INTRODUCTION/GUEST EDITORIAL

Behavioural finance: the roleof psychological factors in

financial decisionsGulnur Muradoglu

Cass Business School, London, UK, and

Nigel HarveyUniversity College London, London, UK

Abstract

Purpose – The purpose of this paper is to introduce the special issue of Review of BehaviouralFinance entitled “Behavioural finance: the role of psychological factors in financial decisions”.Design/methodology/approach – The authors present a brief outline of the origins of behaviouraleconomics; discuss the role that experimental and survey methods play in the study of financialbehaviour; summarise the contributions made by the papers in the issue and consider theirimplications; and assess why research in behavioural finance is important for finance researchers andpractitioners.Findings – The primary input to behavioural finance has been from experimental psychology.Methods developed within sociology such as surveys, interviews, participant observation, focusgroups have not had the same degree of influence. Typically, these methods are even more expensivethan experimental ones and so costs of using them may be one reason for their lack of impact.However, it is also possible that the training of finance academics leads them to prefer methodologiesthat permit greater control and a clearer causal interpretation.Originality/value – The paper shows that interdisciplinary research is becoming more widespreadand it is likely that greater collaboration between finance and sociology will develop in the future.

Keywords Decision making, Psychology, Behavioural finance, Research work

Paper type Research paper

1. IntroductionAccording to Glaser et al. (2004, p. 527): “Behavioural finance as a subdiscipline ofbehavioral economics is finance incorporating findings from psychology and sociologyinto its theories. Behavioral finance models are usually developed to explain investorbehaviour or market anomalies when rational models provide no sufficientexplanations”.

Modern economics assumes that people choose between alternatives in a rationalmanner (von Neumann and Morgenstern, 1944) and that they know the probabilitydistribution of future states of the world (Arrow and DeBreu, 1954). Modern financeassumes that markets are efficient and that agents know the probability distribution offuture market risk (Markowitz, 1952; Merton, 1969). Research has been geared towardssearching for a better risk factor/pricing model.

In parallel with these theoretical developments, psychologists studying decisionmaking were collecting data that suggested that individuals do not always makedecisions in an optimal manner that those working in finance and economics assumed(e.g. Edwards, 1954, 1955). After a large corpus of data had accumulated, Bell et al.(1988) argued that it is worth making a conceptual distinction between normative

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/1940-5979.htm

Review of Behavioral FinanceVol. 4 No. 2, 2012pp. 68-80r Emerald Group Publishing Limited1940-5979DOI 10.1108/19405971211284862

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models of decision making that identified optimal ways of making decisions,descriptive models that identified how people actually make decisions under differentconditions, and prescriptive models that identified ways of improving decision makingwhen no normative models were available. They argued that economists may havebeen unwise to assume that normative models are descriptive.

For many years, this behavioural research had little impact on economics.Behavioural economics did not exist. Kahneman (2011) argues that it originated in theearly 1970s when Richard Thaler, then a graduate student in economics, demonstratedthat one of his professors was highly susceptible to the cognitive bias that is nowknown as the endowment effect. Arguably, behavioural economics came of agewhen Kahneman and Tversky (1979) published prospect theory and maturedafter Kahneman’s receipt of the Nobel Prize for Economics in 2002 demonstrated thateconomists considered behavioural research as worthy of inclusion in their field ofstudy.

Although Slovic (1972) drew the attention of those working within finance to therelevance of research on behavioural decision making to their concerns, behaviouralfinance was slower to develop than behavioural economics. The work of De Bondt andThaler (1985, 1987) can be seen as a landmark that triggered expansion of the field.Later, Thaler (1999) went on to argue that research in the area would soon come to anend because financiers would be so convinced by the behavioural findings that theywould adopt reasonable assumptions. However, although those working in financemay be more sympathetic to the notion of basing their theories on realisticassumptions than those working in other areas of economics, there is, as yet, little signthat the field is contracting.

