,/-0.96/ and /97/ for return (price index), historical beta (risk), OMXS30 (market risk),

weather, dSRIF (fund type) and dSRIF_dWeather (socially responsible fund) consecutively.

Despite the significant t-value, however, the regression result cannot be accepted with 95%

confidence level since the P-value of each relationships are > 0.05. Therefore, the regression

result of capital flow with all the explanatory variables cannot be accepted with 95%

confidence level since the P-value of each relationships are still > 0.05.

5.2 Regression limiting extreme outliers (value greater than 2 and less than -2)

In this part of the regression analysis, to avoid the effect of a few observations with high

values or extreme outlier, capital flow observations with a value exceeding 2 and that are

less than -2 are excluded. The total number of observations, therefore, reduced to 7,207. 40

observations are excluded, which is around 5.35% of the total observation. (See Appendix 4

in the appendix for an observation of the distribution of capital flow observation without the

extreme outliers).

Table 26. Regression result limiting extreme outliers

Table 26. Regression result limiting extreme outliers

66

Overall, the multiple regression analysis result which excludes extreme outliers shows none

of the explanatory variables, that are historical beta, price index, the market risk level

(OMXS30’s) and weather shocks, significantly affect capital flow. The predictability of this

result, however, is almost zero, 0.0035, as can be seen from R-squared and gets even lower

in when adjusted. The overall multifactor regression can be accepted with 95% confidence

level as the significance F value is 0.0003, which is not violating the significance criteria for

the result to be accepted, which is a P-value of <= 0.05.

When looking the individual explanatory variables relationship with capital flow, it is around

12.83% correlation with price index with standard error of around 0.073. Both of which are

significantly lower than the regression result without the exclusion of extreme outlier

observations of capital flow. The p-value at 0.056 rejects the result from being accepted with

95% certainty. The correlation of capital flow with market risk level, OMXS30’s standard

deviation, is negative with a value of around -33.59% and standard error of around 0.497.

The p-value of capital flow and market risk relationship at 0.499, however, rejects the result.

The correlation of capital flow with fund historical beta is almost 0% and the standard error

around 0.002. The p-value of capital flow and fund historical beta relationship at 0.73 rejects

the result as well. The correlation between capital flow and media reported weather shocks

is negative at a value of around -2.23% with a standard error of 0.01. The p-value of capital

flow and media reported weather shocks, at 0.023, accepts the correlation result with a 95%

of certainty. The correlation between capital flow and fund types (represent as dSRIF) is

around 1 % with a standard error of around 0.004. The P-value of capital flow and fund

types, at 0.029, accepts the result with a 95% of certainty. And, the correlation of capital

flow of socially responsible fund with weather shocks (dSRIF_dweather) is around -0.5 %

with a standard error of around 0.014. The P-value of socially responsible funds’ capital flow

with weather shocks at 0.715, rejects the result. Even though, the regression result of capital

flow with the explanatory variables weather shocks and fund types can be accepted with

95%, the result is statistically insignificant at a coefficient value of less than 0.5.While the

capital flow relation with the variables price index , market risk level and the socially

responsible funds’ capital flow with weather shocks cannot be accepted with 95%

confidence level since the P-value of each relationships are > 0.05.

But, when looking the t-value of the relationship of capital flow with all the explanatory

variables return (price index), market risk (OMXS30), Weather and fund types (dSRIF), the

result is significant with a value of greater than /0.5/, that is /1.91/, / -0.68/, /-2.28/ and /2.18/

consecutively. However, since the p-value of return (price index) and market risk level

(OMXS30) is > /0.5), with /0.056/ and /0.499/, the relationship cannot be accepted with 95%

confidence level. On the other hand, due to the reason that the p-value of the variables

weather and fund type (dSRIF) is < /0.5/, which is 0.023 and 0.029 consecutively, the two

relationship results can be accepted with 95% confidence level as statistically significant.

This result clearly differs from the results observed from the initial regression analysis. The

heteroskedasticity test also shows the following result.

