Do Investors Trade Too Much?
By TERRANCE ODEAN*
Trading volume on the world’s markets
seems high, perhaps higher than can be explained by models of rational markets. For example, the average annual turnover rate on the
New York Stock Exchange (NYSE) is currently
greater than 75 percent1 and the daily trading
volume of foreign-exchange transactions in all
currencies (including forwards, swaps, and spottransactions) is roughly one-quarter of the total
annual world trade and investment flow (JamesDow and Gary Gorton, 1997). While this levelof trade may seem disproportionate to investors’ rebalancing and hedging needs, we lackeconomic models that predict what trading volume in these market should be. In theoreticalmodels trading volume ranges from zero (e.g.,
in rational expectation models without noise) toinfinite (e.g., when traders dynamically hedge in
the absence of trading costs). But without amodel which predicts what trading volume

should be in real markets, it is difficult to test
whether observed volume is too high.
If trading is excessive for a market as awhole, then it must be excessive for somegroups of participants in that market. This paper
demonstrates that the trading volume of a particular class of investors, those with discountbrokerage accounts, is excessive.
Alexandros V. Benos (1998) and Odean(1998a) propose that, due to their overconfidence, investors will trade too much. This paper
tests that hypothesis. The trading of discountbrokerage customers is good for testing theoverconfidence theory of excessive trading because this trading is not complicated by agency
relationships. Excessive trading in retail brokerage accounts could, on the other hand, result
from either investors’ overconfidence or frombrokers churning accounts to generate commissions. Excessive institutional trading, too, might
result from overconfidence or from agency relationships. Dow and Gorton (1997) develop amodel in which money managers, who would
otherwise not trade, do so to signal to theiremployers that they are earning their fees andare not “simply doing nothing.”
While the overconfidence theory is tested
here with respect to a particular group of traders, other groups of traders are likely to be
overconfident as well. Psychologists show thatmost people generally are overconfident about
their abilities (Jerome D. Frank, 1935) andabout the precision of their knowledge (Baruch
Fischhoff et al., 1977; Marc Alpert and Howard
Raiffa, 1982; Sarah Lichtenstein et al., 1982).
Security selection can be a difficult task, and it
is precisely in such difficult tasks that people
exhibit the greatest overconfidence. Dale Griffin
and Amos Tversky (1992) write that when predictability is very low, as in securities markets,
experts may even be more prone to overconfidence than novices. It has been suggested thatinvestors who behave nonrationally will not dowell in financial markets and will not continue
to trade in them. There are reasons, though, why
we might expect those who actively trade in* Graduate School of Management, University of California, Davis, CA 95616. This paper is based on my dissertation at the University of California-Berkeley. I would liketo thank Brad Barber, Hayne Leland, David Modest, Richard Roll, Mark Rubinstein, Paul Ruud, Richard Thaler, BrettTrueman, and the participants at the Berkeley Program in
Finance, the National 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, and theRussell Sage Institute for Behavioral Economics, and seminar participants at the University of California-Berkeley,
the Yale School of Management, the University of California-Davis, the University of Southern California, the University of North Carolina, Duke University, the Universityof Pennsylvania, Stanford University, the University of Oregon, Harvard University, the Massachusetts Institute ofTechnology, Dartmouth College, the University of Chicago,
the University of British Columbia, Northwestern University, the University of Texas, UCLA, the University ofMichigan, and Columbia University for helpful comments.
I would also like to thank Jeremy Evnine and especially thediscount brokerage house which provided the data necessary for this study. Financial support from the Nasdaq
Foundation and the American Association of Individual
Investors is gratefully acknowledged.
1
The NYSE website (http://www.nyse.com/public/
market/2c/2cix.htm) reports 1998 turnover at 76 percent.
1279

1280

THE AMERICAN ECONOMIC REVIEW

financial markets to be more overconfident than
the general population. People who are more
overconfident in their investment abilities may
be more likely to seek jobs as traders or toactively trade on their own account. This would
result in a selection bias in favor of overconfidence in the population of investors. Survivorship bias may also favor overconfidence.
Traders who have been successful in the past
may overestimate the degree to which they were
responsible for their own successes—as people
do in general (Ellen J. Langer and Jane Roth,
1975; Dale T. Miller and Michael Ross,
1975)—and grow increasingly overconfident.
These traders will continue to trade and willcontrol more wealth, while others may leave themarket (e.g., lose their jobs or their money).
Simon Gervais and Odean (1999) develop amodel in which traders take too much credit for
their own successes and thereby become overconfident.
Benos (1998) and Odean (1998a) develop
models in which overconfident investors trade
more and have lower expected utilities than theywould if they were fully rational.2 The more
overconfident an investor, the more he trades
and the lower his expected utility. Rational investors correctly assess their expected profits
from trading. When trading is costly rational
investors will not make trades if the expected
returns from trading are insufficient to offset
costs [e.g., Sanford J. Grossman and Joseph E.
Stiglitz (1980) model rational traders who buy
investment information only when the gains in
expected utility due to the information offset its
cost]. Overconfident investors, on the other
hand, have unrealistic beliefs about their expected trading profits. They may engage incostly trading, even when their expected trading
profits are insufficient to offset the costs oftrading, simply because they overestimate themagnitude of expected profits. Benos (1998)

2
Other models of overconfident investors include J.
Bradford De Long et al. (1991), Albert S. Kyle and F.
Albert Wang (1997), Jordi Caballé and József Sákovics(1998), Kent Daniel et al. (1998), and Gervais and Odean(1999), Kyle and Wang (1997) argue that when traders
compete for duopoly profits, overconfident traders may reap
greater profits. However, this prediction is based on several
assumptions that do not apply to individuals trading common stocks.

DECEMBER 1999

and Odean (1998a) model overconfidence withthe assumption that investors overestimate theprecision of their information signals. In thisframework, at the worst, overconfident investors believe they have useful information when
in fact they have no information. These models
do not allow for systematic misinterpretation of
information. Thus the worst expected outcome
for an overconfident investor is to have zero
expected gross profits from trading and expected net losses equal to his trading costs.
This paper tests whether the trading profits of
discount brokerage customers are sufficient tocover their trading costs. The surprising findingis that not only do the securities that theseinvestors buy not outperform the securities theysell by enough to cover trading costs, but on
average the securities they buy underperform
those they sell. This is the case even when
trading is not apparently motivated by liquidity
demands, tax-loss selling, portfolio rebalancing,or a move to lower-risk securities.
While 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. They do not
simply misconstrue the precision of their information, but its very meaning.
The next section of the paper describes thedata set. Section II describes the tests of excessive trading and presents results. Section III
examines performance patterns of securities
prior to purchase or sale. Section IV discusses
these patterns and speculates about their causes.
Section V concludes.
I. The Data

The data for this study were provided by anationwide discount brokerage house. Ten
thousand customer accounts were randomly
selected from all accounts which were active(i.e., had at least one transaction) in 1987.
The data are in three files: a trades file, asecurity number to Committee on Uniform
Securities Identification Procedures (CUSIP)
number file, and a positions file. The trades
file includes the records of all trades made in
the 10,000 accounts from January 1987
through December 1993. This file has
162,948 records. Each record is made up of anVOL. 89 NO. 5

ODEAN: DO INVESTORS TRADE TOO MUCH?