Good reviews on the development of the field of behavioural finance include thoseby De Bondt et al. (2010), Daniel et al. (2002), Glaser et al. (2004) and Garling et al. (2009).These reviews indicate that much behavioural finance uses the corpus of work thatdemonstrates biases in human judgment and decision making (Kahneman et al., 1982)to explain investor behaviour and market anomalies. There is, however, increasingrecognition that we need to move towards a theoretical framework that accounts notjust for the circumstances that produce inefficient information processing but also forthose that produce efficient information processing (Shefrin, 2005).

There have been other developments too. Tversky and Kahneman (1974) arguedthat cognitive biases occur because people use heuristics (mental “rules of thumb”).They use them because they do not have the cognitive resources to carry out theprocedures necessary to make normative decisions. Although Tversky and Kahneman(1974, p. 1131) argued that “these heuristics are economical and usually effective”, theypointed out that their use leads to biases under certain circumstances. They focused onthose circumstances because doing so allowed them to cast light on the nature of theheuristics that produce them – in much the same way that vision scientists studyvisual illusions in their attempts to understand the visual system. However, thisstrategy resulted in many people gaining the impression use of heuristics leads toirrational decisions.

To counter this view, Gigerenzer et al. (1999) instigated a programme of researchgeared to demonstrating that heuristics often produce exceedingly good outcomes.They have demonstrated that, in out-of-sample tests, simple models that ignore someinformation or weight different types of information equally can outperform morecomplex models, such as those based on multiple regression. For example, selectingwho will win a tennis match purely on the basis of choosing the player whose name is

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recognised is a strategy that outperforms the rankings produced by the Associationof Tennis Professionals (Serwe and Frings, 2006; Scheibehenne and Broder, 2007).Similar findings have been reported in other fields, such as medicine (Gigerenzerand Kurzenhauser, 2005), policing (Snook et al., 2005) and marketing (Wubben andvon Wangenheim, 2008).

Within finance, simpler strategies have been found to be superior to more complexones for selecting stocks (DeMiguel et al., 2007). More recently, Haldane (2012),Executive Director for Financial Stability at the Bank of England, has appliedGigerenzer’s approach to bank regulation. He has reported a number of analyses thatdemonstrate that bank regulators would be better able to predict bank failure by usingmuch simpler models than they do at present. He argues that the current regulatoryregime based on the Basel III Accords should be radically simplified if it is to increaseits effectiveness: “Modern finance is complex, perhaps too complex. Regulation ofmodern finance is complex, almost certainly too complex. That configuration spellstrouble. As you do not fight fire with fire, you do not fight complexity with complexity.Because complexity generates uncertainty, not risk, it requires a regulatory responsegrounded in simplicity, not complexity” (Haldane, 2012, p. 19). These examples demonstratethat behavioural finance can provide us with prescriptions as well as descriptions.

2. Experimental work in financeExperimentation is a mainstream methodology within psychology whereas it has beenless common within finance. Here, we shall briefly outline some of issues that thoseworking within finance initially studied non-experimentally but that have morerecently been subject to experimental research.

Researchers within finance have been aware of the potential importance ofpsychologists’ work on cognitive biases for some time. For example, the dispositioneffect (Shefrin and Statman, 1985) refers to the finding that investors are likely to sellshares that have increased in price but tend to keep those that have dropped in price.It is an anomaly that is consistent with what would be expected on the basis ofprospect theory and with what we know about cognitive biases (e.g. the endowmenteffect). It has been demonstrated empirically by a number of researchers (e.g. Barberiset al., 2001; Coval and Shumway, 2005; Frazzini, 2006; Odean, 1998a). However,experiments related to this effect have been comparatively rare: Thaler and Johnson(1990) and Post et al. (2008) report two relevant experimental studies.

Similarly, the implications of overconfidence for finance (e.g. frequent trading) havebeen investigated by a number of authors, including Odean (1998b, 1999), Gervaisand Odean (2001), Daniel et al. (1998) and Bloomfield et al. (2003). However, fewexperiments have been conducted as direct tests of the financial effects ofoverconfidence: they include those carried out on stock market professionals byBiais et al. (2005), Muradoglu (2002), Muradoglu and Onkal (1994) and Onkal andMuradoglu (1994).

