Online Investors: Do the Slow Die First?
Brad M. Barber
University of California, Davis
Terrance Odean
University of California, Berkeley
We analyze 1,607 investors who switched from phone-based to online trading during the
1990s. Those who switch to online trading perform well prior to going online, beating themarket by more than 2% annually. After going online, they trade more actively, more
speculatively, and less proﬁtably than before—lagging the market by more than 3%
annually. Reductions in market frictions (lower trading costs, improved execution speed,
and greater ease of access) do not explain these ﬁndings. Overconﬁdence—augmented by
self-attribution bias and the illusions of knowledge and control—can explain the increasein trading and reduction in performance of online investors.

[The giant] tortoise lives longer than any other animal.
Collier’s Encyclopedia“Online trading is like the old west,” warns Fidelity Investments. “The slow
die ﬁrst.” “Trading at home? Slow can kill you,” echoes a provider of Internet connections. “If your broker’s so great, how come he still has to work?”asks E*TRADE. Another E*TRADE ad notes online investing is “A cinch.
A snap. A piece of cake.” “I’m managing my portfolio better than my broker ever did,” claims a middle-aged woman (Datek Online). In Ameritrade’s“Momma’s Gotta Trade,” two suburban moms return from jogging. Straightto her computer, a few clicks and a sale later, one declares “I think I justmade about $1,700!” Her kids cheer, while her friend laments, “I have mutual
funds.” And then there is Discover Brokerage’s online trading tow-truck
driver. He picks up a snobbish executive who spots a postcard on the dashboard and asks “Vacation?” “That’s my home,” says the driver. “Looks more
like an island,” says the executive. “Technically, it’s a country,” replies thedriver.

We appreciate the comments of an anonymous reviewer, Franklin Allen, Randy Beatty, Ken French, Ravi
Jagannathan, John Heaton, Andrew Metrick, Cesare Robotti, Chuck Schnitzlein, Michael Vetsuypens, andseminar participants at the Berkeley Program in Finance (November 1999), National Bureau of Economic
Research Behavioral Finance Group (December 1999), European Finance Association meetings, Maryland
Finance Symposium, Review of Financial Studies Behavior Finance Conference, Western Finance Association
meetings (June 2000), Princeton, Rodney L. White Conference on Household Financial Decision-Making(March 2001), Securities and Exchange Commission, Southern Methodist University, UC-Berkeley, Universityof Iowa, University of Utah, and Yale University. Michael Foster provided valuable research assistance. Allerrors are our own. Address correspondence to Brad Barber, Department of Finance, Graduate School ofManagement, University of California, Davis, Davis, CA 95616–8609, or e-mail: bmbarber@ucdavis.edu.
The Review of Financial Studies Special 2002 Vol. 15, No. 2, pp. 455–487© 2002 The Society for Financial Studies

The Review of Financial Studies / v 15 n 2 2002

These advertisements entice and amuse. They assure the uninitiated thatthey have what it takes to trade online; tell them what to expect—sudden
wealth; and what will be expected of them—frequent trades. They also reinforce cognitive biases, which, for the most part, do not improve investors’
welfare.
In general, when the price of a product declines, the quantity demanded ofthe product increases, as does consumer welfare. This has been the case, forexample, with personal computers over the last decade. While consumers arealways better off paying less for the same goods, there are situations where
the increased demand associated with lower prices is a questionable boon
to individuals and a clear loss to society. An extreme example is increased
consumption of cigarettes due to price cuts or greater ease of access.
In recent years there has been an explosion of online trading that is likely
to continue. Forrester Research, Inc. [Punishill (1999)] projects “that by
2003, 9.7 million U.S. households will manage more than $3 trillion online—nearly 19 percent of total retail investment assets—in 20.4 million
on-line accounts.” The growth in online trading has been accompanied bya decrease in trading commissions.1 Lower commissions, greater ease ofaccess, and speedier trade executions constitute reductions in market frictions. Such reductions of friction generally improve markets. However, whilethese changes can obviously beneﬁt investors, to the extent that they encourage excessive, speculative trading, this beneﬁt is attenuated.
In this article we provide a description of those who switch from phonebased to online trading. Multivariate analyses document that young men who
are active traders with high incomes and a preference for investing in small
growth stocks with high market risk are more likely to switch to online
trading. We ﬁnd that those who switch to online trading experience unusuallystrong performance prior to going online, beating the market by more than
2% annually.
We also examine the change in trading behavior that takes place wheninvestors go online. In doing so, we test the theory that overconﬁdence leads
to excessive trading. Consistent with that theory, we ﬁnd that, after going
online, investors trade more actively, more speculatively, and less proﬁtably
than before. It is difﬁcult to reconcile these results with rational behavior.
Human beings are overconﬁdent about their abilities, their knowledge,
and their future prospects. Odean (1998b) shows that overconﬁdent investors
trade more than rational investors and that doing so lowers their average utilities, since overconﬁdent investors trade too aggressively when they receive
information about the value of a security. Greater overconﬁdence leads to1

Credit Suisse First Boston Technology Group [Burnham and Earle (1999)] reports a 70% drop in the averagecommission charged by the top 10 online trading ﬁrms from 1996:Q1 to 1997:Q4, though commissions haveremained largely unchanged from 1997:Q4 through 1999:Q1.

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greater trading and to lower average utility.2 Due to several cognitive biases,in addition to selection bias, the investors we observe switching from phonebased trading to online trading are likely to have been overconﬁdent about
their ability to proﬁt from trading online. The reduced costs and increased
ease of online trading are most appealing to active traders. Thus, particularlyin these early years, online trading may have attracted more overconﬁdent,
more active investors. However, this selection bias alone will not cause online
investors to perform worse after they commence online trading. (However,
it is possible that investors whose conﬁdence has recently increased are particularly likely to anticipate more active future trading and to therefore avail
themselves of the beneﬁts of trading online.)
We posit that online investors become more overconﬁdent once online forthree reasons: the self-attribution bias, an illusion of knowledge, and an illusion of control. People tend to attribute their successes to their own abilities,
even when such attribution is unwarranted (self-attribution bias). Thus recent
investment success is likely to foster overconﬁdence in one’s stock picking
abilities. We ﬁnd that those who switched from phone-based to online trading did so after a period of unusually strong performance, which may haveengendered greater overconﬁdence. People also become more overconﬁdent
when given more information on which to base a forecast (the illusion ofknowledge) and they behave as if their personal involvement can inﬂuence
the outcome of chance events (the illusion of control). Online investors haveaccess to vast quantities of information, generally manage their own portfolios, and trade at the click of a mouse. These aspects of online trading foster
greater overconﬁdence. And greater overconﬁdence leads to elevated tradingand poor performance—precisely the portrait of the online investors that westudy.
The article proceeds as follows. We motivate our test of overconﬁdence inSection 1. In Section 2 we describe the data and methods. We present our
main results in Section 3. We discuss our results in Section 4 and conclude
in Section 5.
1. A Test of Overconﬁdence
1.1 Overconﬁdence and trading on ﬁnancial markets
Studies of the calibration of subjective probabilities ﬁnd that people tend
to overestimate the precision of their knowledge [Fischhoff, Slovic, andLichtenstein (1977), Alpert and Raiffa (1982), see Lichtenstein, Fischhoff,
2

Kyle and Wang (1997) and Benos (1998) show that under particular circumstances when both a rational
insider and overconﬁdent insider trade strategically and simultaneously with a market maker, the overconﬁdent
insider may earn greater proﬁts than the rational insider. The overconﬁdent insider earns greater proﬁts by“precommitting” to trading aggressively. For this result to hold, traders must have sufﬁcient resources andrisk tolerance to trade up to the Cournot equilibrium, trade on correlated information, and know each other’s
overconﬁdence. It is unlikely that these models describe individual investors, who have limited resources,
trade asynchronously, and do not know the overconﬁdence levels of those with whom they trade.

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The Review of Financial Studies / v 15 n 2 2002

and Phillips (1982) for a review of the calibration literature]. Such overconﬁdence has been observed in many professional ﬁelds. Clinical psychologists [Oskamp (1965)], physicians and nurses, [Christensen-Szalanski
and Bushyhead (1981), Baumann, Deber, and Thompson (1991)], investmentbankers [Staël von Holstein (1972)], engineers [Kidd (1970)], entrepreneurs[Cooper, Woo, and Dunkelberg (1988)], lawyers [Wagenaar and Keren(1986)], negotiators [Neale and Bazerman (1990)], and managers [Russo andSchoemaker (1992)] have all been observed to exhibit overconﬁdence in their
judgments. [For further discussion, see Lichtenstein, Fischhoff, and Phillips(1982) and Yates (1990).]
Overconﬁdence is greatest for difﬁcult tasks, for forecasts with low predictability, and for undertakings lacking fast, clear feedback [Fischhoff, Slovic,
and Lichtenstein (1977), Lichtenstein, Fischhoff, and Phillips (1982), Yates(1990), Grifﬁn and Tversky (1992)]. Selecting common stocks that will outperform the market is a difﬁcult task. Predictability is low; feedback is noisy.
Thus stock selection is the type of task for which people are most overconﬁdent.
Survey and experimental evidence supports our contention that investors
are overconﬁdent. Since October 1996, Paine Webber has sponsored 13 separate Gallup surveys of individual investors. In each of these 13 surveys,
on average, investors expected their own portfolios to beat the market.3 For
example, in October 1999, investors expected their portfolios to return, on
average, 15.7% over the next 12 months, while they expected the market toreturn 13.3%. Moore et al. (1999) generate similar results in an investment
experiment using 80 MBA students.
DeBondt and Thaler (1995) note that the high trading volume on organizedexchanges is perhaps the single most embarrassing fact to the standard ﬁnanceparadigm, and that the key behavioral factor needed to understand the trading
puzzle is overconﬁdence. DeLong et al. (1991), Kyle and Wang (1997), Benos(1998), Caballé and Sákovics (1998), Daniel, Hirshleifer, and Subramanyam(1998, 2001), Odean (1998b), and Gervais and Odean (2000) develop theoretical models based on the assumption that investors are overconﬁdent.
Most of these models predict that overconﬁdent investors will trade morethan rational investors.
In theoretical models overconﬁdent investors overestimate the precisionof their knowledge about the value of a ﬁnancial security.4 They may also
overestimate the probability that their personal assessments of the security’s
3