account identifier, the trade date, the brokerage house’s internal number for the security
traded, a buy-sell indicator, the quantity
traded, the commission paid, and the principal
amount. Multiple buys or sells of the same
security, in the same account, on the sameday, and at the same price are aggregated. The
security number to CUSIP table translates thebrokerage house’s internal numbers into
CUSIP numbers. The positions file contains
monthly position information for the 10,000
accounts from January 1988 through December 1993. Each of its 1,258,135 records ismade up of the account identifier, the year andmonth, the internal security number, equity,and quantity. Accounts that were closed between January 1987 and December 1993 are
not replaced; thus in the later years of thesample the data set may have some survivorship bias in favor of more successful
investors.
There are three data sets similar to this one
described in the literature. Gary G. Schlarbaum
et al. (1978) and others analyze trading records
for 2,500 accounts at a large retail brokerage
house for the period January 1964 to December
1970; S. G. Badrinath and Wilbur G. Lewellen(1991) and others analyze a second data set
provided by the same retail broker for 3,000
accounts over the period January 1971 to September 1979. The data set studied here differsfrom these primarily in that it is more recent andcomes from a discount broker. By examining
discount brokerage records I can rule out theretail broker as an influence on observed trading
patterns. Brad M. Barber and Odean (1999a)
calculate the returns on common securities in
158,000 accounts. (These accounts are different
from those analyzed in this paper, but comefrom the same discount brokerage.) After subtracting transactions costs and adjusting for risk,these accounts underperform the market. Accounts that trade most actively earn the lowest
average net returns. Using the same data, Barberand Odean (1999b) find that men trade more
actively than women and thereby reduce theirreturns more so than do women. For both men
and women, they also confirm the principal
finding of this paper that, on average, the stocks
individual investors buy subsequently underperform those they sell.
This study looks at trades of NYSE, American

1281

Stock Exchange (ASE), and National Associationof Securities Dealers Automated Quotation(NASDAQ) securities for which daily return information is available from the 1994 Center for
Research in Security Prices (CRSP) NYSE, ASE,
and NASDAQ daily returns file. There are 97,483
such trades: 49,948 purchases and 47,535 sales.
62,516,332 shares are traded: 31,495,296 shares,
with a market value of $530,719,264, are purchased and 31,021,036 shares, with a market
value of $579,871,104, are sold. Weighting eachtrade equally the average commission for a purchase is 2.23 percent and for a sale is 2.76 percent.3 Average monthly turnover is 6.5 percent.4
The average size decile of a purchase is 8.65 andof a sale is 8.68, 10 being the decile of the companies with the largest capitalization.
II. Empirical Study

A. Methodology
In a market with transaction costs we would
expect rational informed traders who trade forthe purpose of increasing returns to increase
returns, on average, by at least enough to covertransaction costs. That is, over the appropriate
horizon, the securities these traders buy will
outperform the ones they sell by at least enoughto pay the costs of trading. If speculative traders
are informed, but overestimate the precision of
their information, the securities they buy will,on average, outperform those they sell, but possibly not by enough to cover trading costs. If
these traders believe they have information, but
actually have none, the securities they buy will,on average, perform about the same as thosethey sell before factoring in trading costs. Overconfidence in only the precision of unbiased
information will not, in and of itself, cause
expected trading losses beyond the loss of transactions costs.
If instead of (or in addition to) being overconfident in the precision of their information,
investors are overconfident about their ability tointerpret information, they may incur average
3
Weighting each trade by its equity value, the averagecommission for a purchase is 0.9 and for a sale is 0.8.
4
I estimate turnover 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.

1282

THE AMERICAN ECONOMIC REVIEW

trading losses beyond transactions costs. Suppose investors receive useful information but
are systematically biased in their interpretationof that information; that is, the investors holdmistaken beliefs about the mean, instead of (orin addition to) the precision of the distributionof their information. If they believe they arecorrectly interpreting information that they misinterpret, they may choose to buy or sell securities which they would not have otherwisebought or sold. They may even buy securities
that, on average and before transaction costs,
underperform the ones they sell.
To test for overconfidence in the precision of
information, I determine whether the securities
investors in this data set buy outperform thosethey sell by enough to cover the costs of trading.
To test for biased interpretation of information,
I determine whether the securities they buy underperform those they sell when trading costs
are ignored. I look at return horizons of fourmonths (84 trading days), one year (252 tradingdays), and two years (504 trading days) following a transaction.5 Returns are calculated fromthe 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 thepurchase (sale), I index each purchase (sale)transaction with a subscript i, i ⫽ 1 to N. Eachtransaction consists of a security, j i , and a date,t i . If the same security is bought (sold) in different accounts on the same day, each purchase(sale) is treated as a separate transaction. The
average return to the securities bought over the
T trading days subsequent to the purchase is:NT¥ 兿 共1 ⫹ R j i ,t i⫹ ␶ 兲(1)

R P,T ⫽ i⫽1 ␶ ⫽1

N

⫺ 1,

where R j,t is the CRSP daily return for security
j on date t. Note that return calculations begin

5
Investment horizons will vary among investors andinvestments. Shlomo Benartzi and Richard H. Thaler (1995)
have estimated the average investor’s investment horizon to
be one year and, during this period, NYSE securities turned
over about once every two years. At the time of this analysis, CRSP data was available through 1994. For this reason
two-year subsequent returns are not calculated for transactions dates in 1993.

DECEMBER 1999

the day after a purchase or a sale so as to avoid
incorporating the bid-ask spread into returns.
In this data set, the average commission paid
when a security is purchased is 2.23 percent of
the purchase price. The average commission ona sale is 2.76 percent of the sale price. Thus if
one security is sold and the sale proceeds areused to buy another security the total commissions for the sale and purchase average about 5
percent. To get a rough idea of the effective
bid-ask spread I calculate at the average difference between the price at which a security ispurchased and its closing price on the day of thepurchase and calculate the average difference
between the closing price on the day of the saleand the selling price. These are 0.09 percent and0.85 percent, respectively. I add these together
to obtain 0.094 percent as an estimate of theaverage effective spread for these investors.6
Thus the average total cost of a round-trip tradeis about 5.9 percent. An investor who sells
securities and buys others because he expects
the securities he is buying to outperform theones he is selling will have to realize, on average and weighting trades equally, a returnnearly 6 percent higher on the security he buys
just to cover trading costs.
The first hypothesis tested here is that, overhorizons of four months, one year, and two
years, the average returns to securities boughtminus the average returns to securities sold areless than the average round-trip trading costs of
5.9 percent. This is what we expect if investors
are sufficiently overconfident about the precision of their information. The null hypothesis(N1) is that this difference in returns is greaterthan or equal to 5.9 percent. The null is consistent with rationality. The second hypothesis isthat over these same horizons the average returns to securities bought are less than those to
securities sold, ignoring trading costs. This hypothesis implies that investors must actually
misinterpret useful information. The null hypothesis (N2) is that average returns to securities bought are greater than or equal to thosesold.
6
Barber and Odean (1999a) estimate the bid-ask spreadof 1.00 percent for individual investors from 1991 to 1996.
Mark M. Carhart (1997) estimates trading costs of 0.21
percent for purchases and 0.63 percent for sales made byopen-end mutual funds from 1966 to 1993.

VOL. 89 NO. 5

ODEAN: DO INVESTORS TRADE TOO MUCH?

