paper for review order choice taiwan

7/29/2019 Paper for Review Order Choice Taiwan

1/47

Investors Information Advantage and Order Choices in

an Order-driven Market

____________________________________________________________________

ABSTRACT

We set out in this study to examine investors information advantage and order choices by

computing the gains and losses from the executed orders in a pure order-driven stock

market, the Taiwan Stock Exchange. We carry out an event study on the profitability of

each type of order around annual earnings announcements which exhibit significant

abnormal price increases during the pre-event period. Our study uses a unique and

extremely comprehensive dataset which can accurately classify executed orders by order

size, order aggressiveness and the type of investors responsible for submitting the orders.

We find that, as a group, individual investors are less informed about imminent corporate

earnings announcements and the related value implications. Domestic institutions with

b tt l l ti h t i il d i f ti lti i i ifi t

*Manuscript, excluding Author DetailsClick here to view linked References
http://ees.elsevier.com/pbfj/viewRCResults.aspx?pdf=1&docID=949&rev=0&fileID=10074&msid={FF8BD954-1911-40BC-809B-9A7EE33C044B}http://ees.elsevier.com/pbfj/viewRCResults.aspx?pdf=1&docID=949&rev=0&fileID=10074&msid={FF8BD954-1911-40BC-809B-9A7EE33C044B}


2/47

b tt l l ti h t i il d i f ti lti i i ifi t

1. INTRODUCTION

Investors order choices are the foundation of security market operation. They

determine the interaction between liquidity supply and demand and, most importantly,

the price formation process. The strategic behavior of investors order placement

hence influences market dynamics, with a number of the prior studies having already

documented the presence of non-monotonicity between trade size and price impact for

both the stock and options markets.1 It is also noted that when determining their level

of order aggressiveness, informed investors are essentially faced with a tradeoff

between execution certainty and transaction costs.2

Informed investors placing

aggressive orders also run the risk of their superior information potentially being

incorporated into the price prior to them acquiring their desired position.

Motivated by the strategic behavior of investors order submission, the present


3/47

(TWSE), one of the major emerging markets, around the time of annual earnings

announcements made between 1 January 2005 and 31 December 2006. Since some

traders may have possessed valuable private information in the pre-announcement

period, there is a greater likelihood of such traders adopting an order placement

strategy that would generate the greatest profits.

In order to maximize the probability of detecting informed trading, our attention

in the present study focuses on a sample of earnings announcements which display

significant abnormal price increases in the pre-event period. As the private

information soon gets incorporated into the prices, if the information happens to be

important and unexpected, this will lead to large abnormal returns.4

In this particular setting, we presume that the different types of investors that

participate during the period leading up to the earnings announcements are likely to


4/47

investors in this study. While informed investors have access to private information at

lower costs, skilled investors are better capable of analyzing value-relevant public

information, including technical and fundamental data. Investors with short-lived

private information tend to trade aggressively. In contrast, skilled investors may adopt

stealth trading strategies.

Hakansson (1977) demonstrates that when investor groups differ, in term of their

information acquisition ability and resources, distinct patterns of information

acquisition emerge. The geographical information asymmetry hypothesis further

suggests that domestic institutions with better local connections may be better

informed regarding the information leaks about forthcoming earnings announcements

[Brennan and Cao, 1997; Coval and Moskowitz, 1999; Hau, 2001; Dvok, 2005].5

Skilled foreign institutions, however, may have potential advantage due to their

i t t ti d i t ti l i [G i bl tt d K l h j 2000


5/47

and are therefore willing to pay a premium to get their orders executed quickly, the

same logic cannot be applied to a call auction market, where buy and sell orders are

accumulated over a certain period of time prior to the market clearing, at which point,

everyone pays or receives the same price, regardless of their quotes. No one actually

initiates a trade under a call auction market.

Thus, as opposed to the approach taken in several of the prior studies, where the

computation of the weighted cumulative price impact critically hinges on the initiator

of a trade,8 in the present study, we calculate the daily trading profits earned by each

order category, which thereby provides precise accounting of the gains and losses

from trades. As in Barber, Lee, Liu and Odean (2009), we construct portfolios which

mimic the buying and selling in each order category, with the order category being

more informative if stock purchases reliably outperform stocks sold. In contrast to

t i t di hi h l ith t l h ldi d t bli l t d


6/47

informed, many of the prior studies have provided ample evidence to show that

institutional investors have information advantages, insofar as their trading predicts

future abnormal returns.9 However, empirical findings on the relative performance of

foreign and domestic institutions are rather mixed. While foreign institutions have

potential advantages based upon their superior investment experience and analytical

expertise, domestic institutions are less subject to issues such as distance, linguistic or

cultural barriers. We distinguish skilled investors from informed investors and

measure the trading profitability of each investor group to enhance our understanding

of the differential information advantages between individual and institutional investors,

and between domestic and foreign institutions.

Secondly, while prior studies have examined informed investors order choices in

quote-driven and hybrid limit order-specialist markets, the size and aggressiveness of

d h b i f d t d i d d i t t h th TWSE


7/47

implications of the preferences of informed investors by examining the gains and

losses of orders with different sizes and aggressiveness.

Thirdly, most prior studies use trade size as a proxy for order size, which places

an important limitation on their ability to examine investors trading behavior due to

the possibility that the true trade size choice of investors is not reflected in the

realized trade size. Furthermore, in an order-driven market, discrepancies between the

submitted order prices and the realized trade prices can be problematic if the trade

prices are used to measure order aggressiveness. In the present study, the

comprehensiveness of our dataset, which contains limit order book data linked to each

trade in the transaction, allows us to accurately classify the underlying trades into the

appropriate order categories. In our analysis of the informativeness of orders, by

avoiding making any particular assumptions such as a high correlation between

l / ll t d d l / ll d th t b itt d d i ill b


8/47

overall make significant profits for all holding periods in the pre-event window,

indicating that informed investors prefer to trade actively with competitive prices to

ensure the execution of their orders.

Without presuming a particular group of investors has some specific information

advantages, we measure the informativeness of investor groups based upon their net

daily dollar profits. We find that individual investors, as a group, are less informed on

upcoming corporate earnings and related value implications, whilst geographical

proximity, consistent with Lee, Liu, Roll and Subrahmanyam (2004), represents an

important source of information advantage for institutional investors. In contrast to

Barber et al. (2009), which find that foreign institutions is the most profitable group in

the TWSE during the whole 1995-1999 period, we focus on the profitability in the

pre-announcement window during the 2005-2006 period and document superior

f f d ti i tit ti Gi th i b tt l l ti d ti


9/47

non-event periods, with the profitability of orders of different size and aggressiveness

tending to vary with the length of the holding period. Institutional investors do not

outperform individual investors in regular periods, as individual investors can also

make profits when 10- and 30-day holding periods are considered.

Not all domestic institutions are equally informed, nor do they have information

advantage at all time for all stocks. Consistent with Ascioglu et al. (2005), informed

domestic institutions tend to submit large-sized orders during a pre-announcement

period to take up all of the available liquidity and thereby ensure their trading profits,

given that the private information acquired by domestic institutions is likely to be

short-lived due to upcoming official announcements and intensive informed trading.

