Microsoft Word - SensationSeekingFinal 01092008 submitted.doc

Sensation Seeking, Overconfidence,

and Trading Activity

Mark Grinblatt

UCLA Anderson School of Management and NBER

Matti Keloharju

Helsinki School of Economics and CEPR

January 9, 2008

ABSTRACT

This study analyzes the role that two psychological attributes—sensation seeking

and overconfidence—play in the tendency of investors to trade stocks. Equity

trading data from Finland are combined with data from investor tax filings,

driving records, and mandatory psychological profiles. We use these data,

obtained from a large population, to construct measures of overconfidence and

sensation seeking tendencies. Controlling for a host of variables, including

wealth, income, age, number of stocks owned, marital status, and occupation, we

find that overconfident investors and those investors most prone to sensation

seeking trade more frequently.

Keywords: Sensation Seeking, Overconfidence, Trading Activity

JEL classification: G110, G120

We would like to thank the Finnish Vehicle Administration, the Finnish Armed Forces, the

Finnish Central Securities Depository, and the Finnish Tax Administration for providing access to

the data, as well as the Office of the Data Protection Ombudsman for recognizing the value of this

project to the research community. Our appreciation also extends to Antti Lehtinen and Juan

Prajogo, who provided superb research assistance, and to Narasimhan Jegadeesh, Samuli Knüpfer,

Lisa Kramer, Juhani Linnainmaa, Tyler Shumway, Ivo Welch, and seminar participants at the

Hong Kong University of Science and Technology, the University of Illinois, the London Business

School, the London School of Economics, the University of Mannheim, the University of

Michigan, Oxford University, the University of Texas, the University of Vienna, the SFM, and the

Western Finance Association, who generated many insights that benefited this paper. We also

thank Seppo Ikäheimo for his help in obtaining the data and Markku Kaustia, Samuli Knüpfer,

Lauri Pietarinen, and Elias Rantapuska for participating in the analysis of the Finnish Central

Securities Depository data. Finally, we are especially thankful for the detailed comments of an

anonymous referee, an associate editor, and the editor, Campbell Harvey. Financial support from

the Academy of Finland, the Foundation for Economic Education, and the Paulo Foundation is

gratefully acknowledged.

Recently, empiricists have begun to study and document that behavioral attributes influence

trading volume.

This evidence is compelling but it is difficult to conclusively argue that

particular traits influence trading without better data. Much of the data used in the past to

establish a connection between behavioral attributes and trading is experimental or aggregated

across individuals. When actual trades are studied at the individual level, the results come

from self-reported surveys, sometimes combined with brokerage trading records. These

surveys and trading records often are based on a limited number of individuals, and

sometimes have timing issues where performance and turnover affect an investor’s desire to

respond to the survey. They also tend to lack data on variables that might disparage claims of

omitted variable or endogeneity biases as the source of the results. Even in the best case

scenarios, control variables are self-reported, with no consequences for distortion by the

reporting investor.

Even studies that avoid the inherent problems of surveys can leave many questions

unanswered for lack of better data. As just one example, the seminal paper on

overconfidence, Barber and Odean (2001), uses gender as an instrument for overconfidence.

Since gender is related to trading—the portfolios of males exhibit greater turnover—they

conclude that overconfidence is responsible for trading. Gender, however, is linked to a

substantial number of other attributes that might affect trading. For instance, sensation

seeking, a measurable psychological trait linked to gambling, driving, drug abuse, and a host

of other behaviors, is more abundant in males. This variable, which is not controlled for in

earlier studies, could account for some of the differences in trading activity between genders.

The contribution of this paper lies in being the first study to specifically focus on

sensation seeking as a motivation for trade. It also is the first study to employ comprehensive

data from a validated psychological assessment to directly measure overconfidence and

analyze its relationship to trading. Using a comprehensive dataset from Finland, which offers

what arguably might be the best set of control variables available for a study of this kind, we

show that investors who are most prone to sensation seeking and those who are most

overconfident trade the most. We now define these concepts.

Sensation seeking is a stable personality trait, studied in the psychology literature,

which varies across individuals.

Those who are sensation seekers search for novel, intense,

and varied experiences generally associated with real or imagined physical, social, and

financial risks. The trait generates behaviors in many arenas that are less frequently observed

among those endowed with lower degrees of the sensation seeking trait: These include risky

driving, risky sexual behavior, frequent career changes, drug and alcohol abuse, participation

in certain types of sports and leisure activities (like bungee jumping or roller coaster riding),

and gambling.

Sensation seeking behavior crosses many domains; hence, a poker player or a

traffic violator may show sensation seeking behavior in other arenas.

Trading fits the

definition of a sensation seeking behavior. Participation in the stock market is perceived to be

financially risky, but in the absence of trading, lacks novelty and variety. Gambling is also

risky, but repeated gambling adds novelty and variety. A single bet may not be as satisfying

to the sensation seeker as a series of smaller and distinct bets (even though the latter has less

volatility).

It is the novelty of the new stock in one’s portfolio, or the change in one’s

position in a stock that provides consumptive utility to the sensation seeker. Because of this,

a diversified portfolio can be as stimulating to the sensation seeker as a non-diversified

portfolio. However, a stale portfolio is not as exciting as a fresh one.

One could argue that a series of stock positions in a single stock is more stimulating to

a sensation seeker than a diversified portfolio where one has minute changes to each position.

This would mean that there are some stock investment behaviors that can be driven either by

sensation seeking or by particular risk aversion parameters. Our analysis, however, is focused

on trading per se, which (except for negligible rebalancing motivations) is not driven by risk

aversion parameters.

Moreover, we control for the number of stocks in the investor’s

portfolio. Among all investors with the same degree of diversification, the sensation seekers

should trade more.

Sensation seekers find trading entertaining, but that does not mean that those who find

trading entertaining are sensation seekers. It is the variety, novelty, and perceived risk of

trading that makes trading (as well as other sensation-related activities) entertaining. If

trading is merely entertaining, in the same sense that television or golf is entertaining, there

would be no difference in the proclivity to trade between sensation seekers and those who

lack the trait. If trading is purely a leisure activity, those motivated by a relatively greater

utility from leisure would trade the most frequently. We would observe that golfers and

“couch potatoes” would trade the most, ceteris paribus. If trading is motivated by sensation

seeking, those who take pleasure in sensation seeking activities—risky driving, drugs, risky

sports, gambling, etc.—would trade the most.

Zuckerman (1994), one of the pioneers of the concept, developed an assessment scale

for sensation seeking, for which we lack data. Our study makes use of a rare dataset as a

substitute. We measure sensation seeking as the number of automobile speeding convictions

earned by an investor over a multi-year period. Zuckerman (1994), as well as Jonah (1997),

suggest that driving behavior may be one of the best observed behaviors for assessing

sensation seeking. Data on speeding tickets from Finland is particularly pertinent with respect

to the financial risks associated with this trait. In Finland, the fine for substantive automobile

violations is a function of income. Thus, those who risk breaking the law do so under severe

financial penalty as well as enduring possible physical risks.

Overconfidence is the tendency to place an irrationally excessive degree of confidence

in one’s abilities and beliefs. This definition has evolved into two different interpretations.

The first is hubris or what is sometimes referred to as the “better than average effect.” One

can think of this as an irrational shift in the perceived mean. The other is “miscalibration.”

This arises when the confidence interval around the investor’s private signal is tighter than it

is in reality. This can be thought of as an irrational shift in perceived variance.

Both forms of overconfidence lead the overconfident investor to form posteriors with

excessive weight on private signals. In the former case, the weight on one’s private signals

irrationally ignores Bayes rule and says “I am right;” in the latter case, Bayes’ rule is known

but not implemented properly because the variance parameter in the weight is incorrect. In

either case, the private valuation of a stock will differ from that of the market as the

overconfident investor places more validity on his private valuation and less on the market’s

valuation. This generates a larger willingness to trade than would be observed in a less

confident investor. The link between overconfidence and trading activity has a recent

theoretical and empirical literature behind it.

We derive the overconfidence measure from a standard psychological assessment.

This test is given to all Finnish males upon induction into mandatory military service.

(Generally, this is at the age of 19 or 20, and, for most investors, it is many years prior to the

trading activity we observe.) One of the scales from the test measures self-confidence. As

this confidence measure is a combination of competence and overconfidence, we use

regression analysis to control for competence and obtain overconfidence as the residual effect.

Because of the mandatory and comprehensive nature of the psychological examination, the

responses lack the bias typically associated with the decision of whether to answer a survey.

Our data are based on a scientifically designed assessment, not a survey. From the description

and details we have obtained about the test, (which are largely confidential for obvious

reasons), it appears as if few, if any of the questions are related to standard calibration

assessment. The assessment is more geared to one’s views of personal abilities, social image,

and self-worth. Hence, after we take out competence, our overconfidence measure appears

to be far closer to a better than average effect than to a miscalibration bias.

The correlation between our sensation seeking and overconfidence measures is very

low, so both behavioral attributes have relatively independent influence on trading. Sensation

seeking is less related to the decision of whether to trade at all and more related to the

decision of how much to trade. Although the number of trades is influenced by

overconfidence, our analysis does not find a relationship between overconfidence and

turnover. The lack of findings here (as in any study) could be due to noisy measurement.

This also applies to the comparisons between sensation seeking and overconfidence.

Our paper also studies portfolio performance after transaction costs. Every investor

group, sorted on the basis of its sensation seeking and overconfidence tendencies, exhibits

negative performance after transaction costs. We measure performance as the returns of past

buys less the returns of past sells for that investor group, adjusted for transaction costs. There

is no support for a claim that trading is rational and profitable for any grouping of investors

sorted on the basis of their psychological traits.

