Accepted ManuscriptTitle: Investors’ trading activity: A behavioural perspectiveand empirical results.Authors: Dimitrios Kourtidis, ˇ Zeljko ˇSevi´c, ProdromosChatzoglouPII: S1053-5357(11)00039-4DOI: doi:10.1016/j.socec.2011.04.008Reference: SOCECO 1054To appear in: The Journal of Socio-EconomicsReceived date: 26-5-2010Revised date: 22-2-2011Accepted date: 6-4-2011Please cite this article as: Kourtidis, D., ˇSevi´c, ˇ Z., Chatzoglou, P., Investors’ tradingactivity: A behavioural perspective and empirical results., Journal of Socio-Economics(2010), doi:10.1016/j.socec.2011.04.008This is a PDF file of an unedited manuscript that has been accepted for publication.As a service to our customers we are providing this early version of the manuscript.The manuscript will undergo copyediting, typesetting, and review of the resulting proofbefore it is published in its final form. Please note that during the production processerrors may be discovered which could affect the content, and all legal disclaimers thatapply to the journal pertain.

Investors’ trading activity: A behavioural perspective and empiricalresults.AbstractThis study attempts to group investors (individuals and professionals) into differentsegments based on their psychological biases and personality traits and, then, toexamine whether, and how, these biases and traits drive their investment behaviour.The behavioural finance literature suggests four main factors that influenceinvestment behaviour: overconfidence, risk tolerance, self-monitoring and socialinfluence. Adopting this approach, a cluster analysis of data from a representativesurvey of 345 investors in Greece identified three main segments of investors: Highprofile investors (a high degree of overconfidence, risk tolerance, self-monitoring andsocial influence), moderate profile investors (a moderate level of overconfidence, risktolerance, self-monitoring and social influence) and low profile investors (a lowdegree of overconfidence, risk tolerance, self-monitoring and social influence). Themajor finding of the analysis shows that the higher the investors‟ profile, the higherthe performance of these investors on stock trading. The results will expand investors‟knowledge about the financial decision-making process and trading behaviour.Keywords: Behavioural Finance; Trading Behaviour; Psychological Biases;Personality TraitsJEL Classification: C30, D14, G11, O16

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1. IntroductionTraditional finance theories such as Efficient Market Theory (Fama, 1965a;1965b) and Modern Portfolio Theory (Markowitz, 1952) support the hypotheses ofrational investors and efficient markets. However, it is obvious that there are irrationalinvestors in the market, making random transactions that can not adequately beexplained by traditional finance theories (Chang, 2008).Many scholars, such as Kahneman and Tversky (1979), believe that the studyof psychology and other social science theories can shed considerable light on theefficiency of financial markets, as well as explain many stock market anomalies,market bubbles and crashes. Thus, a relatively new theory, called behavioural finance,has emerged in an attempt to understand the human psychological biases that arerelated to the financial markets. In contrast to traditional finance, which examineshow people should behave in order to maximize their wealth, behavioural financeinvestigates how people actually behave in a financial setting (Nofsinger, 2005a).The Behavioural finance literature has developed a number of behaviouralconcepts that explain investment behaviour. This paper reviews some of the mostsignificant and reliably measurable concepts to classify investors into profiles and,then, to compare their personal characteristics and their trading behaviour. Thebehavioural characteristics (concepts) that have been selected for classifying investorsinto profiles are: Overconfidence (OV), Risk Tolerance (RT), Self-Monitoring (SM)and Social Influence (SI). Thus, this paper examines whether the differentpsychological and personal characteristics lead to differences in investment behaviourand trading performance among the group of investors with different profiles. Thisframework will, hopefully, help investors understand how biases and traits affect theirinvestment decisions. The paper is organized as follows: first, the paper discussesselected psychological biases and personality traits that are involved in behaviouralfinance. Then, a brief description of the methodology design is presented and finallythe results of the cluster analysis are presented.

2. Literature reviewAlthough the relevant literature suggests that there are many factors affectingpeople‟s behaviour, the emphasis there was to explore the most importantpsychological biases and personality traits affecting investment behaviour. These areOverconfidence, Risk tolerance, Self-monitoring and Social influence. An analyticdiscussion follows in the next sections and an attempt is made to link them withinvestment behaviour.

2.1 OverconfidenceOverconfidence causes investors to be too certain about their own abilities andnot to weight the opinion of others sufficiently. Furthermore, overconfident investorsunder react to new information, or overweight the value of information, but they alsohold unrealistic beliefs about how high their returns will be (Barber and Odean,2000). Chen et al. (2004) examined brokerage accounts in China and reported thatindividual investors exhibit overconfidence.In spite of the fact that some studies have found no difference in

