Demographic variables on investment

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Overconfidence, overreactionand personality

Robert Durand Department of Finance and Banking, Curtin University, Perth, Australia

Rick Newby Department of Accounting and Finance, University of Western Australia,

Perth, AustraliaKevin Tant

Department of Accounting and Finance, Monash University, Melbourne, Australia, and

Sirimon Trepongkaruna Department of Accounting and Finance, University of Western Australia,

Perth, Australia

AbstractPurpose The purpose of this paper is to systematically profile investors personality traits toexamine if, and how, those traits are associated with phenomena observed in financial markets. Inparticular, the paper looks at overconfidence and overreaction in an experimental foreign exchangemarket.Design/methodology/approach The paper measures the personality of the subjects using theshort form of the NEO-PIR instrument, the NEO-FFI developed by Costa and McRae (1992) which isbased on Normans (1963) Big Five personality constructs of negative emotion , extraversion ,openness to experience , agreeableness and conscientiousness . The paper measures psychologicalgender using questions developed by Bem (1994). Preference for innovation and risk-taking propensityare measured using instruments developed by Jackson (1976). The paper then examines the behaviorof the subject who traded interactively in real time in an interactive-simulated foreign exchangemarket where price discovery was instantaneous and pricing decisions were made instantaneouslyas items of news, determined by the researchers, were released.Findings The paper demonstrates that personality traits are associated with overconfidence andoverreaction in financial markets. The paper presents meta-analysis which facilitates the developmentof a posteriori theories of how particular traits affect investment; there are important roles for risk-taking propensity , negative emotion , extraversion , masculinity, preference for innovation andconscientiousness .Originality/value A typical behavioral finance paper might find an empirical regularity in pricesand, on the basis of such patterns, infer the underlying psychology motivating the behaviorof investors. The approach differs from this caricature of the typical behavioral finance paper.The paper does not infer the underlying psychology of investors from patterns in prices. Rather, thepaper learns about investors by systematically profiling their personality traits. The paper then

demonstrates how those traits are associated with the prices generated by the investors the authorsstudy. In focussing on the role of individual personality, the paper refocusses behavioral finance on theindividuals who set prices.Keywords Behavioural finance, Jacksons personality inventory, Normans Big Five, Overreaction,Psychological genderPaper type Research paper

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

Review of Behavioral FinanceVol. 5 No. 2, 2013pp. 104-133r Emerald Group Publishing Limited1940-5979DOI 10.1108/RBF-07-2012-0011 JEL classification G02

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IntroductionBehavioral finance looks to psychology to explain phenomena observed in financialmarkets. A typical behavioral finance paper might find an empirical regularity inprices and, on the basis of such patterns, infer the underlying psychology motivating

the behavior of investors. There are many regularities to be explained and manypotential explanations have been utilized. Fama (1998) suggested that [ y ] given thedemonstrated ingenuity of the theory branch of finance and given the long litany of apparent judgment biases unearthed by cognitive psychologists (De Bondt and Thaler,1985), it is safe to predict that we will soon see a menu of behavioral models that canbe mixed and matched to explain specific anomalies. My view is that any new modelshould be judged [y ] on how it explains the big picture [y ] (p. 291). To illustrateFamas point, momentum ( Jegadeesh and Titman, 1993, 2001) has been explained usingunderreaction (Hong et al., 2000), overreaction ( Jegadeesh and Titman, 2001), thedisposition effect (Grinblatt and Han, 2005) and biased self-attribution andoverconfidence (Chui et al., 2010); the absence of an attempt to compare competingbehavioral explanations is striking[1].

The papers cited above seek to model a particular phenomenon based on anassumption, or set of assumptions, about the individuals who set prices in the market.Someone, whether she invests on her own behalf or on behalf of someone else, transactsto create each price which is used in these studies. These prices are aggregated toisolate the phenomenon being studied; in doing so, the individual decisions involved inmaking those transactions are also aggregated. Behavioral finance theories are basedon individuals yet our analyses lose sight of these individuals.

Our approach differs from this caricature of the typical behavioral finance paper.We do not infer the underlying psychology of investors from patterns in prices. Rather,we learn about investors by systematically profiling their personality traits. We thendemonstrate how those traits are associated with the prices generated by the investorswe study. In focussing on the role of individual personality, we refocus behavioralfinance on the individuals who set prices. Our study, like Durand et al. (2008) (DNS) andDurand et al. (2013) (DNPS), argues that individuals propensity to make certaindecisions is a function of the type of person the individual is. That is, a personspersonality influences the decisions she makes. Overconfidence and overreaction arethe foci of this paper and we confirm DNS and DNPS in demonstrating that personalityis the wellspring of investors behavior.

DNS find that Normans (1963) Big Five personality inventory ( emotional stability ,extraversion , openness to experience , agreeableness and conscientiousness ), thepsychological gender constructs of masculinity and femininity (Constantinople, 1973)and the personality traits of preference for innovation and risk-taking propensity areassociated with the investment decisions and resulting performance of a sample of 21 individual investors from Australia[2]. DNS subjects are real investors who held aportfolio of Australian equities in the financial year ending on the 30th of June 2005.Their results (summarized in tables XII and XII of their paper) indicate that preference for innovation and masculinity were associated with portfolio performance, negativeemotion , risk-taking propensity and openness to experience were associated withexposure to risk, negative emotion , risk-taking propensity and extraversion wereassociated with trading.

DNPS replicate DNS using a larger sample of 115 student investors who completedan investment exercise over a shorter period (March 17, 2008-May 9, 2008). Theyconfirm DNS finding that personality is associated with investment decisions

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and resulting performance. DNPS also extend DNS in an important way. DNPS arguethat there is the easy, Dear Abby, interpretation of [DNS] finding: an advisor mightprovide the best advice for a client by knowing her better. There is, however, a morechallenging interpretation of the DNS results: the auction interpretation. This

suggests that investors with particular personality traits are the marginal pricesetters for securities with particular traits (Durand et al., 2013, p. 2). DNPS findthat personality is associated with investors utilization of the disposition effect andthe availability heuristic. The disposition effect and the availability heuristic havebeen utilized to explain a wide range of financial phenomena. DNPS argue that if the disposition effect and the availability heuristic explain price movements,and if personality explains the disposition effect and the availability heuristic,then personality explains price movements. The evidence in DNPS is supportiveof the investors with particular personality traits being the marginal price setters infinancial markets.

We extend DNS and DNPS in showing that personality is related to overconfidenceand overreaction. Overconfidence and overreaction have been used to model a range of financial phenomena. As we have noted, DNPS demonstrate the association of personality to the disposition effect and the availability heuristic. In demonstratingthat overconfidence and overreaction are also associated with personality, we providefurther evidence that the plethora of explanations that have been utilized in behavioralfinance are a function of personality.

Both DNS and DNPS use personality traits but they do not propose hypotheseson why, and how, particular traits might work. Neither DNS nor DNPS need to do this;they ask only whether personality per se is associated with investment. This study,together with DNS and DNPS, presents 34 analyses where an aspect of investment hasbeen used as a dependent variable. The independent variables used in the three studiesare the same. This allows us to present a meta-analytical analysis of the personalitytraits used in these studies in the final section of this paper. We propose a posteriorihypotheses to guide future studies on personality and investment.

PersonalityDNS quote one of the Oxford English Dictionarys definitions of personality as [ y ]the quality or collection of qualities which makes a person a distinctive individual; thedistinctive personal or individual character of a person, especially of a marked orunusual kind (Durand et al., 2008, p. 206, footnote 1). The Oxford English Dictionarytracks the development of personality in English from something pertaining to Godto something distinctive about each man and woman. A clear definition of personalityis elusive but it appears to involve what makes us different and, from this difference,special. McDougall (1932) is a prominent and early example of a researcher arguingthat it is the traits possessed by individuals that is, their personality that motivatesbehavior. Understanding an individuals personality should therefore help explaintheir interactions with the world.

