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Just How Much Do Individual Investors Lose by Trading?



Individual investor trading results in systematic and economically large losses. Using a complete trading history of all investors in Taiwan, we document that the aggregate portfolio of individuals suffers an annual performance penalty of 3.8 percentage points. Individual investor losses are equivalent to 2.2% of Taiwan's gross domestic product or 2.8% of the total personal income. Virtually all individual trading losses can be traced to their aggressive orders. In contrast, institutions enjoy an annual performance boost of 1.5 percentage points, and both the aggressive and passive trades of institutions are profitable. Foreign institutions garner nearly half of institutional profits.
Electronic copy available at:
Just How Much Do Individual Investors Lose by Trading?
Brad M. Barber
Graduate School of Management
University of California, Davis
Davis, CA 95616
(530) 752-0512
Yi-Tsung Lee
Department of Accounting
National Chengchi University
Taipei, Taiwan
(886-2) 2939-3091 # 81027
Yu-Jane Liu
Department of Finance
Guanghua School, Peking University, China and
Department of Finance,
National Chengchi University
Taipei, Taiwan
(886-2) 2939-3091 # 81123
Terrance Odean1
Haas School of Business
University of California, Berkeley
Berkeley, CA 94720
(510) 642-6767
May 2007
1 We are grateful to the Taiwan Stock Exchange for providing the data used in this study. Michael
Bowers provided excellent computing support. Barber appreciates the National Science Council of
Taiwan for underwriting a visit to Taipei, where Timothy Lin (Yuanta Core Pacific Securities) and
Keh Hsiao Lin (Taiwan Securities) organized excellent overviews of their trading operations. We
appreciate the comments of Ken French, Charles Jones, Owen Lamont, Mark Kritzberg, Victor W.
Liu and seminar participants at UC Berkeley School of Law, UC-Davis, University of Illinois, the
Indian School of Business, National Chengchi University, University of North Carolina, University
of Texas, Yale University, the Wharton 2004 Household Finance Conference, American Finance
Association 2006 Boston Meetings, the Taiwan Financial Supervisory Commission, and the 12th
Conference on the Theory and Practice of Securities and Financial Markets (Taiwan). Terrance
Odean is grateful for the financial support of the National Science Foundation (Grant 0222107).
Electronic copy available at:
Just How Much Do Individual Investors Lose by Trading?
We document that individual investor trading results in systematic and, more importantly,
economically large losses. Using a complete trading history of all investors in Taiwan, we
document that the aggregate portfolio of individual investors suffers an annual performance
penalty of 3.8 percentage points. Individual investor losses are equivalent to 2.2 percent of
Taiwan’s GDP or 2.8 percent of total personal income – nearly as much as the total private
expenditure on clothing and footwear in Taiwan. Using orders underlying trade, we
document that virtually all of individual trading losses can be traced to their aggressive
orders; passive orders placed by individuals are profitable at short horizons and suffer
modest losses at longer horizons. In contrast, institutions enjoy an annual performance
boost of 1.5 percentage points (after commissions and taxes, but before other costs) and
both the aggressive and passive trades of institutions are profitable. Foreign institutional
investors garner nearly half of the institutional profits. Finally, the introduction of a legal
lottery in Taiwan in 2002 coincided with a 25 percent reduction in turnover on the Taiwan
Stock Exchange.
Financial advisors recommend that individual investors refrain from frequent trading.
Investors should buy and hold diversified portfolios, such as low cost mutual funds. If skill
contributes to investment returns, individual investors are obviously at a disadvantage when trading
against professionals. What is less clear is just how much do individual investors lose by trading?
In this paper, we document that trading in financial markets leads to economically large losses for
individual investors and virtually all of the losses of individual investors can be traced to their
aggressive (rather than passive) orders. To do so, we use a unique and remarkably complete
dataset, which contains the entire transaction data, underlying order data, and the identity of each
trader in the Taiwan stock market – the World’s twelfth largest financial market. With these data,
we provide a comprehensive accounting of the gains and losses from trade during the period 1995
to 1999.
Our data allow us to identify trades made by individuals and by institutions, which fall into
one of four categories (corporations, dealers, foreigners, or mutual funds). To analyze who gains
and loses from trade, we construct portfolios that mimic the purchases and sales of each investor
group. If stocks bought by an investor group reliably outperform those that they sell, the group
benefits from trade. In addition, using the orders underlying each trade, we are able to examine
whether gains and losses can be attributed to aggressive or passive orders.
Our empirical analysis presents a clear portrait of who benefits from trade: Individuals
lose, institutions win. While individual investors incur substantial losses, each of the four
institutional groups that we analyze – corporations, dealers, foreigners, and mutual funds – gain
from trade. Though we analyze horizons up to one year following a trade, our empirical analyses
indicate that most of the losses by individuals (and gains by institutions) accrue within a few weeks
of trade and reach an asymptote at a horizon of six months.
Several prior studies provide evidence that individual investors lose from trade, 1 while
institutions profit.2 Relative to prior research, the combination of a comprehensive dataset (all
1 For studies of the performance of individual investors, see Schlarbaum, Lewellen, and Lease (1978a,
1978b), Odean (1999), Barber and Odean (2000, 2001), Grinblatt and Keloharju (2000), Goetzmann and
Kumar (2005), and Linnainmaa (2003a, 2003b). Recent research suggests some trades by individual
investors are systematically profitable. Ivkovich and Weisbenner (2004) document the local holdings of
individual investors perform well, while Ivkovich, Sialm, and Weisbenner (2004) document individuals with
concentrated portfolios perform well. Coval, Hirshleifer and Shumway (2003) provide evidence that some
individual investors are systematically better than others. Other related work includes Lee, Shleifer, and
Thaler (1991), Sias and Starks (1997), Bartov, Radhakrishnan, and Krinsky (2000), Chakravarty (2001),
and Poteshman and Serbin (2003).
trades for an entire market) and the empirical methods we employ provide more convincing
evidence that individuals lose from trade.
The comprehensiveness of our dataset allows us to go beyond the mere documentation of
trading losses and make two important contributions relative to prior research. First, we document
the losses incurred by individual investors are economically large. We estimate the total losses to
individual investors to be $NT 935 billion ($US 32 billion) during our sample period or $NT 187
billion annually ($US 6.4 billion). (The average exchange rate that prevailed during our
sample period was $NT 29.6 per $US 1 with a low of 24.5 and a high of 34.7 $NT/$US.) This is
equivalent to a staggering 2.2 percent of Taiwan’s gross domestic product or roughly 33, 85, and
170 percent of total private expenditures on transportation/communication, clothing/footwear, and
fuel/power (respectively). Put differently, it is a 3.8 percentage point annual reduction in the return
on the aggregate portfolio of individual investors. These losses can be broken down into four
categories: trading losses (27 percent), commissions (32 percent), transaction taxes (34 percent),
and market-timing losses (7 percent).
The trading and market timing losses of individual investors represent gains for
institutional investors. The institutional gains are eroded, but not eliminated by the commissions
and transaction taxes that they pay. We estimate that aggregate portfolio of institutional investors
enjoys annual abnormal returns of 1.5 percentage points after commissions and transaction taxes
(but before any fees the institutions might charge their retail customers). When profits are tracked
over six months, foreigners earn nearly half of all institutional profits; at shorter horizons,
foreigners earn one fourth of all institutional profits. The profits of foreigners represent an
unambiguous wealth transfer from Taiwanese individual investors to foreigners. Whether the
remaining institutional profits represent a wealth transfer depends on who benefits when domestic
institutions profit.
2 For studies of mutual fund performance, see Carhart (1997), Chan, Jegadeesh, and Wermers (2000), Coval
and Moskowitz (2001), Daniel, Grinblatt, Titman, and Wermers (1997), Grinblatt and Titman (1989, 1993),
and Wermers (2000). For studies of pension fund performance, see Ferson and Khang (2002), Lakonishok,
Shleifer, and Vishny (1992), Coggin, Fabozzi, and Rahman (1993), Christopherson, Ferson, and Glassman
(1998), Delguercio and Tkac (2002), Coggin and Trzcinka (2000), Ikenberry, Shockley, and Womack (1998).
In analyses of hedge funds, Ackermann, McEnally, and Ravenscraft (1999), Brown, Goetzmann, and
Ibbotson (1999), Liang (1999), and Agrawal and Naik (2000) provide evidence of superior returns, though
Amin and Kat (2003) argue that hedge fund performance results may be attributable to the skewed nature of
hedge fund payoffs, which when appropriately accounted for, renders hedge fund performance unremarkable.
A distinguishing feature of our dataset is data on the orders underlying each trade. This
feature of our dataset leads to the second main contribution of our study: Virtually all of the losses
incurred by individuals can be traced to their aggressive orders. In contrast, institutions profit from
both their passive and aggressive trades.3 (All orders on the Taiwan Stock Exchange are limit
orders. We define aggressive limit orders to be buy limit orders with high prices and sell limit
orders with low prices—both relative to unfilled orders at the last market clearing; we define
passive limit orders to be buy limit orders with low prices and sell limit orders with high prices. 64
percent of all trades emanate from aggressive orders.) At short horizons (up to one month), the
majority of institutional gains can be traced to passive trades. The profits associated with passive
trades are realized quickly, as institutions provide liquidity to aggressive, but apparently
uninformed, investors. The profits associated with the aggressive trades of institutions, which are
likely motivated by an informational advantage, are realized over longer horizons.
The remainder of the paper is organized as follows. Our data, the Taiwan market, and
empirical methods are described in detail in Section I. We present our main results in Section II,
where we estimate the magnitude of losses and trace these losses to aggressive and passive orders
underlying trade. In Section III, we discuss the economic significance of the gains and losses. In
Section IV, we discuss possible reasons why Taiwanese investors trade actively. We make
concluding remarks in Section V.
I. Background, Data, and Methods
I.A. Taiwan Market Rules
The TSE operates in a consolidated limit order book environment where only limit orders
are accepted. During the regular trading session, from 9:00 a.m. to noon during our sample period,
buy and sell orders interact to determine the executed price subject to applicable automatching
rules. During our sample period, trades can be matched one to two times every 90 seconds
throughout the trading day. Orders are executed in strict price and time priority. Although market
orders are not permitted, traders can submit an aggressive price-limit order to obtain matching
priority. During our study period, there is a daily price limit of seven percent in each direction and
a trade-by-trade intraday price limit of two ticks from the previous trade price.
