Whose trades move stock prices? Evidence from the Taiwan Stock Exchange

Journal Pre-proof Whose trades move stock prices? Evidence from the Taiwan Stock Exchange Donald Lien, Pi-Hsia Hung, Zong-Wei Lin

PII:

S1059-0560(18)30240-5

DOI:

https://doi.org/10.1016/j.iref.2019.10.011

Reference:

REVECO 1849

To appear in:

International Review of Economics and Finance

Received Date: 15 January 2018 Revised Date:

18 October 2019

Accepted Date: 31 October 2019

Please cite this article as: Lien D., Hung P.-H. & Lin Z.-W., Whose trades move stock prices? Evidence from the Taiwan Stock Exchange, International Review of Economics and Finance (2019), doi: https:// doi.org/10.1016/j.iref.2019.10.011. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Inc.

Whose Trades Move Stock Prices? Evidence from the Taiwan Stock Exchange Donald Lien University of Texas at San Antonio Pi-Hsia Hung* National Chi Nan University Zong-Wei Lin National Chi Nan University Current Draft: October 2019

Abstract Analyzing firms listed on the Taiwan Stock Exchange, this research examines whose trades move stock prices. To emphasize the roles of different types of investors, we construct the weighted price contribution (WPC), explore sensitivities of the price contribution to order submission behavior, and then test whether their order aggressiveness and trade size, along with current price contribution, affect future stock returns. Our empirical results reveal several new findings. First, individual traders account for more than 80% of WPC, while institutional investors make up less than 20% of WPC. Second, although mutual funds make up the smallest proportion of WPC, their WPC per order (or per lot) and their order aggressiveness are both the highest among all types of traders. Finally, price contributions generated by large orders of professional institutions are closely related to future stock performance, although they account only for a small proportion of total WPC. JEL Classification: D10, G20, G23, G82. Key Words: market microstructure, order aggressiveness, price contribution, trade size, trading behavior.

*

Corresponding author: Pi-Hsia Hung, Department of Banking and Finance, National Chi Nan University. Address: No. 1, University Rd., Puli, Nantou 54561, Taiwan, R.O.C. Tel.: (011) 886-49-2910960 ext. 4624. E-mail: [email protected]. The authors gratefully acknowledge the Ministry of Science and Technology, Taiwan, R.O.C. for financial support under grant MOST 105-2410-H-260-010 -. The authors are indebted to the editor and two anonymous reviewers whose excellent comments and suggestions have enhanced this paper. 1

Whose Trades Move Stock Prices? Evidence from the Taiwan Stock Exchange Current Draft: October 2019 Abstract Analyzing firms listed on the Taiwan Stock Exchange, this research examines whose trades move stock prices. To emphasize the roles of different types of investors, we construct the weighted price contribution (WPC), explore sensitivities of the price contribution to order submission behavior, and then test whether their order aggressiveness and trade size, along with current price contribution, affect future stock returns. Our empirical results reveal several new findings. First, individual traders account for more than 80% of WPC, while institutional investors make up less than 20% of WPC. Second, although mutual funds make up the smallest proportion of WPC, their WPC per order (or per lot) and their order aggressiveness are both the highest among all types of traders. Finally, price contributions generated by large orders of professional institutions are closely related to future stock performance, although they account only for a small proportion of total WPC. JEL Classification: D10, G20, G23, G82. Key Words: market microstructure, order aggressiveness, price contribution, trade size, trading behavior.

Whose Trades Move Stock Prices? Evidence from the Taiwan Stock Exchange 1.

Introduction The issue of whose trades move stock prices is very important not only to

academicians, but also to market traders and regulators. Traditionally, the degree of a stock’s cumulative price changes correlates to the informativeness of trades, which can be measured by the stock price contribution. As it is commonly known that investor order submission behavior is linked to how stock prices are set and transactions are executed, such behavior has become the key determinant of price contribution in stock markets. This study thus empirically investigates the stock price contribution across investor types, deepening the literature’s understanding of the determinants of stock price movements in an order-driven market.

