The Anatomy of Day Traders
Juhani Linnainmaa
University of California, Los Angeles
∗
This version: June, 2003
First version: December, 2002
This paper examines the complete trading records of all day traders in
Finland. A typical day trader is a male in his late 30s, who lives in the
metropolitan area and trades in larger quantities than an investor in a sizematched control group even after ignoring “day trades.” These traders
day-trade stocks that grab their attention, that they own, or that they
have day-traded before. They pay close attention to the state of the limit
order book, are very active near the end of the trading session, and are
strongly deterred by losses. Day traders do not earn better returns than
investors in the control group. Their realized returns from day trades
are high, but these returns are not representative of overall performance
because of their strong reluctance to realize losses.
Day traders have attracted a significant amount of attention in the popular
press. A search of the LexisNexisTM Academic service reports over 2,500 articles
on day traders. A popular Internet bookstore lists over 100 titles for aspiring
day traders, including at least one murder mystery, Stephen W. Frey’s The
Day Trader, which the New York Times (February 10, 2002) calls “fast-paced.”
This interest has not been matched in academic journals. A similar search on
day trading finds a single citation, and that work deals with the performance
of technical trading rules at the daily level, not day trading in the sense we
understand it today (see Landingham, 1980). This is not because of a lack of
interest, but rather a lack of data. Barber and Odean (2001b, pp. 51) point out
∗ Contact information: Juhani Linnainmaa, [email protected], Tel.
(310) 825 8160. I wish to thank Mark Grinblatt, Matti Keloharju, Michael Brennan, and
Monika Piazzesi for many suggestions on how to improve this paper. Special thanks are due to
Matti Keloharju for his comments and help with the FCSD registry. I gratefully acknowledge
the financial support provided by the Graduate School of Finance and Financial Accounting,
the Foundation for the Development of Finnish Securities Markets, the OKOBANK Group
Research Foundation, and the Foundation of Jenny and Antti Wihuri. Henri Bergström from
the Finnish Central Securities Depository and Pekka Peiponen, Timo Kaski, and Jani Ahola
from the Helsinki Exchanges provided data used in this study.
1
that “little is known about their [day traders’] trading strategies, because firms
that cater to day traders have been generally reluctant to provide access to the
trading records of their clients.”1
We address this gap by examining the complete trading records of all individual investors engaged in day trading activity, along with complete limit order
data for intraday analysis. These data offer a comprehensive look at the day
trading phenomenon.
We focus on the behavior and performance of day traders. For example, how
often does a typical day trader turn over a portfolio? How does a day trader pick
a stock to day-trade? How does he act in the market when executing a trade?
Do day traders earn better returns than similar, non-day trading, investors? Do
traders’ behavior change as their careers progress?
These questions are important because of the degree of uncertainty surrounding day trading, and because of the possibility that day trading may increase
stock market volatility (Campbell, Lettau, Malkiel, and Xu, 2001).
Our results can be summarized as follows.
Demographic Characteristics. A typical day trader is a man in his late
30s who lives in the metropolitan area and turns over his portfolio about once
a month. Even after ignoring actual day trades, he trades more and hold stocks
for a shorter time than investors in a size-matched control group.
Stock Selection. Day traders tend to day-trade in stocks that (1) grab
their attention (through excess returns during previous trading sessions), (2)
they already own, or (3) they have day-traded before. Market volatility raises
and trading losses in the same stock diminish the likelihood of day trading.
Intraday Behavior. Day traders show a high overall level of activity and
demand for liquidity at the opening and the close of the trading session. Day
traders who are about to realize a loss from a day trade are very likely to take
the loss at the very end of the trading session. We show that day traders try
to imitate the behavior of other market participants when they execute trades;
they buy (sell) after an uptick (downtick) and after a buyer-initiated (sellerinitiated) trade. They also pay attention to the depth of the book and react to
changes in the bid-ask prices. Their decisions on passive or active limit orders
depend strongly on the current state of the market and changes in the market.
Performance. The average return from day trades is very high, but returns
are not representative of overall performance because of day traders’ reluctance
1 A study that most closely resembles this paper is Harris and Schultz (1998). They use
approximately three weeks’ worth of proprietary data from two brokerage firms that cater to
SOES (Small Order Execution System of the Nasdaq) bandits to study these traders’ strategies
and profitability of round-trip trades. Although these traders are day traders, their strategy
is distinctive: to profit from stale quotes that arise in Nasdaq from fragmented order flow.
Authors credit SOES bandits as a force that keep quotes of different dealers in line more
cost-effectively than if dealers did this themselves, due to differences in incentives. Other
studies on SOES bandits include Battalio, Hatch, and Jennings (1997), Harris and Schultz
(1997), Kandel and Marx (1999), and Foucault, Röell, and Sandås (2003). These authors
rely on indirect evidence, using data on maximum-sized SOES trades to examine bandits’
impact on the market. SOES activity declined significantly with Nasdaq reforms in 1997,
indicating that the influence of SOES bandits reduced as well (see Barclay, Christie, Kandel,
and Schultz, 1999).
2
to realize losses. Day traders’ overall level of performance during the 1998-2000
sample period does not differ from the control group before commissions. This
suggests that day traders are not trading on superior information.
Careers. Day traders become more confident and more aggressive as they
continue to day-trade. They more often sell stocks that do not own and use more
leverage in day trading. Trading losses play an important role in determining
the fate of a day trader’s career; the likelihood of quitting day trading increases
with the extent of the current losses.
I. Data and Demographic Characteristics
The data come from the Helsinki Exchanges, Finland, which maintain a fully automated trading system based largely on an electronic limit order book.2 These
data provide the daily trading records of all individual investors (other investor
groups are discarded) from January 1995 through May 2000, and complete limit
order book data from November 1998 through May 2000.
The first data set is the same as that used by Grinblatt and Keloharju (2001),
including complete information on shareholdings and changes in shareholdings
by each investor. It also includes demographic information, such as birth year,
sex, and postal code.
This data set is supplemented with the supervisory files kept by the Helsinki
Exchanges, which shows all orders submitted to the market, and information
on the status of orders (whether executed, amended, withdrawn, or expired).
These data can be used to reconstruct the state of the limit order book for any
time during a trading day.3 The structure of the limit order data is similar to
data sets used by Biais, Hillion, and Spatt (1995) and Gouriéroux, Jasiak, and
LeFol (1999), but our cover a considerably longer period.
A. Defining Day Traders and Control Group
We define a day trader as an individual investor who makes a complete roundtrip trade during a single trading day, that is, an investor who buys and sells
the same amount of the same stock on one day. Using this criteria, we select
7,686 investors of 413,645 households in the sample who meet this criterion.
Similar to Barber and Odean (2002b), we constitute a size-matched sample of
investors not classified as day traders. We include four times as many investors
as in the group of day traders in this control group, because non-day traders are
significantly less active traders (see below). Before selecting investors for the
2 Institutional features of the Helsinki Exchanges are discussed in detail in Booth, Lin,
Martikainen, and Tse (2002). There were 154 stocks listed on the Helsinki Exchanges with a
combined market value of 192.76 billion euros in late February 2000.
3 One limitation on the limit order data is that no separate time-stamp is given for the time
of order withdrawals. We approximate this time-stamp by determining when each withdrawn
order is about to cause a conflict in the book (i.e., it must already had been withdrawn). The
robustness of this methodology was verified using data from a later time period for which the
stamp is known.
3
control group, we exclude investors with fewer than two trades during the sample
period, to match the same requirement implicitly imposed on day traders.
Investors are matched primarily on the basis of their portfolio values at the
beginning of 1999, and then according to the date they first traded. This sizematching works well. The average (median) day trader has a portfolio worth
148,538 (17,525) euros, and the average investor in the control group has a
portfolio worth 141,086 (17,528) euros.
Panel A of Figure 1 shows the number of day trading events and the fraction
of trades originating from day traders (of all trades in the market) per month
from January 1998 through May 2000. There are 48,661 day trading events,
1,023,562 trades from day traders, and 4,107,328 total trades. There is a significant increase in day trading activity in early 2000; over 55% of all day trading
events took place during the first five months of year 2000. Overall activity of
day traders during this period, as measured by fraction of trades, represents
12.46% of the total trades. That is, either the buyer or the seller (or both) in a
trade is classified as a day trader.4
The Lorenz curves in Panel B show that both trading and day trading events
are concentrated in a small fraction of all stocks. For example, 71% (76%) of
all trades (day trading events) between January 1998 and May 2000 took place
in the most active decile of stocks. Their Gini coefficients are 80.6% and 85.8%,
respectively. Day trading and trading in general are concentrated in the same
stocks; the correlation between the proportion of trades and day trading events
is 0.9.
Panel C shows the number of new day traders entering and old day traders
exiting the market per month between January 1998 and May 2000. We assume
that a day trader has exited if he does not day-trade for one month, and does
not start day trading again (thus, the number of day traders exiting in May
2000 is undefined). Taking this definition as a proxy for the actual exit rate,
the number of day traders exiting the market was approximately equal to the
number of day traders entering the market in March 2000. We report this
figure only for descriptive purposes. When the actual time of exit is unknown,
the upward bias increases with time.
The importance of the day trading phenomenon becomes obvious in Panel
C. In April 2000, over 1,000 individuals tried their luck in day trading (the population of Finland is approximately 5.2 million). The market index is reported
for reference. For example, as of the end of 1999, a sharp increase in the market
index coincided with a surge in the number of day traders. The contemporaneous monthly correlation from January 1999 through May 2000 between the
return for the market index and the relative change in the number of day traders
entering the market is 0.74 (p-value < 0.001).
Although Figure 1 does not report this, some day traders had had very little
investment experience before completing their first day trade. 318 (4.1%) of day
traders had not traded at all before starting day trading at least as of January
4 Angel
(2000) finds that, in spring 2000, 20% of new orders flowing to Nasdaq originated
from firms that cater to day traders.
4
Panel A: Day Trading Activity
8,000
18%
Fraction of All Trades
05/00
04/00
03/00
02/00
01/00
12/99
11/99
10/99
09/99
08/99
07/99
06/99
05/99
04/99
03/99
02/99
01/99
12/98
11/98
10/98
09/98
6%
08/98
0
07/98
9%
06/98
2,000
05/98
12%
04/98
4,000
03/98
15%
02/98
6,000
01/98
# Day Trading Events
# Day Trading Events
Fraction of Trades
Month
Panel B: Number of Day Traders Entering and Exiting the Market
2,000
220
Entering
Exiting
192
Market Index (01/02/1998 = 100)
1,200
164
800
136
400
108
05/00
04/00
03/00
02/00
01/00
12/99
11/99
10/99
09/99
08/99
07/99
06/99
05/99
04/99
03/99
02/99
01/99
12/98
11/98
10/98
09/98
08/98
07/98
06/98
05/98
04/98
03/98
02/98
80
01/98
0
Market Index
# Day Traders
1,600
Month
Panel C: Concentration of All Trades in the Market and Day Trading Events
Cumulative Proportion of Trades
/ DT Events
100%
All Trades in the Market
Day Trading Events
75%
50%
25%
0%
50%
60%
70%
80%
Cumulative Proportion of Stocks
90%
100%
Figure 1. Day Trading Activity and Its Concentration
A day trading event is defined as a day an investor both purchases and sells the same amount of
the same stock. Panel A shows the number of day trading events and the activity of day traders
as a fraction of all trades in the market from January 1998 through May 2000. During this period,
investors classified as day traders traded 1,023,562 times, and there were a total of 4,107,328 trades.
