The Courage of Misguided Convictions
Brad M. Barber and Terrance Odean
The field of modern financial economics assumes that people behave withextreme rationality, but they do not. Furthermore, people’s deviations fromrationality are often systematic. Behavioral finance relaxes the traditional
assumptions of financial economics by incorporating these observable,
systematic, and very human departures from rationality into standard
models of financial markets. We highlight two common mistakes investors
make: excessive trading and the tendency to disproportionately hold on tolosing investments while selling winners. We argue that these systematic
biases have their origins in human psychology. The tendency for humanbeings to be overconfident causes the first bias in investors, and the humandesire to avoid regret prompts the second.

There is one important caveat to the notion that welive in a new economy, and that is human psychology . . . which appears essentially immutable.
Alan Greenspan
Chair of the U.S. Federal Reserve Board
Speech at the University of California at Berkeley
September 4, 1998

B

ehavioral models of financial markets consider not only how people should act but
also how they do act. People do not always
behave rationally, and although departuresfrom rationality are sometimes random, they areoften systematic. For example, far more people
overestimate, rather than underestimate, their driving ability (Svenson 1981).
Consideration for the observed traits of economic agents is not entirely new. In 1738, Daniel
Bernoulli noted that people behave as if they arerisk averse (see Bernoulli 1954). Prior to Bernoulli,most scholars considered it normative behavior for
people to value a gamble at its expected value.
Today, economists usually assume people are risk
averse. Economists of the 19th century believed
that, ideally, the present and the future should betreated equally, but they observed that generally
people value present consumption more highly
than future consumption (Loewenstein 1992).
Today, economists usually assume that people discount the utility of future consumption. In reality,

people are not always risk averse, nor do they
always discount the future. Millions of people
engage in regular risk-seeking activity, such as
buying lottery tickets; others “bite the bullet” to bedone with unpleasant experiences that they mightotherwise delay. Risk aversion and discounting
future consumption are sufficiently pervasive
behaviors, however, that these behaviors are standard assumptions in economic models.
In recent years, psychologists have identified
ways in which people systematically depart from
optimal judgment and decision making. Behavioral
finance enriches economic understanding by incorporating these aspects of human nature into financial models. Doing so is consistent with thetradition, if not the practice, in financial economics
in the past several decades. Behavioral theories,
like traditional theories, provide formal hypotheses and predictions that can be empirically tested.
We describe empirical tests of the predictionsof two behavioral finance theories. Shefrin and Statman (1985) extended the prospect theory of Kahneman and Tversky (1979) to predict that investors,
because of their desire to avoid regret, will tend to
hold their losing investments too long and sell theirwinners too soon; they labeled this tendency the“disposition effect.” Odean (1998b) predicted that,
because of their overconfidence, investors willtrade too frequently and thereby reduce theirreturns.1

The Data
Brad M. Barber is associate professor of management andfinance and Terrance Odean is assistant professor of
finance at the University of California at Davis.
November/December 1999

A national discount brokerage house provided thedata for the studies we summarize. The primarydata set was 10,000 randomly selected accounts that41

Financial Analysts Journal
were active in 1987 (those with at least one transaction). The data included trading and position
records for these accounts from January 1987
through December 1993; 162,948 trades werereported. Each record included an account identifier, a buy/sell indicator, the number of shares
traded, the commission paid, and the principal
amount.
Price and return data are from the 1993 CRSP
daily stock file for NYSE, Amex, and Nasdaq
stocks. The tests were limited to stocks for which
this information was available. Of the 10,000
accounts, 6,380 made 97,483 common stock trades(49,948 purchases and 47,535 sales); 62,516,332
shares were traded (31,495,296 shares, with a market value of $530,719,264, were purchased, and31,021,036 shares, with a market value of$579,871,104, were sold). Average monthly turnover was 6.5 percent.2 With 10 being the decile ofthe companies with the largest capitalizations, theaverage size decile was 8.65 for a purchase and 8.68
for a sale.

Figure 1. Prospect Theory Value Function
Value (Utility)

LossesGains

The Disposition Effect
The disposition effect is one implication of extending Kahneman and Tversky’s prospect theory toinvestments. Under prospect theory, when facedwith choices involving simple two- and threeoutcome lotteries, people behave as if maximizing
an S-shaped value function, as depicted in Figure
1. This value function is similar to a standard utility
function except that it is defined on the basis ofgains and losses rather than levels of wealth. The
function is concave in the domain of gains andconvex in the domain of losses. It is also steeper for
losses than for gains, which implies that people are
generally risk averse.
Critical to this value function is the reference
point from which gains and losses are measured.
Usually, the status quo is taken as the reference
point, butthere are situations in which gains and losses
are coded relative to an expectation or aspiration level that differs from the status quo. . . . A
person who has not made peace with his losses
is likely to accept gambles that would be unacceptable to him otherwise. (Kahneman andTversky, pp. 286–287)

For example, suppose an investor purchases a
stock that she believes to have an expected returnhigh enough to justify its risk. If the stock appreciates and she continues to use the purchase price asa reference point, the stock price will then be in amore concave, risk-averse part of the investor’svalue function. The stock’s expected return may
42continue to justify its risk, but if the investor lowers
her expectation for the stock’s return somewhat,
she will be likely to sell the stock. If instead ofappreciating, the stock declines, its price is in theconvex, risk-seeking part of the value function.
Here, the investor will continue to hold the stockeven if its expected return falls lower than would
have been necessary for her to justify its original
purchase. Thus, the investor’s belief about
expected return must fall farther to motivate thesale of a stock that has already declined rather than
one that has appreciated. Similarly, suppose aninvestor holds two stocks. One is up, and the otheris down. If he is facing a liquidity demand and has
no new information about either stock, he is more
likely to sell the stock that is up.
Throughout this study, investors’ reference
points are assumed to be their purchase prices.
Although the results presented here appear to vindicate that choice, for some investments, particularly those held for a long time over a wide rangeof prices, the purchase price may be only one determinant of the reference point. The price path may
also affect the level of the reference point. For example, a homeowner who bought his home for$100,000 just before a real estate boom and had thehome appraised for $200,000 after the boom may
no longer feel he is “breaking even” if he sells hishome for $100,000 plus commissions.3©1999, Association for Investment Management and Research