By analyzing a unique and detailed dataset, the study investigates the trading behavior among different investor types and addresses the following research questions. First, whose trades move stock prices in view of the weighted price contribution (WPC)? Second, how does stock investors’ order submission behavior (order aggressiveness and trade size) affect the price contribution? Third and finally, how do investors’ order aggressiveness and size, along with the current price contribution, affect future stock returns?

While the extant literature on price contribution mainly concentrates on trade-size categories, time periods, and trading venues, our analysis focuses on investor types, such as foreign investors, mutual funds, other institutions (corporations), and individuals (retail investors). We propose a modified WPC

1

measure by trade-initiated direction to investigate the linkages between various investor types’ order submission decisions and price movements.1 In addition, most prior literature targets well-developed Western markets, such as New York Stock Exchange (NYSE) and National Association of Securities Dealers Automatic Quotation (NASDAQ), while our analysis focuses on the Taiwan Stock Exchange (TWSE), which is an active emerging market. Although there are studies on the relationships between order submissions and stock returns, the effects of order aggressiveness and size by different types of investors on price contribution have not yet been explored. TWSE is a pure order-driven call market and offers a suitable opportunity to expand our understanding of whose trades move stock prices in emerging market equities.

Many empirical studies posit that informed investors’ trades are the main causes of stock price movement.2 It is plausible that some stock investors have privileged information about firms’ prospects and can realize superior profits by trading these stocks (Barber et al., 2009; Hung, 2016; Hung, Chen, and Wu, 2015; Lee et al., 2004). However, little is known about price contributions from the order submissions of different types of investors and the influence of these order submissions on stock prices and returns. This study aims to explore the sensitivities of the price contribution to order aggressiveness and trade size and to examine whether the current price

1

Most existing studies calculate the weighted price contribution (WPC) based on the trade size category; see, for example, Alexander and Peterson (2007), Barclay and Warner (1993), Chakravarty (2001), Kang and Ryu (2010), Lin (2014), O’Hara, Yao, and Ye (2014), and Blau, Van Ness, and Van Ness (2009). Barclay and Hendershott (2003, 2008), Cao, Ghysels, and Hatheway (2000), and Ellul, Shin, and Tonks (2005) use WPC to measure a stock price’s cumulative change over a given time period. On the other hand, Huang (2002) examines in which trading venue price discovery occurs most through the use of WPC.

2

Examples, among others, include Anand, Chakravarty, and Martell (2005), Chou and Wang (2009), Easley and O’Hara (2004), Goettler, Parlour, and Rajan (2009), Hughes, Liu, and Liu (2007), Lee et al. (2004), Shive (2012), and Wang and Chiao (2008). 2

contribution affects future stock returns.

Our results reveal that different traders have varied trading behavior. On average, individual traders contribute more to current price movement than do institutions on both buy and sell sides, echoing their role of being the dominant participants in TWSE. This higher WPC by individuals is closely linked to the call auction trading mechanism and investor distribution in this stock market. Under a call auction, multiple buy and sell orders are batched collectively for a simultaneous execution at a single price. Thus, at each transaction there are manifold buy-sell paired trades at the same price. Without any further information, we equally distribute the impacts of multiple traders at the same execution time for each trade initiator and then aggregate the proportions of total price changes across trader types that occur during specific time intervals. This reasonably reflects the characteristics of order matching designs in an order-driven market. Consequently, WPC measures how much each type of trader participates in market price changes.

A trade that causes a price change does not necessarily become qualified as price discovery. Since individuals are the dominant participants in TWSE, their orders are executed at almost every execution and thus contribute to each transitory trade return at each matching. As a consequence, individual traders contribute more to current WPC than do institutions. On the other hand, institutional traders contribute little to (transitory) market price changes in terms of WPC, but their trades embody strong return predictability at various horizons. Our paper documents that, in an order-driven call market, individual traders’ WPC mainly reflects transitory price changes rather than price discovery. We evaluate price discovery via return predictability.

