Monday, March 29, 2010

Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors

Enjoy for the first time in non PDF form:
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 return
of 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 performance
of individual investors. Our central message is that trading is hazardous to your
wealth.
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 performance
of equities managed by pension funds. Unfortunately, there is little research
on 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 the
discount 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. All
errors are our own.
THE JOURNAL OF FINANCE • VOL. LV, NO. 2 • APRIL 2000
773
In this paper, we attempt to shed light on the investment performance of
common stocks held directly by households. To do so, we analyze a unique
data set that consists of position statements and trading activity for 78,000
households at a large discount brokerage firm over a six-year period ending
in 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 is
equal 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 their
detriment.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 from
abnormal 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! has
a similar result. Daniel, Hirshleifer, and Subrahmanyam ~1998! consider the asset price implications
of overconfidence but do not directly address investor welfare.
774 The Journal of Finance
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. There
is a less obvious tilt toward value ~high book-to-market! stocks.
2 Throughout this paper, “households” and “individual investors” refer to households and
investors with discount brokerage accounts. Though we believe that our findings generalize to
customers 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 these
cases, our observations are limited to their discount accounts.
Figure 1. Monthly turnover and annual performance of individual investors. The white
bar ~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.
Trading Is Hazardous to Your Wealth 775
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 month
and 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 of
investor 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 brokerage
firm 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 hold
their investments at a discount brokerage firm rather than at a retail brokerage
firm. A wide variety of investment advice is available to both retail
and discount investors from sources such as newsletters, Value Line, and the
financial press. Retail brokerage firms also provide stock selection advice to
their clients. If this advice is valuable and if investors attend to it, it is
3 These figures are based on the mean return from February 1991 through January 1997 for
the size and book-to-market factors constructed by Fama and French ~1993!. In the remainder
of this paper, when we refer to a size or value premium, our inference is based on the returns
of these zero-investment portfolios.
776 The Journal of Finance
plausible that individual investors at these firms earn both better gross
returns and net returns. We would welcome the opportunity to test this
hypothesis directly by obtaining a data set similar to that employed in our
study from a retail brokerage firm. Barber et al. ~1998! and Womack ~1996!
present evidence that the recommendations of brokerage-house analysts have
investment 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 not
appear in their study.
Third, although Schlarbaum et al. ~1978a, 1978b! evaluate performance
using 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 of
individual investors. He analyzes the timing of trades made by individual
investors at a large discount brokerage firm during the seven years ending
in December 1993, a sample period that overlaps with ours. ~The data sets
employed in Odean ~1999! and this study are different.! He documents that
the 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 the
aggregate performance of all stocks held by individuals. Consequently, he is
unable 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 January
1991 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.
Trading Is Hazardous to Your Wealth 777
the 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 on
whether the discount brokerage firm labeled the household as a general
~60,000 households!, affluent ~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 in
equity 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 the
active trader label. In 1997, approximately 61 percent of all retail accounts
at this brokerage firm were classified as general, 28 percent as aff luent, and
11 percent as active. Sampled households were required to have an open
account with the discount brokerage firm during 1991. Roughly half of the
accounts 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 the
sample 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 for
our 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 of
stocks 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 $31 per share. The value of trades and the transaction price of
trades are positively skewed; the medians for both purchases and sales are
substantially less than the mean values.
778 The Journal of Finance
Table I
Descriptive Statistics on Trade Size, Trade Price,
Transaction Costs, and Turnover
The sample is account records for 66,465 households at a large discount brokerage firm from
January 1991 to December 1996. Spread is calculated as the transaction price divided by the
closing price on the day of the transaction minus one ~and then multiplied by minus one for
purchases!. Commission is calculated as the commission paid divided by the value of the trade.
Monthly turnover is the beginning-of-month market value of shares purchased in month t 2 1
~or sold in month t! divided by the total beginning-of-month market value of shares held in
month t. Trade-weighted spread and commission are averages weighted by trade size. Aggregate
turnover is the aggregate value of sales ~or purchases! divided by the aggregate value of
positions held during our sample period.
Mean
25th
Percentile Median
75th
Percentile
Standard
Deviation
No. of
Obs.
Panel A: Purchases
Trade size ~$! 11,205 2,513 4,988 10,500 32,179 1,082,107
Price0share 31.06 11.00 23.00 40.00 117.82 1,082,107
Monthly turnover ~%! 6.49 0.54 2.67 7.08 11.89 66,465
Commission ~%!* 1.58 0.78 1.29 2.10 1.45 966,492
Spread~%! 0.31 1,028,087
Panel B: Sales
Trade size ~$! 13,707 2,688 5,738 13,000 38,275 887,594
Price0share 31.22 12.00 24.00 41.00 113.03 887,594
Monthly turnover ~%! 6.23 0.39 2.58 6.95 11.36 66,465
Commission ~%!* 1.45 0.70 1.16 1.91 1.06 785,206
Spread ~%! 0.69 845,644
Panel C: Trade-Weighted and Aggregate Purchases
Aggregate monthly
turnover ~%!