67

Table 27. Breusch-pagan heteroskedasticity test on the regression result limiting extreme

outliers

For the variable Capital flow, the Breusch-Pagan heteroskedasticity statistical test result of

33.27 is with 1 degree of freedom as can be observed from chi2(1). For the other six

variables, it is 407.04 with a 6 degree of freedom as can be observed from chi2(6). This is

significantly lower in comparison to Breusch-Pagan heteroskedasticity test result on the

regression conducted without limiting the extreme outliers. And, the nolle hypothesis

referrers to the variability of the errors is the same throughout the data. However, the chi2

value on which the probability statistics bases on is smaller in relation to the total number of

observations. The data in this case may not be considered as having the problem of

heteroskedasticity. The nolle hypothesis may, therefore, be accepted.

Table 28. White’s heteroskedasticity test limiting extreme outliers

Table 28. White’s heteroskedasticity test limiting extreme outliers

Table 27. Breusch-pagan heteroskedasticity test on the regression result limiting

extreme outliers

68

White’s general heteroskedasticity test, the result shows relatively higher statistical number

of Chi2, with 21 degree of freedom and significantly lower P-value in comparison to the

preceding white’s test for general heteroskedasticity. However, the test is comparative to

Breusch-Pagan heteroskedasticity test conducted limiting for extreme outliers. (See

Appendix 4 to 8 on the histograms to observe the Skewness and Kurtosis of the variables)

5.2.1 Robustness test of results found limiting extreme outliers

The robustness of the regression result found from the analysis of the data excluding

extreme outliers is presented in the following table.

Table 29. Robustness test of results found limiting extreme outliers

Robust multiple regression analysis result which excludes extreme outliers shows none of

the explanatory variables, that are historical beta, price index, the market risk level

(OMXS30’s) and weather shocks, significantly affect capital flow. The predictability of this

result, however, is almost zero, 0.0035, as can be seen from R-squared and gets even lower

in when adjusted. The overall multifactor regression can be accepted with 95% confidence

level as the significance F value is 0.0034, which is not violating the significance criteria for

the result to be accepted, which is a P-value of <= 0.05.

When looking the individual explanatory variables relationship with capital flow, it is around

12.83% correlation with price index with standard error of around 0.076. Both of which are

similar to the preceding regression result with the exclusion of extreme outlier observations

of capital flow. The p-value at 0.092, however, rejects the result from being accepted with

95% certainty. The correlation of capital flow with market risk level, OMXS30’s standard

deviation, is negative with a value of around -33.59% and standard error of around 0.512.

Table 29. Robustness test of results found limiting extreme outliers

69

The p-value of capital flow and market risk relationship at 0.512, however, rejects the result.

The correlation of capital flow with fund historical beta is almost 0% and the standard error

around 0.001. The p-value of capital flow and fund historical beta relationship at 0.456

rejects the result as well. The correlation between capital flow and media reported weather

shocks is negative at a value of around -2.23% with a standard error of 0.01. The p-value of

capital flow and media reported weather shocks, at 0.013, accepts the correlation result with

a 95% of certainty. The correlation between capital flow and fund types (represent as dSRIF)

is around 1 % with a standard error of around 0.004. The P-value of capital flow and fund

types, at 0.027, accepts the result with a 95% of certainty. And, the correlation of capital

flow of socially responsible fund with weather shocks (dSRIF_dweather) is around -0.5 %

with a standard error of around 0.017. The P-value of socially responsible funds’ capital flow

with weather shocks at 0.757, rejects the result. Therefore, even though, the regression result

of capital flow with the explanatory variables weather shocks and fund types can be accepted

with 95%, the result is statistically insignificant at a coefficient value of less than 0.5.While

the capital flow relation with the variables price index , market risk level and the socially

responsible funds’ capital flow with weather shocks cannot be accepted with 95%

confidence level since the P-value of each relationships are > 0.05.