In each of these surveys, investors were asked two questions: “What overall rate of return do you expect toget on your portfolio in the next 12 months,” and “Thinking about the stock market more generally, what
overall rate of return do you think the stock market will provide investors during the coming 12 months?”
Across the 13 surveys, the average investor expects a return of 15% on their own portfolio, while they expect
the market to return 13%.

4

Odean (1998b) points out that overconﬁdence may result from investors overestimating the precision of theirprivate signals or, alternatively, overestimating their abilities to correctly interpret public signals.

458

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value are more accurate than the assessments of others. Thus overconﬁdent
investors believe more strongly in their own valuations and concern themselves less with the beliefs of others. This intensiﬁes differences of opinion.
And differences of opinion cause speculative trading [Varian (1989); Harrisand Raviv (1993)]. Rational investors only trade and purchase information
when doing so increases their expected utility [e.g., Grossman and Stiglitz(1980)]. Overconﬁdent investors, on the other hand, lower their expected utility by trading too much and too speculatively; they hold unrealistic beliefs
about how high their returns will be and how precisely these can be estimated; and they expend too many resources (e.g., time and money) on investment information [Odean (1998b).]5
Odean (1999) and Barber and Odean (2000) test whether investors decrease
their expected utility by trading too much. Using the same dataset from which
the sample analyzed here is drawn, Barber and Odean (2000) show thatafter accounting for trading costs, individual investors underperform relevantbenchmarks. Those who trade the most realize, by far, the worst performance.
This is what the models of overconﬁdent investors predict. Barber and Odean(2001) show that men, who tend to be more overconﬁdent than women,
trade nearly one and a half times more actively than women and thereby
reduce their net returns more so than do women. With a different dataset,
Odean (1999) ﬁnds that the securities individual investors buy subsequently
underperform those they sell. When he controls for liquidity demands, taxloss selling, rebalancing, and changes in risk aversion, investors’ timing oftrades is even worse. This result suggests that not only are investors too
willing to act on too little information, but they are too willing to act when
they are wrong.
The overconﬁdence models predict that more overconﬁdent investors willtrade more actively and will thereby reduce their net returns. In this article weargue that investors in our sample who switch to online trading are likely to
be more overconﬁdent than the average investor and to be more overconﬁdent
after going online than before. We test for differences in the turnover andreturns of online and phone-based investors and for differences before andafter investors go online.
1.2 Online investing and overconﬁdence
1.2.1 Self-attribution bias. Online investors in our sample outperform themarket before going online. People tend to ascribe their successes to their personal abilities and their failures to bad luck or the actions of others [Langer
and Roth (1975), Miller and Ross (1975)], and self-enhancing attributions
following success are more common than self-protective attributions following failures [Fiske and Taylor (1991), see also Miller and Ross (1975)].
5

In some models that assume investors are overconﬁdent, the overconﬁdent investors may improve their welfare
by trading aggressively. However, the assumptions required to generate this result are unlikely to apply toindividual investors, which is the focus of our empirical investigation (see footnote 2).

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The Review of Financial Studies / v 15 n 2 2002

Gervais and Odean (2000) demonstrate that this self-attribution bias willcause successful investors to grow increasingly overconﬁdent about their general trading prowess. [Daniel, Hirshleifer, and Subrahmanyam (1998) further
argue that self-attribution bias can intensify overreactions and lead to shortterm momentum and long-run reversals in stock prices.] Investors whose
recent successes have increased their overconﬁdence will trade more actively
and more speculatively. Because they anticipate that the effort of switching to online trading will be amortized over more trades, these investors
are more likely to go online. If self-attribution-induced overconﬁdence triggers investors to go online, online investors will tend to be more overconﬁdent than phone-based investors and more overconﬁdent subsequent to going
online than in the period before.
1.2.2 The illusion of knowledge. When people are given more information on which to base a forecast or assessment, the accuracy of their forecasts
tends to improve much more slowly than their conﬁdence in the forecasts.
While the improved accuracy of forecasts yields better decisions, additional
information can lead to an illusion of knowledge and foster overconﬁdence,
which leads to biased judgments. In a widely cited study, Oskamp (1965)
documents that pyschologists’ conﬁdence in their clinical decisions increased
with more information, but accuracy did not. Several subsequent studies conﬁrm the illusion of knowledge [e.g., Hoge (1970), Slovic (1973), Peterson
and Pitz (1988)].
Online investors have access to vast quantities of investment data. We
posit that online investors are more likely to access and use these data thaninvestors with traditional brokerage accounts, thus fostering greater overconﬁdence for online investors. Online brokerages often tout their data offerings
to customers. Waterhouse Securities, for example, claims to offer more “freeinvestment research and research information on-line and in print than any
other discount broker” including “real-time quotes, historical charts, real-timenews, portfolio tracking, S&P stock reports, [and] Zack’s earnings estimates.”
Data provider eSignal promises investors that “You’ll make more, because
you know more.” Indeed, online investors have access to nearly all the samedata as professional money managers, though in most cases they lack thesame training and experience. Investors may be tempted to believe that somuch data confers knowledge. Yet past data may not predict the future. Andeven when data does offer insights, investors may catch only glimmers ofthese. Individual investors, whose purchases habitually underperform theirsales by approximately three percentage points in a year [Odean (1999)],
need more than a glimmer of additional insight to proﬁt from trading.
The tendency of more information to increase trading by increasing overconﬁdence may be augmented by “cognitive dissonance” [Festinger (1957)].
Investors who spend a considerable amount of time (or money) gathering data
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Online Investors: Do the Slow Die First?

will generally believe themselves to be reasonable people. Since a reasonable person would not spend so much time gathering useless data, investors
are motivated to believe that the data are useful. Furthermore, it would beunreasonable to spend so much time gathering data on how to trade if one
didn’t trade. And so, to resolve cognitive dissonance, information-gathering
investors are disposed to trade.
1.2.3 The illusion of control. Langer (1975) and Langer and Roth (1975)
ﬁnd that people behave as if their personal involvement can inﬂuence theoutcome of chance events—an effect they label the “illusion of control.”
These studies document that overconﬁdence occurs when factors ordinarily
associated with improved performance in skilled situations are present insituations at least partly governed by chance. Langer ﬁnds that choice, task
familiarity, competition, and active involvement all lead to inﬂated conﬁdence
beliefs. Presson and Benassi (1996) review 53 experiments on the illusion ofcontrol and conclude that “illusion of control effects have been found acrossdifferent tasks, in many situations, and by numerous independent researchers”(p. 506).
Of the key attributes that foster an illusion of control (choice, task familiarity, competition, and active involvement), active involvement is most relevant
for online investors. Online investors place their orders without the intermediation of a telephone broker. They may feel that such active involvement
improves their chances of favorable outcomes and therefore choose to trademore. Balasubramanian, Konana, and Menon (1999) study survey resultsfrom 832 visitors to an online brokerage house’s website. Survey respondents list “feeling of empowerment” as one of seven basic reasons given forswitching to online trading.6
Advertisements for online brokerages often emphasize the importance oftaking control of one’s investments. Young Suretrade investors boast, “We’renot relying on the government. We’re betting on ourselves.” Discover Brokerage states bluntly that online investing is “about control.” A young woman
in an Ameritrade ad proclaims, “I don’t want to just beat the market. I want
to wrestle its scrawny little body to the ground and make it beg for mercy.”
Such advertisements reinforce investors’ illusion of control.
In summary, overconﬁdent investors trade more actively and more speculatively. Excessive trading lowers their returns. Due to selection bias, those
who go online (especially during the early years of online trading) are likely
to be more overconﬁdent than other investors. Because of self-attribution
bias, the switch to online trading is likely to coincide with an increase inoverconﬁdence. Furthermore, the illusion of knowledge and the illusion of
6

The other six reasons are cost, speed and availability, convenience, easy access to reliable information, lack oftrust in and unsatisfactory experiences with traditional brokers, and investor discomfort when communicating
directly with traditional brokers.

