How do Individual, Institutional, and Foreign Investors Win and Lose in Equity Trades? Evidence from Japan

How do Individual, Institutional,and Foreign Investors Win and Lose

in Equity Trades? Evidence fromJapann

KEE-HONG BAEw, TAKESHI YAMADA

¼AND KEIICHI ITO§

wQueen’s University, Kingston, Ontario, Canada¼National University of Singapore, Singapore

§Nomura Securities, Tokyo, Japan

ABSTRACT

We investigate the gains and losses from equity trades of individualinvestors, various institutional investors, and foreign investors in the TokyoStock Exchange. We develop a trade-weighted performance measure andexamine the impact of trading intervals, price spreads, and market timing onperformance. We find that different investor types gain or lose from differentsources. For example, we discover that individual investors have poor markettiming ability but potentially gain during short-run trading intervals as theiraverage sell price is consistently higher than the average purchase price. Incontrast, we find that foreign investors consistently generate gains fromtrade due to good market timing, although their average sell price is lowerthan the average purchase price. Also, we find that foreign investors extractsignificant portion of their gains by trading against Japanese institutionalinvestors when Japanese investors trade before their fiscal-year end.

n We received useful suggestions from participants of The 5th Behavioral Economics Workshopco-sponsored by Aoyama Gakuin University and Osaka University Center for Research inBehavioral Economics. For earlier versions of the paper, we thank Junji Kawahara, SrinivasanSankaraguruswamy, Yasuhiko Tanigawa, participants of the NFA/APFA/FMA Annual Meetings,and seminar participants at the Hong Kong University of Science and Technology, KeioUniversity, Musashi University, Nanyang Technological University, National University ofSingapore, and The University of Hong Kong for helpful comments. We appreciate the effortsof Masato Hirota and Hirotaka Kawai of the Tokyo Stock Exchange in answering our questions oninstitutional details. We appreciate Alisa Larson for excellent editorial assistance. Yamadaacknowledges the support during stay at the Graduate School of International Corporate Strategyof Hitotsubashi University and financial support from the National University of SingaporeAcademic Research Grant R-315-000-047-112. The contents expressed in the paper do not reflectopinions of the institutions with which authors are affiliated.

r 2007 The Authors. Journal compilation r International Review of Finance Ltd. 2007. Published by BlackwellPublishing Ltd., 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

International Review of Finance, 6:3–4, 2006: pp. 129–155

I. INTRODUCTION

A growing number of empirical studies in recent years have examined thetrading behavior of diverse investor types such as individual investors, variousinstitutional investors, and foreign investors.1 These studies have shown thatthere are significant differences in the trading behavior of various investortypes. However, it is not clear whether the different trading behaviors of variousinvestor types result in significant differences in trade performances. In efficientmarkets, we expect no investor types to perform persistently better or worsethan other investor types. Our paper investigates this issue using data thatinclude trades of all investor types that trade on the Tokyo Stock Exchange(TSE). Our paper not only compares the trading performance of all investortypes across the entire equity market but also measures trading gains and lossesfrom different sources. We examine the impact of trading intervals, pricespreads, and market timing on the trading performance of various investortypes. For this purpose, we develop a trade-weighted measure of tradingperformance using buy and sell volumes.

We raise two issues regarding previous studies that examined the tradeperformance of various investor types (see Section II for a review of priorstudies). First, because different studies have measured trade performancesdifferently, it is difficult to compare results from divergent studies and drawgeneral conclusions. Second, prior studies typically examined single measuresof trade performance in each study. However, some investor types might havepoor market timing in the long run but might generate profits by churningstocks in the short run and compensate for the loss from poor market timing. Ifsuch gains outweigh the losses from poor market timing, these types ofinvestors can continue to participate in a competitive market.

The important result of our paper is that we find some investor types showcomparative advantages in different trading abilities. We find that foreigninvestors consistently have better market timing abilities compared with otherdomestic investors. In fact, we provide evidence that foreign investors cannotmake positive trading gains unless they have good market timing abilitybecause foreign investors buy portfolio of stocks at higher average prices thanthe portfolio of stocks they sell. In contrast, we find that individual investorsoffset losses from poor market timing by selling portfolio of shares atsignificantly higher prices than the purchase prices on average. We find that

1 Among other studies, Choe et al. (1999, 2004) examined trading behavior of various investor

types for the Korean equity market; Grinblatt and Keloharju (2000, 2001), for the Finnish

market; and Hamao and Mei (2001), Kamesaka et al. (2003), and Karolyi (2002), for the Japanese

market. Barber and Odean (2000) and Odean (1999) studied the trading of individual investors

in the United States. Cai et al. (2000), Lakonishok et al. (1992), and Nofsinger and Sias (1999)

studied the trading behavior of US institutional investors. Barber and Odean (2003), Cohen

(1999), Cohen et al. (2002), and Griffin et al. (2003) compared trading behavior of individual

investors and institutional investors. For various markets, Bailey et al. (2007), Brennan and Cao

(1997), Froot, et al. (2001), and Seasholes (2000) examined trading of foreign investors.

International Review of Finance

r 2007 The AuthorsJournal compilation r International Review of Finance Ltd. 2007130

individual investors perform better than other investor types for very shorttrading horizons due to the positive price spreads. However, for long-runtrading intervals, we find that the trading gains from market timing dominatethe overall trading performance for various investor types.

One of the interesting aspects of examining equity trades in the TSE duringour observation period (1991–1999) is that foreign investors’ trades significantlyincreased during the period. Because foreign investors do not always have thesame institutional constraints as domestic investors, we are able to examinehow foreign investors and domestic investors interact with each other in theirtrades. We find evidence that foreign investors buy from Japanese domesticinstitutions that sell a large volume of shares possibly for window-dressingpurposes as well as for adjusting their stock ownership near the fiscal year-end(FYE). Our results suggest that foreign investors exploit significant trading gainsfrom domestic institutions that sell shares during the FYE period.