Panel B shows the number of day traders entering and exiting the market. The dotted line is the
level of the value-weighted and weight-constrained market index. A day trader is assumed to have
exited if he does not day-trade in one month and does not start day trading again. Panel C reports
Lorenz curves for day trading events and all trades.
1995 when the data starts. Of all the remaining day traders, 1,255 (16.3%) had
traded for the first time no more than three months before.
5
Table I
Sample Descriptive Statistics
Descriptive statistics of demographic factors and investor characteristics are shown for day traders
and investors in a size-matched control group. A day trader is defined as an investor who at least
once during the sample period completes a round-trip trade during a single trading day. Panel A
reports differences in sex, mother tongue, and whether the investor lives in the metropolitan area.
Panels B and C report age and investor characteristics such as number of trades. Each investor
is a single observation. The last column in Panel B reports z-values from the Mann-Whitney test
for the difference between day traders and investors in the control group. Trade size and portfolio
values are reported in thousands of euros. Holding period is calculated using the FIFO principle
and reported in the number of days. Daily turnover is the capital-weighted average over the entire
sample period. Portfolio values are as of January 1, 1999. For day traders, this table reports the
number of trades and the average trade size separately for a sample excluding day trading events
and for a sample consisting only of day trading events.
Panel A: Distributions of Sex, Mother Tongue, and Metropolitan Area
Day
Control
Traders
Group
z-value
N
7,686
30,748
Women
16.2%
34.9%
−31.7
Swedish speaking
6.1%
10.1%
−10.7
Metropolitan
39.9%
33.5%
10.5
Panel B: Day Traders
Age
# Days Active
Excluding DT Events
# Trades
Trade Size
Holding Period
Only DT Events
# Trades
Trade Size
Daily Turnover
Portfolio Value
# DT events
Mean
41.5
67.2
10%
25
10
25%
31
20
137.9
7.5
58.8
12
1.5
7.2
29
2.5
15.4
24.0
15.1
10.1%
148.5
7.1
2
1.7
0.6%
1.6
1
2
3.2
1.4%
5.0
1
Mean
48.7
16.0
6.2
167.7
4.6%
141.1
10%
25
2
0.7
23.0
0.1%
1.6
25%
35
3
1.4
50.3
0.2%
5.0
75%
52
85
90%
60
150
z-value
(DT−CG)
−34.8
112.3
70
4.5
35.3
154
8.2
72.0
313
15.1
135.5
106.0
35.5
−60.7
5
6.3
3.9%
17.5
2
15
13.5
11.0%
62.2
5
47
31.9
26.4%
194.0
15
−10.0
55.7
79.2
0.0
Percentiles
50%
75%
50
61
6
15
2.8
5.9
112.5
224.0
0.5%
1.7%
17.5
62.2
90%
72
36
12.1
384.2
7.1%
194.1
Percentiles
50%
39
42
Panel C: Control Group
Age
# Trades
Trade Size
Holding Period
Daily Turnover
Portfolio Value
B. Demographics of Day Traders and Control Group
Table I reports demographics and investor characteristics for both day traders
and the control group. A typical day trader is younger, more often male, lives
more often in the metropolitan area, and is less often Swedish speaking than
an investor in the control group.5 The median day trader is 39 years old, while
the median investor in the control group is 50. All these differences are highly
5 Approximately 6% of the Finnish population speak Swedish as their native language.
Karhunen and Keloharju (2001) document that Swedish speaking individuals in Finland are
on average over three times wealthier than Finnish speaking individuals.
6
significant, and are consistent with the analysis of Odean (1999) and Barber and
Odean (2001a) on sex, overconfidence, and decision to engage in online trading.6
A comparison of investor characteristics shows that the day trading and
control group investor categories are very different, even excluding trades related
to the actual day trading events (i.e., a full round-trip during a trading day).
This finding of higher activity is not surprising, and is consistent with Harris and
Schultz (1998). Day traders also trade in larger quantities. This is especially
true for trades related to day trading events: median trade sizes (in thousands of
euros) are 2.83 for the control group, 4.53 for day traders excluding DT events,
and 6.26 for day traders (only DT events). 72.38% of day traders trade in larger
average trade sizes for DT events than for their other trades. The difference is
highly significant with a t-value of 39.08.
Day trader average holding periods (excluding day trading events) are considerably shorter than the average holding periods of investors in the control
group. The median day trader holds shares on average for 35 trading days, while
the median investor in the control group holds them for 113 trading days.7
The capital-weighted average daily percentage turnover over a T -day period
is defined as:8
T
turnovert
T
t=1 position valuet−1 +
t=1 turnovert
t=1
Daily TurnoverT = T
(1)
This measure shows that the median day trader turns the portfolio over once
every 25th trading day, while the median investor in the control group does so
once every ten months.
Panel B of Table I also reports the number of day trading events for day
traders (the number of round-trip trades completed during a single day) for day
traders. Even though a considerable number of investors day-trade only once,
one-quarter of day traders engage in at least 5 day trades, and the most active
decile shows at least 15 day trading events. Given the size of the sample, this
reveals a significant number of very active day traders. It can also be observed
that day traders do not always use two trades to complete a round-trip within
a single trading day; in fact, the average (median) day trader uses an average of
2.8 (2.3) trades, and the highest decile uses an average of at least four trades.
We examine differences between day traders and investors in the control
group in greater detail (full results not reported here). First, the concentration
of day traders’ trading activity in liquid stocks is manifested in a difference
in spreads for day traders and investors in the control group. The median
6 There is some variation in demographic and investor characteristics within day traders.
Those who day-trade the most are younger and have smaller portfolios than the majority of
day traders. These results are not reported for the reasons of space.
7 The perhaps surprisingly short holding period can be explained by the fact that we do
not includes sales of stock acquired before 1995 because of the absence of data on purchase
dates. Furthermore, holding periods are evaluated only on the basis of actual sales, not on
stocks in the portfolio that are never sold during the period.
8 The reason for including turnover in the denominator is that some investors have so small
(or zero) positions that the unstandardized measure would have a very large variance.
7
percentage spread for day traders is 0.696%; the median spread for investors in
the control group is 0.725% (difference significant with a z-value of −17.7 by
the Mann-Whitney signed rank test).9 This finding that day traders prefer to
trade in more liquid stocks is consistent with the behavior of SOES bandits, as
reported in Harris and Schultz (1998) and Foucault, Röell, and Sandås (2003).
Second, day traders and investors in the control group have different types
of order submission strategies (Linnainmaa, 2003)see. We measure order submission strategies by examining the placement of the limit order relative to
the spread (outside, at, or inside), and whether the order is placed before the
trading session begins (pre-open). 47.5% of day traders’ limit orders are placed
inside the spread (i.e., they improve the spread). The corresponding figure is
38.6% for investors in the control group. These proportions are significantly
different with a z-value of 28.6. Furthermore, investors in the control group
favor placing orders before the exchange opens: 21.2% of their limit orders are
of this type, compared to 6.4% for day traders. The independence of limit order
submission strategies between day traders and investors in the control group is
rejected with a χ2(3) -value of 17, 598.
II. The Decision to Day-Trade
How much do marketwide and stock-specific factors influence day traders’ decision to day-trade? Are day traders more likely to day-trade in stocks that have
experienced large excess returns? Barber and Odean (2002a) find individual
investors seem to chase the action by trading on salient news.10
Figure 2 plots cumulative excess returns over the previous five trading sessions before a day trading event, conditional on both ownership and strategy
(buy first, sell later / sell first, buy later). If the investor day-trades a stock he
does not own, cumulative excess returns through the previous day’s close are
significantly higher than if the investor owns the stock. This finding is consistent
with the attention hypothesis of Barber and Odean (2002a).
Also, an investor who first sells a stock and later buys it back is drawn by
stocks that have appreciated more than those that the investor is betting to rise
in value. This is consistent with research finding that individual investors are
9 Spreads are measured by calculating the average spread for each stock-day and then the
median spread over all trades for day traders and investors in the control group. We use
stock-day spreads instead of actual realized spreads to ensure that differences in non-stockspecific trading behavior do not affect the results. For reference, the actual median spread
paid (earned) by a day trader is 0.513% (0.568%) and the median spread paid (earned) by an
investor in the control group is 0.564% (0.632%). These differences are similar to the results
obtained using stock-specific spreads.
10 Froot, Scharfstein, and Stein (1992) note the literature offers two reasons for trading with
short horizons: (1) money managers need to prove their skills, and (2) speculators who, in the
fear of future credit constraints, do not wish to tie up their money in long-horizon investments.
It is also possible that a day trader tries to act as a market maker. Because individuals pay
commissions and financial institutions do not, it is unlikely that any equilibrium would support
their existence. Furthermore, individuals are likely to be in an information disadvantage
(Schultz, 2003).
8
Cumulative Excess Return (%)
8%
Buy First, Does Not Own
Buy First, Owns
Sell First, Does Not Own
Sell First, Owns
6%
4%
2%
0%
-2%
-5
-4
-3
-2
Trading Day Relative To Day Trading Event
-1
Figure 2. Cumulative Excess Returns on Stock Selected for Day Trading,
Conditional on Strategy and Ownership
For each day trading event for which the strategy is identified (buy first, sell later / sell first, buy
later), the cumulative excess return on the stock traded over the previous five trading sessions is
calculated. The darker line indicates the investor owned the stock he day traded at t = 0. The
lighter line indicates the investor did not own the stock. The solid line indicates the investor first
bought the stock; the dashed line indicates the investor first sold the stock.
contrarians (e.g., Grinblatt and Keloharju, 2000).
We use a multivariate logistic regression to simultaneously account for different factors affecting day trader decisions. The dependent variable takes the
value of one if a day trader day-trades and zero otherwise. For each investor,
we include in the analysis daily observations of all stocks, even if the investor
does not trade in that stock. A stock enters the sample when the investor first
day-trades in it.
Our purpose is to construct a sample of stocks the investor is familiar with.
We assume the investor becomes familiar with a stock as he first day-trades it.