While all individuals are different, they share common features, or traits. Thesetraits seem to capture aspects of individuals inner lives which are manifested in theirinteraction with the world. Psychologists have modeled, or summarized, individualspersonalities by creating measurable indices around these shared traits. DNS, DNPSand this paper use the same traits, and methodology of measuring those traits. Thispermits us to develop the findings in DNS and DNPS and also to validly conduct themeta-analysis of personality and investment that will be reported later in this paper.

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Following DNS and DNPS, we capture our subjects personalities using a number of metrics. We use the short form of the NEO-PIR instrument, the NEO-FFI developedby Costa and McRae (1992) which is based on Normans (1963) Big Five personalityconstructs of negative emotion , extraversion , openness to experience , agreeableness

and conscientiousness . The names of these traits are themselves good summariesof the aspect of personality which each captures, although more detail may befound in Durand et al. (2008, pp. 194 and 195). The questionnaire includes 60 items(12 for every construct) with each measured using a five-point agreement scale.Therefore, summary statistics for the five traits presented for the 61 subjects in Table Iwill range from 12 (suggesting that they carry little of that personality trait) to amaximum of 60.

DNS and DNPS have argued that the biological differences between men andwomen are, as far as finance goes, superficial. Their argument, however, iscontroversial given work, starting with Barber and Odean (2001), arguing thatbiological gender is an important feature of financial decision making. While we wouldwish to further analyze this issue, our sample size precludes a rigorous investigationof this issue. We follow DNS and DNPS in using sex roles (Bem, 1977) in our analysis.Psychological masculinity is associated with being task focussed. Psychological femininity is associated with a relationship focus (ONeill and Blake-Beard, 2002).Using the same questionnaire as DNS and DNPS, we asked 30 questions developedby Bem (1994) to determine psychological gender and the results for our subjects arereported in Table I.

The remaining questions assessed subjects preference for innovation and risk-taking propensity using instruments developed by Jackson (1976). We follow DNS andDNPS and measure risk-taking propensity (the main focus of which is financial risk,Stewart et al., 2003) and preference for innovation which measures the ability tomonitor and be open to adapting to changes in their environment (Welsch and Young,1982). Preference for innovation has been found to apply to financial environments inentrepreneurship research (Roberts and Robins, 2000).

Experimental designWe follow, and confirm, DNS and DNPS in demonstrating that personality isthe wellspring of investors behavior. The personality metrics described in theprevious section are captured by the standard instruments used in both DNS andDNPS and described in detail in the previous section as well as in Durand et al.(2008, p. 196).

The study seeks to examine the relationship of personality to overconfidenceand overreaction. To achieve this, we require an experimental setting which providesa realistic situation in which subjects might have the opportunity to displayoverconfidence (or not to display this behavior) and to overreact (or underreact) tostimuli. DNPS utilize student subjects in their study of the availability heuristic andthe disposition effect by monitoring the activity of subjects engaged in a portfoliomanagement exercise over a number of weeks (March-May 2008). DNPS thereforeconduct a clinical study rather than an experiment. They observe their subjectspersonality metrics and then monitor them in an exercise as close as possible to thereal investors studied by DNS. DNPS work cannot be considered an experiment asthey do not utilize inputs which can be manipulated and controlled.

This study is similar to DNPS in that we piggy back on students undertaking aninvestment exercise. The 61[3] student subjects in this study traded interactively in

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Table I.Summary statistics personality traits

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real time in an interactive-simulated foreign exchange market where pricediscovery was instantaneous and pricing decisions were made instantaneously asitems of news, determined by the researchers, were released. In total, 51 percent (30) of the subjects were male. The overwhelming majority of subjects, 97 percent (59), were

from an Asian cultural background.The subjects were enrolled in a 13-week unit related to foreign exchange trading ata leading Australian university[4] in the second semester of 2010. Like DNPS, eachstudent was required to reflect on their actions in their designated role (which we willdiscuss below) and provide a written report to the unit leader (as chief executive officer)outlining their performance and actions, including self-appraisal on the positive andnegative aspects of their actions in relation to their predetermined strategy. UnlikeDNPS, students were rewarded for their performance; trading reports submitted aftertrading represented 25 percent of the total mark.

Each subject in our study acted as a dealer for a bank in one trading exercise.However, foreign exchange trading is complex and the exercise was conducted as partof a postgraduate finance program. Each subject was a member of a team of threewhich acted as a bank. Each subject acted as a dealer once but was present in two othersessions; once in the role of position keeper and once in the role of risk manager. Theroles reflect the reality of a trading floor; in this exercise, as well as reality, dealers wereconstrained by the policies of their bank. The dealer was expected to trade withinthe teams agreed strategy, the position keeper was expected to input and matchtransactions as soon as they were completed and the risk manager was responsible forensuring that the risk profile remained in accordance with the pre-agreed tradingstrategy and trading limits before trading commenced. Profitability of the bankrepresented 5 percent of the assessment for the subject; therefore, there was also anincentive to act as a good team member.

Subjects commenced trading at a zero AUD and USD position. By the end of tradingthey were expected to have a zero AUD position with a resultant USD profit or loss.No trading limits were imposed on subjects, other than to have a zero trading positionat the end of trading. If this was not the case, the central bank closed them out at a 300basis point penalty; there was a strong incentive for subjects to manage their positionin conjunction with the risk manager. Each week, prior to trading, banks (teams) wererequired to develop and submit to the subject leader their strategy (based on economicscenarios that are reproduced in Appendix) in writing. This required the developmentof an economic and investment perspective about future events and how this mayaffect price discovery and returns. Each scenario was different to ensure that, as far aspossible, traders responses would be unique and as closely determined by theirpersonality as possible. The measures we adopted to control for different scenarios arediscussed later in this section.

Price discovery occurred through real time prices displayed via computer screen.Subjects were required to display two prices (buy/sell) on the screen at all times. Theywere required to deal at the standard market parcel of AUD 10 m at their screen rate;they could not refuse to deal. For amounts above AUD 10 m, the dealers price wassubject to negotiation or the dealer could refuse to deal; we identify transactions overAUD 10 m and analyse them separately. Deals were completed by the dealer throughthe real time pricing system or by telephone. All decision making was motivated by thesubjects quest for financial profit within a risk/return profile they developed forthemselves. Once deals were completed they were passed to the position keeperwho input the transaction and ensured that the deal matched real time with the

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counterparty bank. The risk manager managed the exposure and passed informationto the dealer.

During the simulation news events were released on the dealers pricing screens.The subject could not be influenced by fellow members of his or her bank, the position

keeper did not have access to this information on screen and the risk managercould only view this information on a different screen. Some news events were relevantto economic activity that should have influenced changes in the foreign exchange.The time of the release of such data were known by the dealer prior to thecommencement of trading. Other news events that had little relevance to the price inthe foreign exchange rate (sporting and human interest events) were releasedrandomly and consistently to distract the dealer from their tasks. The history of theexchange rate during one session and responses to news events, are illustratedin Figure 1. Overreaction (underreaction) was measured by the subjects priceadjustment one minute after the news release. If the subjects response was more (less)

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1 2 : 4 7 p . m .

1 2 : 5 3 p . m .

1 2 : 5 9 p . m .

1 : 0 5 p . m .

1 : 1 1 p . m .