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The Review of Financial Studies / v 15 n 2 2002

control will lead online investors to become more overconﬁdent once they
are online. Thus online investors will tend to be more overconﬁdent thanother investors and more overconﬁdent after going online than before.
1.2.4 Selection bias. Online trading is a recent innovation during our sample period (1991–1996). The effort and perceived risks of switching to trading online were probably greater then than they are today. Overconﬁdence
reduces the perception of risk [Odean (1998b)]. Furthermore, more overconﬁdent investors tend to trade more actively and thus potentially beneﬁt
more from online trading. If overconﬁdent investors exist in ﬁnancial markets, it is likely that they were disproportionately represented among early
online investors. Thus there is a selection bias in our sample of investors
who switched to online trading.
In summary, we have the following testable hypotheses:
Hypothesis 1. Online investors trade more actively once online.
Hypothesis 2. Online investors trade more actively than phone-based investors.
Hypothesis 3. Online investors trade more speculatively once online.
Hypothesis 4. Online investors trade more speculatively than phone-based
investors.
Hypothesis 5. By trading more, online investors hurt their performance
more after going online than before.
Hypothesis 6. By trading more, online investors hurt their performance
more than do phone-based investors.
2. Data and Methods
2.1 The online and size-matched samples
The primary focus of our analysis is 1,607 investors who switched fromphone-based trading to online trading; we refer to these investors as ouronline sample. These investors were identiﬁed from 78,000 households withbrokerage accounts at a large discount brokerage ﬁrm.7 (For expositional ease
we often refer to households as investors.) For these households, we have all
trades and monthly position statements from January 1991 through December
1996.8 The trade data document how each trade was initiated (e.g., by phone
7

Previous studies of the behavior of individual investors include Lewellen, Lease, and Schlarbaum (1977),
Schlarbaum, Lewellen, and Lease (1978a, b), Odean (1998a, 1999), Barber and Odean (2000, 2001); Grinblattand Keloharju (2001), and Shapira and Venezia (2001). These studies do not analyze online trading.

8

The month-end position statements for this period allow us to calculate returns for February 1991 throughJanuary 1997. Data on trades are from January 1991 through November 1996. See Barber and Odean (2000)
for a detailed description of these data.

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or by personal computer). The online sample represents all households that
had a common stock position in each month of our six-year sample period
and initiated their ﬁrst online (i.e., computer-initiated) trade between January
1992 and December 1995. We require six years of position statements so that
we can analyze changes in household investment behavior subsequent to theadvent of online trading.
To understand how the trading and performance of the online sample differs from other investors, we employ a matched-pair research design. Each
online investor is size matched to the investor whose market value of common stock positions is closest to that of the online investor; this size matchingis done in the month preceding the online investor’s ﬁrst online trade. As isthe case for the online sample, the matched investor must have a common
stock position in each month of our six-year sample period and at least one
common stock trade during the six years. However, the size-matched households differ from the online households in that they made no online trades
during the six years.9
Our size matching works well. The mean value of month-end common
stock positions held by online investors is $135,000, while that for the control sample is $132,000; median values are $45,400 and $42,700, respectively. Of the 78,000 households from which these samples are drawn, 27,023
have common stock positions in all months. For these households, the mean
(median) value of positions is $62,700 ($21,900). Thus the online investors
have larger mean common stock positions than the sample at large.
We present descriptive statistics for the online and size-matched samples inTable 1. Data on marital status, children, age, and income are from Infobase
Inc. as of June 1997. Self-reported data are information supplied to the discount brokerage ﬁrm by account holders when they opened their accounts.
Income is reported within eight ranges, where the top range is greater than$125,000. We calculate means using the midpoint of each range and $125,000
for the top range. Equity:net worth (%) is the proportion of the market valueof common stock investment at this discount brokerage ﬁrm as of January1991 to total self-reported net worth when the household opened its ﬁrst
account at this brokerage. Those households with an equity:net worth greaterthan 100% are deleted when calculating means and medians.
Relative to the size-matched investors, online investors are more likely tobe younger men with higher income and net worth. Their common stock
investments also represent a smaller proportion of their total net worth.
Online investors also report having more investment experience than the size9

In auxiliary analyses, we matched the online sample to households with the most similar gross return in the
12 months prior to the switch to online traded. Our main results are very similar to those reported later in thearticle. Based on this analysis, the self-attribution bias alone cannot explain the increased trading and resulting
poor performance of online investors. It may be that the successful investors most prone to self-attribution
bias are the ones who, in anticipation of increased trading, go online. But we believe that other factors speciﬁcto the online environment, such as the illusion of knowledge and the illusion of control, also contribute to
trading increases and poor performance.

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Table 1
Demographics of online investors and size-matched investors
Variable

OnlineSize-matched

All accounts

Panel A: Infobase data
Percentage men
Percentage married
Percentage with children
Mean ageMedian age
Mean income ($000)
Percentage with income > $125,000

857
769
265
496
480
796
129

797
749
251
531
520
749
111

772
764
266
516
500
739
98

Panel B: Self-reported data
Net worth ($000)
90th percentile
75th percentile
Median25th percentile
10th percentile

8000
3500
1750
875
375

7000
3000
1250
750
375

5250
2500
1000
750
370

Equity:net worth (%)
MeanMedian

167
83

196
117

164
90

Investment experience (%)
None
Limited
Good
Extensive

18
190
572
220

36
287
493
184

38
337
479
146

Online investors are 1,607 investors with 72 consecutive months of common stock positions, no online trades prior to January
1992, and at least one online trade between January 1992 and December 1995. Each size-matched investor is the investor withthe closest account size (market value of common stocks held) to the sample ﬁrm in the month preceding its ﬁrst online trade.
The matched household must also have 72 consecutive months of common stock positions, no online trades during the 72
months, and at least one trade between January 1992 and December 1995. All accounts are 27,023 accounts with 72 months ofcommon stock positions and no online trades during the 72 months. Data on marital status, children, age, and income are from
Infobase Inc. as of June 1997. Self-reported data are information supplied to the discount brokerage ﬁrm by account holders
on opening their account. Income is reported within eight ranges, where the top range is greater than $125,000. We calculatemeans using the midpoint of each range and $125,000 for the top range. Equity:net worth (%) is the proportion of the market
value of common stock investment at this discount brokerage ﬁrm as of January 1991 to total self-reported net worth when thehousehold opened its ﬁrst account at this brokerage. Those households with a proportion of equity:net worth greater than 100%are deleted when calculating means and medians. Infobase data (panel A) are available for approximately 900 online investors,
900 size-matched investors, and 15,000 of all accounts. Self-reported data (panel B) are available for 510 online investors, 360
size-matched investors, and 9,500 of all accounts.

matched investors. For example, 79% of online investors report having goodor extensive investment experience, while 68% of the size-matched investors
report the same level of experience and 63% of all accounts report similarlevels of experience. In Section 3 we provide a comprehensive multivariate analysis of the characteristics of those who switch from phone-based toonline trading.
2.2 Calculation of trading costs
For each trade we estimate the bid-ask spread component of transaction costs
for purchases (sprb ) and sales (sprs ) as
 P cl
spr s = Pdss − 1
and
ds
 cl

(1)
P
spr b = − Pdbb − 1 db464

Online Investors: Do the Slow Die First?

Table 2
Mean transaction costs for trades of more than $1,000 for online and size-matched households before
and after online trading
Online households

Panel A: Before online trading
No. of trades
Trade size ($)
Trade price ($)
Spread (%)
Commission (%)
Panel B: After online trading
No. of trades
Trade size ($)
Trade price ($)
Spread (%)
Commission (%)

Buys

Sells

27,371
9,074
30.85
0.297
1.697

19,834
11,414
32.84
0.836
1.568

42,296
13,719
31.60
0.223
1.315

34,878
16,795
32.53
0.636
1.192

Size-matched households

Roundtrip

Buys

Sells

Roundtrip

1.133
3.265

20,73911,059
32.61
0.324
1.637

14,784
14,076
35.25
0.761
1.473

1.085
3.110

0.859
2.507

17,886
13,780
33.62
0.363
1.348

15,001
16,735
34.44
0.622
1.246

0.985
2.594

Online investors are 1,607 investors with 72 consecutive months of common stock positions, no online trades prior to January
1992, and at least one online trade between January 1992 and December 1995. Each size-matched investors is the investor
with the closest account size (market value of common stocks held) to the sample ﬁrm in the month preceding its ﬁrst online
trade. The matched household must also have 72 consecutive months of common stock positions, no online trades during the 72
months, and at least one trade between January 1992 and December 1995. 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.

Pdcls and Pdclb are the reported closing prices from the Center for Research inSecurity Prices (CRSP) daily stock return ﬁles on the day of a sale and purchase, respectively; Pdss and Pdbb are the actual sale and purchase price from
our account database.10 Our estimate of the bid-ask spread component oftransaction costs includes any market impact that might result from a trade.
It also includes an intraday return on the day of the trade. The commission
component of transaction costs is calculated as the dollar value of the commission paid scaled by the total principal value of the transaction, both ofwhich are reported in our account data.
We present descriptive statistics on the cost of trading for the online
and size-matched samples for trades of more than $1,000 in Table 2. For
both online investors and their-size matched counterparts, commissions andspreads are lower in the online periods, reﬂecting a market-wide decline intrading costs. For online investors, average round-trip commissions drop from
3.3% to 2.5% after they go online, while average round-trip spreads dropfrom 1.1% to 0.9%.11 12 Transaction costs are similar for the size-matchedhouseholds.
10

Kraus and Stoll (1972), Holthausen, Leftwich, and Mayers (1987), Laplante and Muscarella (1997), andBeebower 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 calculatingtransaction costs.