The paper is organized as follows: Section I offers motivation for the paper and

describes the data. Section II presents the results on sensation seeking, overconfidence, and

trading activity. It also includes a discussion of performance after transaction costs. Section

III concludes the paper.

I. Motivation and Data

The literature in finance is ripe with stylized facts about investor behavior. One of the

most prominent is that trading propensity is related to gender.

Figure 1 Panel A plots the

average number of trades per year as a function of age and gender. Consistent with earlier

findings, men trade more than women within all age groups. Panel B effectively offers the

same plot but takes out the effect of income, wealth, and the number of stocks in the portfolio.

It does this by plotting coefficients and sums of coefficients from a regression of a person’s

average number of trades on age dummies, the product of age dummies and a male gender

dummy, and control variables for income and wealth deciles. The plot for females represents

the coefficients on the age dummies; the plot for males represents the sum of the coefficient

on the age dummy and the product of the age dummy and male gender dummy.

The relation between age and trading in Panel B differs a bit from that in Panel A.

Except for very old and very young people, Panel A suggests that age has little effect on

trading. By contrast, age is inversely related to trading for most ages in Panel B except for the

very young. In both graphs, those under 23 at the start of the sample period experience a

positive relationship between age and trading. This is expected: as one moves from the

college (and military service) years to one’s early career years, we would expect trading to

increase. There also are large periods in the Panel A graph where age does not influence the

gap in trading between men and women. The “gender gap” in trading is about the same for

those born between 1940 and 1960, and it is wide in the middle while narrow at the tails. By

contrast, when we control for income and wealth differences related to age and gender, Panel

B indicates that the gender gap diminishes with age among those who are middle aged. Still,

both panels indicate that males trade more than females, irrespective of age.

What lies behind the greater tendency of males to trade? One possibility, advanced by

Barber and Odean (2001), is that males are more overconfident than females. Another is that

males are more prone to sensation seeking, and thus enjoy the thrill of trading to a greater

extent than females. Panel C plots the number of speeding tickets, a proxy for an investor’s

degree of susceptibility to sensation seeking, as a function of age and birth year. Except for

those under 23 at the start of the trading sample period, there are similarities between the two

graphs in Panels B and C. There is a gender gap in speeding tickets and it diminishes with

age, provided one was born prior to or during 1973. Of course, for those born after 1973,

particularly the youngest males, tickets diminish with age, quite dramatically, but trading in

the stock market increases.

One has to be cautious about drawing conclusions from the similarities between

Panels B and C. As Ameriks and Zeldes (2004) and others point out, it is very difficult to

disentangle cohort, age, and time effects from each other. Still, the intriguing similarity

between Panels B and C for those born before 1974 suggests that it might be interesting to run

a horse race between sensation seeking and overconfidence if one had reasonable measures of

these attributes for each investor. We are fortunate to be able to analyze such data.

A. Sensation Seeking

The classic characterization of sensation seeking is found in Zuckerman (1994, p. 27).

He labels sensation seeking as “… a trait defined by the seeking of varied, novel, complex,

and intense sensations and experiences, and the willingness to take physical, social, legal, and

financial risks for the sake of such experience.“

With respect to trading activity, sensation seeking is distinct from the magnitude or

sign of the risk aversion parameter. For example, the willingness to take on an undiversified

trading strategy may be encouraged by the consumptive value associated with sensation

seeking, yet deterred by a high degree of risk aversion. The mix of these two competing

forces may determine the degree of diversification. However, as Barber and Odean (2001)

observe, an investor’s risk aversion parameter has little bearing on desired trading frequency.

The mere act of trading and the monitoring of a constant flow of “fresh stocks” in one’s

portfolio may create a more varied and novel experience than a buy and hold strategy, and it

is likely to have adverse financial consequences because of trading costs, but it does not

increase volatility per se.

Sensation seeking also appears to be distinct from the self-monitoring dimension

studied by Biais et al. (2005).

Bell et al. (2000) analyzed what accounts for differences in

risky behavior across groups of students who differed in their self-monitoring and sensation

seeking tendencies. They found that any differences are largely accounted for by differences

in the sensation seeking attribute. Group differences in risky behavior across the self-

monitoring dimension are due to a correlation between the self-monitoring and sensation

seeking attributes.

There is reason to believe that males are more prone to sensation seeking behavior.

As Zuckerman (1994) points out, males are more prone to risky sporting activities. While

some of this may be explained by physical traits, there also is a greater tendency among males

towards violence, alcohol, drugs, gambling, and most forms of illicit activity that is not as

easily explained. Even relatively safe sensation seeking behaviors, like high speed

amusement park rides, are more popular among males.

A review article by Jonah (1997)

documents that sensation seeking is significantly related to risky driving.

Men also differ from women with respect to the type of gambling they do. Potenza et

al. (2001) find that men prefer action-oriented forms of gambling, like blackjack, craps, or

sports betting, as opposed to passive escape-oriented gambling (e.g., slot machines, lotteries).

Biaszcynski et al. (1997) as well as Vitaro et al. (1997) suggest that action-oriented gambling

reflects a higher level of sensation seeking among males. Comings (1998) shows that

pathological gambling behavior may be transmitted genetically. Pavalko (2001, p. 34) likens

trading (as opposed to investing) to action-oriented gambling.

B. Overconfidence

The second explanation we investigate for the greater trading of males is

overconfidence. The literature suggests that there are significant gender differences in

overconfidence, measured as a better-than-average effect. For example, Deaux and Farris

(1977), Beyer and Bowden (1997), Beyer (1998), and Johnson et al. (2006) all find that men

have higher self perceptions than women despite the general lack of difference in their test

performance.

To assess whether this form of overconfidence explains trading, it would be useful to

directly observe a measure of overconfidence, rather than a measure that is tied to a gender-

based instrument. We have overconfidence data on a large sample of subjects, assessed from

an extensive psychological profile of those subjects. Our data also offer the possibility of a

much cleaner test of whether overconfidence causes trading. Ideally, in a controlled

experiment of whether overconfidence affects trading activity, all other attributes of the

subjects would be identical and only overconfidence would vary. In a social science

experiment, this ideal is not attainable. However, in our study, all of the subjects for whom

we have a direct measure of overconfidence happen to be male. Moreover, the age at which

we measure overconfidence is approximately the same across subjects (about 20). To

demonstrate a link between such a measure of overconfidence and trading activity would

indeed be remarkable, as it may imply that differences in overconfidence across individuals

persist throughout one’s lifetime and influence economic behavior. We also have data on a

large number of control variables that allow us to use traditional regression analysis to assess

overconfidence, with fewer concerns about omitted variables than one typically has in studies

of economic behavior.

C. Data Sources

Our paper’s analysis requires us to combine information from a number of datasets:

• FCSD data. This dataset records the portfolios and trading records from January 1,

1995 through November 29, 2002 of all household investors domiciled in Finland. The

daily electronic records we use are exact duplicates of the official certificates of

ownership and trades, and hence are very reliable. Details on this dataset, which

includes trades, holdings, and execution prices, are reported in Grinblatt and Keloharju

(2000, 2001). We study trading data from July 1, 1997 on for those individuals who

held stocks at some point between January 1, 1995 and June 30, 1997. The latter

requirement allows us to focus on the determinants of trading activity rather than on

whether an investor participates in the stock market in the first place. (The results are

qualitatively similar if we use all individuals in lieu of individuals who have invested

in the market before.) In addition to trading data, we use this dataset to measure

financial wealth and number of stocks held.

• HEX stock data. Closing transaction prices are obtained from a dataset provided by

the Helsinki Exchanges (HEX). In combination with the FCSD data, this dataset is

used to measure financial wealth and assess portfolio performance.

• FVA driver data. Data from the Finnish Vehicle Administration (FVA) were used to

obtain a set of subjects who have a normal vehicle driving license (as opposed to a

motorcycle or commercial driving license) as of July 1, 1997. The FVA data contain

all driving-related final judgments on each motorist in the provinces of Uusimaa and

East Uusimaa between July 1, 1997 and December 31, 2001. (These provinces

contain Greater Helsinki and represent the most densely populated areas in Finland.)

The judgments contain paragraphs about the nature of the violation that we coded

either as “speeding related” or “not speeding related.” Thus, we have comprehensive

records of tickets for speeding that were finalized over a four and a half year period.

We use these data to measure differences in the sensation seeking attribute across

investors. Driving record data is from drivers who both own and do not own a car.

The data also contain car ownership records, which we use in a robustness test.

• FAF psychological profile. This dataset, from the Finnish Armed Forces, helps us to

measure cross-sectional variation in overconfidence among investors. Around the

time of induction into mandatory military duty in the Finnish armed forces, typically

at ages 19 or 20, males in Finland take a battery of psychological tests. It includes a

leadership inventory test for which we have comprehensive data beginning January 1,

1982 and ending December 31, 2001. The leadership inventory assessment, which

includes 218 “agree” or “do not agree” questions, provides eight scales for leadership.

One of these scales is self-confidence, which is reported as a number from 1 to 9 (and

is designed to approximate a stanine in the overall sample of test takers). The

military’s self confidence measure combines data from 30 different self confidence

related questions. We convert this measure to an overconfidence measure using

regression techniques described later in the paper for all shareholders who have

driver’s licenses prior to July 1, 1997. The psychological profile also contains an

intellectual ability score. The test measures intellectual ability in three areas:

mathematical ability, verbal ability, and logical reasoning. FAF forms a composite

ability score from the results in these three areas. We use the composite ability score

in our analysis.

• FTA dataset. This dataset, from the Finnish Tax Administration, contains annual data

from the 1998 and 1999 tax returns of Finnish investors in the provinces of Uusimaa

and East Uusimaa, as well as data from a population registry. Variables constructed

from this source include income, age, gender, marital status, occupation, and

homeownership status. These variables are used as controls in regressions that explain

trading activity and regressions used to construct a measure of overconfidence for an

individual. We use 1998 data for all of the variables except for employment status,

which is first reported in 1999.