Market Average Bid

Notes: The market average bid and ask rate as of August 17, 2010 is plotted.Trading starts at 11a.m. and closes at 1.15 p.m. A total of eight news itemswere announced during the trading session (labeled A-H). News A (actualdeficit is $25Blns) is announced at 11.15 a.m. News B (unemploymentrate is 8 percent) is announced at 11.30 a.m. News C (there is a rumor thatRBA committee will decrease the interest further by 1 percent in the nextquarter) is announced at 11.45 a.m. News D (the age: the aging populationand a sustained low fertility among populations are expected to lower our growth significantly) is announced at 11.55 a.m. News E (Bloomberg: USinvestment banks are facing with lowest profits in the history the recentlow level of investors money inflow is reflecting the alarming fact that USAis no longer a world financial hub) is announced at 12.25p.m. News F (G8meeting reconvenes for last session: likelihood of concert intervention in thenear future) is announced at 12.31 p.m. News G (Australian resourcescompanies raise iron ore output by 11 percent this year to meet Chinesedemand) is announced at 12.36 p.m. News H (Japanese exit polls: no surprise!Swing against ruling party from the poll) is announced at 12.45p.m.

Market Average Ask

Rate Movement Tuesday, August 17, 2010

Figure 1.Foreign exchange ratemovement as of August17, 2010

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than the mean (or median) response, the subject is treated as overreacting(underreacting) to the news.

Like the differing economic scenarios, differing news items also required differenttreatment. We follow DNS and DNPS and utilize a stepwise regression methodology to

examine whether there are relationships between the personality metrics and thedependent variables. The stepwise regression methodology omits statisticallyinsignificant variables and, following the lead of DNS and DNPS, we deleted theleast significant personality variable in a regression until a model containing onlycoefficients with p-values o 0.10 was obtained. DNS utilize a stepwise approach asthey have a small sample. DNPS found that the stepwise approach was appropriate fortheir data due to the relationships between the dependent variables (see Durand et al.,2013, p. 11, footnote 14). Our data set is similar to that of DNPS; the interrelationshipsbetween the dependent variables do not result in clear results when they are allincluded in the analysis and therefore we also utilize a stepwise approach. In order toexamine whether different sessions affect results, we conduct the stepwise approach onall variables, including dummy variables, for the sessions. We omit statisticallyinsignificant session dummies before conducting the stepwise analysis on thepersonality metrics used as independent variables in our analyses. DNPS also conductseparate analyses on subsets of male and female subjects as well as subsets of subjectswho are from Asian and non-Asian cultural backgrounds. DNPS state that theanalyses for these sub-groups do not result in materially different conclusions fromthose reported in the main body of the paper and do not report these results. The ratioof subjects from each gender to independent variables indicates that conductingfurther analysis like DNPS is not appropriate given our sample size. Adding dummyvariables to capture gender and cultural differences without considering interactionswould also be inappropriate. Questions of the interaction of personality, gender andculture is an important question which we, sadly, must leave to future research.

In the stepwise procedure, the resulting model after deleting insignificant variablesis considered the best one. In all cases we confirmed this by comparing the Akaikeinformation criterion with the next best model; in all cases, the model we present isoptimal by this criterion. Tobit regression, which allows for dependent variables to betruncated, has been used to model the bid-ask spread and the number of transactionsover $10m and in all of the overreaction analyses[5]. In the case of transactions over$10 m, the summary statistics reported in Table II show that the minimum observationfor this observation is zero. In the case of the bid-ask spread , the theoretical minimum iszero and, while the minimum value reported for the average bid-ask spread is not zero,it is very small. Given the theoretical bound and the difficulties due to the dependentvariable approaching this theoretical boundary (Greene, 2003, Chapter 22), use of Tobitanalysis is advisable. In the case of the models of overreaction we study, the numberof reactions is standardized to percentage terms; this recognizes that the number of opportunities for reaction differed between sessions. We model the number of transactions using ordinaryleast squares regression. The z-and t -statistics, and resulting p-values, reported in Table IV have been adjusted for heteroscedasticity.

OverconfidenceOverconfidence has proved a useful concept in explaining financial phenomena.Glaser and Weber (2010) provide a useful overview of overconfidence; they report thatBusiness Source Premier (EBSCO Host) shows 144 peer-reviewed journal articlespublished in 2008 and 1,189 articles since 2000 [and] ScienceDirect indicates 250

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peer-reviewed journal articles were published in 2008 and 1,556 articles since 2000(p. 241). In real, as opposed to experimental, markets, investors participate voluntarily.Confidence in financial markets might be manifested in a number of ways. If confidence can be understood to be an individuals belief that he or she can choose thebest course of action, investors can be presumed to be confident in their abilities asinvestors; it is doubtful that underconfident investors would enter into, or continuein, markets[6].

Overconfidence is characterized by an individuals belief that the precision of theirforecasts is greater than is actually warranted. We cannot measure overconfidencedirectly given the experimental design we are utilizing. We are, however, able to

measure the manifestations of overconfidence. We follow Barber and Odean (2001), forexample, who have argued that excessive trading is a manifestation of over-confidence.Overconfident investors engage in more trading. We capture the amount of trading of our subjects in three ways:

(1) The average bid-ask spread for each trader. We refer to this variable as the bid-ask spread . The bid-ask spread represents the liquidity the trader is willing toprovide to the market and a trader with a lower spread is assumed to be morewilling to trade than a trader posting a wider spread. A trader more willing totrade is therefore taken to be a trader who is more confident than one who isless willing to trade.

(2) The number of times a subject trades (we refer to this dependent variable as

number of transactions ). The more times a subject trades, the more confidentwe assume the trader is.(3) The number of times a trader traded an amount 4 $10 m (we refer to this

dependent variable as transactions 4 $10 m ). A trader trading $100 m istherefore assumed to be more confident than a trader who might trade $10 m.

Summary statistics for these three overconfidence metrics are reported in Table II. Bid-ask spreads are, on average, under one-tenth of an Australian cent but range fromover one-fifth of a cent to 6/100th of a cent; the subject we assume to be least willing totrade posted a spread approximately four times that of the keenest trader. The average

Bid-ask spread Number of transactions Transactions 4 $10 m

Mean 0.000980 73 17Median 0.000947 61 10Maximum 0.002302 203 57Minimum 0.000581 16 0SD 0.000270 47 15Skewness 2.2 1.4 1.2Kurtosis 11.1 4.2 3.4 Jarque-Bera 216.7 23.7 15.1 p-value 0.0000 0.0000 0.0005

Notes: Summary statistics for the proxies for overconfidence which are the dependent variables in theregression analyses reported in Table IV is displayed. The bid-ask spread is average bid ask spread foreach subject. The number of transactions is the number of times a subject traded in a session.Transactions 4 $10 m is the number of times a trader traded an amount 4 $10 m (we refer to thisdependent variable as transactions 4 $10 m ). Jarque-Bera is a test of the null hypothesis that thedistribution of observations conforms to a Gaussian normal distribution

Table II.Summary statistics foroverconfidence metrics

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(median) number of trades was 73 (61) but most of these transactions were small; theaverage (median) number of trades over $10 m was 17 (ten). All of the overconfidencemetrics are positively skewed.

Correlations between the overconfidence metrics are reported in Table III. Pearsons

correlation coefficients are reported above the diagonal and Spearmans rankcorrelation coefficients are reported below the diagonal. The three metrics are designedto proxy overconfidence yet the most striking aspect of Table III is that the proxiesdo not exhibit the relationships that would be expected if the metrics were capturingthe same effect. There are no significant relationships between the bid-ask spread andeither of the transaction metrics. There is a slight negative correlation between thenumber of transactions and the number of transactions over $10 m. Subjects seem tohave preferred a large number of smaller transactions over deals involving largeramounts. Remember that deals over $10 m were subject to negotiation and this aspectof the experimental design may have militated against larger transactions.