The Courage of Misguided Convictions
Taxes. Investor reluctance to realize losses is
at odds with optimal tax-loss selling for taxable
investments. For tax purposes, investors should
postpone taxable gains by continuing to hold theirprofitable investments. They should capture tax
losses by selling their losing investments, although
not necessarily at a constant rate. Constantinides(1984) showed that when there are transaction costs
and no distinction is made between short-term andlong-term tax rates (as was approximately the casefor 1987–1993 for U.S. federal taxes4), investors
should gradually increase their tax-loss sellingfrom January to December. Dyl (1977), Lakonishok
and Smidt (1986), and Badrinath and Lewellen(1991) reported evidence that investors do sell morelosing investments near the end of the year.
Shefrin and Statman proposed that investors
choose to sell their losers in December as aself-control measure. They reasoned that investors
are reluctant to sell for a loss but recognize the tax
benefits of doing so. The end of the year is thedeadline for realizing these losses, so each year,
investors postpone realizing losses until December,at which time they sell losers before the deadline
passes.

By going through each account’s trading
records in chronological order, we constructed aportfolio of securities for which the purchase dateand price are known. Clearly, this portfolio represented only part of each investor’s total portfolio.
Most accounts will have contained securities thatwere purchased before January 1987 for which thepurchase price was not available, and investors
may also have had other accounts that were notpart of the data set. However, although the portfolios constructed from the data set were only part ofeach investor’s total portfolio, the selection process
is unlikely to have biased the partial portfolios
toward stocks for which investors had unusual
preferences for realizing gains or losses.
For each day that a sale took place in a portfolio of two or more stocks, we compared the selling
price for each stock sold with its average purchaseprice to determine whether that stock was sold fora gain or a loss. Each stock that was in that portfolio
at the beginning of that day but was not sold wasconsidered to be a paper (unrealized) gain or loss
(or neither). We determined whether it was apaper gain or loss by comparing its high and low
price for that day (as obtained from CRSP) with its
average purchase price. If both its daily high andlow were above its average purchase price, we
counted it as a paper gain; if they were both below
its average purchase price, we counted it as a paper
loss; if its average purchase price lay between thehigh and the low, we counted it neither as a gain
nor a loss. On days when no sales took place in an
account, no gains or losses, realized or paper, werecounted.
For example, consider two investors, Borgg
and Wellbaum, who have stocks in their portfolios
as depicted in Table 1. Borgg had five stocks in his
portfolio. A and B were worth more than he paid

Methodology. To determine whether investors sell winners more readily than losers, one cannot simply look at the number of securities sold forgains versus the number sold for losses. Suppose
investors are indifferent to selling winners or losers? Then, in an upward-moving market, they will
have more winners in their portfolios and will tend
to sell more winners than losers even though they
have no preference for doing so. To test whether
investors are disposed to sell winners and holdlosers, therefore, one must look at the frequency
with which they sell winners and losers relative totheir opportunities to sell each.

Table 1. Calculation of Proportion of Gains and Losses Realized
Category

Borgg

Wellbaum

Positions
Holdings
Winners
LosersA, B, C, D, E
A, B
C, D, EF, G, H
F, G
H

Sales
Sales on Monday
Sales on Wednesday

A, C
None

None
F

Calculation of gains and losses
Paper gains
Paper losses
Realized gains
Realized losses

1 (B)
2 (D, E)
1 (A)
1 (C)

1 (G)
1 (G)
1 (F)
0

November/December 1999

43

Financial Analysts Journal
for them; C, D, and E were worth less. Wellbaum
had three stocks in her portfolio. F and G were
worth more than she paid for them; H was worth
less. On Monday, Borgg sold shares of A and C. On
Wednesday, Wellbaum sold shares of F. Borgg’s
sale of A and Wellbaum’s sale of F were counted asrealized gains. Borgg’s sale of C was a realized loss.
Because B and G could have been sold for a profit
but were not, they were counted as paper gains. D,
E, and G were paper losses. So, for these two investors over these two days, two realized gains, one
realized loss, two paper gains, and three paper
losses were counted.
We summed realized gains, paper gains, realized losses, and paper losses for each account andacross accounts. Then, we calculated two ratios:
Proportion of gains realized
Realized gains(PGR) = ---------------------------------------------------------------------------- ;Realized gains + Paper gains
Proportion of losses realized
Realized losses(PLR) = ------------------------------------------------------------------------------- .
Realized losses + Paper losses