This universe of stocks is an appropriate benchmark when the investor decides
which stock to day trade. To keep the sample size manageable, we restrict the
analysis to days when the investor traded or had traded the preceding day in
any stock.
The explanatory variables for stock i (with five daily lags) are: excess return
conditional on ownership, dummy variables for investor (1) trading; (2) day
trading; (3) generating a moderate trading loss (< 3%); and (4) generating a
large trading loss (≥ 3%).11 Working with investor holdings at the previous
day’s close, we include a dummy for ownership, and a paper return from the
time of the purchase (set to zero if the investor does not own the stock). We
also include event dummies (1)-(4) for all other stocks the investor is familiar
with (i.e., has day traded). Finally, we control for the return on the market
11 Trading loss is defined as the intraday return due to trading for days when an investor
buys (and possibly sells) a stock.
9
Table II
Daily Analysis of Factors Affecting Day Trader Decision to
Day-Trade
Results of a multivariate logistic regression where the dependent variable takes the value of one if
a day trader day trades in stock i and zero otherwise. The sample consists of daily observations
of all stocks that the investor has previously day-traded. For stock i, the RHS of the regression
includes (five daily lags) the excess return conditional on ownership, and four dummy variables for
whether the investor (1) trades, (2) day trades, (3) generates a moderate trading loss (< 3%) , and
(4) generates a large trading loss (≥ 3%). These dummies are also included for all other stocks
the investor is familiar with (e.g., whether the investor traded in any other stocks besides i at date
t − 3). Ownership is a dummy that takes the value of one if investor owns the stock at the previous
close. Paper return is calculated as the unrealized return on stock i from the time of purchase. The
model also includes an intercept (not reported). t-values are reported in parentheses.
Variable
Category
Market
Variable
Return
Volatility
Stock Used
for
Day Trading
Excess Return
Excess Return × Own.
Traded
Day Traded
Trading Loss, Low
Trading Loss, High
Other Stocks
Traded
Day Traded
Trading Loss, Low
Trading Loss, High
Ownership
Ownership
Min(Paper Return, 0)
Max(Paper Return, 0)
Model
Summary
t−1
−0.026
(−11.4)
0.009
(13.1)
0.044
(50.6)
−0.040
(−27.8)
0.454
(29.9)
0.694
(40.4)
−0.016
(−0.8)
−0.116
(−3.6)
−1.074
(−91.3)
0.699
(51.3)
−0.032
(−1.9)
0.012
(0.5)
0.420
(44.6)
−0.020
(−48.3)
0.002
(12.5)
N
Block χ2
Pseudo-R2
t−2
−0.023
(−9.9)
0.009
(12.4)
0.017
(23.4)
−0.008
(−6.3)
0.536
(32.0)
0.423
(21.8)
0.062
(2.8)
−0.105
(−2.8)
−0.232
(−18.9)
0.375
(26.3)
−0.019
(−1.1)
−0.029
(−1.1)
Trading Day
t−3
−0.025
(−10.7)
0.003
(4.2)
0.008
(10.7)
−0.011
(−8.1)
0.354
(20.0)
0.320
(15.4)
0.095
(4.0)
−0.108
(−2.7)
−0.213
(−17.2)
0.285
(19.6)
−0.015
(−0.9)
0.050
(1.9)
t−4
0.020
(8.8)
0.003
(3.8)
0.002
(2.0)
−0.005
(−3.5)
0.312
(17.2)
0.313
(14.5)
0.130
(5.3)
−0.072
(−1.7)
−0.231
(−18.5)
0.288
(19.7)
0.004
(0.3)
0.005
(0.2)
t−5
0.010
(4.3)
0.006
(8.4)
0.003
(3.2)
−0.002
(−1.2)
0.367
(20.2)
0.352
(16.1)
0.105
(4.2)
−0.192
(−4.4)
−0.249
(−20.1)
0.276
(19.0)
0.019
(1.1)
0.048
(1.8)
2,012,543
78,393.0
13.9%
index (mt ) and market volatility (m2t ).
The estimated coefficients are shown in Table II. At short lags, market
volatility, trading activity in the same stock, and the stock’s excess return have a
significantly positive impact on the likelihood to day trade. A strong relationship
between ownership and excess returns persists; an investor is more likely to
day trade in a stock that he owns. In these cases, the excess return is not as
significant a determinant. For example, at day t − 1, the effect of excess returns
is only 0.003 (0.044 − 0.040 without rounding) if the investor owns the stock.
10
If the investor owns the stock, the unrealized return (paper gain or loss)
is important. Day traders choose to day-trade in stocks that they have paper
losses in. There is also evidence that the likelihood of day trading also increases
with paper gains. In other words, day traders choose to day-trade in stocks that
have been good investments. Although this type of behavior could be evidence
of irrationality (because of tax considerations), this effect is economically less
significant than the impact of paper losses (0.002 vs. −0.020).
Large trading losses in the same stock reduce the likelihood of day trading;
interestingly, for moderate losses at lags from t − 3 to t − 5, the coefficients
are positive. This could indicate that day traders are not permanently intimidated by such losses. A comparison of day trading and loss coefficients shows
that losses do not seem to be as important as the act of day trading itself.
Furthermore, trading losses in other stocks seem to make little difference.
III. Intraday Trading Behavior
To analyze the intraday trading behavior of day traders, we use limit order data
to recreate the limit order book and combine this information with transaction
records of day traders and investors in the control group.12 This allows us to
directly observe whether an investor initiated trade or supplied liquidity to the
market. More generally, the data let us to determine what the limit order book
looks like at the time each order is submitted.
A. Activity, Liquidity, and Buy-Sell Ratios During a Trading Day
We categorize trades into three groups on the basis of type: (1) trades originating from the control group, (2) trades that are part of a day trader’s day trade,
and (3) trades that originate from a day trader but are not part of a day trade.
We divide the trading day into five-minute intervals and calculate trading activity, the supply of liquidity (passive versus active orders), and buy-sell ratios
for each interval as follows:13
Activity
=
Supply of Liquidity
=
Buy-Sell Ratio =
# trades
total # trades over all periods
# passive trades
# trades
# buys
# buys + # sells
12 See
(2)
(3)
(4)
Appendix for description of the matching procedure.
are no designated market makers on the HEX. All liquidity is supplied by market
participants. A limit order is called passive if it is not immediately executed and active
otherwise. There are no market orders; an investor wishing to trade in larger quantity than is
available at the best price level must submit separate active limit orders at each price level.
13 There
11
Panel A: Activity
8%
Fraction of Trades
Control Group
Day Traders (Non-RT)
6%
Day Traders (RT)
4%
2%
1715
1700
1645
1630
1615
1600
1545
1530
1515
1500
1445
1430
1415
1400
1345
1330
1315
1300
1245
1230
1215
1200
1145
1130
1115
1100
1045
1030
0%
Time of Day
Panel B: Supply of Liquidity
Fraction of Passive Orders
70%
60%
50%
Control Group
Day Traders (Non-RT)
Day Traders (RT)
1500
1515
1530
1545
1600
1615
1630
1645
1700
1715
1500
1515
1530
1545
1600
1615
1630
1645
1700
1715
1445
1430
1415
1400
1345
1330
1315
1300
1245
1230
1215
1200
1145
1130
1115
1100
1045
1030
40%
Time of Day
Panel C: Buy-Sell Ratio
Buy-Sell Ratio
60%
50%
40%
Control Group
Day Traders (Non-RT)
Day Traders (RT)
1445
1430
1415
1400
1345
1330
1315
1300
1245
1230
1215
1200
1145
1130
1115
1100
1045
1030
30%
Time of Day
Figure 3. Intraday Trading Activity, Supply of Liquidity, and Buy-Sell
Ratios for Day Traders and Control Group
This figure plots average intraday trading activity (Panel A), supply of liquidity (Panel B), and
buy-sell ratios (Panel C) of day traders and investors in the control group. Trades of day traders
are divided into two exclusive groups: (1) trades originating from day traders that are part of roundtrip trading (i.e., day trading event) and (2) other trades. Trading day is divided into five-minute
intervals, and time of the day is shown on x-axis.
These statistics are plotted in Figure 3. Table III shows daily totals and
statistics for the opening call that takes place at 10:10a.m.
Panel A of Figure 3 shows that in all categories trading is concentrated
near the opening and the close. Day traders (round-tripping or not), however,
are significantly more active during intervals immediately before the close than
investors in the control group.
Panel B shows that day traders engaged in round-trip trading are more
aggressive in their demand for liquidity near the close. Day traders appear to
start to use active orders only when they are running out of time. Investors
12
Table III
Intraday Trading Activity, Supply of Liquidity, and Buy-Sell Ratios
Similar to the graphical presentation in Figure 3, this table shows (1) the intraday trading activity
(N ), (2) the supply of liquidity (Supp.), and (3) buy-sell ratios (B-S) of day traders and investors in
the control group. Trades of day traders are divided into two exclusive groups: (1) trades originating
from day traders that are part of round-trip trading (RT) and (2) other trades (Non-RT). Statistics
are given for the opening call and for the first and last 15 minutes of the continuous trading session.
Time
Open
10:30
10:35
10:40
···
17:15
17:20
17:25
Total
Control Group
N
Supp.
B-S
1.6
53.6
51.0
6.2
58.6
48.5
3.0
52.0
52.2
2.2
52.5
50.8
···
···
···
0.9
58.8
50.8
1.0
62.0
52.2
1.4
68.9
50.3
52.6
51.1
Day Traders (Non-RT)
N
Supp.
B-S
0.7
50.6
47.1
4.0
49.1
48.8
2.7
47.9
50.4
2.1
51.3
50.9
···
···
···
1.7
53.8
50.8
2.0
54.2
51.3
3.1
56.4
52.5
55.6
49.9
Time
Open
10:30
10:35
10:40
···
17:15
17:20
17:25
Total
z-values (Non-RT
vs. Control Group)
N
Supp.
B-S
−3.3
−2.2
−2.8
−7.7
−14.6
0.5
−0.9
−4.7
−2.0
−0.2
−1.1
0.1
···
···
···
2.1
−3.4
0.0
2.8
−5.5
−0.6
4.2
−10.8
1.9
19.3
−7.7
z-values (RT
vs. Non-RT)
N
Supp.
B-S
−0.9
3.4
8.0
0.0
−9.2
14.1
0.2
2.5
11.2
0.8
1.0
6.6
···
···
···
1.4
−3.4
−19.6
2.2
−6.8
−22.1
4.7
−14.2
−30.4
11.0
−14.1
Day Traders (RT)
N
Supp.
