THE JOURNAL OF FINANCE • VOL. LV, NO. 2 • APRIL 2000

Trading Is Hazardous to Your Wealth:
The Common Stock Investment Performanceof Individual Investors
BRAD M. BARBER and TERRANCE ODEAN*

ABSTRACT
Individual investors who hold common stocks directly pay a tremendous performance penalty for active trading. Of 66,465 households with accounts at a large
discount broker during 1991 to 1996, those that trade most earn an annual returnof 11.4 percent, while the market returns 17.9 percent. The average household
earns an annual return of 16.4 percent, tilts its common stock investment toward
high-beta, small, value stocks, and turns over 75 percent of its portfolio annually.
Overconfidence can explain high trading levels and the resulting poor performanceof individual investors. Our central message is that trading is hazardous to yourwealth.

The investor’s chief problem—and even his worst
enemy—is likely to be himself.
Benjamin Graham

In 1996, approximately 47 percent of equity investments in the United States
were held directly by households, 23 percent by pension funds, and 14 percent by mutual funds ~Securities Industry Fact Book, 1997!. Financial economists have extensively analyzed the return performance of equities managed
by mutual funds. There is also a fair amount of research on the performanceof equities managed by pension funds. Unfortunately, there is little researchon the return performance of equities held directly by households, despite
their large ownership of equities.

* Graduate School of Management, University of California, Davis. We are grateful to thediscount brokerage firm that provided us with the data for this study. We appreciate the comments of Christopher Barry, George Bittlingmayer, Eugene Fama, Ken French, Laurie Krigman, Bing Liang, John Nofsinger, Srinivasan Rangan, Mark Rubinstein, René Stulz ~the editor!,
Avanidhar Subrahmanyam, Kent Womack, Jason Zweig, two anonymous reviewers, seminar
participants at the American Finance Association Meetings ~New York, 1999!, the 9th Annual
Conference on Financial Economics and Accountancy at New York University, Notre Dame University, the University of Illinois, and participants in the Compuserve Investor Forum. Allerrors are our own.

773

774

The Journal of FinanceIn this paper, we attempt to shed light on the investment performance ofcommon stocks held directly by households. To do so, we analyze a uniquedata set that consists of position statements and trading activity for 78,000
households at a large discount brokerage firm over a six-year period endingin January 1997.
Our analyses also allow us to test two competing theories of trading activity. Using a rational expectation framework, Grossman and Stiglitz ~1980!
argue that investors will trade when the marginal benefit of doing so isequal to or exceeds the marginal cost of the trade. In contrast Odean~1998b!,
Gervais and Odean ~1998!, and Caballé and Sákovics ~1998! develop theoretical models of financial markets where investors suffer from overconfidence. These overconfidence models predict that investors will trade to theirdetriment.1
Our most dramatic empirical evidence supports the view that overconfidence leads to excessive trading ~see Figure 1!. On one hand, there is very
little difference in the gross performance of households that trade frequently~with monthly turnover in excess of 8.8 percent! and those that trade infrequently. In contrast, households that trade frequently earn a net annualized
geometric mean return of 11.4 percent, and those that trade infrequently
earn 18.5 percent. These results are consistent with models where trading
emanates from investor overconfidence, but are inconsistent with models
where trading results from rational expectations. Though liquidity, riskbased rebalancing, and taxes can explain some trading activity, we argue
that it belies common sense that these motivations for trade, even in combination, can explain average annual turnover of more than 250 percent for
those households that trade most.
We also document that, overall, the households we analyze significantly
underperform relevant benchmarks, after a reasonable accounting for transaction costs. These households earn gross returns ~before accounting for transaction costs! that are close to those earned by an investment in a valueweighted index of NYSE0AMEX0Nasdaq stocks. During our sample period,
an investment in a value-weighted market index earns an annualized geometric mean return of 17.9 percent, the average household earns a gross
return of 18.7 percent, and in aggregate households earn a gross return of
18.2 percent. In contrast, the net performance ~after accounting for the bidask spread and commissions! of these households is below par, with the average household earning 16.4 percent and in aggregate households earning
16.7 percent. The empirical tests supporting these conclusions come fromabnormal return calculations that allow each household to self-select its own
1
In an exception to this finding, Kyle and Wang ~1997! argue that when traders compete for
duopoly profits, overconfident traders may reap greater profits. This prediction is based on
several assumptions that do not apply to individuals trading common stocks. Benos ~1998! hasa similar result. Daniel, Hirshleifer, and Subrahmanyam ~1998! consider the asset price implications of overconfidence but do not directly address investor welfare.

Trading Is Hazardous to Your Wealth775

Figure 1. Monthly turnover and annual performance of individual investors. The whitebar ~black bar! represents the gross ~net! annualized geometric mean return for February 1991
through January 1997 for individual investor quintiles based on monthly turnover, the average
individual investor, and the S&P 500. The net return on the S&P 500 Index Fund is that earned
by the Vanguard Index 500. The gray bar represents the monthly turnover.

investment style and from time-series regressions that employ either the
Capital Asset Pricing Model ~CAPM! or the three-factor model developed by
Fama and French ~1993! as our benchmark.
Our descriptive analysis provides several additional conclusions that are
noteworthy:
1. Households2 trade common stocks frequently. The average household
turns over more than 75 percent of its common stock portfolio annually.
2. Trading costs are high. The average round-trip trade in excess of $1,000
costs three percent in commissions and one percent in bid-ask spread.
3. Households tilt their investments toward small, high-beta stocks. Thereis a less obvious tilt toward value ~high book-to-market! stocks.
2
Throughout this paper, “households” and “individual investors” refer to households andinvestors with discount brokerage accounts. Though we believe that our findings generalize tocustomers at other discount brokerages, we suspect that the trading practices of retail customers differ. Some of our sample households may have both retail and discount accounts. In thesecases, our observations are limited to their discount accounts.

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The Journal of Finance

It is the cost of trading and the frequency of trading, not portfolio selections,that explain the poor investment performance of households during our sample period. In fact, the tilt of households toward small stocks and, to a lesser
extent, value stocks helps their performance during our sample period ~during which small stocks outperform large stocks by 15 basis points per monthand value outperforms growth by 20 basis points per month!.3
The remainder of this paper is organized as follows. We discuss related
research in Section I and our data and empirical methods in Section II. Our
main descriptive results are presented in Section III. We test the models ofinvestor overconfidence in Section IV. We discuss the impact of price momentum on individual investor performance in Section V and liquidity, risk,
and taxes as motivations for trading in Section VI. Concluding remarks are
made in Section VII.

I. Related Research
To our knowledge, the current investigation is the first comprehensive
study of the aggregate common stock performance of individual investors
who manage their own equity investments without the advice of a fullservice broker. Schlarbaum, Lewellen, and Lease ~1978a! analyze the aggregate common stock performance of investors at a full-service brokerage firm.
Odean ~1999! and Schlarbaum, Lewellen, and Lease ~1978b! analyze the profitability of common stock trades ~as distinct from positions held! by individual investors.
Schlarbaum et al. ~1978a! calculate monthly gross and net portfolio returns for 2,500 accounts at a retail brokerage firm over a seven-year period
ending in December 1970. In a separate paper, Schlarbaum et al. ~1978b!
analyze the gross and net returns of round-trip trades made by the same
2,500 accounts over the same period. Though they emphasize that their results are conjectural, they conclude that their results “portray an overall
picture of quite respectable individual investor security selection acumen.”
In contrast, we document that individual investors at a discount brokeragefirm during the six-year period ending January 1997 perform poorly.
There are at least three reasons why our results might differ from those in
Schlarbaum et al. ~1978a, 1978b!. First, we analyze households that holdtheir investments at a discount brokerage firm rather than at a retail brokerage firm. A wide variety of investment advice is available to both retailand discount investors from sources such as newsletters, Value Line, and thefinancial press. Retail brokerage firms also provide stock selection advice totheir clients. If this advice is valuable and if investors attend to it, it is3

These figures are based on the mean return from February 1991 through January 1997 forthe size and book-to-market factors constructed by Fama and French ~1993!. In the remainderof this paper, when we refer to a size or value premium, our inference is based on the returnsof these zero-investment portfolios.

Trading Is Hazardous to Your Wealth777

plausible that individual investors at these firms earn both better gross
returns and net returns. We would welcome the opportunity to test thishypothesis directly by obtaining a data set similar to that employed in ourstudy from a retail brokerage firm. Barber et al. ~1998! and Womack ~1996!
present evidence that the recommendations of brokerage-house analysts haveinvestment value.
Second, the analysis in Schlarbaum et al. ~1978b! focuses on the returns
from round-trip trades. There is now evidence that investors have a tendency to sell winning investments and hold on to losing investments ~Odean~1998a!!. Thus, by analyzing trades rather than position statements ~as we
do in the current study!, Schlarbaum et al. may upwardly bias their return
estimates. Schlarbaum et al. ~1978a! do attempt to reconstruct monthly positions from trading records and partial end-of-period positions. However, as
they point out, stocks purchased before 1964 and sold after 1970 may notappear in their study.
Third, although Schlarbaum et al. ~1978a, 1978b! evaluate performanceusing a variety of market indexes, they do not consider the tendency for
individual investors to tilt toward small stocks ~though of course firm size
did not have the same celebrity status in 1978 that it enjoys today!. They do
not explicitly address whether such a tilt exists among the individual investors they analyze, but we suspect that it does. This small-stock tilt is likely
to be extremely important because small stocks outperform large stocks by
67 basis points per month during their sample period.
As do Schlarbaum et al. ~1978b!, Odean ~1999! focuses on the trades ofindividual investors. He analyzes the timing of trades made by individual
investors at a large discount brokerage firm during the seven years endingin December 1993, a sample period that overlaps with ours. ~The data sets
employed in Odean ~1999! and this study are different.! He documents thatthe stocks individuals sell subsequently outperform the stocks they buy. Thus,
the implications of his study and the current investigation are similar: Individual investors trade too much. However, Odean does not analyze theaggregate performance of all stocks held by individuals. Consequently, he isunable to conclude whether individual investors perform well in aggregate,
which is the focus of our investigation.