In the example of Borgg and Wellbaum, PGR
equaled 1/2 and PLR equaled 1/4.
A large difference in the proportion of gains
realized and the proportion of losses realized indicates that investors are more willing to realize
either gains or losses.
Any test of the disposition effect is a joint testof the hypothesis that people sell gains more
readily than losses and of the specification of thereference point from which gains and losses are
determined. Some possible choices of a reference
point for stocks are the average purchase price, thehighest purchase price, the first purchase price, or
the most recent purchase price. The findings ofthis study were essentially the same for eachchoice; thus, we report results for average purchase price.
Commissions and dividends may or may not
be considered when determining reference points
or profits and losses. Although investors may notconsider commissions when they remember what
they paid for a stock, commissions do affect capitalgains and losses. And because the normative standard to which the disposition effect is being contrasted is optimal tax-motivated selling, we added
commissions to the purchase price and deductedcommissions from the sales price, except where
noted. In determining whether the stocks that were
not sold on a particular day could have been soldfor a gain or a loss, the commission for the potential
sale was assumed to be the average commission per
share paid when the stock was purchased.5 We did
not include dividends when determining whichsales were profitable because dividends do notaffect capital gains and losses for tax purposes. Allgains and losses were calculated after adjusting for
splits.
The primary finding of these tests, that investors are reluctant to sell their losers and prefer tosell winners, was unaffected by the inclusion or
exclusion of commissions or dividends.
Results. The data in this study show thatinvestors did sell a higher proportion of their winners than of their losers. Table 2 reports the proportions of gains and losses realized throughout the
average year, for January through November, andfor December.
Suppose investors frequently realize small
gains and less frequently take large losses. Then,they may be selling similar proportions of the values of their gains and losses, although they are
realizing gains at a higher rate on a trade-counted
basis. The data indicate that investors are not, however, realizing gains and losses this way. The average PGR and PLR per account can be calculated bymeasuring losses, gains, potential losses, andpotential gains in terms of dollars rather thanshares or trades. The dollar-based PGR (averaged
across accounts) was found to be 0.58, and theaverage dollar-based PLR (averaged acrossaccounts) was found to be 0.42.6
In Table 2, the ratio of PGR to PLR for the year
is a little more than 1.5, which indicates that a stock
whose value was up was more than 50 percent wasmore likely to be sold from day to day than a stock
whose value was down. In Weber and Camerer’s(1998) experimental studies of the disposition

Table 2. Proportion of PGR and PLR
Proportion Realized
PLR
PGR

All Months

January–November

December

0.098
0.148

0.094
0.152

0.128
0.108

Note: Realized gains, paper gains, realized losses, and paper losses were aggregated over 1987–1993 andacross all accounts in the data set. The t-statistic tested the null hypotheses that, assuming that all realized
gains, paper gains, realized losses, and paper losses result from independent decisions, the differencesin proportions are equal to zero.

44©1999, Association for Investment Management and Research

The Courage of Misguided Convictions
effect, a stock that was up was also about 50 percent
more likely to be sold than one that was down.
Figure 2 charts the ratio of PGR to PLR for eachmonth in our study. The ratio declines from 2.1 inJanuary to 0.85 in December. This decline is consistent with Constantinides’ tax-loss selling modeland suggests that at least some investors pay attention to tax-motivated selling throughout the year.
From January through November, however, theobserved ratio of PGR to PLR is reliably greaterthan 1.7
The results described here hold up to a classic
principle of scientific inquiry: They are robust to
out-of-sample testing. Specifically, subsequent toOdean (1998a), we obtained trading records for
78,000 households for 1991–1996 from the same
discount brokerage house used for the previous
study. (These data are described in more detail inthe section titled “Additional Tests of Overconfidence.”) For this new data set, we found the PGR
measure to be 0.1442 and the PLR measure to be
0.0863. During this sample period, stocks that had
increased in value were approximately 65 percent
more likely to be sold than stocks that had declined
in value.
Alternative Reasons to Hold Losers andSell Winners. Previous research offers some support for the hypothesis that investors sell winners
more readily than losers, but this research is generally unable to distinguish among various motivations investors might have for doing so. 8 Recent
studies have found evidence of the disposition

effect in the exercise of company stock options(Heath, Huddart, and Lang 1999), in the sale ofresidential housing (Genesove and Mayer 1999),
and among professional futures traders (Locke andMann 1999), Israeli investors (Shapira and Venezia
1998), and Finnish investors (Grinblatt and Keloharju 1999). We believe the disposition effect basedon loss aversion best explains the tendency forinvestors to hold losers and sell winners. In thissection, we present evidence that allows us to discount competing explanations for this investor
behavior.
■ Anticipation of changes in tax law. One reason investors might choose to sell winners rather
than losers is that they anticipate a change in the
tax law that will increase capital gains tax rates. The
Tax Reform Act of 1986 made such a change. If
investors were selling off winners in 1986 in anticipation of higher tax rates, they might have entered1987 with a larger percentage of losers in theirportfolio than usual. Because such stocks were purchased prior to 1987, the stocks would not show up
in the portfolios reconstructed here. Therefore, therate at which winners were being realized relative
to losers could have been lower in the investors’
total portfolios than in the partial reconstructed
portfolios. As old stocks were sold and new ones
purchased, however, the partial portfolios would
have become more and more representative of thetotal portfolios. So, if a sell-off of winners in anticipation of the 1986 tax law affected the observed

Figure 2. Ratio of PGR to PLR by Month
PGR/PLR
2.5

2.0

1.5

1.0

0.5

0
Jan

Feb

November/December 1999

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

Nov

Dec

45

Financial Analysts Journal
rate at which gains and losses were realized in thepartial portfolios, that effect should have been
greater in the first part of the sample period than in
the last part. However, the ratio PGR/PLR is virtually the same for the 1987–90 and 1991–93 periods.
■ Desire to rebalance. Lakonishok and Smidt
suggested that investors might sell winners andhold losers in an effort to rebalance their portfolios.
For example, if one stock in an investor’s portfolio
appreciates considerably, the investor may sellpart of that holding and buy others to rebalance
the portfolio. In that case, the investors who sell
winners would be likely to make new purchases.
To eliminate trades that could have been motivated by a desire to rebalance, we calculated PGRand PLR by using only sales and dates for which
no new purchase was made for a portfolio on thesale date or during the following three weeks.
When sales motivated by a desire to rebalance
were eliminated in this way, we found that investors continued to prefer to sell winners. Onceagain, investors realized losses at a higher rate
than gains in December.
■ Belief that the losers will bounce back. Anotherreason investors might sell winners and hold losers
is that they expect the losers to outperform thewinners in the future. An investor who buys a stock
because of favorable information may sell thatstock when it goes up on the theory that the information is now reflected in the price. On the other
hand, if a stock goes down, the investor may continue to hold it on the theory that the market has
not yet come to appreciate the information. Investors might also choose to sell winners and holdlosers simply because they believe prices may
revert to the mean. Whether such beliefs are justified can be tested ex post.
To test whether the losing stocks that investors
hold outperform the winners they sell, Odean(1998a) calculated market-adjusted returns for losing stocks held and winning stocks sold subsequent
to each sales date. For winners that were sold, hecalculated market-adjusted returns (the average
return in excess of the CRSP value-weighted index)
starting the day after the transaction for the next 84
trading days (four months), 252 trading days (oneyear), and 504 trading days (two years). For thesame horizons, he calculated market-adjusted
returns subsequent to paper losses. That is, forstocks held for a loss in portfolios from which sales
did take place, market-adjusted returns were calculated starting the day after the sale for the next 84,
252, and 504 trading days.
46