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overconfidence between men and women (Lundeberg et al., 2000; Deaves et al.,2003; Biais et al., 2005), the majority of the literature suggests that men areapparently more predisposed to overconfidence than women (Lundeberg et al., 1994;Barber and Odean, 2001a). Barber and Odean (2001a) have found that males trade 45per cent more actively than females, and earn lower returns, while Shu et al. (2004)have shown that, even though men trade more excessively than women, theirperformance is not dramatically lower than that of women.This research assumes that Overconfidence leads to higher trading frequencyand volume. Deaves et al. (2003), and Grinblatt and Kelojarju (2009) for example,have documented that overconfidence causes additional trading frequency. Glaser andWeber (2007) have concluded that “The higher the degree of overconfidence of aninvestor the higher her or his trading volume” (Glaser and Weber, 2007, 13).Αdditionally, Dow and Gorton (1997) have found that trading volume increases whenindividuals and insiders are overconfident. Moreover, Gervais and Odean (2001) havefound that overconfident investors trade too aggressively and this increases theexpected trading volume. A similar argument that overconfidence leads to greatertrading activity is made by Daniel et al. (2001), Hirshleifer and Luo (2001), Wang(2001) and Scheinkman and Xiong (2003).Research has shown that overconfidence leads not only to increased tradingactivity but also to increased probabilities of taking wrong decisions (e.g. buying thewrong stocks). For example, Odean (1998) supports that an overconfident tradermakes biased judgements that may lead to lower returns. Similarly, Fenton-O‟Creevyet al. (2003) and Philip (2007) have documented that overconfidence has a negativeimpact on trading performance. On the other hand, DeLong et al. (1990) and Wang(2001) support that overconfident investors earn higher returns than less confidentinvestors.Overconfident investors believe they can achieve high returns, thus they tradeoften and they underestimate the associated risks (Benos, 1998; Odean, 1998; Wang,2001). Barber and Odean (2001a) and Chuang and Lee (2006) argue thatoverconfident investors underestimate risk and trade more in riskier securities.

have shown that, even though men trade more excessively than women, theirperformance is not dramatically lower than that of women.This research assumes that Overconfidence leads to higher trading frequencyand volume. Deaves et al. (2003), and Grinblatt and Kelojarju (2009) for example,have documented that overconfidence causes additional trading frequency. Glaser andWeber (2007) have concluded that “The higher the degree of overconfidence of aninvestor the higher her or his trading volume” (Glaser and Weber, 2007, 13).Αdditionally, Dow and Gorton (1997) have found that trading volume increases whenindividuals and insiders are overconfident. Moreover, Gervais and Odean (2001) havefound that overconfident investors trade too aggressively and this increases theexpected trading volume. A similar argument that overconfidence leads to greatertrading activity is made by Daniel et al. (2001), Hirshleifer and Luo (2001), Wang(2001) and Scheinkman and Xiong (2003).Research has shown that overconfidence leads not only to increased trading

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activity but also to increased probabilities of taking wrong decisions (e.g. buying thewrong stocks). For example, Odean (1998) supports that an overconfident tradermakes biased judgements that may lead to lower returns. Similarly, Fenton-O‟Creevyet al. (2003) and Philip (2007) have documented that overconfidence has a negativeimpact on trading performance. On the other hand, DeLong et al. (1990) and Wang(2001) support that overconfident investors earn higher returns than less confidentinvestors.Overconfident investors believe they can achieve high returns, thus they tradeoften and they underestimate the associated risks (Benos, 1998; Odean, 1998; Wang,2001). Barber and Odean (2001a) and Chuang and Lee (2006) argue thatoverconfident investors underestimate risk and trade more in riskier securities.

Last, the trend of using online brokerage accounts is making investors moreoverconfident than ever before. Barber and Odean (2001b) have provided evidencethat investors, after going online, tend to trade more actively and their performancedrops. On the other hand, Choi et al. (2002) have investigated the performance ofonline investors and found no significant difference in the performance of Webtraders and phone traders.

2.2 Risk toleranceFinancial risk tolerance, defined as “the maximum amount of uncertainty thatsomeone is willing to accept when making a financial decision, reaches into almostevery part of economic and social life” (Grable, 2000, 625).This study supports the hypothesis that demographics influence risk tolerancebehaviour. This claim is supported by the following authors. Schooley and Worden(2003) have found that “Gen Xers” (defined as being born in 1964 to 1980) generallyhave a low propensity for risk taking. Hira et al. (2007) have found that higher agedecreases risk tolerance, while higher income increases risk tolerance. Cicchetti andDubin (1994) and Grable et al. (2004) have also found that people with high incomeshave higher risk tolerance than people with lower incomes.Roszkowski (1998) and Hartog et al. (2002) assume that single, rather thanmarried, individuals tend to be more risk tolerant. Similarly, Yao and Hanna (2005)have documented that risk tolerance is higher for single males, followed by marriedmales, then unmarried females and then married females. Furthermore, Hariharan etal. (2000) have investigated the behaviour of investors who are about to retire andthey have found that women are more likely to invest in risk-free securities than men.Grable et al. (2004) and Weber et al. (2002) have also found that men are more risktolerant than women.Additionally, the level of formal education is found to influence risk tolerance.Grable and Lytton (1998) and Sung and Hanna (1996) suggest that greater levels ofattained education are associated with increased risk tolerance. Hallahan et al. (2003)considered education and marital status but they have not found evidence to supportthat they are significant determinants of individuals‟ attitude towards risk.Moreover, Keller and Siergist (2006) argue that financial risk tolerance is asignificant positive predictor of willingness to invest in stocks. Specifically, they have