Although limited in terms of private information, given their superior expertise, skilled

foreign institutions can accrue profits by trading conservatively with medium- and

ll i d d d l i i I di id l i t ll f d


10/47

Informed domestic institutions are more likely to employ large-sized orders when

trading in liquid stocks. Whilst institutions have a clear preference for liquid stocks,

individual investors have a comparative advantage with regard to trading in illiquid

stocks. Moreover, although order imbalance does not notably affect order choices of

foreign institutions and individual investors, informed domestic institutions tend to

partially replace large-sized orders with medium- and small-sized orders to reduce

market impacts when buy orders far exceeds sell orders.

The remainder of this paper is organized as follows. Section 2 provides a

description of the institutional background of the Taiwan stock market and details of the

data and variables adopted for this study. The empirical findings on trading profits for

each order category and the order choices of informed investors are presented in

Section 3. Section 4 verifies the robustness of the empirical results by examining large

t i d l i th ff t f t di l d d


11/47

which clearly makes the TWSE one of the most important emerging financial

markets.

The TWSE operates in a limit-order book environment, which means that only

limit orders are accepted, with this environment having no market makers or

specialists. Orders begin to accumulate from 8:30 a.m. onwards, and unless cancelled,

any non-executed orders will remain on the limit order book until the end of the day.

During the regular trading session, from 9:00 a.m. to 1:30 p.m., buy and sell orders

interact in the central automated trading system to determine a single market-clearing

price subject to applicable auto-matching rules aimed at maximizing transaction

volume for each match.

Orders are executed in strict price and time priority, and are matched two to three

times per minute throughout the regular trading session. The actual time interval for


12/47

those stocks that hit their price limits can still be traded as long as the transaction

prices remain within the limits.

Taiwan imposes a transaction tax of 0.3 per cent on stock sales and no tax on

capital gains (both realized and unrealized). Cash dividends are taxed at a maximum

rate of 25 per cent for domestic corporations and 40 per cent for individuals; for

foreign investors, they are taxed at 20 per cent. The maximum commission for trading

on the TWSE is 0.1425 per cent of the trade value, with some brokers offering a lower

commission for larger trades.

2.2 Data

The complete transaction and limit order history of all traders on the TWSE between

1 January 2005 and 31 December 2006 is acquired for this study. Both the trade and

order data include the date and time of the transaction/order, a stock identifier, order


13/47

collected all of the annual earnings announcements available from the Taiwan

Economic Journal (TEJ) databank for the years 2005 and 2006, providing a total of

802 annual earnings announcements from firms listed on the TWSE. In Taiwan,

annual earnings announcements are a regular occurrence; indeed, they are mandated,

which ensures that any market surprise as a result of an announcement is due to the

information provided within the announcement, as opposed to the simple fact that an

announcement has taken place.

The information provided by such announcements can significantly alter the

beliefs of investors with regard to the value of a firm, thereby becoming incorporated

into the stock price through trading. In order to maximize the probability of the

detection of informed trading, we carry out sample partitioning similar to that used in

Chakravarty (2001), restricting our attention to only those earnings announcements

h i ifi b l i i di ibl i h i d12


14/47

event window is defined as: (i) the pre-event period, which is 20 trading days before

the earnings announcement (t=20 to t=1); and (ii) the post-event period, which is

20 trading days after the earnings announcement (t= 0 to t= 19).13 The non-event

period is therefore the sample period which excludes the event window. The earnings

announcements are divided into five groups according to their cumulative abnormal

returns (CARs) in the pre-event period. The descriptive statistics for the five groups

are provided in Table 1.

The average CAR for the top group is 16.55 per cent (7.25 per cent) in the

pre-event (post-event) period, whilst the average CAR for the bottom group is 13.91

per cent (3.09 per cent) in the pre-event (post-event) period. The finding of persistent

abnormal returns in the period after earnings announcements suggests that such


15/47

associated with the 321 announcements are found to have occurred prior to the

official public announcement day; in particular, the average CAR in the [20,1]

pre-event period is found to be 9.89 per cent.

2.3 Variables

The daily returns of an individual stockj are calculated as Equation (1).

)Pln()Pln(R t,jt,jt,j 1 (1)

where Pj,t is the closing price for stockj on day t. The same method is applied in

computing the returns of the market index (Rm,t). The abnormal returns are estimated

based on the market model as outlined by MacKinlay (1997), with parameters

estimated from the estimation period.


16/47

the variance and std( ) represents the standard deviation.

The cumulative abnormal returns (CARs) are computed by aggregating abnormal

returns over the event window t = T1 to t = T2 for each announcement of stockj,

2

1

21

Tt

Tt

tjj AR)T,T(CAR (5)

Using the complete dataset of orders and trades, we trace all trades back to their

underlying orders and determine the order category of the executed orders according to

the characteristics of the original order submission.14 Consistent with Barclay and

Warner (1993) and taking into account of 1,000 shares as the trading unit of stocks in

the TWSE, we define small-sized orders as those involving 1,000-4,000 shares;

medium-sized orders as those involving 5,000-99,000 shares; and large-sized orders as

those involving 100,000 shares or more.15


17/47

Although large-sized orders account for only about 25 per cent of transactions,

they nevertheless account for approximately 80 per cent of all trading volume, of

which, 80 per cent of trades are found to be aggressive orders. Approximately 90 per

cent of all transactions and 70 per cent of all trading volume is attributable to

individual investors, whilst institutional investors are found to be more active in the

pre-event period than in the post-event period.

We adopt similar steps to those proposed in Barber et al. (2009) to compute the

daily dollar profits for each order category, as follows:

1. For each day, we sum up all of the executed orders for each stock in a

particular order category to determine net trading in that order category.

2 F h k d d b f li hi h


18/47

6. The daily dollar return for a portfolio (net of market gains) is therefore

computed by taking the total value of the portfolio from the previous close

and multiplying this by the daily abnormal return.

7. The daily dollar profit for a particular order category is the difference

between the daily dollar return on the buy portfolio and the daily dollar

return on the sell portfolio for that order category.

For each stock, we obtain a time series of daily dollar profits (net of market gains)

for each order category during both the non-event period and the event window for

those earnings announcements which display significant abnormal price increases

prior to the announcement day. It is assumed that each daily profit represents an

independent observation of the profits earned by a particular order category (Barber et

al., 2009).16


19/47

category during the pre-event, post-event and non-event periods. The daily dollar

profits provide precise accounting of the trading gains and losses between groups and

are precisely equal to zero when summed across groups under each categorization. We

compute the mean daily profit by averaging the daily profits across stocks and days

for each order category, and then apply the Hausman test to determine the appropriate

panel models, testing the null hypothesis that the mean will be equal to zero; the

results are presented in Table 3.