11

If trades valued at less than $1,000 are included in these calculations, average round-trip commissions dropfrom 5.1 percent to 3.4 percent when investors go online. Average round-trip spreads drop from 1.2 percent
to 0.9 percent.

12

Since 1996, online commissions have continued to drop while investor trading has increased. To determine
whether investors have beneﬁted from these offsetting trends will require additional research.

465

The Review of Financial Studies / v 15 n 2 2002

2.3 Measuring return performance
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 ourhousehold data and the CRSP monthly returns ﬁle. In so doing, we maketwo 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.
Second, we ignore intramonth trading (e.g., a purchase on March 6 and asale of the same security on March 20), though we do include in our analysis short-term trades that yield a position at the end of a calendar month. In
the current study, an accounting for intramonth trades and the exact timing oftrades would increase the performance of online investors before the switch
to online trading by 10 basis points per year and decrease the performance of
online investors after the switch by 25 basis points per year (see Appendixes
A and B).
Consider the common stock portfolio for a particular household. The grossgrmonthly return on the household’s portfolio Rht  is calculated as
gr

Rht =

sht

i=1

gr

pit Rit(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
grof all stocks held by household h, Rit is the gross monthly return for stocki, and sht are 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 Rnetit  as 
gr  1 − spr ib  1 − comis = 1 + Rit
1 + Rnetit
1 + spr is  1 + comib (3)where spr is and comis are the estimated spread and commission associatedwith a sale and spr ib and comib are the estimated spread and commission
associated with a purchase.13 Because the timing and cost of purchases andsales vary across households, the net return for security i in month t will varyacross households. The net monthly portfolio return for each household is
Rnet
ht =

13

sht

i=1

pit Rnetit (4)

Had we estimated spreads by dividing transaction prices by closing prices, net returns would be calculated as
gr 1−spris  1−comis 
1 + Rnetit  = 1 + Rit  1+spr  1+com  .
ib

466

ib

Online Investors: Do the Slow Die First?

If only a portion of the beginning-of-month position in stock i was purchased
or sold, the transaction cost is only applied to the portion that was purchased
or sold.
In our analysis of returns, we focus on four monthly return series. The
ﬁrst is the average experience of online investors before online trading. In
each month we average the returns of online investors who have not yet
begun online trading. Thus we end up with a 58-month return series foronline investors before switching to online trading (February 1991–November
1995; December 1995 being the month when the last online investors begin
online trading). The second return series is the average experience of online
investors after switching to online trading. In each month, we average thereturns of online investors who have begun trading online. Thus we end up
with a 60-month return series for online investors after online trading begins(February 1992–January 1997; January 1992 being the month when the ﬁrst
online investors begin online trading). We calculate two analogous returnseries for the size-matched investors.14
2.4 Risk-adjusted return performance
We calculate four measures of risk-adjusted performance. In the discussionthat follows we describe the performance measures for the online sample;
there are analogous calculations for their size-matched counterparts. First,
we calculate the mean monthly market-adjusted abnormal return for online
investors by subtracting the return on a value-weighted index of NYSE/
AMEX/Nasdaq stocks from the return earned by online investors.
Second, we calculate an own-benchmark abnormal return for online
investors, which is similar in spirit to that proposed by Grinblatt and Titman(1993) and Lakonishok, Shleifer, and Vishny (1992). In this abnormal returncalculation, the benchmark for household h is the month t return of the
beginning-of-year portfolio held by household h.15 It represents the returnthat the household would have earned had it merely held its beginningof-year portfolio for the entire year. The own-benchmark abnormal returnis the return earned by household h less the own-benchmark return; if thehousehold did not trade during the year, the own-benchmark abnormal returnwould be zero for all 12 months during the year. In each month, the abnormal
returns for all online investors are averaged, yielding a monthly time seriesof mean monthly own-benchmark abnormal returns. Statistical signiﬁcanceis calculated using t-statistics based on this time series. The advantage of theown-benchmark abnormal return measure is that it does not adjust returns
14

The general tenor of our results are the same if we weight the returns of the online and size-matched samples
by account size rather than equally.

15

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 theremainder of the household’s portfolio.

467

The Review of Financial Studies / v 15 n 2 2002

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 proﬁleof its benchmark (i.e., the portfolio it held at the beginning of the year), thusemphasizing the effect trading has on performance. Own-benchmark returns
are our primary measure of the detrimental effect of overconﬁdence (andexcessive trading) on returns.
Third, we employ the theoretical framework of the capital asset pricing
model (CAPM) and estimate Jensen’s alpha by regressing the monthly excess
return earned by online investors on the market excess return. For example,
to evaluate the gross monthly return earned by the average online investorROgrt , we estimate the following monthly time-series regression:
ROgrt − Rft  = i + i Rmt − Rft  + it(5)where Rft is the monthly return on Treasury bills,16 Rmt is the monthly returnon a value-weighted market index, i is the CAPM intercept (Jensen’s alpha),i is the market beta, and it is the regression error term. The subscript i
denotes parameter estimates and error terms from regression i, where weestimate four regressions for online investors and four for the size-matched
control sample: one each for the gross and net performance before online
trading and one each for the gross and net performance after online trading.
Fourth, we employ an intercept test using the three-factor model developed
by Fama and French (1993). For example, to evaluate the performance of
the average online investor, we estimate the following monthly time-series
regression:
ROgrt − Rft  = j + j Rmt − Rft  + sj SMBt + hj HML t + jt(6)where SMBt is the return on a value-weighted portfolio of small stocks minus
the return on a value-weighted portfolio of big stocks and HML t is the returnon a value-weighted portfolio of high book-to-market stocks minus the returnon a value-weighted portfolio of low book-to-market stocks.17 The regression
yields parameter estimates of j , j , sj , and hj . The error term in the regression is denoted by jt . The subscript j denotes parameter estimates and error
terms from regression j, where we again estimate four regressions for the
online sample and four for the control sample.18
16

The return on Treasury bills is from Stocks, Bonds, Bills, and Inﬂation, 1997 Yearbook, Ibbotson Associates,
Chicago, IL.

17

The construction of these portfolios is discussed in detail in Fama and French (1993). We thank Kenneth
French for providing us with these data.

18

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 and French intercept tests employed
here account well for the small stock tilt of individual investors.

468

Online Investors: Do the Slow Die First?

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 − Rft ,
the size zero-investment portfolio SMBt , and the book-to-market zeroinvestment portfolio HML t  from the three-factor regressions. Portfolios
with above-average market risk have betas greater than one, j > 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 themarket excess return j  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. Furthermore, theory supports the link
between ﬁrm size and returns [Berk (1995)]. Thus we would willingly accept
a stronger tilt toward small stocks as evidence that a particular group ofinvestors is pursuing a strategy that they perceive as riskier. It is less clear
to us whether investors believe a tilt toward high book-to-market stocks(which tend to be ugly, ﬁnancially distressed ﬁrms) or toward low bookto-market stocks (which tend to be high-growth ﬁrms) is perceived as riskier
by investors. Daniel and Titman (1997) provide evidence that book-to-market
effects are attributable to ﬁrm characteristics rather than covariance with factor risks, while Davis, Fama, and French (2000) provide contrary evidence.
Daniel, Hirshleifer, and Subrahmanyam (2001) argue that overconﬁdence
itself can lead to a positive relation between book-to-market and returns.
As such, we interpret the coefﬁcient estimates on the book-to-market zeroinvestment portfolio with a bit more trepidation. Nonetheless, our primary
results are unaffected if we exclude HML t from the time-series regressions.
3. Who Goes Online?
The multivariate analyses presented in this section provide a proﬁle of those
who go online. Young men who are active traders with high incomes and nochildren are more likely to switch to online trading. Those who switch also
have higher levels of self-reported investment experience and a preference
for investing in small growth stocks with high market risk. We also document that those who switch to online trading experience unusually strong
performance prior to going online.
These conclusions are based on a pooled time-series cross-sectional logistic regression from January 1992 through December 1995. All householdswith available data and six years of common stock positions (from January469

The Review of Financial Studies / v 15 n 2 2002

1991 through December 1996) are included in the regression. The dependent
variable for the regression is a dummy variable that takes on a value of one
in the month that a household begins online trading and a zero otherwise.
Households that go online are excluded from the sample after going online.19
The independent variables in the regressions fall into three broad categories: demographic characteristics, investor characteristics, and self-reported
data. Demographic characteristics include gender, marital status, presence ofchildren in the household, age, and income. The effect of gender, marital
status, and the presence of children on the probability of going online aremeasured with dummy variables that take on a value of one for women, single investors, and households with children, respectively. Age is measured inyears. Income is reported within eight ranges, where the top range is greaterthan $125,000. When estimating the logistic regression, we use the midpointof each income range and include a dummy variable that takes on a value of
one for households with income greater than $125,000.
Investor characteristics include account size, market-adjusted return,turnover, and preferences for market risk, small ﬁrms, and value stocks.
Account size is measured as the log of the dollar value of common stock
investments in January 1991, the start of our sample period. The marketadjusted return for month t is the mean gross monthly return on the household’s common stock portfolio less a value-weighted market index frommonth t − 12 to t − 1. Monthly turnover for month t is one-half the sumof purchases and sales divided by the sum of month-end positions frommonth t − 12 to t − 1; when estimating the regression we use the log of one
plus monthly turnover. Market-adjusted returns and monthly turnover are theonly variables that vary from one month to the next for a particular household. Preferences for market risk, small ﬁrms, and value stocks are inferredfrom the Fama and French three-factor model, where a separate regression
is estimated for each household.20
Self-reported data include net worth, the ratio of equity to net worth,and investment experience. Net worth is the log of self-reported net worth.
Equity:net worth is the proportion of the market value of common stock
investment at this discount brokerage ﬁrm as of January 1991 to total selfreported net worth when the household opened its ﬁrst account at this brokerage. Three dummy variables, which take on a value of one for those reporting
limited, good, or extensive investment experience (respectively), are used tomeasure the effect of experience. We suspect that while self-reported experience is largely based on objective criteria such as the number of years or the19

We estimate the logistic regression by pooling over time so that we can more precisely measure turnover andreturn performance in the period preceding a switch to online trading. When we estimate the regression using
households rather than household months as the unit of observation, the results for the remaining independent
variables are qualitatively similar.