In finance, judgment is often used to make forecasts from time series data. Itseffectiveness can depend on forecasters’ beliefs about the presence of regime shifts inthose data. Historically, those working in finance have examined size of errors in realforecasts but such studies did not permit researchers to examine the features of timeseries that make forecasting difficult. Experiments allow factors that affect bothjudgmental forecasting (De Bondt, 1993; Lawrence et al., 2006; Harvey and Reimers,2012; Reimers and Harvey, 2011) and beliefs about regime change (Bloomfield andHayes, 2002; Speekenbrink et al., 2012) to be studied systematically.

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Failure to ignore sunk costs is an issue in finance: for instance, it is likely to havea role in producing the disposition effect. The phenomenon has been demonstratedexperimentally in both children (Krouse, 1986; Webley and Plaisier, 1998) andadults (Arkes and Blumer, 1985). It has even been possible to investigate whether innon-human animals (in the context of which it is known as the Concorde fallacy):Dawkins and Carlisle (1976) concluded that animals suffer from the fallacy whereasother researchers have failed to find any evidence that they are susceptible to it(Dawkins and Brockman, 1980; Maestripieri and Alleva, 1991). These latter resultsled Arkes and Ayton (1999) to question whether humans behave less rationally thanlower animals.

Within finance, there is a concern about the validity of studies that have usedparticipants drawn from the general population. Conclusions drawn from such studiesmay need some modification if they are to be applied to investors, either individual orcorporate. Thus, Burns (1985) argues that finance professionals’ behaviour may differfrom non-professionals’ behaviour due to training, reputation, etc. Similarly, Haigh andList (2005, p. 524) argue that Locke and Mann (2000) take the argument a step furtherby suggesting that any research that ignores the use of professional traders is likelyto be received passively because “ordinary” individuals are unlikely to have anysubstantial impact on market price since they are too far removed from the pricediscovery process. Similar views have been expressed by Christensen-Szalanski andBeach (1984) and Frederick and Libby (1986).

Of course, the similarity in the behaviour of finance professionals and lay people isactually an issue that needs to be addressed via empirical studies. What have suchstudies shown? In one of the first experimental papers to be published in the Journal ofFinance, Haigh and List (2005) reported an experiment using 54 professional futuresand options pit traders from the Chicago board of trade and showed that tradersexhibited more myopic risk aversion than students. Onkal and Muradoglu (1994, 1995,1996) conducted a series of experiments comparing finance professionals and novicesin a task requiring probabilistic forecasting of stock prices. They found that financeprofessionals were more over confident than novices but that they could reduce thisbias if they were given feedback. Thus, at least in certain financial tasks, differencesbetween professionals and lay people occur. So the research shows that, at least insome financial tasks, conclusions drawn from studies of lay people do need to bemodified if they are to be applied to professionals. However, they need to be modified ina surprising direction: biases have been found to be larger not smaller in professionals.Clearly, experience does not always produce expertise.

This issue need not concern us when the finance tasks of interest are ones that arenormally carried out by lay people. Thus, for example, two of the papers in the currentissue are concerned with credit markets: they examine factors that influencing the useof credit by lay people. Here sampling participants from the general population isclearly the most appropriate approach. Furthermore, lay people now have increasingaccess to stock markets via the internet. The distinction between individual investorswho are professional and those who are not is much less clear than it has been in thepast. Even in stock investment tasks, such as that reported in third paper in thisspecial issue, non-professional participants validly represent a section of the generalpopulation that invests in stock markets.

More generally, experimenters are increasingly adopting web-basedexperimentation (e.g. Lo and Harvey, 2012; Lo et al., 2012; Reimers and Harvey,2011; Harvey and Reimers, 2012). Links to an experiment are posted in various forums.

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This allows participants to be drawn from a much broader demographic than thatprovided by local students. It is even possible to carry out studies on specificpopulations drawn from various countries by selecting particular forums on which topost the web link to the experiment. For example, studies reported by Lo and Harvey(2011) used this approach to show that availability of credit cards differentiallyaffects purchasing behaviour of compulsive and non-compulsive shoppers in the UKand Taiwan.