II. Data and Methods
A. Household Account Data
The primary data set for this research is information from a large discount brokerage firm on the investments of 78,000 households from January1991 through December 1996.4 Of the sampled households, 42 percent are in
4
The month-end position statements for this period allow us to calculate returns for February 1991 through January 1997. Data on trades are from January 1991 through November
1996.

778

The Journal of Financethe western part of the United States, 19 percent in the East, 24 percent in
the South, and 15 percent in the Midwest. The data set includes all accounts
opened by each household at this discount brokerage firm. The sample selection was performed at the household level and was stratified based onwhether the discount brokerage firm labeled the household as a general~60,000 households!, aff luent ~12,000 households!, or active trader household ~6,000 households!. The firm labels households that make more than 48
trades in any year as active traders, households with more than $100,000 inequity at any point in time as aff luent, and all other households as general.
If a household qualifies as either active trader or aff luent, it is assigned theactive trader label. In 1997, approximately 61 percent of all retail accounts
at this brokerage firm were classified as general, 28 percent as aff luent, and11 percent as active. Sampled households were required to have an open
account with the discount brokerage firm during 1991. Roughly half of theaccounts in our analysis were opened prior to 1987 and half were opened
between 1987 and 1991.
In this research, we focus on the common stock investments of households. We exclude from the current analysis investments in mutual funds~both open-end and closed-end!, American Depositary Receipts ~ADRs!, warrants, and options. Of the 78,000 sampled households, 66,465 have positions in common stocks during at least one month; the remaining accounts
hold either cash or investments in other than individual common stocks.
Households have, on average, two accounts: 48 percent have a single account, 27 percent have two, 14 percent have three, and the remaining
11 percent have more than three. The most common reason for two accounts is the tax-preferred status of retirement accounts ~e.g., IRAs and
Keoghs!. Some households also have different accounts for different household members ~e.g., custodial accounts for children!. Roughly 60 percent of
the market value in the accounts is held in common stocks. In these households, more than 3 million trades are made in all securities during thesample period, with common stocks accounting for slightly more than 60
percent of all trades. On average during our sample period, the mean household holds 4.3 stocks worth $47,334, though each of these figures is positively skewed. The median household holds 2.61 stocks worth $16,210. In
December 1996, these households held more than $4.5 billion in common
stock.
In Table I, we present descriptive information on the trading activity forour sample. Panels A and B show there are slightly more purchases ~1,082,107!
than sales ~887,594! during our sample period, though the average value ofstocks sold ~$13,707! is slightly higher than the value of stocks purchased~$11,205!. As a result, the aggregate value of purchases and sales is roughly
equal ~$12.1 and $12.2 billion, respectively!. The average trade is transacted
at a price of $31 per share. The value of trades and the transaction price oftrades are positively skewed; the medians for both purchases and sales aresubstantially less than the mean values.

Trading Is Hazardous to Your Wealth779

Table I

Descriptive Statistics on Trade Size, Trade Price,Transaction Costs, and Turnover
The sample is account records for 66,465 households at a large discount brokerage firm fromJanuary 1991 to December 1996. Spread is calculated as the transaction price divided by theclosing price on the day of the transaction minus one ~and then multiplied by minus one forpurchases!. Commission is calculated as the commission paid divided by the value of the trade.
Monthly turnover is the beginning-of-month market value of shares purchased in month t 2 1~or sold in month t! divided by the total beginning-of-month market value of shares held inmonth t. Trade-weighted spread and commission are averages weighted by trade size. Aggregate turnover is the aggregate value of sales ~or purchases! divided by the aggregate value of
positions held during our sample period.

Mean

25th
Percentile

Median75th
Percentile

Standard
Deviation

No. ofObs.

10,500
40.00
7.08
2.10

32,179117.82
11.89
1.45

1,082,107
1,082,107
66,465966,492
1,028,087

13,000
41.00
6.95
1.91

38,275113.0311.36
1.06

887,594
887,594
66,465785,206
845,644

Panel A: Purchases
Trade size ~$!
Price0share
Monthly turnover ~%!
Commission ~%!*
Spread~%!

11,205
31.06
6.49
1.58
0.31

2,513
11.00
0.54
0.78

4,988
23.00
2.67
1.29

Panel B: Sales
Trade size ~$!
Price0share
Monthly turnover ~%!
Commission ~%!*
Spread ~%!

13,707
31.22
6.23
1.45
0.69

2,688
12.00
0.39
0.70

5,738
24.00
2.58
1.16

Panel C: Trade-Weighted and Aggregate Purchases
Aggregate monthly
turnover ~%!
Trade-weighted
commission ~%!
Trade-weighted
spread ~%!

6.05
0.77

Not Applicable

0.27

Panel D: Trade-Weighted Sales
Aggregate monthly
turnover ~%!
Trade-weighted
commission ~%!
Trade-weighted
spread ~%!

6.06
0.66

Not applicable

0.61*Commissions are calculated based on trades in excess of $1,000. Including smaller trades
results in a mean buy ~sale! commission of 2.09 ~3.07! percent.

780

The Journal of FinanceFor each trade, we estimate the bid-ask spread component of transaction
costs for purchases ~sprdb ! and sales ~sprds ! assprds 5

S

Pdcls
Pdss

D

21

and

sprdb 5 2

S

Pdclb
Pdbb

D

21 ,

~1!

where Pdcls and Pdclb are the reported closing prices from the Center for Research in Security Prices ~CRSP! daily stock return files on the day of a saleand purchase, respectively, and Pdss and Pdbb are the actual sale price andpurchase price from our account database.5 Our estimate of the bid-ask spreadcomponent of transaction costs includes any market impact that might result from a trade. It also includes an intraday return on the day of the trade.
~In Appendix A, we provide a detailed reconciliation of our return calculations.! The commission component of transaction costs is estimated as thedollar value of the commission paid scaled by the total principal value of thetransaction, both of which are reported in our account data.
The average purchase costs an investor 0.31 percent, and the average salecosts an investor 0.69 percent in bid-ask spread. Our estimate of the bid-ask
spread is very close to the trading cost of 0.21 percent for purchases and 0.63
percent for sales paid by open-end mutual funds from 1966 to 1993 ~Carhart~1997!!.6 The average purchase in excess of $1,000 costs 1.58 percent in commissions, and the average sale in excess of $1,000 costs 1.45 percent.7
In Panels C and D of Table I, we calculate the trade-weighted ~weighted bytrade size! spreads and commissions. These figures can be thought of as thetotal cost of conducting the $24 billion in common stock trades ~$12 billion
each in purchases and sales!. Trade size has little effect on spread costs ~0.27
percent for purchases and 0.69 percent for sales! but substantially reduces
the commission costs ~0.77 percent for purchases and 0.66 percent for sales!.
In sum, the average trade incurs a round-trip transaction cost of about one
percent for the bid-ask spread and about three percent in commissions. In
aggregate, round-trip trades cost about one percent for the bid-ask spread
and about 1.4 percent in commissions.
5
Kraus and Stoll ~1972!, Holthausen, Leftwich, and Mayers ~1987!, Laplante and Muscarella ~1997!, and Beebower and Priest ~1980! use closing prices either before or following atransaction to estimate effective spreads and market impact. See Keim and Madhavan ~1998!
for a review of different approaches to calculating transactions costs.
6
Odean ~1999! finds that individual investors are more likely to both buy and sell particular
stocks when the prices of those stocks are rising. This tendency can partially explain the asymmetry in buy and sell spreads. Any intraday price rises following transactions subtract from ourestimate of the spread for buys and add to our estimate of the spread for sells.
7
To provide more representative descriptive statistics on percentage commissions, we exclude trades of less than $1,000. The inclusion of these trades results in a round-trip commission cost of five percent on average ~2.1 percent for purchases and 3.1 percent for sales!.

Trading Is Hazardous to Your Wealth

781

Finally, we calculate the monthly portfolio turnover for each household. In
each month during our sample period, we identify the common stocks heldby each household at the beginning of month t from their position statements. To calculate monthly sales turnover, we match these positions to sales
during month t. The monthly sales turnover is calculated as the shares soldtimes the beginning-of-month price per share divided by the total beginningof-month market value of the household’s portfolio. To calculate monthly
purchase turnover, we match these positions to purchases during month t 2 1.
The monthly purchase turnover is calculated as the shares purchased times
the beginning-of-month price per share divided by the total beginning-ofmonth market value of the portfolio.8 In Panels A and B of Table I we report
that, on average, households purchase 6.49 percent and sell 6.23 percent of
their stock portfolio each month, though the median household trades muchless frequently ~buying 2.67 percent of their stock portfolio and selling 2.58
percent!. In Panels C and D, we calculate aggregate purchase ~sales! turnover by summing all purchases ~sales! and dividing by the sum of all positions during our sample period. The aggregate purchase turnover is 6.05
percent and the aggregate sales turnover is 6.06 percent.
In sum, these investors trade their common stocks quite frequently. The
average household turns over more than 75 percent of its common stock
portfolio each year. This result is uncannily close to the average turnover of
77 percent reported by U.S. common stock mutual funds for the period 1966
to 1993 ~Carhart ~1997!!. In aggregate, these investors turn over more than70 percent of their invested wealth each year.
B. Measuring Return Performance
The focus of our analysis is the return performance of investments in common stocks by households. We analyze both the gross performance and net
performance ~after a reasonable accounting for commissions, the bid-ask
spread, and the market impact of trades!.
We estimate the gross monthly return on each common stock investmentusing the beginning-of-month position statements from our household data
and the CRSP monthly returns file. In so doing, we make two simplifying
assumptions. First, we assume that all securities are bought or sold on thelast day of the month. Thus, we ignore the returns earned on stocks purchased from the purchase date to the end of the month and include thereturns earned on stocks sold from the sale date to the end of the month.
8

If more shares are sold than were held at the beginning of the month ~e.g., because aninvestor purchases additional shares after the beginning of the month!, we assume the entire
beginning-of-month position in that security is sold. Similarly, if more shares were purchased in
the preceding month than are held in the position statement, we assume the entire position ispurchased in the preceding month. Thus, turnover, as we have calculated it, cannot exceed 100
percent in a month.