The data from the TSE uniquely suit our objective because the data not onlyrecord buy and sell trades of different investor types but also enable us tocompute trade-weighted average prices of both buy and sell trades for allinvestor types. For each investor type, the trade-weighted average prices reflectthe average selection of stocks traded by each investor type. Also, our data coverportfolio trading across the entire market. Because a sell trade in the marketmust clear every buy trade, we are able to examine the correlations of nettrading gains among all investor groups for the entire market.

This paper is organized as follows. Section II reviews the literature. Section IIIdiscusses the data and provides descriptive statistics on trading by differentinvestor types. Section IV reports correlations of trades among different investortypes, correlations between trades and market returns, and seasonal tradingpatterns. Section V investigates the impacts of trading price spreads, markettiming, and trading frequencies on trade performance for different investortypes. Section VI provides concluding remarks.

II. PRIOR STUDIES OF TRADING PERFORMANCE OF VARIOUSINVESTOR TYPES

Although an extensive number of studies have explored the trading behavior ofvarious investor types, not many studies have addressed their tradingperformances. Among those that have, most studies have found poorperformance of individual investors. For example, Barber and Odean (2000)and Odean (1999) reported that individual investors in the United States tradeexcessively, which results in poor portfolio performance. Barber and Odeanused individual investors’ household portfolio returns and compared themagainst the various benchmarks, including the market portfolio and the multi-factor benchmark. They found that individual investors gain poor net returnsafter adjusting for trading costs. By using market-wide data from the Finnishstock market, Grinblatt and Keloharju (2000) found individual investors do not

How do Investors Win and Lose in Equity Trades?

r 2007 The AuthorsJournal compilation r International Review of Finance Ltd. 2007 131

pick future winning stocks better than domestic institutional investors andforeign investors. Cohen (1999) found that individual investors buy stock frominstitutions after price increases and sell to institutions after price decreases inthe United States, which suggests that individual investors are bad markettimers and institutions are good market timers. In a related study, Cohen et al.(2002) examined the interactions between individuals and institutions in theUS stock market and found that institutions profited from the underreaction ofstock prices by buying shares from individuals in response to good cash flownews. A recent paper by Barber et al. (2007) examine trade performances ofindividual investors and various institutional investors using a comprehensivedata from the Taiwan Stock Exchange. They find that individual investors incurtrading losses after costs due to aggressive trading, whereas institutionalinvestors profit from trade after cost. Altogether, previous empirical resultssuggest that individual investors are generally poor performers of equitytrading.

A number of researchers have also compared the performance of foreigninvestors with domestic investors. However, these studies show mixed results.Seasholes (2000) found that foreigners generally perform well compared withdomestic investors in emerging markets. He found that foreign investors’ tradespredict future price movements and earn abnormal profits. Froot et al. (2001)and Froot and Ramadorai (2001) examined international portfolio flows forvarious countries and also found that foreign investors trades forecast futureequity returns relatively well. Karolyi (2002) and Kamesaka et al. (2003) alsoshowed that foreign investors in the Japanese equity market have good marketpredicting ability of the market index.2 In contrast, other studies have shownthat foreign investors do not necessarily have good trade performance.Dahlquist and Robertsson (2004) suggested foreign investors are not necessarilygood at picking future winning stocks for the Swedish market. Choe et al.(2005) suggested that foreign investors do not have a private informationadvantage over Korean individual investors. They showed that individualinvestors have a higher proportion of buy trades than sell trades preceding largeprice movements, whereas foreigners have a higher proportion of buy tradesthan sell trades after the event. They also showed that foreign investors trade atworse prices than individual investors. While Dvorak (2005) found domesticinvestors have an information advantage over foreign investors on average, healso showed evidence that some global brokerages have better informationbecause of their experience and expertise. In sum, the prior studies show thatwhether foreign investors perform better or worse than domestic investors isinconclusive.

Although several other papers have compared the trading performance ofvarious investor types across the entire equity market (Grinblatt and Keloharju

2 Hamao and Mei (2001) found that foreign investors show insignificant degree of timing ability

in the Japanese stock market during most of the 1980s. On the other hand, Karolyi (2002) and

Kamesaka et al. (2003) used data from the 1990s.



2000; Karolyi 2002; Kamesaka et al. 2003), most of these studies each examinedonly single aspect of equity trading performance. Our paper examines differentsources of trading performances such as trading prices, market timing, andtrading frequencies of various investor types, which provides us with a morecomplete picture of the performance of various investor types in the entireequity market.3

III. DATA AND SUMMARY STATISTICS

A. Data

We use weekly trading data on the First Section of the TSE. The TSE categorizesthe member securities companies’ brokered trades by classifying trades intothose by individuals, foreigners, and institutions. Institutions are furtherclassified into nonfinancial corporations, mutual funds, insurance companies,and banks. The data comprise the volume (i.e., number of shares traded) andthe amount of trade in yen (f) for both buy and sell trades for each investortype. The TSE reports trades that are aggregated across individual stocks for eachinvestor type. The data cover all trades brokered by TSE’s member securitiescompanies that have a capitalization of at least f3 billion. The data also includethe proprietary trades of these securities companies. Altogether, the dataaccount for approximately 90% of trades in the First Section of the TSE.

Among the domestic institutions, professional fund managers undertake theequity trading for mutual funds, insurance companies, and banks. A large partof trades by banks are by trust banks, which manage equity funds that includecorporate pensions.4 The foreign investors category includes both institutionaland individual investors, but most trades are believed to be from professionalfund managers. On the other hand, individual investors and nonfinancialcorporations mostly consist of investors that are not professional fundmanagers (i.e., nonprofessional investors). Particularly, the equity trading bynonfinancial corporations arises not only through corporate asset managementbut also from adjustment of cross-shareholdings in Japanese corporate groups.It is understood among TSE member firms that one of the functions of theproprietary trading of securities companies is to facilitate the execution ofcustomer orders. Therefore, proprietary trades include both liquidity-providingactivity and the autonomous trades of the member securities firms.