D. Variable Description and Summary Statistics

Our analysis largely consists of cross-sectional regressions, with some measure of

trading activity as a left hand side variable. The variables and the data sources for them are

described in Table I Panel A. The remainder of the table provides summary statistics on the

data. Panel B describes means, medians, standard deviations, and interquartile ranges for

most of the variables. Panel C provides detailed summary statistics on the self-confidence

measure. Panel D presents the correlation matrix for relevant variables.

As can be seen from Table I, Panel B, stock trades and speeding tickets are rare. Panel

C’s distribution of the self-confidence measure indicates that the highest and lowest measures

of self-confidence (1 and 9) also are relatively rare. Our sample of male drivers is a bit more

self-confident than the universe of males taking the assessment. Some of this may have to do

with the fact that we limit our sample to individuals who own stocks between January 1995

and June 1997. Thus, our sample is wealthier than the population at large. Panel D indicates

that the number of speeding convictions, self-confidence, and gender all have a fairly large

correlation with various measures of a subject’s trading activity, but self-confidence,

described later, has a negligible correlation with the number of tickets earned.

Consistent

with Figure 1 Panel A, age (without controls for income) does not display an obvious

relationship with trading activity. Panel D also indicates that gender per se (with a dummy

value of one being male) is more correlated with all measures of trading activity than are

measures of sensation seeking and self-confidence. However, gender also is highly correlated

with the sensation seeking attribute, as we hypothesized earlier.

II. Results

Our analysis has three parts to it. The first part studies sensation seeking and the role

it plays in trading activity. This analysis makes use of both males and females. The second

part jointly focuses on sensation seeking and overconfidence as explanations for trading

activity. Because our overconfidence score can only be computed for young and middle-aged

males, it contains fewer observations. The third part analyzes performance after transaction

costs, categorized by the investor’s degree of sensation seeking and overconfidence.

A. Sensation Seeking Results

Earlier, we mentioned that our proxy for sensation seeking is the number of final

convictions for speeding. Admittedly, speeding convictions are not a perfect instrument for

speeding because not all violators are caught. However, in Finland, where many fines are tied

to income, there is less reason to believe that the motivation for traffic violations is a rational

calculation based on the cost of one’s time. For example, Jussi Salonoja, a wealthy

businessman, received a 170,000 euro fine for driving 80km/hour in a 40km/hour zone, while

Anssi Vanjoki, a Nokia executive, received an 80,000 euro ticket for driving 75km/hr in a

50km/hr zone.

Moreover, because of the extreme cost of being caught, compliance with

traffic laws is likely to be greater in Finland than in the United States and most parts of

Europe. Speeding convictions are not a signal that one is simply the unlucky driver who is

almost randomly “fished out” from a sea of violators.

Table II reports regressions that explain three different measures of trading as a

function of this measure of sensation seeking and a host of control variables. The first

column, which uses probit estimation to study the decision of whether to trade or not, employs

all investors in the sample. The second column employs investors who trade at least once

and uses the natural logarithm of the number of trades over the sample period as the

dependent variable.

Because this sample is censored to exclude those who do not trade, we

use Heckman’s two stage procedure to estimate the coefficients. The first stage obtains a

Mill’s ratio from the probit regression in the first column. The second stage, estimated with

ordinary least squares, adds Mill’s ratio as an additional regressor to obtain consistent

estimates on the remaining variables. The third column uses the log of the Barber and Odean

(2000, 2001) measure of turnover as the dependent variable.

The coefficients in this column

are estimated with ordinary least squares.

The rightmost three columns report the

corresponding t-statistics for the coefficients. All t-statistics and standard errors in the paper

are robust, in that they are computed using White’s heteroskedasticity-consistent standard

error estimation procedure.

The regressors for Table II include the number of ticket convictions as a predictor of

trading activity. As can be seen from the bottom row, this measure of sensation seeking has

coefficients that are highly significant for all of the measures of trading activity. The first

column indicates that the z-score increases by .047 for each additional speeding ticket. For a

propensity to trade of 0.5, (which is approximately the unconditional probability of trading),

each additional ticket generates approximately a 2% increase in the probability of trading.

The second column indicates that the number of trades increases by a factor of 10% (that is,

multiplies the base number of trades by a factor of exp(.098)) for each additional speeding

ticket. The third column implies that each additional speeding ticket tends to increase

turnover by about 11% (that is, multiplies base turnover by a factor of exp(0.101)). These

effects control for age dummies and dummies for the number of stocks held in addition to the

controls reported in Table II.

The speeding ticket coefficients for the second and third columns in Table II are

similar when we run the regressions separately for males and for females, and are 50-100%

larger for car owners than for non-car owners. For males, the speeding ticket coefficients for

the number of trades and turnover regressions are .084, and .101, while for females, they are

.092, and .085, respectively. The probit regression in the first column has a coefficient on the

tickets variable of .033 for males and .067 for females. For car owners, (with coefficients

nearly identical to those reported in Table II) all of the coefficients are highly significant. For

non-car owners only, the coefficients for the three regression specifications have t-statistics of

1.96, 3.74, and 5.71, respectively.

We also obtain similar coefficients on the speeding tickets variable when we run the

regressions in the first two columns separately for buys and sells. For example, the probit

regression in the first column generates a coefficient of .045 (t = 5.75) when the buy dummy

is the dependent variable and .053 (t = 6.78) when the sell dummy is the dependent variable.

The fact that these are similar and that the regression with the buy dummy as the dependent

variable is highly significant dispels the notion that Table II’s results are driven by asset sales

to finance high fines for speeding.

In Finland, there are two types of speeding tickets. Mild violations—typically less

than 15 kilometers per hour over the speed limit—receive a flat fine and more severe

violations receive a fine related to income. When the Table II regressions employ both the

number of flat fine tickets and the number of income-related fines as proxies for sensation

seeking, we obtain similar coefficients on both regressors. For example, each additional

income-related fine increases the number of trades by a factor of 10.6% while each additional

flat fine increases the number of trades by a factor of 9.7%. However, the t-statistic on the

coefficient for the income-related fine, 9.00, is about three times larger than the t-statistic for

the number of flat fines. 3.05. The most likely explanation for the flat fine’s larger standard

error is that flat fines are rare (constituting 15.6% of tickets); officers rarely choose to enforce

the law for mild speeding violations. This standard error pattern, which gives a far larger

confidence interval for the flat fine coefficient, occurs for the other specifications as well.

Other things equal, the number of flat fines is positively and marginally significantly related

to log of turnover (beta = .064, t = 1.96), but the number of income-related fines has a

coefficient that is 70% larger (beta = .108, t = 9.60). For the trade dummy probit regression,

flat fines have a slightly larger coefficient but a far smaller significance level (beta = .052, t =

2.07) than that for the number of income-related fines (beta = .046, t = 5.09).

Greater degrees of sensation seeking should also be associated with the severity of a

speeder’s typical driving violation. Fines for the more severe violations in Finland, known as

“day fines,” are assessed (approximately) as a number of half days of foregone income. The

number of half days assessed, referred to as “days fined,” is based on the severity of the

infraction. The mean days fined, averaged only across day fine penalties earned by each

driver who has earned at least one day fine, is another proxy for sensation seeking. It

measures the average severity of an investor’s speeding violations. It has a significantly

positive coefficient when it replaces number of speeding tickets in the Table II specifications.

Compared to speeding tickets in the same regression, mean days fined also is a more

significant predictor of the decision to trade and the log of the number of trades when added

as an additional regressor to Table II’s specifications. In the regressions, each additional day

fine (about a half day of salary as a measure of severity) amounts to 0.77% more trades. The

standard deviation of the average number of day fines is 4.39. Thus, a one standard deviation

increase in the average number of day fines increases the number of trades by roughly 3.4%.

This is almost 60% larger than the effect of a one standard deviation increase in the number of

speeding tickets (0.586 more tickets) when the number of speeding tickets is used in the same

regression.

One also can conclude that Table II’s results are not driven by liquidity shocks that

jointly affect driving and trading behavior. The inclusion of four additional liquidity shock

variables—a dummy for getting married, divorced, or becoming unemployed, and change in

the number of dependents—lead to similar results while the shock variables are by and large

insignificant.

The reported coefficients on the control variables from Table II’s regression are

interesting in their own right and sensible. Financial wealth, income, and whether one is

employed in a finance-related profession are positively related to trading activity even after

controlling for the number of stocks in the investor’s portfolio. Also, being unemployed is

positively related to trading activity. Possibly, independently wealthy individuals trade for

their own account rather than work.

The gender effect in Table II’s regression—men trade more—is extremely strong,

even more so for single or widowed men. Thus, our proxy for sensation seeking does not

explain the relationship between gender and trading that has been documented in the

literature.

B. Overconfidence, Sensation Seeking, and Trading Activity

Barber and Odean (2001) contend that the relationship between gender and trading

activity is due to the greater overconfidence of men. We investigate this by controlling for

gender (focusing only on males) and looking at how variation in a direct measure of

overconfidence influences their trading activity. Our analysis also controls for number of

speeding tickets, a sensation seeking proxy, to assess whether overconfidence has any

marginal explanatory power for trading activity.

Overconfidence is derived from the FAF self-confidence scale, which is interpreted by

the FAF as follows:

“A person with a high score believes in himself. He views himself at least

as intelligent as others and believes he will manage in life, if necessary,

even without the help from others. He does not feel nervous or anxious in

social situations; he does not expect others’ approval and is not afraid of

others’ possible critique. A person with a low score is uncertain of

himself and he may hate himself and his outlook. He gives up easily when

facing difficulties and can even blame others for his failures: ‘he has been

given too difficult tasks.’ As a result of lack of self-confidence he feels

himself unsure and anxious in social situations, and can therefore avoid

particular individuals who are self-confident and view him critically.”