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The Journal of Finance

Second, we ignore intramonth trading ~e.g., a purchase on March 6 and asale of the same security on March 20!, though we do include in our analysis
short-term trades that yield a position at the end of a calendar month.
In Appendix A, we document that accounting for the exact timing of trades
would reduce the performance of individual investors by about two basis
points per month. In Appendix B, we document that accounting for intramonth trades would improve the performance of individual investors reported in our main results by less than one basis point per month. More
important, a careful accounting for both the exact timing of trades and the
profitability of intramonth trades indicates that the results we report in themain text are slightly high for our full sample and for every sample partition that we analyze.
Consider the common stock portfolio for a particular household. The grossgrmonthly return on the household’s portfolio ~R ht ! is calculated as
sht
gr

gr

R ht 5 ( pit R it ,

~2!

i51

where pit is the beginning-of-month market value for the holding of stock i
by household h in month t divided by the beginning-of-month market value
grof all stocks held by household h, R it is the gross monthly return for stocki, and sht is the number of stocks held by household h in month t.
For security i in month t, we calculate a monthly return net of transaction
costs ~R itnet ! asgr

~1 1 R itnet ! 5 ~1 1 R it !

~1 2 cits !
~1 1 ci,b t21 !

,~3!

where cits is the cost of sales scaled by the sales price in month t and ci,b t21 is
the cost of purchases scaled by the purchase price in month t 2 1. The costsof purchases and sales include the commissions and bid-ask spread components, which are estimated individually for each trade as previously described. Thus, for a security purchased in month t 2 1 and sold in month t,
both cits and ci,b t21 are positive; for a security neither purchased in montht 2 1 nor sold in month t, both cits and ci,b t21 are zero. Because the timing andcost of purchases and sales vary across households, the net return for security i in month t varies across households. The net monthly portfolio returnfor each household issht
net
5 ( pit R itnet.
R ht
i51

~4!

Trading Is Hazardous to Your Wealth783

If only a portion of the beginning-of-month position in stock i is purchased orsold, the transaction cost is applied only to that portion. We estimate theaggregate gross and net monthly return earned by individual investors as
n ht

n ht

gr

gr

RAGt 5 ( x ht R ht

and

h51

net
RAGtnet 5 ( x ht R ht
,

~5!

h51

where n ht is the number of households with common stock investment inmonth t and x ht is the beginning-of-month market value of common stocks
held by household h divided by the beginning-of-month market value of common stock held by all households. We estimate the gross and net monthly
return earned by the average household as
gr

RHt 5

1

n ht
gr

( R htn ht h51

andRHtnet 5

1

n ht

(
n ht h51

net
R ht
.

~6!

C. Risk-Adjusted Return Performance
We calculate four measures of risk-adjusted performance.9 First, we calculate an own-benchmark abnormal return for individual investors, which issimilar in spirit to that proposed by Grinblatt and Titman ~1993! and Lakonishok, Shleifer, and Vishny ~1992!. In this abnormal return calculation,
the benchmark for household h is the month t return of the beginning-ofyear portfolio held by household h.10 It represents the return that the household would have earned had it merely held its beginning-of-year portfolio forthe entire year. The own-benchmark abnormal return is the return earned
by household h less the own-benchmark return; if the household did nottrade during the year, the own-benchmark return is zero for all 12 months
during the year. In each month, the abnormal returns across households areaveraged, yielding a 72-month time-series of mean monthly own-benchmark
abnormal returns. Statistical significance is calculated using t-statistics basedon this time-series. The advantage of the own-benchmark abnormal return9
A fifth alternative measure of risk-adjusted returns is the Sharpe ratio, the mean excess
return divided by its standard deviation. The average Sharpe ratio for the gross ~net! return of
the average household in our sample is 0.179 ~0.134!. The Sharpe ratio for the market during
our sample period is 0.366 5 ~1.057802.8880!. We do not report Sharpe ratios for most partitions of the data because we do not observe the entire portfolios of these households. Unobserved assets such as equities at other brokerage firms and mutual fund holdings are unlikely
to greatly change average observed portfolio returns, but they are likely to reduce averageobserved volatility. Thus we tend to underestimate the total portfolio Sharpe ratios of investors
with significant unobserved assets.
10
When calculating this benchmark, we begin the year on February 1. We do so because ourfirst monthly position statements are from the month end of January 1991. If the stocks heldby a household at the beginning of the year are missing CRSP returns data during the year, we
assume that stock is invested in the remainder of the household’s portfolio.

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The Journal of Financemeasure is that it does not adjust returns according to a particular risk
model. No model of risk is universally accepted; furthermore, it may be inappropriate to adjust investors’ returns for stock characteristics that they do
not associate with risk. The own-benchmark measure allows each household
to self-select the investment style and risk profile of its benchmark ~i.e., theportfolio it held at the beginning of the year!, thus emphasizing the effecttrading has on performance.
Second, we calculate the mean monthly market-adjusted abnormal returnfor individual investors by subtracting the return on a value-weighted index
of NYSE0AMEX0Nasdaq stocks from the return earned by individual investors.
Third, we employ the theoretical framework of the capital asset pricing
model and estimate Jensen’s alpha by regressing the monthly excess returnearned by individual investors on the market excess return. For example, toevaluate the gross monthly return earned by individual investors in aggregate, we estimate the following monthly time-series regression:
gr~RAGt 2 R ft ! 5 ai 1 bi ~R mt 2 R ft ! 1 eit ,~7!

where R ft 5 the monthly return on T-bills,11 R mt 5 the monthly return on avalue-weighted market index, ai 5 the CAPM intercept ~Jensen’s alpha!,bi 5 the market beta, and eit 5 the regression error term. The subscript i
denotes parameter estimates and error terms from regression i, where weestimate four regressions: one each for the gross and net performance ofindividual investors in aggregate, and one each for the gross and net performance of the average household.
Fourth, we employ an intercept test using the three-factor model developed by Fama and French ~1993!. For example, to evaluate the performanceof individuals in aggregate, we estimate the following monthly time-series
regression:
gr~RAGt 2 R ft ! 5 aj 1 bj ~R mt 2 R ft ! 1 sj SMBt 1 h j HML t 1 ejt ,~8!

where SMBt is the return on a value-weighted portfolio of small stocks minus the return on a value-weighted portfolio of large stocks and HML t is thereturn on a value-weighted portfolio of high book-to-market stocks minus
the return on a value-weighted portfolio of low book-to-market stocks.12 The
regression yields parameter estimates of aj , bj , sj , and h j . The error term in
the regression is denoted by ejt . The subscript j denotes parameter estimatesand error terms from regression j, where we again estimate four regres11

The return on Treasury bills is from Stocks, Bonds, Bills, and Inflation, 1997 Yearbook,
Ibbotson Associates, Chicago, Ill.
12
The construction of these portfolios is discussed in detail in Fama and French ~1993!. We
thank Kenneth French for providing us with these data.

Trading Is Hazardous to Your Wealth785

sions. We place particular emphasis on the Fama–French intercept tests,
since individual investors tilt their portfolios toward small stocks. The threefactor model provides a reasonable adjustment for this small stock tilt.13
Fama and French ~1993! argue that the risk of common stock investments
can be parsimoniously summarized as risk related to the market, firm size,
and a firm’s book-to-market ratio. We measure these three risk exposuresusing the coefficient estimates on the market excess return ~R mt 2 R ft !, thesize zero investment portfolio ~SMBt !, and the book-to-market zero-investment
portfolio ~HML t ! from the three-factor regressions. Portfolios with aboveaverage market risk have betas greater than one, bj . 1. Portfolios with atilt toward small ~value! stocks relative to a value-weighted market index
have size ~book-to-market! coefficients greater than zero, sj . 0 ~h j . 0!.
We suspect there is little quibble with interpreting the coefficient on themarket excess return ~ bj ! as a risk factor. Interpreting the coefficient estimates on the size and the book-to-market zero-investment portfolios is more
controversial. For the purposes of this investigation, we are interested inmeasuring risk as perceived by individual investors. As such, it is our casual
observation that investors view common stock investment in small firms as
riskier than that in large firms. Thus, we would willingly accept a stronger
tilt toward small stocks as evidence that a particular group of investors ispursuing a strategy that it perceives as riskier. It is less clear to us whether
a tilt toward high book-to-market stocks ~which tend to be ugly, financiallydistressed, firms! or toward low book-to-market stocks ~which tend to behigh-growth firms! is perceived as riskier by investors. As such, we interpretthe coefficient estimates on the book-to-market zero-investment portfolio witha bit more trepidation.14

III. Results
A. Full Sample Results
Our main findings for the full sample can be summarized simply. The
grgross return earned by individual investors in aggregate ~RAGt ! and thegrgross return earned by the average household ~RHt ! are remarkably close
to that earned by an investment in a value-weighted index of NYSE0AMEX0
Nasdaq stocks.15 The annualized geometric mean return earned by individ13
Lyon, Barber, and Tsai ~1999! document that intercept tests using the three-factor model
are well specified in random samples and samples of large or small firms. Thus, the Fama–
French intercept tests employed here account well for the small stock tilt of individual investors.
14
Some authors have also identified price momentum effects in stock returns. We discuss
momentum in Section V.
15
We use the NYSE0AMEX0Nasdaq value-weighted market index constructed by Fama andFrench ~1993!. Firms comprising the index must have data for firm size and book-to-market
ratio. The correlation between this market index and the NYSE0AMEX0Nasdaq value-weighted
index from CRSP is 99.9 percent.