3 A recent study by Barber et al. (2007) is among the few studies that examine stock selection as

well as market timing abilities for different investor types.

4 The bank category also includes trading by commercial banks. Although the nature of equity

trading by commercial banks differs from equity trading by professional fund managers of trust

banks, trading by commercial banks is 10% or less of all trades in this category. Data on the

breakdown of bank trades into commercial and trust banks are available only for September

1996 and later.



Our sample period begins with the first week of January 1991 and ends withthe last week of April 1999. We choose 1991 as the beginning of our observationperiod because the period immediately before 1990 is the so-called ‘asset pricebubble’ period in Japan. Because the Japanese government allowed investors toexecute equity trades outside the stock exchanges after April 1999, we chosethis month as the end of our observation period as our data do not includetrades executed outside the TSE. Unless otherwise noted, all data used in thispaper are from the Nomura Research Institute.

B. Descriptive statistics of trades

In Table 1, Panel A shows the weekly trading volume of different investor typesand the proportion of that volume to the total trading volume between January1991 and April 1999. Of all investor types, the major traders are the proprietarytraders of securities companies, individuals, foreigners, and banks. Theseinvestors account for between 75% and 80% of the trades. Other investortypes, such as mutual funds, insurance companies, and nonfinancial corpora-tions, account for relatively small shares of the trades. Panel B shows theaverage yen amount of the net buys of different investor types. During ourobservation period, foreign investors and banks were net buyers, and all otherdomestic investors were net sellers on average.

In Table 1, Panel C shows the percentage spread between trade-weightedaverage sell price and trade-weighted average buy price for each investor type(i.e., average sell price/average buy price-1). We calculate the spread for both 1-week trades and accumulated 52-week trades. The 52-week spread has smallerstandard deviations because short-term fluctuations are smoothed out. Fromboth 1-week and 52-week average price spreads, we find that investors such asforeign investors and insurance companies have negative average spreads thatare significantly different from zero, which implies that these investors onaverage lose money from equity turnovers. Other investors such as individualinvestor, nonfinancial corporations and banks have significant positive spreads.Particularly, we find that banks and individual investors have the largestpositive spreads.

In Table 1, Panel D shows the average turnover ratios for each investor type.5

The average turnover of the entire market is 0.45 times per year. The turnoverratio of individual investors (0.37) is lower than the market average.Nonfinancial corporations have a low turnover ratio (0.09), because a largepart of their shares are cross-held among group companies and are not tradedfrequently. Insurance companies also have a low turnover ratio (0.09) as theynaturally have long investment horizons. The turnover ratio of banks (0.29)

5 We calculate the turnover ratio by dividing the annual trading volume (for major exchanges) by

the ownership of shares outstanding at the beginning of the year of all stock exchanges in

Japan. (Similar data for the TSE are not available.) The percentage share of trades for the TSE is

more than eighty-five percent of total trades of all stock exchanges in Japan.



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reflects the low turnover of commercial banks, as their shareholdings are part ofthe cross-holdings of corporate groups (i.e., keiretsu). However, trust banks,whose trading volume is around 90% of the entire trading volume in the Bankscategory, have higher turnover because they are professional fund managers.For example, after 1997, when a breakdown of the data is available, the averageturnover ratio of trust banks is 0.60 per year, compared with 0.14 per year forcommercial banks. Foreigners and mutual funds have higher turnover than themarket average (1.13 for foreigners, 0.84 for mutual funds). The turnover ratioof proprietary traders (12.44) is the highest among all investor types, which isnot surprising given the liquidity-providing role of proprietary traders.

IV. CORRELATIONS AND PATTERNS OF TRADE AMONG INVESTORTYPES

A. Correlations among net buy trades and market returns

In Table 2, Panel A shows the contemporaneous correlations of the volume ofnet buys among different investor types. We find that foreign investors tend totrade in opposite directions to all domestic investors (except for the proprietarytraders), which is indicated by the negative correlations between the net buys offoreigners and those of domestic investors. We also find that nonprofessionalinvestors such as individual investors and nonfinancial corporations tend totrade in opposite directions to all institutional investors including foreigninvestors. In Panel B, we show the correlations between lagged, contempora-neous, and future market returns (i.e., returns on the Tokyo Stock Price Index[TOPIX] of the First Section) and the net buys of different investors. The resultsshow that the net buys of all domestic investors except proprietary traders havenegative correlations with lagged and contemporaneous market returns,whereas the net buys of foreign investors have positive correlation with lagged,contemporaneous, and some future market returns. The results imply thatdomestic investors (other than proprietary traders) buy more than they sellwhen market is falling, whereas foreign investors tend to buy more than theysell when market is rising.6 Also foreign investors’ net buy trade is significantlypositively correlated with future returns, which might imply their good marketpredicting ability.

6 Our results are similar to the findings from other markets, which report momentum trading

patterns of foreign investors (Brennan and Cao 1997; Choe et al. 1999; Grinblatt and Keloharju

2000, 2001; Froot et al. 2001). We note that Japanese institutions appear to follow contrarian

trading patterns (see also Kim and Nofsinger 2005; Karolyi 2002; Kamesaka et al. 2003), which

contrasts with US institutions that follow momentum-trading strategies (Lakonishok et al.

1992; Nofsinger and Sias 1999; Cai et al. 2000).



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ark

etre

turn

sas

wee

kly

log

pri

cech

an

ges

fro

mth

eT

OPIX

.T

he

sam

ple

per

iod

isfr

om

the

firs

tw

eek

of

Jan

uary

1991

thro

ugh

the

last

wee

ko

fA

pri

l1999.