The findings reported in Table IV confirm that there is a relationship betweenpersonality and overconfidence. Personality traits are found to have statisticallysignificant relationships with each of the dependent variables. The bid-ask spread (the first equation) has statistically significant positive relationships withagreeableness and masculinity. The bid-ask spread has a statistically significantand negative relationship with extraversion . Bearing in mind that a lower (higher) bid-ask spread is a proxy for overconfidence (underconfidence) our findings points tooverconfidence increasing with extraversion and decreasing with agreeableness andmasculinity . DNS (in their discussion of table VII of their paper) find that their subjectsexposure to the stock market was positively associated with extraversion andnegatively related to agreeableness . DNS postulated that their finding for extraversionwas perhaps due to [y ] exposure to equities [giving] investors more to socialiseabout (p. 202). Additionally, DNS find that increasing extraversion is associated withholding stocks with lower market capitalization. The relationships betweenextraversion and agreeableness in this paper, however, suggest that the finding inDNS may be interpreted as supporting a positive relationship of overconfidence toexposure to the stock market. In addition to its association with sociability, research inneurofinance has found evidence that extraversion is positively associated with rewardseeking (Peterson, 2010, p. 77).

The finding of a positive association of masculinity with the bid-ask spread (i.e. higher masculinity is associated with less confidence) is at odds with Barber andOdean (2001). Barber and Odean argue that masculinity is associated with greateroverconfidence but their argument is based on biological gender. The finding that

Bid-ask spread Number of transactions Transactions 4 $10 m

Bid-ask spread 0.15 0.17 Number of transactions 0.29 0.21*Transactions 4 $10 m 0.20 0.27**

Notes: Correlation coefficients for the proxies for overconfidence which are the dependent variables inthe regression analyses reported in Table IV is displayed. Bid-ask spread, number of transactions andtransactions 4 $10 m are defined in the text accompanying Table II. Pearsons correlation coefficientsare reported above the diagonal and Spearmans rank correlations below the diagonal.*,**,***Significant at the 10, 5, 1 percent level, respectively

Table IIICorrelations

overconfidence metri

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overconfidence has a negative relationship to overconfidence in our analysis of thebid-ask spread is, however, consistent with the general finding for psychologicalgender in DNS, where masculinity is associated with lower risk-taking and increasingexposure to larger stocks.

Dependent variableIndependent variables Bid-ask spread Number of transactions Transactions 4 $10m

Constant 0.000547 146.017900 10.982910 z( t )-statistic 1.9388 1.5796 0.4500 p-value 0.0525 0.1200 0.6527 Negative emotion 3.280859 z( t )-statistic 2.6664 p-value 0.0101 Extraversion 0.000012 z( t )-statistic 1.6919 p-value 0.0907Openness to experience z( t )-statistic p-value Agreeableness 0.000014 1.167496 z( t )-statistic 1.7755 2.4263 p-value 0.0758 0.0153Conscientiousness 3.478960 1.449318 z( t )-statistic 2.6393 2.4305 p-value 0.0108 0.0151 Masculinity 0.000008 0.522408 z( t )-statistic 1.7563 1.7773 p-value 0.0790 0.0755 Femininity z( t )-statistic p-value Preference for innovation 3.331258 0.624332 z( t )-statistic 2.8052 1.8985 p-value 0.0070 0.0576 Risk-taking propensity 2.392784 z( t )-statistic 1.8748 p-value 0.0662Significant sessions 0 2 and 3 0Summary statistics R 2 0.3036 R 2 (adjusted) 0.2262Reset ( F -stat/ p-value) 3.9234 (0.0025)AIC 13.5181 10.3957 8.2514

Notes: Results for the optimal parsimonious variable analysis of the dependent variables proxyingfor overconfidence the bid-ask spread , number of transactions and transactions 4 $10 m (defined inthe text accompanying Table II) obtained using a backwards step-wise deletion procedure isreported. The independent personality variables are described in the text accompanying Table I. Forthe bid-ask spread and transactions 4 $10 m , the models are run via Tobit methodology, with p-valuescalculated using quasi-maximum likelihood standard errors that are robust to heteroscedasticity. Fornumber of transactions , the model is run via OLS regression methodology, with whiteheteroscedasticity adjusted p-values. Results of Ramseys RESET test (which is reported as an F -statistic) are reported for the OLS regression only. Significant sessions indicates if a dummy variablefor a particular session was statistically significant at the 10 percent level

Table IV.Regression analysesfor overconfidence

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The number of transactions (the second equation in Table IV) has statisticallysignificant positive relationships with negative emotion (neuroticism),conscientiousness and risk-taking propensity (although, in this case, the p-value is0.0662). It has a negative relationship to preference for innovation . The variables

that are statistically significant for the number of transactions are not significantin the analysis of the bid-ask spread and vice versa. That risk-taking propensity isassociated with overconfidence is perhaps not surprising; people who are notconfident about an activity are less likely to risk undertaking that activity (trekking inthe Himalayas is an example which comes to mind).

The number of transactions is the only dependent variable in common with thispaper, DNS and DNPS. DNS and DNPS find that personality is related to the numberof trades. DNS (table VIII, Equation (2)) also find a positive relation between negativeemotion and their dependent variable called number of times an investor trades.DNS argue that [y ] it is probably unsurprising that investors with a greaterpropensity for risk trade more often. That investors who are more anxious should trademore is, however, more difficult to interpret. It might also be the case that anxietyand nervousness are associated with greater levels of activity. We suspect that thepositive association of negative emotion to trading may be associated with an increasedpropensity to respond to stimuli in a way that results in trading. If such stimuliincrease the psychological discomfort of neurotic investors, trading might be perceivedas a means to reduce these unpleasant feelings (DNS, p. 202)[7]. Such a proposal,however, while consistent with our data, is at odds with the findings of neurofinancewhich associate neuroticism with loss avoidance (Peterson, 2010, pp. 77-78). DNPS (intheir table VIII) find, like us, a positive relationship of conscientiousness to trading,which they suggest may be a function of the experimental design (as our studentsubjects may associate increased trading with better performance in the exercise).

The analysis of number of transactions in Table IV also finds a statisticallysignificant and negative coefficient for preference for innovation . This result isconsistent with DNPS (in their table VIII, Equation (1))[8]. Given that preference for innovation is associated with seeking out information to adapt to new opportunities,higher preference for innovation may be associated with a more realistic appreciation of ones abilities (i.e. less overconfidence).

As we examine the third proxy for overconfidence, the number of transactions4 $10 m , there is a positive and statistically significant coefficient for preference for innovation (the p-value is 0.0576)[9]. This finding appears inconsistent with thefindings discussed in the preceding paragraph (where preference for innovation wasnegative). Once investors have chosen to trade larger amounts, it may be the case thatthis process is facilitated by increasing information seeking and adaptation[10].Consistent with the analysis for the bid-ask spread , we find agreeableness is alsostatistically significant and positive for transactions 4 $10 m [11]. Table III reveals thattransactions and transactions 4 $10 m are negatively correlated. Thus variableshaving different signs in equations which model variables which are negativelycorrelated should not be unexpected.

Masculinity is positive and statistically significant for transactions 4 $10m : thegreater an investors psychological masculinity, the greater is his or her confidenceceteris paribus . This finding is not consistent with the role we find for masculinity inthe bid-ask spread . It is also inconsistent with the role for masculinity found in DNS.It is, however, consistent with the role Barber and Odean find for biological genderin their analysis of overconfidence. Indeed, in this study, positive coefficient for

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masculinity may be consistent with being task focussed; larger trades may help getthe job done. We leave this tension unresolved for the moment but refer to it in themeta-analysis discussed below.