For winners that were sold, the average excess
return over the following year was a highly statistically significant 3.4 percent more than it was forlosers that were not sold.9 (Winners sold subsequently outperformed paper losses by 1.03 percent
over the following four months and 3.58 percent
over the following two years.) Investors who sell
winners and hold losers because they expect thelosers to outperform the winners in the future are,on average, mistaken. The superior returns toformer winners noted here are consistent withJegadeesh and Titman’s (1993) finding of pricemomentum in security returns at horizons of up to
18 months.10■ Attempt to limit transaction costs. Harris(1988) suggested that investors’ reluctance to sell
losers may result from their sensitivity to higher
trading costs at lower stock prices. To contrast thishypothesis to the disposition effect, one can investigate the rates at which investors purchase additional shares of stocks they already own. If investors
are avoiding the sale of losing investments becauseof the higher transaction costs associated with selling low-price stocks, the expectation is that theyalso are avoiding the purchase of additional sharesof these losing investments. In fact, we found thatinvestors are more inclined to purchase additional
shares of their losing investments than additional
shares of their winning investments. In our sample,
investors were almost 1.5 times more likely to purchase additional shares of any losing position they
already held than any winning position.
■ Belief that all stocks revert to the mean. The
results presented so far do not distinguish the disposition effect of prospect theory from the mistaken
belief that losers will bounce back to outperformcurrent winners. Both prospect theory and a belief
in mean reversion posit that investors will hold theirlosers too long and sell their winners too soon. And
both predict that investors will purchase a greaternumber of additional shares of losers than of winners. A belief in mean reversion should, however,
apply to stocks that an investor does not alreadyown as well as those an investor does own, whereas
prospect theory would apply only to the stocks theinvestor owns. That is, a belief in mean reversion
implies that investors will tend to buy stocks that
previously declined even if they do not already own
those stocks, whereas prospect theory makes no
prediction in this case. Odean (forthcoming 1999)
found that the same group of investors as studied
here tended to buy stocks that had on average outperformed the CRSP value-weighted index over theprevious two years. This result appears to be inconsistent with a pervasive belief in mean reversion.

©1999, Association for Investment Management and Research

The Courage of Misguided Convictions

Overconfidence and Excessive
Trading
Reconciling the volume of trading observed inequity markets with the trading needs of rational
investors is difficult. Rational investors make periodic contributions and withdrawals from theirinvestment portfolios, rebalance their portfolios,
and trade to minimize their taxes. Those possessedof superior information may trade speculatively,
but rational speculative traders will generally notchoose to trade with each other. Thus, rational trading needs are unlikely to account for a 1998 turnover rate on the NYSE of 76 percent.
We believe there is a simple and powerfulexplanation for high levels of trading on financial
markets—overconfidence. Human beings are overconfident about their abilities, their knowledge,
and their future prospects. Odean (1998b) showedthat overconfident investors trade more than rational investors and that doing so lowers their
expected utilities.11 Greater overconfidence leads
to greater trading and to lower expected utility.
Overconfidence increases trading activity
because it causes investors to be too certain about
their own opinions and to not consider sufficiently
the opinions of others. The result is an increase in
the heterogeneity of investors’ beliefs, and as Harris and Raviv (1993) and Varian (1989) pointed out,
heterogeneous beliefs are needed to generate significant trading. Overconfident investors also perceive their actions to be less risky than generally
proves to be the case.
The study reported in this section tested
whether a particular class of investors, those withaccounts at discount brokerages, trade excessively,in the sense that their trading profits are insufficient
to cover their trading costs. The surprising findingis that not only do the securities these investors buy
not outperform the securities they sell by enoughto cover trading costs but, on average, the securities
they buy underperform those they sell. This result
held even when trading was not apparently motivated by liquidity demands, tax-loss selling, portfolio rebalancing, or a move to lower-risk
securities.
Although investors’ overconfidence in the precision of their information may contribute to thisfinding, it is not sufficient to explain it. These investors must be systematically misinterpreting information available to them. That is, they do not
misconstrue simply the precision of their information; they misconstrue its very meaning.