B-S
0.4
58.0
64.5
4.0
42.6
58.7
2.8
49.9
59.9
2.3
52.3
57.1
···
···
···
2.1
50.4
31.3
2.6
48.0
31.4
4.3
46.3
30.9
57.1
47.9
in the control group by contrast become a significant source of liquidity to the
market near the end of the trading session.
Table III shows that differences in the supply of liquidity are highly significant among all three groups. Day traders are net suppliers of liquidity, which
means they effectively frequently act as “market makers,” not only as speculators betting on the direction of the price movement. This directly suggests that
day trader behavior differs from the behavior SOES bandits, who initiate their
positions through SOES with market orders but dispose of them using limit
orders on Instinet or SelectNet (Harris and Schultz, 1998). In fact, the behavior
of day traders indicates that day traders are more like dealers than bandits (see
Foucault, Röell, and Sandås, 2003, p. 29).
Panel C of Figure 3 shows that day traders engaged in intraday trading
behave asymmetrically with respect to trading strategies.14 During the first
five-minute period of the continuous trading session, 58.73% of trades originating from day traders (round-trip) are buys. During the opening call the figure
is 64.50%. As the trading session progresses, the buy-sell ratio decreases monotonically. In the very last interval, the buy-sell ratio is 30.88%. The timing of
14 Table V shows that day traders prefer to first purchase and then sell stock when they
engage in complete round-trip trades. The net effect on the market is that there is more sell
pressure toward the end of the day.
13
Panel A: Activity
8.00%
6.00%
No Loss
Loss
4.00%
2.00%
134500
140000
141500
143000
144500
150000
151500
153000
154500
160000
161500
163000
164500
170000
171500
134500
140000
141500
143000
144500
150000
151500
153000
154500
160000
161500
163000
164500
170000
171500
133000
131500
130000
124500
123000
121500
120000
114500
113000
111500
110000
104500
103000
0.00%
Panel B: Supply of Liquidity
70.00%
60.00%
50.00%
No Loss
40.00%
Loss
133000
131500
130000
124500
123000
121500
120000
114500
113000
111500
110000
104500
103000
30.00%
Figure 4. Intraday Trading Activity and Supply of Liquidity, Conditional
on Loss
The analysis of Figure 3 (except for buy-sell ratios) is repeated by comparing behavior of day traders
on days they are day trading, conditional on realizing a loss (gross return strictly smaller than zero).
buys and sells seems to be unimportant for day traders when they are not day
trading.
Figure 4 repeats the analysis for round-tripping day traders, conditional on
realizing a loss. We define a loss as a gross return strictly lower than zero. Panel
A shows that after very closely trailing the “no loss” category for the early part
of the day, day traders who are about to realize a loss become significantly more
active approximately half an hour before the session ends. Panel B shows that
investors who are about to realize a loss revert more quickly to active orders
than when realizing a gain.
Although the last result could be affected by confusion between cause and
consequence (i.e., a trader might realize a loss because of demanding liquidity
from the market, rather than demand liquidity from the market because he is
about to realize a loss), the intraday variation is not explained by this argument.
Investors who are about to realize a loss become gradually more impatient and
shift to the use of active orders.
B. Intraday Analysis of Trade Execution
Is a day trader more likely to buy after another buyer has demanded liquidity
from the market, and is a day trader more likely to submit a passive order when
he wants to trade in larger quantities? To investigate questions such as these,
we specify two multivariate regressions: (1) in a sample consisting of all buys
14
and sells where the day trader demands liquidity, wr set the dependent variable
to one for buys and zero for sells and (2) in a sample consisting of buys (sells),
we set the dependent variable to one if the day trader demands liquidity.
As explanatory variables, we include trading activity (measured as logarithm
of the sum of number of orders and number of trades); logarithm of the time
since the previous trade (following Dufour and Engle, 2000); the logarithmic
difference between ask and bid prices (spread); and changes in the size of the
spread, the midpoint of the spread, and the bid and ask depths. We calculate
the depth of the book at bid and ask sides at a 2.5% interval from the midpoint
of the spread (i.e., if the spread midpoint is 10e, the buy depth is defined as the
total volume outstanding on the interval [9.75, 10]). Changes are measured by
comparing the state of the book five minutes before the trade and immediately
before the trade. For the previous five transactions, we include dummy variables
to capture whether the trade is an up- or downtick and whether it is buyeror seller-initiated.15 We include separate dummies to capture cases where all
previous five trades are either buyer- or seller-initiated. The second regression
also includes logarithm of order value as an explanatory variable.
The Columns (a) in Table IV present the results for the buy vs. sell decision.
Dummies for previous ticks and trade initiator show that day traders follow
momentum in their intraday trading; they are more likely to buy when the
price has risen and sell when the price has dropped, as well as buy (sell) when
the previous trade was initiated by a buyer (seller). This intraday momentum
should not be confused with the conventional definition of the dependence of
buys and sells on past (longer term) price movements.
The direction of a trade is more important than whether the trade generates
an up- or downtick. At one trade lag, for example, coefficients for the uptick and
the buyer-initiated trade dummies are 0.06 and 0.56, respectively. Furthermore,
these effects are asymmetric; an uptick does not increase the propensity to buy
as strongly as the downtick reduces it. The change in the midpoint of the spread
during the past five minutes is insignificant, suggesting that transaction time
variables better capture the factor that drives day trader decisions.
Increase in the volume at the bid (ask) side of the book increases (reduces)
the propensity to buy. This also indicates that day traders attempt to trade in
the same direction as the incoming order flow. The coefficient of the percentage
spread shows that day traders are more likely to buy when the spread is wide.
Columns (b) and (c) of Table IV analyze the choice between active and
passive orders, conditional on whether the order is a buy or sell.16 For passive
orders, we look at the state of the book at the time an order is submitted (i.e.,
when the investor enters the passive order in the book) instead of the time of
15 Trades
can also also occur in the upstairs market as prenegotiated trades, which ensures
that some 10% of trades in the sample are neither buyer- nor seller-initiated (Linnainmaa,
2003).
16 Bae, Jang, and Park (2002) analyze the submission of limit and market orders on the
NYSE and find that liquidity is provided when the spread and the order size are large. Griffiths, Smith, Turnbull, and White (2000) and Ranaldo (2002) study the determinants of order
aggressiveness. Our analysis differs significantly from theirs in that we focus on a specific
group of investors.
15
Table IV
Intraday Analysis of Factors Influencing Day Trader Decision to
Trade
Limit order book data is matched with transaction records of day traders to analyze the impact
of market microstructure variables on day traders’ buy/sell decision (Columns a) and decision to
use active versus passive order (Columns b and c). In Columns a, the sample is limited to cases
where the investor is demanding liquidity from the market and the dependent variable takes the
value of one if the investor buys. In Columns b (c), the sample consists of all buys (sells), and the
dependent variables takes the value of one if the investor demands liquidity. Activity is defined as
logarithm of the number of new orders and trades in the stock during the past five minute period.
Last Trade is defined as logarithm of the time from the previous trade (in seconds + 1). Spread is
the log-difference between best ask and bid prices. Change in Spread is the percentage change in
the size of the spread during the past five minute period. Change in Mid Quote is the log-difference
between the midpoint of the spread immediately before the trade and the midpoint five minutes
earlier. Change in Bid Depth (Ask Depth) is calculated as the change in the volume outstanding in
the book within 2.5% interval from the midpoint of the spread during the past five minute period.
Upticki−j (Downticki−j ) takes the value of one if trade i − j takes place at a higher (lower) price
than trade i − j − 1. Buyer-Initiatedi−j (Seller-Initiatedi−j ) takes the value of one if trade i − j
is initiated by the buyer (seller). Buyer-Initiatedall (Seller-Initiatedall ) takes the value of one if all
previous five trades are initiated by the buyer (seller). Logarithm of the order value is included as
an additional explanatory variable in Columns b and c. All regressions also include time dummies
for all 30-minute periods except for the one from noon to 12:30p.m. and an intercept (not reported).
Variable
Activity
Last Trade
Spread
Change in Spread
Change in Mid Quote
Change in Bid Depth
Change in Ask Depth
Upticki−1
Upticki−2
Upticki−3
Upticki−4
Upticki−5
Downticki−1
Downticki−2
Downticki−3
Downticki−4
Downticki−5
Buyer−Initiatedi−1
Buyer−Initiatedi−2
Buyer−Initiatedi−3
Buyer−Initiatedi−4
Buyer−Initiatedi−5
Buyer−Initiatedall
Seller−Initiatedi−1
Seller−Initiatedi−2
Seller−Initiatedi−3
Seller−Initiatedi−4
Seller−Initiatedi−5
Seller−Initiatedall
Ln(Order Value)
N
Block χ2
Pseudo-R2
Dependent Variable
(b) Market vs.
Limit (Buys)
Coeff.
t-value
−0.102
−26.3
−0.184
−72.4
−12.829
−38.8
−0.021
−13.9
−6.039
−15.5
0.071
15.8
−0.093
−20.8
−0.010
−1.0
−0.006
−0.6
−0.021
−2.1
−0.022
−2.1
−0.043
−4.1
−0.054
−4.8
−0.032
−2.9
−0.049
−4.4
−0.051
−4.6
−0.002
−0.2
0.409
31.9
0.086
6.5
0.020
1.5
−0.039
−2.9
−0.036
−2.8
−0.045
−3.3
−0.086
−6.2
−0.092
−6.6
−0.138
−9.9
−0.195
−14.6
0.047
3.7
−0.100
−6.0
−0.174
−56.4
321,904
25,366.3
5.7%
(a) Buy vs. Sell
for Market Orders
Coeff.
t-value
0.105
26.1
0.016
7.3
2.582
7.9
−0.004
−2.8
−0.050
−0.1
0.224
42.5
−0.193
−37.0
0.060
5.3
0.045
3.9
0.027
2.3
0.000
0.0
−0.041
−3.5
−0.219
−19.4
−0.099
−8.6
−0.055
−4.7
−0.019
−1.6
0.028
2.5
0.560
38.9
0.145
10.0
0.110
7.5
0.085
5.8
0.109
7.8
0.118
7.8
−0.659
−44.9
−0.175
−11.9
−0.095
−6.4
−0.063
−4.3
−0.081
−5.7
−0.094
−5.8
301,204
51,551.9
12.3%
16
(c) Market vs.
Limit (Sells)
Coeff.
t-value
−0.137
−34.2
−0.191
−76.7
−16.006
−45.0
−0.025
−14.5
4.478
10.3
−0.105
−21.3
0.060
12.3
−0.036
−3.2
−0.048
−4.3
−0.041
−3.7
−0.051
−4.6
−0.021
−1.9
0.134
13.4
0.052
5.0
0.042
4.0
−0.022
−2.1
−0.028
−2.6
−0.062
−4.6
−0.061
−4.4
−0.144
−10.5
−0.146
−10.7
−0.259
−19.6
−0.071
−4.7
0.532
39.8
0.136
10.0
−0.011
−0.8
−0.024
−1.8
−0.102
−7.6
0.018
1.3
−0.265
−84.1
328,866
41,099.7
9.1%
the trade.