6.05
Trade-weighted
commission ~%!
0.77 Not Applicable
Trade-weighted
spread ~%!
0.27
Panel D: Trade-Weighted Sales
Aggregate monthly
turnover ~%!
6.06
Trade-weighted
commission ~%!
0.66 Not applicable
Trade-weighted
spread ~%!
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.
Trading Is Hazardous to Your Wealth 779
For each trade, we estimate the bid-ask spread component of transaction
costs for purchases ~sprdb! and sales ~sprds! as
sprds 5 SPds
cl
Pds
s 2 1D and sprdb52SPdb
cl
Pdb
b 2 1D, ~1!
where Pds
cl and Pdb
cl are the reported closing prices from the Center for Research
in Security Prices ~CRSP! daily stock return files on the day of a sale
and purchase, respectively, and Pds
s and Pdb
b are the actual sale price and
purchase price from our account database.5 Our estimate of the bid-ask spread
component 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 the
dollar value of the commission paid scaled by the total principal value of the
transaction, both of which are reported in our account data.
The average purchase costs an investor 0.31 percent, and the average sale
costs 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 by
trade size! spreads and commissions. These figures can be thought of as the
total 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 a
transaction 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 our
estimate 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!.
780 The Journal of Finance
Finally, we calculate the monthly portfolio turnover for each household. In
each month during our sample period, we identify the common stocks held
by 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 sold
times 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 t21.
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 much
less 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 than
70 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 investment
using 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 the
last 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 the
returns 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 an
investor 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 is
purchased in the preceding month. Thus, turnover, as we have calculated it, cannot exceed 100
percent in a month.
Trading Is Hazardous to Your Wealth 781
Second, we ignore intramonth trading ~e.g., a purchase on March 6 and a
sale 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 the
main 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 gross
monthly return on the household’s portfolio ~Rht
gr! is calculated as
Rht
gr 5 (
i51
sht
pit Rit
gr, ~2!
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
of all stocks held by household h, Rit
gr is the gross monthly return for stock
i, 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 ~Rit
net! as
~1 1 Rit
net! 5 ~1 1 Rit
gr!
~1 2 cit
s !
~1 1 ci, t21
b !
, ~3!
where cit
s is the cost of sales scaled by the sales price in month t and ci, t21
b is
the cost of purchases scaled by the purchase price in month t 2 1. The costs
of 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 cit
s and ci, t21
b are positive; for a security neither purchased in month
t 2 1 nor sold in month t, both cit
s and ci, t21
b are zero. Because the timing and
cost of purchases and sales vary across households, the net return for security
i in month t varies across households. The net monthly portfolio return
for each household is
Rht
net 5 (
i51
sht
pit Rit
net. ~4!
782 The Journal of Finance
If only a portion of the beginning-of-month position in stock i is purchased or
sold, the transaction cost is applied only to that portion. We estimate the
aggregate gross and net monthly return earned by individual investors as
RAGt
gr 5 (
h51
nht
xht Rht
gr and RAGt
net 5 (
h51
nht
xht Rht
net, ~5!
where nht is the number of households with common stock investment in
month t and xht 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
RHt
gr 5
1
nht
(
h51
nht
Rht
gr and RHt
net 5
1
nht
(
h51
nht
Rht
net. ~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 is
similar 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 for
the entire year. The own-benchmark abnormal return is the return earned
by household h less the own-benchmark return; if the household did not
trade during the year, the own-benchmark return is zero for all 12 months
during the year. In each month, the abnormal returns across households are
averaged, yielding a 72-month time-series of mean monthly own-benchmark
abnormal returns. Statistical significance is calculated using t-statistics based
on this time-series. The advantage of the own-benchmark abnormal return
9 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 average
observed 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 our
first monthly position statements are from the month end of January 1991. If the stocks held
by 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.
Trading Is Hazardous to Your Wealth 783
measure 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., the
portfolio it held at the beginning of the year!, thus emphasizing the effect
trading has on performance.
Second, we calculate the mean monthly market-adjusted abnormal return
for 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 return
earned by individual investors on the market excess return. For example, to
evaluate the gross monthly return earned by individual investors in aggregate,
we estimate the following monthly time-series regression:
~RAGt
gr 2 Rft ! 5 ai 1 bi ~Rmt 2 Rft ! 1 eit , ~7!
where Rft 5 the monthly return on T-bills,11 Rmt 5 the monthly return on a
value-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 we
estimate four regressions: one each for the gross and net performance of
individual 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 performance
of individuals in aggregate, we estimate the following monthly time-series
regression:
~RAGt
gr 2 Rft ! 5 aj 1 bj ~Rmt 2 Rft ! 1 sjSMBt 1 hjHMLt 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 HMLt is the
return 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 hj . The error term in
the regression is denoted by ejt . The subscript j denotes parameter estimates
and error terms from regression j, where we again estimate four regres-
11 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.