tend to overestimate the precision of their knowledge (Alpert and Raiffa 1982; Fischhoff, Slovic, andLichtenstein 197712). Such overconfidence has been
observed among many professionals: clinical psychologists (Oskamp 1965), physicians and nurses(Christensen-Szalanski and Bushyhead 1981; Baumann, Deber, and Thompson 1991), investmentbankers (Staël von Holstein 1972), engineers (Kidd
1970), entrepreneurs (Cooper, Woo, and Dunkelberg 1988), lawyers (Wagenaar and Keren 1986),
negotiators (Neale and Bazerman 1990), and managers (Russo and Schoemaker 1992).13
Miscalibration is only one manifestation of
overconfidence. Researchers have also found thatpeople overestimate their ability to do well on
tasks, and these overestimates increase with theimportance of the task to the person (Frank 1935).
People are also unrealistically optimistic about
future events. They expect good things to happen
to them more often than to their peers (Weinstein
1980; Kunda 1987). They are even unrealistically
optimistic about pure chance events (Marks 1951;
Irwin 1953; Langer and Roth 1975).
Moreover, people have unrealistically positive
self-evaluations (Greenwald 1980). Most individuals see themselves as better than the average person
and as better than others see them (Taylor and
Brown 1988). They rate their abilities and theirprospects higher than those of their peers. For
example, when a sample of U.S. students—averageage 22—assessed their own driving safety, 82 percent judged themselves to be in the top 30 percentof the group (Svenson). And 81 percent of 2,994
new business owners thought their businesses had
a 70 percent or better chance of succeeding, but only
39 percent thought that any business like theirs
would be so likely to succeed (Cooper, et al.).
In addition, people overestimate their contributions to past positive outcomes; they recall information related to their successes more easily thaninformation related to their failures. Fischhoff
wrote that “they even misremember their own predictions so as to exaggerate in hindsight what theyknew in foresight” (1982, p. 341). And when people
expect a certain outcome and the outcome thenoccurs, they often overestimate the degree to which
they were instrumental in bringing it about (Miller
and Ross 1975). Taylor and Brown argued thatexaggerated beliefs in one’s abilities and unrealistic
optimism may lead to “higher motivation, greater
persistence, more effective performance, and ultimately, greater success” (p. 199). These beliefs canalso lead to biased judgments.

Overconfidence. Studies of the calibration ofsubjective probabilities have found that people

Overconfidence in Financial Markets. In amarket with transaction costs, one would expect

November/December 1999

47

Financial Analysts Journal
rational informed traders who trade for the purpose of increasing returns to increase returns, on
average, by at least enough to cover their transaction costs. That is, over the appropriate horizon,
the securities these traders buy should outperform the ones they sell by at least enough to pay
the costs of trading. If, however, speculative traders are informed but overestimate the precision of
their information (one form of overconfidence),
the securities they buy may outperform those theysell but possibly not by enough to cover trading
costs. If these traders believe they have information but actually have none, the securities they buy
should perform, on average, about the same as
those they sell, before factoring in trading costs.
That is, overconfidence in only the precision ofunbiased information should not, in and of itself,cause expected trading losses beyond the loss oftransaction costs.
If in addition to being overconfident about the
precision of their information, investors are overconfident about their ability to interpret information, they may incur trading losses beyond
transaction costs. For instance, suppose investors
receive useful information but are systematicallybiased in their interpretation of that information;that is, the investors hold mistaken beliefs about themean, instead of (or in addition to) the precision, ofthe distribution of their information. If they unwittingly misinterpret information, they may choose
to buy or sell securities that they would not haveotherwise bought or sold. They may even buy securities that, on average and before transaction costs,
underperform the ones they sell.
Methodology. To test for overconfidence inthe precision of information, our approach was todetermine whether the securities bought by theinvestors in this data set outperformed those theysold by enough to cover the costs of trading. To test
for biased interpretation of information, theapproach was to determine whether the securities
they bought underperformed those they sold when
trading costs were ignored. We examined returnhorizons of four months, one year, and two years
following each transaction.14 We calculated returns
from the CRSP daily return files.
To calculate the average return to securities
bought (sold) in these accounts over the T (T = 84,
252, or 504) trading days subsequent to the security’s purchase (sale), we indexed each purchase(sale) transaction with a subscript i, i = 1 to N. Eachtransaction consisted of a security, ji, and a date, ti.
If the same security was bought (sold) in different
accounts on the same day, each purchase (sale) was48

treated as a separate transaction. Market-adjusted
returns were calculated as the security return lessthe return on the CRSP value-weighted index. The
market-adjusted return for the T trading days subsequent to a purchase was calculated asTT

i=1 τ=1

τ=1N

1
RP, T = ---- ∑ ∏ ( 1 + Rj , t + τ ) – ∏ ( 1 + RVW, t + τ ) ,
i i
i
N

where
RP,T

= the market-adjusted return on portfolio P from Day 1 to T
= the CRSP daily return for security j on
Rj,tdate t
RVW,t = the daily return for the CRSP
value-weighted market index ondate t(Note that return calculations began the day after apurchase or a sale to avoid incorporating the bid–
ask spread into returns.)
In this data set, the (equally weighted) averagecommission paid when a security was purchased
was 2.23 percent of the purchase price. The averagecommission on a sale was 2.76 percent of the saleprice.15 Thus, if one security was sold and the saleproceeds were used to buy another security, thetotal commissions for the sale and purchase averaged about 5 percent. The average effective bid–ask
spread was 0.94 percent.16 The average total cost ofa round-trip trade was thus about 5.9 percent. In
short, an investor who sells some securities andbuys others because he expects the securities he isbuying to outperform the ones he is selling will
have to realize, on average and weighting trades
equally, a return nearly 6 percent higher on the
security bought to cover trading costs.
The first hypothesis was that over horizons offour months, one year, and two years, the average
returns to securities bought minus the average
returns to securities sold were less than the averageround-trip trading costs of 5.9 percent. Thishypothesis is what we expected if investors are
overconfident about the precision of their information. The first null hypothesis was that this difference in returns is greater than or equal to 5.9
percent. This null is consistent with rationality. The
second hypothesis was that for the same horizons(and ignoring trading costs), the average returns tosecurities bought were less than those to securities
sold. If this hypothesis were to hold, the implicationis that investors must actually misinterpret useful
information. The second null hypothesis was that
average returns to securities bought are greaterthan or equal to those sold.