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found that highly risk-tolerant investors have high-value portfolios and they tradesecurities frequently (Keller and Siergist, 2006). Further, Dorn and Huberman (2005)have investigated the determinants of portfolio diversification and turnover and foundthat risk-tolerant investors and trade more aggressively. Additionally, a number ofresearch studies have found that people who are risk tolerant trade more often thanless risk-tolerant people (Tigges et al., 2000; Wärneryd, 2001; Clark-Murphy andSoutar, 2004; Wood and Zaichkowsky, 2004; Durand et al., 2008).

2.3 Social influenceA concept that also explains behavioural dispositions is social influence.Social attitude has played an important role in these attempts to predict and explainhuman behaviour (Campbell, 1963; Sherman and Fazio, 1983; Ajzen, 1988). Thisresearch supports that Social Influence has an impact on investors‟ trading behaviour.This claim is also supported by Nofsinger‟s (2005b) findings. Individual investorsdiscuss with, and are affected by (to an extent), their family members, neighbors,colleagues and friends, as far as their investment decisions are concerned (Nofsinger,2005b). In addition, investors in financial markets imitate each other. Thisphenomenon is referred to as herding (Hirshleifer and Teoh, 2003). Evidence ofherding behaviour among stock-market participants is “Wall Street”, which sharesaspects of a crowd (Prechter, 2001). When a large number of investors make similardecisions, it is a possible cause of market booms and bursts. This is the reason whythe popular press often holds investors‟ tendency to herd as responsible.Hong et al. (2004) have investigated the participation of households in thestock market and they have concluded that social households are 4 per cent morelikely to invest in the stock market than nonsocial households. Along the same line,De Marzo et al. (2003) suggest that individuals form their opinions by interacting withothers and an obvious example is that investors‟ decisions are usually affected by therecommendations made by friends and/or analysts. Whereas some studies confirm theexistence of herding in financial markets (Guedj and Bouchaud, 2005), others do not(Drehmann et al., 2005).Furthermore, technological advancements have made the production, retrievaland distribution of information much easier, faster and cheaper than ever before(Johnson, 2001). The Internet also seems likely to change the information investorslook for and focus on. For instance, the Internet facilitates comparisons of real-timedata, and this has changed investors‟ focus by emphasising the importance of speedand immediacy.

2.4 Self-monitoringSelf-monitoring is a personality trait, a sort of social intelligence. It is adisposition to attend to social cues and to adjust one‟s behaviour to one‟s socialenvironment (Biais et al., 2005). It refers to the sensibility for what is considered as adesirable expressive behaviour in different situations and the ability to control andmodify this behaviour (Snyder, 1974). People high on self-monitoring have greatersocial sensitivity than people low on self-monitoring (Snyder, 1987). Individuals highon self-monitoring alter their expressive self-presentation for the sake of desired

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public appearances and are, therefore, highly responsive to social and interpersonalcues of situational appropriate performances (Snyder and Gangestad, 1986). On theother hand, individuals low on self-monitoring are not able, or they have nomotivation, to control their expressive self-presentations. Therefore, their expressivebehaviour depicts their own feelings and thoughts, without being concerned muchabout what would be right in the social sense.This research assumes that high self-monitoring influences investors‟ tradingbehaviour. Highly self-monitored people are less likely to underestimate the extent towhich other players‟ actions are correlated with their information (Eyster and Rabin,2005) and, thus, they should avoid the winner‟s curse (because of incompleteinformation and emotional bidders, there is a difficulty in determining an item‟sintrinsic value). Indeed, Biais et al. (2005) have found that investors high onselfmonitoring are unlikely to fall into winner‟s curse traps and behave strategically,achieving high stock returns. Further, Alemanni and Franzosi (2006) have found thatself-monitoring increases trading frequency.

3. MethodologyThis study attempts to group investors (individuals and professionals) intodifferent segments based on their psychological biases and personality traits and, then,to examine whether, and how, these biases and traits drive their investment behaviour.The selected factors that classify investors into profiles through cluster analysis arethe following: overconfidence, risk tolerance, self-monitoring and social influence.This study makes an attempt to confirm the relationship between these factors, alsotaking into account differences in the investors‟ profiles and their trading behaviour.