3 Parlour (1998), Foucault (1999) and Handa, Schwartz and Tiwari (2003) explore the choice between
demanding liquidity with market or marketable limit orders and supplying liquidity with limit orders that
cannot be immediately executed. Griffiths et al. (2000) find that aggressive buys are more likely than sells to
be motivated by information.
The TSE caps commissions at 0.1425 percent of the value of a trade. Some brokers offer
lower commissions for larger traders, though we are unable to document the prevalence of these
price concessions. Taiwan also imposes a transaction tax on stock sales of 0.3 percent. Capital
gains (both realized and unrealized) are not taxed, while cash dividends are taxed at ordinary
income tax rates for domestic investors and at 20 percent for foreign investors. Corporate income is
taxed at a maximum rate of 25 percent, while personal income is taxed at a maximum rate of 40
I.B. Trades Data and Descriptive Statistics
We have acquired the complete transaction history of all traders on the TSE from January
1, 1995, through December 31, 1999. The trade data include the date and time of the transaction, a
stock identifier, order type (buy or sell), transaction price, number of shares, and the identity of the
trader. The trader code allows us to broadly categorize traders as individuals, corporations, dealers,
foreign investors, and mutual funds. The majority of investors (by value and number) are
individual investors. Corporations include Taiwan corporations and government-owned firms (e.g.,
in December 2000 the government-owned Post, Banking, and Insurance Services held over $NT
213 billion in Taiwanese stock). Dealers include Taiwanese financial institutions such as Fubon
Securities, Pacific Securities, and Grand Cathay Securities. Foreign investors are primarily
foreign banks, insurance companies, securities firms, and mutual funds. During our sample period,
the largest foreign investors are Fidelity Investments, Scudder Kemper, and Schroder Investment
Management. Mutual funds are domestic mutual funds, the largest being ABN-
AMRO Asset Management with $NT 82 billion invested in Taiwanese stocks in December 2000.
We present basic descriptive statistics on the market during the 1995 to 1999 period in
Table 1. In contrast to the U.S., which enjoyed an unprecedented bull market in the late 1990s,
Taiwan experienced average annual return of 6.9%. The main index for the Taiwan market (the
TAIEX – a value-weighted index of all listed securities) enjoyed gains of over thirty percent in
1996 and 1999 and losses of over twenty percent in 1995 and 1998. Our sample period also
includes the period of the Asian Financial crisis, which began in May 1997 with a massive sell-off
of the Thai Baht.
The stock market is important in Taiwan. The number of firms listing in Taiwan grew at
average annual rate of over 7 percent between 1995 and 1999. (This growth continues to date, with
700 firms listed on the TSE at the end of 2004.) The market value of the TSE nearly doubled from
1995 to 1999 – growing from $NT 5.2 trillion ($US 198 billion) in 1995 to over $NT 10 trillion
($US 313 billion) in 1999. In 1994, the ratio of external capital (i.e., stock market valuation
corrected for inside ownership) to GDP in Taiwan was 0.88 and was the sixth highest of 49
countries analyzed by La Porta et al. (1997); Taiwan’s ratio was slightly higher than the ratios for
Japan and the U.S., but somewhat lower than the ratios for the U.K., Hong Kong, and Singapore.
At the end of 1999, the Taiwan market ranked as the 12th largest financial market in the world (by
market capitalization), though it was only slightly greater than two percent of the total U.S. market.
Turnover in the TSE is remarkably high – averaging almost 300 percent annually during
our sample period. (We calculate turnover as ½ the sum of buys and sells in each year divided by
the average daily market cap for the year.) In contrast, annual turnover on the New York Stock
Exchange (NYSE) averaged 97 percent annually from 2000 through 2003. The high turnover rates
observed in Taiwan, though unusual, are not unique to Taiwan. During our sample period, the
annual turnover rate was 511 percent in China and 181 percent in Korea (peaking at 345 percent in
1999).4 Day trading is also prevalent in Taiwan (see last column of Table 1). We define day
trading as the purchase and sale of the same stock on the same day by an investor. Over our sample
period, day trading accounted for 23 percent of the total dollar value of trading volume. (See
Barber, Lee, Liu, and Odean (2004) for a detailed analysis of day trading on the TSE.)
We restrict our analysis to ordinary common stocks. In Table 2, we present the total value
of buys and sells of stocks for each investor group by year. Individual investors account for roughly
90 percent of all trading volume and place trades that are roughly half the size of those made by
institutions (corporations, dealers, foreigners, and mutual funds). Each of the remaining groups
accounts for less than five percent of total trading volume. During our five-year sample period,
there were approximately 3.9 million individual investors, 24,000 corporations, 83 dealers, 1,600
foreigners, and 289 mutual funds that traded on the TSE.
Equities are an important asset class for Taiwanese. According to the 2000 Taiwan Stock
Exchange Factbook (table 24), individual investors accounted for between 56 and 59 percent of
total stock ownership during our sample period. Taiwan corporations owned between 17 and 23
percent of all stocks, while foreigners owned between 7 and 9 percent. At the end of 2000,
4 Turnover data for China are from table 30 of Gao (2002). Turnover data for Korea are from the Taiwan
Financial Supervision Commission.
Taiwan’s population reached 22.2 million; 6.8 million Taiwanese (31 percent) placed orders
through a brokerage account.5
Stocks are broadly held in Taiwan and are an important asset class for many households in
Taiwan. Each year, the Taiwan Ministry of Finance collects the asset holdings for all households
with taxable and nontaxable income). We analyze these data over the period 1997 to 2002. On
average, about half of reporting households own equities (ranging from 49 to 56 percent). For those
who own equity, the majority (70 percent) of these equity holdings are public equities. Less than
one percent of equities are held through mutual funds, while the remaining equities are privately
held stock.6 We present in Table 3 the ratio of equity value to total assets and to total assets
excluding real estate. For all households owning equity, equities average 24 percent of total assets
and 45 percent of non-real-estate assets. We further partition households into quartiles based on net
worth and separately report results for households with negative net worth (about 3 percent of
households report negative net worth). Though the wealthy no doubt own the majority of equities,
the less well off have substantial portions of their assets invested in equities. By comparison, less
wealthy investors in the U.S. tend to have a somewhat lower proportion of their assets invested in
equities than do wealthier investors (Polkovnichenko, 2005). One possible reason why less wealthy
Taiwanese households participate so actively in the stock market is that the market provides an
opportunity to gamble. We discuss this possibility further in Section IV.
I.C. Aggressive and Passive Trades
In addition to trade data, we have all orders (both filled and unfilled) that underlie trades.
Using these order data, we categorize each trade as aggressive or passive based on the order
underlying the trade. This categorization involves three steps. First, for each stock, we construct a
time series of clearing prices, the lowest unfilled sell limit order price, and the highest unfilled buy
limit order price. These data are compiled by the TSE (the market display data) and are presented
to market participants in real time. Second, we categorize all orders as aggressive or passive by
comparing order prices to the most recent unfilled limit order prices. Orders to buy with prices in
excess of the most recent unfilled sell limit order are categorized as aggressive; those with prices
below the most recent unfilled buy limit order are categorized as passive; those with an order price
5 The data of Taiwan’s population are from the Directorate-General of Budget, Accounting and Statistics,
Executive Yuan, Taiwan. We report 6.8 million Taiwanese open accounts using the order data from Taiwan
stock exchange. The number of opened accounts is 12.3 millions. (Data are from the website of the Taiwan
stock exchange).
6 Data are from Major Indicators of Securities & Futures Market, Financial Supervisory Commission,
Executive Yuan, Taiwan and Annual Statistical Data, Taiwan Stock Exchange;
between the two unfilled limit order prices are categorized as indeterminant. There is an analogous
algorithm for sells. Third, we match all orders to trades. This matching allows us to determine
whether a trade emanated from a passive or aggressive order.
Using this algorithm, we categorize 90 percent of all trades as passive or aggressive.7 The
majority of executed trades – 64 percent – emanate from aggressive orders. Overall, individuals are
slightly more aggressive than institutions (64.9 percent vs. 64.2 percent of trades emanate from
aggressive orders). However, there is considerable variation in the aggressiveness of institutions.
Corporations are the most passive group of traders (52.2 percent aggressive), while foreigners are
the most aggressive group (68.4 percent aggressive). (Linnainmaa (2003b) documents that
individuals and institutions in Finland use roughly similar proportions of market orders—48.4 for
individuals and 50.9 percent for institutions).
I.D. Dollar Profits
In our main analysis, we calculate a time-series of daily trading profits earned by each
investor group. We focus on dollar profits rather than abnormal returns so as to precisely calculate
the trading gains and losses between investor groups. Abnormal returns might be artificially high if
returns earned are high on days with low trading volume. In contrast, the calculation of dollar
profits provides a precise accounting for the gains from trade, since the dollar profits are precisely
equal to zero when summed across investor groups. We test the robustness of our results by
analyzing abnormal returns as described later in this section.
To calculate daily dollar profits, we first aggregate all trades made by investor group,
stock, and day. We then construct two portfolios for each investor group: one that mimics the net
daily purchases and one that mimics the net daily sales. To focus on trading that occurs between
groups, we only analyze net trades. For example, if individuals buy 1,100 shares of Micron and
sell 1,000 shares of Micron on January 15, 1995, we would add 100 shares of Micron to the
individual investor buy portfolio on January 15, 1995, while no Micron shares would be added to
the individual investor sell portfolio on that day. The purchase price is recorded as the difference
between the total value of buys and the total value of sells divided by the net shares bought. Shares
are included in the portfolio for a fixed horizon; we consider horizons of 1, 10, 25, and 140 trading
days. Shares are marked to market daily. The daily dollar profits for the buy portfolio are
7 The indeterminant category also includes trades that we are unable to match to an order. We discussed this
issue with the TSE and they suspect data entry errors in the order records is the source of the problem.
Though annoying, this type of data error should not introduce any bias into our results.
calculated net of market gains as the total value of the buy portfolio at the close of trading on day t-
1 multiplied by the spread between the return on the buy portfolio and the market on day t. There
is an analogous calculation for the sell portfolio. Ultimately, our statistical tests use a time-series of
daily dollar profits from January 1995 to December 1999. Thus, it is assumed that each day
represents an independent observation of the total profits earned by a particular group. To control
for the low levels of autocorrelation in profits observed at a one-day horizon, we use a Newey-
West procedure to correct the estimated standard errors using an assumed lag length of six days.8
I.E. Return Calculations
To test the robustness of our dollar profit calculations, we also calculate monthly abnormal
returns on the buy portfolio, sell portfolio, and buy less sell portfolio for all investor partitions.