20

Using the full six-year sample period to estimate household investment preferences assumes that preferencesof online households do not change signiﬁcantly after going online. In Section 4.6 we document that there is
not a signiﬁcant change in investment style once households begin trading online.

470

Online Investors: Do the Slow Die First?

amount of money that one has invested, people vary in how they interpretthese criteria. We also suspect that, ceteris paribus, the more overconﬁdent
investors are likely to overstate their experience.
The results of this analysis are presented in Table 3. Self-reported data
are available for only about one-third of the total sample. Consequently weestimate two regressions—one that includes only independent variables for
demographics and investor characteristics and one that includes independent
variables for demographics, investor characteristics, and self-reported data.
The results of the ﬁrst logistic regression are presented in columns 2 and 3of Table 3. Of the variables considered, only marital status has no signiﬁcanteffect on the probability of going online. The remaining coefﬁcient estimates
are all reliably nonzero p < 10. The results of the regressions requiring
Table 3
Logistic regression of the probability of switching to online trading
Number of households
going on line
Observations(household-months)
VariableIntercept
Demographics
Women
SingleChildrenAge (years)
Income ($000)
Income > $125,000
Investor characteristics
Account size (log)Market-adjusted return
Monthly turnover (log)Market risk preference
Small ﬁrm preference
Value ﬁrm preference
Self-reported data
Net worth (log)
Stock/net worth
Limited experience
Good experience
Extensive experience

Coefﬁcient

727

377

682,121208,598p

Coefﬁcientp−9197< 0001∗∗∗−10209< 0001∗∗∗−0419−0087−0158−00140003
0050< 0001∗∗∗
0368
0068∗< 0001∗∗∗0081∗
0694−0262−0018−0191−00140002
0020

0082∗
0899
0105∗
0005∗∗∗
0258
0906

0165
4629
0352
0225
0169
−0256< 0001∗∗∗0002∗∗∗< 0001∗∗∗0012∗∗
0002∗∗∗< 0001∗∗∗

0082
3106
0182
0120
0133
−019201350180< 0001∗∗∗
0357
0101
0027∗∗

—
—
—
—
—

—
—
—
—
—

0085
−0146
0974
1503
1684

0161
0721
0174
0035∗∗
0019∗∗∗∗∗ ∗∗ ∗ Signiﬁcant at the 1%, 5%, and 10% levels, respectively.
A pooled time-series, cross-sectional, logistic regression is estimated from January 1992 through December 1995. The dependent
variable for the regression is a dummy variable that takes on a value of one in the month that a household begins online trading
and a zero otherwise. All households with six years of common stock positions are included in the estimation of the regression.
Woman, single, and children are dummy variables that take on a value of one for households headed by a woman, a single
person, and households with children. Age is measured in years. Income is reported in eight ranges, where the top range isover greater than $125,000. We also include a dummy variable that takes on a value of one if income is greater than $125,000.
Account size is the log of the dollar value of common stock positions held in January 1991. Market-adjusted return in month t
is the mean monthly return on the household’s portfolio less a value-weighted market index from month t − 12 to t − 1. Monthly
turnover in month t is one-half the sum of purchases and sales divided by the sum of monthly positions from month t − 12 tot − 1. The regression is estimated using the log of one plus monthly turnover. Market risk, small ﬁrm, and value preferences are
based on coefﬁcient estimates from the Fama and French three-factor model, which are estimated separately for each household.
Net worth is the log of self-reported net worth. Proportion of equity to net worth is the value of common stock investments
in January 1991 to self-reported net worth. Limited, good, extensive investment experience are dummy variables that take on avalue of one for households reporting that level of experience.

471

The Review of Financial Studies / v 15 n 2 2002

self-reported data are present in the last two columns of Table 3. Of theself-reported data, experience has a signiﬁcant effect of the probability ofgoing online; those with greater self-reported investment experience (perhaps
because they are more overconﬁdent) are more likely to make the switch. The
remaining independent variables have coefﬁcient estimates that are generally
consistent with those in the regression that exclude self-reported data, though
the statistical signiﬁcance of some coefﬁcients is weakened—likely the resultof the smaller sample size.
4. Results
In this section we test and conﬁrm our hypotheses about the trading activity
and return performance of online investors. Our four principal results arethat those who switch from phone-based trading to online trading experience
unusually strong performance prior to going online, accelerate their trading
after going online, trade more speculatively after going online, and experience subpar performance (as a result of the accelerated trading) after going
online. We consider each of these results in turn.
4.1 Performance before the switch
Before the switch to online trading, the average online investor outperforms
both the market and the average size-matched investor. The returns bothgroups earned before the switch are presented in columns 2–4 of Table 4.
Before switching, the gross returns of the average online investor beat themarket by 35 basis points a month (4.2% annually, p = 009, and outpaced
the average size-matched investor by 18 basis points a month (2.2% annually,p = 002). Even after netting out trading costs the average online investor stillbeat the market by 20 basis points a month (2.4% annually, not statisticallysigniﬁcant) and outpaced the net returns of the average size-matched investor
by 14 basis points a month (1.7% annually, p = 008).21
We argue that the strong performance of online investors before the switch
fostered greater overconﬁdence. Thus our primary interest in this analysis is
to identify how investors perceived their own performance before the switch
to online trading. We believe market-adjusted returns are the most relevantmeasure of performance during this period. To the extent individual investors
evaluate their own performance against a benchmark, it is likely that they use
the market return. The average online investor comfortably beat the market
before the switch to online trading. Many investors likely gauge the success of their investments on nominal rather than market-adjusted returns.
Those that do so may attribute their high returns to their own abilities rather
21

Before going online, the gross performance of online traders is positive for all of our return measures except
the own-benchmark measure, which is essentially zero −0014 t = −055. Thus the superior returns earned
before going online were due primarily to the portfolios these investors held at the beginning of our evaluation
period, not to the trades they made during this period.

472

Online Investors: Do the Slow Die First?

Table 4
Percentage monthly returns for online and size-matched investors
Before online trading

OnlineSizematched

Panel A: Gross percentage monthly return
Raw
1649
1467
Market adjusted
Own benchmark
CAPM alpha
Fama and French
alpha

0354
171∗−0003−010
0275
127
0120
088

0172
110
−0083−175∗0152
092
−0015−015

Panel B: Net percentage monthly return
Raw
1492
1355
Market adjusted
Own benchmark
CAPM alpha
Fama and French
alpha

0197
097
−0160−583∗∗∗
0126
059
−0023−017

0060
039
−0196−421∗∗∗
0044
027
−0117−118

After online trading

Difference(onlinelessmatch)
0182
232∗∗
0182
232∗∗
0080
165
0123
154
0135
201∗∗
0137
178∗
0137
177∗
0036
076
0082
103
0094
142

OnlineSizematched

1198

1174

−0094−049
−0100−223∗∗−0324−173∗−0182−138

−0118−094
−0028−092
−0220−169∗−0193−185∗

1002

1082

−0291−152
−0297−650∗∗∗−0519−277∗∗∗−0363−276∗∗∗−0211−169∗−0120−391∗∗∗−0311−240∗∗−0280−267∗∗∗Difference(onlinelessmatch)
0024
024
0024
024
−0073−193∗−0104−107
0011
015
−0080−078
−0080−078
−0177−450∗∗∗−0208−210∗∗−0083−111

∗∗∗ ∗∗ ∗ Signiﬁcant at the 1%, 5%, and 10% levels, respectively (two-tailed).
Returns are based on month-end position statements for 1,607 online investors and their size-matched counterparts from January1991 to December 1996. Panel A (panel B) presents results for the gross (net) return on a portfolio that mimics the average
investor. The columns labeled “Before online trading” are the returns of the average online and size-matched investor before
online trading (February 1991–November 1995); those labeled “After online trading” are the returns of the average online andsize-matched investor after online trading (February 1992–January 1997). Market-adjusted return is the return on the household
portfolio less the return on a value-weighted NYSE/AMEX/Nasdaq index. Own-benchmark abnormal return is the return on thehousehold portfolio minus the return on the portfolio the household held at the end of the previous January. CAPM alpha is theestimated intercept from a time-series regression of the household excess return on the market excess return Rmt − Rft . Fama
and French alpha is the estimated intercept from a time-series regression of household excess return on the market excess return,a zero-investment book-to-market portfolio HML t , and a zero-investment size portfolio SMBt . t-statistics are in parentheses.