In this issue, we have two papers that use experimental methods. The first paper iswritten by Maria Andersson, Tommy Garling, Martin Hedesstrom and Anders Bielfrom the University of Gothenburg. They studied impact of the length of a time serieson the predictions of stock prices and investment decisions. The effectiveness ofjudgment as a means of making forecasts from time series and as a basis for makingdecisions using those forecasts is a well-researched area (Harvey and Bolger, 1996;Lawrence et al., 2006). However, Andersson and colleagues were interested in it becauseof their concern about short termism in financial markets (Stiglitz, 1989). They defineshort termism as a preference for actions in the near term that have detrimentalconsequences in the long term. They argue that bonuses based on the performanceover the last year or even over the last quarter are signs of short termism. In threeexperiments, they investigate the effect of longer evaluation intervals on financialdecisions.

In their first experiment, on students from the University of Gothenburg, theyconducted a laboratory experiment that lasts about 30 minutes. Participants played therole of an investor employed by a company. They were presented with price series fornine shares over five, ten or 15 days. Trends in the price series were systematicallyvaried. Participants were asked to make a prediction about the price before makinga purchase decision for up to 100 shares. No significant effects of the length of the priceseries were observed either in predictions or in investments. In Experiment 2, theauthors added graphs of the price information because it is known that visuallydisplaying the data can make trends more salient. This time, they observed that thepredictions based on the longer price series of ten to 15 data points yielded smallerprediction errors but there was no impact on investment decisions. Finally, inExperiment 3, the authors added a condition intended to reduce participants’information processing load: in this condition, each of five points representedaggregated data over three days, thereby lowering the number of data points that hadto be processed. Price prediction errors were smaller in this condition than inconditions in which either five or 15 non-aggregated points were presented and risktaking for investments was closer to optimal.

Their paper is important for its policy implications in the context of the currentdebate on bonuses. People are in general myopic. Graphs may counteract the myopictendency to a certain extent by defocusing the attention from the most recentinformation. When the number of data points is reduced and averages are presented toreduce local variation, both prediction and investment performance are improved.Thus, to distract investors from myopic decisions, aggregation over time is a usefulstrategy.

Our second paper is written by Sandie McHugh and Rob Ranyard from theUniversity of Bolton. They examined the effects of information about the long-termfinancial consequences of different types of loans on credit repayment decisions. Theyconducted two experiments with a random sample of 2,000 people from a high streetbank’s database of personal account customers. They processed 242 replies for the

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paper that they present here. Two credit repayment scenarios, one with a credit cardbalance of £1,500 and one involving re-mortgage of a property loan of £40,000, weredevised. In these scenarios, participants selected a monthly payment level whengiven either no additional information, total cost information, loan durationinformation, or both total cost and loan duration information. Controlling fordemographic information, the authors showed that provision of additional informationproduced higher repayment levels.

In their second experiment, McHugh and Ranyard again examined the effects ofprovision of information about the long-term consequences of repayment decisions.However, in this experiment, which was conducted just after the 2008 financial crisis,they used a wider variety of credit repayment scenarios. They also added questionsasking participants to estimate the likelihood that redundancy or illness would lead torepayment difficulties, to assess levels of worry this likelihood would produce, andto assess their levels of worry arising from the possibility of future rises in the cost ofliving. Higher estimates of the likelihood of personal circumstances leading torepayment difficulties and worry about future increases in the cost of living reducedrepayment levels. In contrast, higher levels of education and worry about personalcircumstances causing repayment difficulties raised repayment levels.

The policy implications of the paper are important for retail banks. If theyprovided information on the cost and duration of debt repayments, they couldspeed up the repayment of loans, credit cards debts or mortgages. Selecting a lowerpayment plan is associated with worry about a change in personal circumstancescausing repayment difficulties. Maybe lower repayment levels can be used as a meansof credit risk management and be associated with taking payment protectioninsurance.