3

We improve the traditional WPC measure by distinguishing the trade-initiated direction (i.e., buy- or sell-initiator). Most existing studies calculate WPC based on the trade size categories (Kang and Ryu, 2010; Lin, 2014; O’Hara, Yao, and Ye, 2014; Blau, Van Ness, and Van Ness, 2009), time periods (Barclay and Hendershott, 2003, 2008; Cao, Ghysels, and Hatheway, 2000; Ellul, Shin, and Tonks, 2005), or trading venues (Huang, 2002). In a buy-sell paired trade, both buyer and seller have the same trade size, trade in the same time period, and at the same trading venue. Consequently, the conventional price contribution measure does not need to identify the trade direction. Our WPC is based on investor types. The matched buyer and seller do not necessarily come from the same investor category. Using Lee and Ready’s (1991) trade classification algorithm to identify the paired trade as a buyer- or seller-initiated trade, we propose WPC sorted by the trade-initiated direction, which is very different from that in the U.S. markets.3

Since individuals account for the largest proportion of orders and trade value (more than 67%) in TWSE, they provide about 80% of WPC, but their average trade value either per lot or per order is the smallest across investor types. Individuals’ order aggressiveness is lower than professional institutions’. Among institutions, foreign investors consist of about 17% of trade value, but only around 10% of WPC. Their average trade value per order is smaller than domestic institutions’. On the other

3

The Lee and Ready (1991) method may not be perfect for inferring the direction of unrecognized trades in U.S. markets due to the absence of order-level data. However, it is a common process for identifying the trade initiation to determine which counterparty drives transaction more powerfully. Several previous studies use the Lee-Ready procedure to identify the trade-initiator in a buy-sell paired transaction in order-driven markets (e.g., Aktas et al., 2007; Huang and Chou, 2007; Hung, Chen, and Wu, 2015; Lu and Wei, 2009). In TWSE, order- and trade-level data do contain identified trade directions, in which the transaction file covers definitive buy-sell paired trades. Thus, trade directions are explicit in TWSE, and so we do not need to infer the directions of trades. Nevertheless, to judge which side has more power to drive the stock price, we use the Lee and Ready (1991) approach to determine who is the trade-initiator - that is, we use this method to determine the trade initiator between buy-sell-paired trades, instead of the trade direction. 4

hand, mutual funds make up the smallest proportion of orders and trade value (less than 2%) and have the lowest percentage of WPC (about 2%) in TWSE, but their average trade value per order is much larger than foreign investors’.

In terms of WPC per order, we find foreign investors’ WPC per lot or per order is the smallest across investor types, which is consistent with their order-splitting behavior. Although mutual funds’ total WPC is the lowest, their WPC per lot or per order is the largest across investor types, in accordance with their most aggressive order submission behavior.

Regarding the sensitivities of the price contribution to investor types and order submission behavior, our empirical results show that, regardless of order direction, institutional investors always exhibit a smaller price contribution than do individuals in TWSE after controlling for related factors. Our empirical results also display that both order aggressiveness and trade value are positively related to price contribution. On average, foreign investors’ larger trade value and mutual funds’ more aggressive orders lead to greater price contribution. In addition, the sensitivity of price contribution to trade size is much larger than to order aggressiveness.

Although institutional investors provide smaller current price contribution than individuals do, this is related to their sophistication and thus connected to future stock returns. On the buy side, larger orders from institutions contain trading information and are positively related to future stock returns. The size of an individual’s buy order is negatively related to future returns, albeit individuals, as a group, produce the largest price contributions. For sell orders, foreign investors’ and mutual funds’ larger orders are negatively related to future short-term stock returns, indicating that stock returns decline right after these professional institutions sell in the markets. Overall, 5

professional institutions have better information collection and processing abilities, can predict stock prices more precisely, and make the more correct trading decisions. Stocks with larger institutional buying (selling) have better (worse) performance. Professional institutions’ buying helps them make profits when the market is up, and their selling also reduce their losses when the market is down. Thus, for both bull and bear markets, professional institutions exhibit better stock picking or market timing abilities versus individual investors. We conclude that institutional investors are better engaged in information-based trading.