Th

esa

mp

leco

mp

rise

s435

ob

serv

ati

on

s.nSi

gn

ifica

nce

at

the0.10

level

.nnSignificance

atthe0.05

level

.nnnSignificance

atthe0.01

level

.



B. Seasonal trading patterns: FYE effect

Several studies have found seasonal trading patters in the Japanese equitymarket during the FYE period. Bremer and Kato (1996) reported that stocks withgains in Japan have higher turnover in March. Kato and Loewenstein (1995)suggested that overall increase in trading volumes of most domestic institutionsaround the FYE could reflect the temporary stock ownership adjustments ofgroup company (keiretsu) firms.7 In our paper, we examine if seasonal tradingpatterns near the FYE affect the trading performance of various investor types.Therefore, in this section we investigate if there are any seasonal patterns of buyand sell trades for different investor types.

We estimate the following regression model for volumes of each investortype:

Volumet ¼ aþX12

j¼1bjMonthDumj;t þ e

t

where X12

j¼1wjbj ¼ 0: ð1Þ

Volumet denotes buy or sell volume data in week t, and MonthDumj, t is a dummyvariable that takes the value of 1 if Volumet is from month j and zero otherwise.The intercept measures the average volume, and bj measures the deviation fromthe average volume for month j. The weight, wj, is the proportion of month j inthe sample. Because we constrain the weighted sum of 12 monthly coefficientsto be zero in the estimation procedure, each coefficient indicates the monthlydeviation from the average volume for the observation period, which is theintercept.

Panels A and B in Table 3 show that during the period from January throughMarch, most domestic institutions sell more than they buy compared withother periods. We find that the sell volume of domestic institutions, particularlythat of banks, is greatest in March. Whereas the adjustments of stock holdingsby institutional investors explain the increase in the overall trading around theFYE (Kato and Loewenstein 1995), a unique Japanese corporate accounting rulemight partly explain the sell trading patterns of domestic institutional investorsbefore the FYE. Under this accounting rule, capital gain is realized andrecognized as income only if the shares are sold. Observers of the Japaneseequity market point to the fact that this rule motivates domestic institutions tosell shares to realize capital gains and to window-dress their profits during the

7 He et al. (2004) showed that US institutions, particularly investment advisors who act as

external managers such as mutual funds, tend to sell loser stock at year-end to window-dress

their portfolios. Ng and Wang (2004) also observed similar year-end trading patterns for small

stocks that are held by US institutions.



Ta

ble

3Se

aso

nali

tyo

fb

uy

an

dse

llvo

lum

esb

yin

ves

tor

typ

es

Ind

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nfi

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ns

Mu

tual

fun

ds

Insu

ran

ceco

s.B

an

ks

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reig

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pri

etary

trad

ers

Panel

A:

Seaso

nality

inbuy

volu

mes

Inte

rcep

t339.8

48nnn

60.1

19nnn

85.3

29nnn

16.2

34nnn

220.8

80nnn

348.7

07nnn

446.1

64nnn

Jan

uary

�53.6

40n

�10.2

69n

�17.7

79nn

�2.2

69

�59.7

51nnn

�33.5

67

�86.9

81nnn

Feb

ruary

76.7

30nn

14

.76

9nnn

3.0

56

7.7

08nnn

�12.6

27

40.0

48

54.2

72n

Marc

h1

14

.17

8nnn

37

.53

9nnn

13.3

26

14

.97

1nnn

45

.03

7nnn

11

1.5

60nnn

13

4.6

64nnn

Ap

ril

14

7.7

72nnn

13.3

50nn

18.3

29nn

�3.9

38nn

50

.61

6nnn

40.1

79

64.0

24nn

May

�28.3

84

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38n

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37

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90nnn

�5.0

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�36.1

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�30.2

81

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July

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47n

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1.9

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�16.0

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�23.9

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Wald

test

50.6

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76.6

2nnn

22.4

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95.4

6nnn

46.3

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29.0

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50.9

3nnn

Panel

B:

Seaso

nality

inse

llvo

lum

esIn

terc

ept

333.0

45nnn

84.5

69nnn

103.3

03nnn

33.8

99nnn

195.4

83nnn

315.1

68nnn

449.9

28nnn

Jan

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�48.8

08n

�12.3

25n

�21.3

56nn

�0.8

48

�29.2

55n

�41.3

64n

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68nnn

Feb

ruary

53.8

17n

30

.96

8nnn

22.6

45nn

15

.21

4nnn

42

.94

9nnn

0.7

37

14.7

94

Marc

h8

5.2

20nnn

59

.73

1nnn

38

.00

1nnn

19

.04

1nnn

10

4.9

65nnn

25.0

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13

9.7

01nnn

Ap

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13

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13nnn

1.0

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25.4

26nn

�5.0

89

22.4

56

41.2

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11

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86nnn

May

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�17.3

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�19.9

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Jun

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�20.8

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�25.8

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66



Ta

ble

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)

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uals

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�13.2

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�23.6

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�40.8

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FYE period (see Bremer and Kato 1996).8 Our results in Table 3 are consistentwith this explanation. In contrast, foreign investors are significant buyers inMarch (see Panel A), whereas their sell volume in Panel B does not show anysignificant seasonal patterns. Foreign investors, who do not need to follow thesame tax and accounting rules as domestic investors, could purchase shares atbetter terms before the FYE and improve their trading performance, whereasdomestic institutional investors might sell shares at unfavorable conditions.Individual investors also tend to buy more than they sell in March, which isconsistent with the fact that individual investors do not have any specialincentives to sell shares before end-March.

One might argue that foreign investors buy shares in March before the FYEbased on information. However, because most public information related to thelatest financial reports become available after April, information-based tradingmight not explain the large buy trades of foreign investors in March. Also, theinformation story cannot explain the greater buy trades of individual investorsin March as most individual investors only have access to public information.Therefore, our results suggest that foreign investors and some individualinvestors are timing their buy trades before the FYE when domestic institutionssell their shares.