However, concurrent to the presiding result, here also, the t-value of the relationship of

capital flow with all the explanatory variables return (price index), market risk (OMXS30),

Weather and fund types (dSRIF), the result is significant with a value of greater than /0.5/,

that is /1.69/, / -0.66/, /-2.48/ and /2.21/ consecutively. In addition, the capital flow- fund

risk (H.beta) relationship shows significant result with a value of /0.75/. However, since the

p-value of return (price index), fund risk (H.beta) and market risk level (OMXS30) is > /0.5/,

with /0.092/,/0.456/ and /0.512/, the capital flow and those variables relationship cannot be

accepted with 95% confidence level. On the other hand, due to the reason that the p-value

of the variables weather and fund type (dSRIF) is < /0.5/, which is 0.013 and 0.027

consecutively, the two relationship results can be accepted with 95% confidence level as

statistically significant. This result confirms the results observed from the preceding

regression analysis made limiting extreme outliers.

5.3 Result

The result, therefore, shows a negative correlation between the media reported weather

shocks and the flow of both socially responsible- and conventional mutual funds, the

statistical significance of the correlation is very significant with value of -2.28, and even

stronger when tested for robustness with a value of -2.48. This result could be interpreted in

relation to Hirshleifer & Shumway (2003)’s study indicating whether conditions affecting

investors behavior. The two major issues specific to this study are their study indicates a

positive mood or sunshine and this comfortable weather conditions having positive impact

on the financial market performance regardless of the investment types on the market. While

this study mainly focused on understanding the relationship between negative weather

condition or weather shock and socially responsible investment (socially responsible mutual

trust funds). What is interesting, is the result showing the negative role bad weather (weather

shocks) have on the flow capital regardless of the fund types. The study result, therefore,

70

accepts H1 which states that there is significant relation between weather shocks and socially

responsible investment capital flow. Similarly, it accepts H2, which states that there is no

significant difference between the capital flow and weather shock relationship of socially

responsible funds and that of conventional mutual funds. Even though, the main interest of

the study was to observe if there is a difference between SRI and conventional investments

in relation to weather shock, the study result showing a significant relationship for both fund

types could give an insight on the nature of the Swedish mutual fund market. The fact that,

there is significant relation between weather shocks and socially responsible investment

capital flow and the significant weather shocks-capital flow relationship is applicable to

conventional mutual funds could motivate for further explanation in addition to weather

shocks affection investors mood negatively. Two possible explanations could be that the

Swedish mutual trust fund investors, regardless of in which fund types they invest, might

not be predictable by the neoclassical economic theories and variables in general, since the

study result did not show any statistically significant relationship between capital flow and

fund return, fund risk level as well as market risk level.

The study, therefore, rejects H3 which states that the capital flow and weather shock

relationship is stronger for socially responsible funds than that of the conventional funds.

The implication of this result could be explained in relation to Bollen (2007), Lopez-Arceiz

et al. (2018) and Renneboog et al. (2011)’s idea of multi-utility factor, where socially

responsible investors derive additional utilities from investing in social causes than only

financial rewards. Thus, if weather shock occurs conventional investors might likely be

affected in their investment decision and that they might place their capital in SR mutual

funds if they have similar financial performance as conventional mutual funds Bollen

(2007). However, given that weather shocks affect both fund types negatively (or affecting

investors mood negatively and shadowing their values for social causes), the concern has

shifted form a choose between investment types towards whether if they invest at all.

Therefore, since capital flow is negatively affected in general, the flow of capital between

fund types might not be feasible.

71

6. Conclusion

This study gathered a total of 7247 observation from a total of 127 active mutual trust funds

traded in the Swedish financial marked with the Swedish currency. It thoroughly

investigated the relationship capital flows of fund types to infer the role weather shocks, that

are media reported, have on the capital flow of socially responsible- and conventional mutual

funds. It is performed taking all accessible socially responsible mutual trust funds in the

Swedish financial market as well as matching conventional mutual trust funds that have

similar maturity period. The result of the study shows that both the capital flow of socially

responsible- and conventional mutual funds have statistically strong relationship with media

reported weather shocks in Sweden. Which can be taken as a confirmation of Hirshleifer &

Shumway (2003)’s result indicating weather conditions affecting investors mood which

subsequently affecting investment behaviors. The study result shows that there is

relationship between weather shocks and the capital inflow and out flow of socially

responsible and conventional mutual trust funds. Accordingly, the most probable

explanation for factors affecting the flow of capital is psychological and emotional rather

than rational economic interests, for example return and risk.