This study offers several contributions to the literature on investors’ trading behavior and stock price movements. First, it deepens the understanding of order submission in an order-driven call market and to the best of our knowledge is the first study that provides direct empirical evidence on price contribution across investor types.4 Second, we propose a modified WPC measure by trade-initiated direction, which extends the traditional WPC measure in the market microstructure literature. Third, our study combines three types of data, order-, transaction-, and quote-level data, to analyze the link between investors’ order aggressiveness (or size) and price contribution (details of data combination are in Section 4.2.). Most studies in the existing literature use transactions, TAQ (Trade and Quote), or TORQ data to calculate price contribution (Alexander and Peterson, 2007; Barclay and Warner, 1993; Chakravarty, 2001; Choe, Chung, and Lee, 2008). As the impact of investors’ order submission behavior on final price contribution has not yet been examined, our study offers new insights on this issue. Fourth and finally, we link the current price

4

The measures of price contribution are originally inspired by Barclay and Warner (1993). Chakravarty (2001), Huang (2002), Blau, Van Ness, and Van Ness (2009), and Lin (2014) extend Barclay and Warner’s work and determine the price contribution across trade-size categories. 6

contribution to future stock returns, providing convincing evidence that at least a portion of future stock returns are attributable to informed trades.

The remainder of this paper is organized as follows. Section 2 introduces the institutional background. Section 3 reviews the current literature related to investor types, traders’ order submission, and stock returns. Section 4 describes the data and methodology, including data sources, data construction process, variable definitions, and analytical models. Section 5 presents and discusses the empirical results. Section 6 concludes this paper. 2.

Institutional Background As of the end of 2018, the total market capitalization of 928 firms listed on

TWSE amounted to about US$954 billion, ranking it as the fourteenth largest stock exchange in the world. TWSE is also widely known for being one of the most active stock exchanges with the sixth highest turnover rate (92.55% in 2018) and the third highest ratio of market capitalization to gross domestic production (1.82 times in 2017) around the globe.

TWSE is a pure order-driven call market with price variation limits and a multiple tick-size system. In this consolidated limit order book environment, only limit orders can be accepted by the Fully Automated Securities Trading (FAST) system. All orders are matched and executed sequentially based on the principles of price-then-time priorities in the regular trading session. In terms of the price criterion, the computer processing design primarily matches the waiting limit orders in sequence of high bid and low ask quotes, respectively. According to the principle of the maximized quantity of transactions, the determined price can be set under which

7

all limit orders with higher (lower) bid (ask) prices are given absolute priority. At that moment, at least one party’s buy or sell orders standing at the determined price have been fully matched, since quantity supplied does not necessarily equal quantity demanded in the market. The unfilled shares wait there for the next call auction. Judging by the time sequence, an order entered into the consolidated limit order book at an earlier time must be executed in full before another order at the same price but entered later can be executed.

As to trading information disclosure, since January 1, 2003, TWSE discloses both price and volume of unexecuted orders at the best five bids and offers on a real-time basis. This allows all market participants to receive the same transparent information so as to make a fair judgment when placing orders. Within this consolidated limit order book system, stock investors’ order submission behavior plays a central role not only in how transactions are executed, but also toward the level of stock price changes.

TWSE adopts a price limit system as a policy tool to stabilize security prices and to prevent dramatic losses to investors caused by large price fluctuations. During our sample period, security prices have ±7% upper and lower bounds. In addition, TWSE carries out a multiple tick-size system, which produces several benefits, such as a simplified trading environment, contracted bid-ask spread, reduced negotiation time, and increasing likelihood of order matching. The TWSE market architecture is very different from developed countries without price limits and which have a uniform tick-size rule. We expect dissimilar market mechanisms have distinct price contributions among various types of stock markets.

8

3.

Literature Review

3.1. Relationships between Investor Types and Stock Returns Lee et al. (2004) note that large domestic institutional investors conduct the most informed trades, and that large individuals play the role of liquidity traders in TWSE. Barber et al. (2009) also use TWSE data and document that the aggregate portfolio of individuals suffers significant losses, while institutions enjoy trading profits. Chou and Wang (2009) find that foreign institutional traders and futures proprietary firms (i.e., futures dealers) are likely to trade on the Taiwan Futures Exchange based on private information. Huang and Shiu (2009) also confirm that stocks with high foreign ownership outperform stocks with low foreign ownership in Taiwan.