Consider, for example, the portfolio that mimics the buys of individual investors. We first
calculate the daily returns on this portfolio (again, assuming a holding period of 1, 10, 25, or 140
days). Daily returns are compounded within a month to yield a time-series of 60 monthly returns
for the individual investor buy portfolio.
Statistical tests are based on the monthly time-series of the portfolio return and abnormal
returns from a four-factor model; results are qualitatively similar if we use market-adjusted returns
or the intercept from a one-factor model with the market risk premium as the sole factor. For
example, we calculate the abnormal return on the corporate investor buy portfolio as the intercept
from the following four-factor model:
jttjtjtjftmtjjftt WMLwHMLhSMBsRRRR
+++++=)()( corp (1)
where Rft is the monthly return on T-Bills,9 Rmt is the monthly return on a value-weighted Taiwan
market index, SMBt is the return on a value-weighted portfolio of small stocks minus the return on
a value-weighted portfolio of big stocks, HMLt is the return on a value-weighted portfolio of high
book-to-market stocks minus the return on a value-weighted portfolio of low book-to-market
stocks, and WMLt is the return on a value-weighted portfolio of stocks with high recent returns
minus the return on a value-weighted portfolio of stocks with low recent returns. The construction
of the size and book-to-market portfolios is identical to that in Fama and French (1993). The WML
return is constructed based on a six-month formation period and a six-month holding period. The
8 There is a small, but reliably positive autocorrelation of total profits at one day horizon (ranging from 6.3
percent to 14.2 percent). No autocorrelations beyond one day are reliably different from zero. To test the
robustness of our profit results, we also calculate monthly returns on the buy and sell portfolios. Monthly
portfolio returns for all investor partitions have no reliable serial dependence.
9 We use the series of one-month deposit rates of the First Commercial Bank as the risk-free rate. This
interest rate series is taken from Financial Statistics Monthly, Taiwan District, R.O.C., and is compiled by
the Central Bank of China.
regression yields parameter estimates of
jjjj j
sh w, , , and for regression j. The error term in
the regression is
jt .
II. Results
II.A. Event-Time Results
To provide an overview of our results, we first present the results of an event-time analysis,
where day 0 represents the day of a trade. Consider the buys of individual investors. We begin by
aggregating all purchases by individual investors by stock and day. We then calculate the mean
market-adjusted abnormal return on event day τ (MAτ) (weighted by the value of stocks bought).
There is a similar calculation for the sales of individuals. Finally, we calculate the cumulative
(market-adjusted) abnormal return on stocks bought less the cumulative (market-adjusted)
abnormal return on stocks sold as:
uy sell
. (2)
There is an analogous calculation for the purchases and sales of institutional investors.
The results of this analysis are presented in Figure 1, panel A. Consider first the results for
institutions. Institutions appear to gain from trade, though the gains from trading reach an
asymptote at approximately six months (140 trading days). After one month (roughly 23 trading
days), the stocks bought by institutions outperform those sold by roughly 80 basis points. After six
months, stocks bought outperform those sold by roughly 150 basis points.
In contrast, stocks sold by individuals outperform those bought. The magnitude of the
difference is smaller than for institutions since most trades by individuals are with other individuals
and do not contribute to the difference in performance between stocks sold and stocks bought. The
large gains by institutions map into small losses by individuals merely because individuals
represent such a large proportion of all trades. After one month, stocks bought by individuals lag
those sold by roughly 10 basis points. After six months, the difference grows to roughly 20 basis
Another way of viewing the gains to institutions (and losses to individuals) is to calculate
cumulative abnormal returns based on whether institutions are net buyers (or sellers) of a stock.
Thus, the mean market-adjusted abnormal return on event day τ (MAτ) is identical to that described
before, except for the weighting scheme. For example, a stock enters the institutional buy portfolio
on a particular day only if institutions are net buyers of the stock, and the buy portfolio is weighted
by the net purchases of institutional investors (i.e., the value of buys less the value of sells). There
is an analogous calculation for the sale portfolio.
The results of this analysis are presented in Figure 1, panel B. Stocks that are net bought by
institutions outperform those that are net sold by 4 percentage points after 140 trading days. Of
course, the performance of individual investors is now the mirror image of institutions. This
method magnifies the return differences described above, since we now focus on stocks where
individuals are trading with institutions.
Though these results provide a powerful visual representation of our primary results, we do
not draw inferences from this event time analysis because of the well-known problems associated
with constructing a well-specified test of the null hypothesis that abnormal returns are zero using
long-run event-time returns. We base our statistical tests on the daily time-series of dollar profits
and the monthly time-series of portfolio returns earned on stocks bought (or sold) by each of the
investor groups that we analyze. (See Lyon, Barber, and Tsai (1999) and Mitchell and Stafford
(2000)) These statistical tests rely on the reasonable assumption, which we empirically verify, that
daily profits (or monthly returns) are serially independent.
II.B. Dollar Profits
In Table 4, we present our main results on the dollar profits (and losses) from trade for
each investor group. We present the profits from the buy portfolio, sell portfolio, and total profits
from all trades. Of course, in aggregate the dollar profits from trade are precisely zero. We also
present total profits that can be traced to aggressive and passive orders.
Individual investors incur losses that grow from mean daily losses of $NT 35.3 million
after one day to $NT 178.7 million after 140 trading days (Table 4, Column 1). At each horizon,
the losses are highly significant with test-statistics ranging from -4.68 to -13.42. Stocks bought by
individuals lose money at horizons of one day and 10 days, but their losses on purchases are
indistinguishable from zero at the longer horizons of 25 and 140 trading days (Table 4, Column 2).
In contrast, stocks sold by individuals subsequently perform well at all horizons, resulting in
trading losses to individuals. 10 In general, taxes and the disposition effect (the propensity to hold
losers and sell winners) might affect investors’ selling decisions, but not purchase decisions.
10 Stocks bought and stocks sold by individuals (or by institutions) can both perform well if market gains are
concentrated in high volume stocks. In the U.S., Gervais, Kaniel, and Mingelgrin (2001) document that high-
volume stocks subsequently earn high returns.
Taiwanese investors do not face capital gains taxes, but do exhibit a strong disposition effect
(Barber, Lee, Liu, and Odean, 2007). It is possible that the disposition effect contributes to the poor
sales decisions of Taiwanese individual investors.
Institutions, as a group, earn profits that are identical to the losses of individuals.
Furthermore, each of the institutional subcategories (Corporations, Dealers, Foreigners, and Mutual
Funds) earn reliably positive overall trading profits with the exception of corporations at a horizon
of 140 trading days.11
The results of our abnormal return and dollar profit calculations raise the obvious question
of whether these gains grow at longer horizons. We also analyze holding periods of one year. The
dollar profits remain reliably positive for institutions and reliably negative for individuals. The
average daily institutional gains from trade (and individual losses) are virtually identical at the one
year and six month horizon (see also Figure 1). To test the robustness of these results, we calculate
the average daily institutional gross profits for each calendar year from 1995 to 1999. In each year,
mean daily institutional profits are positive (reliably so in four of the five sample years).
Furthermore, when we sum daily profits within each month, institutions profit in 44 out of 60
months during our sample period.
II.C. Tracing Profits to Passive and Aggressive Trades
The fourth and fifth columns of numbers in Table 4 present the total profits that can be
traced to passive and aggressive trades. The last two columns of the table present the associated
test statistics. Summing the profits of aggressive and passive trades does not precisely equal the
total profits from all trades, since we are unable to categorize all trades.
Consider first the passive trades. Both individuals and institutions profit in the short-run
from their passive trades. However, as we increase the horizon over which the trading profits are
evaluated from one day to 140 trading days, the profitability of the passive trades of individual
investors erodes and is indistinguishable from zero at 25 and 140 trading days. In contrast, the
passive profits of institutions remain reliably positive at all horizons.
11 The profits of stocks bought (and sold) by each of the four institutional subcategories do not sum to the
profits for all institutions because we only analyze net purchases (or sales) for each stock within a
subcategory or across all institutions. However, total profits (profits of buy portfolio less sell portfolio) for
each of the four institutional subcategories sum to the total profits for all institutions.
When an investor places a passive order, he is essentially offering to provide liquidity to
market participants who demand it. Our results indicate that though individuals initially profit by
providing liquidity to market participants, these profits erode perhaps because those to which
individuals provide liquidity have information about the future prospects of a stock. While some
individuals undoubtedly unwind these positions for a profit, in aggregate, individuals hold
positions initiated with liquidity providing trades until initial profits are lost. In contrast,
institutions are much better at sustaining profits through the provision of liquidity.
The pattern of profits for aggressive orders is quite different. Individual investors lose
large sums immediately on their aggressive orders. Apparently, individual investors are
demanding liquidity when they have no information about the future prospects of a stock. This
observation is quite consistent with models that assume investors are overconfident and, as a result,
trade too aggressively and to their detriment. In striking contrast, institutions immediately profit
from their aggressive trades and these profits grow dramatically at longer horizon – perhaps as the
information that institutions possess about the prospects for a stock are more widely appreciated by
market participants.
In summary, virtually all of individual trading losses can be traced to their aggressive
trades. On the other hand, institutions profit from both their passive and aggressive trades.
II.D. Results by Firm Size
Investors can earn trading profits by exploiting information asymmetries or by selling
liquidity to those who are impatient to trade. Both information asymmetry and the cost of liquidity
are likely to be greater for smaller firms. Thus a simple way to test whether the losses that we
document increase as information asymmetries and the cost of liquidity increase is to partition our
sample on the basis of firm size.
In each month, we rank firms on the basis of market capitalization. The largest firms that
represent 70 percent of total market value are defined as large firms, while remaining firms are
defined as small. Though the market capitalization that defines a firm as large varies from month to
month, the average cutoff during our sample period is $NT 24 billion. In the average month, 72
firms are defined as large. Having defined large (and small) firms, we construct buy and sell
portfolios based on the trades of large (and small) firms.
The mean daily dollar profits by firm size are presented in Table 5.12 The qualitative
patterns for all trades, passive trades, and aggressive trades are similar for large firms and small
firms. By construction, large firms represent 70 percent of total market capitalization. Institutional
trading is more concentrated in large firms (64 percent of all institutional trades are in large firms)
than individual trading (58 percent). At horizons of 1, 10, and 25 trading days, roughly half of the
individual losses can be traced to their trading in large stocks. At the longer horizon of 140 trading
days, approximately 60 percent of their losses can be traced to trading in large stocks. Thus,
individual investors lose on both their trades in large and small stocks, though their losses per
dollar traded, particularly at short horizons, are greater for small stocks.