(A potential problem with the analysis of passive orders is that the sample
consists only of orders that are ultimately executed; e.g., orders that are put
further away from the spread are less likely to get executed (see, e.g., Handa
and Schwartz, 1996; Lo, MacKinlay, and Zhang, 2002). Thus, the sample is
not representative of order submission strategies. We see no reason to believe
this filtration would bias analysis of the differences between active and passive
orders.)
Changes in the depth of the book and initiator coefficients show that when
day traders demand liquidity, they are trying to go with the flow more than
when they are supplying liquidity. For example, if the day trader buys after a
buyer-initiated trade, he is more likely to do so using a market order rather than
a limit order. Changes in the depth of the book also show that when investors
demand liquidity, they base their decisions more heavily on the behavior of
other market participants (incoming order flow). Negative coefficients on the
time since last trade and trading activity show that a day trader is more likely
to supply liquidity when there has been more activity in the stock (i.e., the
probability of execution is higher).
Order size, spread, and change in the spread show that day traders are
supplying liquidity when they (1) want to trade in larger quantities, (2) are
facing wider spreads, and (3) when the spread has worsened. The converse is
more important: Day traders supply liquidity when it is valued the most, thus
improving market quality. This finding is not sufficient to conclude that the
overall impact on market quality is positive. For example, it could be that day
traders’ high demand for liquidity near the open and the close disrupt the price
discovery process and add unnecessary volatility.
The same asymmetry between buys and sells is apparent in Columns (b) and
(c). First, the overall explanatory power of the model (judging by the pseudoR2 statistic) is higher for sells. Second, individual coefficients indicate stronger
relationships, e.g., the trade size is unimportant for buys but highly significant
for sells. Overall, the results in Table IV suggests that market microstructure
considerations are more important for an investor wishing to sell.17
IV. Performance of Day Traders
Are day traders able to take advantage of minute price movements? Or do
they day-trade because they have superior information, at least compared to
similar investors? If day trading is driven by these factors, day traders would
be expected to outperform the control group that never day-trades.
To see whether this is the case, we analyze rates of return. First, we study
17 A potential explanation for the asymmetry between buys and sells is that the sell transaction is most often the trade that “realizes” the return. An investor buying a stock may
believe the trade will generate enough return to dilute the importance of the spread and the
intraday timing of the transaction. When the investor is selling the stock, the price taken
from the market is directly reflected in the return.
17
Table V
Realized Returns from Day Trading Events
A day trading event is defined as a day an investor both buys and sells stock i. Average gross
returns – assuming no commissions and taking the value of buys as capital – are partitioned by
strategy (buy first, sell later / sell first, buy later) and stock ownership (owns the stock / does not
own). F -value is from analysis of variance of the means.
Strategy
Buy First
Buy First
Sell First
Sell First
Total
Ownership
No
Yes
No
Yes
N
19,817
7,417
6,425
5,019
38,678
Mean
1.50%
1.20%
0.79%
2.18%
1.41%
Median
1.21%
0.90%
0.72%
1.72%
1.13%
s.e.
0.16%
0.22%
0.26%
0.30%
0.11%
F -value
p-value
88.21
< 0.001
the returns day traders realize from day trades. While the realized returns are
highly positive, this appears to be a consequence of the fact that day traders
are reluctant to realize losses. Second, we compare the overall performance of
day traders and the control group. Their performance records do not differ
significantly. Finally, we show that neither group seems to have stock selection
skills.
A. Day Traders’ Day Trades
Our investigation of the returns that day traders realize from day trades reveal that returns are high because of a strong reluctance to sell losers.18 The
hypothesis that day traders have information places a testable restriction on
the profitability of day trades; if day trading is nothing more than investor exploitation of private signals, returns should be largely independent of ownership,
strategy, and amount of leverage.
We classify each day trading event according to whether an investor owns the
stock he day trades and whether he (1) first buys and later sells or (2) first sells
and later buys the stock.19 Finland is different from many other markets in that
some (online) brokerage firms allow intraday short-selling without a requirement
to borrow the stock or to post additional capital.
Table V reports the gross rate of return that day traders realize from full
round-trips, and frequencies of different strategies. In 70.4% of all events, an
investor first buys a stock and later sells it. This finding could be particular to
the period under study (which represents a bull market), and not necessarily
an indication of a reluctance to assume short positions that could result in
18 Odean (1998) and Grinblatt and Keloharju (2001) offer empirical evidence. At a theoretical level, the disposition effect is proposed in Shefrin and Statman (1985), based on the
prospect theory of Kahneman and Tversky (1979). Shefrin and Statman augment prospect
theory with mental accounting, regret aversion, and self-control to describe a general tendency
to sell winners and to hold losers.
19 It is not possible to classify all events based on strategy because of an ambiguity in the
sequence of trades within a trading day when trading records and limit order data do not
match each other. This results in a loss of 13,517 observations, or 28.9% of the sample.
18
unbounded losses.20
A large share (32%) of day trading activity takes place in stocks that the
investor owns. Day traders earn very high returns, 1.41%, on full round-trips
(no commissions are assumed; for full round-trips, k commissions would reduce
estimates by 2k). These returns, which are similar to the findings in Schlarbaum,
Lewellen, and Lease (1978), would appear high even if traders paid, say, 0.25%
in trading commissions.
Schlarbaum, Lewellen, and Lease (1978) hypothesize that investors have
some target in mind, and when this target is reached, they sell the stock. Categorization of results according to strategy and ownership indicates that reluctance to realize losses plays a role. The third row in Table V shows that when an
investor first sells a stock he does not own, he earns only 0.79% in comparison
to 2.18% when he owns the stock.
In the first case, the investor is constrained. He either has to be able to lend
the stock, or he has to buy back the shares. In the latter case, the investor
faces no constraint at all. This difference could indicate that when the investor
is less likely to be able to keep a losing position, he also displays more modest
returns.21
There is evidence of reluctance to realize losses, in the relationship between
leverage and returns. An investor might be forced to close a position if he is
highly leveraged (except when the investor sells something he owns). A major
online brokerage firm in Finland determines investors’ allowed trading positions
based on their (1) shareholdings and (2) balances kept in the broker’s account.
Because we do not have data on the second component, we proxy for overall
leverage as follows:
Trading Capitalt
(5)
Leveraget = ln
Position Valuet−1
when defined (i.e., the investor must have holdings at t − 1). Trading capital is
the value of buys, or the value of sells if the investor is known to sell first. The
position value is the value of shareholdings at the close on day t − 1. We form
deciles based on this variable and calculate mean day trading event return for
each category.
Figure 5 shows that leverage has an almost monotonic impact on day trading
event returns. Starting from an impressive return in excess of 2.5%, the return
20 There is evidence in the research to support the reluctance hypothesis. For example,
Almazan, Brown, Carlson, and Chapman (2001) find that only 10% of mutual fund managers
who are allowed to take short positions actually do so. In unreported analysis, we find that
the relative frequency of the “sell first, buy later” strategy remains about the same over the
entire sample period and that it is in fact below average in the start of the downturn in May
2000. This suggests that reluctance to assume short positions is the cause, not the market
conditions.
21 In unreported work, we examine the returns from partial day trades (where the investor
reverses at least 50% of the position accumulated during the trading day). We find the same
pattern in returns across strategy and ownership categories but at a significantly lower overall
level. This could be interpreted as evidence that day traders do partial day trades when things
go awry.
19
Gross Return from a Round-Trip (%)
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
Low
2
3
4
5
6
Leverage (Deciles)
7
8
9
High
Figure 5. Impact of Leverage on Day Trading Event Returns
Amount of leverage is proxied by logarithm of the trading capital divided by portfolio value at
yesterday’s close, ignoring cases where the investor does own any shares. Mean gross returns of
day trading events are calculated for each deciles formed on the basis of the leverage variable. The
dashed line shows the 95% confidence interval around the mean.
drops to approximately 0.5% when leverage becomes high. This is consistent
with a conclusion that investors are unwilling to realize losses. If possible, they
prefer to hold on to their losing trades.22
B. Measuring Returns
We investigate differences in the overall performance of day traders and the
control group by comparing their daily capital-weighted average total returns.
We also decompose this return into the rate of return earned on stocks held in
the portfolio and the gain from intraday trading (e.g., if an investor buys stock,
we call the change from purchase price to the close the intraday return). We
calculate returns as capital-weighted averages to account for investors’ timevarying market participation; more weight is given to periods the investor is
more highly invested in the market.
We define trading, position, and total profits as follows:
22 In unreported work, we find that day traders are less likely to close their positions when
the stock price moves against them. We proxy for a day trader’s intention to day-trade by
focusing on days he buys a stock and does not sell it after day trading in it at least once
during the previous five trading sessions. The stock on average depreciates by 0.93% to the
close on these days, while the change in value for buys that are not surrounded by day trading
events is not significantly different from zero.
20
Πtrading
=
max[buy volume − sell volume, 0] Pt −
max[sell volume − buy volume, 0] Pt−1 +
Πposition
=
value of sells − value of buys
rt (nt−1 − sell volume) Pt−1
Πtotal
=
Πtrading + Πposition
(6)
(7)
(8)
where P is the stock price at the close (t for today and t − 1 for yesterday), r
is the stock’s return from close to close, and n is number of shares held.
In calculating the trading profit (6), we first record the gain from the trade to
the close for buys and the gain from the previous close to the time of trade (i.e.,
the price at which the trade takes place) for sells. Then, we adjust the profit
for the cash flows generated by the buys and sells. This definition of trading
profit captures the effect of trading activity (including day trading activity) on
performance; Barber and Odean (2000) attribute this difference to the spread.
Our definition of the position profit (7) is the gain on shares held from close
to close. The total profit (8) is the sum of these two components.
We define the amount of capital (C) required to generate the profit as follows:
Ctrading
= value of buys + max(sell volume − buy volume, 0) Pt−1 (9)
Cposition
Ctotal
= (nt−1 − sell volume) Pt−1
= Ctrading + Cposition
(10)
(11)
For Equation (9), we assume that buys precede sells. If the sell volume is
higher than the buy volume (i.e., the investor is selling shares from the portfolio),
we adjust capital by the price at the previous close (ct−1 ) times the sell volume
exceeding the buy volume.
The capital required by the position (10) is simply the value of shares at
t − 1, after subtracting shares that were possibly sold at t. The total capital
(11) is the sum of the capital used in trading and the capital needed to uphold
the position. By these definitions, the average capital-weighted total return over
a period of T days can be calculated by taking the sum over all I stocks:
rtotal =
I
i=1
I
i=1
T
t=1
Πtotal,i,t
t=1
Ctotal,i,t
T
Table VI demonstrates the calculation of returns for three examples.23
21
(12)
Table VI
Example of Measuring Returns
Three examples of calculation of capital-weighted average total return and its components are shown.