784 The Journal of Finance
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 exposures
using the coefficient estimates on the market excess return ~Rmt 2 Rft !, the
size zero investment portfolio ~SMBt !, and the book-to-market zero-investment
portfolio ~HMLt ! from the three-factor regressions. Portfolios with aboveaverage
market risk have betas greater than one, bj . 1. Portfolios with a
tilt toward small ~value! stocks relative to a value-weighted market index
have size ~book-to-market! coefficients greater than zero, sj . 0 ~hj . 0!.
We suspect there is little quibble with interpreting the coefficient on the
market 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 in
measuring 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 is
pursuing 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, financially
distressed, firms! or toward low book-to-market stocks ~which tend to be
high-growth firms! is perceived as riskier by investors. As such, we interpret
the coefficient estimates on the book-to-market zero-investment portfolio with
a bit more trepidation.14
III. Results
A. Full Sample Results
Our main findings for the full sample can be summarized simply. The
gross return earned by individual investors in aggregate ~RAGt
gr! and the
gross return earned by the average household ~RHt
gr! 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 individ-
13 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 and
French ~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.
Trading Is Hazardous to Your Wealth 785
ual 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 ~RAGt
net! and
the net return earned by the average household ~RHt
net! underperform the
value-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 performance
measures 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; they
hurt 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 our
period 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 performance
of 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! documents
that 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 prefer
large growth stocks. It is likely that these foreign investors are predominantly institutions.
786 The Journal of Finance
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 discount
brokerage firm from January 1991 to December 1996. Panel A ~Panel C! presents results for the
gross ~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 investment
of 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 a
value-weighted NYSE0AMEX0Nasdaq index. CAPM is the results from a time-series regression
of the household excess return on the market excess return ~Rmt 2 Rft !. 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.
Coefficient Estimate on:
Excess
Return ~Rmt 2 Rft ! HMLt SMBt
Adjusted
R2
Panel A: Gross Percentage Monthly Returns in Aggregate
Own-benchmark abnormal return 20.049**
~0.013!
Market-adjusted return 0.038
~0.723!
CAPM 20.067 1.100*** 92.9
~0.543! ~0.007!
Fama–French three-factor 20.076 1.082*** 20.035 0.231*** 96.3
~0.357! ~0.005! ~0.324! ~0.000!
Panel B: Gross Percentage Monthly Returns for the Average Household
Own-benchmark abnormal return 20.048**
~0.010!
Market-adjusted return 0.078
~0.672!
CAPM 20.014 1.087 80.3
~0.944! ~0.177!
Fama–French three-factor 20.154 1.120*** 0.140*** 0.516*** 93.0
~0.205! ~0.005! ~0.008! ~0.000!
Panel C: Net Percentage Monthly Returns in Aggregate
Own-benchmark abnormal return 20.155***
~0.000!
Market-adjusted return 20.073
~0.496!
CAPM 20.175 1.096*** 93.0
~0.113! ~0.009!
Fama–French three-factor 20.180** 1.077*** 20.040 0.225*** 96.3
~0.031! ~0.009! ~0.251! ~0.000!
Panel D: Net Percentage Monthly Returns for the Average Household
Own-benchmark abnormal return 20.194***
~0.000!
Market-adjusted return 20.090
~0.621!
CAPM 20.177 1.082 80.7
~0.360! ~0.194!
Fama–French three-factor 20.311** 1.113*** 0.131** 0.506*** 93.0
~0.011! ~0.008! ~0.012! ~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 Ho: b 5 1.
Trading Is Hazardous to Your Wealth 787
Table III
Descriptive Statistics, Gross Returns, and Net Returns for
Household Quintiles formed on Beginning Position Value
The sample is account records for 66,465 households at a large discount brokerage firm from
January 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 value
of 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 are
those from a time-series regression of the gross average household excess return on the market
excess return ~Rmt 2 Rft !, a zero-investment book-to-market portfolio ~HMLt !, 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 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 a value-weighted NYSE0
AMEX0Nasdaq index. CAPM intercept is the estimated intercept from a time-series regression
of the household excess return on the market excess return ~Rmt 2 Rft !. 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 ~HMLt !, and a zero-investment
size portfolio ~SMBt !. p-values are presented in parentheses.
Quintile
1
~Small! 2 3 4
5
~Large!
Difference:
Lrg 2 Sml
Panel A: Descriptive Statistics
Mean beginning position value 1,581 4,653 8,599 16,725 149,710 N.A.
Mean monthly turnover ~%! 6.68 6.35 6.31 6.13 6.33 20.35***
~0.000!
Coefficient estimate on:
~Rmt 2 Rft ! 1.21*** 1.13*** 1.11*** 1.11*** 1.06** 20.15***
~0.004! ~0.005! ~0.009! ~0.005! ~0.035! ~0.006!