3. Use of surveys in financeUse of surveys in finance does not have a long history. In one relatively early study,Muradoglu (1989) surveyed about 500 stock investors in Turkey. At the time, there wasmuch discussion in the country about the possibility of privatisation promotingdemand among workers and about the possible effects of privatisation of companies oninhabitants in the neighbourhoods in which they were located. “The typicalstockholder is from the upper social class [y] Stock demand increases as education,income, savings and wealth increases” (p. 167). Some of the findings have since beenconfirmed by the literature on home bias: “[y] those investors who have personal andbusiness relations with the management of the companies invest more in thosecompanies because they feel confident in their action” (p. 169) and by research onmental accounting: “Turkish investors do not sell the stocks when the price is fallingbut they do not hesitate to sell them when the price is rising. They do not want torealise losses due to price movements. They can internalise losses only in the case ofcatastrophic situations” (p. 171). Yet others have been supported by work on corporategovernance: “[y] investors prefer to buy the stocks of companies that are owned bya well-known group or individual” (p. 173). Among the many recommendations wasthe suggestion that “Further research may [y] be conducted by savings surveys justlike the consumer surveys” (p. 179).

Nowadays, such surveys are carried out in much of the world, with high qualitydata available from the USA, the UK, and Scandinavia and other European countries.These include household level data on consumption and savings and debt. Mostly, it iseconomists who work in this area. However, as the third paper in this issue

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demonstrates, financial survey research includes an agenda that can be addressed bythose working in behavioural finance.

This paper by Stefano Cosma and Francesco Pattarin focuses on the role of attitudesin the use of consumer credit. They report a survey of 2,000 Italian households thatwas conducted in 2009. They distinguish consumer credit users from non-users.Importantly, their definition of consumer credit refers to institutional credit involving arequest by a household that the banker considers solvent. This is contrasted withconsumer debt which refers to debts that arise when someone does not fulfil theirrepayment obligations against their intentions and those of their creditor.

The survey covered households in both the north and south of Italy and includeddifferent household sizes and different income earners in each household. Age,education and gender of credit users and non-users were reasonably well balanced.The questionnaire collected information about psychological characteristics as well asdetails of cognitive, emotional and behavioural attitudes towards consumer credits.First, Cosma and Pattarin show that cognitive and behavioural components ofattitudes towards consumer credit differentiate credit users and non-users. Theseresults were robust to the needs of the household. Second, they show that, as creditusers’ attitude towards credit becomes more positive, they are more likely to financeconsumption with credit cards or point-of-sale lending than by using personal bankcredit or salary loans. In contrast, as the attitude of non-users becomes more favourabletowards credit, they increasingly prefer point-of-sale lending to credit cards. Finally,Cosma and Pattarin show that the probability of taking on debt increases as theattitude towards debt becomes more favourable. The probability of using credit cardsalso increases as the number of income earners in the household increases and whenthere are strong expectations that income will rise.

The paper is important in showing that, among the many determinants of credituse, attitude plays a significant role. The cognitive component which determinesthe individual’s decision-making framework is crucial. The psychological profile of theborrower is an important factor in consumer credit decisions.

4. Why is work in behavioural finance important for finance?Behavioural finance is used to make recommendations to finance professionals abouthow to change their behaviour or how to communicate with their clients. Kahnemanand Riepe’s (1998) list of recommendations includes the following:

. keep track of instances of your overconfidence;

. communicate realistic odds of success to your clients;

. resist the natural urge to be optimistic;

. ask yourself whether you have real reasons to believe that you know more thanthe market;

. make sure the frame chosen has relevance for the client; and

. assess how risk averse your client is.

Further suggestions to financial advisors on how to take findings from behaviouralfinance into account have recently been outlined by Benartzi (2011).

As we have seen, simple (fast-and-frugal) heuristics can provide an effective meansof making complex decisions (Gigerenzer et al., 1999). We mentioned Haldane’s (2012)application of them to the problem of bank regulation above. They can be useful in

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others areas of finance as well, such as in the expectation formation processesunderlying selection of the contents of portfolio. Thus, for example, Muradoglu et al.(2005) examined the effectiveness of an expectation formation process heuristicallybased on the subjective forecasts of finance professionals. The portfolio performance ofsubjective forecasts was superior to that of standard time series modelling.