In example 1, an investor buys 100 shares (with an average price of 1050/100 = 10.5). In example
2, the investor sells 100 shares, and in example 3, the investor buys 100 shares and sells 200 shares.
(1)
10.0
10.5
500
100
0
1,025
0
Yesterday’s Close (Pt−1 )
Today’s Close (Pt )
Position Yesterday (nt−1 )
Buy Volume
Sell Volume
Value of Buys
Value of Sells
+ Value of Buys at t
− Value of Sells at t − 1
+ Cash-Flow from Sells
− Cash-Flow from Buys
= Trading Profit (Πtrading )
Position Profit (Πposition )
Total Profit (Πtotal )
Example
(2)
10.5
11.0
600
0
100
0
1,100
(3)
11.0
10.0
500
100
200
1,050
2,200
1,050
0
0
−1,025
25
250
275
0
−1,050
1,100
0
50
250
300
0
−1,100
2,200
−1,050
50
−300
−250
+ Value of Buys
+ Net Sells
= Trading Capital (Ctrading )
Position Capital (Cposition )
Total Capital (Ctotal )
1,025
0
1,025
5,000
6,025
0
1,050
1,050
5,250
6,300
1,050
1,100
2,150
3,300
5,450
Trading Component
Position Component
Total Return
0.4%
4.1%
4.6%
0.8%
4.0%
4.8%
0.9%
−5.5%
−4.6%
C. Evaluating Performance
Table VII reports capital-weighted trading, position, and total return estimates
for day traders and the control group. Total return is reported (1) without
commissions (Panel C), and (2) assuming that the commission per trade is
10 euros and 0.25% of the value of the trade (Panel D). This assumption of
commissions is based on the lowest rate advertised by a major online brokerage
firm in Finland at the time.
A comparison of total returns shows that before commissions, day traders
perform no better but no worse than investors in the control group. The average
daily total returns for day traders are 0.093% compared to 0.102% for the control
group, not significantly different. After assuming 10 euros plus 0.25% commission, the control group generates a significantly higher total return (0.095%)
than the day traders (0.057%). This suggests that day traders are not trading
on superior information. This finding is important because it shows that day
traders, unlike devoted SOES bandits, are unable to earn returns that would
justify their existence (Harris and Schultz, 1998).
23 In calculating returns, we ignore days when the investor appears to have a negative position. This decision does not affect the results because there are few such observations, but is
consistent with the treatment in Grinblatt and Keloharju (2001).
22
Table VII
Comparison of Performance of Day Traders and Control Group
This table reports the capital-weighted average total return, the trading component, and the position
component of the total return separately for day traders and the control group. All returns are
reported in basis points. Means and medians reported in this table are calculated across investors.
The total return is reported (1) without commissions and (2) assuming that commission per trade
is 10 euros and 0.25% of the trade value. The position return is the rate of return an investor earns
on stocks held in the portfolio from close to close. t-value (z-value from the Mann-Whitney test)
reports the difference in means (in mean ranks) between day traders and investors in the control
group.
Panel A: Trading Component (No Commissions)
Day Traders
Control Group
Period
Mean
Median
s.e.
Mean
Median
1998 / I
2.43
0.00
0.38
−0.03
0.00
1998 / II
2.65
0.00
0.36
0.17
0.00
1999 / I
2.36
0.03
0.39
1.00
0.00
1999 / II
1.74
0.60
0.91
−4.50
0.00
3.87
0.87
0.59
0.81
0.00
2000 / I†
All
3.68
1.10
0.44
−0.82
0.01
s.e.
0.11
0.07
0.19
0.53
0.26
0.24
t-value
6.3
6.8
3.2
5.9
4.8
9.0
MannWhitney z
25.6
23.7
24.9
34.9
−27.7
40.0
Panel B: Position Component
1998 / I
24.20
20.73
1998 / II
10.67
0.00
1999 / I
14.73
13.03
1999 / II
47.37
40.22
−17.90
−13.23
2000 / I†
All
5.61
9.45
26.00
3.69
13.59
44.32
−9.73
11.02
22.49
−5.74
12.07
37.81
−6.56
10.45
0.15
0.34
0.16
0.32
0.26
0.19
−4.9
9.0
3.2
4.2
−14.5
−12.7
−8.5
11.3
3.5
5.5
−22.2
−13.5
25.97
3.87
14.59
39.82
−8.92
10.20
22.60
−5.75
12.30
38.38
−6.26
10.70
0.19
0.35
0.26
0.63
0.37
0.31
1.2
10.9
4.4
7.2
−6.0
−1.4
−2.5
14.5
11.1
11.1
−13.8
−1.8
Panel D: Total Return (10 Euros per Trade + 0.25% of Trade Value)
1998 / I
25.16
21.58
0.51
25.65
22.49
0.19
1998 / II
11.41
1.01
0.79
3.53
−5.87
0.35
1999 / I
15.26
13.67
0.50
14.14
12.18
0.26
1999 / II
46.72
42.32
1.13
39.15
38.14
0.64
−18.38
−13.25
0.77
−9.58
−6.56
0.37
2000 / I†
All
5.66
10.58
0.58
9.50
10.46
0.32
† Consists of only the first five months of year 2000.
−0.9
9.1
2.0
5.8
−10.4
−5.8
−4.9
12.4
6.8
8.4
−20.5
−9.2
0.33
0.70
0.31
0.65
0.50
0.38
Panel C: Total Return (No Commissions)
1998 / I
26.63
22.31
0.52
1998 / II
13.32
2.18
0.80
1999 / I
17.09
14.46
0.50
1999 / II
49.11
44.29
1.12
−14.03
−10.81
0.76
2000 / I†
All
9.28
12.40
0.57
The performance records of the two investor categories vary across subperiods. Day traders perform better from late 1998 through the end of 1999, but
during the peak of day trading in early 2000, their performance is significantly
worse. These losses are weighted more heavily than earlier gains, because day
traders were more heavily invested in the market during this period.
The extent of the increase in their trading activity becomes apparent when
we compare total returns with and without commissions. In early 1999, the
difference in mean total returns without and with commissions for day traders
is only 1.8 basis points (0.171%−0.153%), compared to 4.4 bps a year later. The
position component of returns indicates that the reason for the poorer returns
in early 2000 is the return day traders earn on their portfolios.
23
Table VIII
Own Benchmark-Adjusted Position Returns
This table shows a modified version of the Grinblatt and Titman (1993) own benchmark measure.
The realized capital-weighted average position return is compared to the capital weighted average
position return an investor would have earned had he held the same portfolio as one month earlier.
Average returns are calculated over days when the investor is both (1) currently invested and (2)
has a non-zero lagged portfolio to avoid effects of market timing. Returns are reported in basis
points. It is required that the investor is invested in the market for at least a month.
Day Traders
Control Group
Period
Mean
Median
s.e.
Mean
Median
1998 / I
−1.17
−0.25
0.25
0.06
0.00
1998 / II
6.63
2.79
0.39
4.28
0.83
1999 / I
−1.39
−0.67
0.27
−2.61
−1.13
1999 / II
−3.13
−0.93
0.30
−1.28
−0.10
−1.05
−0.33
0.60
−0.70
−0.01
2000 / I†
All
−0.85
−0.51
0.37
−0.30
−0.09
† Consists of only the first five months of year 2000.
s.e.
0.11
0.16
0.14
0.09
0.20
0.09
t-value
−4.5
5.6
4.1
−5.9
−0.6
−1.4
MannWhitney z
−5.2
9.9
8.8
−8.8
−1.8
−6.0
The overall returns are driven largely by the position component, but the
trading returns of day traders are consistently better than those earned by the
control group. This could reflect the fact that day traders are more likely to
sell stocks when they have appreciated from the previous close; 60.72% of all
day trader sells take place at a price higher than the previous close compared
to 59.51% for the control group (results not tabulated here). The difference of
121 bps is highly significant with a z-value of 10.3.
If a day trader sells a stock after it has appreciated in value from the previous
close, the resulting gain is recorded as a short-term (trading) profit. If an
investor in the control group keeps the stock in the same situation, the resulting
gain will be recorded as a position profit.
It is possible that day traders may show similar performance to the control
group and yet possess superior information, if they take less risk than the control
group. To evaluate this proposition, we estimate the own benchmark adjusted
measure of Grinblatt and Titman (1993). For each investor, we calculate the
realized capital-weighted average total return and the return the investor would
have earned had he held the portfolio he owned one month earlier. We assume
an investor changes the position at each close without paying commissions. We
require that the investor is both (1) currently invested in the market and (2)
holder of a non-zero lagged portfolio to eliminate the effects of market timing.
The estimates are reported in Table VIII. For the entire sample, the average
day trader (investor in the control group) was approximately 0.9 bps (0.3 bps)
worse off each day with his new portfolio. The difference between investor
categories is highly significant with a t-value of −6.8. Like the results on total
returns, this result militates against a conclusion that day traders have better
stock picking skills than the control group.24
24 In unreported work, we examine whether this result could be explained by increasing day
trader risk aversion. We compare the number of investors switching to a strictly inferior
portfolio (higher volatility, lower returns) to the number of day traders switching to a strictly
24
Table IX
Day Trading Returns at Different Career Points
This table shows the average rate of return day traders earn at three different career points. Returns
are defined as follows: the return from the first day trade, the return from the last day trade, and
the capital-weighted average return from all other day trades. Only investors who day traded on at
least three different dates are included.
Career Point
First Day Trade
Other Day Trades
Last Day Trade
Mean
1.94%
1.58%
1.18%
s.e.
0.11%
0.06%
0.08%
Median
1.33%
1.24%
0.96%
N
3041
3041
3041
F -value
p-value
19.02
< 0.001
V. Careers
Why do day traders stop day trading, and what are their careers from the first
day trade to the last like? Does their trading behavior remain similar throughout
their careers, or are there systematic changes? For example, conditional on
staying in the market, do day traders start to take on more risk? This section
examines these questions.
A. Comparison of Returns and Decision to Quit
We have shown that trading losses have little effect on day trader selection of
stock to trade. It is conceivable that losses still have a greater impact on day
traders’ careers. A sufficiently large loss, or the accumulation of smaller losses,
might persuade a day trader to exit the market. We look at this relationship
in two ways. First, we compare returns day traders earn during different points
in their careers. Second, we use a logistic regression to show that there is a
monotonic relation between contemporary trading loss and the likelihood of
exit.