HMLt 0.36*** 0.12** 0.09* 0.09* 0.06* 20.30***
~0.000! ~0.022! ~0.067! ~0.053! ~0.079! ~0.000!
SMBt 0.97*** 0.56*** 0.45*** 0.39*** 0.27*** 20.70***
~0.000! ~0.000! ~0.000! ~0.000! ~0.000! ~0.000!
Adjusted R2 86.1 92.8 93.2 94.3 95.8 68.4
Panel B: Gross Average Household Percentage Monthly Return
Raw return 1.722 1.511 1.473 1.424 1.400 20.322
Own-benchmark 20.071 20.051** 20.038* 20.038* 20.037* 0.034
abnormal return ~0.101! ~0.022! ~0.070! ~0.061! ~0.077! ~0.487!
Market-adjusted return 0.302 0.091 0.053 0.004 20.020 20.322
~0.370! ~0.648! ~0.755! ~0.980! ~0.857! ~0.185!
CAPM intercept 0.182 20.015 20.043 20.089 20.072 20.253
~0.612! ~0.942! ~0.811! ~0.570! ~0.541! ~0.328!
Fama–French intercept 20.137 20.152 20.149 20.186* 20.140 0.003
~0.510! ~0.227! ~0.206! ~0.082! ~0.101! ~0.983!
Panel C: Net Average Household Percentage Monthly Return
Raw return 1.478 1.328 1.313 1.280 1.279 20.199
Own-benchmark 20.270*** 20.206*** 20.178*** 20.169*** 20.150*** 0.120**
abnormal return ~0.000! ~0.000! ~0.000! ~0.000! ~0.000! ~0.023!
Market-adjusted return 0.059 20.092 20.107 20.140 20.141 20.199
~0.860! ~0.635! ~0.521! ~0.339! ~0.200! ~0.404!
CAPM intercept 20.056 20.193 20.198 20.229 20.189 20.133
~0.875! ~0.350! ~0.264! ~0.140! ~0.105! ~0.602!
Fama–French intercept 20.366* 20.323** 20.298** 20.319*** 20.254*** 0.112
~0.079! ~0.011! ~0.013! ~0.003! ~0.004! ~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 Ho: b 5 1 except in the difference
column, where the null hypothesis is Ho: b 5 0.
788 The Journal of Finance
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 the
Fama–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 style
preference 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, these
results 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 by
partitioning the households into quintiles on the basis of portfolio size. We
define portfolio size as the market value of common stocks held in the first
month 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 in
Table 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 mean
gross household excess return for each quintile.19 The coefficient estimates
on 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 report
results for the aggregate performance of each partition. We note when conclusions are different
using the aggregate performance.
Trading Is Hazardous to Your Wealth 789
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 find
that small portfolios ~quintile 1! earn higher average returns than large
portfolios ~quintile 5!, though the difference is not reliably different from
zero. This difference is likely attributable to the fact that small portfolios tilt
more heavily toward small value stocks, which performed well during our
sample 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 the
quintiles. In sum, after a reasonable accounting for the size and value tilts
of 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 results
presented in Table II, the median household earns a gross monthly marketadjusted
return of 20.01 percent and a net return of 20.14 percent. Though
49.3 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 the
market, 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 and
Raiffa ~1982!, Griffin and Tversky ~1992!; see Odean ~1998b! for a more
detailed review!. Odean ~1998b!, Gervais and Odean ~1998!, and Caballé and
Sákovics ~1998! develop theoretical models of financial markets where in-
20 We omit from this analysis accounts that held common stocks for fewer than 12 months
during our 72-month sample period.
790 The Journal of Finance
vestors suffering from overconfidence trade too much ~i.e., trading, at the
margin, 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 expected
utilities. Active traders must earn higher expected gross returns in order to
Table IV
Cross-Sectional Distribution of Percentage Monthly Gross
and Net Market-Adjusted Household Returns
The sample is account records for 66,465 households at a large discount brokerage firm from
January 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 these
households are included, more extreme values are observed.
Gross Monthly
Market-Adjusted Return
~%!
Net Monthly
Market-Adjusted Return
~%!