The self-confidence measure for an individual is transformed into an overconfidence measure,

which is a residual from a regression that uses controls for competence from the FAF, FTA,

and FCSD datasets.

Table III Panel A reports the coefficients and test statistics for this

regression. The controls include the regressors from Table II (except for number of speeding

tickets and the finance professional dummy) as well as the composite intellectual ability score

from the FAF assessment, which measures verbal, mathematical, and logical ability. (Our

results are virtually identical if we enter these dimensions of ability as separate scores in lieu

of the composite score.) Table III Panel A indicates that individuals’ self-confidence scores

are somewhat prescient about their future life success. Those who have greater FAF ability

scores, who later in life achieve greater income, marry, and hold down jobs, tend to be the

most self confident.

There also is an age pattern to the assessment. As age dummies represent an age range

of the subject in 1997, higher age dummies generally correspond to those who took the

leadership assessment in the more distant past (and to a small extent, those who entered

military service at a later age).

Those who took the leadership assessment most recently

exhibit the greatest self-confidence. One can only speculate about the reasons for this. On

the one hand, it may reflect generational differences and economic changes in Finland. The

successful economic growth of Finland and the waning influence of the Soviet Union (and

later Russia) may have produced ever growing confidence among army recruits. On the other

hand, our sample is filtered for those who own at least one stock during the 2 ½ years that

precede our sample period. This may select more confident subjects among the very young.

The residual from Panel A’s regression is our measure of overconfidence. The idea

behind this is that self-confidence, as measured by a scale from the Finnish Armed Forces

leadership assessment, is a combination of competence and overconfidence. Panel A’s

regression controls for competence and the residual represents overconfidence.

The first two rows of Table III Panel B provide direct evidence on the joint impact of

sensation seeking and overconfidence on trading activity. Sensation seeking is highly

significant except when measuring whether someone trades or not. The number of trades and

turnover are significantly related to sensation seeking, even after controlling for

overconfidence and the other regressors listed in the table, as well as unreported dummies for

birth year and number of stocks in the portfolio. The economic effect, where significant,

seems meaningful. For example, the middle regression indicates that each additional

speeding ticket generates approximately 7% more trades.

Overconfidence also is significantly related to trading (at the 5% level), except when

logged turnover is the dependent variable. In the case of logged number of trades, a unit

increase in overconfidence (a regression adjusted stanine) generates almost a 4% increase in

trades. Turnover is a bit of a mystery here. Barber and Odean (2001) found that the turnover

of males exceeded that of females and attributed that to the greater overconfidence of males.

However, within the male sample that took the FAF assessment, the sensation seeking proxy

appears to be better at explaining turnover than overconfidence.

Why is it that sensation seeking has little effect on the decision of whether to trade or

not, but overconfidence has such a large effect? One possibility is that sensation seekers

achieve stimulation with each trade; a single trade offers very little stimulus. However, this is

more likely a sample-specific finding: Restricting the sample so that it excludes older citizens

as well as women significantly weakens the predictive power of sensation seeking on the

decision to trade. This occurs even without the addition of the overconfidence variable. It

appears that women and older male investors who receive few speeding tickets do not trade.

The same cannot be said for the relatively younger and exclusively male group who are in the

FAF data sample. Even though their turnover ratios and number of trades are low, these

young males still trade on rare occasions.

One should also not read too much into comparisons between the overconfidence and

sensation seeking coefficients. Both of our measures of these concepts are noisy, downwardly

biasing estimates of their coefficients. Not all sensation seekers are caught speeding, nor is

sensation seeking the only motivation for a speeding ticket. Similarly, overconfidence is

ultimately derived from an assessment taken years before we observe trading. Even if this

assessment was able to generate a perfect measure of overconfidence at the time it was taken,

(which is unlikely), the measure becomes a noisy proxy for our purposes if overconfidence

changes over time.

This gives us some assurance that our conclusions are conservative

when we observe a significant effect. On the other hand, one should pause before drawing

strong conclusions from the absence of a significant effect.

C. Overconfidence, Sensation Seeking, and Performance after Trading Costs

If inside information motivates trades and this information is somehow related to the

sensation seeking or overconfidence attribute, the overconfident and sensation seeking

investors might trade more than others for rational reasons. If this was the case, the trades of

sensation seekers and overconfident individuals would exhibit superior performance. In the

model of Kyle and Wang (1997), for example, overconfidence survives as an attribute, as the

trades of these investors do not harm them. Here, we show the opposite. All classes of

investors, including those most overconfident and those most prone to sensation seeking,

exhibit negative portfolio performance. This is because transaction costs exceed any potential

profit that might be earned. This negative performance holds for multiple holding periods.

To assess performance, we examine the executed price of each buy trade and sell trade

for all investors in our sample. Table IV, using daily share price and return data, reports the

average of the differences between the subsequent one-day, five-day, twenty-day, and sixty-

day returns between each day’s (value-weighted) portfolio of buys and portfolio of sells,

formed from categories of investors grouped by their overconfidence and sensation seeking

attributes. Because we know transaction prices, the reported performance numbers in Table

IV include the intra-day return on the day of transaction. A five-day return thus contains a bit

more than five full days of returns. Reported performance also is net of the lowest available

commission rate, 8.42 euros + 0.15% of value, for each buy or sell transaction, making our

numbers conservative. Indeed, such a low commission was not available before the year

2000, a period that constitutes a portion of our sample. The lowest possible commission costs

can be computed quite accurately because we have the necessary details of each trade.

If the buy portfolios have the same risk as the sell portfolios, their differenced returns

should have means of zero. This is the standard test we look at. However, just averaging each

multi-day return, using daily data, generates a classic overlapping data problem. If factor

exposures on the buy and sell side are consistently different, consecutive multi-day returns

observed at daily frequencies are positively correlated; this contaminates standard test

statistics. The problem has to be resolved before assessing whether the average of the buy

minus sell return differences significantly differs from zero.

To better understand this overlap, consider the one-day and five-day returns of

portfolios of buys and sells on June 13 and 14 of the year 2000. For the June 13

portfolios,

execution took place sometime on June 13. The one-day returns of the June 13 buy and sell

portfolios stretch from a variety of intraday points on June 13 to the close on June 14. At

least a portion of the June 14 return difference from the June 13 trades overlaps with the

intraday returns of the one-day portfolio formed from buys and sells executed on June 14.

Similarly, the five-day returns of the June 13 portfolio stretch from the middle of June 13 to

the close on June 20. On average, 4 ½ days of returns overlap with the five-day returns of the

June 14 buys and sells. Hence, standard t-tests exaggerate significance here.

In lieu of a GLS procedure, such as Hansen and Hodrick (1980), to address the

exaggerated significance, we employ a variation of the technique used in Jegadeesh and

Titman (1993). The innovation we present has to do with assigning transaction costs and

intra-day returns to these trades. To understand the mathematics of this procedure, it is useful

to illustrate it with an example, which will be a 5-day return. For simplicity, initially ignore

the intra-day return and the commission cost. We also will ignore compounding, which is

negligible, so that, for example, we view a five-day return as the sum of the daily returns on

the five days comprising it.

The Jegadeesh and Titman (1993) insight is immediately apparent. With daily

observations, the full-day return from say Wednesday, June 14, 2001 (close on the 13

close on the 14

) appears in a rolling average of five-day returns as if it is computed as the

sum of five buy portfolio minus sell portfolio return differences, with initial transaction dates

of June 7, 8, 9, 12, and 13. Thus (except for compounding), the time series of daily buy-sell

return differences, from portfolios generated by the five most recent buy-sell portfolios, is

identical to the average of a rolling series of five-day returns, formed from each day’s buy-sell

portfolios. The advantage to the former is that the average daily buy-sell returns, computed

with the former method, are likely to be uncorrelated, due to market efficiency. The lack of

serial correlation within the sequence of such daily observed return differences allows an

ordinary t-test of significance: the ratio of the average to the standard error of the average. An

analogous approach can be applied to other rolling multi-day horizons.

Our innovation is that we account for intra-day returns and commission costs in the

computation. This is a bit trickier than one might think. The June 13, 2001 intraday return

difference from buys and sells on June 13 has to be added to the June 13 observed return

difference for the t-test to remain legitimate. If the intra-day return difference was added to

June 14, for example, the observed return differences on June 13 and 14, formed as described

above, would no longer be uncorrelated. Commissions are less problematic but we simply

subtract them from the observed buy or sell return on the same day. Recall that the

commission cost is 8.42 euros + 0.15% of the transaction value. If the amount bought (or

sold) by investors of a given category is V euros and the number of buy (sell) transactions is

N, the buy (sell) return is reduced by 8.42N/V + 0.0015. Combining intra-day returns by

adding them to the sum of the five buy returns (or sell returns), and adjusting the return for

commissions in this manner implies the unit of performance from averaging the daily return

difference sums is a 5-day abnormal return difference. Longer or shorter units apply

analogously in the Table IV columns for 1-day, 20-day, and 60-day return differences.

Table IV thus reports appropriate t-statistics for what effectively might be thought of

as the 1-day, 5-day, 20-day, and 60-day differences in the returns of the buy portfolios and the

sell portfolios of investors of a given category—grouped by sensation seeking or

overconfidence. These differences measure abnormal performance per dollar invested, after

transaction costs, of each group of investors. As can be seen, all of the forty differences are

negative, about half significantly so at the 5% significance level.

One cannot conclude from

this evidence that performance is the motivation for trading by these investor categories.