II.E. Portfolio Returns
Dollar profits are calculated assuming only an adjustment for market gains. To test the
robustness of our results, we also analyze the mean monthly abnormal returns on the buy portfolio,
sell portfolio, and buy minus sell portfolio. As was done for daily dollar profits, the buy and sell
portfolios are based on the net daily purchases and net daily sales of each investor group. In Table
6, we present the monthly abnormal return measures (four-factor intercepts) for each investor
Consistent with our prior evidence, the results provide strong evidence that institutions
earn positive abnormal returns, while individuals earn negative abnormal returns. In general, the
monthly abnormal returns decrease with holding horizon.13 For example, the abnormal return of
the buy-sell portfolio (Table 6, Column 1) for all trades shrinks from 10.97 percent per month at
one trading day (t=19.92) to 0.76 percent per month at 140 trading days (t=5.77). The abnormal
return results are qualitatively similar to the profit calculations presented in Table 4. Market-
adjusted returns and alphas from a single factor model are very similar to the results presented in
this table. Thus, style or risk adjustment has virtually no effect on our results.
II.F. Market-timing
To this point, we have focused on the security selection ability of institutions and
individuals. By calculating trading gains net of any market return, we have excluded any profits
12 Adding the profits of small firms and large firms does not precisely equal the profits from all trades in
Table 4 because we are missing firm size data for some stocks (e.g., in the month after an initial public
13 Abnormal returns tend to decrease with horizon while profits increase with horizon. This is so because the
total number of positions held in the buy (or sell) portfolio at longer horizons is much greater than the total
number of positions held at shorter horizons and the ratio of total profits to portfolio value decreases. For
example, at a one day horizon, the buy portfolio will contain only stocks bought in the last day, while at a
140 day horizon the buy portfolio will contain stocks bought over the last 140 trading days (with an average
holding period of 70 days if trading is uniformly distributed over time).
from market-timing. We estimate market-timing losses as follows. On each day, we sum the total
value of stock purchases and the total value of stock sales for each investor group. We then take
the difference of these two sums. If individuals were net buyers of stock (i.e., the total value of
buys exceeds the total value of sales), we construct a long portfolio that invests a dollar amount
equal to their net long position in the market portfolio and a short portfolio that invests an equal
amount in the riskfree asset. Our calculation of dollar profits is analogous to that for security
selection, with one exception. From the realized dollar gain on the long portfolio, we subtract the
expected gain, which is calculated using beginning-of-day portfolio value, the Capital Asset
Pricing Model, and the beta of the long portfolio during the five-year sample period
). Essentially, we are comparing the dollar gain of the long portfolio to the
dollar gain of a portfolio that had a fixed investment in the market and the riskfree asset over the
five-year sample period. There is an analogous calculation of the dollar profit for the short
portfolio. The total gains from market-timing are the sum of the gains on the long and short
portfolio. At horizons of 10, 25, and 140 days, we estimate the market-timing losses of individual
investors to be $NT 9.9, $NT 18.9, and $NT 46.4 million with associated t-statistics of 2.09, 1.93,
and 1.63 (respectively).14
III. Economic Significance
One of our main objectives is assessing the economic significance of the losses incurred by
individual investors. In this section, we document that individual investor trading losses are
equivalent to 2.2 percent of Taiwan’s GDP or 2.8 percent of total personal income – nearly as
much as the total private expenditure on clothing and footwear in Taiwan. The aggregate portfolio
of individual investors suffers an annual performance penalty of 3.8 percentage points. In contrast,
institutions enjoy an annual performance boost of 1.5 percentage points (after commissions and
taxes, but before other costs).
From 1995 to 1999, individual lose $NT 935 billion from their trading in stocks. Losses
can be traced to (1) gross trading losses ($NT 249 billion), (2) commissions ($NT 302 billion), (3)
transaction taxes ($NT 319 billion), and (4) market timing losses ($NT 65 billion).15 These losses
14 These test statistics rely on the assumption that daily market timing profits are serially independent.
Though there is no daily serial dependence for holding periods of 10 and 140 days, there is modest serial
dependence at one day for a holding period of 25 days. Consequently, test statistics are calculated using a
Newey-West adjustment for serial correlation assuming a lag length of six days (one week).
15 Gross trading losses and market timing losses over the entire sample period are calculated as mean daily
losses times 1,397 (the number of trading days during our sample period). Mean daily gross trading losses
and market timing losses are $NT 178.7 and $NT 46.4 million (respectively). Commission costs are the total
represent 2.8 percent of total personal income (including income of non-investors) or 2.2 percent of
Taiwan’s total gross domestic product during our sample period. We can also perform back-of-the-
envelope calculations to estimate the return shortfall suffered by individual investors as 3.8 percent
While exacerbating the losses of individuals, transactions costs put a sizable dent in the
profits of institutions. Nonetheless, the average daily profit net of transaction costs ($NT 126.3) is
reliably positive (t=3.58).17 These daily profits translate into an abnormal return net of transaction
costs of 1.5 percent annually. Not all institutions fair equally well net of trading costs. We conduct
similar calculations for each institutional investor category. Net of transaction costs, the average
daily profits of corporations, dealers, foreigners, and mutual funds are ($NT million) -3.1, 5.0,
75.5, and 48.4 (with t-statistics of -0.12, 1.74, 3.90, and 3.04, respectively).18
Do the trading losses of individuals represent a wealth transfer? Losses and costs of trading
for individual investors fall into three categories of roughly equal magnitude: taxes, commissions,
and trading and market-timing losses.
Transaction taxes are a wealth transfer from investors to the government. It seems likely
that absent this transfer, the government would impose other taxes of similar magnitude.
Commissions are the cost charged by those who provide investors with access to secondary
markets. Secondary markets, in which investors who already own securities sell to investors who
wish to buy those securities, do not directly raise investment capital for firms. However, secondary
markets provide liquidity, price discovery, and regulatory oversight, which ensure primary
value of trade (Table 2) times the commission rate of 0.1425%. Transaction taxes are the total value of sales
times the transaction tax of 0.30%.
16 Individual investors held roughly 60 percent of all outstanding stock during our sample period. The
average market value of all stock during our sample period was $NT 8.1 trillion (Table 1). Thus, trading
losses represent roughly a daily performance penalty of 0.37 basis points ($NT 178.7 million daily trading
losses divided by the product of $NT 8.1 trillion times 60 percent), while commissions, transaction taxes, and
market-timing losses cost investors roughly 0.10 bps, 0.44 bps, and 0.47 bps per day. Annualized, this
represents a return shortfall of 3.8 percentage points.
17 Commissions are capped at 0.1425 percent and the transaction tax is 0.30 percent. Over our sample period,
institutions bought $NT 12.5 trillion and sold $NT 12.5 trillion of common stock (Table 2). Thus, total
commissions and transaction taxes paid during the sample period were $NT 35.6 and $NT 37.5 billion
(respectively). This corresponds to mean daily commissions and transaction taxes of $NT 25.5 million and
$NT 26.9 million.
18 Seasholes (2000) presents evidence consistent with our findings on foreign investors. Using data on cross-
border investments in Korean and Taiwanese stocks, Seasholes (2000) documents that foreigners increase
positions prior to positive earnings surprises and decrease investments prior to negative surprises.
investors of an opportunity to later sell their investments expeditiously and at a reasonable price. It
is difficult to say what the value of this service is to individual investors. We can, however, put a
price on the service in Taiwan: $NT 216 million a day, or 1.2 percentage points annually. These
fees provide a livelihood to employees of the exchange and of brokerage firms as well as profits to
their shareholders.
Combined trading and market-timing losses constitute a wealth transfer from individual
investors to institutional investors. Institutions are agents. Whether the principals represented by
institutions ultimately enjoy this performance boost depends on the costs that institutions charge
their principals for their portfolio management services. In our sample, the most profitable group of
institutional investors is foreign investors who garner 46.2 percent of the trading and market-timing
gross profits of institutional investors. Thus, nearly half of the wealth transfer from domestic
individuals to institutional investors goes to foreign institutions. Whether the institutional profits of
corporations, dealers, and domestic mutual funds represent a wealth transfer depends on many
factors. Corporate profits would be arguably enjoyed by corporate shareholders, but only after the
wages paid to those who manage the equity portfolios of corporations. Based on our discussions
with dealers, their trading operations are primarily a combination of proprietary trading and trading
for high net worth individuals.
For domestic equity mutual funds we can shed some light on whether those who own
mutual funds participate in the trading gains of the funds. Using data between 1995 and 2005,
which contains a record of returns for all domestic equity funds in Taiwan, we are able to construct
a time-series of monthly mutual fund returns weighted by the beginning-of-period total net asset
value (TNA) of funds in each month. These data (from the Securities Investment Trust &
Consulting Association of the ROC) are free of survivorship bias. (Dividend data from the Taiwan
Economic Journal are used to calculate fund returns.) Thus, the time-series of returns represents the
return earned by the average dollar invested in equity mutual funds. To estimate the performance of
mutual funds, we estimate an abnormal return using the four-factor model of equation (1). For the
1995 to 2005 sample period, the abnormal return (four-factor intercept) is 0.43 percent per month
(t=1.90); during our sample period (1995 to 1999), the four-factor intercept is 0.23 percent per
month (t=0.78). Thus, consistent with our evidence that mutual funds profit from trade, the returns
of mutual funds are positive (albeit with marginal statistical significance). The positive net returns
earned by mutual funds is quite remarkable, since the TNA-weighted expenses of these mutual
funds are large – ranging from 2.4 to 3.1 percent annually from 1997 to 2005. While individual
investors could easily have met or beat market rates of return by investing in the average mutual
fund, few did so. Less than one percent of equity held by households was held in the form of
mutual funds.
Individual investors pay an exorbitant price for trading actively. Individual
investors could participate in financial markets at low cost by following a simple buy-and-
hold strategy. Even if poorly diversified, the average performance of individual investors
would be materially improved. Alternatively, individual investors could cheaply diversify
and enjoy market rates of returns by investing in equity mutual funds.
IV. Reasons to trade
Why do individual investors willingly incur such large net trading losses? There are several
reasons why uninformed investors might trade: liquidity requirements, rebalancing needs, hedging
demands, entertainment (or sensation seeking), and the mistaken belief that they are informed, that
is, overconfidence. Turnover in Taiwan during our sample period is nearly 300 percent annually
and two to three times that observed in the U.S in recent years. It strikes us as unlikely that the
liquidity, rebalancing, and hedging needs of Taiwanese investors are two to three times those of
current U.S investors or that these needs warrant a reduction of 3.8 percentage points in the return
on the aggregate portfolio of Taiwanese individual investors. We propose, though do not prove,
that a combination of overconfidence and the desire to gamble account for much of the active
trading and substantial losses of individual investors in Taiwan.