Minimum 219.46 220.85
1st percentile 24.32 24.86
5th percentile 22.12 22.45
10th percentile 21.34 21.60
25th percentile 20.57 20.73
Median 20.01 20.14
75th percentile 0.66 0.50
90th percentile 1.62 1.40
95th percentile 2.41 2.15
99th percentile 4.86 4.44
Maximum 48.53 48.35
Total households 62,439 62,439
Percentage . 0 49.3%*** 43.4%***
Binomial Z-statistic 23.38 233.13
*** indicates significant difference from 50 percent at the 1% level.
Trading Is Hazardous to Your Wealth 791
offset their greater trading costs.21 The Grossman–Stiglitz model therefore
predicts that the gross risk-adjusted return performance of households with
high turnover will be higher than that of households with low turnover, but
there 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 average
of purchase and sale turnover!. Each quintile represents the common stock
investments of more than 12,000 households. Descriptive statistics for each
of 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 average
month. 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 household
excess return for each turnover quintile. The coefficient estimates on the
market, 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 in
Table 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 based
on the Fama–French three-factor model, which accounts for the tendency of
the high turnover portfolio to tilt more heavily toward high-beta, small, growth
stocks, 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 believe
these figures to be economically large ~approximately three to four percent
annually!. Regardless of whether one accepts these results as statistically
significant, 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 of
the 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 the
21 Rather than increasing their gross returns, active traders could alternatively achieve the
same expected utility as less active traders by lowering their volatility through trading. We find
no 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.
792 The Journal of Finance
Table V
Descriptive Statistics, Gross Returns, and Net Returns for
Household Quintiles Formed on Mean Turnover
The sample is account records for 66,465 households at a large discount brokerage firm from
January 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 the
household 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 the
gross average household excess return on the market excess return ~Rmt 2 Rft !, ~HMLt !, and a
zero-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 ~Rmt 2 Rft !. 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 ~HMLt !, and a
zero-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 ~%! 0.19 1.24 2.89 5.98 21.49 N.A.
Mean beginning position value 34,169 26,046 22,945 19,102 21,560 212,609***
~0.000!
Coefficient estimate on
~Rmt 2 Rft ! 1.03 1.06* 1.11** 1.18*** 1.29*** 0.26***
~0.199! ~0.090! ~0.015! ~0.002! ~0.000! ~0.000!
HMLt 0.20*** 0.10*** 0.13** 0.13* 0.12 20.08
~0.000! ~0.012! ~0.020! ~0.065! ~0.195! ~0.333!
SMBt 0.24*** 0.29*** 0.51*** 0.72*** 1.02*** 0.78***
~0.000! ~0.000! ~0.000! ~0.000! ~0.000! ~0.000!
Adjusted R2 96.1 94.7 92.2 90.4 87.6 71.8
Panel B: Gross Average Household Percentage Monthly Return
Raw return 1.483 1.472 1.489 1.511 1.548 0.065
Own-benchmark 20.009 20.026* 20.052** 20.079*** 20.096* 20.087
abnormal return ~0.156! ~0.064! ~0.014! ~0.007! ~0.093! ~0.116!
Market-adjusted return 0.063 0.052 0.069 0.091 0.128 0.065
~0.534! ~0.660! ~0.710! ~0.726! ~0.728! ~0.832!
CAPM intercept 0.090 0.022 20.015 20.078 20.167 20.257
~0.409! ~0.865! ~0.936! ~0.774! ~0.663! ~0.407!
Fama–French intercept 20.048 20.072 20.149 20.237 20.359 20.311*
~0.526! ~0.448! ~0.242! ~0.143! ~0.104! ~0.086!
Panel C: Net Average Household Percentage Monthly Return
Raw return 1.470 1.411 1.361 1.267 1.009 20.460
Own-benchmark 20.021*** 20.079*** 20.167*** 20.300*** 20.587*** 20.566***
abnormal return ~0.000! ~0.000! ~0.000! ~0.000! ~0.000! ~0.000!
Market-adjusted return 0.050 20.009 20.059 20.153 20.411 20.460
~0.625! ~0.937! ~0.749! ~0.547! ~0.253! ~0.124!
CAPM intercept 0.077 20.038 20.140 20.314 20.692* 20.768**
~0.480! ~0.764! ~0.474! ~0.242! ~0.066! ~0.012!
Fama–French intercept 20.061 20.130 20.269** 20.464*** 20.864*** 20.803***
~0.422! ~0.172! ~0.037! ~0.005! ~0.000! ~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 Ho: b 5 1 except in the difference
column, where the null hypothesis is Ho: b 5 0.
Trading Is Hazardous to Your Wealth 793
trading 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 and
Stiglitz 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 turnover
quintile 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 have
recently underperformed the market. This is consistent with the evidence
that individual investors tend to hold their losers and sell their winning
investments ~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 that
have 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! is
20.053 ~20.041! percent per month when price momentum is included as an
additional 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.
794 The Journal of Finance
Our principal finding—that those investors who trade most actively realize,
on average, the lowest net returns—is unaffected by the inclusion of
a 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 hold
poorly performing stocks, those investors who trade least actively achieve
reasonable 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 these
than 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 that
claims 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 performance
of 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 return
of 17.8 percent while the value-weighted market index earned 17.9 percent.
The market-adjusted return, the CAPM intercept, and the Fama–French
intercept 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 individual
common stocks.
Trading Is Hazardous to Your Wealth 795
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 turnover
that 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 that
have 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 both
taxable 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 in
tax-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 in
turnover may result from tax-motivated trading or it may be that investors
associate their retirement accounts with future safety and therefore trade
less 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 by
taxable 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, the
probability 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.