786

The Journal of Financeual investors in aggregate, the average household, and the value-weighted
market index are 18.2, 18.7, and 17.9 percent, respectively. In contrast,
the net returns earned by individual investors in aggregate ~RAGtnet ! andthe net return earned by the average household ~RHtnet ! underperform thevalue-weighted index by more than 100 basis points annually. The net annualized geometric mean return earned by individual investors in aggregate
and by the average household are 16.7 and 16.4 percent, respectively.
The results of this analysis are presented in Table II. Panel A presents
results for the gross performance of individual investors in aggregate, Panel B
presents results for the average household. Three of the four performancemeasures indicate that the gross performance of individual investors is unremarkable; neither the market-adjusted return, Jensen’s alpha, nor the intercept test from the Fama–French three-factor model is reliably different
from zero. The fourth performance measure, the own-benchmark abnormal
return, is reliably negative. This result indicates that the investors would
have earned higher returns from following a buy-and-hold strategy; theyhurt their gross performance by trading.
Also noteworthy in these results are the coefficient estimates on the market, size, and book-to-market factors. Individual investors tilt toward small
stocks with high market risk. The market beta for stocks held by individual
investors is reliably greater than one and the coefficient estimate on SMBt
is reliably positive. Though in aggregate individual investors have no tilt
toward value or growth, the average household has a slight tilt toward value
stocks ~those with high book-to-market ratios! and a more pronounced tilt
toward small stocks.16 These tilts serve individual investors well during ourperiod of analysis; the mean monthly returns on SMBt and HMLt during our
72-month sample period are 0.15 and 0.20 percent, respectively. This observation can account for the fact that the market-adjusted return performanceof individual investors is positive ~albeit unreliably so!, while Jensen’s alpha~CAPM intercept! and the intercept test from the Fama–French three-factor
model are negative.
The style preferences of individual investors complement those of institutions. Institutional investors have a clear preference for large stocks. Gompers and Metrick ~1998! document this preference for large institutions; Carhart~1997! and Falkenstein ~1996! document a similar bias for mutual funds. As
is the case for individual investors, the growth or value preference of institutions is less obvious. Gompers and Metrick ~1998! document that large
institutions prefer value stocks, but Carhart ~1997, Table III! documentsthat mutual fund holdings tilt toward growth stocks.17

16

Aggregate measures weight each household by the value of that household’s common stocks.
Average household measures weight each household equally.
17
Kang and Stulz ~1997! document that foreign investors in Japanese equity markets preferlarge growth stocks. It is likely that these foreign investors are predominantly institutions.

Trading Is Hazardous to Your Wealth787

Table II

Summary of the Percentage Monthly Abnormal Return Measures
for the Average Household and Aggregate Household
Returns are based on month-end position statements for 66,465 households at a large discountbrokerage firm from January 1991 to December 1996. Panel A ~Panel C! presents results for thegross ~net! return on a portfolio that mimics the aggregate investment of all households. Panel
B ~Panel D! presents results for the gross ~net! return on a portfolio that mimics the investmentof the average household. Own-benchmark abnormal return is the return on the household
portfolio minus the return on the portfolio the household held at the end of the previous January. Market-adjusted return is the return on the household portfolio less the return on avalue-weighted NYSE0AMEX0Nasdaq index. CAPM is the results from a time-series regressionof the household excess return on the market excess return ~R mt 2 R ft !. Fama–French threefactor is the results from time-series regression of household excess return on the market excess return, a zero-investment book-to-market portfolio ~HMLt !, and a zero-investment size
portfolio ~SMBt !. p-values are presented in parentheses.
ExcessReturnCoefficient Estimate on:~R mt 2 R ft !

HML t

SMBt

Adjusted
R2

Panel A: Gross Percentage Monthly Returns in Aggregate
Own-benchmark abnormal return
Market-adjusted return
CAPM
Fama–French three-factor

20.049**~0.013!
0.038~0.723!
20.067~0.543!
20.076~0.357!

1.100***~0.007!
1.082***~0.005!

92.9
20.035~0.324!

0.231***~0.000!

96.3

Panel B: Gross Percentage Monthly Returns for the Average Household
Own-benchmark abnormal return
Market-adjusted return
CAPM
Fama–French three-factor

20.048**~0.010!
0.078~0.672!
20.014~0.944!
20.154~0.205!

1.087~0.177!
1.120***~0.005!

80.3
0.140***~0.008!

0.516***~0.000!

93.0

Panel C: Net Percentage Monthly Returns in Aggregate
Own-benchmark abnormal return
Market-adjusted return
CAPM
Fama–French three-factor

20.155***~0.000!
20.073~0.496!
20.175~0.113!
20.180**~0.031!

1.096***~0.009!
1.077***~0.009!

93.0
20.040~0.251!

0.225***~0.000!

96.3

Panel D: Net Percentage Monthly Returns for the Average Household
Own-benchmark abnormal return
Market-adjusted return
CAPM
Fama–French three-factor

20.194***~0.000!
20.090~0.621!
20.177~0.360!
20.311**~0.011!

1.082~0.194!
1.113***~0.008!

80.7
0.131**~0.012!

0.506***~0.000!

93.0***, **, and * indicate significance at the 1, 5, and 10 percent levels, respectively ~two-tailed!. The null
hypothesis for beta ~the coefficient estimate on the market excess return! is H o : b 5 1.

788

The Journal of Finance
Table III

Descriptive Statistics, Gross Returns, and Net Returns forHousehold Quintiles formed on Beginning Position Value
The sample is account records for 66,465 households at a large discount brokerage firm fromJanuary 1991 to December 1996. Households are sorted into quintiles based on the market
value of common stocks in the first month that a household appears during our sample period.
Quintile 1 contains households with the smallest market value of common stock holdings, quintile 5 contains households with the largest value. Beginning position value is the market valueof common stocks held in the first month that the household appears during our sample period.
Mean monthly turnover is the average of sales and purchase turnover. Coefficient estimates arethose from a time-series regression of the gross average household excess return on the market
excess return ~R mt 2 R ft !, a zero-investment book-to-market portfolio ~HML t !, and a zeroinvestment size portfolio ~SMBt !. Raw return is the average monthly return for the average
household. Own-benchmark abnormal return is the return on the household portfolio minus thereturn on the portfolio the household held at the end of the previous January. Market-adjusted
return is the return on the household portfolio less the return on a value-weighted NYSE0
AMEX0Nasdaq index. CAPM intercept is the estimated intercept from a time-series regressionof the household excess return on the market excess return ~R mt 2 R ft !. Fama–French intercept
is the estimated intercept from time-series regressions of household excess return on the market excess return, a zero-investment book-to-market portfolio ~HML t !, and a zero-investment
size portfolio ~SMBt !. p-values are presented in parentheses.
Quintile
1~Small!

2

3

45~Large!

Difference:Lrg 2 Sml

Panel A: Descriptive Statistics
Mean beginning position value
Mean monthly turnover ~%!
Coefficient estimate on:~R mt 2 R ft !
HML t
SMBt
Adjusted R 2

1,581
6.68

4,653
6.35

8,599
6.31

16,725
6.13

149,710
6.33

N.A.
20.35***~0.000!

1.21***~0.004!
0.36***~0.000!
0.97***~0.000!
86.1

1.13***~0.005!
0.12**~0.022!
0.56***~0.000!
92.8

1.11***~0.009!
0.09*~0.067!
0.45***~0.000!
93.2

1.11***~0.005!
0.09*~0.053!
0.39***~0.000!
94.3

1.06**~0.035!
0.06*~0.079!
0.27***~0.000!
95.8

20.15***~0.006!
20.30***~0.000!
20.70***~0.000!
68.4

Panel B: Gross Average Household Percentage Monthly ReturnRaw returnOwn-benchmark
abnormal return
Market-adjusted return
CAPM intercept
Fama–French intercept

1.722
20.071~0.101!
0.302~0.370!
0.182~0.612!
20.137~0.510!

1.511
20.051**~0.022!
0.091~0.648!
20.015~0.942!
20.152~0.227!

1.473
20.038*~0.070!
0.053~0.755!
20.043~0.811!
20.149~0.206!

1.424
20.038*~0.061!
0.004~0.980!
20.089~0.570!
20.186*~0.082!

1.400
20.037*~0.077!
20.020~0.857!
20.072~0.541!
20.140~0.101!

20.3220.034~0.487!
20.322~0.185!
20.253~0.328!
0.003~0.983!

Panel C: Net Average Household Percentage Monthly ReturnRaw returnOwn-benchmark
abnormal return
Market-adjusted return
CAPM intercept
Fama–French intercept

1.478
20.270***~0.000!
0.059~0.860!
20.056~0.875!
20.366*~0.079!

1.328
20.206***~0.000!
20.092~0.635!
20.193~0.350!
20.323**~0.011!

1.313
20.178***~0.000!
20.107~0.521!
20.198~0.264!
20.298**~0.013!

1.280
20.169***~0.000!
20.140~0.339!
20.229~0.140!
20.319***~0.003!

1.279
20.150***~0.000!
20.141~0.200!
20.189~0.105!
20.254***~0.004!

20.1990.120**~0.023!
20.199~0.404!
20.133~0.602!
0.112~0.450!

***, **, and * indicate significance at the 1, 5, and 10 percent levels, respectively ~two-tailed!. The null
hypothesis for beta ~the coefficient estimate on the market excess return! is H o : b 5 1 except in the difference
column, where the null hypothesis is H o : b 5 0.