On the other hand, investors behavior might possibly be explained by the Swedish cultural

environment and way of living. For example, most of the weather shocks occurred during

winter (December, January, and February) and summer (June, July, and August) season, four

weather shocks in each season of the total eleven. Assessing how individual’s mood be

affected by the winter season and given that summer season is when most individuals could

be in a good mood, the effect of weather shocks in those periods of a year on individual

(investors) could be considered. However, this a topic yet to be further studied.

6.1 Recommendation for future related study

One potential study opportunity in this topic is the refinement of this study model by

including expertise in weather measurement parameters. The number of media reported

weather shocks this study identified are few in comparison to the availability of financial

data on the capital flow of funds. Thus, addressing if it could be possible to identify a greater

number of extreme weather shocks can be classified as weather shocks with different

categorization criteria. Another possible study opportunity is to account for all media reports

on extreme weather, not only those that are associated with class 3 warnings, through time

and study its relationship with the growth of socially responsible funds in comparison with

conventional funds to observe if the relatively steady growth of socially responsible

investments funds volume correlate with the number of weather shocks reported . A related

study could also be possible by accounting advertisement expenditures on socially

responsible funds made by financial institutions through time and study its relationship with

the growth of socially responsible funds in comparison to conventional funds.

72

6.2 The studies contribution and implication

The studies theoretical and practical contribution as well as its societal implication are

discussed in a separate part as follows.

6.2.1 Theoretical contribution

Empirical studies on the role of weather related incidents on capital flow of mutual funds, in

general, is limited, and particularly a study that assess the role of weather shocks, that are

inferred from media reports, on how capital flows in and out of funds hasn’t been observed

so far. This study, therefore, contributes to the theoretical studies that are focusing on

identifying factors affecting the in and out flow of capital from an investment fund.

Therefore, it contributes to the refinement of the already existing studies on investors

behavior in relation to socially responsible investment. Potentially, the study model could

also be useful to explore the analysis of more weather-related parameters or how the volume

of different types of funds types change through time and in response to incidents of interest.

Thus, by doing so provide concepts and ideas that can help find ways of informed decision

making in investment process as well as the efficient administration of investment funds.

6.2.2 Practical contribution

For practitioners in the financial market the efficient administration of capital under

management depends on the predictability of the fund’s movement. Therefore, identifying

factors affecting capital flow help better allocate resources to administer the changing capital

under management. Thus, this study is informative for fund managing institutions about the

short-term impact or role weather shocks have on how capital flow and help plan for the

appropriate administrative actions. It can also be a valuable source of information for

investors and financial advisers that might be interested in understanding when or in what

circumstances a certain type of financial product is in high or low demand.

6.2.3 Societal implication

The societal implication this study have is no different than that it contributes to the academic

community and to practitioners in the financial sector. Since institutions both in the academic

field and the financial sector serve the society at large, the societal contribution of this study

is through its contribution to the theoretical and practical field of finance. While conducting

the study, issues related to ethical considerations such as privacy, anonymity, informed

consent and the like has not been the major concern. The main reason for this is most of the

study input accessed from a database that is accessible for study purpose. However, sharing

the source those data required personal interactions and, in this regard, data was accessed in

a cooperative manner with other users of Eikon database Umeå University.

73

6.3 The studies limitations

Factors affecting capital flow in this study is broadly classified as economic interest,

behavioral and market risk condition. However, the behavioral variables that might

potentially affect and explain the trend of capital flow could be several. This study has

considered only the behaviors associated with experiencing extreme weather associated with

class 3 warnings. It did not consider the behaviors that are associated with extreme weather

related to class 2 or class one warnings. In addition, it did have limitation with the

consideration of weather shocks associated with extremely high and low temperature and

the only fishable inference made was from big forest fires in this regard. Thus, lack of

expertise in weather variables is one of the main limitations for this study.