Panel A of Table 3 reveals that in the pre-event window, large-sized orders

consistently result in profits for all holding periods, with the mean profits being

statistically significant. Under the assumption of a one-day holding period, large-sized

orders accrue an average daily profit of NT$14 million, although when considering a


20/47

institutions accrue significant profits for all holding periods, with the mean daily

profits being NT$10.3 million for a one-day holding period, NT$2.6 million for a

10-day holding period, and NT$4.6 million for a 30-day holding period. Foreign

institutions perform well over a one-day horizon, although they tend to make losses

over longer horizons of 10 and 30 trading days. Despite the suggestion in the prior

studies that institutional investors have privileged information on earnings, foreign

institutions are not well informed regarding upcoming earnings numbers.17

Panel A indicates that relative to foreign institutions, domestic institutions are

better informed with regard to optimistic earnings forecasts, and therefore make

significant profits from trading. In contrast to Barber et al. (2009), in which it is

suggested that foreign institutions are, in general, the most profitable group of

institutional investors in Taiwan, the present study demonstrates that geographical

i i i i f i f i l d fi ifi


21/47

being positive, the profits generated by large-sized orders nevertheless become

statistically insignificant. Similarly, when considering one-day and ten-day holding

periods, aggressive orders lead to profits that are both smaller in size and less

significant, as compared to those in the pre-announcement period.

Panel B of Table 3 also indicates that individual investors continue to make losses

in the post-announcement period, whilst domestic institutions do not perform as well as

in the pre-announcement period when one-day and 10-day holding periods are

considered; thus, there is a significant decline in the importance of local private

information after official announcements. Conversely, foreign institutions accrue mean

daily profits of NT$21.3 million over a one-day horizon, significantly better than the

NT$4.2 million in the pre-event window. Whilst domestic institutions are better

informed with regard to optimistic earnings forecasts in the pre-announcement period,


22/47

make poor investment decisions, which thereby gives rise to market inefficiency;18

however, Panel C of Table 3 demonstrates that although they do tend to make losses

over a one-day holding period, individual investors can succeed in making profits

over longer horizons during non-event periods.

When including material earnings announcement event windows, Barber et al.

(2009) report that individual investors incur mean daily losses of about NT$59.4

million over a 10-day horizon, and NT$74.0 million over a 25-day horizon. However,

in the present study, we show that the information disadvantage for individual investors

occurs mainly during important events, such as earnings announcements. By excluding

performance during material earnings announcement event windows, we find that

individual investors accrue mean daily profits of about NT$1.3 million over a 10-day

horizon, and NT$1.7 million over a 30-day horizon.


23/47

pre-announcement periods. However, the informativeness of large-sized orders and

aggressive orders is diminished in both the post-event and non-event windows. Since

information asymmetry is most severe during a pre-announcement period, we place

specific focus on this pre-event window, a period when informed trading activities are

most likely to occur.

3.2 The Order Choice of Investors

We go on to examine the mean daily profit for each investor group for various order

choices with regard to size and aggressiveness during the pre-event period. We

decompose the total profits for each investor group from Table 3, by order size and

order aggressiveness, with appropriate panel models then being used to test the null

hypothesis that the mean dollar profit is equal to zero. The results are presented in

Table 4, with Panel A showing that individual investors who use small-sized or


24/47

found to incur losses. Both passive and aggressive orders placed by domestic institutions

tend to be profitable, with the former demonstrating better short-run profitability, whilst

the latter tend to make reliable returns over longer horizons.

The method by which new earnings information reaches the market in Taiwan may

place individual investors and foreign institutions at a disadvantage. Domestic institutions

with better local connections and relatively lower marginal costs for their information

acquisition usually acquire news leaks ahead of official announcements, thereby

benefiting from information asymmetry. Such private information is, however, likely to

be short-lived, as informed trading leads to the information being incorporated into prices.

Domestic institutions submit large orders so as to take up all of the available liquidity and

accrue the maximum profit from their information advantage.

Whilst foreign institutions are not informed about the impending earnings numbers,


25/47

passive orders consistently make profits for all holding periods, from average daily profits

of NT$1.4 million over a one-day horizon to NT$0.6 over a 30-day horizon. Although

foreign institutions may lose out to domestic institutions in acquiring private information

on earnings, they nevertheless have considerable investment experience and better

international expertise. Skilled foreign institutions with limited private information can

make profits by trading conservatively with small- and medium-sized orders and less

aggressive prices.

Foreign institutions which place too much faith in the private information they

possess, and consequently trade with large orders and aggressive prices, tend to incur

losses from trading. Such losses by these foreign institutions may also come from

overreaction to earnings-related signals. In the presence of local private information,

even in cases where all investors have rational expectations (Froot et al., 2001;


26/47

the 20-day periods by their cumulative returns and select the top ten percent periods

as the sample of large non-event price run-ups. The sample consists of 1,184 20-day

periods of non-event price run-ups, with the average 20-day cumulative return equal

to 16.66%. The average cumulative return in the pre-event periods is 14.36%, similar

to that in the large non-event price run-ups.

Table 5 reports the differential profitability of order submission decisions with

respect to size and aggressiveness in the pre-event periods and the large non-event

price run-ups. The information disadvantage for individual investors is more severe in

the pre-event periods than in the large non-event price run-ups. As Panel A of Table 4

shows that individual investors who use small-sized or passive orders are the most

uninformed, Table 5 suggests that, except for the one-day holding period, individual

investors placing small-sized or passive orders suffer significantly greater losses in


27/47

Except for the one-day holding period, domestic institutions that use aggressive orders

also make significant greater profits in the pre-event periods than in the non-event

price run-ups.

Foreign institutions are more likely to be skilled than informed. The good

performance of foreign institutions in the per-event periods is mainly driven by their

expertise and trend chasing tendency, which is not much different in the non-event

price run-ups. As Panel C of Table 4 shows that skilled foreign institutions tend to use

medium- and small-sized orders, Table 5 demonstrates that foreign institutions placing

medium- and small-sized orders perform as well, if not better, in the non-event price

run-ups. Panel C of Table 4 also suggests that skilled foreign institutions prefer to use

passive orders. Without information disadvantage regarding impending earnings news,

foreign institutions placing passive orders make better profits in the non-event price


28/47

The trading volume is defined as the daily average number of shares traded in the

pre-announcement window. The order imbalance is defined as the average of the daily

order imbalance in the pre-announcement window; the daily order imbalance is

calculated as the number of buy orders less the number of sell orders divided by the

total number of orders. All of the events are divided equally into high and low

groups on the basis of trading volume and order imbalance. Table 6 reports the trading

profits of each order category and the profit differences between high and low groups.

Panel A of Table 6 shows that the profits earned by domestic institutions in the

pre-event periods stem mainly from trading in stocks with high trading volume. Given

that stocks with high trading volume are usually large-sized stocks, it is not surprising

that domestic institutions are better motivated to acquire and trade on pre-event


29/47

more per day over a 10-day horizon and NT$5.3 million more per day over a 30-day

horizon when trading in liquid stocks as compared to their trading in illiquid stocks.