The three proxies used for overconfidence the bid-ask spread , number of

transactions and transactions 4 $10 m are not positively correlated as might beexpected if they are capturing a common underlying reality. All are plausible metricsfor overconfidence. We remain agnostic on the question of which might be the best.None the less, the findings in this section indicate that these proxies are associated withpersonality metrics. The relationships between the overconfidence proxies andpersonality were found to constitute evidence in support of DNPS argument thatinvestors with particular personality traits are the marginal price setters in financialmarkets. Furthermore, not only are we able to establish links, the analysis in this paper,along with evidence provided in DNS and DNPS, allows us to begin to develophypotheses on why these personality constructs are linked to overconfidence.

Overreaction and underreactionLike overconfidence, over, and under, reaction, has been the subject of behavioralmodels and empirical work. For example, we have already noted how Jegadeesh andTitman (2001) use overreaction to explain the presence of momentum in financialmarkets and that Hong et al. (2000) use underreaction to model momentum.Overreaction and underreaction can be illustrated using an example from theexperiment we conduct. Consider a situation where a subjects quote is the same as themarket average, say 0.7352/0.7357. In this case, the subject is offering to buy oneAustralian dollar for US$0.7352 (the bid) and sell one Australian dollar for US$0.7357(the ask). If there is good news about the Australian dollar, the quotes should go up.If there is bad news, the quotes should fall. If there is good news, for example, and themarket average bid price goes to 0.7355, the subject is categorized as overreacting if she posts an ask of 0.7356. Conversely, she is considered to underreact if her quote isbelow the market average, for example, if she posts 0.7354.

Subjects in this study, as we have discussed above, participated in a simulatedforeign exchange market. In each of the markets we conduct, news (stimuli) waspresented to subjects and they were able to respond to this news. To measureoverreaction we observed each subjects response within one minute of theannouncement of the news. If the subjects response was greater than the mean(median) response in the session, we categorized the response as overreaction (andcoded it with a variable taking the value of 1); if the subjects response was below themean (median), we categorized the response as an underreaction (and coded it with avariable taking the value of 0). For example (using, for simplicity, only one quote ratherthan a bid and an ask), let us assume that the market responds to good news bypushing the exchange rate from 0.73 to 0.74; a trader quoting 0.75 has overreactedand a trader quoting 0.735 has underreacted. If there is bad news and the quotemoves to 0.72, a trader moving to 0.71 has overreacted and a trader quoting 0.725has underreacted. There are x items of news in each session. To reduce the noisein the analysis, our analysis of overreaction focussed on each subjects propensityto overreact by summing the variables assigned to each response (i.e. 1 or 0). Therefore,in each session, each subject generates a score between 0 (which indicates thatthe subject always underreacts) and x (which indicates that the subject alwaysoverreacts in that session). To standardize the scores between sessions, we divide x by the number of items of news in a session ( n ); the resulting ratio ( x/n ) takes

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the value between 0 (if a subject never overreacts) and 1 (if a subject alwaysoverreacts). We also categorized the stimuli into good and bad news events andconduct the study using two additional metrics: the propensity to overreact to good news and the propensity to overreact to bad news. Summary statistics for the

overreaction are presented in Table V; maximum values of 1 and minimumvalues of 0 indicate that some subjects overreacted (underreacted) to particulartypes of news.

The findings reported in Table VI confirm that there is a relationship betweenpersonality and overreaction. Personality variables are found to have statisticallysignificant relationships with each of the dependent variables. We note, however,that when the average reaction within one minute is used as a benchmark, novariable was found to have a statistically significant relationship with good newsand, therefore, no analysis is reported for this dependent variable. All of theanalyses reported in Table VI are Tobit as the dependent variable is bounded between0 and 1.

Extraversion is found to have a statistically significant and negative relationshipto overreaction to all items of news when overreaction is measured both by thebenchmark of average and median reaction (Equations (1) and (3)). As an investorbecomes more extraverted, the less likely he or she is to overreact to news,ceteris paribus . We do not see a role for extraversion when we dichotomize news intogood and bad announcements.

When average reaction to all news is the dependent variable, agreeableness is alsofound to have a positive and statistically significant relationship with the dependent

Where the benchmarkis the average reaction

Where the benchmarkis the median reaction

Overall Good news Bad news Overall Good news Bad news

Mean 0.40 0.34 0.26 0.29 0.22 0.21Median 0.39 0.38 0 0.29 0.26 0.22Maximum 0.75 0.75 1 0.52 0.75 1Minimum 0.08 0 0 0.08 0 0SD 0.1 0.2 0.3 0.1 0.2 0.2Skewness 0.4 0.2 1.2 0.2 0.3 1.1Kurtosis 3.2 2.0 3.3 2.7 2.5 4.9 Jarque-Bera 1.5 3.4 15.5 0.5 1.7 21.3 p-value 0.4656 0.1867 0.0004 0.7617 0.4216 0.0000

Notes: Summary statistics for standardized scores for overreaction (the dependent variables in theregression analyses reported in Table VI) is diaplayed. To measure overreaction we first observe eachsubjects response within one minute of the announcement of the news. If the subjects response is

greater than the mean (median) response in the session, we categorize the response as overreaction(and code it with a variable taking the value of 1); if the subjects response is below the mean (median),we categorize the response as underreaction (and code it with a variable taking the value of 0). Thereare x items of news in each session. In each session, each subject generates a score between zero (whichindicates that the subject always underreacts) and x (which indicates that the subject alwaysoverreacts in that session). The standardized scores result from dividing x by the number of items of news in a session ( n ); therefore, the resulting ratio ( x/n ) takes the value between 0 (if a subject neveroverreacts) and 1 (if a subject always overreacts). We also categorize the stimuli into good and badnews events and this table includes summary statistics for these variables. Jarque-Bera is a test of thenull hypothesis that the distribution of observations conforms to a Gaussian normal distribution

Table VSummary statistics fo

overreaction metri

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variable. Overreaction increases as an investor becomes more agreeable. Individualswho score highly on the agreeableness value respect the beliefs of others. Consequently,their overreaction might be seen as excessive agreement with the perceived consensus.DNPS also find a statistically significant and positive relationship of agreeablenessto the disposition effect (but only in a subjects second trade see Durand et al.,2008, table X).

Dependent variableWhere the benchmarkis the average reaction

Where the benchmarkis the median reaction

Independent variables Overall Bad news Overall Good news Bad news

Constant 0.438411 1.172440 0.439405 0.095991 0.668757 z-statistic 4.0510 1.8795 5.7384 0.4076 3.0382 p-value 0.0001 0.0602 0.0000 0.6836 0.0024 Negative emotion z-statistic p-value Extraversion 0.005253 0.003537 z-statistic 1.9128 1.9119 p-value 0.0558 0.0559Openness to experience 0.012223 z-statistic 1.7364

p-value 0.0825 Agreeableness 0.005358 z-statistic 1.9832 p-value 0.0473Conscientiousness z-statistic p-value Masculinity z-statistic p-value Femininity z-statistic p-value Preference for innovation 0.012132

z-statistic 1.8268 p-value 0.0677 Risk-taking propensity 0.040847 0.018332 z-statistic 1.8516 2.3343 p-value 0.0641 0.0196Significant sessions 4 and 6 0 0 0 0Summary statisticsAIC 0.9807 1.9181 1.5605 0.8854 0.2102

Notes: Results for the optimal parsimonious variable analysis of the dependent variables capturingoverreaction (discussed in the text accompanying Table V) obtained using a backwards step-wisedeletion procedure is reported. The independent personality variables are described in the textaccompanying Table I. The models are run via Tobit methodology, with p-values calculatedusing quasi-maximum likelihood standard errors that are robust to heteroscedasticity. Significant

sessions indicates if a dummy variable for a particular session was statistically significant at the10 percent level

Table VI.