©1999, Association for Investment Management and Research

The Courage of Misguided Convictions
Results. For all three follow-up periods, theaverage subsequent market-adjusted return tostocks that were bought is less than the return tostocks that were sold. Figure 3 provides a graph ofthe difference between the market-adjusted returns
to stocks that were bought and the market-adjusted
returns to stocks that were sold. Regardless of thehorizon, the stocks that investors bought underperformed the stocks that they sold. (This outcome wastrue also when actual returns were calculated
instead of market-adjusted returns.) Not only doinvestors pay transaction costs to switch stocks, but
the stocks they buy underperform the ones theysell. For the four-month (84-trading-day) horizon,
the average market-adjusted return on a purchased
stock was 1.45 percentage points (pps) lower than
the average market-adjusted return on a stock sold.
For the one-year (252-trading-day) horizon, theunderperformance was 3.2 pps, and for the
two-year (504-trading-day) horizon, the shortfall
was slightly greater, 3.6 pps.
The first null hypothesis—that the expected
returns to stocks purchased are at least 5.9 percent(the average cost of a round-trip trade) greater thanthe expected returns to stocks sold—is comfortably
rejected (p < 0.001 for all three horizons). The second null hypothesis—that the expected returns tostocks purchased are greater than or equal to thoseof stocks sold (ignoring transaction costs)—is also
comfortably rejected (p < 0.001, p < 0.001, and p <0.002 for horizons of, respectively, four months,
one year, and two years).
These investors did not make profitable trades.
Of course, investors trade for reasons otherthan to increase profits. They trade to meet liquidity demands, to move to more (or less) risky investments, to realize tax losses, and to rebalance theirportfolios. Odean (forthcoming 1999) examinedtrades for which these alternative motivations for
trading had been largely eliminated. The “speculative” trades included (1) only sales and purchases
in which a purchase was made within three weeks
of a sale (such transactions are unlikely to be liquidity motivated because investors who need cash forthree or fewer weeks can borrow more cheaply

Figure 3. Market-Adjusted Returns Subsequent to Buys minus
Market-Adjusted Returns Subsequent to Sells
Market-Adjusted Return (%)
0

–0.5

–1.0

–1.5

–2.0

–2.5

–3.0

–3.5

–4.0
1

51

101

151

201

251

301

351

401

451

501

Trading Days after Purchase or Sale
Notes: Day 0 is the day of a buy or sell. There were 49,948 buys and 47,535 sells.

November/December 1999

49

Financial Analysts Journal
than by selling and buying stocks—for example, byusing credit cards); (2) only sales that were for a
profit (that is, the stocks were not sold to realize tax
losses and were not short sales); (3) only sales of aninvestor’s complete holding in the stock (so, mostof these sales were not motivated by a desire torebalance a portfolio because of an appreciatedstock); and (4) only sales and purchases in which
the purchased stock was from the same or a smaller
size decile as the stock sold (based on CRSP size
deciles for the year of the transaction). Because sizehas been shown to be highly correlated with risk,
this last restriction was intended to eliminate most
instances in which an investor intentionally buys astock of lower expected return than the one being
sold in hopes of reducing risk.
When all these alternative motivations for
trading were eliminated, Odean (forthcoming1999) found that investors actually performed
worse in all three evaluation periods. Over the
four-month horizon, speculative purchases underperformed speculative sells by 2.5 pps; over the
one-year horizon, by 5.1 pps; and over the two-year
horizon, by 8.6 pps. Sample size was greatly
reduced, however, and statistical significance wasslightly lower following the eliminations. Nonetheless, both null hypotheses could be comfortably
rejected (for the first null hypothesis, as p < 0.001
for all three horizons; for the second null hypothesis, at p < 0.001, p < 0.001, and p < 0.002 for, respectively four months, one year, and two years).
As was the case for the tests of the disposition
effect, we were able to replicate these results out ofsample. Subsequent to Odean (forthcoming 1999),
we obtained trading records for 78,000 households
for 1991–1996 from the same discount brokerage
house used for the 1999 Odean study (the data aredescribed in the next section). On average, the1,082,106 stocks that these households bought reliably underperformed (p < 0.001) the 887,638 theysold by 2.35 pps over the 252 trading days subsequent to each transaction.
Overconfidence alone cannot explain theseresults. These investors appear to have had someability to distinguish stocks that would subsequently perform better and worse. Unfortunately,
they somehow got the relationship wrong.
Additional Tests of Overconfidence. Two
tests of overconfidence remain to be examined—
tests involving performance in relation to turnover
and tests involving performance related to genderdifferences in investors.
■ Turnover and performance. Odean (1998b)
predicted that the more overconfident investors
are, the more they will trade and the more they will50

thereby lower their expected utilities. If overconfidence is an important motivation for investor trading, then one would expect that, on average, thoseinvestors who trade most actively will most reduce
their returns through trading. As reported in Barber and Odean (forthcoming 2000), this expectation
is borne out.
We examined trading and position records for
78,000 households with accounts at the same discount brokerage house that supplied the data
described previously. The records were from January 1991 through December 1996 and included allaccounts at this brokerage for each household. Of
the sampled households, 66,465 had positions incommon stocks during at least one month of thesample period; the remaining accounts held cash orheld investments in securities other than individualcommon stocks. Roughly 60 percent of the market
value in the accounts was held in common stocks.
More than 3 million trades occurred in all the securities, and common stocks accounted for slightly
more than 60 percent of all trades. In December
1996, these households held more than $4.5 billion
in common stock. In addition to trade and position
records, our data set also identified demographic
characteristics, such as age, gender, marital status,
and income, for much of the sample.
We partitioned the households into quintiles
on the basis of the average monthly turnover of
their common stock portfolios. Mean monthly
turnover for these quintiles ranged from 0.19 percent (the low-turnover quintile) to 21.49 percent(the high-turnover quintile). Households in thehigh-turnover quintile earned a net annualized
geometric mean return of 11.4 percent; households
in the low-turnover quintile earned 18.5 percent.
Because the households in each quintile could(and did) vary as to the average risk characteristicsof their portfolios, we compared the annual net
return earned by each household with the annual
net return that would have been earned had thehousehold’s beginning-of-the-year portfolio been
held for a year without any trading, which is areasonable measure of the impact of trading on
returns. The quintile of households that tradedmost infrequently underperformed its counterpart
buy-and-hold portfolio by, on average, a mere 0.25
pps annually, whereas the quintile of households
that traded most frequently underperformed its
counterpart buy-and-hold portfolio by, on average,
a whopping 7.04 pps annually. This finding that themore investors trade the more they reduce their
expected returns is consistent with the predictionthat the more overconfident traders will trade more
actively and earn less.