V. TRADING PRICES, MARKET TIMING, AND TRADING INTERVALS

In this section, we examine trading gains and losses of various investor types. InSection V.A, we develop a new trading performance measure and explain thetest methodology. In Section V.B, we present the results.

A. Performance measures and test methodology

We develop a performance metric that gauges the net trading gains of securitiestrading. We define the net trading gains as net cash inflows generated by trades.Specifically, we define the gains as net cash inflows that increase the level ofportfolio holdings after adjusting for trade size and the number of shares traded.Because trade sizes as well as the level of net buy trades are different for variousinvestor types, we create a standardized measure that compares tradingperformances between different investor types. We assume that investorinitially buys (sells) the portfolios of shares during week t and subsequentlysells (buys) the same number of shares during week t1h. Given the samenumber of shares traded, trade performance is determined by the spread

8 A widely used accounting rule is called teikaho, under which a company or financial institution

can choose between cost or market price, whichever is lower, to value its asset. Particularly,

cross-held shares among keiretsu companies have low acquisition costs if they were purchased at

issue many years previously, which gives the companies greater incentives to sell these shares

when they need to increase profits. Under the new accounting standards that became effective

in fiscal year 2001, securities holdings must be valued at market prices.



between trade-weighted buy and sell prices. We also standardize the yenamount of buy and sell trades so that the net buy trade (buy minus sell trade) forthe observation period is zero. Therefore, the trade performance is alsodetermined by the allocation (or the timing) of trades over a specified period.An investor could achieve better market timing performance if he/she allocatedmore buy trades than sell trades before increases in market returns. We definethe overall net trading gains over h-week trading horizon as follows:

Pt � ybt

pstþh

pbt

� �1=h

�yst

pbtþh

pst

!1=h24

35 ð2Þ

where ybt ¼ vb

t pbt ðys

t ¼ vst p

stÞ is the yen amount of buy (sell) trades in week t, ps

t andpb

t are trade-weighted sell and buy prices, and vst and vb

t are sell and buy volumes,respectively. The yen amount of buy and sell trades are adjusted to have thesame median values. We use the median instead of the mean because trades areknown to have skewed distributions. This measure assumes that the investorbuys vb

t shares (sells vst shares) at week t and sells (buys) the same volume of

shares at week t1h, but allowing for different selections of shares for each trade.We can interpret

pstþh

pbt

andpb

tþh

pst

as the intertemporal spreads of trade-weightedaverage prices, which reflect the stock selection as well as the trade weights ofshares each investor type chooses to trade.9 Our definition of stock selectionrefers to the choice of stocks that investors choose to buy and sell, whereas theconventional definition used for portfolio performance measurement refers tothe selection of stocks that investors decide to hold in their portfolios at thebeginning of the holding period.

Our performance measure has the following implications. If our overall nettrading gain is positive (negative), P 4 0 (o 0), it implies that the net cash flowfrom trade at time t and t1h increases (decreases) the level of the underlyingportfolio under the assumption that the same number of shares are traded attime t and t1h. In this regard, we analyze net gains that arise from trades ofmarginal investors but not profits that arise from changes in the valuation ofportfolio holdings that might not be actually traded. We decompose P into twocomponents, net-trading gains arising from price spreads and those arising frommarket timing. We define pS as the net trading gains that arise due to(intertemporal) price spreads in excess of trading the market benchmark:

pSt � yb

t

pstþh

pbt

� �1=h

�yst

pbtþh

pst

!1=h24

35� ðyb

t � yst ÞðRM

tþhÞ1=h

h ið3Þ

where RMtþh is one plus the rate of return of the market index over an h-week

interval. As the second term, ðybt � ys

t ÞðRMtþhÞ

1=h, is the net trading gains wheninvestor trades the market index, pS measures the excess gains that arises wheninvestors trade portfolio of stocks that is different from the market portfolio.

9 Using the notation in this section, the contemporary spread between trade-weighted average

sell price and buy price in Table 1, Panel C is ðpst=p

bt � 1Þ � 100.



We compute pT to measure timing ability in relation to the market index. Aswe have standardized the value of trading to have equal medians, �yb

t ¼ �yst , we

have:

pTt � ðyb

t � yst ÞðRM

tþhÞ1=h � ð�yb

t � �yst ÞðRM

tþhÞ1=h ¼ ðyb

t � yst ÞðRM

tþhÞ1=h: ð4Þ

We can interpret pT as the net trading gains from actual trades in excess of thenet gains from a passive strategy that trades a constant amount, �yb

t and �yst , each

week. In actual trades, ysðbÞt and RM

tþh can be correlated because an investor canallocate, or time, the trades over a period.10 A larger pT implies better timingperformance because the investor buys (sells) before the market returnincreases (decreases). Our timing measure is similar to the portfolio perfor-mance measure developed by Grinblatt and Titman (1993). Because the originalGrinblatt–Titman measure uses the changes in portfolio weights in place oftrades, the interpretation of our measure is slightly different than theirs. In sum,we can express the overall trading profit as a summation of the abovecomponents:

Pt ¼ pSt þ pT

t : ð5Þ

Based on this performance measure, we are able to conduct a test against thenull hypothesis, H0:Pt 5 0. For net gains arising from price spreads, we havethe null hypothesis, H0 : pS

t ¼ 0, which implies that an investor trades themarket benchmark portfolio. For profits arising from market timing, we havethe null hypothesis, H0 : pT

t ¼ 0.We calculate these measures for h 5 1, 4, 8, 26, and 52 weeks over the total

observation period. By comparing the net trading gains for different tradingintervals, we can determine the effect of turnover period on the net gains. Toenable comparisons between trading gains for different trading intervals, allnumbers are expressed in yen per month. In each cell in Table 4, we report themedian net trading gains for each investor type and test the null hypothesis ofzero median using the nonparametric signed-rank test. The sum of the net gainsdoes not equal the overall gains because each component represents the medianfor the sample.