Trading Is Hazardous to Your Wealth789

The more interesting findings of our analysis are contained in Panels C
and D of Table II. Net of transaction costs, individual investors perform
poorly. Both the market-adjusted return and the CAPM intercepts are negative, though unreliably so. The own-benchmark abnormal return and theFama–French intercept provide the most compelling evidence of underperformance. These performance measures indicate significant underperformance of 15 to 31 basis points per month ~1.8 percent to 3.7 percent per year,with t-statistics ranging from 22.20 to 210.21!. These two performance measures are most appropriate in our setting because they control for the stylepreference of individual investors: small stocks with above-average market
risk. In particular, the own-benchmark abnormal returns indicate individual
investors would have increased their annual return by about two percent
had they merely held their beginning-of-year portfolio. In combination, theseresults indicate that the net return performance of individual investors is
reliably negative.
One might wonder whether our results are driven by a short sample period coinciding with an unusual stock market. Though the market returned
about 18 percent per year during our sample period, the market return was
negative in 20 of the 72 months. When we compare the performance of individual investors during the 20 months when the market was down to the
52 months in which the market was up, the performance measures presented in Table II are virtually identical.
B. Sorting on Portfolio Size
We test the robustness of our results across different position sizes bypartitioning the households into quintiles on the basis of portfolio size. We
define portfolio size as the market value of common stocks held in the firstmonth for which there is a position statement.18 Each quintile represents
the common stock investments of more than 12,000 households.
Descriptive statistics on the partition by portfolio size are presented inTable III, Panel A. The largest portfolios have a mean beginning position
market value of $149,750, the smallest portfolios average $1,581. Small portfolios have slightly higher monthly turnover ~6.68 percent! than large portfolios ~6.33 percent!. As before, we estimate the parameters of the Fama–
French three-factor model, where the dependent variable is the monthly meangross household excess return for each quintile.19 The coefficient estimateson the market, size, and book-to-market factors reveal that small portfolios
tilt more heavily toward high-beta, small, value stocks than do large portfolios.
18
If the first position statement appears after January 1991, we do not discount the market
value of the common stocks to January 1991 in our rankings. Our results are virtually identical
if we discount the market value of these common stocks using the return on the value-weighted
market index.
19
In the interest of parsimony, here and in the remainder of the paper we do not reportresults for the aggregate performance of each partition. We note when conclusions are different
using the aggregate performance.

790

The Journal of Finance

The gross and net returns for each quintile are presented in Table III,
Panels B and C. Focusing first on the gross performance ~Panel B!, we findthat small portfolios ~quintile 1! earn higher average returns than large
portfolios ~quintile 5!, though the difference is not reliably different fromzero. This difference is likely attributable to the fact that small portfolios tilt
more heavily toward small value stocks, which performed well during oursample period. The net performance results are presented in Panel C.
The market-adjusted return and Jensen’s alpha are similar to those reported for the full sample for each quintile. Though the point estimates are
consistently negative, they are not reliably so. Of course, these riskadjustments ignore the fact that investors are tilting toward small value
stocks. In contrast, the own-benchmark abnormal returns and the intercept
tests from the Fama–French three-factor model indicate significant underperformance, ranging from 15 to 37 basis points per month, in each of thequintiles. In sum, after a reasonable accounting for the size and value tiltsof small investors, we document that both small and large portfolios
underperform.
C. Cross-Sectional Variation in Performance
We should emphasize that the aggregate performance and average household performance, though germane and interesting, mask considerable crosssectional variation in the performance across households. For each household,we calculate the mean monthly market-adjusted abnormal return. We present
the distribution of these means in Table IV.20 Consistent with the resultspresented in Table II, the median household earns a gross monthly marketadjusted return of 20.01 percent and a net return of 20.14 percent. Though49.3 percent of households outperform a value-weighted market index before
transaction costs, only 43.4 percent outperform the index after costs. Nonetheless, many households perform very well: 25 percent of all households
beat the market, after accounting for transaction costs, by more than 0.50
percent per month ~more than six percent annually!. Conversely, many households perform very poorly: 25 percent of all households underperform themarket, after accounting for transaction costs, by more than 0.73 percent
per month ~more than eight percent annually!.

IV. Overconfidence and Performance
It is well documented that people tend to be overconfident ~e.g., Alpert andRaiffa ~1982!, Griffin and Tversky ~1992!; see Odean ~1998b! for a moredetailed review!. Odean ~1998b!, Gervais and Odean ~1998!, and Caballé and
Sákovics ~1998! develop theoretical models of financial markets where in20

We omit from this analysis accounts that held common stocks for fewer than 12 months
during our 72-month sample period.

Trading Is Hazardous to Your Wealth791

Table IV

Cross-Sectional Distribution of Percentage Monthly Grossand Net Market-Adjusted Household Returns
The sample is account records for 66,465 households at a large discount brokerage firm fromJanuary 1991 to December 1996. Households with position statements in 12 or fewer months
are omitted from this analysis. Though the median values are virtually identical when thesehouseholds are included, more extreme values are observed.
Gross Monthly
Market-Adjusted Return~%!

Net Monthly
Market-Adjusted Return~%!

Minimum
1st percentile
5th percentile
10th percentile
25th percentile
Median75th percentile
90th percentile
95th percentile
99th percentile
Maximum

219.46
24.32
22.12
21.34
20.57
20.01
0.66
1.62
2.41
4.86
48.53

220.85
24.86
22.45
21.60
20.73
20.14
0.50
1.40
2.15
4.44
48.35

Total households
Percentage . 0
Binomial Z-statistic

62,439
49.3%***
23.38

62,439
43.4%***
233.13*** indicates significant difference from 50 percent at the 1% level.

vestors suffering from overconfidence trade too much ~i.e., trading, at themargin, reduces their expected utility!. In contrast, in a rational expectation
framework, Grossman and Stiglitz ~1980! argue that investors will trade
when the marginal benefit of doing so is equal to or exceeds the marginal
cost of the trade ~including the cost of acquiring information!. Odean ~1998b!
analyzes a variation of Grossman and Stiglitz’s model in which investors are
overconfident. The two models yield different predictions about the gains of
trading. The rational expectations model predicts that investors who trade
more ~i.e., those whose expected trading is greater! will have the same expected utility as those who trade less. The overconfidence model predicts
that investors who trade more will have lower expected utility.
Consider the implications of these two models in our empirical setting.
The overconfidence model predicts that the net return performance of households with high turnover will be lower than that of households with low
turnover, while making no prediction about the differences in gross returns.
In Grossman–Stiglitz, active and passive investors have equivalent expectedutilities. Active traders must earn higher expected gross returns in order to792

The Journal of Financeoffset their greater trading costs.21 The Grossman–Stiglitz model therefore
predicts that the gross risk-adjusted return performance of households withhigh turnover will be higher than that of households with low turnover, butthere will be little difference in the net risk-adjusted returns.
To test these competing models, we partition our sample of households
into quintiles on the basis of mean monthly turnover ~defined as the averageof purchase and sale turnover!. Each quintile represents the common stock
investments of more than 12,000 households. Descriptive statistics for eachof the quintiles are presented in Table V, Panel A. The households with low
turnover average 0.19 percent turnover per month, those with high turnover
average 21.49 percent. To qualify as a high turnover portfolio, a household
would need to turn over at least 8.7 percent of its portfolio in an averagemonth. Households with low turnover also tend to have larger accounts.
As before, we estimate the parameters of the Fama–French three-factor
model, where the dependent variable is the monthly mean gross householdexcess return for each turnover quintile. The coefficient estimates on themarket, size, and book-to-market factors reveal that the high turnover households tilt more heavily toward high-beta, small, growth stocks than do the
low turnover households.
The gross and net returns for each turnover quintile are presented inTable V, Panels B and C. Focusing first on the gross performance ~Panel B!,
we find that high turnover households ~quintile 5! do not significantly outperform low turnover households ~quintile 1!. In fact, the intercept test basedon the Fama–French three-factor model, which accounts for the tendency ofthe high turnover portfolio to tilt more heavily toward high-beta, small, growthstocks, indicates that the two high turnover quintiles ~quintiles 4 and 5!
underperform by 24 and 36 basis points per month. Though marginally statistically significant ~ p-values of 0.143 and 0.104, respectively!, we believethese figures to be economically large ~approximately three to four percent
annually!. Regardless of whether one accepts these results as statisticallysignificant, the prediction of the Grossman and Stiglitz model is not supported; those who trade most do not earn higher gross returns.
The analysis of net returns ~Panel C! is quite interesting. Regardless ofthe method used to measure performance, the high turnover households ~quintile 5! underperform the low turnover households ~quintile 1!. The underperformance ranges from 46 basis points per month ~5.5 percent per year,t 5 21.56! using market-adjusted returns to an astoundingly high 80 basis
points per month ~9.6 percent per year, t 5 24.59! based on the Fama–
French intercept. The own-benchmark abnormal returns indicate that the21
Rather than increasing their gross returns, active traders could alternatively achieve thesame expected utility as less active traders by lowering their volatility through trading. We findno evidence of this however. For example, the average ~net! Sharpe ratio of the quintile that
trades most actively ~0.092! is one-half that of the quintile that trades least actively ~0.180!.
Though these Sharpe ratios do not consider investors’ total portfolios of assets ~see footnote 9!,
they indicate that active traders do not have higher volatility adjusted returns within the observed equity portfolios.

Trading Is Hazardous to Your Wealth793

Table V

Descriptive Statistics, Gross Returns, and Net Returns forHousehold Quintiles Formed on Mean Turnover
The sample is account records for 66,465 households at a large discount brokerage firm fromJanuary 1991 to December 1996. Households are sorted into quintiles based on monthly turnover ~the average of sales and purchase turnover! during our sample period. Quintile 1 contains
households with the lowest turnover, quintile 5 contains households with the highest. Beginning position value is the market value of common stocks held in the first month that thehousehold appears during our sample period. Mean monthly turnover is the average of sales
and purchase turnover. Coefficient estimates are those from a time-series regression of thegross average household excess return on the market excess return ~R mt 2 R ft !, ~HML t !, and azero-investment size portfolio ~SMBt !. Raw return is the average monthly return for the average household. Own-benchmark abnormal return is the return on the household portfolio minus
the return on the portfolio the household held at the end of the previous January. Marketadjusted return is the return on the household portfolio less the return on a value-weighted
NYSE0AMEX0Nasdaq index. CAPM intercept is the estimated intercept from a time-series
regression of the household excess return on the market excess return ~R mt 2 R ft !. Fama–
French intercept is the estimated intercept from time-series regressions of household excess
return on the market excess return, a zero-investment book-to-market portfolio ~HML t !, and azero-investment size portfolio ~SMBt !. p-values are presented in parentheses.
Quintile
1
~Low!