3.1 Questionnaire designA structured questionnaire was constructed and used as the main surveyinstrument. First of all, an extensive pretesting with professional and individualinvestors took place in an attempt to improve the format of the questions. Thequestionnaire was delivered to the research subjects face to face, to allow theresearcher to discuss with the participants the main aims of the research and providethem with all the necessary explanations in order to eliminate possible mistakes in theunderstanding and completion of the questionnaires.The questionnaire includes seventy-two questions that collect informationabout investment behaviour, while it is divided into two sections. The first section isbased on the four main constructs. Overconfidence is measured by seven items (fivepoint Likert scale) that have been adapted from Wood and Zaichkowsky (2004).Furthermore, the thirteen items (multiple choices) measuring risk tolerance have beenadapted from Grable and Lytton (1999). Six items (five-point Likert scale) measuringsocial influence and eighteen items measuring self-monitoring have been adaptedfrom Ajzen (1991) and Biais et al. (2005), respectively. The second section includesquestions about participants‟ stock transactions (in their private portfolios). There aresix questions included, collecting information about their portfolio value, stockvolume, stock returns, profitable transactions and frequency of stock transactions.Additionally, ten questions focus on information about people‟s investing behaviour,

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such as types of stocks held and sources of investment information. Moreover, twelvedemographic and socioeconomic items are included in this section. All the necessarystatistical tests have been performed to verify construct validity and to confirm thereliability and validity of the research instrument. The Cronbach alpha statistic hasbeen used to determine the degree of consistency among the measurements of eachconstruct. The final constructs and their internal reliability are shown in Table 1.<Table 1>

3.2 Sample descriptionThe questionnaires were distributed to individuals who make stocktransactions and had at least two transactions in 2007 on the ASE (Athens StockExchange), as well as professional investors (including portfolio analysts as well asstockbrokers) who work in various investment companies all over the country (inorder to achieve the maximum geographical distribution).A total of 200 professional investors from 80 investment companies and 400individual investors were contacted face to face. The individual investors whopositively responded (through face-to-face contact) totalled 263, while 83 professionalinvestors successfully responded to the questionnaire. Therefore, the response rate forthe face-to-face distribution was 66 per cent for individuals and 42 per cent forprofessional investors.Additionally, 67 questionnaires (including a prepaid stamped envelope) weredistributed by mail to 42 investment companies (phone contact had previouslyconfirmed their willingness to answer the questionnaire) in all over the country. In afew weeks, 18 of them were returned completed. Therefore, the response rate onquestionnaires via mail was 26.8 per cent.The total number of questionnaires returned was 373. However, 28questionnaires (individuals) were omitted since some questions had been leftunanswered. Thus, the total number of valid questionnaires was 345. Of these, 235questionnaires were completed by individuals and 110 questionnaires were completedby professionals.

4. ResultsOnce the constructs had been defined, an overall score for each of them wascalculated. Cluster analysis examines whether respondents scored similarly on a set ofvariables and seeks to identify a set of groups with the greatest possible distinction(Keller and Siergist, 2006). This study used K-means cluster analysis because thismethod is appropriate (unlike hierarchical clustering) for large data sets (N > 250).Due to the fact that some variables were measured on different scales, they werestandardised to assume equal impact on the computation of the distances betweencases. A range (2–5) of clusters was tested and the greatest distinctiveness (with theappropriate significance) among the groups was provided by a 3-cluster solution. Theresults suggest that 3 significant subgroups exist within the investor sample, each withdifferent psychological and personality characteristics (Table 2).In addition, an ANOVA test has shown a statistically significant difference (p< 0.05) between the clusters for all the constructs (Table 3). Interpreting the results of

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this table, each investor segment identified could be labelled as follows: high profileinvestors, moderate profile investors and low profile investors. High profile investorsare those who have the following characteristics as far as the four main constructsexamined are concerned: high degrees of overconfidence, risk tolerance, selfmonitoringand social influence. Moderate profile investors are those who scoremoderately on overconfidence, risk tolerance, self-monitoring and social influence,whereas low profile investors are those who have a low degree of overconfidence, risktolerance, self-monitoring and social influence. To validate the distinctiveness of theclusters, independent sample t-tests were conducted on the mean ratings of each of thefour constructs between clusters (Table 3). The results indicate the existence ofstatistically significant differences between each cluster (except for the risk toleranceof low and moderate profile investors). We also ran crosstabulations between clustersfor several demographic and trading characteristics. A detailed discussion of thefindings from these analyses is provided in the next sections.<Table 2><Table 3>

4.1 Profile AnalysisHigh Profile InvestorsThe majority (83 per cent) of the respondents were men, which does not differmuch between profiles (Table 4). High profile investors are mainly young respondentswith 68 per cent of them being less than 45 years old (relatively younger than theinvestors in the other groups), having a higher educational level (42 per cent of themhold a Master degree) and having a significantly (Table 5) higher income level (34 percent of them earn more than 50,000 euros annually) than investors in the other 2profiles. Additionally, the high profile includes significantly more professionals thanany of the other 2 groups.The primary characteristic of this group is the high score on all thepsychological biases and traits compared with the scores of the same traits of the other2 groups of investors (Table 2). Thus, high profile investors have significantly higheroverconfidence (mean 27.42) than investors in any other group. This is expected dueto the large amount of professionals included in this profile. Kourtidis et al. (2011)have found that that professionals score higher on psychological biases andpersonality traits (overconfidence, risk tolerance, social influence, and selfmonitoring)compared to the individual investors. As Chen et al. (2004) have found,experienced investors are more prone to overreact due to behavioural biases. Also,Glaser et al. (2007) have found that professional investors‟ judgement is biased andthat their overconfidence levels are significantly higher than the respective degrees ofindividuals. Furthermore, Griffin and Tversky (1992) have documented evidence thatexperts are more overconfident than non-experts.In addition, these investors also have higher risk tolerance (mean 31.61) thaninvestors in any other group. This can be explained by the fact that low age, highincome and high educational level (which characterize investors who belong to thisprofile) are correlated with high risk tolerance. More specifically, Hira et al. (2007)have found that both low age and high income increase risk tolerance. Similarly,