Regression analysesfor overreaction

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Risk-taking propensity is found to have a statistically significant and negativerelationship to overreaction to items of bad news when overreaction is measured by thebenchmark of average and median reaction (Equations (2) and (4)). Investors who areless willing to commit to a decision that could either lead to success or failure are more

likely to overreact to bad news.As we have noted, no variable was found to have a statistically significantrelationship with good news and, therefore, no analysis is reported for this dependentvariable. Where median reaction is used as the benchmark for overreaction, preference for innovation is found to have a significant and negative relationship while opennessto experience is found to have a statistically significant and positive relationship tooverreaction. The analysis of number of transactions in Table IV also finds astatistically significant and negative coefficient for preference for innovation and weargue that, in that instance, seeking information and adapting is associated witha more realistic appreciation of ones abilities (i.e. less overconfidence). In the case of overreaction to good news, it would also appear that, if preference for innovation isinvolved in a more realistic approach, subjects may not overreact to bad news.

The relationships between measures of overreaction and personality were found toconstitute evidence in support of DNPS argument that investors with particularpersonality traits are the marginal price setters in financial markets. DNPS find thatsubjects personalities are related to their use of the availability heuristic and theirpropensity to be influenced by the disposition effect. This study adds overconfidenceand overreaction to the list of behavioral biases with personality at their core.

A meta-analytical basis for a theory of personality and financeThis study, like DNS and DNPS, focusses on personality as the key driver for theexplanations that have been used to explain financial phenomena. It is now clearthat personality is associated with the choices investors make. Furthermore, it is clearthat investors personality drives phenomena, such as overconfidence andoverreaction, which behavioral finance has used to model movements in the market.These three studies have utilized personality traits to address the big picturequestion of whether personality is associated with investment. None of these papershas proposed a priori theories about how each trait might relate to the financephenomena studied; this is simply not required to address the big picture of whetherpersonality is related to investment. This paper, DNS and DNPS have proposed ad hocexplanations to help interpret their findings. The question as to whether theories abouthow each personality trait can be proposed has been, to now, left open.

In total, DNS, DNPS and the current paper have conducted a total of 34 analysesusing a financial phenomenon as a dependent variable and Normans Big Five, Bemssex roles and two traits from Jacksons personality inventory as independent variables.The results of these 34 experiments are summarized in Table VII. All three papers havereported p-values o 10 percent in their preferred models and we use to denotewhere a statistically significant and positive effect has been found at the 10 percentlevel and where a statistically significant negative effect has been found. Theanalysis adopted on the basis of the summary of results reported in Table VIIcategorizes each variable as either being significant or insignificant and treats theseresults as a yes or no dichotomy which can be analyzed using the binomialdistribution. The significant/insignificant dichotomy we initially adopt is independentof the sign the significant trait takes (either positive or negative): we will utilize thepattern of positive and negative results in the following paragraph to guide us on how

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N o r m a n

s B i g 5

B e m

J a c k s o n

N e g a t i v e

e m o t i o n E x t r a v e r s i o n

O p e n n e s s t o

e x p e r i e n c e A g r e e a b l e n e s s C o n s c i e n t i o u s n e s s M a s c u l i n i t y F e m i n i n i t y


i n n o v a t i o n

R i s k - t a

k i n g

p r o p e n s i t y

O v e r c o n

f i d e n c e

B i d - a s k s p r e a d

N u m b e r o f t r a n s a c t i o n s

T r a n s a c t i o n s 4 $ 1 0 m

O v e r r e a c t i o n

A l l n e w s ( a v e r a g e )

A l l n e w s ( m e d i a n )

G o o d n e w s ( m e d i a n )

B a d n e w s ( a v e r a g e )

B a d n e w s ( m e d i a n )

D u r a n

d e t a l .

( 2 0 0 8 )

I n f o r m a t

i o n s e e k

i n g u s

i n g

T e l e v

i s i o n

I n v e s t m e n t f o r u m s

I n v e s t m e n t a d v i c e

F i n a n c

i a l p u b

l i c a t

i o n s

I n v e s t o r c h o i c e s

P o r t

f o l i o e x p o s u r e

N u m

b e r o f t r a d e s

P r o p o r t i o n o f p o r t

f o l i o

t r a d e d

P r o p o r t i o n o f s h a r e s

i n

l a r g e s t 1 0 % o f

A S X s t o c

k s

P r o p o r t i o n o f s h a r e s

i n

l a r g e s t 2 0 % o f

A S X s t o c

k s

I n v e s t m e n t o u t c o m e s

T o t a l r e t u r n

M o n t h l y s h a r p e r a t i o

D a i

l y s h a r p e r a t i o

S D

( c o n t i n u e

d )

Table VII.Meta-analysis

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N o r m a n

s B i g 5

B e m

J a c k s o n

N e g a t i v e

e m o t i o n E x t r a v e r s i o n

O p e n n e s s t o

e x p e r i e n c e A g r e e a b l e n e s s C o n s c i e n t i o u s n e s s M a s c u l i n i t y F e m i n i n i t y


i n n o v a t i o n

R i s k - t a

k i n g

p r o p e n s i t y

D u r a n

d e t a l .

( 2 0 1 3 )

H e u r i s t

i c s

S a l i e n c e

( t h e a v a i

l a b i l i t y

h e u r

i s t i c )

D i s p o s i t i o n e f

f e c t

( 1 s t t r a d e ) :

N e t r e a l

i z e d g a

i n

P r o p o r t i o n o f g a

i n s

r e a l

i z e d

P r o p o r t i o n o f

l o s s e s

r e a l

i z e d

D i s p o s i t i o n e f

f e c t

( 2 n d t r a d e )

N e t r e a l

i z e d g a

i n

I n v e s t m e n t c h o i c e s

H e r

f i n d a h l ( d i v e r s i

f i c a t

i o n )

S i z e

S h o r t m o m e n t u m

L o n g m o m e n t u m

R i s k ( b )

I n v e s t m e n t o u t c o m e s

N u m

b e r o f t r a d e s

P o r t

f o l i o r e t u r n

R 2

N u m

b e r o f s i g n

i f i c a n t

o b s e r v a t

i o n s

1 4

1 4

5

7

1 1

1 2

7

1 1

1 7

N o t e s :

, a n e x p e r i m e n t w h e r e

a p - v a

l u e o

1 0 p e r c e n t h a s

b e e n r e p o r t e d i n

t h e p r e f e r r e

d m o d e l s

i n t h a t p a p e r ; , i

s u s e d w

h e r e a s t a t

i s t i c a l

l y s i g n

i f i c a n t n e g a t i v e e f

f e c t

h a s

b e e n

f o u n

d . T h e 3 4 a n a l y s e s o f

i n v e s t m e n t p r e s e n t e d

i n D u r a n

d e t a l .

( 2 0 0 8 ) ( D N S ) a n d

D u r a n

d e t a l .