B. Significance Testing
The study compares the average return topurchased securities subsequent to their purchase and the average return to sold securities
subsequent to their sale. These returns areaveraged over the trading histories of individual investors and across investors. Many individual securities are bought or sold on morethan one date and may even be bought or soldby different investors on the same date. Suppose, for example, that one investor purchasesa particular stock and that a month later another investor purchases the same stock. The
returns earned by this stock over four-month
periods subsequent to each of these purchases
are not independent because the periods overlap for three months. Because returns to individual stocks during overlapping periods are
not independent, statistical tests which require independence cannot be employed here.
Instead statistical significance is estimated bybootstrapping an empirical distribution fordifferences in returns to purchased and soldsecurities. This empirical distribution is generated under the assumption that subsequent
returns to securities bought and securities
sold are drawn from the same underlying distribution. The methodology is similar to thatof William Brock et al. (1992) and David L.
Ikenberry et al. (1995). Barber et al. (1999)
test the acceptance and rejection rates for thismethodology and find that it performs well in
random samples. For each security in thesample for which CRSP return data are available a replacement security is drawn, withreplacement, from the set of all CRSP securities of the same size decile and same bookto-market quintile as the original security.
Using the replacement securities together
with the original observation dates, average
returns are calculated for the 84, 252, and 502
trading days following dates on which sales
or purchases were observed. For example,
suppose that in the original data set security A
is sold on October 14, 1987, and August 8,
1989, and is bought on April 12, 1992. If
security B is drawn as security A’s replacement, then in calculating the average return toreplacement securities sold, returns to security B following October 14, 1987, and August 8, 1989, will be computed; and in1283

calculating the average return to replacement
securities bought, returns to security B following April 12, 1992, will be computed.
Replacements are drawn for each security andthen average returns subsequent to dates on
which the original securities were purchased
and were sold are calculated for the replacement securities. These averages and their differences constitute one observation from theempirical distribution. One thousand such observations are made. The null hypothesis (N2)that the securities investors buy outperform(or equally perform) those they sell is rejected
at the ␣ percent level if the average subsequent return of purchases minus that of sales
in the actual data is less than the ␣ percentile
average return of purchases minus that ofsales in the empirical distribution. The null
hypothesis (N1) that the securities investors
buy outperform (or equally perform) thosethey sell by at least 5.9 percent (the cost of
trading) is rejected at the ␣ percent level if theaverage subsequent return of purchases minusthat of sales minus 5.9 percent in the data set
is less than the ␣ percentile average return ofpurchases minus that of sales in the empirical
distribution.
This test tries to answer the following question: Suppose that instead of buying and sellingthe securities they did buy and sell, these investors had randomly chosen securities of similar
size and book-to-market ratios to buy and sell; ifeach security actually traded were replaced, forall of its transactions, by the randomly selected
security, how likely is it that, for the randomly
selected replacement securities, the returns subsequent to purchases would underperform returns subsequent to sales by as much as isobserved in the data?
C. Results
Table 1 presents the principal results in thispaper. Panel A reports results for all purchases
and all sales of securities in the database. Panels
B–F give results for various partitions of thedata.7 The most striking result in Table 1 is that

7
The empirical distributions used for significance testingfor various partitions of the data were derived simultaneously.

1284

THE AMERICAN ECONOMIC REVIEWTABLE 1—AVERAGE RETURNS FOLLOWINGPURCHASES AND SALES

Panel A: All Transactionsn
84 trading
days later
Purchases
49,948
1.83
Sales
47,535
3.19
Difference⫺1.36
N1(0.001)
N2(0.001)

252 trading
days later
5.69
9.00
⫺3.31
(0.001)(0.001)504 trading
days later⫺24.0027.32⫺3.32(0.001)(0.002)

Panel B: Purchases Within Three Weeks of Sales—Sales for
Profit and of Total Position—Size Decile of Purchase Less
Than or Equal to Size Decile of Salen84 trading
252 trading
504 trading
days laterdays laterdays later
Purchases
7,503
0.11
5.45
22.31
Sales
5,331
2.62
11.27
31.22
Difference
⫺2.51
⫺5.82
⫺8.91
N1
(0.001)(0.001)(0.001)
N2(0.002)(0.003)(0.019)Panel C: The 10 Percent of Investors Who Trade the Mostn
84 trading
252 trading
504 trading
days laterdays laterdays later
Purchases
29,078
2.13
7.07
25.28
Sales
26,732
3.04
9.76
28.78
Difference
⫺0.91
⫺2.69
⫺3.50
N1
(0.001)(0.001)(0.001)
N2(0.001)(0.001)(0.010)Panel D: The 90 Percent of Investors Who Trade the Leastn
84 trading
252 trading
504 trading
days laterdays laterdays later
Purchases
20,870
1.43
3.73
22.18
Sales
20,803
3.39
8.01
25.44
Difference
⫺1.96
⫺4.28
⫺3.26
N1
(0.001)(0.001)(0.001)
N2(0.001)(0.001)(0.001)Panel E: 1987–1989
nPurchases
Sales
DifferenceN1
N2

25,256
26,73284 trading
days later
0.05
1.70
⫺1.65
(0.001)(0.001)

252 trading
days later
1.47
4.88
⫺3.41
(0.001)(0.001)504 trading
days later
20.44
22.95
⫺2.51
(0.001)(0.006)84 trading
days later
4.67
5.93
⫺1.26
(0.001)(0.004)252 trading
days later
12.29
16.44
⫺4.15
(0.001)(0.001)504 trading
days later
32.04
38.89
⫺6.85
(0.001)(0.005)

Panel F: 1990–1993
nPurchases
Sales
Difference
N1
N2

29,07826,732

Notes: Average percent returns are calculated for the 84,
252, and 504 trading days following purchases and following sales in the data set trades file. Using a bootstrapped
empirical distribution for the difference in returns following
buys and following sells, the null hypotheses N1 and N2 canbe rejected with p-values given in parentheses. N1 is thenull hypothesis that the average returns to securities subsequent to their purchase is at least 5.9 percent greater than the
average returns to securities subsequent to their sale. N2 is
the null hypothesis that the average returns to securities
subsequent to their purchase is greater than or equal to theaverage returns to securities subsequent to their sale.

DECEMBER 1999

for all three follow-up periods and for all partitions of the data the average subsequent returnto securities bought is less than that to securities
sold. Not only do the investors pay transactions
costs to switch securities, but the securities theybuy underperform the ones they sell. For example, for the entire sample over a one-year horizon the average return to a purchased security is
3.3 percent lower than the average return to asecurity sold.
The rows labeled N1 give significance levels
for rejecting the null hypothesis that the expected returns to securities purchased are 5.9
percent (the average cost of a round-trip trade)or more greater than the expected returns tosecurities sold. Statistical significance isdetermined from the empirical distributions described above; p-values are given in parentheses. For the unpartitioned data (Panel A) N1 canbe rejected at all three horizons with p ⬍0.001. The rows labeled N2 report significancelevels for rejecting the second null hypothesis(N2) that the expected returns to securities purchased are greater than or equal to those of
securities sold (ignoring transactions costs). For
the unpartitioned data (Panel A) N2 can berejected at horizons of 84 and 252 trading days
with p ⬍ 0.001 and at 504 trading days withp ⬍ 0.002.
These investors are not making profitabletrades. Of course investors trade for reasons
other than to increase profit. They trade to meetliquidity demands. They trade to move to more,or to less, risky investments. They trade to realize tax losses. And they trade to rebalance. For
example, if one security in his portfolio appreciates considerably, an investor may sell part ofhis holding in that security and buy others torebalance his portfolio. Panel B examines trades
for which these alternative motivations to trade
have been largely eliminated. This panel examines only sales and purchases where a purchaseis made within three weeks of a sale; suchtransactions are unlikely to be liquidity motivated since investors who need cash for three
weeks or less can borrow more cheaply (e.g.,using credit cards) than the cost of selling andlater buying securities. All of the sales in thispanel are for a profit; so these securities are notsold in order to realize tax losses (and they are
not short sales). These sales are of an investor’scomplete holding in the security sold; so mostVOL. 89 NO. 5