The method used to infer the different economic variables such as fund movement, past-

financial performance and market risk levels are subject to assumptions and is, thus, the

other limitation for this study. This study did not consider dividend that might have been

paid out during a period when inferring capital flow from change in total net asset. This is

due to the assumption that dividend is a capital that can be reinvested in the fund thus a

withdrawal of dividends by investors are considered as capital out flow. This study also took

position to one assumption from three alternatives to infer past financial (return)

performance. Thus, the relationship between capital flow and past-return performance might

differ with the exercise of another assumption from the alternatives. For example, Bollen

(2007, p. 694) estimates return for a period as follows 𝑅𝑡 =(𝑁𝐴𝑉𝑡−𝑁𝐴𝑉𝑡−1+𝐷𝑡)

𝑁𝐴𝑉𝑡−1 , where 𝑁𝐴𝑉𝑡

and 𝑁𝐴𝑉𝑡−1 are net asset value per share in an observation period and prior period

respectively, 𝐷𝑡 is dividend paid in an observation period. Although, Bollen (2007, 686)

assumed rational learning for return inference made. However, this should not affect the

study’s result on the relationship between weather shock and capital flow. Rather, it could

have helped to draw a correct conclusion from the study’s result had weather shocks had a

relationship with how capital flows. In this regard, the study is also limited with its

assumption of the fund’s response period after a weather shock occurs. The subsequent

month after a weather shock occurred is chosen to account for the potential delay in

accounting the changes in the net total asset of a fund due to administrative process.

In addition, the theoretical framework is not limited only on addressing the contents of

previous studies that are focused on comparing socially responsible and conventional funds

as a base for this study. Rather, since those studies also have based their reasoning to an

already existing and well-established theories, this study referred to those original theories

to support the construction of the studies point of departure. This might potentially be a

limitation on giving a brief and concise explanation for factors affecting the flow of capital.

74

6.4 Quality criteria in quantitative study

The widely discussed quality criteria in quantitative study are reliability, validity, and

generalizability. Evaluation of this study based on each of those criteria’s is presented as

follows.

6.4.1 Reliability

The reliability of the study can be confirmed by repeating the study following the same

procedures applied during the study and is related to Objectivity. Objectivity helps avoid or

minimize documentation errors and unrepresentative interpretation of study observation or

data (Swanborn, 1996, p. 24). The fact that all the techniques used for sampling, inference

and measurement tool are discussed can assist for other researchers to repeat the study and

reach on the same result. This assure the non-interference of the researchers bias in the

research (Swanborn, 1996, p. 24). To make repeatability of the study the data inputs, their

categorization as well as the steps followed in the categorization of the fund types by their

maturity or year base also discussed.

6.4.2 Validity

Validity is related to the methodological consistency of the study, or methodological fitness.

Which is weather the study is answering the questing that it set out to answer when starting

the study, weather the appropriate tools are used to answer the research question and result

is related to it (Edmondson, A.C & McManus, 2007,). In the case of this study, the intention

and the plan was to answer the role media reported weather shocks to capital flow(or capital

flow) and to make observation on socially responsible fund and conventional funds as well

as make to a comparison between the two fund types in relation to the weather shock.

Subsequently, a result was reached up on answering the research question set out using the

appropriate sampling and measure tools guided by the philosophical assumptions and the

research design.

6.4.3 Generalizability

The main concern related to reaching on results from quantitative study is whether the

findings can be applicable to other settings and study domain (Bryman, 2012, p. 176). The

study with its scope is limited to Sweden, that is the Swedish financial market and media

reported weather shocks in Sweden. Therefore, the study result may not be generalizable to

other countries’ financial market. However, the study model can be used to observe if the

relationship between the same variables as this study’s variable types can be applicable in in

other study domain or country. On the other hand, based on the result of the study one can

be certain to generalize that a weather shock occurred in Sweden does not have a role in how

both socially responsible- and conventional funds move, or how capital flows in- and out,

75

with 95% confidence. That is, there is only 5 out of 100 chance that it might be possible for

weather shocks to have a role in fund movements in the Swedish financial market.