796 The Journal of Finance
Table VI
Descriptive Statistics, Gross Return, and Net Return
for Taxable and Tax-Deferred Accounts
The sample is account records for 66,465 households at a large discount brokerage firm from
January 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 turnover
is 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
~Rmt 2 Rft !, a zero-investment book-to-market portfolio ~HMLt !, 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 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 NYSE0AMEX0Nasdaq
index. CAPM intercept is the estimated intercept from a time-series regression of the household
excess return on the market excess return ~Rmt 2 Rft !. 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 ~HMLt !, and a zero-investment size
portfolio ~SMBt !. p-values are presented in parentheses.
Taxable
Tax
Deferred Difference
Panel A: Descriptive Statistics
Number of households 54,434 30,554 N0A.
Mean beginning position value 26,303 14,042 12,261***
~0.000!
Mean monthly turnover ~%! 7.45 5.63 1.82***
~0.000!
Coefficient estimate on:
~Rmt 2 Rft ! 1.13*** 1.12*** 0.01
~0.004! ~0.007! ~0.346!
HMLt 0.14*** 0.18*** 20.04***
~0.010! ~0.001! ~0.000!
SMBt 0.56*** 0.52*** 0.04***
~0.000! ~0.000! ~0.000!
Adjusted R2 92.6 92.2 46.7
Panel B: Gross Average Household Percentage Monthly Return
Raw return 1.496 1.532 20.036
Own-benchmark abnormal return 20.048*** 20.037* 20.010
~0.009! ~0.055! ~0.107!
Market-adjusted return 0.076 0.112 20.036
~0.702! ~0.555! ~0.185!
CAPM intercept 20.027 0.031 20.058**
~0.899! ~0.156! ~0.039!
Fama–French intercept 20.174 20.133 20.041*
~0.174! ~0.298! ~0.059!
Panel C: Net Average Household Percentage Monthly Return
Raw return 1.313 1.379 20.066**
Own-benchmark abnormal return 20.203*** 20.166*** 20.036***
~0.000! ~0.000! ~0.000!
Market-adjusted return 20.107 20.042 20.066**
~0.583! ~0.823! ~0.012!
CAPM intercept 20.204 20.119 20.085***
~0.326! ~0.547! ~0.002!
Fama–French intercept 20.344*** 20.278** 20.066***
~0.008! ~0.030! ~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 Ho: b 5 1 except in the difference
column, where the null hypothesis is Ho: b 5 0.
Trading Is Hazardous to Your Wealth 797
returns earned by taxable and tax-deferred accounts are both poor, after a
reasonable 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 a
preference 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 these
bets 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 that
each 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 overconfident, or because they are overconfident
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 through
trading.
798 The Journal of Finance
It is possible that some investors set aside a small portion of their wealth
with 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 in
the 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 for
it. 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 of
a 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 and
net returns that we document for individual investors also exists in mutual
funds ~Carhart ~1997!!. It is unlikely that mutual fund managers buy and
sell 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 in
January 1997. We document that the gross returns ~before accounting for
transaction costs! earned by these households are quite ordinary, on average.
Unfortunately, the net returns ~after accounting for the bid-ask spread
and commissions paid by these investors! earned by these households are
poor. 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 to
the costs associated with this high level of trading.
24 This 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 for
769 all-equity pension funds, though this finding puzzles the authors.
Trading Is Hazardous to Your Wealth 799
Our most dramatic empirical evidence is provided by the 20 percent of
households that trade most often. With average monthly turnover of in excess
of 20 percent, these households turn their common stock portfolios over
more than twice annually. The gross returns earned by these high-turnover
households 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 more
deleterious to performance because individuals execute small trades and face
higher proportional commission costs than mutual funds.
Our main point is simple: Trading is hazardous to your wealth. Why then
do 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 turnover
of 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 them
to 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 a
month. 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.
800 The Journal of Finance
Consistent with the results reported in Odean ~1999!, we document that
the 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 by
more than two basis points per month ~or approximately 0.29 percent
annually!.
For each account with a beginning-of-month position statement in month
t, we identify all purchases in month t 2 1 and sales in month t. For both
purchases and sales, we calculate the compound return on the stock from
the day following the trade to the last day of the month. For purchases this
return 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.
Table AI
The 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 from
January 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 is
calculated 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.
Sample
Monthly
Purchase
Turnover
~%!
Purchase
Abnormal
Return
~%!
Monthly
Sale
Turnover
~%!
Sale
Abnormal
Return
~%!
Estimated
Effect on
Monthly
Abnormal
Return
~%!