Table IX shows the gross returns investors earn on their first, last, and (on
average) all other day trades. The sample is limited to day traders who day
traded at least on three separate dates. The average day trader earns 1.94% on
his first day trade, 1.58% on trades between the first and the last, and 1.18%
on the last day trade. The mean returns differ significantly from each other,
suggesting that there may be a relationship between returns and careers.
Note that the aggregate gross returns from day trading are relatively constant over time (not reported). This means that the result in Table IX is unlikely
to be due to careers coinciding with a downturn in the market.
We examine the relationship between returns and the decision to quit by
investigating to what extent cumulative trading returns help to explain day
traders’ decision to exit. An investor enters the sample as he makes his first
superior portfolio (lower volatility, higher returns). During the sample period, 37.39% of day
traders were able to switch into a superior portfolio compared to 42.45% of the control group.
This difference is highly significant with a t-value of −5.0; providing additional evidence to
support the claim that day traders do not have superior information or skills than the control
group.
25
day trade and exits the sample when he trades for the last time. The last trade
is not required to be a day trade. Each day the investor trades constitutes a
separate observation. The dependent variable takes the value of one if investor
has day traded for the last time (including the day of the last day trade).
We include all trading days after the last day trade to account for the possibility that day traders are reluctant to realize losses. If they are, the loss that
makes them quit the market is not necessarily generated through a day trade
but rather through a non-day trading event that was intended as a day trade.
As explanatory variables, we include monthly dummies for all months starting with January 1998, a dummy for a day trading event, and dummies for the
size of the cumulative trading loss.
The results are shown in Table X. Block χ2 indicates that the day trading
event dummy and cumulative trading loss dummies help to predict the decision to cease day trading. For all cumulative trading loss dummies except the
smallest (between −1% and 0%), losses significantly increase the likelihood that
the day trader will no longer day-trade. The finding that small trading losses
do not persuade an investor to quit is consistent with the results in Table II.
The likelihood that the day trader will quit increases with the extent of the
cumulative trading loss.
This can be confirmed by running a linear regression bi = â + b̂ · i + εi ,
where bi is the ith loss coefficient in the model. The likelihood of exit increases
significantly with the extent of loss with a t-value of 4.9. Together, these findings
show that losses are a very important determinant of a day trader’s decision to
quit day trading.
In unreported work, we find that day traders who are starting out (those
making their first day trades) are more sensitive to small losses than day traders
later in their careers. These results are not tabulated for the sake of brevity.
B. Changes in Behavior
Table XI reports results on whether a day trader changes his behavior as he stays
in the market. We calculate the proportion of the most aggressive strategy (No
Ownership, Sell First), trade size relative to the first day trading event, and
amount of leverage. Panel A includes all day traders. Panel B restricts the
sample to day traders who day-traded at least ten times. Panel B is included
to illustrate that a survival bias does not affect the results. That is, it is not
that less aggressive investors never day-trade more than, say, three times.
The results show that day traders grow more confident or more aggressive as
they remain in the market. First, they switch to more aggressive strategies. In
Panel B, in only 8.1% of first day trade events does the day trader sell a stock
he does not own; this proportion doubles by the fifth day trade.
Second, day traders begin to make larger trades. For example, the median
tenth day trade is over 50% larger than the first day trade.
Third, estimates of the amount of leverage show that the results prevail not
only because day traders become wealthier or invest more into the market, but
also because they trade in larger quantities relative to their portfolios.
26
Table X
Impact of Losses on Day Trader Decision to Leave the Market
This table shows the results from an analysis of how losses affect a day trader’s decision to quit.
A day trader enters the sample as he makes his first day trade and leaves the sample as he trades
(not necessarily a day trade) for the last time. Each day the investor trades is a single observation.
The dependent variable in the multivariate logistic regression is set to one when investor has day
traded for the last time (including that day), and zero for all days before this. Day Trade is a
dummy that takes the value of one if investor day-trades. Cumulative trading loss is calculated
by dividing trading profit (6) up to current day by trading capital (9). This cumulative trading
loss is partitioned into 11 categories with dummy variables. An intercept and monthly dummies
for all months starting with January 1998 are also included (not reported). Block χ2 and Change
in Pseudo-R2 report how the model changes as variables other than the intercept and monthly
dummies are added.
Variable
Coefficient
Day Trade
−1.18
Cumulative Trading Loss
[−1%, 0%)
−0.07
[−2%, −1%)
0.29
[−3%, −2%)
0.55
[−4%, −3%)
0.63
[−5%, −4%)
0.83
[−6%, −5%)
0.88
[−7%, −6%)
1.34
[−8%, −7%)
0.83
[−9%, −8%)
1.01
[−10%, −9%)
0.93
< −10%
1.27
t-value
−87.5
−8.2
16.0
17.8
13.0
12.2
9.5
9.8
5.0
5.7
4.3
9.2
Model Summary
N
Pseudo-R2
Block χ2(12)
Change in Pseudo-R2
294,493
4.8%
7,584.3
2.8%
VI. Conclusions
The daily trading records of 7,686 investors classified as day traders, with complete limit order data for an intraday analysis of their trading behavior, allow
an investigation of behavior and performance of day traders.
Analysis of the factors that influence how day traders pick a stock to daytrade, indicates they day-trade in stocks that they either already own, have
day traded before, or that have experienced high excess returns during previous
trading sessions. Day traders concentrate their trading near the market opening
and close. Because they prefer to first purchase and later sell, the net buy-sell
ratio for round-tripping day traders decreases monotonically during the day.
Day traders who are about to realize a loss from a round-trip do so very late
in the trading session. An intraday analysis of trade execution shows that day
traders attempt to go with the flow; they buy (sell) after an uptick (downtick)
and after a buyer-initiated (seller-initiated) trade. They pay close attention to
changes in the bid and ask prices and depth of the book, and are conscious of
the spread. An analysis of the decision between active and passive limit orders
reveals that day traders are more likely to submit liquidity when it is valued
the most; the likelihood of a passive order increases with trade size and spread.
The performance of day traders does not significantly differ from performance of a size-matched control group. It is thus unlikely that day traders
are exploiting private signals or are engaged in profitable market making. A
potential explanation for day trader failure to achieve superior returns is their
27
Table XI
Changes in Behavior over Day Traders’ Careers
Day traders’ careers are partitioned into eleven dates: (1) first ten day trading event days and (2)
a single category that includes all day trading days larger than ten. Frequencies of the the most
aggressive strategy (“No Ownership, Sell First”), size of each day trade relative to the first day
trading event, and the amount of leverage (see Equation (5)) are calculated for each date. Mean,
standard error (s.e.), and median (Md) are reported. Panel A includes all day traders. Panel
B restricts the sample to day traders with at least ten trading day events to control for possible
survival bias.
Panel A: All Day Trading Events
No Ownership,
Size Relative to
Sell First
First Event
Event
Prop.
s.e.
Mean
s.e.
Md
1
7.2%
0.3%
1.03
0.01
1.00
2
9.6%
0.5%
1.88
0.07
1.04
3
11.2%
0.7%
2.10
0.07
1.13
4
12.5%
0.7%
2.33
0.11
1.18
5
14.7%
0.9%
2.49
0.14
1.28
6
15.3%
0.9%
2.70
0.13
1.31
7
16.5%
1.0%
2.87
0.16
1.35
8
16.5%
1.1%
2.93
0.19
1.34
9
15.1%
1.1%
3.08
0.24
1.35
10
16.5%
1.2%
2.90
0.23
1.44
> 10
22.2%
0.3%
3.82
0.06
1.57
Mean
−1.33
−1.16
−1.07
−0.98
−0.90
−0.86
−0.83
−0.84
−0.83
−0.84
−0.59
Leverage
s.e.
0.02
0.03
0.03
0.03
0.04
0.04
0.04
0.05
0.05
0.05
0.01
Md
−1.41
−1.28
−1.10
−1.00
−0.94
−0.91
−0.91
−0.89
−0.82
−0.91
−0.81
N
8,355
4,688
3,362
2,787
2,296
2,012
1,746
1,565
1,426
1,267
24,868
Panel B: Sample Restricted to Day Traders with # Day Trading Events ≥ 10
No Ownership,
Size Relative to
Sell First
First Event
Leverage
Event
Prop.
s.e.
Mean
s.e.
Md
Mean
s.e.
Md
1
8.1%
0.9%
1.01
0.01
1.00
−0.86
0.05
−0.97
2
12.0%
1.1%
1.87
0.08
1.09
−0.80
0.05
−0.81
3
12.9%
1.1%
2.17
0.09
1.23
−0.78
0.05
−0.85
4
12.5%
1.1%
2.53
0.20
1.29
−0.76
0.05
−0.85
5
14.2%
1.2%
2.55
0.20
1.29
−0.80
0.05
−0.81
6
16.1%
1.2%
2.89
0.18
1.39
−0.78
0.05
−0.84
7
16.9%
1.2%
2.92
0.19
1.39
−0.81
0.05
−0.90
8
17.0%
1.2%
2.96
0.19
1.38
−0.81
0.05
−0.88
9
15.1%
1.2%
3.01
0.24
1.38
−0.82
0.05
−0.82
10
16.5%
1.2%
2.90
0.23
1.44
−0.84
0.05
−0.91
> 10
22.2%
0.3%
3.82
0.06
1.57
−0.59
0.01
−0.81
N
1,195
1,219
1,236
1,267
1,258
1,283
1,278
1,277
1,300
1,267
24,868
tendency to hold on to losers, and perhaps consequently to exhibit poor stock
selection skill.
This paper also demonstrates that trading losses influence day traders’ decision to quit the market. A day trader earns the highest return from his first
day trade and the lowest from his last trade. Moreover, the likelihood of exiting
increases with the extent of the current trading loss. Finally, conditional on
remaining in the market, day traders become more confident and aggressive as
their careers progress; they more often sell stocks that they do not own, trade in
larger sizes, and become more highly leveraged. The decision to try day trading
again after successful first day trade resembles the behavior of problem gamblers. Turner, Littman-Sharp, Zengeneh, and Spence (2003, pp. 3) find that
“problem gamblers were significantly more likely than non-problem gamblers to
have experienced a win the first time they gambled”.
A number of potentially fruitful research avenues are not explored in this
28
paper. Barber and Odean (2001b) and Campbell, Lettau, Malkiel, and Xu
(2001), point to day trading as a possible explanation for increased idiosyncratic
volatility. We might ask whether day traders stabilize a market because they
are net suppliers of liquidity, or destabilize the market because of the strategies
they employ, or perhaps because of their demand for liquidity near the open
and the close?25
It would also be interesting to examine how the bear market that started in
2000 affected day trader behavior, as well as what type of day trader remained
in the market.