Cross-cultural studies of overconfidence report higher levels of overconfidence—by some
measures nearly double—in China and Taiwan compared to United States (Yates et al., 1989;
Yates et al. 1998). Theoretical models of equity markets predict that overconfident investors will
trade to their detriment (Odean (1998), Gervais and Odean (2001), and Caballé and Sákovics
(2003), while empirical work (Grinblatt and Keloharju (2006)) links overconfidence and sensation
seeking with more active trading. Thus overconfidence could contribute to excessive trading in
Another contributing factor may be that Taiwanese investors view trading in the stock
market as an opportunity to gamble (Kumar (2006)) or a sensation-seeking activity (Grinblatt and
Keloharju (2006)). During our sample period, gambling was illegal in Taiwan. Legalized gambling
in the form of a government sponsored lottery (the Public Welfare Lottery) was introduced in
January 2002. To shed light on whether some of the excessive trading in Taiwan is driven by
investors who wish to gamble, we estimate the following regression for the period January 1995
through February 2007.
,1,12,13,4,5TSE t TSE t TSE t HK t SG t t t
ββ βββε
=+ + + + + +, (3)
where ,TSE t
T is month t percent turnover on the TSE, RTSEt-1 is the month t-1 TAIEX index return,
T and ,SG t
Tare month t percent turnovers on the Hong Kong and Singapore exchanges, and Lt is
an indicator variable set to 0 for months prior to January 2002 and to 1 for January 2002 and
subsequent months.
The estimated coefficient on the lottery dummy variable ( 5
) is -5.62 (t = -3.69), while the
mean of monthly TSE turnover from 1995 through 2001 is 22.6 percent. Thus, controlling for other
factors, the introduction of legal gambling in Taiwan reduced turnover on the TSE by about one
To calibrate the reasonableness of this result, we compare lottery losses to stock market
trading losses. Average annual lottery sales in Taiwan from 2002 through 2006 were $NT 82.3
billion (National Treasury Agency, Taiwan, With a
lottery payout rate of approximately 60 percent (ROC Lotto,, lottery
players paid an average net annual cost of about $NT 32.9 billion. In Section III, we estimate
trading total losses to Taiwanese individual investors from 1995 to 1999 averaging $NT 187 billion
per year. If individual investor trading losses are approximately proportional to trading activity, a
25 percent reduction in trading activity would correspond to a reduction in annual trading losses of
about $NT 46.75 billion. Thus the approximate aggregate annual net cost of playing the lottery
($NT 32.9 billion) was somewhat less than the approximate aggregate annual reduction in trading
losses subsequent to the introduction of the lottery ($NT 46.75 billion). If, indeed, the Taiwanese
derived the same utility of gambling from the lottery that they had previously derived from
additional trading, they did so at a lower cost.
Equity options began trading on the Taiwan Futures Exchange (TAIFEX) in January 2003;
index options began trading in December 2001. Individual investors account for the majority of
trading in equity options. However, the total volume of trading in options is small relative to
trading in common stocks. For example, in 2006 the total dollar value of trading in common stocks
was nearly $NT 25 trillion (similar to trading levels during our sample period), while trading in
equity options was only 1.2 percent of total this amount (almost $NT 300 billion). When we
augment the above regression to include the dollar volume of options trading scaled by the market
cap of Taiwan common stocks, the coefficient on options trading variable is negative but not
reliably so (-11.9, t=-0.84), while the coefficient on the lottery dummy remains reliably negative (-
4.6, t=-2.41).
Individual ownership of stock dropped from the late 1990s (when individual
ownership averaged between 56 and 59 percent of stock) to 2006 (when individual
ownership of stock was 42 percent). This reduced ownership of stock by individuals, who
have higher turnover rates than institutions during our sample period, may also explain the
drop in turnover in recent years. Unfortunately, we do not have individual ownership data
by month and so are unable to reliably test the relation between individual ownership and
overall turnover in the monthly regression framework.
V. Conclusion
We estimate that Taiwanese individual investors incur trading losses, trading costs, and
market timing losses that reduce their aggregate portfolio return by 3.8 percentage points annually.
Less comprehensive studies suggest that trading losses and costs for individual investors in the
U.S. are about 2 percentage points a year (Barber and Odean (2000, 2001)). (U.S. individual
investors trade less actively but run a higher risk of trading with better informed institutional
investors.) Countries around the world are increasingly counting on personal investment accounts
to fund their citizens’ retirements. Yet most individuals have no training in investments; many hold
underdiversified portfolios and routinely make poor trading decisions. Over a savings horizon of
twenty or more years, an annual return shortfall of 2 to 3.8 percentage points will result in a
tremendous reduction in potential wealth. In Taiwan, the U.S., and elsewhere, investors who are
saving to meet long term goals would benefit from effective guidance regarding best investment
practices. Until then, the answer to “Just how much do individual investors lose by trading?”
remains: Too much!
Ackermann, Carl, Richard McEnally, and David Ravenscraft, 1999, “The Performance of Hedge Funds:
Risk, Return, and Incentives,” Journal of Finance, 54, 833-874.
Agrawal, Vikas, and Narayan Y. Naik, 2000, “On Taking the Alternative Route: The Risks, Rewards, and
Performance Persistence of Hedge Funds,” Journal of Alternative Investments, 2, 6-23.
Amin, Gaurav S., and Harry M. Kat, 2003, “Hedge Fund Performance 1990-2000: Do the “Money
Machines” Really Add Value?” Journal of Financial and Quantitative Analysis, 38, 251-274.
Barber, Brad M., Yi-Tsung Lee, Yu-Jane Liu, and Terrance Odean, 2004, “Do Individual Day Traders Make
Money? Evidence from Taiwan,” working paper, UC-Davis, Davis, CA.
Barber, Brad M., Yi-Tsung Lee, Yu-Jane Liu, and Terrance Odean, 2007, “Is the Aggregate Investor
Reluctant to Realize Losses?” European Financial Management, 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(2), 773-806.
Barber, Brad M., and Terrance Odean, 2001, “Boys will Be Boys: Gender, Overconfidence, and Common
Stock Investment,” Quarterly Journal of Economics, 116, 261-292.
Bartov, Eli, Suresh Radhakrishnan, and Itzhak Krinsky, 2000, “Investor Sophistication and Patterns in Stock
Returns after Earnings Announcements,” Accounting Review, 75, 43-63.
Brown, Stephen J., William N. Goetzmann, and Roger G. Ibbotson, 1999, “Offshore Hedge Funds: Survival
and Performance, 1989-95,” Journal of Business, 72, 91-117.
Caballé, Jordi, and József Sákovics, 2003, “Speculating Against an Overconfident Market,” Journal of
Financial Markets, 6, 199-225.
Carhart, Mark M., 1997, “On Persistence in Mutual Fund Performance,” Journal of Finance, 52, 57-82.
Chakravarty, Sugato, 2001, “Stealth-trading: Which Traders Trades Move Stock Prices?” Journal of
Financial Economics, 61, 289-307.
Chan, Hsiu-Lang, Narasimhan Jegadeesh, and Russ Wermers, 2000, “The Value of Active Mutual Fund
Management: An Examination of the Stockholdings and Trades of Fund Managers,” Journal of
Financial and Quantitative Analysis, 35, 343-368.
Christopherson, Jon A., Wayne E. Ferson, and Debra A. Glassman, 1998, “Conditional Measures of
Performance and Persistence for Pension Funds,” In: Research in Finance, Vol. 16, JAI Press,
Stamford, CT, pp. 1-46.
Coggin, T. Daniel, Frank J. Fabozzi, and Shafiqur Rahman, 1993, “The Investment Performance of U.S.
Equity Pension Fund Managers: An Empirical Investigation,” Journal of Finance, 48, 1039-1055.
Coggin, T. Daniel, and Charles A. Trzcinka, 2000, “A Panel Study of U.S. Equity Pension Fund Manager
Style Performance,” Journal of Investing, 9, 6-12.
Coval, Joshua D., David Hirshleifer, and Tyler Shumway, 2003, “Can Individual Investors Beat the Market?”
unpublished working paper, Harvard Business School, Cambridge, MA.
Coval, Joshua D., and Tobias J. Moskowitz, 2001, “The Geography of Investment: Informed Trading and
Asset Prices,” Journal of Political Economy, 109, 811-841.
Daniel, Kent, Mark Grinblatt, Sheridan Titman, and Russ Wermers, 1997, “Measuring Mutual Fund
Performance with Characteristic-based Benchmarks,” Journal of Finance, 52, 1035-1058.
Delguercio, Diane, and Paula A. Tkac, 2002, “The Determinants of the Flow of Funds of Managed
Portfolios: Mutual Funds vs. Pension Funds,” Journal of Financial and Quantitative Analysis, 37,
Fama, Eugene F., and Kenneth R. French, 1993, “Common Risk Factors in Returns on Stocks and Bonds,”
Journal of Financial Economics, 33, 3-56.
Ferson, Wayne, and Kenneth Khang, 2002, “Conditional Performance Measurement Using Portfolio
Weights: Evidence for Pension Funds,” Journal of Financial Economics, 65, 249-282.
Foucault, T., 1999, “Order Flow Composition and Trading Costs in a Dynamic Limit Order Market,” Journal
of Financial Markets, 2, 99-134.
Gao, Sheldon, 2002, “China Stock Market in a Global Perspective,” Dow Jones Indexes.
Gervais, Simon, Ron Kaniel, and Dan H. Mingelgrin, 2001, “The High-volume Return Premium,” Journal of
Finance, 56, 877-922.
Gervais, Simon, and Terrance Odean, 2001, “Learning to Be Overconfident,” Review of Financial Studies,
14, 1-27.
Goetzmann, William N., and Kumar, Alok, 2005, “Why Do Individual Investors Hold Under-Diversified
Portfolios? ” Available at SSRN:
Griffiths, M., B. Smith, A. Turnbull, and R. 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, 43-68.
Grinblatt, Mark, and Matti Keloharju, 2006, “Sensation Seeking, Overconfidence, and Trading Activity,”
NBER working paper 12223.
Grinblatt, Mark, and Sheridan Titman, 1989, “Mutual Fund Performance: An Analysis of Quarterly Portfolio
Holdings,” Journal of Business, 62, 393-416.