ODEAN: DO INVESTORS TRADE TOO MUCH?

of these sales are not motivated by a desire torebalance the holdings of an appreciated security.8 Also this panel examines only sales andpurchases where the purchased security is fromthe same size decile as the security sold or froma smaller size decile (CRSP size deciles for theyear of the transaction); since size has beenshown to be highly correlated with risk, thisrestriction is intended to eliminate most instances where an investor intentionally buys asecurity of lower expected return than the one
he sells because he is hoping to reduce his risk.
We see in Panel B that when all of thesealternative motivations for trading are (at least
partially) eliminated, investors actually perform
worse over all three evaluation periods; over a
one-year horizon the securities these investors
sell underperform those they buy by more than
5 percent. Sample size is, however, greatly reduced and statistical significance slightly lower.
Both null hypotheses can still be rejected.
In Panels C–F the data set is partitioned totest the robustness of these results. Panel C
examines the trades made by the 10 percent of
the investors in the sample who make the greatest number of trades. Panel D is for trades madeby the 90 percent of investors who trade least.
The securities frequent traders buy underperform those they sell by a bit less than is the casefor the investors who trade least. It may be thatthe frequent traders are better at security picking. Or it may be that because they hold securities for shorter periods, the average returns inperiods following purchases and sales are morealike. If, for example, an investor buys a security and sells it ten trading days later, the 84trading-day period following the purchase will
overlap the 84-trading-day period following thesale on 74 trading days. Thus the returns for thetwo 84-day periods are likely to be more alikethan they would be if there were no overlap.
Panel E examines trades made during 1987–

8
The profitability of a sale and whether that sale is of acomplete position are determined by reconstructing an investor’s portfolio from past trades. Exactly how this is done
is described in Odean (1998b). It is possible that there aresome cases where it appears that an investor’s entire position has been sold, but the investor continues to hold sharesof that security acquired before 1987. It is also possible thatthe investor continues to hold this security in a different
account.

1285

1989 and Panel F those made during 1990 –1993. For panels C, D, E, and F, we can rejectboth of the null hypotheses at all three horizons.
D. Calendar-Time Portfolios
To establish the robustness of the statisticalresults presented above, I calculate three measures of performance that analyze the returns on
calendar-time portfolios of securities purchased
and sold in this data set. The calendar-time
portfolio method eliminates the problem ofcross-sectional dependence among sample
firms, since the returns on sample firms areaggregated into two portfolio returns.9 These
intercept tests test whether the difference in the
average subsequent returns to securities purchased and to securities sold in the data set issignificantly different than zero. Transactions
costs are ignored. Thus the null hypothesis
tested here is N2, whether average returns tosecurities bought are greater than or equal tothose sold even before subtracting transactions
costs.
I calculate calendar-time returns for securities
purchased as follows. For each calendar month t,
I calculate the return on a portfolio with one position in a security for each occurrence of a purchase of that security by any investor in the data
set during the “portfolio formation period” (of 4,12, or 24 months) preceding the calendar month t.
A security may have been purchased on several
occasions during the portfolio formation period. If
so, each purchase generates a separate position in
the portfolio. Each position is weighed equally.
Similarly I form and calculate returns for a portfolio based on sales.
The first performance measure I calculate issimply the average monthly calendar-time return on the “Buy” portfolio minus that on the“Sell” portfolio. Results for portfolio formation
periods of 4, 12, and 24 months are reported inTable 2, Panel A. For all three periods themonthly returns on this “long-short” portfolio
are reliably negative.
Second, I employ the theoretical framework of
the Capital Asset Pricing Model and estimate

9This discussion of calendar-time portfolio methods
draws heavily on Barber et al’s. (1999) discussion andanalysis of these methods.

1286

THE AMERICAN ECONOMIC REVIEWTABLE 2—MONTHLY ABNORMAL RETURNSFOR CALENDAR-TIME PORTFOLIOSFormation
period4months12monthsPanel A: Raw Returns
Return⫺0.293*** ⫺0.225***(0.081)(0.071)Panel B: CAPM Intercept
Excess return⫺0.311*** ⫺0.234***(0.080)(0.073)
Beta
0.036**⫺0.012(0.018)(0.020)Panel C: Fama-French Three-Factor Intercept
Excess return⫺0.249*** ⫺0.207***(0.075)(0.070)Beta⫺0.001⫺0.007(0.019)(0.020)Size coefficient
0.031
0.075***(0.028)(0.026)
HML coefficient ⫺0.138*** ⫺0.051(0.035)(0.032)24
months⫺0.137**(0.067)⫺0.152**(0.068)0.020(0.018)⫺0.136**(0.065)0.008(0.019)0.068***(0.024)⫺0.025(0.029)Notes: Raw returns (Panel A) are R Bt ⫺ R St , where R Bt is
the percent return in month t on a equally weighted portfoliowith one position in a security for each occurrence of apurchase of that security by any investor in the data set inthe 4, 12, or 24 months (the formation period) preceding
month t and R St is the percent return in month t on a equallyweighted portfolio with one position in a security for eachoccurrence of a sale of that security by any investor in thedata set in the 4, 12, or 24 months preceding month t. The
CAPM intercept is estimated from a time-series regressionof R Bt ⫺ R St on the market excess return R mt ⫺ R rf . The
Fama-French three-factor intercept is estimated from atime-series regressions of R Bt ⫺ R St on the market excess
return, a zero-investment size portfolio (SMB t ), and a zeroinvestment book-to-market portfolio (HML t ). Standard errors are in parentheses.
***,**Significant at the 1- and 5-percent level, respectively. The null hypothesis for beta (the coefficient estimate
on the market excess return) is H 0 : ␤ ⫽ 1.

Jensen’s alpha (Michael C. Jensen, 1969) by regressing the monthly return of the buy-minus-sell
portfolio on the market excess return. That is, I
estimate:(2) R Bpt ⫺ R Spt ⫽ ␣ p ⫹ ␤ p 共R mt ⫺ R ft 兲 ⫹ ⑀ pt
where:R Bpt ⫽ the monthly return on the calendartime portfolio based on purchases;
R Spt ⫽ the monthly return on the calendartime portfolio based on sales;
R Mt ⫽ the monthly return on a valueweighted market index;

DECEMBER 1999

R ft ⫽ the monthly return on T-bills;10
␤ p ⫽ the market beta; and⑀ pt ⫽ the regression error term.
The subscript p denotes the parameter estimatesand error terms for the regression of returns forcalendar-time portfolios with a p month formation period. Results from these regressions arereported in Table 2, Panel B. Excess return
estimates (␣) are reliably negative for all threeportfolio formation periods (4, 12, and 24
months).
Third, I employ an intercept test using thethree-factor model developed by Eugene F.
Fama and Kenneth R. French (1993). I estimate
the following monthly time-series regression:(3) R Bpt ⫺ R Spt ⫽ ␣ p ⫹ ␤ p 共R mt ⫺ R ft 兲
⫹ z p SMB t ⫹ h p HML t ⫹ ⑀ pt
where SMB t is the return on a value-weighted
portfolio of small stocks minus the return on avalue-weighted portfolio of big stocks and
HML t is the return on a value-weighted portfolio of high book-to-market stocks minus thereturn on a value-weighted portfolio of low
book-to-market stocks.11
Fama and French (1993) argue that the risk ofcommon stock investments can be parsimoniously summarized as risk related to the market,
firm size, and a firm’s book-to-market ratio. I
measure these three risk exposures using thecoefficient estimates on the market excess
return R mt ⫺ R ft , the size zero-investment
portfolio (SMB t ), and the book-to-market
zero-investment portfolio (HML t ) from thethree-factor regressions. Portfolios with aboveaverage market risk have betas greater than one,␤ P ⬎ 1. Portfolios with a tilt toward large(growth) stocks relative to a value-weighted
market index have size (book-to-market) coefficients less than zero, z P ⬍ 0 (h P ⬍ 0).
The regression yields parameter estimatesof ␣ , ␤ , z, and h. The error term in the
regression is denoted by ⑀ t . The estimate of10
The return on T-bills is from Stocks, Bonds, Bills, andInflation: 1997 Yearbook (Ibbotson Associates, 1997).
11
The construction of these factors is described in Fama
and French (1993). I thank Kenneth French for providing
these data.