( 2 0 1 3 ) ( D N P S ) i s s u m m a r

i z e d . N

e g a t i v e e m o t i o n , e x t r a v e r s i o n

, o p e n n e s s t o e x p e r i e n c e ,

a g r e e a b l e n e s s

, c o n s c i e n t i o u s n e s s

, m a s c u l i n i t y

, f e m i n i n i t y

, p r e f e r e n c e f o r i n n o v a t i o n a n

d r i s k - t a k i n g p r o p e n s i t y a r e

d e s c r

i b e d

i n t h e t e x t a c c o m p a n y

i n g

T a b

l e I

Table VII

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we might expect the trait to function in financial decision marking. Using a 10 percentlevel of significance, and a simplifying assumption that each test of each trait isindependent, we would expect to find about three significant results by chance. Usingtwo standard deviations from the expected value as a benchmark, seven significant

observations for any variable would be more than might be expected from simplerandom draws; nine significant observations are more than would be expected if a cutoff of three standard deviations from the expected value was adopted[12]. Therefore,the use of the binomial distribution can inform us if the traits that have been utilized inthe analyses are found to be significant in more instances than might be expectedgiven a random draw. Further, the number of times a personality trait is found to besignificant serves as a benchmark for its importance. Risk-taking propensity , with 17significant occurrences, occurs most often in significant roles in explanations of thedependent variables. Following this, with 14 significant observations, we find negativeemotion and extraversion . Masculinity has 12 significant occurrences, preference for innovation and conscientiousness have 11 and agreeableness and femininity each haveseven. While we have been reluctant to hypothesize about the relationships betweenthe traits and financial phenomena, the summary in Table VII may help us developsuch theories a posteriori.

Standard (i.e. non-behavioral) finance is based on risk-aversion. Indeed, aninvestors attitude to risk is the only psychological notion which standard economicsentertains. Risk-taking propensity focusses on financial risk (Stewart et al., 2003) and itis unsurprising that the psychological construct works in the three studies much aswould be expected from a risk-averse or risk-seeking individual. On the basis of thethree studies, risk-taking propensity can be expected to be positively associated withinvestment decisions associated with riskier behavior. Higher (lower) risk-taking propensity will be associated with higher (lower) exposure to risk. DNS document thatthere is a positive association between risk-taking propensity and the standarddeviation of the portfolios of the investors they study. Risky behavior is not simplydefined by exposure to risk which might be captured by a portfolios variance orexposure to prima facie riskier asset classes such as smaller stocks (Berk, 1995; Durandet al., 2007). DNS find that higher risk-taking propensity has a negative relationship toexposure to larger stocks. Risky behavior might also consist of trading more (as is thecase in this paper and DNS) or deviating from a benchmark (as DNPS find in theiranalysis of R 2 ). If investors scoring highly on risk-taking propensity are the marginalprice setters in the market, we would expect demand to be higher for riskier portfolios.Rather than risk aversion, risk-taking propensity might be used to explain the positiverelationship between the equity risk premium and risk (Merton, 1980; Lundblad, 2007;Muller et al., 2011). Variation in the size-premium, as documented by Durand et al.(2007), where emotional arousal is used to explain the variation in the size-premiumand its positive association with trading activity, might also be better explained by themarginal price being set by investors scoring highly on risk-taking propensity .

People who rank highly on negative emotion have a propensity to experiencenegative feelings such as embarrassment, guilt, low self-esteem, emotional instabilityand pessimism (Durand et al., 2008, p. 194). The meta-analysis presented in Table VIIsuggests that its role in financial markets is complex: we find both positive andnegative relationships between negative emotion and the phenomena modeled. Negative emotion appears to have a positive association to reliance on information.DNS find a positive relationship of negative emotion and reliance on investment advice.DNPS find a positive relationship of negative emotion and reliance on the disposition

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effect. On balance, negative emotion tends to have a positive relationship to riskalthough the picture is complex. DNS find a positive relationship between negativeemotion and the standard deviation of portfolios. DNPS find a positive associationbetween negative emotion and the b of a subjects portfolio. DNS also find that

investors scoring higher on this scale hold smaller stocks, although DNPS find theopposite. The picture is also complex with respect to trading activity. This paper andDNS find a positive association between negative emotion and trading activity, whileDNPS find the opposite. Neurotic investors are attracted to risk, perhaps unwittingly,but find such exposure disturbing.

Extraversion is associated with sociability and, as we have noted above,neurofinance has found evidence that extraversion is positively associated withreward seeking. In DNS, this trait has been found to have a positive association toexposure to the stock market, greater exposure to smaller stocks, higher returns(and risk-adjusted returns) and, in this paper, underreaction and overconfidence. Thenegative coefficient for extraversion reported when the bid-ask spread is used as aproxy for overconfidence is interpreted as a positive relationship between extraversionand overconfidence: lower spreads are associated with greater overconfidence. Extraversion would appear to play an important role in investors entry and exitdecisions into the market and asset classes. It may prove to be the case that extravertedinvestors are likely to be in the market but, as returns increase, investors scoringprogressively lower on extraversion may be tempted into the market. An association of market exposure with extraversion may explain increasing market participation whenreturns are increasing.

Masculinity, the trait that captures being focussed on tasks, is found to be associatedwith less exposure to risk. DNS find that masculinity is associated with lower standarddeviations in the portfolio as well as increased holdings of larger stocks. They also findmasculinity is associated with lower exposure to the equity market. Although there isevidence that masculinity is also associated with increased trading which this paperhas argued is in keeping with overconfidence, it might also be a function of rebalancingportfolios toward lower risk positions. Where we argued that negative emotion is apotentially destabilizing influence on markets, masculinity appears a stabilizinginfluence. If investors scoring highly on masculinity are the marginal price setters in themarket, we might expect them to be involved in driving flights to quality (Abel, 1988;Barsky, 1989; Durand et al., 2010). As we have noted, however, we have been unable toconsider any interactions between psychological and biological gender. AlthoughDNPS argue that their findings are robust to these considerations, it might be best toleave more detailed consideration to future research.

Preference for innovation has been associated with higher returns (in DNS), higherrisk-adjusted returns (in DNPS). In both DNS and DNPS, preference for innovation hasbeen found to be associated with lower levels of trading. DNPS find positive andnegative relationships between preference for innovation and the disposition effectwhich is difficult to interpret. Investors with high preference for innovation shouldbe able to monitor and be open to adopting the innovations necessary to adapt tochanges in their environment (Welsch and Young, 1982). Preference for innovation , likerisk-taking propensity , appears to have a role which influences investors towardeconomically rational (i.e. risk averse wealth maximization) behavior.

Conscientiousness , with 11 significant observations in the 34 studies, is threestandard deviations above the expected value, yet it is the variable which this paperand DNPS have suggested should be interpreted with caution. In both this study and

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DNPS, students are used as subjects. It may be the case that conscientiousness is a traitwhich may be exaggerated due to the experimental design adopted in these papers.If the results regarding conscientiousness are generalizable, it would appear that it maybe associated with a propensity to reduce exposure to risk.

Agreeableness and femininity each have seven significant occurrences and aretherefore two standard deviations from the expected value. Agreeableness was found,in this paper, to be associated with overconfidence, but the associations found inDNS and DNPS are difficult to interpret and, therefore, we are reluctant to hypothesizeabout this variable at present. Bems sex role of femininity is found to be associatedwith seeking information, choosing larger stocks, a greater reliance on the availabilityheuristic and lower diversification. Investors scoring highly on the trait of femininitymay wish to hold securities that they know; as such femininity may be associatedwith following trends or herding. As we discussed in our considerations of masculinity,however, further research is required on the relationship of biological andpsychological gender before we feel comfortable about theorizing in this area.

ConclusionFriends, lovers, careers and even your choice to read this paper (unless yourprofessor has forced you) are strongly influenced by the sort of person you are.Personality, the essence of who you are, is at the core of many, if not all, decisions.Financial decisions are important decisions. The core research question examinedin this paper has been whether personality drives investment decision makingand outcomes.