Grinblatt, Mark, and Sheridan Titman, 1993, “Performance Measurement without Benchmarks: An
Examination of Mutual Fund Returns,” Journal of Business, 66, 47-68.
Handa, P., R. Schwartz, and A. Tiwari, 2003, “Quote Setting and Price Formation in an Order Driven
Market,” Journal of Financial Markets, 6, 461-489.
Ikenberry, David, Richard Shockley, and Kent Womack, 1998, “Why Active Fund Managers Often
Underperform the S&P 500: The Impact of Size and Skewness,” Journal of Private Portfolio
Management, 1, 13-26.
Ivkovich, Zoran, Clemens Sialm, and Scott J. Weisbenner, 2004, “Portfolio Concentration and the
Performance of Individual Investors,” Journal of Financial and Quantitative Analysis, forthcoming.
Ivkovich, Zoran, and Scott J. Weisbenner, 2004, “Local Does as Local Is: Information Content of the
Geography of Individual Investors’ Common Stock Investments,” Journal of Finance, 60, 267-306.
Kumar, Alok, 2006, “Who Gambles in the Stock Market?” AFA 2006 Boston Meetings Paper Available at
Lakonishok, Josef, Andrei Shleifer, and Robert Vishny, 1992, “The Structure and Performance of the Money
Management Industry,” Brookings Papers on Economic Activity: Microeconomics 1992, 330-391.
La Porta, Rafael, Florencio Lopez-de-Silanes, Andrei Shleifer, and Robert W. Vishny, 1997, “Legal
Determinants of External Finance,” Journal of Finance, 52, 1131-1150.
Lee, Charles M.C., Andrei Shleifer, and Richard H. Thaler, 1991, “Investor Sentiment and the Closed-End
Fund Puzzle,” Journal of Finance, 46, 75-109.
Liang, Bing, 1999, “On the Performance of Hedge Funds,” Financial Analysts Journal, 55, 72-85.
Linnainmaa, Juhani, 2003a, “The Anatomy of Day Traders,” Available at SSRN:
Linnainmaa, Juhani, 2003b, “Who Makes the Limit Order Book? Implications for Contrarian Strategies,
Attention-Grabbing Hypothesis, and the Disposition Effect” Available at SSRN:
Lyon, John D., Brad M. Barber, and Chih-Ling Tsai, 1999, “Improved Methods for Tests of Long-run
Abnormal Stock Returns,” Journal of Finance, 54, 165-201.
Mitchell, Mark L., and Erik Stafford, 2000, “Managerial Decisions and Long-term Stock Price Performance,”
Journal of Business, 73, 287-329.
Odean, Terrance, 1998, “Volume, Volatility, Price, and Profit When All Traders Are above Average,”
Journal of Finance, 53, 1887-1934.
Odean, Terrance, 1999, “Do Investors Trade too Much?” American Economic Review, 89, 1279-1298.
Parlour, Christine, 1998, “Price Dynamics in Limit Order Markets,” Review of Financial Studies, 11(4), 789-
Polkovnichenko, Valery, 2005, “Household Portfolio Diversification: A Case for Rank-Dependent
Preference,” Review of Financial Studies, 18, 1467-1502.
Poteshman, A.M., and V. Serbin, 2003, “Clearly Irrational Financial Market Behavior: Evidence from the
Early Exercise of Exchange Trade Stock Options,” Journal of Finance, 58, 37-70.
Schlarbaum, Gary G., Wilbur G. Lewellen, and Ronald C. Lease, 1978a, “The Common-Stock-Portfolio
Performance Record of Individual Investors: 1964-70,” Journal of Finance, 33, 429-441.
Schlarbaum, Gary G., Wilbur G. Lewellen, and Ronald C. Lease, 1978b, “Realized Returns on Common
Stock Investments: The Experience of Individual Investors,” Journal of Business, 51, 299-325.
Seasholes, Mark S., 2000, “Smart Foreign Traders in Emerging Markets,” unpublished working paper, UC-
Berkeley, Berkeley, CA.
Sias, Richard W., and Laura T. Starks, 1997, “Return Autocorrelation and Institutional Investors,” Journal of
Financial Economics, 46, 103-131.
Wermers, Russ, 2000, “Mutual Fund Performance: An Empirical Decomposition into Stock-Picking Talent,
Style, Transactions Costs, and Expenses,” Journal of Finance, 55, 1655-1694.
Yates, J. Frank, Ying Zhu, David L. Ronis, Deng-Feng Wang, Hiromi Shinotsuka, and Masanao Toda, 1989,
“Probability Judgment Accuracy: China, Japan, and the United States,” Organizational Behavior
and Human Decision Processes, 43, 145-171.
Yates, J. Frank, Ju-Whei Lee, Hiromi Shinotsuka, Andrea L. Patalano, and Winston R. Sieck, 1998, “Cross-
Cultural Variations in Probability Judgment Accuracy: Beyond General Knowledge
Overconfidence,” Organizational Behavior and Human Decision Processes, 74, 89-117.
Table 1: Basic Descriptive Statistics for Taiwan Stock Exchange
The market index is a value-weighted index of all stocks traded on the TSE. Mean market cap is
calculated as the sum of daily market caps divided by the number of trading days in the year.
Turnover is calculated as half the value of buys and sells divided by market cap. Number of traders
and number of trades are from the TSE dataset. Day trades are defined as purchases and sales of the
same stock on the same day by one investor. Day trade percentage of all trades is based on value of
trade; percentages based on number of trades are similar.
Market Cap
(bil TW $)
No. of
No. of
Day Trade
as % of All
1995 -27.4 347 5,250 195 1,169 120,115 20.6
1996 33.9 382 6,125 214 1,320 149,197 17.3
1997 18.2 404 9,571 393 2,173 310,926 24.8
1998 -21.6 437 9,620 310 2,816 291,876 25.6
1999 31.6 462 10,095 292 2,934 321,926 21.8
8,132 294 2,082 238,808
Table 2: Trade Descriptive Statistics by Trader Type: 1995 to 1999
Data are from the Taiwan Stock Exchange.
Total Value of Trade
($NT billion)
Average Trade Size
Buys Sells Buys Sells
% of all
Individuals 106,323.4 106,344.1 190,656 191,459 89.5
Corporations 5,078.1 5,334.4 380,900 379,232 4.4
Dealers 1,749.5 1,747.4 424,131 411,109 1.5
Foreigners 2,503.5 2,066.9 350,413 310,439 1.9
Mutual Funds 3,193.7 3,355.3 427,355 359,068 2.8
All Investors 118,848.1 118,848.1 201,524 201,519 100.0
Table 3: Equity to Total Assets for Households Owning Equity
Data are from the Taiwan Ministry of Finance. Means are calculated
for each year, 1997 to 2002. The table reports the mean across years.
Quartile of Household Net Worth
(Conditional on Positive Net Worth)
Worth 1 (Low) 2 3 4 (High) All
Equity to Total Assets (%)
17 52 17 14 15 24
Equity to Total Assets Excluding Real Estate (%)
52 62 64 44 38 45
Table 4: Mean Daily Dollar Profit from Trade for Various Trading Groups in Taiwan: 1995 to 1999
On each day, the dollar profit from trade is calculated as the dollar gain on the buy portfolio (net of any market gain) less the dollar gain
on the sell portfolio (net of any market gain). Portfolios are based on net daily buys (or sells) of each investor group. Buy and sell
portfolios are constructed assuming a holding period of 1, 10, 25, and 140 trading days. The table presents the mean daily dollar profit
across all trading days. Test statistics are calculated using the time-series of daily dollar profits. Profits are further partitioned based
upon whether the order underlying the trade was aggressive or passive (see text for definitions of aggressive and passive).
Buys - Sells Buys Sells Buys - Sells Buys - Sells Buys Sells Buys - Sells
All All All Passive Aggressive All All All Passive Aggressive
Profits ($NT Mil) t-statistic
1 days
Corporations 13.9 6.0 -7.9 13.1 0.2 9.32 5.00 -6.47 13.88 0.24
Dealers 3.2 0.4 -2.8 3.3 -0.4 6.28 0.82 -5.53 12.56 -1.11
Foreigners 9.5 5.7 -3.8 5.1 3.5 8.94 6.45 -6.06 13.31 4.91
Mutual Funds 8.4 2.3 -6.2 6.6 1.5 6.61 1.95 -5.48 14.97 1.90
All Institutions 35.3 14.2 -21.1 27.7 5.2 13.42 6.33 -10.16 18.29 3.07
Individuals -35.3 -21.1 14.2 71.5 -100.9 -13.42 -10.16 6.33 12.21 -14.86
10 days
Corporations 22.3 8.6 -13.7 18.4 -0.4 4.95 2.22 -3.16 8.05 4.95
Dealers 3.9 4.1 0.2 3.5 0.1 3.47 1.85 0.11 6.20 3.49
Foreigners 14.2 12.9 -1.3 6.4 5.7 4.16 4.08 -0.59 6.58 4.14
Mutual Funds 18.8 15.9 -2.9 11.2 6.1 3.91 3.16 -0.64 7.79 3.85
All Institutions 59.4 33.1 -26.3 39.2 12.0 7.62 4.37 -3.46 12.18 7.54
Individuals -59.4 -26.3 33.1 70.7 -129.2 -7.62 -3.46 4.37 5.03 -7.54
25 days
Corporations 23.1 6.8 -16.3 18.9 -2.5 2.91 0.85 -1.83 4.95 -0.59
Dealers 3.2 9.1 5.9 2.8 0.2 1.87 1.78 1.16 3.44 0.14
Foreigners 22.5 26.3 3.8 8.0 11.5 3.36 3.83 0.81 4.71 2.41
Mutual Funds 25.0 31.5 6.5 12.8 11.1 2.98 2.89 0.65 5.00 2.10
All Institutions 74.0 52.6 -21.4 42.2 20.8 5.32 3.25 -1.29 7.88 2.29
Individuals -74.0 -21.4 52.6 34.1 -107.7 -5.32 -1.29 3.25 1.47 -4.26
140 days
Corporations 18.9 17.5 -1.4 19.2 -14.0 0.70 0.51 -0.04 1.65 -0.73
Dealers 12.3 40.9 28.6 4.2 8.0 4.09 1.61 1.13 2.25 2.54
Foreigners 84.7 120.5 35.8 21.9 54.2 3.88 3.77 1.82 3.72 3.60
Mutual Funds 62.5 126.3 63.8 22.3 37.2 3.58 2.38 1.24 4.05 3.12
All Institutions 178.7 193.7 15.0 67.3 85.8 4.68 2.57 0.18 4.51 3.22
Individuals -178.7 15.0 193.7 -27.0 -157.6 -4.68 0.18 2.57 -0.35 -1.91
Table 5: Trading Profits by Firm Size for Various Trading Groups in Taiwan: 1995 to 1999
On each day, the dollar profit from trade is calculated as the dollar gain on the buy portfolio (net of any market gain) less the dollar gain
on the sell portfolio (net of any market gain). Portfolios are based on net daily buys (or sells) of each investor group. Buy and sell
portfolios are constructed assuming a holding period of 1, 10, 25, and 140 trading days. The table presents the mean daily dollar profit
across all trading days. Test statistics are calculated using the time-series of daily dollar profits. Profits are further partitioned based
upon whether the order underlying the trade was aggressive or passive (see text for definitions of aggressive and passive).