than market returns (about 18% annually during the six-year sample period).
Regardless of the performance measure employed, the average online investor
earned higher returns than the average size-matched investor prior to theswitch.
4.2 Turnover
We begin our analysis of turnover by calculating aggregate turnover for
online and size-matched investors in event time, where we deﬁne month
0 as the month of the ﬁrst online trade. For example, for online investors
we calculate aggregate turnover in event time as one-half the total value ofpurchases and sales by all online investors in an event month (the numerator)
divided by the sum of month-end position statements for that event month(the denominator).
473

The Review of Financial Studies / v 15 n 2 2002

We present annualized turnover (monthly turnover times 12) in event timein Figure 1. In the two years prior to the switch to online trading, annualized
turnover for the online investors averaged about 70%, while that for thesize-matched investors averaged about 50%. In the month after the switch toonline trading (month 1), annualized turnover of the online investors surges
to 120%. While a temporary surge in trading activity is not surprising, afull two years after the switch to online trading annualized turnover is still
90% for the online investors—well above their turnover rate in the periodprior to the switch. There is no such change in the observed turnover of thesize-matched investors.
To more formally test whether the increase in turnover for online investors
is reliably greater than that for their size-matched counterparts, we calculate turnover separately for each investor. Monthly turnover for each household is one-half the total value of purchases and sales, now summed overtime (the numerator), divided by the sum of month-end position statements
over time (the denominator). For each online and size-matched household
we calculate two turnover measures: one before and one after online trading. (The month of the ﬁrst online trade is excluded from both calculations.)
The annual turnover (monthly turnover times 12) of online investors andtheir size-matched counterparts are presented in panel A of Table 5. Bothbefore and after the switch, online investors trade more actively than theirsize-matched counterparts.
140%
Size-Matched

120%
Annualized Turnover (%)

Online

100%
80%
60%
40%
20%

24

21

18

15

9

12

6

3

0

-3

-6

-9

-1
8
-1
5
-1
2

-2
4
-2
1

0%

Event Month (0 is month of first online trade)
Figure 1
Aggregate turnover for online and size-matched investors in event time (month 0 = month of ﬁrst online
trade)

474

Online Investors: Do the Slow Die First?

Table 5
Mean annual turnover of online households and size-matched households
Beforeonline trading

After
online trading

Change(after online lessbefore online)

Panel A: Total turnover
Online households
Size-matched households
Online less size-matched

737
532
205∗∗∗

955
482
473∗∗∗

218∗∗∗−50∗∗
268∗∗∗

Panel B: Speculative turnover
Online households
Size-matched households
Online less size-matched

164
115
49∗∗∗

302
139
163∗∗∗

138∗∗∗
24
114∗∗∗∗∗∗ ∗∗ ∗ Signiﬁcant at the 1%, 5%, and 10% levels, respectively (two-tailed).
Online households are 1,607 households with 72 consecutive months of common stock positions, no online trades prior to
January 1992, and at least one online trade between January 1992 and December 1995. Each size-matched household is thehousehold with the closest account size to the sample ﬁrm in the month preceding its ﬁrst online trade. The matched householdmust also have 72 consecutive months of common stock positions, no online trades during the 72 months, and at least one
trade between January 1992 and December 1995. Monthly turnover for each household is one-half the sum of all trades forthat household divided by the sum of all month-end positions. Monthly turnover times 12 yields annual turnover. Speculative
turnover is calculated using only trades classiﬁed as speculative. We deﬁne speculative trades as all proﬁtable sales of complete
positions that are followed by a purchase within three weeks and all purchases made within three weeks of a speculativesale. Test statistics for the difference in means are based on a two-sample t-statistic assuming unequal variance for the two
samples.

After switching online, investors trade much more actively than before.
Their average annual turnover increases from 73.7% to 95.5% p < 0.01),
thus conﬁrming Hypothesis 1. While online investors trade more actively
than their size-matched counterparts both before and after the switch, thedifference in average turnover is much greater after the switch. This conﬁrms Hypothesis 2. In the postswitch period, the average turnover of online
investors is nearly double that of their size-matched counterparts (95.5% versus 48.2%, p < 0.01).22
Though the switch to online trading is associated with greater trading
activity, it is possible that those who switched to online trading would have
traded more actively regardless of whether they were online or not. Though
we are unable to dismiss this possibility within the context of our study,
Choi, Laibson, and Metrick (2001) provide corroborating evidence that it is
the online environment that spawns greater trading. They document that atcompanies which adopted web-based interfaces for plan participants during
the 1990s, turnover in 401(k) accounts increased by 50%; there was no suchincrease in trading activity for ﬁrms without web-based access.

22

There is also an increase in the turnover of the median online household after the switch to online trading,though it is much smaller than the increase in average turnover (1.4%, p < 010. Twenty-ﬁve percent of
the online households increase their turnover by 35% or more; 10% of the online households increase their
turnover by 109% or more. Thus most households increase their trading activity, but some increase theirtrading dramatically.

475

The Review of Financial Studies / v 15 n 2 2002

4.3 Speculative trading
An investor may trade common stocks for many reasons. An investor with abonus to invest or a large bill to pay may buy or sell for liquidity reasons.
If one security in his portfolio appreciates considerably, he may rebalance torestore diversiﬁcation to his portfolio (by selling part of his holding in that
security and buying others). He may sell to capture a tax loss. Or he may
trade to speculate by selling one stock from his portfolio and buying anotherin an effort to improve his performance.
To examine how speculative trading changes when investors go online, we
screen out most trades that may have been motivated by liquidity needs, adesire to rebalance, or tax losses. We screen liquidity sales by considering
only sales for which a new purchase follows within three weeks (15 tradingdays) of the sale; most investors who need to raise cash for less than three
weeks have lower cost alternatives available than trading stocks (e.g., credit
cards). We screen rebalancing sales by considering only sales of the complete
holding of a position. We eliminate tax-loss sales by considering only sales
for a proﬁt. In short, we consider all proﬁtable sales of complete positions
that are followed by a purchase within three weeks as speculative and allpurchases made within three weeks of a speculative sale as speculative. It
is unlikely that we identify all (or even most) speculative trades, but those
trades that meet our screens are very likely to be speculative.
As reported in panel B of Table 5, speculative turnover nearly doubles
when investors go online (from 16.4% to 30.2%), conﬁrming Hypothesis 3.
Even with our conservative classiﬁcation, speculative trading accounts for
60% of the increase in turnover for the average online investor. Both before
and after the switch, online investors trade more speculatively than their sizematched counterparts. This conﬁrms Hypothesis 4.
Do investors trade more speculatively because they are better able to identify and execute proﬁtable speculative trades after going online? To answer
this question, we compare the returns earned by stocks subsequent to speculative purchases and to speculative sales. In each month we construct a
portfolio comprised of those stocks purchased speculatively in the preceding
three months. The daily returns on this portfolio are calculated asnpb pb
i=1 Ti Ri
Rpb
=
(7)


pb
n
i=1 Tiwhere Tipb is the aggregate value of all speculative purchases in security ifrom day  − 63 through  − 1 and Rpbi is the gross daily return of stock i
on day . We compound the daily returns within a month, which yields atime series of monthly returns for four portfolios: one for speculative purchases before going online Rpbt , one for speculative purchases after goingsb
online Rpat , one for speculative sales before going online Rt , and onesa
for speculative sales after going online Rt .
476

Online Investors: Do the Slow Die First?

Before going online, the stocks online investors buy speculatively outperform those that they sell speculatively by 59 basis points per monthp = 008.23 After going online, the stocks online investors buy speculatively
underperform those that they sell speculatively by 27 basis points per month,though the underperformance is not reliably different from zero p = 029.
The difference in the relative performance of purchases and sales before andafter going online is signiﬁcant p = 010.24 Though the speculative tradesof online investors performed well prior to switching online, these proﬁts
would not be sufﬁcient to cover average round-trip transaction costs of 4%(see Table 2). Furthermore, online investors were unable to sustain their solid
gross performance.
4.4 Performance after the switch
The returns earned by online investors after the switch to online trading andthose of their size-matched counterparts are presented in columns 5–7 ofTable 4. After the switch to online trading, online investors perform poorly.
The gross returns of online investors underperform the market by 9 basis
points a month (not statistically signiﬁcant), while their net returns underperform by an economically large 29 basis points a month (3.5% annually,p = 013). Net own-benchmark returns indicate that after the switch, online
investors lose 30 basis points a month (3.6% annually) through their trading activities, while the size match group loses only 12 basis points (1.4%
annually); both shortfalls and their difference are signiﬁcant at the 1% level,
conﬁrming Hypothesis 6.
4.5 Changes in performance
In this section we formally test whether the changes in performance—from
before to after online trading—are signiﬁcant. To do so we compare thereturns earned by online investors who have not yet gone online to thoseearned during the same months by online investors who have already begun
trading online. For our online sample, the ﬁrst online trading begins inJanuary 1992 and the last households commence online trading in December
1995. Thus we can calculate the before-after return series for 46 months:
February 1992 to November 1995. The results of this analysis are presented
in Table 6. (Returns in Tables 4 and 6 differ because their observation periods
differ.)Using any of our return measures, the gross and net returns of online
investors who have already switched to online trading are less than those
who have not yet made the switch. For example, the average net raw returnpb
Rt −Rsbt √ .
pb
 Rt −Rsbt  5623

This t-statistic is calculated as t =24

If we look at all trades, not just speculative trades, the stocks online investors buy before going online
outperform those they sell by an insigniﬁcant 29 basis points a month p = 015. After going online theirbuys underperform their sells by a signiﬁcant 31 basis points a month p = 005.