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ODEAN: DO INVESTORS TRADE TOO MUCH?

the intercept term (␣) provides a test of thenull hypothesis that the difference in the mean
monthly excess returns of the “buy” and“sell” calendar-time portfolios is zero.12 As
reported in Table 2, Panel C, excess returns
for this model are reliably negative for all
three portfolio formation periods (4, 12, and24 months). There is some evidence that,compared to the stocks they sell, these investors tend to buy smaller, growth stocks. After
adjusting for size and book-to-market effects,
there is no evidence of systematic differencesin the market risk (␤) of the stocks they buy
and sell.
E. Security Selection vs. Market Timing
The posttransaction returns of the stocks
these investors purchase are lower than thosethey sell. This underperformance could be due
to poor choices of which stocks to buy and sellor poor choices of when, in general, to buy
stocks and when to sell them. That is, the underperformance may be caused by inferior security selection or inferior market timing (orboth).
To test whether the underperformance isdue to poor security selection, I repeat theanalysis of Section II, subsection B, using
market-adjusted returns rather than raw returns. From each return subsequent to a purchase or a sale, I subtract the return on the
CRSP value-weighted index for the same period. This adjustment removes the effect thatmarket timing might have on performance.
Results for all investors during the entire sample period are reported in Table 3. The differences in the market-adjusted returns
subsequent to purchases and sales are reliably
negative at all three horizons (4, 12, and 24
months) and are similar to the difference in
raw returns subsequent to purchases and sales
reported in Table 1, Panel A. For example,
over the following 12 months, marketadjusted returns to purchases are 3.2 percent
less than market-adjusted returns to sales,
12

The error term in this regression may be heteroskedastic, since the number of securities in the calendar-time
portfolio varies from month to month. Barber et al. (1999)
find that this heteroskedasticity does not significantly affectthe specification of the intercept test in random samples.

1287

TABLE 3—AVERAGE MARKET-ADJUSTED RETURNSFOLLOWING PURCHASES AND SALESnPurchases
Sales
Difference
N1
N2

49,94847,53584
trading
days
later

252
trading
days
later

504
trading
days
later

⫺1.33
0.12
⫺1.45
(0.001)(0.001)

⫺2.68
0.54
⫺3.22
(0.001)(0.001)

⫺0.68
2.89
⫺3.57
(0.001)(0.002)

Notes: Average percent returns in excess of the CRSP
value-weighted index are calculated for the 84, 252, and 504
trading days following purchases and following sales in thedata set trades file. Using a bootstarpped empirical distribution for the difference in market-adjusted returns following buys and following sells, the null hypotheses N1 and N2
can be rejected with p-values given in parentheses. N1 is thenull hypothesis that the average market-adjusted returns tosecurities subsequent to their purchase is at least 5.9 percent
greater than the average market-adjusted returns to securities subsequent to their sale. N2 is the null hypothesis thatthe average market-adjusted returns to securities subsequent
to their purchase is greater than or equal to the average
market-adjusted returns to securities subsequent to theirsale. This table reports results for all investors over theentire sample period.

while raw returns to purchases are 3.3 percent
less than raw returns to sales. This supports
the hypothesis that these investors make poor
choices of which stocks to buy and which tosell.
To test whether these investors exhibit anability to time their entry and exit from themarket, I examine whether their entry or exitfrom the market in one month predicts the next
month’s market return. I first calculate monthly
order imbalance as the dollar value of all purchases in a month divided by the dollar value ofall purchases and all sales in that month. I thenregress the current month’s return of the CRSP
value-weighted index on the previous month’sorder imbalance:(4) R mt ⫽ a ⫹ b冉冊Buys t⫺1⫹ et .
Buys t⫺1 ⫹ Sells t⫺1

The coefficient estimate (b) for order imbalanceis statistically insignificant (t ⫽ ⫺0.4, R 2 ⫽0.0). This suggests that poor market timing
does not make an important contribution to thesubsequent underperformance of the stocks
these investors buy relative to those they sell.

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THE AMERICAN ECONOMIC REVIEWDECEMBER 1999

FIGURE 1. AVERAGE RETURNS IN EXCESS OF THE CRSP VALUE-WEIGHTED INDEX FOR ALL SECURITIES BOUGHT AND SOLD
Note: 46,830 bought; 44,265 sold.

III. Returns Patterns Before
and After Transactions

The securities the investors in this data set buy
underperform those they sell. When the investors
are most likely to be trading solely to improve
performance (Table 1, Panel B), performance gets
worse. It appears that these investors have accessto information with some predictive content, butthey are misinterpreting this information. It is possible that they are misinterpreting a wide variety
of information, such as accounting data, technicalindicators, and personal knowledge about an company or industry. A simpler explanation is thatmany of them are misinterpreting the same information. One information set readily available tomost investors is recent historical returns.
This section describes return patterns to securities before and after they are purchased andsold by individual investors.

Figures 1 and 2 graph average marketadjusted returns in excess of the CRSP valueweighted index for sales and purchases of
securities in the database from two years (504
trading days) before the transaction until two
years after it.13 If such graphs were made for allpurchases and sales in the entire market, the13
The average market-adjusted return for a set of N
transactions for a period of T trading days following eachtransaction is calculated as:

R P,T ⫽

N

T

T

i⫽1

␶ ⫽1

␶ ⫽1

¥ 共 兿 共1 ⫹ R j i ,t i⫹ ␶ 兲 ⫺ 兿 共1 ⫹ R M,t i⫹ ␶ 兲兲N

where j i , t i , and R j,t are defined as in equation (1) and R M,tis the day t return on the CRSP value-weighted market
index excluding distributions. If the calculation is done for
the CRSP value-weighted market index inclusive of distributions, daily market-adjusted returns are, on average, one
basis point lower. This change in indices has virtually no

VOL. 89 NO. 5

ODEAN: DO INVESTORS TRADE TOO MUCH?