Large-sized orders generally cause greater market impacts which may hinder

trading performance of informed investors. Such market impacts tend to be moderate

for stocks with high trading volume; hence, informed investors are more likely to

adopt large-sized orders when trading in liquid stocks. While Panel B of Table 4

suggests that informed domestic institutions tend to use large-sized orders, Panel A of

Table 6 further illustrates that these orders are only profitable when trading in stocks

with high trading volume. That is, domestic institutions tend to acquire privileged

earnings-related information of liquid stocks and employ large-sized orders to

maximize their trading profits in those liquid stocks. Informed domestic institutions

placing large-sized orders earn NT$18.9 million more per day over a one-day horizon,


30/47

impacts are greater in the case of large order imbalance; thus, informed investors may

have difficulty to accumulate desired position before their private information is fully

incorporated into stock prices. Informed domestic institutions make NT$8.8 million

less per day over a one-day horizon, NT$1.1 million less per day over a 10-day

horizon and NT$4.5 million less per day over a 30-day horizon in the situation where

sell orders are outnumbered by buy orders.

Informed domestic institutions seem to partially replace large-sized orders with

medium- and small-sized orders in the case of large order imbalance. The market

impact of large-sized orders grows to be an even more serious issue when orders of

the counterparties are relatively scarce. Informed investors may have to strategically

break up their orders into smaller lots so as to disguise their activities and protect their

information advantage.


31/47

5. CONCLUSIONS

A surprising variety of approaches is used within the literature to determine which

investors have information advantages, largely as a result of data restrictions. Using a

unique and remarkably comprehensive dataset in the present study, we adopt a direct

approach of measuring order informativeness by computing the daily dollar profits

(net of market gains) for various order categories. We recognize the investor group

with the higher average profits as the group with the information advantage; that is,

those who know more will ultimately gain more.

We go on to trace the profits of the investor groups to the different order categories,

in terms of size and aggressiveness, in order to investigate the choice of orders made by

well-performed investors. Firm-specific annual earnings news announcements serve as

an ideal setting for examining the comparative short-lived information advantage for


32/47

more conservative trading using smaller orders and less aggressive prices.

Our results provide support for the geographical information asymmetry

hypothesis proposed in many of the prior studies.21 We find that as compared to

foreign institutions, domestic institutions have a clear information advantage relating

to local annual earnings announcements. They exhibit better stock selection ability

and have superior trading performance. The domicile status appears to provide

domestic institutions with access to private earnings information. However, such local

private information advantage enjoyed by domestic institutions is invariably

short-lived. Thus, informed domestic institutions tend to use large-sized orders to

rapidly secure their trading profits.

The superior performance of domestic institutions in the pre-event window is

likely to stem from private information, as the profitability declines in the post-event


33/47

REFERENCE

Ahn H. J., K. H. Bae and K. Chan, 2001, Limit Orders, Depth and Volatility:Evidence from the Stock Exchange of Hong Kong, Journal of Finance 56, 767-788.

Albuquerque, R., G. H. Bauer, and M. Schneider, 2007, International Equity Flows

and Returns: A Quantitative Equilibrium Approach, The Review of Economic Studies

74, 1-30.

Ali, A., C. Durtschi, B. Lev, and M. Trombley, 2004, Changes in Institutional

Ownership and Subsequent Earnings Announcement Abnormal Returns, Journal ofAccounting, Auditing and Finance 19, 221-248.

Amihud, Y. A., and K. Li, 2006, The Declining Information Content of Dividend

Announcements and the Effect of Institutional Holdings, Journal of Financial and

Quantitative Analysis 41, 636-660.

Anand, A., and S. Chakravarty, 2007, Stealth Trading in Options Markets, Journal of

Financial and Quantitative Analysis 42, 167-188.

Anand, A., S. Chakravarty, and T. Martell, 2005, Empirical Evidence on the

Evolution of Liquidity: Choice of Market versus Limit Orders by Informed and

Uninformed Traders,Journal of Financial Markets 8, 289-309.

Atiase, R. K., 1985, Predisclosure Information, Firm Capitalization, and Security

Price Behavior Around Earning Announcement, Journal of Accounting Research


34/47

Move Prices?,Journal of Financial Economics 34, 281-305.

Bartov, E., S. Radhakrishnan, and I. Krinsky, 2000, Investor Sophistication andPatterns in Stock Returns after Earnings Announcements, Accounting Review 75,

4363.

Beber A. and C. Caglio, 2005, Order Submission Strategies and Information:

Empirical Evidence from the NYSE, Working Paper, University of Lausanne.

Bhusan, R., 1989, Firm Characteristics and Analyst Following,Journal of Accounting

and Economics 11, 255274.

Biais B., P. Hillion and C. Spatt, 1995, An Empirical Analysis of the Limit Order

Book and the Order Flow in the Paris Bourse,Journal of Finance 50, 1655-1689.

Bisire C. and T. Kamionka, 2000, Timing of Orders, Order Aggressiveness and the

Order Book at the Paris Bourse,Annales dEconomie et de Statistique 60, 43-72.

Blau, B. M., B. F. Van Ness and R. A. Van Ness, 2009, Intraday Stealth Trading:Which Trades Move Prices During Periods of High Volume, Journal of Financial

Research 32, 1-21.

Brennan, M. J., and H. Cao, 1997, International Portfolio Flows, Journal of Finance,

52, 1851-1880.

Brown, S., W. Goetzmann, and R. Ibbotson, 1999, Offshore Hedge Funds: Survival


35/47

Chan, H., N. Jegadeesh, and R.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, 34368.

Choe, H., B.-C. Kho, and R.M. Stulz, 2005, Do Domestic Investors Have an Edge?

The Trading Experience of Foreign Investors in Korea, Review of Financial Studies,

18(3), 795829.

Cooper, I., and E. Kaplanis, 1994, Home Bias in Equity Portfolios, Inflation Hedging, and

International Capital Market Equilibrium, The Review of Financial Studies,7, 45-60.

Coval, J., D. Hirshleifer, and T. Shumway, 2005, Can Individual Investors Beat the

Market? Available at SSRN:http://ssrn.com/abstract=364000.

Coval, J., and T. Moskowitz, 2001, The Geography of Investment: Informed Trading

and Asset Prices,Journal of Political Economy 109, 81141.

Coval, J., and T. Moskowitz, 1999, Home Bias at Home: Local Equity Preference in

Domestic Portfolios,Journal of Finance, 54, 2045-2073.

Demski, J. S., and G. A. Feltham, 1994, Market Response to Financial Reports,

Journal of Accounting and Economics 17, 3-40.

Dvok, T., 2005, Do Domestic Investors Have an Information Advantage? Evidence

from Indonesia,Journal of Finance, 60(2), 817839.
http://ssrn.com/abstract=364000http://ssrn.com/abstract=364000http://ssrn.com/abstract=364000http://ssrn.com/abstract=364000


36/47

Individual Trading,Journal of Finance 58, 2285-2320.

Griffiths M. D., B. F. Smith, D. A. Turnbull and R.W. White, 2000, The Costs andDeterminants of Order Aggressiveness,Journal of Financial Economics 56, 65-88.

Grinblatt, M., and M. Keloharju, 2000, The Investment Behavior and Performance of

Various Investor Types: A Study of Finlands Unique Data Set,Journal of Financial

Economics 55, 43-67.

Hakansson, N. H., 1977, Interim Disclosure and Public Forecasts: An Econometric

Analysis and a Framework for Choice.Accounting Review 52(2), 396-416.

Harris, L., 1998, Optimal Dynamic Order Submission Strategies in Some Stylized

Trading Problems,Financial Markets, Institutions and Instruments 7, 1-76.