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The Review of Financial Studies / v 15 n 2 2002

Table 6
The performance of investors already online and investors who have not yet traded online (but do sobefore 1996): February 1992–November 1995

Panel A: Gross percentage monthly return
Raw
Market adjusted
Own benchmark
CAPM alpha
Fama and French alphaPanel B: Net percentage monthly return
Raw
Market adjusted
Own benchmark
CAPM alpha
Fama and French alphaInvestors not yet
onlineInvestors alreadyonline

1284

0993

0189
108
0001
003
0083
046
0145
099

−0104−052
−0079−160
−0240−119
−0106−075

1139

0777

0043
025
−0144−460∗∗∗−0057−032
0010
007

−0319−160
−0296−581∗∗∗−0453−223∗∗−0301−211∗∗Difference(already online −not yet online)−0291−315∗∗∗−0291−315∗∗∗−0080−175∗−0323−332∗∗∗−0251−271∗∗∗−0362−372∗∗∗−0362−372∗∗∗−0152−332∗∗∗−0396−385∗∗∗−0311−323∗∗∗∗∗∗ ∗∗ ∗ Signiﬁcant at the 1%, 5%, and 10% levels, respectively (two-tailed).
Online households are 1,607 households with 72 consecutive months of common stock positions, no online trades prior to
January 1992, and at least one online trade between January 1992 and December 1995. From February 1992 to November 1995
these 1,607 online households are broken into two groups, those that have already begun online trading (already online) andthose that have not yet done so (not yet online). Market-adjusted return is the return on the household portfolio less the returnon a value-weighted NYSE/AMEX/Nasdaq index. 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. CAPM alpha is the estimated intercept
from a time-series regression of the household excess return on the market excess return Rmt − Rft . Fama and French alpha isthe estimated intercept from a time-series regression of household excess return on the market excess return, a zero-investment
book-to-market portfolio (HML t ), and a zero-investment size portfolio (SMBt ). t-statistics are in parentheses.

of online investors who have commenced trading online is 36 basis points amonth lower than that of online investors who have not yet made the switch(4.3% annually, p < 001). After going online net own-benchmark returns
are 15 basis points a month lower p < 001, conﬁrming Hypothesis 5.
Without equivocation, we can conclude that there is a dramatic erosion in
the performance of online investors after they switch to online trading. Thiserosion is due to the combination of better than average (gross) performance
before the switch and inferior performance (both gross and net) afterwards.25

25

An analogous analysis for the size-matched households yields differences in net performance (after online lessbefore online) ranging from 8 basis points (own-benchmark abnormal return) to −14 basis points (marketadjusted returns). The change in net performance of the online sample (after online less before online) lessthe change in performance for the size-matched households range from −21 basis points (Fama and Frenchalpha) to −26 basis points (own-benchmark abnormal return); all of the differences between the online andsize-matched samples are statistically signiﬁcant at less than the 5% level (two-tailed).

478

Online Investors: Do the Slow Die First?

4.6 The investment style of online investors
It is natural to wonder whether the investment style of online investors differsfrom other investors. The answer is yes and no. Yes, since relative to the sizematched sample, online investors tilt their investments toward small growthstocks with higher market risk.26 No, since the tilt toward small growth stocks
with high market risk is apparent both before and after online trading. Thereis some evidence that online investors increase their exposure to small growthstocks after going online (the test statistic is 1.36 for the coefﬁcient estimate
on the SMBt and −211 for HML t . However, there is a similar style change
for the size-matched control group; there are no signiﬁcant differences inthese changes between the online investors and the control group. At facevalue it does not appear that the switch from phone-based to online investing is accompanied by a signiﬁcant change in the style of stocks investors own.
5. Discussion
We ﬁnd that those who switch from phone-based trading to online trading
experience unusually strong performance prior to the switch, accelerate theirtrading after going online, trade more speculatively after going online, andexperience subpar performance (as a result of the accelerated trading) after
going online. The strong performance prior to going online is consistent
with self-attribution bias. Investors with unusually good returns are likely
to have taken too much credit for their success and grown more overconﬁdent about their stock-picking abilities. Overconﬁdent investors were more
likely to go online and once online the illusion of control and the illusion ofknowledge further increased their overconﬁdence. Overconﬁdence led themto trade actively and active trading caused subpar performance.
For overconﬁdent investors, the greater effort of initiating trades by phoneor, more recently, the higher commissions associated with phone trades may
serve as unintended barriers to excessive trading. When these market frictions
are reduced, the disutility of increased speculative losses may offset utility
gains from lower trading effort and cost. While our results are consistent withthe overconﬁdence model of trading and conﬁrmed all of our hypotheses
based on that model, other explanations warrant consideration.
Investors may be trading more after they go online simply because lower
trading costs make more trades potentially proﬁtable. If so, online trading
should lead to improved performance, which it does not.
26

These conclusions are based on the coefﬁcient loadings and associated test statistics from the time-series
regressions that employ the Fama and French three-factor model. The online investors also have a preference
for stocks with poor recent return performance relative to their size-matched counterparts. This inference isdrawn by adding a zero-investment portfolio that is long stocks that have performed well recently and shortstocks that have performed poorly [see Carhart (1997)]. None of our conclusions regarding performance arealtered by the inclusion of this price momentum variable. We thank Mark Carhart for providing us with these
return data.

479

The Review of Financial Studies / v 15 n 2 2002

Some investors may anticipate unusual liquidity needs and switch online
in hopes of facilitating liquidity-driven purchases or sales more easily. This
could explain temporary increases in trading after going online. It does notaccount for higher permanent levels of online trading, unless traders experience permanent shifts in their liquidity-based trading needs. Furthermore,changes in liquidity based trading do not explain observed increases in speculative trading after investors go online.
It is conceivable that, through greater speed of execution, online trading
allows investors to make proﬁtable trades that would not have otherwise been
available. If so, online trading should lead to improved performance, which
it does not.
Investors may trade more when they go online simply because of greater
ease of access. For rational investors this implies that there were potentially
proﬁtable trades that the investors declined to make before going online
because the expected proﬁts did not warrant the effort of calling a broker. Due to the greater ease of trading online, these investors now makesuch trades. Such investors must have placed a high cost on the effort
required to phone their discount broker. While this explanation is consistent with increased trading after going online, it does not explain subpar
performance.
Lower trading costs, liquidity needs, speed of execution, and ease of accessdo not explain why rational investors would trade more actively, more speculatively, and less proﬁtably after going online. A ﬁnal possibility is thatrational investors trade more actively online simply because online investingis entertaining. We cannot rule out the possibility that we are observing theactions of fully rational investors who knowingly trade to their ﬁnancial detriment simply for fun. We believe it more plausible that the investors who ﬁnd
trading most fun are those who are overconﬁdent about their chances of success. The Gallup survey and experimental evidence cited earlier (Moore et al.
1999) show that investors expect to beat the market. If investors rationallytrade for entertainment, they will recognize that the price of this entertainment is below-average performance; overconﬁdent investors will expect tobeat the market. For a more detailed discussion of this issue, see Barber andOdean (2000, 2001).
Another reason why investors trade more actively online may be that they
simply misunderstand trading costs. While the commissions they pay are
explicitly reported to investors, bid-ask spreads are not. Our online traders
pay aggregate round-trip commissions of about 3.3% and bid-ask spreads ofabout 1.1% before going online. After going online, they pay about 2.5%
round-trip commissions and 0.9% in spread. Commissions fell faster during
this period, and subsequently, than spreads. If investors ignore spreads whenassessing their trading costs, they may conclude that total costs have fallen480

Online Investors: Do the Slow Die First?

much faster than is actually the case. This misapprehension will encourage
trade.27
When overconﬁdent investors go online, they are likely to trade more forthe same reasons rational investors would trade more: lower costs, faster
executions, easier access (and entertainment). Unlike rational investors, however, overconﬁdent investors tend to incorrectly identify proﬁtable speculativetrades [Odean (1999)]. Therefore increased trading hurts their performance.
Thus overconﬁdence, coupled with the lure of cheaper, faster, easier trading,explains increased trading and speculation as well as impaired performanceafter investors go online.
6. Conclusion
We analyze the characteristics, trading, and performance of 1,607 investors
who switched from phone-based to online trading between 1992 and 1995.
During this period, young men who were active traders with high incomes
and no children were more likely to switch to online trading. Those who
switched also had higher levels of self-reported investment experience and apreference for investing in small growth stocks with high market risk.
Investors who went online during the period 1992–1995 generally earned
superior returns before switching to online trading. After the switch they
increased their trading activity, traded more speculatively, and performed
subpar. Rational investors would not do this. Overconﬁdent investors, on
the other hand, are inclined to trade excessively.
Several cognitive biases reinforce the overconﬁdence of online investors.
Investors who earn strong returns before going online are likely to attribute
this success disproportionately to their own investment ability (rather thanluck) and become overconﬁdent. Once online, investors have access to vastquantities of investment data; these data can foster an illusion of knowledge,
which increases overconﬁdence. Finally, online investors generally manage
their own stock portfolios and execute trades at the click of a mouse; thisfosters an illusion of control, which reinforces overconﬁdence.
Investors who do not increase their trading clearly beneﬁt from the convenience and low costs of trading online. But others, who trade more actively
online, risk offsetting per trade savings with greater cumulative costs andspeculative losses. This is the experience of the online investors we study
here.
27

An additional bias that may encourage some investors to trade online is loss aversion. Kahneman and Tversky(1979) argue that, when faced with losses, people will accept additional risks in hopes of recovering to theformer status quo. While we do not believe that most investors are motivated to go online by loss aversion,some may be. An E*TRADE advertisement reads: “The Tooth Fairy, Santa Claus, Social Security.” The
implied failure of social security would be a loss of anticipated welfare for many people. The prospect ofsuch a loss could prompt people to take risks they might not otherwise take. Some might even go so far as
to open an E*TRADE account.