Kelley and Tetlock (2013) employ stocks listed on NYSE, NASDAQ, and American Stock Exchange (Amex) and analyze the role of retail investors, suggesting that some individuals are “wise”, because those smart individuals positively predict cross-section stock returns, thus contributing to market efficiency. Hung (2016) and Hung, Chen, and Wu (2015) further confirm that mutual funds, compared to other investors, perform better in TWSE. In brief, different types of investors have discrepant stock picking and market timing abilities, and thus their trading behaviors affect stock price changes to a different extent.

The unique dataset we enlist in this study allows us to divide investors into four categories, which is finer than what previous studies have analyzed using data from well-developed markets. For example, Bloomfield, O’Hara, and Saar (2005) classify traders into informed traders, large liquidity traders, and small liquidity traders to investigate the evolution of liquidity in an electronic limit order market. Kaniel and 9

Liu (2005) and Menkhoff, Osler, and Schmeling (2010) discriminate uninformed traders versus informed traders and analyze traders’ order submission behavior. Our investor-type classification presents a better understanding of the link between each category of investor and stock returns. 3.2. Relationships between Investors’ Order Submission and Stock Returns Order submission strategies consist of order direction (buy or sell), order price (i.e. aggressiveness), order quantity (size), timing, etc. Prior research has confirmed that buy and sell orders display different patterns and asymmetric effects (Chou and Wang, 2009; Griffiths et al., 2000; Hung, 2016; Hung, Chen, and Wu, 2015; Keim and Madhavan, 1996), while order aggressiveness and size exhibit a trade-off relationship (Hung, 2016; Lo and Sapp, 2010). Some empirical research examines the relationships between order aggressiveness and stock returns. For instance, Griffiths et al. (2000) reveal that aggressive orders have stronger price impacts, while Bae, Jang, and Park (2003) present that traders submit more limit orders relative to market orders when the spread, the order size, or the expected transitory price volatility increases.

The existing literature has examined in-depth the relationships between order/trade size and stock price drift.5 Chan and Fong (2000) confirm that large size trades have a stronger influence on stock returns than smaller size trades. Some studies investigate the relationships between medium-size trading and stock returns and confirm the stealth-trading hypothesis (Alexander and Peterson, 2007; Barclay

5

Some examples are Alexander and Peterson (2007), Barclay and Warner (1993), Chakravarty (2001), Chan and Fong (2000), Easley and O’Hara (1987), Hvidkjaer (2008), Keim and Madhavan (1996), and Moulton (2005). 10

and Warner, 1993; Chakravarty, 2001).6

Although

many

studies

have

investigated

the

link

between

order

aggressiveness/size and stock returns, the role of price contribution in various investors’ order decisions (regarding aggressiveness and size) has not yet been explored. This issue remains an interesting and valuable topic in market microstructure research. 3.3. A Comparison of Price Discovery Measures This paper adopts WPC as the measure of price information. An alternative measure is information share proposed by Hasbrouck (1995), who notes that information share is originally derived for homogeneous or closely-linked securities traded in multiple markets. Kang, Kang, and Lee (2016) and Brogaard, Hendershott, and Riordan (2019) extend the analysis to the case of multiple types of traders in a single market, thus allowing us to compare WPC to information shares (IS).7

Kang, Kang, and Lee (2016) examine the Hasbrouck information share and Granger-Gonzalo component share for different traders, finding that both identify

6

Barclay and Warner (1993) test the stealth-trading hypothesis and find that stock price changes are mainly due to informed traders’ private information. Chakravarty (2001) extends Barclay and Warner’s work and shows that the disproportionately large cumulative price impact of medium-size trades mainly comes from trades initiated by institutions. Moreover, Alexander and Peterson (2007) present that medium-sized rounded trades have a greater price impact than large-size rounded trades, indicating that stealth traders tend to submit medium-sized rounded trades. Ascioglu, Comerton-Forde, and McInish (2011) reconfirm the stealth trading hypothesis and reveal that price changes are driven by small- and medium-size trades on the Tokyo Stock Exchange.