2

3

4

5
~High!

Difference:High 2 Low

Panel A: Descriptive Statistics
Mean monthly turnover ~%!
Mean beginning position value
Coefficient estimate on~R mt 2 R ft !
HML t
SMBt
Adjusted R 2

0.19
34,1691.24
26,046

2.89
22,945

5.98
19,102

21.49
21,560N.A.
212,609***~0.000!

1.03~0.199!
0.20***~0.000!
0.24***~0.000!
96.1

1.06*~0.090!
0.10***~0.012!
0.29***~0.000!
94.7

1.11**~0.015!
0.13**~0.020!
0.51***~0.000!
92.2

1.18***~0.002!
0.13*~0.065!
0.72***~0.000!
90.4

1.29***~0.000!
0.12~0.195!
1.02***~0.000!
87.6

0.26***~0.000!
20.08~0.333!
0.78***~0.000!
71.8

Panel B: Gross Average Household Percentage Monthly ReturnRaw returnOwn-benchmark
abnormal return
Market-adjusted return
CAPM intercept
Fama–French intercept

1.483
20.009~0.156!
0.063~0.534!
0.090~0.409!
20.048~0.526!

1.472
20.026*~0.064!
0.052~0.660!
0.022~0.865!
20.072~0.448!

1.489
20.052**~0.014!
0.069~0.710!
20.015~0.936!
20.149~0.242!

1.511
20.079***~0.007!
0.091~0.726!
20.078~0.774!
20.237~0.143!

1.548
20.096*~0.093!
0.128~0.728!
20.167~0.663!
20.359~0.104!

0.065
20.087~0.116!
0.065~0.832!
20.257~0.407!
20.311*~0.086!

Panel C: Net Average Household Percentage Monthly ReturnRaw returnOwn-benchmark
abnormal return
Market-adjusted return
CAPM intercept
Fama–French intercept

1.470
20.021***~0.000!
0.050~0.625!
0.077~0.480!
20.061~0.422!

1.411
20.079***~0.000!
20.009~0.937!
20.038~0.764!
20.130~0.172!

1.361
20.167***~0.000!
20.059~0.749!
20.140~0.474!
20.269**~0.037!

1.267
20.300***~0.000!
20.153~0.547!
20.314~0.242!
20.464***~0.005!

1.009
20.587***~0.000!
20.411~0.253!
20.692*~0.066!
20.864***~0.000!

20.46020.566***~0.000!
20.460~0.124!
20.768**~0.012!
20.803***~0.000!

***, **, and * indicate significance at the 1, 5, and 10 percent levels, respectively ~two-tailed!. The null
hypothesis for beta ~the coefficient estimate on the market excess return! is H o : b 5 1 except in the difference
column, where the null hypothesis is H o : b 5 0.

794

The Journal of Financetrading of high turnover households costs them 57 basis points per month~6.8 percent per year! relative to the returns earned by low turnover households. Again, these differences are not consistent with the Grossman andStiglitz model, but are consistent with the predictions of the overconfidence
models.
In sum, differences in gross returns across the turnover quintiles are small.
An investment mimicking that of the average household in each quintile
would have earned a gross annualized mean geometric return that ranged
from 18.5 percent ~for quintile 2! to 18.7 percent ~for quintile 1!. However,there are dramatic differences in the net returns across the turnover quintiles. An investment mimicking the average household of the high turnoverquintile would have earned a net annualized mean geometric return of 11.4
percent, while an investment that mimicked the low turnover quintile would
have earned 18.5 percent. These returns are graphed in Figure 1.

V. Price Momentum
Some authors have identified price momentum effects in stock returns—that is, stocks that have performed well recently tend to earn higher returns
than those that have not ~Jegadeesh and Titman ~1993!!. It is unlikely, however, that individual investors view momentum as a risk factor. Thus, we do
not include momentum when calculating risk-adjusted returns.
Nonetheless, it is interesting to consider how momentum affects the performance of individual investors. In general, the sampled investors are antimomentum investors; that is, on average they tend to hold stocks that haverecently underperformed the market. This is consistent with the evidence
that individual investors tend to hold their losers and sell their winninginvestments ~Odean ~1998a!!.
To investigate the effect of price momentum on the performance of individual investors, we add a zero-investment price-momentum portfolio to the
Fama–French three-factor regressions described in Section II.C.22 This portfolio is long stocks that have performed well recently and short those thathave performed poorly. We then estimate time-series regressions for each of
the sample partitions described in the main text. In all sample partitions,the estimated coefficient estimate on the zero-investment price-momentum
portfolio is negative; individuals tend to tilt their investments toward stocks
that have performed poorly recently.
The net performance of individual investors in aggregate ~on average! is20.053 ~20.041! percent per month when price momentum is included as anadditional characteristic. Though still negative, these intercepts are smaller
in magnitude than those from the Fama–French three-factor regressions
and are not statistically significant.
22

The construction of the zero-investment price-momentum portfolio is described in Carhart~1997!. We thank Mark Carhart for providing us with the returns data.

Trading Is Hazardous to Your Wealth795

Our principal finding—that those investors who trade most actively realize, on average, the lowest net returns—is unaffected by the inclusion ofa momentum characteristic in the regressions. These time-series regressions result in an intercept of 20.398 percent per month for those who
trade most actively ~quintile 5! and 0.070 percent per month for those who
trade least ~quintile 1!. Thus, when one controls for their tendency to holdpoorly performing stocks, those investors who trade least actively achievereasonable performance. More important, however, is the finding that active investors continue to underperform less active investors. The differences in the intercepts remains large and statistically significant: 20.468
percent per month.

VI. Liquidity, Rebalancing, and Tax-Motivated Trading
To this point, we have focused on information-motivated versus overconfidence-motivated trading. The empirical evidence we have presented solidly favors overconfidence as the major motivation for trading, since trading
unambiguously hurts investor performance; however, there are other motivations for trading, which we consider in this section.
A. Liquidity
Investors who face liquidity shocks over time will trade as a rational response to those shocks. Thus, liquidity shocks can explain some trading activity. But, they seem implausible as an explanation of the 75 percent annual
turnover that we document for the average individual investor and belie
common sense as an explanation of the more than 250 percent annual turnover of the households who trade most. Investors facing rapidly f luctuating
liquidity needs can, in most cases, find less expensive means to finance thesethan rapid trading in and out of stocks.
Moreover, the trading that results from liquidity shocks can be accomplished at a much lower cost by investing in mutual funds than by investing
in individual common stocks. To illustrate this point, we analyze the returns
on the Vanguard Index 500 mutual fund, a large passive mutual fund thatclaims to match the performance of the Standard and Poor’s 500. Investors
can move in and out of this fund at no cost. In contrast to the performanceof the average or aggregate household, this index fund does not underperform when compared to any of the standard performance benchmarks. During our sample period, this fund earned an annualized geometric mean returnof 17.8 percent while the value-weighted market index earned 17.9 percent.
The market-adjusted return, the CAPM intercept, and the Fama–Frenchintercept for the Vanguard Index 500 were 20.002, 20.004, and 0.009 percent, respectively. A passively managed mutual fund clearly provides a lower
cost means of managing liquidity shocks than does investment in individualcommon stocks.

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B. Rebalancing
Investors who desire a portfolio with certain risk characteristics will rationally rebalance their portfolio to maintain this risk profile. With an average holding of four common stocks, we believe that risk-based rebalancing
is not a significant motivation for trading in the households that we study.
Risk-based rebalancing as an explanation of the 75 percent annual turnoverthat we document for the average household belies common sense. Investors
can manage the risk composition of their portfolio at much lower cost by
carefully selecting a portfolio of mutual funds.
C. Taxes
The single most compelling reason for investors to hold individual common stocks in lieu of mutual funds is taxes. Investors who hold stocks thathave lost value since their purchase can realize those losses. These losses
can be used to shelter gains and thereby reduce the investor’s tax liability.23
Tax-loss selling cannot completely explain the results that we document
here for three reasons. First, it is implausible that tax-motivated trading
would yield an annual turnover rate of 75 percent. A simple example illustrates this point: Consider an investor who buys the value-weighted market
index on January 1 of each year 1991 to 1996. In December of the average
year, this investor would be able to sell 24 percent of her portfolio for a loss.
Of course, this example assumes a holding period of 12 months. The turnover resulting from tax-loss selling will decline as this holding period increases.
Second, we find high turnover and significant underperformance in bothtaxable and tax-deferred accounts. If tax-loss selling is the major motivation
for trading we would expect to find little trading in tax-deferred accounts.
On the other hand, if overconfidence is the major motivation for trading, we
would expect to find, as we do, active trading and significant underperformance in both taxable and tax-deferred accounts. We partition the accounts
in our sample into taxable and tax-deferred accounts ~i.e., Individual Retirement Accounts and Keogh Accounts!. In Table VI, Panel A, we present descriptive statistics for the taxable and tax-deferred accounts. Turnover intax-deferred accounts is high: 67.6 percent annually ~monthly turnover of
5.63 percent times 12!, though not as high as in taxable accounts: 89.4 percent annually ~monthly turnover of 7.45 percent times 12!. The difference inturnover may result from tax-motivated trading or it may be that investors
associate their retirement accounts with future safety and therefore tradeless speculatively in these accounts.
In Table VI, Panels B and C, we present the gross and net return performances of taxable and tax-deferred accounts. The gross returns earned bytaxable and tax-deferred accounts are quite similar ~see Panel B!. The net
23
Though losses on mutual funds can also be used to reduce an investor’s tax liability, theprobability of having a loss on a mutual fund is less than the probability of observing at least
one losing investment in a well-diversified portfolio of common stocks.