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The Review of Financial Studies / v 15 n 2 2002

Online brokers encourage investors to trade speculatively and often. Someof their advertisements reinforce cognitive biases, such as overconﬁdence.
Others create unrealistic expectations. While rational investors will consider
only the relevant informational content of advertising, SEC Chairman Arthur
Levitt worries that many of us may be unduly inﬂuenced by advertisementsthat “step over the line and border on irresponsibility.”28
Advertisements compare online trading to the old West, where the ﬁrst
to draw prevailed. Investors are led to believe that proﬁtable investment
opportunities are ephemeral events, seized only by the quick and vigilant.
Most investors, however, beneﬁt from a slow trading, buy-and-hold strategy.
Trigger-happy traders are prone to shoot themselves in the foot.
Appendix A: The Analysis of Trade Timing
In this appendix we analyze the timing of purchases and sales within a month. The timing oftrades within a month are ignored in our main analysis, since we assume all purchases andsales are made at month end. The analysis that follows indicates that this assumption does notmaterially affect the conclusions presented in the main text. If anything, this assumption leads us
to underestimate the performance of the online sample prior to the switch, while overestimating
performance after the switch.
For each account with a beginning-of-month position statement in month t, we identify allpurchases in month t − 1 and sales in month t. For both purchases and sales, we calculate thecompound return on the stock from the day following the trade to the last day of the month.
For purchases, this return is excluded in our main results, while for sales this return is included.
Note that in our main results we account for the intraday return on the trade day in our estimateof the bid-ask spread.
The results of our analysis are presented in Table A1. The second (and fourth) column ofthis table presents aggregate purchase (and sale) turnover calculated as the aggregate dollar
value of purchases (or sales) divided by the aggregate dollar value of positions held. (Thisturnover measure is slightly different than that used in the Table 5, where turnover is calculated
for each household and then averaged across households.) Abnormal returns are calculated forpurchases and sales by subtracting the compound return on the CRSP NYSE/AMEX/Nasdaq
value-weighted index. The trade-weighted mean abnormal returns are presented in columns 3(for purchases) and 5 (for sales) of Table A1.
First, consider the results for the online sample (panel A). Prior to the switch to online
trading, from the day following the trade to the end of the month, the stocks that these investorsbought outperformed the value-weighted market index by 14 basis points per month, while thosethat they sold outperformed the index by nine basis points. After the switch to online trading,the stocks that these investors bought underperformed the value-weighted market index by 33
basis points per month, while those that they sold outperformed the index by 21 basis points.
Based on these abnormal returns and our estimates of turnover, we estimate that the results wepresent in the main text underestimate the performance of online investors by 0.23 basis points
per month prior to the switch and overestimate their performance by 3.38 basis points per monthafter the switch.
Second, consider the results for the size-matched sample (panel B). The same analysis asthat outlined in the preceding paragraph indicates that we have overestimated the performance28

Speech at National Press Club, May 4, 1999, http://www.sec.gov/news/speeches/spch274.htm.

482

Online Investors: Do the Slow Die First?

Table A1
The gross abnormal returns for stocks bought and sold from the trade date to the end of the month

Monthlypurchase
turnover(%)

Purchaseabnormalreturn (%)

Monthlysaleturnover (%)

Saleabnormalreturn (%)

Estimated
effect on
monthlyabnormalreturn (%)

4157
6260

0137
−0327

3774
6311

0091
0211

00023−00338

Panel B: Size-matched households
Prior to switch
3395
After switch
3081−0148−0807

3069
3147

−0023−0029−00043−00240

SamplePanel A: Online households
Prior to switch
After switch

Online households are 1,607 households with 72 consecutive months of common stock positions, no online trades prior to
January 1992, and at least one online trade between January 1992 and December 1995. Each size-matched household is thehousehold with the closest account size to the sample ﬁrm in the month preceding its ﬁrst online trade. The matched householdmust also have 72 consecutive months of common stock positions, no online trades during the 72 months, and at least one
trade between January 1992 and December 1995. Purchase turnover is the aggregate value of stocks purchased divided by theaggregate 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 thevalue-weighted NYSE/AMEX/Nasdaq market index. Sales turnover and sales abnormal return are analogously calculated. The
estimated effect on the monthly abnormal return is purchase turnover times purchase abnormal return minus sale turnover times
sale abnormal return.

of the size-matched sample by 0.43 basis points per month prior to the switch and 2.40 basis
points per month after the switch.
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 ispurchased 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 tbcl to t1 minus the return from tscl to ts . Ourestimate of the bid-ask spread is the return from ts to tscl minus the return from tb to tbcl . Whenthe return from timing is added to the main calculation and the spread is subtracted, one getsthe (approximate) return from tb to ts , the period in which the investor held the stock.

cl

tb tb

t1

Close Month 1
Day of Purchase

t2

cl

ts ts

Close Month 2

t3

Close Month 3
Day of Sale

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

483

The Review of Financial Studies / v 15 n 2 2002

Appendix B: The Analysis of Intramonth Trades
In this appendix we analyze the performance of stocks that are bought and then sold withina calendar month (e.g., purchased on January 3 and sold on January 10). These intramonthtrades are excluded from our main analyses, since those analyses are based on monthly position
statements.
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 theﬁrst sold. 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 purchaseand sale date, respectively. We calculate the net returns on these round-trip transactions bysubtracting estimates of the bid-ask spread and commissions as is done in the main text for thecase of monthly returns.
In Table B1 we summarize our analysis of the gross and net returns earned on intramonthtrades. In this table we calculate market-adjusted abnormal returns by subtracting the daily valueweighted NYSE/AMEX/Nasdaq CRSP market index from the return earned on each intramonthtrade. Both the gross and net abnormal returns in this table are weighted by the size of eachtrade, so we can estimate the aggregate impact of these intramonth trades on the performanceof the online sample and their size-matched counterparts.
Panel A presents the results for the online sample. Prior to the switch to online trading,their intramonth trades earn impressive gross abnormal returns of 3.540%; the net abnormal
returns are 1.812%. Since these intramonth trades represent 0.315% of the monthly positions,
we estimate that these intramonth trades would improve the performance of online investors by
0.57 basis points per month (1.812 times 0.00315). After the switch to online trading, intramonthtrades would improve the performance of online investors by 1.26 basis points per month (1.167
times 0.01078).
Panel B presents the results for the size-matched sample. Prior to the switch, intramonthtrades improve the performance of the size-matched sample by 0.41 basis points per month.
After the switch, these trades improve their performance by 0.55 basis points per month. These
small improvements in performance for the online sample and their size-matched counterparts
do not materially affect any of the conclusions that we present in the main text.
Table B1
The gross and net abnormal returns earned on intramonth trades

SampleMean trade
size

Gross
abnormalreturn (%)

Net
abnormalreturn (%)

Intramonthtrades as apercentage of
total position
value

Estimatedchange inmonthlyabnormalreturn (%)

Panel A: Online sample
Prior to switch
After switch$13,941$23,251

3540
2036

1812
1167

0315
1078

0005700126

Panel B: Size-matched households
Prior to switch$20,310After switch$26,562

2971
2562

1447
1163

0286
0470

0004100055

Online households are 1,607 households with 72 consecutive months of common stock positions, no online trades prior to
January 1992, and at least one online trade between January 1992 and December 1995. Each size-matched household is thehousehold with the closest account size to the sample ﬁrm in the month preceding its ﬁrst online trade. The matched householdmust also have 72 consecutive months of common stock positions, no online trades during the 72 months, and at least one trade
between January 1992 and December 1995. The gross abnormal return on intramonth trades is calculated as the compound returnfrom the day following the purchase to the day of the sale less the compound return on a value-weighted NYSE/AMEX/Nasdaq
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 averagemonthly 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.

484Online Investors: Do the Slow Die First?

In conclusion, we should 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 proposedby Shefrin and Statman (1985)], we would expect to observe positive abnormal returns on shortterm round-trip trades.

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