Does performance differ across portfolios with different sensation seeking and

overconfidence characteristics? Hirshleifer and Luo (2001) suggest that the worst

performance should come from the least and most overconfident investors. It is intriguing

that for three of the four investment horizons in Table IV, this holds true. However, in

addition to properly benchmarking the significance level for a multiple comparison,

there

are issues with omitted variables: Someone who trades frequently or in larger trade sizes, or

who is a finance professional, may have lower transaction costs per dollar of trade, yet end up

with higher aggregate costs from trading. Prior drafts of this paper have also analyzed

monthly returns (before transaction costs) of stocks bought and sold in the past by various

groupings of investors and none of these were significant. Therefore, we are reluctant to draw

inferences about relative ability from comparisons of numbers in Table IV.

III. Summary and Conclusion

This paper has shown that some portion of trading is driven by behavioral attributes.

Those who are sensation seekers (as measured by the number of speeding tickets received)

and those who exhibit more overconfidence (as measured by a psychological assessment of

each male entering the armed forces) trade more. Although our measure of sensation seeking,

which is derived from driving records, is correlated with gender, it does not account for

gender differences in trading activity.

These findings beg for investigation of other arenas where the behavioral attributes

studied here tend to operate. Uncovering links between these attributes and other predicted

economic behavior would suggest that our findings are part of a larger picture in which

certain behavioral attributes play a key role in microeconomic behavior. For example, if

sensation seekers are those who live for the moment, and overconfident investors have

irrationally optimistic beliefs about the value of their human capital or their health prospects,

we might expect these investors to have lower demand for insurance products (including life

and health insurance). Moreover, we might expect sensation seekers to have lower savings

rates and greater need for commitment in their consumption/investment plans.

Our results on how trading is affected by these attributes stem from analysis of a

dataset that has several advantages over those used to study related issues in behavioral

finance in the past. It is fairly comprehensive in the subjects it covers, lacks response bias,

and allows us to control for a number of other variables that might explain trading activity.

As a consequence of these controls, as well as additional tests, we do not believe that our

results on the relation between sensation seeking and trading activity are driven by investor

differences in risk aversion. First, our trading activity regressions control for the degree of

diversification in the investor’s portfolio by employing dummy variables for the number of

stocks held and additionally control for both income and wealth. Second, a proxy for an

investor’s risk aversion—the ratio of his equity wealth to his total portfolio wealth—appears

to be unrelated to that investor’s sensation seeking attribute. Therefore, it is not surprising

that employing this risk aversion proxy (in unreported regressions) as an additional control

variable in our trading activity regressions has little impact on the coefficients or test statistics

for the sensation seeking variable.

We have tried to be exhaustive in assessing whether other alternative rational

explanations, besides risk aversion, account for our findings. Examination of these

alternatives goes beyond what is reported in the paper. For example, our sensation seeking

findings might be due to an endogeneity bias arising from active traders locating in urban

areas where speeding enforcement is high. However, when we run Table II’s regression

separately for urban, suburban, and rural locations of residence, we find that there is a

significant relation between speeding tickets and trading activity in all areas, including the

rural areas. Another possibility is that income is not properly controlled for with our decile

proxies, but alternative income and wealth controls, including finer categorizations of income

and wealth at the extreme tails, do not seem to affect our results.

To further assess the validity of the sensation seeking explanation, we analyzed a

second plausible metric of sensation seeking: sports car ownership. Regressions (containing

the usual set of controls) of trading activity on a dummy variable for whether one owns a

sports car indicate that sports car ownership is significantly related to trading activity, albeit to

a lesser degree than the number of speeding tickets.

Despite the surprisingly strong results here, it is important to emphasize that the

degree of trading activity in financial markets remains an anomaly. We have not calibrated

our findings to suggest that sensation seeking and overconfidence explain a large proportion

of observed trading activity. Rather, what we have learned is that fairly stable behavioral

traits explain some cross-sectional differences in trading activity. This adds to the evidence

suggesting that rational motivations, like rebalancing, cannot explain the volume of trade,

because some of that volume appears to be clearly driven by behavioral motivations. Whether

better measurement of the behavioral motivations we have analyzed or whether some other

behavioral motivation can explain most of the observed trading activity is an open question

for future research.

REFERENCES

Agnew, Julie, Pierluigi Balduzzi, and Annika Sundén, 2003, Portfolio choice and trading in a

large 401(k) plan, American Economic Review 93, 193-215.

Ameriks, John, and Stephen Zeldes, 2004, How do household portfolios vary with age?

Working paper, Columbia University.

Barber, Brad and Terrance Odean, 2000, Trading is hazardous to your wealth: The common

stock investment performance of individual investors, Journal of Finance 55, 773-806.

Barber, Brad and Terrance Odean, 2001, Boys will be boys: Gender, overconfidence, and

common stock investment, Quarterly Journal of Economics 116, 261-292.

Ball, S., D. Farnhill, and J. Wangeman, 1984, Sex and age differences in sensation seeking:

some national comparisons, British Journal of Psychology 75, 257-265.

Begg, Dorothy and John Langley, 2001, Changes in risky driving behavior among young

adults, Journal of Safety Research 32, 491-499.

Bell, Nancy, Carol Schoenrock, and Keri O'Neal, 2000, Self-monitoring and the propensity

for risk, European Journal of Personality 14, 107-119.

Benos, Alexandros, 1998, Aggressiveness and survival of overconfident traders, Journal of

Financial Markets 1, 353-383.

Beyer, Sylvia, 1998, Gender differences in self-perception and negative recall biases, Sex

Roles 38, 103-132.

Beyer, Sylvia and Edward Bowden, 1997, Gender differences in self-perceptions: convergent

evidence from three measures of accuracy and bias, Personality and Social Psychology

Bulletin 23, 157-172.

Biaszcynski, A., Z. Steel, N. McCongaghy, 1997, Impulsivity in pathological gambling: the

antisocial impulsivist, Addiction 92, 75-87.

Biais, Bruno, Denis Hilton, Karine Mazurier, and Sébastian Pouget, 2005, Judgmental

overconfidence, self-monitoring and trading performance in an experimental financial market,

Review of Economic Studies 72, 287-312.

Comings, David, 1998, The molecular genetics of pathological gambling, CNS Spectrums 3,

20-37.

Daniel, Kent, David Hirshleifer, and Avanidhar Subrahmanyam, 1998, Investor psychology

and security market under- and over-reactions, Journal of Finance 53, 1839-1886.

Deaves, Richard, Erik Lüders, and GuoYing Luo, 2004, An experimental test of

overconfidence and gender on trading activity, Working paper, McMaster University.

Deaux, Kay and Elizabeth Farris, 1977, Attributing causes for one’s own performance: The

effects of sex, norms, and outcome, Journal of Research in Personality 11, 59-72.

Dickerson, Mark, 1984, Compulsive Gamblers, London, Longman Group.

Dorn, Daniel and Gur Huberman, 2005, Talk and action: What individual investors say and

what they do, Review of Finance 9, 437-481.

Feather, N. T. and J. G. Simon, 1971, Attribution of responsibility and valence of outcome in

relation to initial confidence and success and failure of self and other, Journal of Personality

and Social Psychology 18, 173-188.

Ferris, Stephen, Robert Haugen, and Anil Makhija, 1988, Contemporary volume and historic

volume at differential price levels, Journal of Finance 43, 677-697.

Gervais, Simon and Terrance Odean, 2001, Learning to be overconfident, Review of Financial

Studies 14, 1-27.

Glaser, Markus and Martin Weber, 2007, Overconfidence and trading volume, Geneva Risk

and Insurance Review 32, 1-36.

Graham, John, Campbell Harvey, and Hai Huang, 2005, Investor competence, trading

frequency, and home bias, Working paper, Duke University.

Grinblatt, Mark and Bing Han, 2005, Prospect theory, mental accounting, and momentum,

Journal of Financial Economics 78, 311-339.

Grinblatt, Mark and Matti Keloharju, 2000, The investment behavior and performance of

various investor-types: A study of Finland’s unique data set, 2000, Journal of Financial

Economics 55, 43-67.

Grinblatt, Mark and Matti Keloharju, 2001, What makes investors trade? Journal of Finance

56, 589-616.

Hansen, Lars and Robert Hodrick, 1980, Forward exchange rates as optimal predictors of

future spot rates: An econometric analysis, Journal of Political Economy 88, 829-853.

Hey, John, 1984, The economics of optimism and pessimism, Kyklos 37, 181-205.

Hirshleifer, David and Guo Ying Luo, 2001, On the survival of overconfident traders in a

competitive securities market, Journal of Financial Markets 4, 73-84.

Horvath, Paula and Marvin Zuckerman, 1993, Sensation seeking, risk appraisal, and risky

behavior, Personality and Individual Differences 14, 41-52.

Jegadeesh, Narasimhan and Sheridan Titman, 1993, Returns to buying winners and selling

losers: Implications for stock market efficiency, Journal of Finance 48, 65-91.

Johnson, Dominic D. P., Rose McDermott, Emily S. Barrett, Jonathan Cowden, Richard

Wrangham, Matthew H. McIntyre and Stephen Peter Rosen, 2006, Overconfidence in

wargames: Experimental evidence on expectations, aggression, gender and testosterone,

Proceedings of the Royal Society B 273, 2513-2520.

Jonah, Brian, 1997, Sensation seeking and risky driving: A review and synthesis of the

literature, Accident Analysis and Prevention 29, 651-665.

Kumar, Alok, 2006, Who gambles in the stock market? Working paper, University of Notre

Dame.

Kyle, Albert and F. Albert Wang, 1997, Speculation duopoly with agreement to disagree: Can

overconfidence survive the market test? Journal of Finance 52, 2073-2090.