Trading Is Hazardous to Your Wealth797

Table VI

Descriptive Statistics, Gross Return, and Net Returnfor Taxable and Tax-Deferred Accounts
The sample is account records for 66,465 households at a large discount brokerage firm fromJanuary 1991 to December 1996. Accounts are partitioned as either taxable or tax deferred~IRA, Keogh, SEP-IRA!. Beginning position value is the market value of common stocks held in
the first month that the household appears during our sample period. Mean monthly turnoveris the average of sales and purchase turnover. Coefficient estimates are those from a timeseries regression of the gross average household excess return on the market excess return~R mt 2 R ft !, a zero-investment book-to-market portfolio ~HML t !, and a zero-investment size
portfolio ~SMBt !. Raw return is the average monthly return for the average household. Ownbenchmark abnormal return is the return on the household portfolio minus the return on theportfolio the household held at the end of the previous January. Market-adjusted return is thereturn on the household portfolio less the return on a value-weighted NYSE0AMEX0Nasdaq
index. CAPM intercept is the estimated intercept from a time-series regression of the household excess return on the market excess return ~R mt 2 R ft !. Fama–French intercept is theestimated intercept from time-series regressions of household excess return on the market
excess return, a zero-investment book-to-market portfolio ~HML t !, and a zero-investment size
portfolio ~SMBt !. p-values are presented in parentheses.
TaxableTax
Deferred

Difference

Panel A: Descriptive Statistics
Number of households
Mean beginning position value

54,43426,30330,554
14,042

Mean monthly turnover ~%!

7.45

5.63

1.13***~0.004!
0.14***~0.010!
0.56***~0.000!
92.6

1.12***~0.007!
0.18***~0.001!
0.52***~0.000!
92.2

Coefficient estimate on:~R mt 2 R ft !
HML t
SMBt
Adjusted R 2

N0A.
12,261***~0.000!
1.82***~0.000!
0.01~0.346!
20.04***~0.000!
0.04***~0.000!
46.7

Panel B: Gross Average Household Percentage Monthly ReturnRaw returnOwn-benchmark abnormal return
Market-adjusted return
CAPM intercept
Fama–French intercept

1.496
20.048***~0.009!
0.076~0.702!
20.027~0.899!
20.174~0.174!

1.532
20.037*~0.055!
0.112~0.555!
0.031~0.156!
20.133~0.298!

20.03620.010~0.107!
20.036~0.185!
20.058**~0.039!
20.041*~0.059!

Panel C: Net Average Household Percentage Monthly ReturnRaw returnOwn-benchmark abnormal return
Market-adjusted return
CAPM intercept
Fama–French intercept

1.313
20.203***~0.000!
20.107~0.583!
20.204~0.326!
20.344***~0.008!

1.379
20.166***~0.000!
20.042~0.823!
20.119~0.547!
20.278**~0.030!

20.066**20.036***~0.000!
20.066**~0.012!
20.085***~0.002!
20.066***~0.002!

***, **, and * indicate significance at the 1, 5, and 10 percent levels, respectively ~two-tailed!. The null
hypothesis for beta ~the coefficient estimate on the market excess return! is H o : b 5 1 except in the difference
column, where the null hypothesis is H o : b 5 0.

798

The Journal of Finance

returns earned by taxable and tax-deferred accounts are both poor, after areasonable accounting for the small stock tilt of these individuals ~see Panel C!.
The tax-deferred accounts outperform the taxable accounts by about six basis
points per month. In short, the general tenor of our results is similar for the
taxable and tax-deferred accounts.
Third, Odean ~1998a, 1999! documents that most investor trading activity
is inconsistent with tax-motivated trading. He observes that investors at a
discount brokerage sell profitable investments twice as often as unprofitable
investments ~during the period 1987 to 1993! and that, relative to their opportunities to do so, these investors are about one and one-half times more
likely to realize any gain than any loss. They do engage in tax-loss selling
late in the year, but December is the only month in which they realize losses
at as fast a rate as they do gains.
Finally, we should emphasize that trading not associated with tax-loss
selling will further hurt the after-tax returns of individual investors. Not
only does this trading incur trading costs, when done in a taxable account it
also accelerates the payment of capital gain taxes that could be otherwise
deferred.
D. Gambling
To what extent may a desire to gamble account for the excessive trading
we observe? Many people appear to enjoy gambling. Some buy lottery tickets. Others gamble at casinos. We consider two distinct aspects of gambling:risk-seeking and entertainment. Risk-seeking is when one demonstrates apreference for outcomes with greater variance but equal or lower expected
return. In equity markets the simplest way to increase variance without
increasing expected return is to underdiversify. Excessive trading has a related, but decidedly different, effect; it decreases expected returns without
decreasing variance. Thus risk-seeking may account for underdiversification~though underdiversification could also result from simple ignorance of its
benefits!, but it does not explain excessive trading.
A second aspect of gambling is the entertainment derived from placing
and realizing bets. When coupled with the overconfident belief that thesebets are expected-wealth enhancing, it is easy to see that the entertainment utility of gambling will fuel greater trading. There is also the possibility that people may trade for entertainment while fully realizing thateach trade is more likely than not to reduce their personal future wealth.
~Note that this is different from realizing that the trades of others are
wealth reducing.! We favor the hypothesis that most investors trade excessively because they are overconf ident, or because they are overconf ident and they enjoy trading, over the hypothesis that they trade purely
for entertainment and expect thereby to lower their wealth. Many studies
have established that people are overconfident. We know of no study demonstrating that ordinary investors expect to lower their wealth throughtrading.

Trading Is Hazardous to Your Wealth799

It is possible that some investors set aside a small portion of their wealthwith which they trade for entertainment, while investing the majority more
prudently. If “entertainment accounts” are driving our findings, we would
expect turnover and underperformance to decline as the common stocks inthe accounts we observe represent a larger proportion of a household’s total
wealth. We are able to test this hypothesis directly and find no support forit. For approximately one-third of our sample, the households reported their
net worth at the time they opened their accounts. We calculate the proportion of net worth invested at the discount broker as the beginning value ofa household’s common stock investments scaled by its self-reported net worth.24
We then analyze the turnover and investment performance of 2,333 households with at least 50 percent of their net worth in common stock investments at this discount broker. These households have similar turnover ~6.25
percent per month, 75 percent annually! to our full sample ~see Table I!.
Furthermore, these households earn gross and net returns that are very
similar to the full sample. The monthly net return, own-benchmark abnormal return, market-adjusted return, CAPM intercept, and Fama–French intercept for these households are 1.285, 20.173, 20.135, 20.221, and 20.285
percent, respectively.
Finally, it is worth noting that the negative relation between turnover andnet returns that we document for individual investors also exists in mutual
funds ~Carhart ~1997!!. It is unlikely that mutual fund managers buy andsell stocks for the pure joys of trading despite the fact that this trading
lowers the expected returns of their shareholders.25

VII. Conclusion
We analyze the returns earned on common stock investments by 66,465
households at a large discount brokerage firm for the six years ending inJanuary 1997. We document that the gross returns ~before accounting fortransaction costs! earned by these households are quite ordinary, on average. Unfortunately, the net returns ~after accounting for the bid-ask spreadand commissions paid by these investors! earned by these households arepoor. The average household underperforms a value-weighted market index
by about 9 basis points per month ~or 1.1 percent annually!. After accounting for the fact that the average household tilts its common stock investments toward small value stocks with high market risk, the underperformance
averages 31 basis points per month ~or 3.7 percent annually!. The average
household turns over approximately 75 percent of its common stock portfolio
annually. The poor performance of the average household can be traced tothe costs associated with this high level of trading.
24This estimate is upwardly biased because the account opening date generally precedes our
first portfolio position observation and net worth is likely to have increased in the interim.
25
Lakonishok et al. ~1992! report a positive relation between turnover and performance for769 all-equity pension funds, though this finding puzzles the authors.

800

The Journal of FinanceOur most dramatic empirical evidence is provided by the 20 percent ofhouseholds that trade most often. With average monthly turnover of in excess of 20 percent, these households turn their common stock portfolios overmore than twice annually. The gross returns earned by these high-turnoverhouseholds are unremarkable, and their net returns are anemic. The net
returns lag a value-weighted market index by 46 basis points per month ~or
5.5 percent annually!. After a reasonable accounting for the fact that the
average high-turnover household tilts its common stock investments toward
small value stocks with high market risk, the underperformance averages
86 basis points per month ~or 10.3 percent annually!.
The investment experience of individual investors is remarkably similar
to the investment experience of mutual funds. As do individual investors,
the average mutual fund underperforms a simple market index ~Jensen ~1969!
and Malkiel ~1995!!. Mutual funds trade often and their trading hurts performance ~Carhart ~1997!!. But trading by individual investors is even moredeleterious to performance because individuals execute small trades and facehigher proportional commission costs than mutual funds.
Our main point is simple: Trading is hazardous to your wealth. Why thendo investors trade so often? The aggregate turnover of the individual investor portfolios we analyze is about 70 percent; the average turnover is about
75 percent. The New York Stock Exchange reports that the annual turnoverof stocks listed on the exchange hovered around 50 percent during our sample period. Mutual funds average an annual turnover of 77 percent ~Carhart~1997!!. We believe that these high levels of trading can be at least partly
explained by a simple behavioral bias: People are overconfident, and overconfidence leads to too much trading.
Based on rational agents free from such behavioral biases, the efficient
markets hypothesis has been central to both the theory and practice of investment management. The efficiency research posits that private information is rare. Thus, active investment strategies will not outperform passive
investment strategies. Both the theoretical and empirical work on efficiency
supporting this view have led to a rise of passive investment strategies that
simply buy and hold diversified portfolios ~Fama ~1991!!.
Behavioral finance models that incorporate investor overconfidence ~e.g.,
Odean ~1998b!! provide an even stronger prediction: Active investment strategies will underperform passive investment strategies. Overconfident investors will overestimate the value of their private information, causing themto trade too actively and, consequently, to earn below-average returns. Consistent with these behavioral models of investor overconfidence, we provide
empirical evidence that households, which hold about half of U.S. equities,
trade too much, on average. Those who trade the most are hurt the most.
Appendix A. The Analysis of Trade Timing
In this appendix, we analyze the timing of purchases and sales within amonth. The timing of trades within a month is ignored in our main analysis where we assume all purchases and sales are made at month end.