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Cicchetti and Dubin (1994) and Grable et al. (2004) have found that people with ahigh income level have higher risk tolerance than people with lower incomes.Moreover, Grable and Lytton (1998) suggest that greater levels of attained educationare associated with increased risk tolerance. High risk tolerance can also be explainedby the large number of professionals included in this profile. There are studies thatshow a positive relation between risk taking and experience (Hong et al., 2000;Lamont, 2002).Also, there is evidence in the literature suggesting that high overconfidence isassociated with high risk tolerance and, more specifically, some research studiesconclude that overconfident investors underestimate risk taking (Barber and Odean,2001a; Wang, 2001; Chuang and Lee, 2006). Additionally, a statistically significantpositive relationship has been found between overconfidence and risky assets, whichis similar to other studies (Benos, 1998; Odean, 1998; Wang, 2001). Moreover, selfmonitoring(mean 11.52) and social influence (mean 16.05) are significantly higher inthe high profile investors‟ group compared with investors from the other two groups.<Table 4><Table 5>

Moderate Profile InvestorsModerate profile investors are significantly older (41 per cent of them areabove 45 years old) and have a significantly lower educational level (only 16 per centhold a Master degree) and lower income (only 23 per cent earn more than 50,000euros annually) than high profile investors (Table 4). However, there is no statisticallysignificant difference between moderate and low profile investors (as far as age,education and income level are concerned). This group has a moderate profile, as faras the psychological biases and personality traits are concerned, which means thattheir levels of overconfidence (mean 24.87), risk tolerance (mean 26.03), self monitoring(mean 10.85) and social influence (12.02) range between the high and lowprofile investors‟ level of means (Table 2).

Low Profile InvestorsThe group of low profile investors includes a significantly lower percentage ofmen than the high profile investors. Further, they are significantly older (43 per centof them are above 45 years old) and have a significantly lower educational level (only16 per cent hold a Master degree) and a significantly lower income than the investorsincluded in the high profile group. In addition, this group of investors mainly consistsof individual investors (88 per cent) in contrast to the high profile investors‟ groupwhere 58 per cent are professionals.The major characteristic of this group of investors is the low score on all thepsychological biases and traits, which characterizes investors who are included in thisgroup. Low profile investors have a low score on overconfidence (mean 19.52), whichmay be due to their small investment experience. Glaser et al. (2005) have found thatindividuals‟ degree of overconfidence is significantly lower than the respective degreeof professionals. Furthermore, Griffin and Tversky (1992) have found that nonexpertsare less overconfident than experts.

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Moreover, they are significantly less risk tolerant (mean 25.39) compared withhigh profile investors (but without having a statistically significant difference fromthe moderate profile investors). This can be explained by the high age, low incomeand low educational level that the investors with this profile have, which are found tobe related to risk intolerance. More specifically, Hira et al. (2007) have found thathigher age and lower income decreases risk tolerance. Similarly, Cicchetti and Dubin(1994) and Grable et al. (2004) have found that people with low incomes have lowerrisk tolerance than people with higher incomes. Low risk tolerance can also beexplained by the fact that more individuals are included in this profile than in any ofthe other profiles. There are previous studies that have examined the level ofinvestors‟ (in)experience and their risk (in)tolerance and have found that there is astatistically significant relationship between these two parameters (Hong et al., 2000;Lamont, 2002). Additionally, another parameter that may influence the level of risktolerance of investors in this profile is that the number of women included in thisprofile is higher than in any of the other two profiles. Weber et al. (2002) and Grableet al. (2004) claim that women are less risk tolerant than men. Also, low profileinvestors are low self-monitoring (mean 8.52) investors and they have a significantlylower degree of social influence (mean 11.28) compared with the other groups.<Table 6>