76

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Appendix 1. SRIF convenient population

Appendix 1. SRIF convenient population

RIC Name Issue Name LP60048092 CATELLA SVERIGE AKTIV HALLBARHET Catella Sverige Aktiv Hallbarhet

LP60048410 CATELLA SVERIGE HALLBARHET BETA A Catella Sverige Hallbarhet Beta A

LP68191683 CATELLA SVERIGE HALLBARHET BETA A Catella Sverige Hallbarhet Beta B

LP68249790 ETISK INDEX USA B Etisk Index USA B

LP68244842 HALLBAR FORETAGSOBLIGATION Hallbar Foretagsobligation

LP68281834 HANDELSBANKEN HALLBAR ENERGI (A1 SEK) Handelsbanken Hallbar Energi (A1 SEK)



LP68534732 HANDELSBANKEN HALLBAR GLOBAL OBLIGATION

(A1 SEK)

Handelsbanken Hallbar Global Obligation

(A1 SEK)


(A1 SEK)


(B1 SEK)


(A1 SEK)


A9 SEK

LP60048440 KPA ETISK AKTIEFOND KPA Etisk Aktiefond Open Fund

LP60048442 KPA ETISK BLANDFOND 2 KPA Etisk Blandfond 2 Open Fund

907963.FBF KPA ETISK RANTEFOND KPA Etisk Rantefond Open Fund

LP68529726 LANNEBO SVERIGE HALLBAR B Lannebo Sverige Hallbar A

LP68087307 LANNEBO SVERIGE HALLBAR B Lannebo Sverige Hallbar B

LP60047718 LANSFORSAKRINGAR GLOBAL HALLBAR A Lansforsakringar Global Hallbar A

LP68389085 LANSFORSAKRINGAR GLOBAL HALLBAR A Lansforsakringar Global Hallbar B

LP68162572 OHMAN ETISK EMERGING MARKETS Ohman Etisk Emerging Markets

LP68422558 OHMAN ETISK EMERGING MARKETS Ohman Etisk Emerging Markets B

LP60048998 OHMAN ETISK INDEX EUROPA Ohman Etisk Index Europa Open Fund

LP60048999 OHMAN ETISK INDEX JAPAN Ohman Etisk Index Japan Open Fund

LP60049000 OHMAN ETISK INDEX PACIFIC Ohman Etisk Index Pacific Open Fund

LP65011787 OHMAN ETISK INDEX SVERIGE A Ohman Etisk Index Sverige A

LP68249759 OHMAN ETISK INDEX SVERIGE B Ohman Etisk Index Sverige B

LP60048997 OHMAN ETISK INDEX USA A Ohman Etisk Index USA A

LP68143193 OHMAN FORETAGSOBLIGATIONSFOND HALLBAR A Ohman Foretagsobligationsfond Hallbar A

LP68206520 OHMAN FORETAGSOBLIGATIONSFOND HALLBAR A Ohman Foretagsobligationsfond Hallbar B