Hau, H., 2001, Location Matters: An Examination of Trading Profits, Journal of

Finance 56, 1959-1983.

Huang, R., 2002, The Quality of ECN and NASDAQ Market Maker Quotes, Journalof Finance 57, 12851319.

Ikenberry, D., R. Shockley, and K. Womack, 1998, Why Active Fund Managers

Often Underperform the S&P 500: The Impact of Size and Skewness, Journal of

Private Portfolio Management1, 1326.

Ivkovich, Z., C. Sialm, and S. Weisbenner, 2008, Portfolio Concentration and the


37/47

Kim, O., and R. Verrecchia, 1994, Market Liquidity and Volume around Earnings

Announcements,Journal of Accounting and Economics 17, 41-67.

Kim, O., and R. Verrecchia, 1997, Pre-Announcement and Event-Period Private

Information,Journal of Accounting and Economics 24, 395-419.

Lakonishok, J., A. Shleifer, and R. Vishny, 1992, The Structure and Performance of

the Money Management Industry, Brookings Papers on Economic Activity:

Microeconomics 1992, 330391.

Lee Y. T., Y. J. Liu, R. Roll and A. Subrahmanyam, 2004, Order Imbalances andMarket Efficiency: Evidence from the Taiwan Stock Exchange,'Journal of Financial

and Quantitative Analysis 39, 327-341.

Lee, C. M. C., A. Shleifer, and R. Thaler, 1991, Investor Sentiment and the

Closed-End Fund Puzzle,Journal of Finance 46, 75-109.

Liang, B., 1999, On the Performance of Hedge Funds, Financial Analysts Journal 55,

7285.

Lo, A. W., and A. C. MacKinlay, 1990, When are Contrarian Profits Due to Stock

Market Overreaction?Review of Financial Studies 3, 175206.

Maug, E., 1998, Large Shareholders as Monitors: Is there a Tradeoff between

Liquidity and Control?,Journal of Finance 53, 65-98.


38/47

Table 1 Descriptive Statistics for Cumulative Abnormal Returns

The table reports descriptive statistics for the five groups of annual earningsannouncements classified by cumulative abnormal returns (CAR) in the pre-eventperiod during 2005 and 2006. The abnormal returns are obtained from the marketmodel with corrected beta according to the methodology provided in Scholes andWilliams (1977). *, **, and *** indicate significance at the 10, 5, and 1 percent level,respectively.

Top Group 2nd Group 3rd Group 4th Group Bottom Group

CAR [-1, -20]Mean 16.5450 4.9364 0.3321 -4.4010 -13.9075

Median 14.4810 4.5704 0.4997 -4.4979 -12.5139

Std. dev.

(t-statistics)

8.3486

(25.15)***

1.6579

(37.66)***

1.2441

(3.38)***

1.5280

(-36.43)***

6.6272

(-26.63)***

CAR [0, 19]

Mean 7.2515 5.2705 1.9134 -1.0867 -3.0871

Median 5.2718 2.3450 0.7014 -1.6836 -3.9366

Std. dev.

(t-statistics)

14.6818

(6.27)***

13.8601

(4.81)***

11.8285

(2.04)**10.1111

(-1.36)9.8161

(-3.99)***

Number of

Observations

161 160 160 160 161


39/47

Table 2 Percentage of Executed Orders by Order Categories

The table reports the number of executed orders (Panel A) and the shares of executedorders (Panel B) in percentage term by order categories for the sample of top twoCAR groups during 2005 and 2006. Only fully and partially executed orders areconsidered. Panel C reports the number of executed orders and the shares of executedorders in percentage term for each investor group with respect to order size andaggressiveness in the pre-event period. Order sizes are classified as small(1,000-4,999 shares), medium (5,000-9,999 shares), and large (10,000 + shares).Order aggressiveness is classified as aggressive orders (buy limit orders with priceshigher than or equal to the last market price and sell limit orders with prices lower

than or equal to the last market price) and passive orders (buy limit orders with priceslower than the last market price and sell limit orders with prices higher than the lastmarket price).

Panel A Number of Executed Orders

Pre-Event Period Post-Event Period Non-Event Period

Buy Sell Buy Sell Buy Sell

Order SizeSmall Orders 28.04 29.71 28.58 28 29.66 28.47

Medium Orders 8.22 8.67 8.48 8.48 8.43 8.26

Large Orders 12.65 12.7 13.26 13.2 12.66 12.51

Aggressiveness


40/47

Aggressive Orders 41.28 39.56 41.15 39.89 40.63 40.42

Passive Orders 8.95 10.22 8.95 10.02 9.46 9.49

Trader Type

Individuals 35.26 37.21 36.91 38.31 34.91 35.62

Domestic

Institutions6.63 5.57 5.72 5.84 6.16 6.3

ForeignInstitutions

8.34 7 7.46 5.76 9.01 8

Panel C Percentage of Order Choices by Investor Groups in Pre-event Window

Individuals Domestic Institutions Foreign Institutions

Aggress Passive Aggress Passive Aggress Passive

Number of Executed Orders

Small Orders 45.52 13.16 21.83 3.13 44.78 6.46

Medium Orders 13.59 3.64 10.27 3.14 13.60 2.36

Large Orders 19.22 4.87 45.77 15.86 27.99 4.81

Shares of Executed Orders


41/47

41

Table 3 Mean Daily Dollar Profit for Various Order Categories

The table reports the mean daily dollar profit ($NT million) in the pre-event (Panel A), post-event (Panel B), and non-even periods (Panel C). The dailydollar profits are calculated as the difference between the daily dollar returns on the buy portfolio and the daily dollar returns on the sell portfolio, net of themarket gains. Portfolios are constructed based on net daily purchases and sales of each order category, assuming a holding period of 1, 10, and 30 tradingdays. Only fully and partially executed orders are considered. Order sizes are classified as small (1,000-4,999 shares), medium (5,000-9,999 shares), andlarge (10,000 + shares). Order aggressiveness is classified as aggressive orders (buy limit orders with high price and sell limit orders with low price) andpassive orders (buy limit orders with low prices and sell limit orders with high prices). The figures in parenthesis are t statistics in panel models. *, **, and*** indicate significance at the 10, 5, and 1 percent level, respectively.