Trading Is Hazardous to Your Wealth801

Table AIThe Gross Abnormal Returns for Stocks Bought and Sold
from the Trade Date to the End of the Month
The sample is account records for 66,465 households at a large discount brokerage firm fromJanuary 1991 to December 1996. Purchase turnover is the average value of stocks purchased
divided by the average value of stocks held in each month. The purchase abnormal return iscalculated by compounding the daily returns on the purchased security from the day following
the purchase to the end of the month less the compound return on the value-weighted NYSE0
AMEX0Nasdaq market index. Sales turnover and sales abnormal return are analogously calculated. The estimated effect on the monthly abnormal return is the purchase turnover times
the purchase abnormal return minus the sale turnover times the sale abnormal return.

SampleMonthly
Purchase
Turnover~%!

All households

4.92

Purchase
Abnormal
Return~%!

Monthly
Sale
Turnover~%!

Sale
Abnormal
Return~%!

Estimated
Effect on
Monthly
Abnormal
Return~%!

0.021

20.0242

Panel A: Aggregate
20.472

4.93

Panel B: Households Partitioned by Beginning Position Value
1 ~Small!
2
3
4
5 ~Large!

6.85
5.83
5.82
5.55
4.41

20.65020.38120.38620.44520.486

6.06
5.16
5.25
5.25
4.23

20.11620.0190.437
0.030
20.03520.0375
20.021320.0454
20.0263
20.0199

Panel C: Households Partitioned by Turnover
1 ~Low!
2
3
4
5 ~High!

0.26
1.37
3.07
6.46
21.81

20.18420.176
20.12620.23420.674

0.23
1.14
2.57
6.13
20.75

0.068
20.0890.041
0.102
20.00320.0006
20.001420.0049
20.0214
20.1464

Consistent with the results reported in Odean ~1999!, we document thatthe stocks investors buy subsequently underperform the stocks they sell.
In aggregate, we estimate that an exact accounting for the timing of purchases and sales would reduce the performance of individual investors bymore than two basis points per month ~or approximately 0.29 percent
annually!.
For each account with a beginning-of-month position statement in montht, we identify all purchases in month t 2 1 and sales in month t. For bothpurchases and sales, we calculate the compound return on the stock fromthe day following the trade to the last day of the month. For purchases thisreturn is excluded from our main results; for sales this return is included.
Note that in our main results, we account for the intraday return on the
trade day in our estimate of the bid-ask spread.

802

The Journal of Finance

Figure A1. Time line of returns calculations. The time of purchase (sale) is tb (ts ). The
close on the purchase ~sale! day is tbcl ~tscl !. The close on the last day of the purchase ~sale! monthis t1 ~t3 !.

The results of our analysis are presented in Table AI. The second~fourth! columns of this table present aggregate purchase ~sale! turnover
calculated as the aggregate dollar value of purchases ~sales! divided by
the aggregate dollar value of positions held. ~This turnover measure isslightly different from that used in the main text, where turnover is calculated based on market values contained in position statements and isthus capped at 100 percent per month for each household.! Abnormal returns are calculated for purchases and sales by subtracting the compound return on the CRSP NYSE0AMEX0Nasdaq value-weighted index.
The trade-weighted mean abnormal returns are presented in columns 3~for purchases! and 5 ~for sales! of Table AI. In aggregate ~Panel A!,
from the day following the trade to the end of the month, the stocks
that investors buy underperform the value-weighted market index by
47 basis points, and those they sell outperform the index by two basis
points. Based on these abnormal returns and our estimates of aggregate
turnover, we calculate that the results we present in the main text overestimate the performance of individual investors by 2.42 basis points permonth.
We also analyze the timing of trades by partitioning households on thebasis of account size ~Panel B! and turnover ~Panel C!. In each of the sample
partitions, the timing of their trades hurts investors. In short, the results inthe main text overestimate the performance of individual investors by ignoring the exact timing of purchases and sales.
Consider how the accounting for the exact timing of trades relates to thereturn calculations contained in the main text. In Figure A1, we present an
example of a security that is purchased in month 1 and sold in month 3. Atime line for these transactions is depicted in Figure A1.
In the main text, we calculate the return for this security from t1 to t3 . In
this appendix, we calculate the return from timing as the return from tbcl to
t1 minus the return from tscl to ts . Our estimate of the bid-ask spread is thereturn from ts to tscl minus the return from tb to tbcl . When the return fromtiming is added to the main calculation and the spread is subtracted, onegets the ~approximate! return from tb to ts , the period in which the investor
held the stock.

Trading Is Hazardous to Your Wealth803

Appendix B. The Analysis of Intramonth Trades
In this appendix, we analyze the performance of stocks that are bought andthen sold within a calendar month ~e.g., purchased on January 3 and sold on
January 10!. These intramonth trades are excluded from our main analyses,
since those analyses are based on monthly position statements. In aggregate,
we estimate that intramonth trades would improve the performance of individual investors by less than one basis point per month ~or approximately 0.06
percent annually!. Though profitable, the aggregate value of intramonth trades
accounts for less than one percent of the aggregate value of positions held.
For each account, we identify all purchases followed by a sale within thesame month. In accounting for multiple purchases and sales, we assume
that the first securities purchased are the first sold. Over our 72-monthsample period, we identify 87,095 round-trip intramonth trades worth approximately $27 million per month, on average. In contrast, the average
beginning-of-month value of positions held, which we analyze in the main
text, is over $2.7 billion.
We calculate the gross returns on these round-trip transactions using the
CRSP daily return files assuming the security is purchased and sold at theclose of trading on the purchase and sale dates, respectively. We calculate
the net returns on these round-trip transactions by subtracting estimates ofthe bid-ask spread and commissions as is done in the main text for the caseof monthly returns. The average round-trip trade involves a purchase of$22,275, is held for 6.16 days, and costs 2.08 percent in commissions and0.30 percent for the bid-ask spread. ~In aggregate, these round-trip trades
cost 0.87 percent in commissions and 0.27 percent for the bid-ask spread.!
Note that the bid-ask spread is lower than that documented for trades that
we analyze in the main text, which have an average round-trip bid-ask spreadof one percent ~see Table I!. This lower spread is likely a result of the intraday return earned by investors from the transaction price through theend of the trading day ~which is included in our estimate of the spread!
rather than a smaller bid-ask spread for these intramonth trades.
In Table BI, we summarize our analysis of the gross and net returns earned
on intramonth trades. In this table, we calculate market-adjusted abnormal
returns by subtracting the daily value-weighted NYSE0AMEX0Nasdaq CRSP
market index from the return earned on each intramonth trade. Both thegross and net abnormal returns in this table are weighted by the size of eachtrade, so that we can estimate the aggregate impact of these intramonthtrades on the performance of individual investors.
Panel A presents results for all households. In aggregate, the intramonthtrades earn impressive gross abnormal returns of 1.64 percent. The net abnormal returns are 0.50 percent. Since these intramonth trades average 0.99
percent of the average value of positions held, we estimate that these intramonth trades would improve the performance of individual investors by 0.49
basis points per month ~0.0050 times 0.0099! in aggregate. This small improvement in performance does not affect any of the conclusions that we
present in the main text.

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Table BIThe Gross and Net Abnormal Returns earned on Intramonth Trades
The sample is account records for 66,465 households at a large discount brokerage firm fromJanuary 1991 to December 1996. The gross abnormal return on intramonth trades is calculated
as the compound return from the day following the purchase to the day of the sale less thecompound return on a value-weighted NYSE0AMEX0Nasdaq index. The net abnormal return isthe gross abnormal return adjusted for the return earned on the day of the purchase or sale, thebid-ask spread, and the commission cost. The intramonth trades as a percentage of total position value are the average monthly value of intramonth purchases divided by the average monthly
value of all stocks held. The estimated effect on monthly abnormal return is the net abnormal
return times the intramonth trades as a percentage of total position value.

Sample

Mean
Trade
Size

Gross
Abnormal
Return~%!

Net
Abnormal
Return~%!

Intramonth
Trades as a
Percentage
of Total
Position
Value

Estimated
Change inMonthly
Abnormal
Return~%!

0.99

0.0049Panel A: Aggregate
All households$22,275

1.636

0.496

Panel B: Households Partitioned by Beginning Position Value
1 ~Small!
2
3
4
5 ~Large!

17,45912,57917,173
20,25528,387

2.376
2.082
1.757
1.363
1.563

0.904
0.248
0.486
0.351
0.526

1.42
0.92
1.17
1.33
0.86

0.01280.00230.00570.00460.0045

0.00
0.02
0.08
0.36
6.92

0.00000.00000.00020.00500.0312

Panel C: Households Partitioned by Turnover
1 ~Low!
2
3
4
5 ~High!

10,63812,876
11,886
13,83823,70220.003
3.006
1.843
2.925
1.545

20.026
0.200
0.220
1.378
0.451

We also analyze the profitability of intramonth trades by partitioning households on the basis of account size ~Panel B! and turnover ~Panel C!. In short,none of these results are so dramatic that they would lead us to qualify anyof the results that we present in our main text. Those who benefit most fromintramonth trades are those who trade most. Their intramonth trades improve their performance by 3.12 basis points per month ~last row and lastcolumn of Panel C!. Yet, we estimate that these investors underperform by awhopping 86 basis points per month ~last row, Table V!.
In conclusion, we emphasize that the positive net returns earned on intramonth trades do not necessarily imply that individual investors have superior short-term trading ability. If investors have a disposition to sell winninginvestments and ride losing investments ~as proposed by Shefrin and Statman ~1985!!, we would expect to observe positive abnormal returns on shortterm round-trip trades.

Trading Is Hazardous to Your Wealth805

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