Langer, Ellen, 1975, The illusion of control, Journal of Personality and Social Psychology 32,

311-328.

Larrick, Richard, Katherine Burson and Jack Soll, 2005, Social comparison and confidence:

When thinking you’re better than average predicts overconfidence (and when it does not),

Organizational Behavior and Human Decision Processes 102, 76-94.

Lichtenstein, Sarah and Baruch Fischhoff, 1981, The effects of gender and instructions on

calibration, Decision Research Report 81-5, Decision Research (Eugene, OR).

Lundeberg, Mary, Paul Fox, and Judith Punćochaŕ, 1994, Highly confident but wrong: gender

differences and similarities in confidence judgments, Journal of Educational Psychology 86,

114–121.

Lundeberg, Mary, Paul Fox, Amy Brown, and Salman Elbedour, 2000, Cultural influences on

confidence: Country and gender, Journal of Educational Psychology 92, 152-159.

Milburn, Michael, 1978, Sources of bias in the prediction of future events, Organizational

Behavior and Human Performance 21, 17-26.

Nicholson, Nigel, Emma Soane, Mark Fenton-O'Creevy, and Paul Willman, 2005, Personality

and domain-specific risk taking, Journal of Risk Research 8, 157-176.

Odean, Terrance, 1998, Volume, volatility, price, and profit when all traders are above

average, Journal of Finance 53, 1887-1934.

Odean, Terrance, 1999, Do investors trade too much? American Economic Review 89, 1279-

1298.

Pavalko, Ronald, 2001, Problem Gambling and its Treatment, Springfield, IL, Charles, C.

Thomas Publisher, Ltd.

Potenza, Marc, M.A. Steinberg, S.D. McLaughlin, et al., 2001, Gender-related differences in

the characteristics of problem gamblers using a gambling helpline, American Journal of

Psychiatry 158, 1500-1505.

Pulford, Briony and Andrew Colman, 1997, Overconfidence: feedback and item difficulty

effects, Personality and Individual Differences 23, 125-133.

Raylu, Namrata and Tian Oei, 2002, Pathological gambling: A comprehensive review,

Clinical Psychology Review 22, 1009-1066.

Salminen, Simo and Markku Heiskanen, 1995, Correlations between traffic, occupational,

sports, and home accidents, Accident Analysis and Prevention 29, 33-36.

Shefrin, Hersh and Meir Statman, 1985, The disposition to sell winners too early and ride

losers too long: Theory and evidence, Journal of Finance 40, 777-790.

Vitaro, F., I. Arnseneault, and R.E. Tremblay, 1997, Dispositional predictors of problem

gambling in male adolescents, American Journal of Psychiatry 154, 1769-1770.

Zuckerman, Marvin, 1994, Behavioral expression and biosocial bases of sensation seeking,

Cambridge University Press.

Zuckerman, M., Eysenck, S., and Eysenck, H., 1978, Sensation seeking in England and

America: Cross-cultural, age, and sex comparisons, Journal of Consulting and Clinical

Psychology 46, 139-149.

Table I

Variable Descriptions and Descriptive Statistics

Table I describes the variables used in this study and provides summary statistics on them. Panel A provides detailed descriptions of the variables used, date or

interval of measurement, and the source for the data used to construct the variable. Panel B reports means, medians, standard deviations, and interquartile ranges

for the variables used in the study. Panel C contains the histogram for the scores reported on the self-confidence measure. Panel D is the correlation matrix for

key variables used in the study. The sample is restricted to drivers in the province of Uusimaa or East Uusimaa who got their AB or B license before July 1,

1997, who owned stocks between January 1, 1995 and June 30, 1997, and for whom there is tax data from 1998. For the first two columns of Panel C and for the

self-confidence correlation in Panel D, the sample is further restricted to males who took the FAF leadership inventory between January 1, 1982 and December

31, 2001. To assess the representativeness of the sample of drivers and 1995-97 stockholders whose overconfidence we study, the last two columns of Panel C

report on the stanine distribution and reliability rates for all subjects who took the leadership inventory assessment.

Panel A. Variable Description

Variable Data source Measuring time More details

Age FTA + FCSD Measured at 1997 Determined based on social security code

Male FTA + FCSD Does not change Determined based on social security code

Married FTA / Pop. Register End 1998

Cohabitor FTA / Pop. Register End 1998

Unemployed FTA Year 1999 Drew unemployment benefits for at least one day in 1999

Homeowner FTA End 1998 Declared either real estate or apartment wealth at end 1998

Finance professional FTA End 1998 Empoyment in finance-related profession in 1998

Total income FTA Year 1998 Declared total ordinary income + total capital income from 1998

Value of stock portfolio FCSD 6/30/1997 Market value of stock portfolio

# stocks in portfolio FCSD 6/30/1997 Number of different stock exchange listed stocks

# stock trades FCSD 7/1/1997-11/29/2002 Number of open market trades of stocks

Portfolio turnover FCSD 7/1/1997-11/29/2002 Computed as in Barber and Odean (2001) for stocks

# of speeding tickets FVA 7/1/1997–12/31/2001 Total number of speeding tickets

Self confidence FAF When test taken Psychological test self-confidence scores. The test scores

are (approximately) stanine scores and vary between

1 (lowest) and 9 (highest). 0 denotes an unreliable score.

Ability score FAF When test taken Psychological test ability scores. Each test score combines

results from three separate tests that measure mathematical

ability, verbal ability, and logical reasoning. The test scores are

(approximately) stanine scores.

Explanations of abbreviations: FTA = Finnish Tax Administration; FCSD = Finnish Central Securities Depositary; FTA / Pop. Register = Tax authorities have

obtained the information from the Finnish Population Register; FVA = Finnish Vehicle Administration; FAF = Finnish Armed Forces

Represents one of the following professions (# in the sample): Portfolio manager or professional investor (117), dealer (FX and money market, 47), bank

manager (mostly commercial banking, manager of branch, 297), stockbroker (61), stockbroker or portfolio manager assistant (29), investment advisor (generally

low level, in bank branches, 20), miscellaneous investment baking or other higher level finance professional (68), financial manager (corporation, 45), equity

analyst (33), miscellaneous low level investment banking related job (33), loan officer (commercial banking, 138), retired bank manager (23), CFO (227), analyst

(may be other than equity analyst, 104). The tax authorities do not update the profession information often, as there was very little change in the profession data

between 1998 and 2000.

Panel B. Means, Medians, Standard Deviations, and Interquartile Ranges of Variables

Mean Std. Deviation Percentiles N

25 50 75

Total income, EUR 35,559 73,337 14,888 25,474 40,425 95,804

Portfolio value, EUR 24,620 289,019 221 1,571 7,844 95,804

# stocks 2.0502.54611295,804

# stock trades 14.67 119.4001695,804

Monthly portfolio turnover 0.019 0.056 0 0.002 0.014 90,467

Age 44.40 14.39 32 46 54 95,804

# speeding tickets 0.2000.58600095,804

Ability score 6.425 1.74956812,466

Fraction of sample individuals

Traded stocks 0.5570.49701195,804

Male 0.4710.49900195,804

Married 0.5840.49301195,804

Cohabitor 0.0240.15200095,804

Unemployed 0.0600.23700095,515

Homeowner 0.7590.42711195,804

Finance professional 0.0140.11600091,129

Panel C. Distributions of the Self-confidence Measure (Males only)

This sample Full sample

Stanine score # observations % of reliable scores % of reliable scores Stanine distribution

No reliable result 207

1 (low self confidence) 135 1% 3% 4%

2 203 2% 5% 7%

3 432 3% 9% 12%

4 844 6% 14% 17%

5 1,941 15% 20% 20%

6 1,903 14% 14% 17%

7 2,961 23% 16% 12%

8 3,620 28% 15% 7%

9 (high self confidence) 1,114 8% 4% 4%

Totals 13,360 100% 100% 100%

Average 6.54 5.48 5.00

Panel D. Correlations between Key Variables

Trade dummy ln (#trades) ln (Turnover) Male Age # tickets Self confidence

Trade dummy 1.000 N.A. 0.017 0.146 0.026 0.047 0.100

ln (#trades) 1.000 0.451 0.243 -0.007 0.095 0.058

ln (Turnover) 1.000 0.148 -0.204 0.101 0.022

Male 1.000 0.005 0.206 N.A.

Age 1.000 -0.108 -0.020

# tickets 1.000 0.012

Self confidence 1.000

Table II

Regressions of Trading Activity on Sensation Seeking and Control Variables

Table II reports coefficients and robust test statistics for a probit regression (column 1), a

Heckman two-stage regression (column 2, which also reports the correlation coefficient between

the residuals in the two stages), and an OLS regression (column 3). These regressions explain

three measures of trading activity as a function of the number of speeding tickets and a host of

control variables. Income and other socioeconomic data are from 1998. Unreported are

coefficients on a set of dummies for the number of stocks in the investor's portfolio and birth

year dummies. The sample is restricted to drivers in the province of Uusimaa or East Uusimaa

who got their AB or B license before July 1, 1997, who owned stocks between January 1, 1995

and June 30, 1997, and for whom there is tax data from 1998.