4.2 Trading BehaviourTable 6 presents the trading characteristics of the 3 profiles. In detail, therespondents reported that their average portfolio value is about 70,000 euros whileeach stock transaction is about 9,000 euros. Regarding their stock performance, theyclaim that their average stock return is about 14 per cent, while their profitable stocktransactions are about 56 per cent of the total number of transactions.Moreover, the respondents were asked to describe their portfolio allocation(Table 6). The results have shown that, on average, 55 per cent of their portfolio isinvested in stocks, 24 per cent in saving accounts and the remaining 21 per cent inmutual funds, bonds and other investment products. It is interesting to see that thesepeople mainly invest in ASE 20 (blue chips), 53 per cent of their stock portfolio,while the remaining 47 per cent is invested in stocks of firms with medium or smallcapitalisation. Another finding worth mentioning is that 42 per cent of the respondentsadmitted that they insist on buying some specific stocks despite their consistently badperformance. It is not surprising, therefore, that it is found that the majority (61 percent) of investors do not have a clear investment policy when they buy stocks. Only20 per cent of the participants follow a stop loss policy, 17 per cent prefer a maxprofit policy and 16 per cent adopt a target price policy.In addition, the respondents were asked to define the major factor thatsignificantly influences their investment decisions. They reported that the mostimportant factors are the negative climate in the Athens Stock Exchange (21 per cent),the stock prospects (15 per cent) and various personal/psychological reasons (10 percent). Additionally, the respondents were asked to define the information sources theyuse during their decision-making process. The findings reveal that newspapers, TVnews, and fundamental and technical analyses are the most important information

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sources (Table 8). As it is probably expected, investors with different profiles differ asfar as their attitude toward stock trading is concerned. A detailed discussion aboutthese differences follows in the next few paragraphs.<Table 7><Table 8>

High profileThis is the most experienced group as far as investments are concerned (70 per

cent of them have more than 10 years of investment experience). Regarding theirinvestment horizon, fewer of them (48 per cent) invest on a long-term basis,compared with investors from the other profiles with a similar strategy. On the otherhand, the number of high profile investors following a medium-term investmentstrategy is significantly higher (37 per cent) than the number of medium or low profileinvestors who follow a similar strategy (Table 8).Surprisingly, even though short-term investment was expected to be morepopular among high profile investors, since investors in this profile trade morefrequently, it was found that the percentage of them following this strategy is higher(but with no significance) only compared with the percentage of moderate profileinvestors who follow a similar strategy. This result contradicts that of Wood andZaichkowsky (2004), who found that overconfident investors have a shorterinvestment horizon than low confident investors.

Moreover, they have a significantly higher portfolio value (average 111,136euros) compared with low and moderate profile investors. The higher portfolio valueof this group may be explained by the large number of professional investors who areincluded in this group. Sharma (2006) has found that professional investors investlarger amounts than non-professional investors. Coval et al. (2005) also claim thatindividual traders almost always trade smaller positions than professional traders.

Furthermore, high profile investors have higher stock volume (average 13,542euros per transaction) compared with investors in the other 2 groups. Glaser (2003)has documented that the higher the stock portfolio value, the higher is the averagetrading volume per stock market transaction. In addition, there are lots of studies thatrelate the high degree of overconfidence (high profile investors are overconfident) andtrading volume. For example, Dow and Gorton (1997), Gervais and Odean (2001) andGlaser and Weber (2007) suggest that the higher the degree of one‟s overconfidence,the higher the trading volume.

Additionally, they check stock prices more frequently (86 per cent of themcheck stock prices daily) and, also, trade more frequently (32 per cent make stocktransactions at least weekly) than investors in either of the other 2 groups. Thistrading behaviour may be the result of high overconfidence, high risk tolerance andgood investment experience. To support this argument, Deaves et al. (2003) andGrinblatt and Keloharju (2009) have found that overconfidence increases tradingfrequency. Moreover, a number of studies have shown that risk-tolerant investorstrade more frequently than risk-intolerant investors (Tigges et al., 2000; Wärneryd,2001; Clark-Murphy and Soutar, 2004; Wood and Zaichkowsky, 2004; Dorn andHuberman, 2005; Durand et al., 2008). Also, Shapira and Venezia (2001) have found

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that professionals trade much more frequently than individuals. Further, Alemanniand Franzosi (2006) have found that high self-monitoring (as high profile investorshave) increases trading frequency.

The most important finding is that investors from the high profile group reportsignificantly higher stock returns (average 21 per cent) and significantly moreprofitable stock transactions (average 60 per cent) than investors in the other groups.This is expected as there are other studies that underline the relationship between highoverconfidence and stock profits. DeLong et al. (1990) and Wang (2001), forexample, have found support for overconfident investors earning higher returns thanless confident investors. In addition, Biais et al. (2005) have found that high selfmonitoringinvestors earn higher stock returns than low self-monitoring investors.Further, Statman and Thorely (1999) claim that high stock returns are correlated witha high trading volume. The high stock returns of this group may again be explained bythe large amount of professional investors. Chan et al. (2004), investigating the meanMonday returns, have found that professional investors‟ stock portfolios earn higherreturns than the non-professional investors‟ stock portfolios. Additionally, Kourtidiset al. (2011), comparing professional and individual investors, have shown evidencesthat professionals have higher performance than the individual ones as far as stocktrading is concerned.