More generally, Ricciardi and Simon (2000) have argued that behavioural financeenables those who invest in stock and mutual funds to avoid common “mental mistakesand errors” and develop effective investment strategies. Others have argued thatknowledge of behavioural finance should enable investors to become aware of howpotential biases can affect investment their decisions and thereby to avoid such errors.This, in turn, should act to promote the efficiency of the market and so limit the needfor regulation and improve information dissemination (Daniel et al., 2002). Similarly,awareness of findings in behavioural finance may lead to a change in working practicesthat improve performance: for example, use of feedback and a change in the wayinformation is presented can improve forecasting performance (Harvey and Bolger,1996; Onkal and Muradoglu, 1995, 1996).

A common objection is that incorporating behavioural data into theories offinance would produce results that would be too complex to be useful in practice. Thus,Bloomfield (2006) has argued: “No behavioural alternative will ever rival theparsimony and power of traditional efficient markets theory, because, psychologicalforces are too complex” (p. 11). Even Thaler (2000, p. 140) has seen this as a problem:“One reason economics did not start this way is that behavioural models are harderthan traditional models”. We have two responses to this objection.

First, during the early development of traditional economic theory, the processesinvolved were seen as too complex to allow them to be described formally. Thus, whenhe wrote the Wealth of Nations, Smith (1776/1976) had to describe those processes interms of an invisible hand; he could not describe them formally in the way we do today.The invisible hand was a metaphor that he used to communicate his view of aneconomic reality in which people act in their own self-interest but in which themarket has the ability to correct itself without intervention. It was not untilthe late nineteenth century that Walras (1874/1954) modelled these economicprocesses in a rigorous manner and not until the mid-twentieth century that thelaw that he identified was proved formally. Behavioural finance is still in its earlydays: the path along which it develops may result in it becoming a more rigorousdiscipline.

Second, we acknowledge that psychological processes are highly complex and thoseinvolved in the social cognition underlying financial behaviour especially so. Neuro-economics and social neuroscience are still in their infancy. However, as we have seen,responses to complexity need not themselves be complex: in fact, they are more likelyto be effective if they are simple (Astebro and Elhedhli, 2006; Gigerenzer et al., 1999;Haldane, 2012; Holte, 1993). The problem is in identifying the simple solutions that areappropriate for dealing with complex problems. Succeeding in this is still likely torequire the development of a more rigorous approach.

Finance has always borrowed methodologies from other disciplines. Methodsdeveloped in mathematics, physics and economics are now standard in finance.Methods developed in psychology have been imported more slowly. There areprobably a number of reasons for this. For example, experiments are difficult andcostly to conduct with investors and market professionals because their participationin experiments requires funds that exceed those available under standard finance

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academic research budgets. Nevertheless, although progress has been slow, work hasbeen done and more will be done.

The primary input to behavioural finance has been from experimental psychology.Methods developed within sociology such as surveys, interviews, participantobservation, focus groups have not had the same degree of influence. Typically,these methods are even more expensive than experimental ones and so costs ofusing them may be one reason for their lack of impact. However, it is also possible thatthe training of finance academics leads them to prefer methodologies that permitgreater control and a clearer causal interpretation. Nevertheless, interdisciplinaryresearch is becoming more widespread and it is likely that greater collaborationbetween finance and sociology will develop in the future.

Academics often cross disciplinary boundaries. They may simply borrow a singleidea or concept from another discipline. They may work with those from anotherdiscipline but with only limited integration between disciplines taking place. Bothof these are examples of multidisciplinary research (Klein, 1990). However, in trueinterdisciplinary research, disciplines or research methods are integrated into a newfield of study (Mitchell, 1995). Behavioural finance may have started as amultidisciplinary endeavour but it is now an interdisciplinary field with its ownlearned societies, journals and conferences. However, it is still developing and continues toborrow methods and ideas from other disciplines. This bodes well for its future.

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Further reading

Ricciardi, V. and Tomic, I. (2004), “An introduction to mutual fund investing: a practical approachfor busy people”, unpublished book.

Tversky, A. and Kahneman, D. (1981), “The framing of decisions and the psychology of choice”,Science, Vol. 211 No. 4481, pp. 453-8.

Corresponding authorGulnur Muradoglu can be contacted at: g.muradoglu@city.ac.uk

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