All Pass. Agg. All Pass. Agg. All Pass. Agg. All Pass. Agg.
Profits ($NT Mil) t-stat Profits ($NT Mil) t-stat
1 day 1 day
Corporations 6.8 7.5 -0.6 6.99 12.25 -1.17 7.1 5.5 0.8 9.22 11.42 2.07
Dealers 1.2 2.0 -1.0 3.03 10.67 -3.18 1.9 1.2 0.5 8.52 10.53 3.20
Foreigners 6.5 3.8 2.2 7.13 11.29 3.55 3.0 1.3 1.3 9.08 11.97 6.04
Mutual Funds 1.8 3.4 -1.4 1.90 10.98 -2.10 6.4 3.1 2.9 10.55 13.74 7.86
All Institutions 16.5 16.6 -0.4 8.56 16.35 -0.31 18.6 11.1 5.6 15.82 16.38 8.47
Individuals -16.5 52.2 -64.2 -8.56 11.59 -13.55 -18.6 19.5 -36.6 -15.82 9.49 -13.64
10 days 10 days
Corporations 9.1 9.3 -1.5 2.61 5.06 -0.87 13.2 9.0 1.1 6.22 8.75 0.88
Dealers 1.7 2.2 -0.4 1.93 4.84 -0.57 2.1 1.3 0.5 3.83 5.02 1.23
Foreigners 10.0 4.9 3.9 3.39 6.06 1.82 4.2 1.4 1.9 3.83 3.66 2.76
Mutual Funds 7.4 5.7 2.0 2.19 5.51 0.86 11.4 5.5 4.2 4.38 6.74 2.67
All Institutions 28.3 22.0 4.3 4.95 9.11 1.05 31.0 17.2 7.7 8.47 11.98 3.35
Individuals -28.3 52.3 -79.0 -4.95 4.62 -7.31 -31.0 18.5 -49.7 -8.47 3.76 -8.97
25 days 25 days
Corporations 5.8 7.0 -3.3 0.93 2.04 -1.06 17.4 11.9 0.7 4.91 6.82 0.30
Dealers 2.2 2.1 0.2 1.69 3.25 0.21 1.0 0.7 0.0 1.17 1.56 0.00
Foreigners 16.3 5.6 9.5 2.78 3.81 2.17 6.2 2.4 2.1 3.34 3.90 2.00
Mutual Funds 12.8 6.7 6.9 2.31 3.76 1.86 12.5 6.3 4.5 2.74 4.50 1.65
All Institutions 37.3 21.2 13.7 3.88 5.13 1.97 37.3 21.2 7.4 5.50 8.67 1.74
Individuals -37.3 22.1 -58.0 -3.88 1.21 -3.06 -37.3 12.3 -50.3 -5.50 1.47 -5.02
140 days 140 days
Corporations -13.1 0.2 -15.8 -0.65 0.02 -1.36 31.4 18.9 1.3 3.18 3.36 0.13
Dealers 8.5 2.4 6.7 3.34 2.00 2.56 3.1 1.6 0.7 1.96 1.49 0.48
Foreigners 67.1 16.2 47.5 3.28 2.97 3.23 17.5 5.6 6.9 3.90 3.75 2.76
Mutual Funds 41.0 13.3 27.8 3.01 3.47 2.69 19.0 8.7 8.5 1.92 2.52 1.57
All Institutions 103.7 32.0 66.6 3.67 2.71 3.09 71.1 34.8 17.6 4.25 5.16 1.34
Individuals -103.7 -16.4 -95.7 -3.67 -0.28 -1.62 -71.1 -9.7 -57.9 -4.25 -0.35 -1.57
Table 6: Percentage Monthly Abnormal Returns for Various Trading Groups in Taiwan: 1995 to 1999
A buy (and sell) portfolio is constructed that mimics the daily net purchases (and sales) of each investor group at holding periods of 1,
10, 25, or 140 trading days. The daily returns on the portfolios are compounded to yield a monthly return series. Abnormal returns are
calculated as the intercept from a time-series regression of the portfolio excess return on the market excess return, a firm size factor, a
value-growth factor, and a momentum factor (4-factor).
Buys - Sells Buys Sells Buys - Sells Buys - Sells Buys Sells Buys - Sells
All All All Passive Aggressive All All All Passive Aggressive
Monthly Alpha t-stat
1 Days
Corporations 6.078 2.560 -3.518 11.682 0.560 10.40 7.52 -9.33 16.38 1.25
Dealers 5.515 1.859 -3.656 12.460 1.035 10.64 4.90 -8.76 15.62 2.11
Foreigners 9.455 5.167 -4.288 15.305 5.920 13.45 10.82 -9.46 21.28 8.11
Mutual Funds 6.576 2.726 -3.850 12.804 2.796 13.49 7.98 -10.07 21.73 5.84
All Institutions 10.969 5.002 -5.968 17.069 4.314 19.92 13.54 -16.62 24.28 9.24
Individuals -10.969 -5.968 5.002 9.046 -14.028 -19.92 -16.62 13.53 12.13 -19.14
10 Days
Corporations 2.388 0.776 -1.612 3.941 0.109 5.67 2.35 -4.99 8.47 0.32
Dealers 1.183 0.475 -0.708 3.228 -0.152 4.78 1.52 -2.21 10.06 -0.65
Foreigners 2.288 1.325 -0.963 3.804 1.253 4.45 3.66 -2.45 8.29 2.37
Mutual Funds 2.183 1.299 -0.884 4.094 0.986 4.34 3.41 -2.04 9.19 1.95
All Institutions 3.269 1.394 -1.875 5.197 0.909 8.93 5.23 -5.94 14.26 2.52
Individuals -3.269 -1.875 1.394 2.996 -4.720 -8.93 -5.94 5.23 8.78 -13.61
25 Days
Corporations 1.372 0.271 -1.101 1.905 0.193 4.30 0.88 -3.80 6.04 0.65
Dealers 0.308 0.213 -0.095 1.125 -0.251 1.72 0.70 -0.31 5.26 -1.56
Foreigners 1.599 1.154 -0.445 2.158 1.089 3.18 3.47 -1.11 5.49 2.10
Mutual Funds 1.251 0.930 -0.321 2.218 0.731 3.83 2.58 -0.82 7.21 2.23
All Institutions 1.914 0.850 -1.064 2.609 0.747 6.47 3.55 -3.59 11.24 2.56
Individuals -1.914 -1.064 0.850 1.153 -2.193 -6.47 -3.59 3.55 4.88 -8.47
140 Days
Corporations 0.486 0.183 -0.303 0.521 0.207 3.02 0.80 -1.46 4.14 1.09
Dealers 0.247 0.233 -0.014 0.475 0.074 3.42 0.78 -0.04 3.58 0.96
Foreigners 0.727 0.799 0.072 0.769 0.620 3.15 2.98 0.31 3.18 3.00
Mutual Funds 0.512 0.575 0.063 0.748 0.387 3.27 1.66 0.18 5.54 2.33
All Institutions 0.757 0.494 -0.263 0.842 0.438 5.77 2.40 -1.12 8.24 3.07
Individuals -0.757 -0.263 0.494 0.296 -0.666 -5.77 -1.12 2.40 2.17 -4.80
Figure 1: Cumulative (Market-Adjusted) Abnormal Returns (CARs) in Event Time
for Stocks Bought less Stocks Sold by Institutions and Individuals
Panel A: CARs are weighted by aggregate value of stocks bought and stocks sold
Panel B: CARs are weighted by net value of stocks bought and sold
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280
Event Day 0 = Day of Trade
0 20 40 60 80 100 120 140 160 180 200 220 240 260 280
Event Day 0 = Day of Trade
... The authors have a stream of future papers that document further retail underperformance across time and geography. One example is Barber et al. (2009) who analyze the trading records of Taiwanese investors and document underperformance. ...
... Kaniel et al. (2012) find similar results of informed trading around earnings announcements. Similarly, Barber et al. (2009) document that stocks heavily bought in retail books positively predict performance. Boehmer et al. (2021) document retail skill over a week-long horizon. ...
In early 2021, several stocks receiving attention from retail traders known as "meme stocks" soared in value. A primary source of information regarding these stocks is from the social media platform Reddit, specifically from a subreddit known as WallStreetBets (WSB).This paper investigates whether a simple and easily implementable trading strategy following the WallStreetBets (WSB) subreddit can produce alpha. We document no evidence this is the case. Though we do observe a positive relation between WSB submissions and abnormal trading volume, we find that a portfolio that goes long buy recommendations and short sell recommendations each day is not profitable on a risk-adjusted basis. Holding periods from one day to one year fail to produce alpha. These findings are robust to a variety of different portfolio formation strategies. Our results provide an early look at the data following the explosion of interest in social media inspired retail investing.
... At the level of interpretation, the decrease in confidence with forecast extremity connects with studies suggesting that private investors tend for contrarian trading (e.g., Grinblatt and Keloharju, 2000;Baltzer et al., 2019) and institutional investors benefit from momentum or feedback trading on the expense of private investors (Grinblat and Han, 2005;Barber et al., 2009;Economou et al., 2022). The results also relate to the literature on overconfidence and trading (Glaser and Weber, 2007;Ben-David et al., 2013). ...
... Contrarian response to recent quarterly returns is reported for Finnish households (Grinblatt and Keloharju, 2000), German private investors (Baltzer et al., 2019), and Indian retail investors (Chhimwal and Bapat, 2021). The literature moreover argues that institutional investors benefit from momentum or feedback trading at the expense of private investors (Grinblat and Han, 2005;Barber et al., 2009;Baltzer et al., 2019;Economou et al., 2022). Connecting to the current results, the decrease in confidence with forecast extremity may deter private investors from chasing trends, explaining the prevalence of contrarian trading in empirical records. ...