1289

FIGURE 2. AVERAGE RETURNS IN EXCESS OF THE CRSP VALUE-WEIGHTED INDEX FOR SECURITIES BOUGHT AND SOLD BY
THE 90 PERCENT OF INVESTORS WHO TRADED LEAST
Note: 20,870 bought; 20,803 sold.

paths for returns to sales and to purchases would
coincide, since for every purchase there is asale. The differences in these paths here reflect
differences in returns to the securities that these
traders in aggregate sold to and bought from therest of the market.
Figure 1 graphs average market-adjusted returns for all purchases and all sales of securities
in the data set for which daily returns are available from CRSP. On average these investors
both buy and sell securities which have outperformed the market over the previous two years.
This is consistent with the findings of Josef
Lakonishok and Seymour Smidt (1986) andothers that trading volume is positively correlated with price changes. The securities the investors buy have appreciated somewhat morethan those they sell over the entire previous two
years, while the securities they sell have appreciated more rapidly in the months preceding
sales. Securities purchased underperform themarket over the next year, while securities soldperform about as well as the market over theeffect on the market-adjusted returns of purchases and sales
relative to each other.

next year. If there were no predictive information in the purchase or sale of a security, and if
investors traded in a mix of securities representative of the market, we would expect securities
to perform about as well as the market after
being purchased or sold. If trading were concentrated in a particular segment of the market,
such as small capitalization companies, wewould expect that if there were no predictive
value to a transaction these securities would
perform about as well, relative to the market, as
the segment of the market from which they weredrawn.14 In the overall sample the securities thatwere bought and sold are from about the same
average size deciles (8.65 and 8.68). Nevertheless, securities purchased subsequently underperform those sold. The difference in average
market-adjusted returns to purchases and sales
is statistically significant at the three time horizons for which it is tested: 84, 252, and 504
trading days (Table 3).
As discussed at the end of the Section II,
14
The data analyzed in these graphs extends from 1985
through 1994. Barber and Lyon (1997) find that big firms
outperformed small firms from 1984 to 1988 and that small
firms outperformed big firms from 1989 to 1994.

1290

THE AMERICAN ECONOMIC REVIEW

subsection C, when securities are held only ashort time between purchase and sale, the average returns to purchases and sales over longer
horizons will tend to converge. Investors who
trade most frequently tend to hold their positions for shorter periods than those who tradeless. Active traders may also have shorter trading horizons and so looking at returns one to
two years after a transaction may not be relevant
for the most active traders. Concentrating on
trades of the 90 percent of investors who tradethe least accentuates, and facilitates identifying,differences in the returns patterns of securities
purchased and sold. Figure 2 graphs average
market-adjusted returns for the purchases andsales made by these investors. The differencesin returns to purchases and sales is greater inFigure 2 than in Figure 1. Prior to the transaction, purchases have been rising steadily for two
years; sales, on the other hand, only started
rising a little over a year before the sale but haverisen more rapidly in recent months. After apurchase the market-adjusted returns to securities fall over the next eight months or so, nearly
as rapidly as they rose over the eight months
prior to the purchase. The difference in marketadjusted returns to securities bought and to securities sold following the transactions are
statistically significant for all three time horizons at which I have tested, 84, 252, and 504
trading days ( p ⬍ 0.001).
While investors buy and sell securities that
have, on average, appreciated prior to purchaseor sale, some of the securities they buy and sell
have depreciated. The decision to buy or sell aprevious winner may be motivated differently
than the decision to buy or sell a previous loser.
In Figures 3 and 4 the purchases and sales of the
90 percent of investors who trade least are partitioned into previous winners and losers. A
security that had a positive raw return over the
126 trading days (six months) preceding a purchase or sale is classified as a previous winner.
A security that had negative raw return over thisperiod is a previous loser. Because of the selection criteria, market-adjusted returns are steep
and nearly straight for both winners and losers
during the evaluation period (⫺126 to ⫺1 trading days).
In Figure 3 previous winners that are boughtby the infrequent traders outperform the market
by 60 percent over the entire two years preced-

DECEMBER 1999

ing a purchase. They then underperform themarket by about 5 percent over the next two
years. Previous winners that are sold outperform the market by almost 40 percent over the
15 months before the sale; over the 24th to 16thmonth before the sale their return is similar to
the market’s. After the sale they outperform themarket by 3 percent over the next two years.
Using the tests described in Section II, subsection B, the differences in market-adjusted returns subsequent to transactions for previous
winners sold and previous winners bought are
statistically significant for time horizons at
which I have tested, namely, 84 trading days
( p ⫽ 0.002), 252 trading days ( p ⫽ 0.001),
and 504 trading days ( p ⫽ 0.001).
Figure 4 graphs average market-adjusted returns for previous losers that are bought andsold by the infrequent traders. Those that arebought rise, relative to the market, nearly 4
percent over the 24th to 18th month prior to apurchase; then they fall 28.5 percent. Securities
sold rise about 1 percent (relative to the market)
over the 24th to 19th month prior to the sale andthen fall 24.5 percent. After being purchased
previous losers continue to underperform themarket by about 5.5 percent over the next year.
They regain most of this loss in the next year.
Previous losers which are sold outperform themarket by 1 percent over the next three months.
They then lose 5 percent more than the market
over the next nine months and finally regainsome of this loss. The difference in marketadjusted returns to previous losers bought andprevious losers sold following the transactions
are statistically significant for the first two time
horizons at which I have tested, namely, 84
trading days ( p ⫽ 0.001), and 252 trading days
( p ⫽ 0.003). The difference is not statisticallysignificant for 504 trading days.
In Figures 3 and 4 we see that both securities
that previously outperformed the market andthose that previously underperformed it, underperform it subsequent to being purchased. Thereis another class of securities, recent initial public offerings, that have neither previously outperformed or underperformed the market.
Figure 5 graphs the average market-adjusted
returns for a proxy for newly issued securities
over the two years following a purchase. Purchases are included in this graph if the beginning date for the security’s listing in the CRSP

VOL. 89 NO. 5

ODEAN: DO INVESTORS TRADE TOO MUCH?

1291

FIGURE 3. AVERAGE RETURNS IN EXCESS OF THE CRSP VALUE-WEIGHTED INDEX FOR SECURITIES BOUGHT AND SOLD BY
THE 90 PERCENT OF INVESTORS WHO TRADED LEAST, FOR SECURITIES THAT HAD POSITIVE RAW RETURNS OVER THE 126
TRADING DAYS PRECEDING A PURCHASE OR A SALE
Note: 9,688 bought; 12,250 sold.

daily returns file is no more than five trading
days prior the date of the purchase. This is nota perfect proxy for new issues, but it does give
us some indication of how new issues perform
after being purchased. When the trades of all
investors are considered, 398 purchases meetthis “new issue” criteria. Only 25 sales meet thecriteria; because of this small sample size sales
are not graphed. (If sales are graphed their return pattern is very similar to that of the purchases.) The “new issues” that the investors buy
underperform the market by an average of about
25 percent over the 14 months following thepurchase. They recover about half of this loss inthe next ten months. The underperformance ofthe market by new issues noted here is consistent with, though more extreme than, Jay R.
Ritter’s (1991) and Tim Loughran and Ritter’s(1995) findings that after the first day’s close
initial public offerings tend to underperform themarket. When compared to the empirical benchmark distribution the underperformance of
these new issues is statistically significant ( p ⬍0.05) over the 84-trading-day horizon. The underperformance is not statistically significant
for the 252- and 504-trading-day horizons.
Figure 6 graphs average market-adjusted returns over the 20 trading days preceding a transaction for securities bought and sold by the 90
percent of investors who traded least. In thisgraph securities are classified as previous winners or losers on the basis of their raw returns
over the period of 146 to 21 trading days (theseventh through the second month) preceding apurchase or a sale. The securities which investors sell rise sharply in the 20 days preceding a

1292

THE AMERICAN ECONOMIC REVIEWDECEMBER 1999

FIGURE 4. AVERAGE RETURNS IN EXCESS OF THE CRSP VALUE-WEIGHTED INDEX FOR SECURITIES BOUGHT AND SOLD BY
THE 90 PERCENT OF INVESTORS WHO TRADED LEAST, FOR SECURITIES THAT HAD NEGATIVE RAW RETURNS OVER THE 126
TRADING DAYS PRECEDING A PURCHASE OR A SALE
Note: 8,971 bought; 6,602 sold.