Panel A: Aggregate
All households 4.92 20.472 4.93 0.021 20.0242
Panel B: Households Partitioned by Beginning Position Value
1 ~Small! 6.85 20.650 6.06 20.116 20.0375
2 5.83 20.381 5.16 20.019 20.0213
3 5.82 20.386 5.25 0.437 20.0454
4 5.55 20.445 5.25 0.030 20.0263
5 ~Large! 4.41 20.486 4.23 20.035 20.0199
Panel C: Households Partitioned by Turnover
1 ~Low! 0.26 20.184 0.23 0.068 20.0006
2 1.37 20.176 1.14 20.089 20.0014
3 3.07 20.126 2.57 0.041 20.0049
4 6.46 20.234 6.13 0.102 20.0214
5 ~High! 21.81 20.674 20.75 20.003 20.1464
Trading Is Hazardous to Your Wealth 801
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 is
slightly different from that used in the main text, where turnover is calculated
based on market values contained in position statements and is
thus 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 per
month.
We also analyze the timing of trades by partitioning households on the
basis 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 in
the 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 the
return 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. A
time 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 tb
cl to
t1 minus the return from ts
cl to ts. Our estimate of the bid-ask spread is the
return from ts to ts
cl minus the return from tb to tb
cl . When the return from
timing is added to the main calculation and the spread is subtracted, one
gets the ~approximate! return from tb to ts, the period in which the investor
held the stock.
Figure A1. Time line of returns calculations. The time of purchase (sale) is tb (ts). The
close on the purchase ~sale! day is tb
cl ~ts
cl !. The close on the last day of the purchase ~sale! month
is t1 ~t3!.
802 The Journal of Finance
Appendix B. The Analysis of Intramonth Trades
In this appendix, we analyze the performance of stocks that are bought and
then 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 the
same month. In accounting for multiple purchases and sales, we assume
that the first securities purchased are the first sold. Over our 72-month
sample 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 the
close of trading on the purchase and sale dates, respectively. We calculate
the net returns on these round-trip transactions by subtracting estimates of
the bid-ask spread and commissions as is done in the main text for the case
of 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 and
0.30 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 spread
of one percent ~see Table I!. This lower spread is likely a result of the intraday
return earned by investors from the transaction price through the
end 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 the
gross and net abnormal returns in this table are weighted by the size of each
trade, so that we can estimate the aggregate impact of these intramonth
trades on the performance of individual investors.
Panel A presents results for all households. In aggregate, the intramonth
trades 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.
Trading Is Hazardous to Your Wealth 803
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 any
of the results that we present in our main text. Those who benefit most from
intramonth trades are those who trade most. Their intramonth trades improve
their performance by 3.12 basis points per month ~last row and last
column of Panel C!. Yet, we estimate that these investors underperform by a
whopping 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 winning
investments and ride losing investments ~as proposed by Shefrin and Statman
~1985!!, we would expect to observe positive abnormal returns on shortterm
round-trip trades.
Table BI
The Gross and Net Abnormal Returns earned on Intramonth Trades
The sample is account records for 66,465 households at a large discount brokerage firm from
January 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 the
compound return on a value-weighted NYSE0AMEX0Nasdaq index. The net abnormal return is
the gross abnormal return adjusted for the return earned on the day of the purchase or sale, the
bid-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 in
Monthly
Abnormal
Return
~%!
Panel A: Aggregate
All households $22,275 1.636 0.496 0.99 0.0049
Panel B: Households Partitioned by Beginning Position Value
1 ~Small! 17,459 2.376 0.904 1.42 0.0128
2 12,579 2.082 0.248 0.92 0.0023
3 17,173 1.757 0.486 1.17 0.0057
4 20,255 1.363 0.351 1.33 0.0046
5 ~Large! 28,387 1.563 0.526 0.86 0.0045
Panel C: Households Partitioned by Turnover
1 ~Low! 10,638 20.003 20.026 0.00 0.0000
2 12,876 3.006 0.200 0.02 0.0000
3 11,886 1.843 0.220 0.08 0.0002
4 13,838 2.925 1.378 0.36 0.0050
5 ~High! 23,702 1.545 0.451 6.92 0.0312
804 The Journal of Finance
REFERENCES
Alpert, Marc, and Howard Raiffa, 1982, A progress report on the training of probability assessors;
in Daniel Kahneman, Paul Slovic, and Amos Tversky, eds.: Judgment Under Uncertainty:
Heuristics and Biases ~Cambridge University Press, Cambridge!.
Barber, Brad M., Reuven Lehavy, Maureen McNichols, and Brett Trueman, 1998, Can investors
profit from the prophets? Consensus analyst recommendations and stock returns, Working
paper, Graduate School of Management, University of California, Davis.
Beebower, Gilbert, and William Priest, 1980, The tricks of the trade, Journal of Portfolio Management
6, 36–42.
Benos, Alexandros V., 1998, Overconfident speculators in call markets: Trade patterns and
survival, Journal of Financial Markets 1, 353–383.
Caballé, Jordi, and József Sákovics, 1998, Overconfident speculation with imperfect competition,
Working paper, Universitat Autònoma de Barcelona, Spain.