Panel A The Pre-Announcement Period

1-Day Holding Period 10-Day Holding Period 30-Day Holding Period

Buys Sells Buys-Sells Buys Sells Buys-Sells Buys Sells Buys-Sells

Size

Large 8.3(3.29)*** -5.7(-0.92) 14(2.89)*** 2.4(4.55)*** 1.2(4.99)*** 1.2(2.65)*** 12.9(7.64)*** 11.3(8.95)*** 1.5(1.99)**

Medium 0.4(0.26) 1.7(0.94) -1.2(-0.5) 0.3(5.11)*** 0.5(4.95)*** -0.2(-2.2)** 2.8(9.72)*** 3.2(9.04)*** -0.4(-1.42)Small -6.2(-1.23) 6.6(3.1)*** -12.8(-2.36)** 0.9(4.46)*** 1.9(4.11)*** -1(-2.55)** 8.5(7.97)*** 9.7(6.71)*** -1.2(-1.62)

Aggressiveness

Aggressive 87.2(1.98)** 16.2(2.55)** 71.1(2.97)*** 6.7(4.76)*** 3.5(7.46)*** 3.3(3.07)*** 24.1(7.11)*** 20(9.6)*** 4.1(2.08)**

Passive 16.2(2.55)** 87.2(1.98)** -71.1(-2.97)*** 3.5(7.46)*** 6.7(4.76)*** -3.3(-3.07)*** 20(9.6)*** 24.1(7.11)*** -4.1(-2.08)**

Trader

Individual 4.1(0.88) 18.7(3.91)*** -14.5(-2.17)** 2.4(6.31)*** 3.5(4.87)*** -1.1(-1.66)* 18.2(9.63)*** 19.8(8.58)*** -1.6(-2.17)**

Domestic Institution 8.7(3.2)*** -1.6(-0.46) 10.3(2.91)*** 2.7(6.22)*** 0.2(0.66) 2.6(4.77)*** 10(8.52)*** 5.5(6.1)*** 4.6(3.97)***

Foreign Institution 10(4.54)*** 5.8(0.81) 4.2(1.76)* 0.8(1.9)* 2.2(4.29)*** -1.5(-2.55)** 9.8(5.51)*** 12.7(8.28)*** -2.9(-2.01)**

Panel B The Post-Announcement Period




42/47

42

Size

Large 8.6(2.8)*** 6.6(0.67) 2(0.2) 1.5(1.58) 1.1(3.02)*** 0.4(0.59) 11.9(2.84)*** 7.2(5.51)*** 4.7(1.49)

Medium -1.3(-0.83) 1.7(1.5) -3(-1.54) 0.2(2.09)** 0.5(1.51) -0.3(-1.07) 1.6(4.19)*** 3.6(2.44)** -1.9(-1.7)*

Small 7.8(0.85) 6.8(2.93)*** 1(0.1) 0.9(3.11)*** 1(1.59) -0.1(-0.2) 5.6(5.84)*** 8.3(3.02)*** -2.7(-1.35)

AggressivenessAggressive 76.6(2.17)** 30.2(2.37)** 46.4(1.24) 7.2(3.23)*** 3.9(4.41)*** 3.3(1.79)* 24.1(4.39)*** 16.2(6.1)*** 7.9(2.26)**

Passive 30.2(2.37)** 76.6(2.17)** -46.4(-1.24) 3.9(4.41)*** 7.2(3.23)*** -3.3(-1.79)* 16.2(6.1)*** 24.1(4.39)*** -7.9(-2.26)**

Trader

Individual -6.2(-1.86)* 24(3.88)*** -30.2(-4.31)*** 0.8(1.71)* 1.7(1.99)** -0.9(-1.09) 9.2(6.41)*** 10.1(2.86)*** -1(-0.35)

Domestic Institution 6.3(1.29) -2.7(-0.99) 8.9(1.61) 0.9(2.51)** 0.3(0.83) 0.6(1.28) 12.5(8.19)*** 9.1(5.89)*** 3.4(5.23)***

Foreign Institution 17.7(2.52)** -3.5(-1.31) 21.3(2.82)*** 0.7(0.86) 0.5(1.33) 0.3(0.29) -2.4(-0.71) 0(0.05) -2.4(-0.59)

Panel C The Non-Event Period



Size

Large 4.3(2.57)** 0(-0.01) 4.3(1.78)* -0.3(-1.63) 0.2(1.48) -0.4(-2.9)*** -3.6(-9.85)*** -2.6(-7.15)*** -1(-3.86)***

Medium -0.8(-2.01)** 1(1.33) -1.8(-2.1)** 0.1(2.25)** -0.1(-2.02)** 0.2(3.25)*** -0.7(-8.56)*** -1.4(-12.73)*** 0.7(7.57)***

Small 0.7(0.39) 3.3(2.91)*** -2.6(-1.22) 0.1(1.06) -0.1(-1.32) 0.2(2.46)** -1.9(-6.04)*** -2.2(-7.94)*** 0.3(1.51)

Aggressiveness

Aggressive 20.9(3.83)*** 9.2(1.84)* 11.7(1.58) 0.1(0.26) 1.7(4.41)*** -1.7(-5.71)*** -3.5(-2.38)** 2(1.73)* -5.6(-4.78)***

Passive 9.2(1.84)* 20.9(3.83)*** -11.7(-1.58) 1.7(4.41)*** 0.1(0.26) 1.7(5.71)*** 2(1.73)* -3.5(-2.38)** 5.6(4.78)***

Trader

Individual -0.3(-0.18) 8.8(1.82)* -9.1(-1.77)* 0(0.02) -1.3(-5.66)*** 1.3(5.31)*** -5.5(-8.75)*** -7.3(-15.97)*** 1.7(10.51)***Domestic Institution 4(0.86) 0.3(0.23) 3.6(0.75) 0.2(0.87) -0.3(-3.57)*** 0.5(2.43)** -1.2(1.80)* -1.5(-10.79)*** 0.3(4.59)***

Foreign Institution 4.8(3.87)*** -0.7(-0.54) 5.5(3.14)*** -1.4(-9.51)*** 0.3(2.79)*** -1.8(-10.25)*** -3(-6.21)*** -1(-2.51)** -2.0(-4.71)***


43/47

Table 4 Investors Order Choices in the Pre-Announcement Period

The table reports the mean daily dollar profit (in $NT million) for investors orderchoices regarding size and aggressiveness respectively in the pre-event period. Thedaily dollar profits are calculated as the difference between the daily dollar returns on

the buy portfolio and the daily dollar returns on the sell portfolio, net of the marketgains. Portfolios are constructed based on net daily purchases and sales of eachsubcategory, assuming a holding period of 1, 10, and 30 trading days. Only fully andpartially executed orders are considered. Order sizes are classified as small(1,000-4,999 shares), medium (5,000-9,999 shares), and large (10,000 + shares).Order aggressiveness is classified as aggressive orders (buy limit orders with highprice and sell limit orders with low price) and passive orders (buy limit orders withlow prices and sell limit orders with high prices). The figures in parenthesis are tstatistics in panel models. *, **, and *** indicate significance at the 10, 5, and 1percent level, respectively.

Mean Daily Dollar Profit (Buys-Sells in $NT million)

Holding Period 1 Day 10 Days 30 Days

Panel A. Individuals

Total -14.5(-2.17)** -1.1(-1.66)* -1.6(-2.17)**

Size

Large -6(-1.26) 0(0.1) 0.1(0.09)

Medium -2.1(-1.55) -0.2(-1.6) -0.5(-1.67)*

Small -6.4(-2.75)*** -0.9(-3.04)*** -1.3(-2.03)**

Aggressiveness

Aggressive -7.4(-1.08) 0.3(0.51) 0.8(0.71)


44/47

44

Table 5 Comparison between Pre-Event Periods and Non-Event Price Run-ups

The table reports the mean daily dollar profit (in $NT million) of investors order choices regarding size and aggressiveness in the pre-event period andnon-event price run-ups. The non-event price run-ups are non-overlapping 20-day windows with cumulative returns in the top ten percent. The meandifference in profits between the pre-event period and non-event price run-ups for each category is also computed (Diff). The figures in parenthesis are tstatistics for mean profits and unpaired t statistics for mean differences. *, **, and *** indicate significance at the 10, 5, and 1 percent level, respectively.