sale; previously winning securities rise 4.1 percent and previous losers rise 2.8 percent. Previous winners that they buy also rise while losers
they buy fall. When compared to the empiricalbenchmark distributions, the 20-trading-day
market-adjusted returns for previous winners
bought, previous winners sold, previous losers
bought, and previous losers sold are all significantly different than 0 ( p ⬍ 0.001 in all fourcases).
IV. Discussion

The previous section identifies a number ofregularities in the return patterns of securities
before they are bought or sold by individual
investors. These investors buy securities thathave experienced greater absolute price changes

over the previous two years than the ones theysell (Figures 3 and 4). They buy similar numbers of winners and losers, but they sell far
more winners than losers (Figures 3 and 4).
Investors sell securities which have risen
sharply in the weeks prior to sale. This is true
for securities that were previous winners and forprevious losers (Figure 6).
I propose that, at least in part, these patterns canbe explained quite simply. The buying patterns arecaused by the large number of securities fromwhich investors can choose to buy and by thetendency of investors to let their attention be directed towards securities that have experienced
abnormally good or bad performance. The selling
patterns result from investors’ reluctance to sell
short and from the disposition effect (i.e., investors’ reluctance to realize losses).

VOL. 89 NO. 5

ODEAN: DO INVESTORS TRADE TOO MUCH?

1293

FIGURE 5. AVERAGE RETURNS IN EXCESS OF THE CRSPVALUE-WEIGHTED INDEX FOR SECURITIES BOUGHT THATWERE ISSUED (LISTED ON CRSP) WITHIN FIVE DAYSPRIOR TO PURCHASE

FIGURE 6. AVERAGE RETURNS IN EXCESS OF THE CRSPVALUE-WEIGHTED INDEX OVER THE 20 DAYS PRECEDING A
TRANSACTION FOR SECURITIES BOUGHT AND SOLD BY THE
90 PERCENT OF INVESTORS WHO TRADED LEAST

Note: 398 bought.

Notes: Previously profitable securities had positive raw returns over the period from 146 to 21 trading days precedinga purchase or a sale. Previously not profitable securities had
negative raw returns over the same period. 26,434 previous
winners sold. 17,078 previous losers sold. 26,133 previous
winners bought. 18,964 previous losers bought.

In Section II, formal hypotheses are subjected
to rigorous tests. In this section, conjectures areproposed to explain the return patterns described in Section III. These conjectures are not,however, tested.
Investors face a formidable challenge whenlooking for a security to buy. There are well over
10,000 securities to be considered. These investors do not have a retail broker available to suggest
purchase prospects. While the search for potential
purchases can be simplified by confining it to a
subset of all securities (e.g., the S&P 500), eventhen the task of evaluating and comparing eachsecurity is beyond what most nonprofessionals areequipped to do. Unable to evaluate each security,
investors are likely to consider purchasing securities to which their attention has been drawn. Investors may think about buying securities they
have recently read about in the paper or heard
about on the news. Securities that have performed
unusually well or poorly are more likely to be

discussed in the media, more likely to be considered by individual investors and, ultimately, more
likely to be purchased.
Once their attention has been directed to potential purchases, investors vary in their propensity to buy previous winners or previous losers.
The null hypothesis that the probability of buying previous winners (or losers) is the same forall investors in this data set can be rejected ( p ⬍0.001) using a Monte Carlo test described in
the Appendix. The separation between those
who buy previous winners and those who buy
previous losers is greatest for securities whichhave experienced large price changes.
It may be that those who buy previous winners believe that securities follow trends while
those who buy previous losers believe they

1294

THE AMERICAN ECONOMIC REVIEW

revert. The investors who believe in trend may
buy previous winners to which their attention
has been directed, while those who believe inreversion buy previous losers to which theirattention has been directed. If investors were as
willing to sell securities short as to buy, we
might expect them to actively sell as well as toactively buy securities to which their attention
was directed. But mostly these investors do notsell short—less than 1 percent of the sales inthis data set are short sales. The cost of shortingis high for small investors who usually receive
none of the interest on the proceeds of the shortsale. Furthermore short selling is not limited inliability and may be considered too risky bymany investors.
While theoretical models of financial markets
often treat buying and selling symmetrically, formost investors the decision to buy a security isquite different from the decision to sell. In thefirst place, the formidable search problem forpurchases does not apply to sales. Since mostinvestors do not sell short, those seeking a security to sell need only consider the ones they
already own. This is usually a manageable
handful—in this data set the average number of
securities, including bonds, mutual funds, andoptions as well as stocks, per account is 3.6.
Investors can carefully consider selling eachsecurity they own regardless of the attention
given it in the media.
Though the search for securities to sell is far
simpler, in other respects the decision to sell asecurity is more complex than the decision tobuy. When choosing securities to buy, an investor only needs to form expectations about the
future performance of those securities. Whenchoosing securities to sell, the investor will consider past as well as future performance. If theinvestor is rational he will want to balance theadvantages or disadvantages of any tax losses orgains he realizes from a sale against future
returns he expects a security to earn. If an
investor is psychologically motivated he may
wish to avoid realizing losses and prefer to sell
his winners. In Figures 3 and 4 investors sell
nearly twice as many previous winners as previous losers. Using this same data set, Odean(1998b) shows that these investors strongly prefer to sell their winning investments and to holdon to their losing investment even though thewinning investments they sell subsequently out-

DECEMBER 1999

perform the losers they continue to hold. Jeffrey
Heisler (1997) and Chip Heath et al. (1999) findthat investors display similar behavior when
closing futures contracts and exercising employee stock options. This behavior is predicted
by Hersh Shefrin and Meir Statman’s disposition theory (1985) and, in more general terms,
by Daniel Kahneman and Tversky’s prospect
theory (1979). It appears that for many investors
the decision to sell a security is more influenced
by what that security has done than by what it islikely to do.
Disposition theory predicts that investors will
evaluate investments relative to a reference
point or “break even” price. An investment soldfor more than its reference point will be perceived as a gain. An investment sold for lesswill be perceived as a loss. Investors do not liketo accept a loss so investments above the reference point are more likely to be sold than thosebelow it. The reference point for an investment
is sometimes assumed to be its purchase price.
However for investments that have been held
over a wide range of prices, purchase price may
be only one determinant of the reference point.
For example, a homeowner who bought his
house for $100,000 just before a real-estate
boom, and had the house appraised for$200,000 after the boom, may no longer feel heis “breaking even” if he sells his house for$100,000. Alternatively, suppose an investor
buys a security at $20 a share. The share pricefalls over a few months to about $10 where it
stays for the next year. If the share price thenstarts to rise rapidly, the investor may happilychoose to sell for much less than $20, because
his reference point has fallen below the original
purchase price.
Suppose that reference points are moving averages (with some weighting function) of past
prices.15 When securities appreciate quickly
they gain relative to their moving averages. A
security that has lost value in recent months willprobably be below its reference point. If the
security rises rapidly over a few weeks, it mightpass its reference point and thus become a candidate for a sale.

15
Heath et al. (1998) find that the decision to exercise
employee security options is a function maximum price of
the underlying security over the previous year.

VOL. 89 NO. 5

ODEAN: DO INVESTORS TRADE TOO MUCH?