©1999, Association for Investment Management and Research

The Courage of Misguided Convictions
But we still have not examined directly
whether overconfidence is motivating trading. For
such a test, the behavior or groups with clearly
different profiles of overconfidence is needed.
■ Gender, overconfidence, and performance. To
test directly the role of overconfidence in motivating trading, we partitioned our data into twogroups that psychologists have shown to differ intendency toward overconfidence—men andwomen. Although both men and women exhibit
overconfidence, men have been found to be generally more overconfident than women. Genderdifferences as to overconfidence are, however,highly task dependent (Lundeberg, Fox, and Puncochar 1994). Although Lichtenstein and Fischhoff(1981) did not find gender differences in calibration of general knowledge, Lundeberg et al.
argued that the reason is that gender differences
in calibration are strongest for topics in the masculine domain (e.g., mathematics). Deaux and Farris wrote, “Overall, men claim more ability than
do women, but this difference emerges moststrongly on . . . masculine task[s]” (1977, p. 64).
And several studies have confirmed this difference (Deaux and Emswiller 1974; Lenney 1977;
Beyer and Bowden 1997). Specifically, Prince(1993) found that men are inclined to feel morecompetent than women do in financial matters.
Indeed, casual observation reveals that men aredisproportionately represented in the financial
industry. We expected men, therefore, to be generally more overconfident than women about
their ability to make financial decisions.
Additionally, Lenney reported that gender differences in self-confidence depend on the presenceor lack of clear, unambiguous feedback. She statedthat when feedback is
unequivocal and immediately available,
women do not make lower ability estimates
than men. However, when such feedback isabsent or ambiguous, women seem to havelower opinions of their abilities and often dounderestimate relative to men. (p. 3)

Unfortunately, the stock market does not generally
provide clear, unambiguous feedback—which is all
the more reason to expect men to be more confident
than women about their ability to make common
stock investments.
Our prediction, then, was clear: We expected
men, the more overconfident group, to trade more
actively than women and, in doing so, to detract morethan women from their net return performance.
As reported in Barber and Odean (1999), we
found this prediction to hold true: In this study,
men traded 45 percent more actively than women(76.9 percent versus 52.8 percent turnover annually), and men reduced their net annual returns
November/December 1999

through trading by 0.94 pps more than women.
(Men underperformed their buy-and-hold portfolios by 2.652 pps annually; women underperformed their buy-and-hold portfolios by 1.716 pps
annually.) We found the differences in the turnoverand performance of men and women to be highly
statistically significant and robust to the introduction of other demographic variables, such as marital status, age, and income.

Conclusion
One of the major contributions of behavioral
finance is that it provides insights into investor
behavior when such behavior cannot be understood under traditional theories. We tested two
behavioral finance theories and found, as predicted, that investors tend to sell their winners andhold their losers. Also, as a result of overconfidence,
investors trade to their detriment. Because thesebehaviors reduce investor welfare, understanding
them is important for investors and for those who
advise them.
But the welfare consequences of investor
behavior extend beyond individual investors andtheir advisors to the market itself. Modern financial
markets depend on trading volume for their very
existence. It is trading—commissions andspreads—that pays for the brokers and market makers, without whom these markets would not exist.
Traditional models of financial markets provide little insight into why people trade as much as theydo. In some models, investors seldom trade or do
not trade at all (e.g., Grossman 1976). Other models
simply stipulate a class of investors—noise or
liquidity traders—who are required to trade (e.g.,
Kyle 1985). Harris and Raviv (1993) and Varian(1989), however, pointed out that heterogeneous
beliefs are needed to generate significant trading.
And behavioral finance throws light on why andwhen investors form heterogeneous beliefs.
Both behavioral theories tested offer insights
into trading volume. The disposition effect saysthat investors will generally trade less actively
when their investments have lost money. The overconfidence theory suggests that investors will trade
more actively when their overconfidence is high.
Psychologists find that people tend to give themselves too much credit for their own success and do
not attribute enough of that success to chance oroutside circumstance. Gervais and Odean showedthat this bias leads successful investors to become
overconfident. So, in a market where most investors are successful (such as today’s long bull market), trading rises. Statman and Thorley (1999)
found that over even short horizons, such as amonth, current market returns predict subsequent
trading volume.
51

Financial Analysts Journal
Finally, the investor behaviors discussed inthis article have the potential to influence asset
prices.17 The tendency to refrain from selling losers
may, for example, slow the rate at which negative
news is translated into price. The tendency to buy
stocks with recent extreme performance could
cause recent winners to overshoot. For biases toinfluence asset prices, investors must be systematicin their biases and willing to act on them.18
Our common psychological heritage ensuresthat we systematically share biases. We are disposed to hold our losers and sell our winners. Andin our overconfidence, we act on our misguided
convictions.
We would like to thank for helpful comments Peter Klein,
Hayne Leland, Richard Lyons, David Modest, John
Nofsinger, James Poterba, Mark Rubinstein, Paul Ruud,
Richard Sansing, Richard Thaler, Brett Trueman, andparticipants at the Berkeley Program in Finance, theNational Bureau of Economic Research behavioral
finance meetings, the Conference on Household Financial Decision Making and Asset Allocation at the Wharton School, the Western Finance Association meetings,
the Financial Management Association Conference, theRussell Sage Institute for Behavioral Economics, andseminar participants at Columbia University, the Yale
School of Management, Duke University, the Wharton
School at the University of Pennsylvania, Stanford University, Harvard University, the Massachusetts Institute of Technology, the Amos Tuck School of Business at
Dartmouth College, Northwestern University, and theuniversities of British Columbia, Chicago, Michigan,
North Carolina, Oregon, Southern California, Texas,
and California at Berkeley, Davis, and Los Angeles. We
would also like to thank Jeremy Evnine and the discountbrokerage house that provided the data for this study.
Financial support from the Nasdaq Foundation and theAmerican Association of Individual Investors is gratefully acknowledged.