Appendix: Matching Data Sets
Much of the analysis relies on a matching procedure that combines two data
sets: (1) the Finnish Central Securities Registry, which includes the daily trading
records of all investors, and (2) limit order data, which provide information on
all orders and trades for the underlying market, the Helsinki Exchanges.
We create a match separately for each day and each stock. If there is only a
single transaction during a day, the trading record data will include two entries
(one for the buyer and one for the seller), which can be directly matched to the
trade. If there is more than one transaction, as usually is the case, the matching
procedure is as follows. Table XII provides examples.
In the first stage, we identify trades that have a unique price/volume combination, and thus can be identified with certainty. For example, trade number
1 in the limit order data is unique because no other trade took place at price
10.00 and had a volume of 100 shares. This can be directly matched with two
trading record entries, numbers 1 and 15 (negative volume indicates a sell). By
the same principle, all entries in the limit order data except for entries 4, 8, and
9 can be matched using the trading record data.
In the second stage, the results from this initial match for the entire sample can be used to assess the brokers different investors use the most. Using
this information, we determine whether there are trades that are unique with
respect to broker; that is, there are no two or more trades than have the same
price, volume, and broker, and only one investor with a potentially potentially
matching trading record (correct price and volume), has been determined to use
this broker.
For example, three of the remaining trades took place at 10.20 and had a
volume of 150 shares. These trades have brokers 3, 4, and 1 as the buyer,
respectively, and there are three possible investors, 3, 5, and 7. After the match
in stage 1, it can be determined that investor 5 is using broker 3. This leads us
to match trade number 8 (investor 5 who uses broker 3) with trade 4 (a trade
25 The literature documents that SOES bandits do not have an adverse effect on the market
(Battalio, Hatch, and Jennings, 1997; Foucault, Röell, and Sandås, 2003). Yet SOES bandits
use a very distinctive day trading strategy that is closer to arbitrage than it is to speculation.
Hence, the question whether the increase in day trading activity near the end of the millennium
affected volatility remains.
29
Table XII
Example of Matching of Trading Records and Limit Order Data
This table shows a hypothetical example of matching trading record data and limit order data for a
single stock on a single day. There are 10 trades and 20 trading record entries (10 for the buyers and
10 for the sellers). In the first stage of the matching, trades with unique volume/price combinations
are identified and matched with corresponding trading record entries. In the second stage, this
initial match over the entire sample is used to infer which broker each investor uses the most. This
information is used to match trades that appear to be unique with respect to volume, price, and
broker. In the third stage, ambiguous cases are matched by randomly selecting a trading record
entry with the correct price and volume, and a preference for the correct broker is given. Column
“match stage” displays at which stage the trading record entry is combined with the limit order
data entry.
#
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Inv.
1
1
1
2
3
4
5
5
6
7
8
9
9
10
10
11
12
13
14
14
Trading Record Data
Match
Size
Pri.
Stage
100
10.00
1
1,000
10.10
1
300
10.20
1
125
10.25
1
150
10.20
3
200
10.00
1
100
10.20
1
150
10.20
2
100
10.10
1
150
10.20
3
−1,000
10.10
1
−100
10.20
1
−200
10.00
1
−150
10.20
3
−100
10.00
1
−125
10.25
1
−300
10.20
1
−150
10.20
3
−100
10.10
1
−150
10.20
3
Size
100
200
100
150
1,000
300
125
150
150
100
Pri.
10.00
10.00
10.20
10.20
10.10
10.20
10.25
10.20
10.20
10.10
Limit Order Data
Broker
Buy
Sell
1
2
2
3
3
3
3
2
1
1
1
2
1
2
4
3
1
2
1
2
Match Stage
Buy
Sell
1
1
1
1
1
1
2
3
1
1
1
1
1
1
3
3
3
3
1
1
with broker 3 as the buyer).26
In the third stage, we match the remaining cases. For each trade that has
not been matched in the previous two stages, we randomly draw an unmatched
trading record entry with the correct price and volume. For each trade, trading
records that have correct price, volume, and broker are preferred over records
that only have correct price and volume.
For example, on the sell side, both investors 10 and 14 are known to use broker 2 as a result of the match in the first stage. They are potential sellers in the
trades 4, 8, and 9 who have brokers 2, 3, and 2 as the seller, respectively. Hence,
we randomly match trading record entries 14 (investor 10) and 20 (investor 14)
with trades 4 and 9 (both have broker 2 as the seller). After this, only trading
record entry 18 remains to be matched with the trade 8. Similarly, on the buy
side, either investor 3 or 7 could be the buyer in either trade 8 or 9, because the
broker these investors are using has not been identified. We randomly match
these trading record entries with trades.
The matching process described allows us to match 74.8% of all trading
26 In the actual matching process, we use the entire sample to determine the brokers each
investor uses. Here, for the sake of clarity, we use only data given in Table XII.
30
records. 44.2% of the records are matched in the first stage. 26.9% and 28.8%
entries are matched in the next two stages.
References
Almazan, Andres, Keith C. Brown, Murray Carlson, and David A. Chapman,
2001, Why constrain your mutual fund manager?, University of Texas, working paper.
Angel, James, 2000, 404 error: Liquidity not found, Georgetown University,
working paper.
Bae, Kee-Hong, Hasung Jang, and Kyung Suh Park, 2002, Traders’ choice between limit and market orders: Evidence from NYSE stocks, Journal of Financial Markets, forthcoming.
Barber, Brad M., and Terrance Odean, 2000, Trading is hazardous to your
wealth: The common stock investment performance of individual investors,
Journal of Finance 55, 773–806.
Barber, Brad M., and Terrance Odean, 2001a, Boys will be boys: Gender, overconfidence, and common stock investment, Quarterly Journal of Economics
116, 261–292.
Barber, Brad M., and Terrance Odean, 2001b, The Internet and the investor,
Journal of Economic Perspectives 15, 41–54.
Barber, Brad M., and Terrance Odean, 2002a, All that glitters: The effect of
attention and news on the buying behavior of individual and institutional
investors, Berkeley, working paper.
Barber, Brad M., and Terrance Odean, 2002b, Online investors: Do the slow
die first?, Review of Financial Studies 15, 455–487.
Barclay, Michael J., William G. Christie, Eugene Kandel, and Paul H. Schultz,
1999, Effects of market reform on the trading costs and depths of Nasdaq
stocks, Journal of Finance 54, 1–34.
Battalio, Robert, Brian Hatch, and Robert Jennings, 1997, SOES trading and
market volatility, Journal of Financial and Quantitative Analysis 32, 225–
238.
Biais, Bruno, Pierre Hillion, and Chester Spatt, 1995, An empirical analysis
of the limit order book and the order flow in the Paris Bourse, Journal of
Finance 50, 1655–1689.
Booth, G. Geoffrey, Ji-Chai Lin, Teppo Martikainen, and Yiuman Tse, 2002,
Trading and pricing in upstairs and downstairs stock markets, Review of
Financial Studies 15, 1111–1135.
31
Campbell, John Y., Martin Lettau, Burton G. Malkiel, and Yexiao Xu, 2001,
Have individual stocks become more volatile? An empirical exploration of
idiosyncratic risk, Journal of Finance 56, 1–43.
Dufour, Alfonso, and Robert F. Engle, 2000, Time and the price impact of a
trade, Journal of Finance 55, 2467–2498.
Foucault, Thierry, Ailsa Röell, and Patrik Sandås, 2003, Market making with
costly monitoring: An analysis of the SOES controversy, Review of Financial
Studies 16, 345–384.
Froot, Kenneth A., David S. Scharfstein, and Jeremy C. Stein, 1992, Herd on the
street: Informational inefficiencies in a market with short-term speculation,
Journal of Finance 47, 1461–1484.
Gouriéroux, Christian, Joanna Jasiak, and Gaëlle LeFol, 1999, Intra-day market
activity, Journal of Financial Markets 2, 193–226.
Griffiths, Mark D., Brian F. Smith, D. Alasdair S. Turnbull, and Robert W.
White, 2000, The costs and determinants of order aggressiveness, Journal of
Financial Economics 56, 65–88.
Grinblatt, Mark, and Matti Keloharju, 2000, The investment behavior and performance of various investor-types: A study of Finland’s unique data set,
Journal of Financial Economics 55, 43–67.
Grinblatt, Mark, and Matti Keloharju, 2001, What makes investors trade?,
Journal of Finance 56, 589–616.
Grinblatt, Mark, and Sheridan Titman, 1993, Performance measurement without benchmarks: An examination of mutual fund returns, Journal of Business
66, 47–68.
Handa, Puneet, and Robert A. Schwartz, 1996, Limit order trading, Journal of
Finance 51, 1835–1861.
Harris, Jeffrey H., and Paul H. Schultz, 1997, The importance of firm quotes
and rapid executions: Evidence from the January 1994 SOES rules change,
Journal of Financial Economics 45, 135–166.
Harris, Jeffrey H., and Paul H. Schultz, 1998, The trading profits of SOES
bandits, Journal of Financial Economics 50, 39–62.
Kahneman, Daniel, and Amos Tversky, 1979, Prospect theory: An analysis of
decision under risk, Econometrica 47, 263–291.
Kandel, Eugene, and Leslie M. Marx, 1999, Odd-eighth avoidance as a defense
against SOES bandits, Journal of Financial Economics 51, 85–102.
Karhunen, Jussi, and Matti Keloharju, 2001, Shareownership in Finland 2000,
Finnish Journal of Business Economics 50, 188–226.
32
Landingham, M. H. Van, 1980, The day trader: Some additional evidence,
Journal of Financial and Quantitative Analysis 15, 341–355.
Linnainmaa, Juhani, 2003, Does it matter who trades? Investor sophistication,
broker identity, and permanent price impacts, working paper.
Lo, Andrew W., A. Craig MacKinlay, and June Zhang, 2002, Econometric models of limit-order executions, Journal of Financial Economics 65, 31–71.
Odean, Terrance, 1998, Are investors reluctant to realize their losses?, Journal
of Finance 53, 1775–1798.
Odean, Terrance, 1999, Do investors trade too much?, American Economic Review 89, 1279–1298.
Ranaldo, Angelo, 2002, Order aggressiveness in limit order book markets, Journal of Financial Markets, forthcoming.
Schlarbaum, Gary G, Wilbur G. Lewellen, and Ronald C. Lease, 1978, Realized
returns on common stock investments: The experience of individual investors,
Journal of Business 51, 299–325.
Schultz, Paul H., 2003, Who makes markets, Journal of Financial Markets 6,
49–72.
Shefrin, Hersh, and Meir Statman, 1985, The disposition to sell winners too
early and ride losers too long: Theory and evidence, Journal of Finance 40,
777–790.
Turner, Nigel E., Nina Littman-Sharp, Masood Zengeneh, and Warren Spence,
2003, Winners: Why do develop gambling problems while others do not?,
working paper.
33