What was probably not expected is the favourable investment policy thepeople of this group prefer. It is found that 60 per cent of them prefer the “wait andsee” policy. However, another 63 per cent (the sum is higher than 100 per centbecause they could choose more than one option) make use of the target price (25 percent), max profit (19 per cent) and stop loss (19 per cent) investment policies. It isreasonable then to assume that most of the high profile investors always have in minda “plan B” strategy. Of course, this plan “B” seems to differ depending on the profileeach investor belongs to. More specifically, high profile investors prefer the “targetprice” policy, while moderate profile investors prefer the “max profit” policy. On theother hand, low profile investors definitely prefer the “stop loss policy”.The most significant sources of information are the fundamental and technicalanalyses (64 per cent) followed by balance sheets (54 per cent) and financialannouncements (42 per cent). These sources have a significantly greater impact onhigh profile investors than on investors of either of the other 2 profiles. This wasrather expected since high profile investors trade more frequently than other investors.Wood and Zaichkowsky (2004), for example, have found that overconfident investors(as high profile investors are), rely more heavily on financial statements.

The investment decision making of most (28 per cent) of the high profileinvestors is mainly influenced by the environment of the Athens Stock Exchange.Stock prospects, their personal and psychological status and stock returns are theother more influential parameters. In addition, almost half (46 per cent) of the highprofile investors admit that they insist on holding or even buying specific stocksdespite their consistently bad performance. Surprisingly, almost the same number (52per cent) of the moderate profile investors but only 30 per cent of the low profileinvestors exhibit a similar investment behaviour.<Table 9>

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Moderate profileThe investment experience of moderate profile investors is significantly lessthan high profile investors (only 47 per cent have more than 10 years of investmentexperience) but significantly higher than low profile investors. They have a moderateportfolio value (62,211 euros) which is significantly less than the portfolio value ofhigh profile investors but (insignificantly) higher than the portfolio value of the lowprofile investors. Their stock volume (6,599 euros per transaction) is significantly lessthan that of the high profile investors but it does not significantly differ from that ofthe low profile investors. Furthermore, the frequency of stock transactions, the levelof stock returns and the profitable stock transactions are moderate (significantlydifferent compared with the investors of the 2 other profiles except for profitablestock transactions between the moderate and low profiles) compared with the stockreturns of the investors in the 2 other profiles (Table 4). Moderate profile investorshave also reported that their preferred investment policy is “wait and see” (53 percent). In the second place of their preferences is the max profit policy (23 per cent),which is different from the second option of the high profile investors (target price, 25per cent) and the low profile investors (stop loss, 23 per cent).Further, as far as the sources on investment information are concerned,moderate profile investors are significantly influenced by financial announcements,fundamental and technical analyses and balance sheets but also by TV news,newspapers and the Internet. In addition, stock prospects and the Athens StockExchange environment have a significant impact while personal and psychologicalreasons also play their role in the decision-making process of investors in themoderate profile group.

Low profileLow profile investors have less investment experience than investors in anyother group (only 30 per cent have more than 10 years of investment experience).They check stock prices rarely (37 per cent of them check stock prices daily) and alsoinvest in stocks rarely (only 4 per cent of them make stock transactions daily while 64per cent of them semi-annually). This may be explained by the risk intolerance thatcharacterizes investors in this profile.A number of studies have shown that risk-intolerant investors trade lessfrequently than risk-tolerant investors (Tigges et al., 2000; Wärneryd, 2001; Clark-Murphy and Soutar, 2004; Wood and Zaichkowsky, 2004; Dorn and Huberman, 2005;Durand et al., 2008). Moreover, this would be a result of the large number ofindividuals in the profile. Shapira and Venezia (2001) have found that individualstrade less frequently than professionals.Furthermore, low profile investors have a significantly lower portfolio value(32,845 euros) and lower stock volume (6,421 euros per transaction) than high andprofile investors and medium profile investors (with the exception of stock volume,which is not statistically significantly different from that of moderate profileinvestors). The low portfolio value of this group may again be explained by the largeamount of individual investors, which is in line with Sharma (2006). For example,Coval et al. (2005) and Sharma (2006) claim that individual investors trade smaller

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positions than professional investors. The main finding is that stock returns (9.81 percent) are significantly less than those of investors in any other group, while theprofitable stock transactions (52.53 per cent) are significantly lower compared withthose of high profile investors (there was no statistically significant relationship withmoderate profile investors). This may be explained by the low confidence level thatlow profile investors have. DeLong et al. (1990) and Wang (2001) suggest that lessconfident investors earn lower returns than overconfident investors. In addition, Biaiset al. (2005) have found that low self-monitoring investors earn lower stock returnsthan high self-monitoring investors.Further, they have a higher level of short-term investments compared withmoderate profile investors, which is also slightly higher than the high profileinvestors. On the other hand, there is a significantly lower level of medium-terminvestments compared with high profile investors (there is no statistically significantdifference from moderate profile investors). Their sources of investment informationmainly include newspapers (44 per cent) and TV news (36 per cent) and the impact offriends is significantly higher than in any other investors‟ profiles (maybe becausethey have lower investment experience).As far as the investment policy followed is concerned, while low profileinvestors prefer (except for the “wait and see policy” of 70 per cent) the stop losspolicy (23 per cent), there is no significant difference between all the other investors‟profiles. Additionally, they have a significantly lower preference for max profit (10per cent) and target price policy (4 per cent) compared with the other investors‟groups.Low profile investors reported that mainly the Athens Stock Exchange has asignificantly greater impact on their investment decision making than high profileinvestors (without having a statistically significant difference from the moderateprofile). Specifically, low profile investors are more influenced by their liquidity thanthe other two groups. This was expected since this group has a lower income level.Additionally, investors with a low profile who insist on buying specific stocks (inspite of their bad performance) are fewer than the investors from the other twoprofiles who have the same investing behaviour.