Coefficient t -value

Dependent variable Dependent variable

Trade Trade

Independent variables dummy ln (#trades) ln (Turnover) dummy ln (#trades) ln (Turnover)

# speeding tickets 0.047 0.098 0.101 5.75 9.68 9.98

Total income dummies

Lowest -0.133 -0.105 -0.081 -6.21 -3.30 -2.48

2 -0.039 -0.047 0.019 -1.90 -1.60 0.61

3 -0.030 -0.077 0.009 -1.52 -2.72 0.29

4 -0.031 -0.021 0.004 -1.58 -0.75 0.12

6 0.072 0.128 0.019 3.74 4.69 0.66

7 0.093 0.201 0.005 4.74 7.34 0.17

8 0.127 0.301 0.063 6.40 10.79 2.20

9 0.200 0.454 0.079 9.75 15.41 2.75

Highest 0.394 0.863 0.422 17.60 25.28 14.53

Financial wealth dummies

Lowest -0.994 -0.740 -0.193 -48.96 -8.67 -5.90

2 -0.788 -0.735 -0.177 -42.38 -10.90 -6.21

3 -0.504 -0.645 -0.048 -28.91 -14.37 -1.88

4 -0.335 -0.526 0.090 -19.27 -15.56 3.71

6 -0.037 -0.168 -0.008 -2.16 -7.91 -0.36

7 0.064 0.041 0.004 3.67 2.01 0.20

8 0.193 0.240 -0.199 4.84 7.70 -5.32

Highest 0.361 0.406 -0.106 12.85 13.05 -3.51

Other dummies

Male 0.347 0.762 0.503 23.08 25.45 23.63

Married 0.029 0.062 0.164 2.19 3.41 8.48

Cohabitor -0.070 -0.034 0.093 -1.79 -0.57 1.53

Male * married -0.107 -0.351 -0.245 -5.55 -13.49 -9.10

Male * cohabitor 0.022 -0.082 -0.106 0.37 -1.03 -1.31

Unemployed 0.083 0.166 0.215 4.28 6.11 7.16

Homeowner 0.111 0.094 -0.100 8.84 4.99 -5.34

Finance professional 0.539 0.426 0.358 12.11 8.37 8.06

(Constant) -0.325 0.603 -4.204 -5.32 4.45 -47.45

Inverse Mill's ratio 0.476 3.88

ρ 0.358

Pseudo R

0.153

0.236 0.180

Number of observations 90,868 50,713 50,224

Table III

Regressions of Trading Activity on both Sensation Seeking and Overconfidence

Table III reports coefficients and robust test statistics for regressions. Panel A’s cross-sectional

regression uses ordered probit to estimate competence as the predicted value from a regression of self-

confidence (from the FAF leadership assessment) on control variables that measure success in later life.

Overconfidence is the residual from the regression. Panel B’s probit, 2-stage Heckman (which also

reports the correlation between the residuals of the two stages), and OLS regressions explain three

measures of trading activity as a function of overconfidence, the number of speeding convictions, and a

host of control variables. Income and other socioeconomic data are from 1998. Unreported in Panel B are

coefficients on a set of dummies for the number of stocks in the investor's portfolio and birth year

dummies. The sample is restricted to male drivers in the province of Uusimaa or East Uusimaa who got

their AB or B license before July 1, 1997, who owned stocks between January 1, 1995 and June 30, 1997,

and for whom there is tax data from 1998.

Panel A. Parsing out Competence from Self-confidence to Derive Overconfidence

Independent variables Coefficient t -value

Total income dummies

Lowest -0.031 -0.75

2 0.027 0.63

3 -0.061 -1.35

4 -0.128 -2.74

6 0.013 0.29

7 0.102 2.30

8 0.181 4.08

9 0.268 5.70

Highest 0.367 6.75

Portfolio value dummies

Lowest -0.032 -1.10

2 -0.080 -2.25

3 -0.020 -0.56

4 0.002 0.05

6 0.014 0.36

7 0.033 0.85

8 -0.004 -0.05

Highest -0.020 -0.32

Other dummies

Married 0.156 6.37

Cohabitor -0.117 -2.17

Unemployed -0.238 -4.85

Homeowner 0.009 0.43

Ability score 0.119 20.82

Age dummies

23-29 0.420 5.59

30-34 0.358 4.97

35-39 0.129 1.82

40-44 -0.012 -0.16

Pseudo R

0.021

Number of observations 12,379

Panel B. Sensation Seeking, Overconfidence, and Trading Activity

Coefficient t -value

Dependent variable Dependent variable

Trade Trade

Independent variables dummy ln (#trades) ln (Turnover) dummy ln (#trades) ln (Turnover)

# speeding tickets -0.001 0.070 0.090 -0.05 3.48 4.82

Overconfidence 0.037 0.037 0.013 4.81 2.93 1.28

Total income dummies

Lowest -0.023 -0.203 -0.209 -0.40 -2.37 -2.64

2 0.075 0.051 -0.030 1.30 0.59 -0.38

3 0.135 -0.015 -0.006 2.25 -0.17 -0.07

4 0.029 -0.122 0.043 0.48 -1.31 0.52

6 0.267 0.303 0.133 4.47 3.13 1.66

7 0.307 0.386 0.127 5.22 3.95 1.66

8 0.493 0.679 0.272 7.96 5.97 3.57

9 0.564 0.777 0.237 8.63 6.37 2.93

Highest 0.868 1.293 0.665 10.36 8.65 7.52

Financial wealth dummies

Lowest -1.142 -1.010 -0.290 -21.39 -4.37 -4.33

2 -0.892 -0.923 -0.278 -16.91 -5.25 -4.33

3 -0.522 -0.812 -0.231 -9.88 -7.39 -3.82

4 -0.321 -0.588 -0.021 -5.87 -6.99 -0.36

6 0.023 -0.067 0.029 0.39 -1.01 0.47

7 0.206 0.157 0.127 3.20 2.20 2.05

8 0.275 0.317 -0.111 1.40 2.32 -0.77

Highest 0.362 0.346 -0.219 2.95 3.17 -2.10

Other dummies

Married -0.092 -0.324 -0.039 -2.74 -6.94 -1.00

Cohabitor -0.134 -0.183 0.033 -1.80 -1.78 0.39

Unemployed -0.072 0.050 0.257 -1.12 0.50 2.81

Homeowner 0.144 0.134 -0.016 4.77 2.86 -0.45

Finance professional 0.692 0.577 0.448 6.47 4.60 5.43

(Constant) -0.639 1.502 -3.460 -1.67 2.17 -4.74

Inverse Mill's ratio 0.816 2.45

ρ 0.532

Pseudo R

0.156

Adjusted R

0.154 0.178

Number of observations 11,521 7,359 7,271

Table IV

Performance after Transaction Costs for Investors Categorized by Sensation Seeking and

Overconfidence

For holding periods of 1, 5, 20, and 60 days, Table IV reports the average difference between

the returns of a daily sequence of buy portfolios and sell portfolios, formed from the buys and

sells of a group of investors, after transaction costs. Each T-day holding period return is

approximated as a sum of T 1-day returns formed with the procedure of Jegadeesh and Titman

(1993) to get around the overlapping horizon problem. The table also reports t-statistics for the

associated returns. These are based on Newey-West standard errors for 13 cases where the

first-order autocorrelation coefficient is significant at the 5% level. The transaction cost of

each trade, be it a buy or a sell, is assumed to be 8.42 euros + 0.15% · (the value of the trade).

Groupings of investors are based on the number of speeding tickets (Panel A) and the

investor’s overconfidence quintile (Panel B). Days with no buys or no sells are eliminated

from all computations.

Panel A. Performance by Number of Speeding Tickets

Performance evaluation period

1 day 5 days 20 days 60 days

# speeding tickets Mean t -value Mean t-value Mean t -value Mean t -value

0 -0.53% -13.22 -0.27% -2.66 -0.57% -2.04 -0.68% -0.93

1 -0.42% -9.68 -0.27% -2.97 -0.41% -1.84 -0.47% -0.84

2 -0.29% -5.20 -0.15% -1.48 -0.14% -0.69 -0.50% -1.13

3 -0.64% -5.79 -0.26% -1.19 -0.43% -0.98 -0.65% -0.75

>3 -0.51% -4.27 -0.36% -1.91 -0.13% -0.34 -0.45% -0.61

Panel B. Performance by Overconfidence Quintile

Performance evaluation period

Overconfidence 1 day 5 days 20 days 60 days

quintile Mean t-value Mean t-value Mean t -value Mean t -value

Lowest -0.66% -9.85 -0.56% -4.10 -1.08% -3.44 -1.89% -2.60

2 -0.62% -7.75 -0.44% -2.98 -0.62% -2.04 -1.16% -1.74

3 -0.43% -6.60 -0.18% -1.39 -0.14% -0.49 -0.98% -1.40

4 -0.44% -6.07 -0.22% -1.62 -0.43% -1.31 -0.56% -0.76

Highest -0.74% -10.03 -0.58% -3.80 -0.61% -1.76 -0.81% -1.08

Figure 1

The joint effect of age and gender on trading activity and sensation seeking

Figure 1 plots trades and speeding tickets as a function of age and gender. Panel A plots

number of trades from 7/1/1997-11/29/2002. Panel B effectively plots number of trades over

the same period, controlling for income, wealth, and number of stocks in the portfolio. It

reports coefficients from a regression of number of trades on birth year dummies (Females

line) as well as the sum of the former coefficients and the product of birth year dummies and a

male gender dummy (Males line). Regressors for income deciles, wealth deciles, and number

of stocks are also controlled for. Panel C plots the number of speeding tickets from 7/1/1997-

12/31/2001. The sample is restricted to drivers in the province of Uusimaa or East Uusimaa

who got their AB or B license before July 1, 1997, who owned stocks between January 1, 1995

and June 30, 1997, and for whom there is tax data from 1998.