We follow DNS and DNPS in using Costa and McRaes (1992) operationalizationof Normans (1963) Big Five ( negative emotion , extraversion , openness to experience ,agreeableness and conscientiousness ), Bems (1977) psychological gender traits( masculinity and femininity ) and Jacksons (1976) personality traits of preference for innovation and risk-taking propensity to model the personalities of the 61 subjectsengaged in a foreign exchange trading exercise. Our analysis confirms that personalityis associated with overconfidence and overreaction.

Finding that personality is linked with overreaction and overconfidence supportsDNS and DNPS findings that personality is associated with investment decisions andoutcomes. DNPS findings that personality traits are associated with the availabilityheuristic and the disposition effect support the auction interpretation of analyses of personality and investment: investors with particular personality traits are themarginal price setters for securities with particular traits (Durand et al., 2013, p. 2).The availability heuristic, the disposition effect, overreaction and overconfidence arepredominant phenomena utilized in behavioral finance to explain financial markets.The evidence in DNS, DNPS and this paper is that behavioral finance has focussed onepiphenomena. Understanding personality is at the core to developing a coherenttheory of finance from well-grounded behavioral underpinnings.

This paper, DNS and DNPS have addressed the question of whether personality per se is associated with investment. All three papers have recognized the difficulty of developing a priori hypotheses about which particular traits might influenceinvestment and, if a trait influences investment, how might it do so. This paper utilizesthe 33 analyses presented in DNS, DNPS and in this paper to conduct a meta-analyticalanalysis of the personality traits used in these studies. This analysis indicates that theimportant traits for behavioral finance are (in order of their suggested importance) risk-taking propensity , negative emotion , extraversion , masculinity, preference for innovation ,

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conscientiousness , agreeableness and femininity. A posteriori theories for the influenceof these traits are then proposed.

This paper, DNS and DNPS convey a challenge to future research in behavioralfinance. The claim that personality is the driver of investment behavior is far reaching

yet testable. This paper, DNS and DNPS have tackled a limited, though important,subset of the behavioral phenomena utilized in finance; more can be tested.Research on personality and finance requires data on individuals transactions and

their personality traits. DNS utilize actual investors and their observable transactions.Their small sample illustrates the difficulty of obtaining such data. DNPS utilizestudent subjects and obtain a larger sample. DNPS seek to replicate the real investmentbehavior studied in DNS by using prices taken live from the Australian StockMarket. This study also uses student subjects to obtain a workable number of observations. In contrast to DNPS, our subjects interact with each other in a clinicalsetting to create a market in which prices rise and fall in response to news, subjectsexpectations and their trading positions. We have utilized a clinical setting to createa market but, unlike purely experimental work, we do not control the price stimulito which subjects respond. Future research might be usefully conducted focussingon the effect of these variables in purely experimental settings. To our mind, achallenge in purely experimental work will be to create settings that reflect themarket conditions whereby researchers can relate to personality traits and reachgeneralizable conclusions.

Traits which are taken from Normans Big Five negative emotion , extraversion andconscientiousness may be further decomposed into sub-traits (using more detailedquestionnaires) which may result in a more detailed understanding about why andhow these variables influence investment decisions and, through these decisions, theoften-dramatic movements of financial markets. Furthermore, consideration of theinteraction of personality and situations would be of interest: are the effects of personality traits stable or do they vary by situation.

AcknowledgementThe authors have benefited from comments and suggestions from Graeme Harrisonand seminar participants at the Academy of Behavioral Finance and Economics(Los Angeles, September 2011), Curtin University, Colorado State University, theUniversity of Otago and Macquarie University. The authors are particularly gratefulfor the helpful comments received from the referee of the paper.

Notes1. It is important to note that there are also a considerable number of competing explanations

that are not behavioral. Conrad and Kaul (1998) have argued that momentum captures

cross-sectional variation in expected returns; that is, momentum is a function of risk.Moskowitz and Grinblatt (1999) argued that momentum is a function of shared industryclassification. Lesmond et al. (2004) contend that momentum is illusory and that any attemptto exploit the phenomenon would not be profitable due to high trading costs. Chordia andShivakumar (2002) consider whether momentum is a function of state variables capturingthe business cycle. In the cases of Conrad and Kaul (1998), Chordia and Shivakumar (2002)and Lesmond et al. (2004) assumptions are still made about what motivates investors and, if our argument is sound, what is found to motivate investors setting prices in these scenariosshould be a function, in some way, of their personalities.

2. We discuss below how these traits are measured.

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3. In total, 69 students completed the questionnaires. Four had missing student numbers (andcould not be matched with the trading data), three had missing items and one studentcompleted the form but did not complete the trading exercise.

4. The university is a member of the Group of Eight (Go8) (see www.go8.edu.au). DNPS alsostudy subjects enrolled at a Go8 university (although not the university in which the subjectsin this study were enrolled).

5. As DNPS remind us that although Tobit has a superficial resemblance to ordinary leastsquares regression, the maximum likelihood estimation procedure used for estimation meansthat the value of R 2 is not valid (Durand et al., 2013, p. 14, footnote 12).

6. Bitmead et al. (2004) link exposure to the internet bubble to overconfidence, arguingthat [y ] the role of overconfidence before the Crash should not be surprising. Investorswithout adequate levels of confidence probably wouldnt have been in the market [ y ](p. 168).

7. DNS find that, contrary to their expectations, extraversion has a negative relationship to thenumber of shares traded. We do not find a statistically significant relationship betweenextraversion and number of shares traded in this study.

8. DNS find a marginally significant negative relationship of preference for innovation tonumber of trades (their table VIII, Equation (1)) but the variable does not survive in theirpreferred equation.

9. There is evidence of non-linearity in the analysis of number of transactions : the reset test isstatistically significant. Consideration of non-linear transformations of variables found to besignificant in both the model of number of transactions and transactions 4 $10 m did notresult in a resolution of this issue. We reiterate that the standard errors used to makeinferences about the significance of variables are adjusted for heteroskedasticity and,therefore, allow us to make robust inferences about the variables.

10. In Table II the correlations reported for number of transactions and transactions 4 $10 m arenegative. The differing signs for preference for innovation may be related to this negative

relationship.11. This finding might be a function of the experimental design. These transactions had to be

negotiated and it simply may be the case that subjects who are more agreeable are morelikely to reach a deal when negotiation is required.

12. We use a binomial distribution as our rule of thumb. The expected value (mean) of abinomially distributed variable is a function of the number of trials ( n 34 in this case) andthe p-value ( p ) chosen (in this case 10 percent): np 34 0.1 3.4. The variance of abinomially distributed variable is given by np(1 p ) which is 34(0.1)(1 0.1) 3.06 and thestandard deviation is therefore O3.06 1.75. A binomial distribution is applicable whenthe n trials are independent and we note that this may not be the case in the studieswe analyse; our emphasis in the text is that we are using the distribution only as a ruleof thumb.

References

Abel, A.B. (1988), Stock prices under time-varying dividend risk. An exact solution in aninfinite-horizon general equilibrium model, Journal of Monetary Economics , Vol. 22 No. 3,pp. 375-393.

Barber, B.M. and Odean, T. (2001), Boys will be boys: gender, overconfidence, and common stockinvestment, Quarterly Journal of Economics , Vol. 116 No. 1, pp. 261-292.

Barsky, R.B. (1989), Why dont the prices of stocks and bonds move together?, American Economic Review, Vol. 79 No. 5, pp. 1132-1145.

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Bem, S.L. (1977), Bem Sex-Role Inventory (BRSI). The 1977 Annual Handbook for Gro

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