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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
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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
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(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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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
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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,
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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.
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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
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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,
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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;
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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
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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
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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
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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,
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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.
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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
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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
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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
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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
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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
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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
1-Day Holding Period 10-Day Holding Period 30-Day Holding Period
Buys Sells Buys-Sells Buys Sells Buys-Sells Buys Sells Buys-Sells
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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
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 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)***
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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)
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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)***
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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)***
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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)***
7/29/2019 Paper for Review Order Choice Taiwan
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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)*
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