Panel A. Average Number of Trades as a Function of Gender and Birth Year

100

1920 1930 1940 1950 1960 1970

Birth year

Average # of trade

Females Males

Panel B. Marginal Effects of Gender and Birth Year on Average Number of Trades with

Effects of Control Variables Taken out

-0.5

0.5

1.5

1920 1930 1940 1950 1960 1970

Birth year

Coefficien

Females Males

Panel C. Speeding Convictions as a Function of Gender and Birth Year

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1920 1930 1940 1950 1960 1970

Birth year

Mean # speeding ticket

Females Males

ENDNOTES

Shefrin and Statman (1985), Ferris, Haugen, Makhija (1988), Odean (1999), Grinblatt

and Keloharju (2001), and Grinblatt and Han (2005) argue that trading can arise as a

consequence of a disposition effect. Graham, Harvey, and Huang (2005) contend that

competence drives trading. There also is a large literature on overconfidence and trading,

which we discuss later.

See Zuckerman (1994), a key founder of the concept, for an excellent summary of this

literature.

For a review of the sensation seeking literature on gambling, see Raylu and Oei

(2002). They document that active gambling games, like craps or poker, are more attractive to

a sensation seeker than passive games with repeated small bets, like slot machines. Kumar

(2006) concludes that investor-types with characteristics associated with an attraction to

gambling prefer lottery-like stocks.

Horvath and Zuckerman (1993) find that sensation seeking is significantly positively

related to risky behavior in the following four areas of risk: criminal, minor rule violations

(such as traffic offenses), financial (including gambling), and sports risks. Nicholson et al.

(2005) find that safety risks (e.g., fast driving and cycling without a helmet) is significantly

positively related to recreational, health, career, finance, and social risks. Salminen and

Heiskanen (1997) show that traffic accidents are significantly correlated with home, work-

related, and sports accidents.

Dickerson (1984) discusses how the repetition of stimuli in gambling settings delights

the sensation seeker. In roulette for example, he notes the stimulation from the spinning of the

wheel, the croupier’s calls, and the placing of bets.

There is, however, empirical evidence tying risk aversion to trading. Dorn and

Huberman (2005) use survey data to document that a sample of German investors who self-

report that stock investing is a low risk endeavor churn their portfolios. The survey asks

questions of investors after experiencing five years of trades.

Kyle and Wang’s (1997) model has overconfidence as a commitment device for

trading intensity. Odean (1998) and Benos (1998) develop a model in which overconfidence

leads to trading. Daniel, Hirshleifer, and Subrahmanyam (1998) show that overconfident

investors overweight private signals. Gervais and Odean (2001) show that investors whose

overconfidence is a function of experience trade more in response to a given signal than less

confident investors. Odean (1999) suggests that overconfidence may be responsible for some

portion of trading. Barber and Odean (2001) test whether overconfidence drives trading using

gender as a proxy for overconfidence. Glaser and Weber (2007), using data on 215 online

investors who responded to a survey, find that the better than average effect is related to

trading frequency. Using experimental data, Deaves et al. (2004) observe that miscalibration

based overconfidence is positively related to trading activity, while Biais et al. (2005) find that

miscalibration-based overconfidence reduces trading performance.

As just one hypothetical example, assume that positive past performance generates

lower self-assessed risk aversion among both passive and active investors. Further assume that

(in contrast to passive investors) those who traded a lot and did poorly do not answer the

survey (with embarrassment as the explanation). In this case, it seems plausible that one might

spuriously infer a positive correlation between past churning and self-assessed aversion to risk

from a survey.

The questions also clearly differ from the types of questions offered in tests of

optimism, like the LOT-R test. The use of “confidence” for skill-related outcomes and

“optimism” for exogenous outcomes is common. See Feather and Simon (1971), Hey (1984),

Langer (1975), and Milburn (1978).

See, for example, Barber and Odean (2001) and Agnew et al. (2003).

High self monitors are more aware of how their behavior influences others. They

also tend to be more aware of strategic behavior on the part of others.

See, for example, Zuckerman, Eysenck, and Eysenck (1978) and Ball, Farnhill, and

Wangeman (1984).

See Begg and Langley (2001).

The literature offers differing views on whether males actually are more

miscalibrated than women. Lundeberg, Punćochaŕ, and Fox (1994) and Pulford and Colman

(1996) argue that men are less well calibrated than women, particularly for tasks that are

perceived to be in the masculine domain, whereas Beyer and Bowden (1997) and Beyer (1998)

find women to be better calibrated. Lichtenstein and Fishhoff (1981), Lundeberg et al. (2000),

Deaves et al. (2004), and Biais et al. (2005) find no difference in miscalibration between men

and women.

Non speeding offenses are fewer in number, varied across many categories, and

difficult to interpret. For example, tickets do get issued for driving too slowly on a freeway.

For these reasons, we focus only on speeding offenses in the sample. When we pool speeding

with all other driving offenses as our measure of sensation seeking, we obtain highly similar

results.

Car owners are individuals who had a car registered in their name as of June 10,

2002. (Ownership of a truck, bus, or a related commercial vehicle is not considered in the

analysis.) The mean number of tickets is lower for non-owners, as they tend to drive less than

owners. Many Finnish families have just one car, which usually is registered in the name of

the spouse who uses the vehicle more (typically, the male).

The correlations of the variables in the table with overconfidence, which is derived

from self-confidence with a procedure described later, are similar to their correlations with

self-confidence.

Source: “Finn’s speed fine is a bit rich,” BBC News, February 10 2004. Mr.

Vanjoki’s fine was later reduced by 95% due to a drop in his executive stock option income.

We also used Poisson estimation to obtain coefficients for a regression with the

number of trades (rather than the log of trades) as the dependent variable. The t-statistic on the

speeding conviction coefficient was 5.48.

This is the average of buy turnover plus sell turnover. Buy turnover for a given

month is the investor’s portfolio weighted average of the ratio of shares bought of a stock to

shares owned in the stock at the end of the month (or one if the ratio exceeds one). Sell

turnover is the investor’s portfolio weighted average of the ratio of shares sold of a stock to

shares owned in the stock at the beginning of the month (or one if the ratio exceeds one). We

average monthly buy turnover and sell turnover over all months to obtain an investor’s overall

buy turnover and sell turnover ratios. Months for which there is no end of month holding (for

buy turnover) or beginning of month holding (for sell turnover) are excluded from the average.

The number of observations for this measure of trading activity is slightly smaller than the

sample for number of trades because of the absence of computable portfolio holdings.

Although not reported formally, adjusting our turnover measure in each month by

subtracting the average turnover across all investors for that month, before averaging across

months, yields approximately the same results as we report here. This robustness applies,

irrespective of whether the subtracted average for the month equally weights all investors or

weights them in proportion to their portfolio value.

As in the log trades specification, we analyzed turnover with the Heckman two-stage

procedure to account for self selection in the trading decision. The inverse Mill’s ratio does not

significantly differ from zero, so we only report the results from the more parsimonious OLS

specification for observations with strictly positive turnover. The reported results are very

similar to the results from the Heckman estimation.

We obtain highly similar results when we run a Heckman regression using the log of

the number of different stocks traded in lieu of the log of number of trades as the LHS-variable

(t-value 8.11). The speeding ticket variable is also highly significant if we use the log of the

ratio between the number of trades and the number of different stocks traded as the LHS

variable (t-value = 7.41). Thus, doubling or halving your position in a stock also appears to be

stimulating to sensation seekers.

If a propensity to trade of 0.5 corresponds to a z-score of zero, the coefficient’s .047

increase in the z-score per ticket has the cumulative normal probability moving from 0.5 (z=0)

to approximately 0.52 (z=.047).

The coefficients for a turnover regression specification that employs dummies for

one speeding ticket, two speeding tickets, and three or more speeding tickets are .111, .209,

and .367, respectively. Because so few subjects have four or more tickets, these coefficients

are consistent with the reported regression in Table II, which has a .101 coefficient on number

of speeding tickets. For the other two regressions as well, we obtain similar results to those

reported in Table II when we employ dummies for tickets in lieu of number of speeding tickets.

Significance in Table II also is not driven solely by the relatively infrequent trading among

zero ticket investors. A regression analogous to that in Table II with one dummy for investors

that have at least one ticket and another for those that have at least two tickets has significant

coefficients on both dummies. This indicates that having two tickets leads to significantly

more trades than having one ticket.

Depending on the specification, adding the product of unemployment and log of

wealth as an additional control either makes the unemployment dummy significantly negative

or insignificant. Also, we checked the robustness of our results by omitting finance

professionals and unemployed investors from the sample. The results are virtually identical to

those presented here.

Our overconfidence measure is closely related to Larrick et al’s (2007)

overplacement measure, which is defined as the difference between a subject’s perceived

percentile in a test and the actual percentile she belongs to.

Entrance to military service generally is from ages 18-20, but is never later than age

28.

Daniel, Hirshleifer, and Subrahmanyam (1998), as well as Gervais and Odean

(2001), suggest that overconfidence can change over time. This would bias us to finding no

relationship between our overconfidence measure and trading activity.

Because autocorrelated daily portfolio returns can be induced by microstructure

considerations, like non-synchronous trading, we compute autocorrelation coefficients for the

return difference series. Most of the first-order autocorrelations do not significantly differ

from zero. We apply Newey-West standard errors to compute the t-statistics for those 13

holding period – portfolio combinations where the first-order autocorrelation coefficient differs

significantly from zero at the 5% level. The adjustment has very little effect on our results and

does not alter any of our conclusions.

If a particular day lacks any buys or any sells for an investor category, we define it as

a missing value for the summation of buy-sell portfolio differences for each return day it might

apply to. This assignment arises from a desire to make each day’s return difference sums

approximate a market-neutral strategy. The reported numbers in Table IV best approximate

the average of the overlapping multi-day return differences by dividing the sum of all daily

return difference sums by the number of non-missing daily observations over the entire sample

period for that category.

The results are qualitatively similar for longer horizons as well.

For example, it would be natural to test whether the performance of the extreme

groups of investors (e.g., most vs. least overconfident) differ from each other. We find no

significant differences between the performance of the extremes for any of the eight pairings in

Table IV.