5. Summary and ConclusionsBased on the evidence provided by the literature it becomes apparent thatinvestors‟ stock trading behaviour (including stock performance, stock volume andstock frequency) is affected by personality traits and psychological biases(Overconfidence, Risk tolerance, Self-monitoring, Social influence).However, it should be stressed that each of these psychological biases affect ina different way each of the three dimensions of trading beahaviour examined in thisstudy. For example, although, overconfidence positively affects stock trading volume(Dow and Gorton, 1997; Glaser and Weber, 2007, Deaves et al., 2003), as well asstock trading frequency (Alemanni and Franzosi, 2006; Grinblatt and Keloharju,2009; Glaser and Weber, 2007), the evidences about its relation with stockperformance are both positive (DeLong et al., 1990; Wang, 2001) as well as negative(Barber and Odean, 2001a; Benos, 1998; Daniel et al., 1998; Odean, 1998b; Philip,

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2007).The main concern, therefore, is that one should look at the relationshipsbetween trading behaviour dimensions and the various psychological biases at an oneto-one basis in order to be able to come up with a meaningful conclusion concerningthe overall effect at these biases on trading behaviour.In this study, cluster analysis identified three investor profiles, the low,moderate and high investor profiles, with each one of them exhibiting differenttrading behaviour. The results of the analysis show that the higher the investors‟profile, the higher the performance of these investors on stock trading. Unfortunately,one may assume that the characteristics of high profile investors lead to a winningstrategy in stock markets. In this research, high profile investors are those scoringhigh levels on the psychological biases and personality traits examined. Specifically,they are overconfident and risk-tolerant investors with, also, a high degree of socialinfluence and self-monitoring, who have better performance than investors from otherprofiles. Thus, these investors own high-value portfolios, trade high volumes of stocksand make transactions more frequently compared with investors from the otherprofiles. Therefore, high trading frequency and high stock volume do not negativelyaffect investment performance but may lead, under specific conditions, to a betterperformance. Also, high risk taking and high overconfidence seem to influence stockreturns positively (high profile investors‟ results).Furthermore, high profile investors, among other sources of investmentinformation, emphasize the information provided by fundamental and technicalanalyses and, generally, financial statements. Some other characteristics of investorsin this profile are the target price investment policy they adopt and their largeinvestment experience. High profile investors‟ trading behaviour may be explained bythe large proportion of professional investors (Shapira and Venezia, 2001; Sharma,2006) who are included in this group, but also by their high degree of overconfidence,risk tolerance and self-monitoring (Wang, 2001; Biais et al., 2005; Dorn andHuberman, 2005; Durand et al., 2008; Grinblatt and Keloharju, 2009). On the otherhand, low profile investors underperform in stock markets, trade rarely and theirmajor characteristics, compared with the investors in other groups, are their lowscores on psychological biases, personality traits and investment experience.Additionally, moderate profile investors‟ level of psychological biases, personalitytraits and trading performance is somewhere between the high profile and low profilelevels.This is an exploratory study to be used as a starting point for the understandingof the characteristics (Overconfidence, Risk Tolerance, Social Influence, Self-Monitoring) of investors (including both individuals and professionals) and theirtrading behaviour. The results show that high scores on psychological biases andpersonality traits (thus Overconfidence, Risk Tolerance, Social Influence and Self-Monitoring) are associated with high scores on aspects of trading behaviour such astrading performance, trading frequency and trading volume. This study may provideinvestment advisors with a framework to understand clients‟ attitude and thus allowadvisors to give better advice to their clients depending on each client‟s profile.Finally, this study also offers insights into investors, as they can understand the

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trading behaviour of each investor‟s profile and compare it with their own investmentcharacteristics, their trading behaviour and their performance. Ultimately, it willprovide a framework that will help investors understand how biases and traits affectinvestment decisions and thus they may be able to become aware of and overcomethem.One major limitation of this study is that it is based on the self-assessed biases,traits and trading behaviour of each respondent. It is important for future research tobe directed towards collecting more objective data as far as these crucial parametersare concerned.Moreover, it is not clear which are the stronger parameters that actuallyinfluence trading behaviour. Further research could expand the scope of this researchby examining the magnitude of the effect of these parameters on investor tradingbehaviour. Also, a direct comparison between individual and professional investors‟trading behaviour would enhance our knowledge/comprehension of investors‟ tradingbehaviour.

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