1 Day 10 Days 30 Days

Pre-Event Non-Event Diff Pre-Event Non-Event Diff Pre-Event Non-Event Diff

Panel A. Individuals

Large -6(-1.26) -0.1(-0.56) -5.9(-1.24) 0(0.1) 0.1(0.28) -0.1(-0.28) 0.1(0.09) -0.4(-0.86) 0.5(0.42)

Medium -2.1(-1.55) -4.1(-2.78)*** 2(1.00) -0.2(-1.6) -0.1(-1.18) -0.1(-0.66) -0.5(-1.67)* -0.7(-3.44)*** 0.2(0.55)

Small -6.4(-2.75)*** -11.2(-3.68)*** 4.8(1.01) -0.9(-3.04)*** 0.2(0.84) -1.1(-2.90)*** -1.3(-2.03)** -0.5(-0.86) -0.8(-1.92)*

Aggressive -7.4(-1.08) 2.8(0.42) -10.2(-1.07) 0.3(0.51) -0.1(-0.31) 0.4(0.60) 0.8(0.71) -3(-4.49)*** 3.8(2.90)***

Passive -7.1(-1.67)* -18.1(-3.2)*** 11(1.33) -1.4(-2.86)*** 0.5(1.66)* -1.9(-3.31)*** -2.5(-2.58)*** 1.3(3.48)*** -3.8(-3.66)***Panel B. Domestic Institutions

Large 9.7(2.94)*** 3.5(1.57) 6.2(2.13)** 2.7(5.11)*** 0(-0.05) 2.7(5.11)*** 5.8(5.69)*** -0.3(-0.5) 6.1(5.16)***

Medium 0.2(0.22) 0.6(1.88)* -0.4(-0.42) -0.1(-1.76)* 0(-0.37) -0.1(-1.76)* -0.5(-2.73)*** 0(-0.2) -0.5(-2.73)***

Small 0.5(1.48) -0.7(-0.94) 1.2(1.47) 0(-0.78) -0.1(-1.36) 0.1(1.36) -0.7(-3.03)*** -0.2(-1.82)* -0.5(-1.95)*

Aggressive 2.3(0.38) 4.8(1.99)** -2.5(-0.38) 1.9(4.21)*** -0.4(-2.21)** 2.3(4.73)*** 2.4(2.58)*** -2.6(-4.75)*** 5(4.63)***

Passive 8(1.65)* -1(-0.73) 9(1.58) 0.7(4.06)*** 0.4(3.26)*** 0.3(1.42) 2.2(5.68)*** 2.2(8.03)*** 0(0.00)

Panel C. Foreign Institutions

Large -0.6(-0.09) 11.3(2.89)*** -11.9(-1.54) -1.7(-3.32)*** -0.3(-0.97) -1.4(-2.34)** -4.1(-3.24)*** -1(-1.2) -3.1(-2.05)**

Medium 2.3(2.9)*** 2.6(2.86)*** -0.3(-0.25) 0.1(2.09)** 0.1(1.98)** 0(0.00) 0.6(2.57)** 0.9(3.76)*** -0.3(-0.90)Small 2.6(1.72)* 1.9(1.78)* 0.7(0.38) 0.1(1.09) 0.2(1.4) -0.1(-0.59) 0.6(2.02)** 2.2(3.96)*** -1.6(-2.54)**

Aggressive 2.9(0.39) 8.9(2.3)** -6(-0.72) -1.6(-2.78)*** -0.4(-1.6) -1.2(-1.91)* -3.6(-2.65)*** -0.2(-0.33) -3.4(-2.29)*

Passive 1.4(1.66)* 6.8(3.57)*** -5.4(-2.39)** 0.1(1.72)* 0.4(2.87)*** -0.3(-1.89)* 0.6(2.45)** 2.2(5.3)*** -1.6(-3.32)***


45/47

45

Table 6 Sensitivity Analyses: Trading Volume and Order Imbalance

The table reports the mean daily dollar profit (in $NT million) of investors order choices, assuming a holding period of 1, 10, and 30 trading days, by tradingvolume (Panel A) and order imbalance (Panel B) in the pre-event period. For each stock, the trading volume is defined as the daily average number of sharestraded in the pre-announcement window. The order imbalance is defined as the average of the daily order imbalance in the pre-announcement window; the

daily order imbalance is calculated as the number of buy orders less the number of sell orders divided by the total number of orders. The mean of thedifference in profits between firm groups is also computed. The daily dollar profits are calculated as the difference between the daily dollar returns on thebuy portfolio and the daily dollar returns on the sell portfolio, net of the market gains. Portfolios are constructed based on net daily purchases and sales ofeach subcategory, assuming a holding period of 1, 10, and 30 trading days. Only fully and partially executed orders are considered. The figures in parenthesisare t statistics for mean profits and paired t statistics for mean differences. *, **, and *** indicate significance at the 10, 5, and 1 percent level, respectively.

Panel A By Trading Volume

Mean Daily Dollar Profit (Buys-Sells) in $NT million Mean Profit Difference in $NT million

High Trading Volume Firms Low Trading Volume Firms High - Low

1 day 10 days 30 days 1 day 10 days 30 days 1 day 10 days 30 days

Individuals -28.2(-2.12)** -2.5(-1.9)* -3.6(-1.27) -0.9(-0.54) 0.3(2.45)** 0.3(0.76) -27.3(-2.03)** -2.8(-2.11)** -3.9(-1.35)

Size

Large -11.6(-1.24) 0(-0.02) -0.2(-0.1) -0.4(-0.23) 0.1(0.91) 0.4(1.9)* -11.2(-1.17) -0.1(-0.14) -0.6(-0.29)

Medium -4.6(-1.75)* -0.5(-2.23)** -1.1(-2.05)** 0.4(0.6) 0.1(3.64)*** 0.2(1.58) -5(-1.85)* -0.7(-2.83)*** -1.3(-2.33)**

Small -12(-2.63)*** -1.9(-3.14)*** -2.3(-2.04)** -0.9(-1.31) 0.1(1) -0.3(-1.53) -11.1(-2.51)*** -2(-3.23)*** -2(-1.98)**

Aggressiveness

Aggressive -14.9(-1.09) 0.7(0.59) 1.5(0.63) 0.1(0.03) -0.1(-0.74) 0.1(0.64) -15(-1.09) 0.8(0.67) 1.4(0.58)

Passive -13.2(-1.11) -3.2(-3.29)*** -5.2(-2.66)*** -1(-0.43) 0.4(3.98)*** 0.1(0.46) -12.2(-1.01) -3.6(-3.67)*** -5.3(-2.69)***

Domestic

Institutions20.6(2.91)*** 5.4(5.05)*** 9.7(4.22)*** 0.1(0.08) -0.3(-2.24)** -0.5(-1.79)* 20.5(2.9)*** 5.7(5.24)*** 10.2(4.39)***