LP68190295 OHMAN FRN HALLBAR A Ohman FRN Hallbar A

LP68212335 OHMAN FRN HALLBAR A Ohman FRN Hallbar B

LP68459823 OHMAN FRN HALLBAR A Ohman FRN Hallbar C

LP60048394 OHMAN GLOBAL HALLBAR A Ohman Global Hallbar A

LP68424415 OHMAN GLOBAL HALLBAR A Ohman Global Hallbar B

LP68445644 OHMAN GLOBAL MARKNAD HALLBAR A Ohman Global Marknad Hallbar A

81

LP68445643 OHMAN GLOBAL MARKNAD HALLBAR A Ohman Global Marknad Hallbar B

LP68586096 OHMAN GLOBAL SMABOLAG HALLBAR A Ohman Global Smabolag Hallbar A

LP68586095 OHMAN GLOBAL SMABOLAG HALLBAR A Ohman Global Smabolag Hallbar B

LP68445642 OHMAN GRON OBLIGATIONSFOND A Ohman Gron Obligationsfond A

LP68474394 OHMAN GRON OBLIGATIONSFOND A Ohman Gron Obligationsfond B

LP68311253 OHMAN RANTEFOND KOMPASS HALLBAR A Ohman Rantefond Kompass Hallbar A

LP68551932 OHMAN RANTEFOND KOMPASS HALLBAR A Ohman Rantefond Kompass Hallbar B

LP68226676 OHMAN SVERIGE HALLBAR A Ohman Sverige Hallbar A

LP68294912 OHMAN SVERIGE HALLBAR A Ohman Sverige Hallbar B

LP68454460 OHMAN SVERIGE MARKNAD HALLBAR A Ohman Sverige Marknad Hallbar B

LP68429057 OHMAN SVERIGE MARKNAD HALLBAR A Ohman Sverige Marknad Hallbar A

LP68543653 SEB FRN FOND HALLBAR A SEB FRN Fond Hallbar C

LP68543652 SEB FRN FOND HALLBAR A SEB FRN Fond Hallbar A

LP68543649 SEB FRN FOND HALLBAR A SEB FRN Fond Hallbar B Utd

LP68543650 SEB FRN FOND HALLBAR A SEB FRN Fond Hallbar D

LP68543651 SEB FRN FOND HALLBAR A SEB FRN Fond Hallbar E

LP68566886 SEB FRN FOND HALLBAR A SEB FRN Fond Hallbar F

LP68501710 SEB HALLBAR FAKTOR EMERGING MARKETS SEB Hallbar Faktor Emerging Markets

LP68509064 SEB HALLBAR FAKTOR EMERGING MARKETS SEB Hallbar Faktor Emerging Markets

Utd

LP68501711 SEB HALLBAR FAKTOR GLOBAL SEB Hallbar Faktor Global

LP68509065 SEB HALLBAR FAKTOR GLOBAL SEB Hallbar Faktor Global D

LP68509066 SEB HALLBAR FAKTOR GLOBAL SEB Hallbar Faktor Global Utd

LP60046884 SEB HALLBARHETSFOND GLOBAL SEB Hallbarhetsfond Global

LP68536409 SEB HALLBARHETSFOND GLOBAL SEB Hallbarhetsfond Global C

LP68203429 SEB HALLBARHETSFOND GLOBAL SEB Hallbarhetsfond Global utd

LP68420078 SEB HALLBARHETSFOND SVERIGE INDEX SEB Hallbarhetsfond Sverige Index

LP68044690 SEB HALLBARHETSFOND SVERIGE INDEX SEB Hallbarhetsfond Sverige Index utd

LP68459900 SKANDIA SVERIGE HALLBAR Skandia Sverige Hallbar

LP68300943 SPP GRON OBLIGATIONSFOND A SPP Gron Obligationsfond A

LP68420280 SPP GRON OBLIGATIONSFOND A SPP Gron Obligationsfond B

LP60046568 SWEDBANK ROBUR ETHICA SVERIGE Swedbank Robur Ethica Sverige

BZ010S5 SEB FOTF.HALLBAR SEB fotf Hallbar

BZ010R4 SEB FOTF.HALLBAR INST SEB Fotf Hallbar Inst

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Appendix 2. SRIF excluded from the convenient sample size due to lack of full data

Appendix 2. SRIF excluded from the convenient sample size due to lack of full data

Name Symbol Start Date

SEB HALLBARHETSFOND SVERIGE INDEX 9065XN 2017-04-04

HANDELSBANKEN HALLBAR ENERGI (A10 SEK) 9293D5 2018-03-13

SEB HALLBAR FAKTOR GLOBAL D 9437JA 2018-11-19

LANNEBO SVERIGE HALLBAR A 94395K 2018-12-21

HANDELSBANKEN HALLBAR GLBL OBLIG (B1 SEK) 9442EZ 2019-02-20

HANDELSBANKEN HALLBAR ENERGI (A9 SEK) 9660CM 2020-01-17

HANDELSBANKEN HALLBAR GLOBAL OBLIGATION A9 SEK 9661XK 2020-01-17

Appendix 3. A histogram of capital flow observation distribution without excluding

extreme outliers.

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Appendix 4. A histogram of capital flow observation distribution excluding extreme

outliers.

Appendix 5. A histogram of price index observation distribution.

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Appendix 6. A histogram of historical beta observation distribution.

Appendix 7. A histogram of market risk level (OMXS30, S.D) observation distribution.

85

Appendix 8. A histogram of weather shocks observation distribution.

Appendix 9. Scatter plots of capital flow

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Appendix 10. A scatter plot of price index

Appendix 11. A scatter plot of historical beta

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Appendix 12. A scatter plot of market risk level (OMXS30, S.D)

Appendix 13. A plot of capital flow to each explanatory variable.

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