We compute the p-values of the signed-rank test statistics using a blockbootstrap method. We cannot use the conventional standard errors for thesigned-rank tests as the standard signed-rank test assumes independent data. Aswe use overlapping observations, we introduce serial dependence in theperformance measures. Also, we expect seasonal patterns in the performancemeasures arising from seasonal patterns in buy and sell trades (see Section IV.B).Therefore, we use a block bootstrap method which resamples the data in blocksto maintain the serial dependence and periodicity (i.e., seasonality) in the

10 If we use the mean trades to standardize both buy and sell trades, the expectation of pT is the

covariance between trade today and the future market returns, covðybt � ys

t ;RMtþhÞ.



Ta

ble

4So

urc

eso

ftr

ad

ing

per

form

an

ceb

yin

ves

tor

typ

es

Panel

A:

Tra

din

gper

form

ance

for

all

mon

ths

Tra

din

gfr

equ

ency

Ind

ivid

uals

No

n-fi

nan

cial

corp

ora

tio

nM

utu

al

fun

ds

Insu

ran

ceco

mp

an

ies

Ban

ks

Fo

reig

ner

sPro

pri

etary

trad

ers

1w

eek

Spre

ad

29.7

10nnn

10.0

37

2.2

60

�120.2

77nnn

�11.5

65

�14.3

44

0.4

25

Tim

ing

�15.8

44nnn

�58.0

92nnn

�15.1

56nnn

�52.9

36nnn

10.4

92

8.8

11nnn

0.4

73

Over

all

15.6

13

�41.4

82nnn

�12.3

76nn

�169.5

3nnn

�0.0

79

�1.4

83

1.9

74

4w

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Spre

ad

8.2

77nnn

2.8

61

0.4

09

�26.7

31nnn

�2.7

12

�3.5

32

�0.0

61

Tim

ing

�15.4

10nnn

�60.6

94nnn

�14.9

20nnn

�51.9

68nnn

10.1

58

8.0

79nnn

0.4

14

Over

all

�6.0

40

�53.7

69nnn

�15.7

50nnn

�81.1

58nnn

7.7

61

5.1

79

0.6

18

8w

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ad

3.8

76nnn

1.4

83

0.6

34

�12.7

27nnn

�1.0

11

�1.7

67

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43

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�15.4

47nnn

�62.0

46nnn

�15.8

73nnn

�51.0

12nnn

10.0

13

8.3

06nnn

0.9

54

Over

all

�9.7

79nn

�58.1

30nnn

�16.0

77nnn

�65.2

32nnn

6.7

46

5.9

93nn

0.4

78

26

wee

ksSp

read

1.2

31nnn

0.3

47

0.0

91

�4.3

61nnn

�0.4

19

�0.4

28

�0.0

58

Tim

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�17.6

37nnn

�58.7

36nnn

�16.7

27nnn

�47.3

32nnn

13.8

62n

8.3

74nn

0.6

73

Over

all

�15.6

27nnn

�56.9

53nnn

�16.8

91nnn

�50.5

66nnn

14.1

08n

7.7

31nn

0.7

19

52

wee

ksSp

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0.5

84nnn

0.1

26

�0.0

18

�2.0

06nnn

�0.1

59

�0.1

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�0.0

17

Tim

ing

�17.6

22nnn

�56.0

25nnn

�15.7

20nnn

�40.8

98nnn

15.6

40nn

10.1

13nnn

1.8

76

Over

all

�16.7

54nnn

�54.8

16nnn

�15.6

07nnn

�44.2

89nnn

15.3

97nn

10.1

44nnn

1.9

68

Panel

B:

Tra

din

gper

form

ance

for

January

–Marc

hand

Apri

l–D

ecem

ber

Tra

din

gfr

equ

ency

Ind

ivid

uals

No

nfi

nan

cial

corp

ora

tio

ns

Mu

tual

fun

ds

Insu

ran

ceco

mp

an

ies

Ban

ks

Fo

reig

ner

sPro

pri

etary

trad

ers

Jan

uary

–M

arc

hA

pri

l–D

ecem

ber

Dif

Jan

uary

–M

arc

hA

pri

l–D

ecem

ber

Dif

Jan

uary

–M

arc

hA

pri

l–D

ecem

ber

Dif

Jan

uary

–M

arc

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original sample, as resampling of the data using conventional bootstrap methodinevitably will destroy information included in the sequence of the originaldata.11 To determine the block size for bootstrapping, we first examine the serialdependence of performance measures by fitting an ARMA model after adjustingfor seasonality.12 We find that the maximum lag length among all performancemeasures is 5 weeks. To bootstrap time series data with seasonality, Politis(2003) proposed a method to resample the data in periodic blocks. Our naturalperiodic block length is 52 weeks because our data has monthly patterns. As the52 weeks block length subsumes the serial dependence of 5 weeks identified bythe ARMA model, we randomly resample the original data with replacement inblock length of 52 weeks. As we test the null hypothesis of zero median, wesubtract the median value from the original sample to generate the bootstrapdistribution under the null hypothesis. We conduct 2000 replications for eachcase.