Attention focusing, the disposition effect, andthe reluctance to sell short explain some of the
security return patterns noted in Figures 1– 6.
These are patterns that precede sales and purchases. They are indications of the trading practices and preferences of investors. It is useful tounderstand these patterns, but it is not surprisingthat they exist. The patterns that are surprising
to find are those that follow purchases and sales.
These patterns indicate that these investors are
informed but misuse their information. In Figures 1 and 2 the securities investors buy underperform those they sell. When these trades are
partitioned into purchases and sales of previous
winners (Figure 3), the previous winners investors buy underperform those they sell. Thesewinners have been outperforming the market for
at least two years prior to being purchased.
After purchase they underperform the market.
It is possible that the return pattern for previous winners is caused by investors who buy atthe top of a momentum cycle. Narasimhan Jegadeesh and Sheridan Titman (1993) document
momentum patterns in security returns. They
sort securities into those which have performed
well or poorly during six-month formation periods. In the subsequent year the securities that
previously did well continue to outperform
those that previously did poorly. After one year
these trends reverse somewhat. John R. Nofsinger and Richard W. Sias (1999) find that thereversals are mostly confined to securities withhigh percentages of individual investor ownership. If the rise of momentum securities is, inpart, driven by the purchases of “momentum
traders,” then, when the last momentum trader
has taken his position, the rise may stall. If
momentum traders have pushed price beyond
underlying value then the price is likely to fallwhen new information becomes available. Individual investors who follow momentum strategies may be among the last momentum traders
to buy these securities and among the first to
suffer losses when trends reverse. Some of theunderperformance of securities these investors
buy relative to those they sell may be due tomistiming of momentum cycles.
The same reasoning would not necessarily
apply on the down side. Investors who follow
momentum strategies might not sell securities
that have fallen simply because they do notalready own these securities and they do not like1295

to sell short. If they do own securities that havefallen they may choose not to sell them becauseof disposition effects (i.e., they do not like torealize losses).
These explanations for the return patterns
found in these data are speculations. Further
research is needed to understand why individual
investors choose the securities they choose andwhy they choose so poorly.
Whenever it is suggested that investors behave suboptimally the question arises: “why
don’t they learn?” It is possible that they dolearn, but slowly. Equity markets are noisy
places to learn. Most of the inferences drawn inthis paper could not be made with the sample
sizes available to most investors. It is likely thatmany investors never make the sort of evaluative comparisons made here. They do not, forexample, routinely look up the performance of asecurity they sold several months ago and compare it to the performance of a security theybought in its stead. The disposition effect, too,
may slow learning. Investors tend to sell winning investments and hold on to losers. If theyweigh realized gains more heavily than “paper”
losses when evaluating their personal performance, they may feel they are doing better thanthey are. During the seven years covered by thedata, 55 percent of the original accounts dropout of the sample. About half of these drop in
the first year, perhaps as a response to the market crash of October 1987. While there aremany reasons to close an account, some investors may have closed their accounts because
they did learn that they were not as good at
picking securities as they had anticipated.
In aggregate the investors in this study maketrading choices which lead to below-market returns. This does not mean these investors losemoney. 1987 through 1993 were good years to
be in the stock market and most of these investors are probably happy that they were.
The discount brokerage customers in thisstudy make some poor trading choices. Other
groups of traders make bad choices as well.
Jensen (1968), Lakonishok et al. (1992), andBurton G. Malkiel (1995) show that active
money managers underperform relevant market
indices. While this may indicate poor judgment,
agency considerations could also motivate
active managers to make choices they would
not otherwise make. Investors with discount1296

THE AMERICAN ECONOMIC REVIEW

brokerage accounts are studied in this paper for
two reasons. First, a discount brokerage firm
was generous enough to make the data available. Second, discount customers trade mostly
for themselves and without agency concerns;
they are therefore well suited for testing behavioral theories of finance. It would be instructive
to repeat this study for other groups of traders.
This is a study of the trading of individual
investors with discount brokerage accounts.
What effect, if any, the trading of these investors will have on market prices will also dependon the trading of other market participants who
may follow very different trading practices.
V. Conclusion

This paper takes a first step towards demonstrating that overall trading volume in equity
markets is excessive by showing that it is excessive for a particular group of investors: thosewith discount brokerage accounts. These investors trade excessively in the sense that theirreturns are, on average, reduced through trading. Even after eliminating most trades thatmight be motivated by liquidity demands, taxloss selling, portfolio rebalancing, or a move tolower-risk securities, trading still lowers returns. I test the hypothesis that investors tradeexcessively because they are overconfident.
Overconfident investors may trade even when
their expected gains through trading are notenough to offset trading costs. In fact, evenwhen trading costs are ignored, these investors
actually lower their returns through trading.
This result is more extreme than is predicted byoverconfidence alone.
I examine return patterns before and after thepurchases and sales made by these investors.
The investors tend to buy securities that haverisen or fallen more over the previous six
months than the securities they sell. They sell
securities that have, on average, risen rapidly inrecent weeks. And they sell far more previous
winners than losers. I suggest that these patterns
can be explained by the difficulty of evaluating
the large number of securities available for investors to buy, by investors’ tendency to let
their attention be directed by outside sources
such as the financial media, by the disposition
effect, and by investors’ reluctance to sell short.
Return patterns after purchases and sales are

DECEMBER 1999

more difficult to understand. It is possible thatsome of these investors are among the last buyers to contribute to the rise of overvalued momentum securities and are among the first to
suffer losses when these securities decline.
What is more certain is that these investors dohave useful information which they are somehow misinterpreting.
APPENDIX
I use a Monte Carlo simulation to test the
hypothesis that investors vary in their propensity to buy previous winners and previous losers. Two test statistics are employed: theproportion of accounts buying only previous
winners or only previous losers, and the averageof 兩N w ⫺ N l 兩 where N w and N l are the numberof previous winners and previous losers purchased in an account. These two statistics are
first calculated from the data and then simulated
under the null hypothesis that each investor has
the same probability of buying a previous winner as every other investor. For the simulation
the probability of buying a previous winner is
set to be the empirically observed ratio of previous winners bought to previous winners plus
previous losers bought. Observations are taken
only from accounts with more than one purchase of a previous winner or previous loser.
For each account the same number of simulated
purchases are generated as are observed in thesample. Each simulated purchase is drawn as
either a previous winner or previous loser.
When simulated purchases have been drawn foreach account the two test statistics are calculated. This process is repeated 1,000 times andfor each test statistic the 1,000 observations
constitute a simulated distribution. When previous winners (losers) are simply defined to be
securities which had a positive (negative) return
over the six months prior to purchase (as in
Figures 3 and 4), the average number of purchases per account is 8.4. The fraction of accounts buying only previous winners or
previous losers is 0.265, while in the 1,000
simulations the largest fraction of accounts buying only previous winners or previous losers is
0.252. In the actual data 兩N w ⫺ N l 兩 is 3.6 whilein the 1,000 simulations the largest value of兩N w ⫺ N l 兩 is 2.6. Using either statistic we canreject the null hypothesis that each investor has

VOL. 89 NO. 5

ODEAN: DO INVESTORS TRADE TOO MUCH?

the same propensity for buying winners andlosers ( p ⬍ 0.001). If big winners are defined
to be securities that returned 60 percent or morein the previous six months (about the average in
Figure 3) and big losers are those that returned⫺40 percent or less (about the average in Figure4), then of the 1,197 investors who bought morethan one big winner or big loser (4.5 such
purchases on average), 555 bought only big
winners or only big losers. In 1,000 simulations
based on the assumption that all investors had
the same probability as each other for buying
big winners (or big losers), at most 457 bought
only winners or only losers. The null hypothesisthat each investor has the same propensity forbuying big winners and big losers can be rejected ( p ⬍ 0.001).
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