Size

Large 19.2(2.92)*** 5.7(5.41)*** 12.2(6.01)*** 0.3(0.3) -0.3(-2.39)** -0.5(-1.94)* 18.9(2.88)*** 5.9(5.61)*** 12.7(6.2)***

Medium 0.5(0.32) -0.2(-1.91)* -1(-2.72)*** -0.1(-0.76) 0(0.96) 0(-0.29) 0.6(0.42) -0.2(-2.02)** -1(-2.69)***


46/47

46

Small 1(1.57) -0.1(-0.77) -1.5(-3.08)*** -0.1(-1.11) 0(-0.23) 0(1.1) 1.1(1.66)* -0.1(-0.75) -1.5(-3.12)***

Aggressiveness

Aggressive 4.5(0.37) 4(4.6)*** 5.3(2.89)*** 0.2(0.22) -0.3(-2.77)*** -0.5(-2.02)** 4.3(0.35) 4.3(4.88)*** 5.9(3.13)***

Passive 16.2(1.46) 1.4(3.95)*** 4.4(5.67)*** -0.1(-0.53) 0(1.94)* 0(0.37) 16.3(1.47) 1.3(3.81)*** 4.4(5.62)***

Foreign

Institution7.6(0.51) -2.9(-2.51)** -6.1(-2.07)** 0.8(0.8) 0(-0.89) 0.2(1.09) 6.8(0.45) -2.9(-2.47)** -6.4(-2.15)**

Size

Large -2.4(-0.18) -3.5(-3.33)*** -8.4(-3.28)*** 1.1(1.16) 0(0.27) 0.2(1.15) -3.6(-0.26) -3.5(-3.34)*** -8.6(-3.34)***

Medium 4.6(2.88)*** 0.3(2.21)** 1.1(2.44)** 0(0.33) 0(-1.47) 0.1(1.64) 4.6(2.85)*** 0.3(2.31)** 1.1(2.26)**

Small 5.5(1.84)* 0.3(1.88)* 1.1(2.03)** -0.3(-1.93)* 0(-1.91)* 0(0.18) 5.8(1.94)* 0.3(1.86)* 1.1(1.97)**

Aggressiveness

Aggressive 5.1(0.34) -3.1(-2.72)*** -7.3(-2.68)*** 0.6(0.62) -0.1(-1.68)* 0.1(0.59) 4.5(0.3) -3(-2.65)*** -7.4(-2.71)***

Passive 2.5(1.08) 0.1(1.12) 1.2(2.23)** 0.2(1.5) 0(1.52) 0.1(2.51)** 2.3(0.99) 0.1(0.91) 1(1.96)*

Panel B By Order Imbalance

Mean Daily Dollar Profit (Buys-Sells) in $NT million Mean Profit Difference in $NT million

High Order Imbalance Firms Low Order Imbalance Firms High - Low

1 day 10 days 30 days 1 day 10 days 30 days 1 day 10 days 30 days

Individuals -6.3(-0.66) -1(-1.52) -1.4(-0.89) -22.9(-2.44)** -1.3(-1.13) -1.9(-0.8) 16.5(1.23) 0.3(0.22) 0.4(0.16)

Size

Large 6(0.95) 1.1(2.15)** 4(4.3)*** -18.1(-2.55)** -1(-1.59) -3.8(-2.37)** 24.1(2.55)** 2.1(2.57)** 7.8(4.2)***

Medium -3.3(-1.72)* -0.4(-2.69)*** -1.4(-3.55)*** -0.9(-0.48) -0.1(-0.25) 0.5(1.26) -2.3(-0.85) -0.3(-1.28) -1.9(-3.38)***

Small -9(-1.36) -1.7(-3.2)*** -4(-3.56)*** -3.9(-1.19) -0.2(-0.64) 1.5(2.6)*** -5.2(-0.7) -1.5(-2.47)** -5.5(-4.33)***

Aggressiveness

Aggressive 6.6(0.59) 2.2(2.54)** 6.9(4.22)*** -21.5(-2.65)*** -1.7(-2.3)** -5.2(-3)*** 28.1(2.03)** 3.9(3.43)*** 12.1(5.07)***


47/47

47

Passive -13(-1.3) -3.2(-3.77)*** -8.4(-4.7)*** -1.3(-0.18) 0.4(0.8) 3.3(4.23)*** -11.7(-0.97) -3.6(-3.71)*** -11.7(-6)***

Domestic

Institutions6(0.77) 2(1.88)* 2.3(1.4) 14.8(2.96)*** 3.1(3.79)*** 6.8(5.11)*** -8.8(-2.81)*** -1.1(-2.03)** -4.5(-3.46)***

Size

Large 4.8(0.71) 1.7(1.43) 1.7(1.7)* 14.8(3.98)*** 3.6(3.77)*** 10(5.12)*** -10(-2.99)*** -2(-2.27)** -8.3(-2.64)***

Medium 0.5(2.73)*** 0.2(2.11)** 0.6(3.08)*** -0.2(-0.72) -0.4(-1.75)* -1.6(-2.17)** 0.6(2.44)*** 0.6(2.01)** 2.2(2.93)***

Small 0.8(2.18)** 0.1(1.74)* 0.1(1.44) 0.2(1.07) -0.1(-1.71)* -1.5(-3.23)*** 0.7(1.99)** 0.2(1.72)* 1.6(3.03)***

Aggressiveness

Aggressive -7(-0.68) 0.6(2.81)*** -0.6(-0.4) 11.7(1.82)* 3.2(3.16)*** 5.3(4.32)*** -18.7(-1.75)* -2.6(-2.97)*** -5.9(-3.19)***

Passive 13(3.18)*** 1.5(1.83)* 2.9(2.63)*** 3.1(1.71)* -0.1(1.36) 1.5(2.36)** 9.8(2.88)*** 1.6(1.76)* 1.4(2.53)***

Foreign

Institution0.3(0.02) -1(-1.06) 0.8(0.42) 8.1(1.84)* -1.8(-2.82)*** -6.7(-2.96)*** -7.8(-0.52) 0.8(0.7) 7.5(2.57)**

Size

Large -8(-0.6) -1.5(-1.76)* -2.8(-2.14)** 6.6(1.77)* -1.9(-3.11)*** -5.5(-2.51)** -14.6(-1.06) 0.3(0.33) 2.7(1.08)

Medium 3.2(2.14)** 0.2(1.88)* 1.5(3.4)*** 1.5(2.41)** 0.1(0.98) -0.3(-2.39)** 1.7(1.07) 0.2(1.14) 1.8(4.02)***

Small 5.1(1.8)* 0.3(1.31) 2(3.92)*** 0(-0.01) 0(-0.74) -0.9(-3.77)*** 5.1(1.73)* 0.3(1.41) 2.9(5.24)***

Aggressiveness

Aggressive -1.3(-0.09) -1(-1.09) -0.3(-0.2) 7(1.78)* -2(-3.16)*** -6.8(-3.16)*** -8.3(-0.56) 1(0.87) 6.5(2.44)**

Passive 1.6(0.73) 0(-0.05) 1.1(2.4)** 1.1(1.4) 0.2(2.69)*** 0.2(0.72) 0.5(0.22) -0.2(-1.39) 0.9(1.77)*

paper for review order choice taiwan

Documents