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Three return forecasting experiments and a panel of more than 14,000 CFOs' forecasts of the S&P 500 annual return suggest that forecast confidence decreases as the forecasts diverge from zero, in the positive or negative direction. The decrease in confidence reflects in longer forecast intervals, weaker belief in the accuracy of the forecasts, and larger perceived volatility estimates. Assuming cumulative prospect theory, the increase in perceived volatility with forecast optimism is fast enough to fully offset the CFOs' response to more optimistic expectations in about 20% of the cross-sample comparisons. Permutation tests, more generally, confirm that the decrease in confidence significantly delays the response to optimistic forecasts. The decrease in confidence alleviates the underestimation of volatility in cases of optimistic forecasts, but even the optimistic CFOs underestimate the VIX volatility by more than 50%. A complementary empirical analysis reveals significant cross-sectional and time-series correlations between the absolute realized returns on the stocks composing the S&P 500 list and estimates of the stocks' contemporaneous volatility. The correlations emerge in five levels of analysis and separately show for positive and negative sub-sequences of the returns.
... In addition, Johnson (2009) cites the perception that many VC professionals (the GPs) "do not give much thought to tax." Various findings in the finance literature suggest that investors do not always make optimal choices regarding taxes. Barber et al. (2009) show that, in Taiwan, transaction taxes make up 34% of investors' losses, consistent with their not being salient. Sialm and Zheng (2020) document that tax-efficient mutual funds save on taxes without a clear reduction in returns, yet they are relatively uncommon. ...
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We examine the choice of organizational structure for VC-backed startup firms. These firms overwhelmingly organize as C-corporations rather than as tax advantaged limited liability companies (LLCs). This results in foregone tax savings of $43.9 billion, or 4.9% of the total equity invested in the sample firms. The decision is puzzling, given plausible estimates of the direct costs involved, but appears related to “hassle” and other transition costs generated by participants implementing a new form. Firms with more employees and investors are likely to choose the C-corporation. VCs appear to prefer the C-corporation form, as receiving VC money is associated with most LLC firms switching to a C-corporation within 30 days. Greater VC preferences for C-corporations are linked to a preference for familiarity, and less attention to taxes.
Using the brokerage analyst reports released from 2014 to 2019, this paper examines the effects of the information role of brokerage firms and the information transmission role of the media on investor trading activity in the Taiwan stock market. Trading activity in the market can be categorized by investor types, including retail investors and institutional investors (foreign institutions, mutual funds, and broker dealers). This paper reviews the “buy the rumor, sell the news” trading strategy, which refers to institutional investors acting as early-informed traders and exhibiting short-term profit-taking characteristics. Notably, mutual funds have the best informational advantage among institutional investors, whose trading activities are significantly and positively related to the abnormal returns around the release of brokerage reports. On the other hand, retail investors may follow the trading activity of institutional investors without making a significant profit. The empirical findings are robust to include several firm and market characteristics. Overall, the empirical results provide in-depth insights into the regulation of brokerage analyst report disclosures.
We investigate the impact of Google search volume on stock prices and trading volume around recommendation revision announcements made by securities firms in the Taiwan stock market. Our results reveal that for upgrade announcements, the internet searches that are driven by investor attention lead to a temporary increase in positive price pressure around the revision announcements, whereas the price effect of the information extracted from the Google searches is persistent. We also find that trading volume reflects the incoming news around the revision announcements. Our results suggest that individual investors, who receive analyst revision reports gratis from securities firms before these reports are made public, will search through the Internet to obtain valid firm-specific information relating to the recommendation revisions.
The purpose of the present investigation was to determine the prevalence, demographic profile, and correlates of financial specula- tion in Canada, with a focus on its relationship to gambling. In cooperation with Statistics Canada, a brief assessment of financial speculation was developed and included in the 2018 Canadian Community Health Survey (CCHS) and administered to 23,952 adults. The past year national prevalence of financial speculation was found to be 3.6%, with the province of Alberta having the only higher inter- provincial rate. Speculators were predominantly male, White, with a modal age of 30–49, and with higher educational attainment and household income. In terms of health indicators, they were more likely to be regular alcohol drinkers and occasional smokers, but to have better self-reported general health, mental health, and life satisfaction. However, the strongest multivariate predictor of spec- ulation was higher frequency of gambling involvement. Speculators had higher levels of participation in all types of gambling (especially skill-based formats) with the exception of instant lotteries. This high level of involvement contributes to somewhat higher rates of pro- blem gambling symptomatology. The present investigation confirms that speculation not only has a strong conceptual relationship to gambling but also a strong empirical relationship.
We study a quasi-natural experiment that altered the structure of commission fee schedules applied to retail investors in the Portuguese stock market in 2003. Using a difference-in-differences analysis, we show that investors with a university degree, financial knowledge and numerical skills demonstrate greater ability and eagerness in adjusting their trading patterns to the new commission schedules. The impact of education was stronger for those lacking trading experience. Investors with a university degree that were more prone to issue small orders before the event were also more eager to scale up their order size.
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In this paper, we analyze the effects of abnormal capital investment and financial flexibility on firm performance following actual share repurchases by using a unique Taiwanese dataset. The results indicate that superior operational performance is associated with share repurchases; however, share repurchases may crowd out required capital investments. Further examination reveals that firms with adequate financial flexibility outperform firms with inadequate financial flexibility after actual share repurchases. In summary, our findings suggest that financial flexibility can mitigate the investment crowding out effect and positively affect firms' operational performance post-repurchase.
As hospitality and tourism (H&T) businesses mature, they often seek institutional equity financing to support their growth. Capital intensive H&T firms, such as cruise operators, casinos and large restaurant and hotel chains, continuously rely on institutional capital to fund their operations. This study examines which corporate social responsibility dimensions affect H&T firms’ ability to attract institutional equity capital providers. We document that firms with better social and governance performance have higher institutional ownership, particularly by investors focused on long-term growth and value creation, such as dedicated institutional investors, domestic investors and blockholders. Community and environmental performance do not increase institutional holdings.
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Abstract The performance,of actively managed,U.S. equity funds is often naively compared,to that of the S&P 500 index. In recent years, this comparison has generally cast an unfavorable impression of active fund managers and has led many investors to embrace index funds. , Systematic deviations from the benchmark,are affected by two conventional practices of active
We ask whether the typical investor and the aggregate investor exhibit a bias known as the disposition effect, the tendency to sell investments that are held for a profit at a faster rate than investments held for a loss. We analyse all trading activity on the Taiwan Stock Exchange (TSE) for the five years ending in 1999. Using a dataset that contains all trades (over one billion) and the identity of every trader (nearly four million), we find that in aggregate, investors in Taiwan are about twice as likely to sell a stock if they are holding that stock for a gain rather than a loss. Eighty-four percent of all Taiwanese investors sell winners at a faster rate than losers. Individuals, corporations, and dealers are reluctant to realise losses, while mutual funds and foreigners, who together account for less than 5% of all trades (by value), are not.
We investigate the value of active mutual fund management by examining the stockhold? ings and trades of mutual funds. We find that stocks widely held by funds do not outper? form other stocks. However, stocks purchased by funds have significantly higher returns than stocks they sell?this is true for large stocks as well as small stocks, and for value stocks as well as growth stocks. We find that growth-oriented funds exhibit better stock selection skills than income-oriented funds. Finally, we find only weak evidence that funds with the best past performance have better stock-picking skills than funds with the worst past performance.
Applying a geographic lens to mutual fund performance, this study finds that fund managers earn substantial abnormal returns in nearby investments. These returns are particularly strong among funds that are small and old, focus on few holdings, and operate out of remote areas. Furthermore, we find that while the average fund exhibits only a modest bias toward local stocks, certain funds strongly bias their holdings locally and exhibit even greater local performance. Finally, we demonstrate that the extent to which a firm is held by nearby investors is positively related to its future expected return. Our results suggest that investors trade local securities at an informational advantage and point toward a link between such trading and asset prices.
We develop a multiperiod market model describing both the process by which traders learn about their ability and how a bias in this learning can create overconfident traders. A trader in our model initially does not know his own ability. He infers this ability from his successes and failures. In assessing his ability the trader takes too much credit for his successes. This leads him to become overconfident. A trader's expected level of overconfidence increases in the early stages of his career. Then, with more experience, he comes to better recognize his own ability. The patterns in trading volume, expected profits, price volatility, and expected prices resulting from this endogenous overconfidence are analyzed. Article published by Oxford University Press on behalf of the Society for Financial Studies in its journal, The Review of Financial Studies.
The risk–return characteristics, risk exposures, and performance persistence of various hedge fund strategies remains an area of interest to alternative asset investors. Using a database on hedge fund indices and individual hedge fund managers in a mean–variance framework, results show that a combination of alternative investments and passive indexing provides a significantly better risk–return tradeoff than passively investing in the different asset classes. Moreover, using parametric and non–parametric methods, a reasonable degree of persistence is found for hedge fund managers. This seems to be attributable more to the losers continuing to be losers instead of winners continuing to be winners and highlights the importance of manager selection in case of hedge funds.
Using data from Finland, this study analyzes the extent to which past returns determine the propensity to buy and sell. It also analyzes whether these differences in past-return-based behavior and differences in investor sophistication drive the performance of various investor types. We find that foreign investors tend to be momentum investors, buying past winning stocks and selling past losers. Domestic investors, particularly households, tend to be contrarians. The distinctions in behavior are consistent across a variety of past-return intervals. The portfolios of foreign investors seem to outperform the portfolios of households, even after controlling for behavior differences.
This article develops and applies new measures of portfolio performance which use benchmarks based on the characteristics of stocks held by the portfolios that are evaluated. Specifically, the benchmarks are constructed from the returns of 125 passive portfolios that are matched with stocks held in the evaluated portfolio on the basis of the market capitalization, book‐to‐market, and prior‐year return characteristics of those stocks. Based on these benchmarks, “Characteristic Timing” and “Characteristic Selectivity” measures are developed that detect, respectively, whether portfolio managers successfully time their portfolio weightings on these characteristics and whether managers can select stocks that outperform the average stock having the same characteristics. We apply these measures to a new database of mutual fund holdings covering over 2500 equity funds from 1975 to 1994. Our results show that mutual funds, particularly aggressive‐growth funds, exhibit some selectivity ability, but that funds exhibit no characteristic timing ability.