Carhart, Mark M., 1997, On persistence in mutual fund performance, Journal of Finance 52,
57–82.
Daniel, Kent, David Hirshleifer, and Avanidhar Subrahmanyam, 1998, Investor psychology and
security market under- and overreactions, Journal of Finance 53, 1839–1885.
Falkenstein, Eric G., 1996, Preferences for stock characteristics as revealed by mutual fund
portfolio holdings, Journal of Finance 51, 111–135.
Fama, Eugene F., 1991, Efficient capital markets II, Journal of Finance 46, 1575–1618.
Fama, Eugene F., and Kenneth R. French, 1993, Common risk factors in returns on stocks and
bonds, Journal of Financial Economics 33, 3–56.
Gervais, Simon, and Terrance Odean, 1998, Learning to be overconfident, Working paper, Wharton
School, University of Pennsylvania.
Gompers, Paul, and Andrew Metrick, 1998, How are large institutions different from other
investors? Why do these differences matter?, Working paper, Harvard Business School.
Griffin, Dale, and Amos Tversky, 1992, The weighing of evidence and the determinants of confidence,
Cognitive Psychology 24, 411–435.
Grinblatt, Mark, and Sheridan Titman, 1993, Performance measurement without benchmarks:
An examination of mutual fund returns, Journal of Business 66, 47–68.
Grossman, Sanford J., and Joseph E. Stiglitz, 1980, On the impossibility of informationally
efficient markets, American Economic Review 70, 393–408.
Holthausen, Robert, Richard Leftwich, and David Mayers, 1987, The effect of large block transactions
of security prices: A cross-sectional analysis, Journal of Financial Economics 19,
237–267.
Jegadeesh, Narisimhan, and Sheridan Titman, 1993, Returns to buying winners and selling
losers: Implications for stock market efficiency, Journal of Finance 48, 65–91.
Jensen, Michael C., 1969, Risk, the pricing of capital assets, and evaluation of investment
portfolios, Journal of Business 42, 167–247.
Kang, Jun-Koo, and René Stulz, 1997, Why is there a home bias? An analysis of foreign portfolio
equity ownership in Japan, Journal of Financial Economics 46, 3–28.
Keim, Donald, and Ananth Madhavan, 1998, The cost of institutional equity trades, Financial
Analysts Journal 54, 50–69.
Kraus, Alan, and Hans Stoll, 1972, Price impacts of block trading on the New York Stock Exchange,
Journal of Finance 27, 569–588.
Kyle, Albert S., and F. Albert Wang, 1997, Speculation duopoly with agreement to disagree: Can
overconfidence survive the market test?, Journal of Finance 52, 2073–2090.
Lakonishok, Josef, Andrei Shleifer, and Robert W. Vishny, 1992, The structure and performance
of the money management industry; in Martin Neil Baily and Clifford Winston, eds.: Brookings
Papers on Economic Activity: Microeconomics, ~Brookings Institution, Washington, D.C.!.
LaPlante, Michele, and Chris Muscarella, 1997, Do institutions receive comparable execution in
the NYSE and Nasdaq markets? A transactions study of block trades, Journal of Financial
Economics 45, 97–134.
Lyon, John D., Brad M. Barber, and Chih-Ling Tsai, 1999, Improved methods for tests of longrun
abnormal stock returns, Journal of Finance 54, 165–201.
Trading Is Hazardous to Your Wealth 805
Malkiel, Burton G., 1995, Returns from investing in equity mutual funds 1971 to 1991, Journal
of Finance 50, 549–572.
Odean, Terrance, 1998a, Are investors reluctant to realize their losses?, Journal of Finance 53,
1775–1798.
Odean, Terrance, 1998b, Volume, volatility, price, and profit when all traders are above average,
Journal of Finance 53, 1887–1934.
Odean, Terrance, 1999, Do investors trade too much?, American Economic Review 89, 1279–
1298.
Schlarbaum, Gary G., Wilbur G. Lewellen, and Ronald C. Lease, 1978a, The common-stockportfolio
performance record of individual investors: 1964–70, Journal of Finance 33, 429–441.
Schlarbaum, Gary G., Wilbur G. Lewellen, and Ronald C. Lease, 1978b, Realized returns on
common stock investments: The experience of individual investors, Journal of Business 51,
299–325.
Security Industry Association, 1997, Securities Industry Fact Book ~Security Industry Association,
New York!.
Shefrin, Hersh, and Meir Statman, 1985, The disposition to sell winners too early and ride
losers too long: Theory and evidence, Journal of Finance 40, 777–790.
Womack, Kent L., 1996, Do brokerage analysts’ recommendations have investment value?, Journal
of Finance 51, 137–167.
806 The Journal of Finance

1 comments:

Liquid Rubber Videos said...

90% of the wealth is in trading. God has put 90% of the wealth in trading. So one who is involved in trading will certainly have the more wealth any other business other than trading.