B. Results

In Table 4, Panel A, our result shows that the overall net trading gain, P, for 1week trading interval is greatest for individual investors, which is due to thelarge trading gain arising from price spreads for short trading intervals. Forinitial buy and sell trades, both having median value of f100, average individualinvestors generate a median trading profit of f15.61 for turning over the samenumber of shares 1 week subsequent to the initial trade.13 On the other hand,our result shows that individual investors had a median trading loss of f16.75 ifthey turned over the same number of shares 52 weeks later. Although thepositive gains from the intertemporal price spreads for individual investorscould partly reflect higher risk premium of (possibly smaller) stocks preferred byindividual investors, they reflect the positive spread difference between thetrade-weighted sell and buy prices (see Table 1, Panel C). The positive pricespreads for individual investors might reflect the investors’ disposition to sellwinning investments and hold onto losing investments. Our result is alsoconsistent with the findings by Barber and Odean (2000) for US individualinvestors that show positive abnormal return for short-term round-trip trades.As trading intervals become longer, our result shows that the overall tradinggain of individual investors worsens. Because gains from price spreads becomesrelatively smaller for longer trading intervals, trading losses arising from poormarket timing dominates the overall performances for longer trading intervals.We also observe a similar pattern of trading performances for nonfinancial

11 For general discussion on block bootstrap methods, see Davison and Hinkley (1997).

12 We use the Minimum Information Criterion Method that estimates ARMA with various lag

lengths and tentatively identify the order of the process. See Box et al. (1994).

13 In effect, the median net initial trade is zero yen as we assume each investor type buys and sells

100 yen each. After 1 week, investor sells and buys the same numbers of shares as the initial

trade and generates a median trading gain of 15.61 yen.



corporations, although their trading gains from price spreads are notstatistically significant.

In contrast, we find that foreign investors have negative trading perfor-mances (while not statistically significant) arising from intertemporal pricespreads. As foreign investors have significant negative price spreads (see Table 1,Panel C), a short-term turnover of their portfolios would not be a sustainabletrading strategy in the long run. In effect, we find that foreign investors havepositive performances arising from good market timing that largely offset thelosses arising from negative price spreads.

Although banks have relatively large positive contemporaneous spreadsbetween sell prices and buy prices (see Table 1, Panel C), we do not find anypositive performance arising from intertemporal price spreads, which must bedue to banks’ poor price spread performance relative to the market movements.However, our result shows that banks have positive market timing perfor-mances that generate significant and positive overall trading gains for longertrading intervals.

Other professional money managers such as mutual funds and insurancecompanies, on the other hand, have trading losses arising from poor markettiming.14 As Japanese mutual funds face considerably higher fund churningratios compared with US and UK mutual funds (see Takehara and Yamada 2004),high levels of fund flows might negatively affect funds’ market timingperformance as fund managers face greater short-term liquidity trading of fundassets. Edelen (1999) found for US mutual funds that funds’ short-term liquiditytrading explains the negative market-timing performance of mutual funds.15

Similarly, the poor performances of insurance companies might be explained byunexpected asset liquidations to meet insurance claims. Our result points to thefact that insurance companies not only have significantly poor market timingperformance but also significantly poor performance from price spreads. Ourresult shows that insurance companies are the only investor type that performsvery poorly in both performances. A caveat is that our trading intervals mightbe too short to evaluate net trading gains of insurance companies as theiraverage holding period could be around 10 years, which we infer from theaverage annual turnover ratio of 0.09 (Table 1, Panel D).

Figure 1 shows overall trading gains (Pt) cumulated over the observationperiod for different investor types. Panel A of Figure 1 shows cumulated profits

14 Also using data from the TSE, Froot et al. (2001) and Kamesaka et al. (2003) found comparable

results of timing performance. Because Kamesaka, Nofsinger, and Kawakita found a similar

result using longer time series from January 1980 to October 1997, the result seems robust for

different observation periods. Hamao and Mei (2001) examined monthly data and found that

banks have better market timing than foreign investors before early 1990s.

15 Cai et al. (1997) discovered that Japanese equity mutual funds show unusually large

underperformance regardless of various benchmarks. Our result points to the fact that poor

market timing might explain part of the underperformance.



Panel A: 1 week trading interval

−30000

−25000

−20000

−15000

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5000

10000

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Panel B: 4 week trading interval

−30000

−25000

−20000

−15000

−10000

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Panel C: 8 week trading interval

−50000

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10000

20000

Panel D: 26 week trading interval

−35000

−30000

−25000

−20000

−15000

−10000

−5000

0

5000

10000

15000

Figure1 Cumulated Overall Trading Gains of Different Investor Types for 1, 4, 8, and26 Week Trading Horizons

This figure shows the cumulated overall net trading gains for different investor typesfrom January 1991 to April 1999. We define the overall trading gain over h-week tradinginterval from week t as follows:

Pt � ybt

pstþh

pbt

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�yst

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pst

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35

where ybt ¼ vb

t pbt ðys

t ¼ vst p

stÞ is the yen amount of buy (sell) trades in week t, ps

t and pbt are trade-

weighted sell and buy prices, and vst and vb

t are sell and buy volumes, respectively. Both buyand sell trades are standardized to have a median value of f100. The trading gains are expressedin yen per month. In Panel A, we draw the cumulated gains computed for 1 week tradinghorizon. We observe a downward shift of the cumulative gains for individual investor in thelast week of October 1993, which reflects the post-IPO purchasing price of East Japan RailwayCompany. As the post-IPO price of the share was much higher than average price of the sharesusually traded by individual investors, the trade-weighted purchase price increased by morethan 200% from the previous week. We do not include the graph of cumulated gains forinsurance companies in Panel A (1 week trading interval) and Panel B (4 week trading interval)because the graph does not comfortably fit in the panels due to large negative numbers.



for 1-week-trading intervals.16 We find that the net trading gains of individualinvestors for a very short trading interval exceed those of other investor typesdue to the large trading gains arising from positive price spreads (see Table 4).17

We do not include the graph of the cumulated gains for insurance companies inPanel A (1 week trading interval) and Panel B (4 week trading interval) becausethe graph does not comfortably fit in the panels due to their large cumulativelosses. In Panels C and D, we draw cumulated gains computed for 8- and 26-week trading intervals for all investor types. The figures confirm the result thatthe impact of price spreads on overall trading performance becomes lessdominant and the impact of market timing becomes more as trading intervalsbecome longer for all investor types.