Notes
1.

2.

3.

4.

5.

6.

7.

52

Many of the results presented here were first reported in
Odean (1998a, forthcoming 1999) and in Barber and Odean(1999, forthcoming 2000).
Turnover was estimated as one-half the average monthly
equity value of all trades (purchases and sales) divided by
the average equity value of all monthly position statements.
If purchase price is a major component but not the solecomponent of the reference point, it may serve as a noisy
proxy for the true reference point. Using the proxy in placeof the true reference point would make a case for the disposition effect being more difficult to prove. Therefore, if thetrue reference point were available, the evidence reported
here would probably be even stronger.
Prior to 1987, the long-term capital gains tax rates were 40
percent of the short-term capital gains rates. For 1987–1993,
long-term and short-term gains were taxed at the same
marginal rates for low-income taxpayers. The maximum
short-term rate at times exceeded the maximum long-term
rate; in 1987, the maximum short-term rate was 38.5 percent
and the maximum long-term rate was 28 percent. In 1988–
1990, the highest income tax payers paid a marginal rate of
28 percent on both long-term and short-term gains. In 1991
and 1992, the maximum long-term and short-term rates
were, respectively, 28 percent and 31 percent. In 1993, themaximum long-term and short-term rates were, respectively, 28 percent and 39.6 percent.
When we assumed that the commission for potential sales
was the same percentage of principal as paid when the stock
was purchased, the results did not significantly change.
Note that the PGR and the PLR measures depend on the
average size of the portfolios from which they were calculated. When the portfolio sizes were small and when we
calculated average account proportions rather than aggregate sample proportions, both of these proportions tended
to be larger.
In the reported PLR and PGR calculations, realized andunrealized losses were tabulated on days that sales took
place in portfolios of two or more stocks. One objection tothis formulation is that for portfolios that hold only winners

8.

9.

or only losers, an investor cannot choose whether to sell awinner or to sell a loser but only which winner or loser tosell. Another objection is that if an investor has net capital
losses of more than $3,000 for the current year (innon-tax-deferred accounts), it may be normative for thatinvestor to choose to sell a winner rather than a loser.
Therefore, we repeated the analyses under the following
additional constraints: that a portfolio hold at least one
winner and one loser on the day of a sale for that day to be
counted and that the net realized capital losses for the year
to date in the portfolio be less than $3,000. When these
constraints were imposed, the difference in PGR and PLR
was greater for each analysis. For example, for the entire
sample and the entire year (as in Table 2), the result was
10,111 realized gains, 71,817 paper gains, 5,977 realized
losses, and 94,419 paper losses. Thus, the PLR was 0.060, thePGR was 0.123, their difference was 0.063, and the t-statistic
for the difference in proportions was 47.
Starr-McCluer (1995) found that 15 percent of thestock-owning households interviewed in the 1989 and 1992
Surveys of Consumer Finances had paper losses of 20 percent or more. She estimated that in the majority of cases, thetax advantages of realizing those losses would have morethan offset the trading costs and time costs of selling. Heisler(1994) documented loss aversion in a small sample offutures speculators. In a study of individual federal tax
returns, Poterba (1987) found that, although many investors
do offset their capital gains with losses, more than 60 percent of the investors with gains or losses realized only gains.
Weber and Camerer reported experimental evidence of thedisposition effect.
Here, and for the findings reported in the section titled“Overconfidence and Excessive Trading,” we determinedstatistical significance by using a bootstrapping techniquesimilar to the techniques discussed in Brock, Lakonishok,
and LeBaron (1992); Ikenberry, Lakonishok, and Vermaelen(1995); and Lyon, Barber, and Tsai (1999). This procedure is
described in greater detail in Odean (1998a) and Odean(forthcoming 1999).

©1999, Association for Investment Management and Research

The Courage of Misguided Convictions
10. At the time of this study, CRSP data were available through1994. For this reason, we did not calculate two-year subsequent returns for sales dates in 1993.
11. Other theoretical treatments of overconfident investors arefound in DeLong, Shleifer, Summers, and Waldmann(1991); Benos (1998); Kyle and Wang (1997); Daniel,
Hirshleifer, and Subrahmanyam (1998); and Gervais andOdean (1999).
12. See Lichtenstein, Fischhoff, and Phillips (1982) for a reviewof the calibration literature.
13. For further discussion, see Yates (1990) and Lichtenstein,
Fischhoff, and Phillips.
14. Benartzi and Thaler (1995) estimated the average investor’s
investment horizon to be one year; from 1987 to 1993, NYSE
securities turned over about once every two years.
15. Weighting each trade by its equity value, rather than equal

weighting, produced an average commission for a purchase(sale) of 0.9 (0.8) percent.
16. Barber and Odean (forthcoming 2000) estimated the bid–
ask spread at 1.00 percent for individual investors for 1991–
1996. Carhart (1997) estimated trading costs of 0.21 percent
for purchases and 0.63 percent for sales made by open-end
mutual funds in 1966–1993.
17. In the past two decades, researchers have discovered many
anomalies that apparently contradict established financetheories. New theories, both behavioral (e.g., Barberis, Shleifer, and Vishny 1998; Daniel et al.) and rational (e.g., Berk
1995), have been devised to explain anomalies in asset
prices. It is not yet clear, however, what contribution behavioral finance will make to asset-pricing theory.
18. Of course, there must also be limits to arbitrage (see Shleiferand Vishny 1997).

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