In Table 4, Panel B, we show trading profits that arise from trades conductedduring January through March and April through December. We find that thenet trading gains of banks worsen during the FYE for both price spreads andmarket timing. This result is consistent with the fact that banks might beobliged to sell near the FYE for accounting purposes as well as for other reasonssuch as adjusting the stock ownership among the group companies (see SectionIV.B). Nonfinancial corporations and insurance companies also perform worseduring FYE because some of them have the same trading incentives as banks tosell equities before the FYE. In contrast, we find very large positive timingperformances for foreign investors during the FYE period. Our result shows thatforeign investors take advantage of the trading opportunities during the FYEperiod to buy up equities from domestic institutions. We note that the markettiming performance of individual investors and proprietary traders alsoimprove during the FYE period, because these investors most likely do nothave special incentives to sell shares during the period and also can maketrading gains by buying from domestic institutions.18

In Table 5, we examine the correlations of overall trade performance (P),market adjusted price spread performance (pS), and timing performance (pT)between various investor types calculated for the trading interval of 8 weeks. Wedo not report results for other trading intervals as the results are qualitatively

16 The downward shift of the cumulative profits for individual investor in the last week of October

1993 reflects the purchasing price of East Japan Railway Company of individual investors

immediately after the IPO. As the post-IPO price of the share was much higher than average

price of the shares usually traded by individual investors, the trade-weighted purchase price

increased by more than 200% from the previous week.

17 The increase in trading profits for individual investors and the decrease for foreign investors

after 1996 might affect the results in Table 4 that use data for the entire observation period. To

check the robustness of the results, we estimate the profit measures for the period before 1995.

We find that, although the absolute magnitudes of pS for individual investors, non-financial

corporations, and foreign investors are smaller before 1995 for shorter trading horizons

compared with the results in Table 4, all crucial signs remain the same and our overall

conclusion is not affected. The results for longer trading horizons are not much different

between before and after 1995.

18 To compute p-values using block bootstrap method for January–March and April–December, we

generate separate bootstrap samples for each period using data from respective months.



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similar. In Table 5, Panel A shows that P for foreign investors and proprietarytraders tend to increase when P for other domestic investors decrease, which isindicated by large and negative correlations of the last two rows of Panel A. Wefind in Panel B of Table 5 that the correlations of net trading gains arising fromprice spreads, pS, are negative between the group of nonprofessional investors(i.e., individual investors and nonfinancial corporations) and the rest ofprofessional investors (see negative correlations of the first two columns inPanel B). These negative correlations imply that profits arising from pricespreads shift between professional and nonprofessional investors. As the trade-weighted prices reflect the selection of stocks being bought and sold, thenegative correlations suggest that professional and nonprofessional investorsselect equities to trade in opposite directions to each other. We find large andnegative correlations of pT between domestic investors and foreign investors,which suggests that trading gains arising from market timing mostly shiftbetween the group of domestic investors and foreign investors (as well asproprietary traders). (See the last two rows of Table 5, Panel C.) As thecorrelation pattern for pT is similar to that of the overall trade performance inPanel A of Table 5, we find that the correlations in market timing performancelargely determines the correlation of the overall performance among differentinvestor types.

VI. CONCLUSION AND DISCUSSION

In this paper, we examine the trading performance of different investor typessuch as individual investors, various institutional investors, and foreigninvestors. We develop a method that gauges the performance of equity tradesof marginal investors. We use data from the TSE that allow us to examineperformances of all investor types across the entire market. Our main resultimplies that different investor types have different sources of equity tradinggains and losses. In particular, we find that average foreign investors make largetrading gains from good market timing but likely to incur minor losses fromnegative spreads between sell and buy prices. On the other hand, we find thatnonprofessional investors such as individual investors make trading gains frompositive spreads between sell and buy prices, especially in the short-term, butlose from bad market timing. The positive price spreads for individual investorsmight reflect the investors’ disposition to sell winning investments and holdonto losing investments. The poor market timing ability of individual investorscould indicate poor ability in predicting market. However, as average individualinvestors can generate gains from short-term turnover of their portfolios, suchgains might justify their continued participation in a competitive equity marketdespite their poor market timing ability.

Our results also shed light on conflicting empirical results regarding foreigninvestors. Some papers have reported results that indicate foreign investorshave good performance in equity trade (Grinblatt and Keloharju 2000;



Seasholes 2000; Bailey et al. 2007), whereas other papers have found thatforeign investors trade in a manner similar to less informed investors (Dahlquistand Robertsson 2004; Choe et al. 2005). The momentum trading patterns thatwe find for foreign investors hint that these investors are less informed investorswith respect to individual stock information as suggested by Brennan and Cao(1997). Also, we find that foreign investors pay higher average prices for theportfolio of stocks they buy than for the portfolio of stocks they sell. However,because foreign investors draw their trading gains from good market timing, ourresults suggest that they are actually smart traders that seek more trading gainsfrom macromanagement (e.g., market prediction and/or asset allocation) thanfrom micromanaging (e.g., stock picking) of their portfolios. Our result isconsistent with the recent findings by Thomas et al. (2004) that showed thatthe performances of US investors’ international portfolio depend on thesuccessful exploitation of public information and not on private information.

Our paper also finds that trading gains and losses arise from trades betweeninvestors that have different institutional backgrounds. During the monthsbefore the FYE, domestic institutions, particularly banks, sell a significantlylarge number of shares for possibly window-dressing purposes and for adjustingtheir stock ownership among the group (keiretsu) firms. On the other hand,foreign investors take advantage of this trading opportunity and buy up a largeamount of shares from domestic institutions on favorable terms. Our resultshows that foreign investors generate significant trading gains during thisperiod. Individual investors also benefit to a lesser extent because they, too, arenot subject to the institutional constraints that institutions face.

Takeshi YamadaNUS Business [email protected]

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Documents

How do Individual, Institutional, and Foreign Investors Win and Lose in Equity Trades? Evidence from Japan