7

Wang and Yang (WY, 2015) consider a sequential market (a single market in different consecutive trading periods) and construct the Hasbrouck-type information share. Clearly, WY does not fit our case since we do not consider sequential markets. Brogaard, Hendershott, and Riordan (2019) extend Brogaard, Hendershott, and Riordan (2014) by including trades and limit orders into the IS framework. Other related papers include Chen and Lien (2016), Fassas and Siriopoulos (2019), Lien and Shrestha (2014), Ozturk, Van der Wel, and Van Dijk (2017), etc. 11

foreign investors as the biggest contributor; however, the rankings between institutional investors and individuals are not consistent. Putnins (2013) concludes that information share, component share, and information leader share tend to produce different conclusions. Each measure offers its own insights. We expect WPC to provide independent insights as well.

As a final remark, WPC is a non-parametric measure where the contribution of each price refers to the observed price. Information share is a parametric measure, and the reference price is the efficient price. However, assumptions are required to generate the efficient price. The original Habrouck framework requires each price series to be non-stationary with a specific cointegration vector; also, the data generation process is assumed to be a linear vector error correction model (VECM) with homoskedastic errors. When any of these assumptions is invalid, the model becomes misspecified and the resulting IS measure calculation is incorrect and unreliable. 8 On the other hand, WPC is model-free and not subjected to the misspecification concern. This is clearly an advantage of WPC over IS. 4.

Data and Methodology

4.1. Data Sources and Sample Period Our study relies upon two data sources. First, we obtain intraday data from the Taiwan Stock Exchange Corporation, including order-, transaction-, and quote-level

8

The above assumptions have been gradually relaxed in the literature and several modified IS measures are proposed. Examples include Dolatabadi, Nielsen, and Xu (2015), and Lien and Shrestha (2014). Thus, one may need to go beyond the Hasbrouck framework if the IS measure is the focal point. Fleming, Mizrach, and Nguyen (2018) incorporate trades and limit orders into the price dynamics to calculate information shares - that is, in addition to market prices, trades and limit orders both contribute to variations in equilibrium prices. Brogaard, Hendershott, and Riordan (2014) allow the trading behavior of high-frequency traders to affect the deviation between the true price and the observed market price and hence the information share. 12

data. Second, we obtain daily market trading information and yearly financial data from the Taiwan Economic Journal (TEJ) database. The sample period ranges from July 2009 to May 2015, for a total of five years and eleven months. According to the regulations of “Personal Information Protection Act,” the Taiwan Stock Exchange Corporation has withheld dealers’ intraday-level trading information since July 2009 and only reveals trading data for foreign investors, mutual funds, other institutions, and individuals since then. Thus, for comparison purpose, our sample starts at the beginning of July 2009. Moreover, TWSE changed the price variation limit from 7% to 10% on June 1, 2015. To isolate possible effects of this policy, our sample period ends at May 31, 2015. Overall, our sample period covers a total of nearly six years.

Our sample includes all common stocks listed on TWSE over the sample period, including 84 newly added and 47 delisted firms. To the best of our ability and to avoid survivorship bias, we seek to cover the most common stocks with available data. As a consequence, the complete data consist of 845 non-overlapping common stocks during 1,468 trading days, for a total of 1,126,024 firm-day observations. In unreported results (but available upon request), we find newly added firms have higher turnover rates and book-to-market ratios than the overall average. By contrast, delisted firms tend to have lower returns, turnover rates, market capitalization, and book-to-market ratios than the average. In brief, our data consist of all industry categories in Taiwan, maturely exhibiting a representative dataset of stock components listed on TWSE. 4.2. Data Construction Process Our primary intraday data consist of three categories of historical trading data from TWSE:

the order, transaction, and quote files. We combine the order-level 13

data, the transaction data (based on each order), and the quote data (based on the time records). The resulting files comprise many one-to-one correspondences with the transactions that make up the original orders, along with the prevailing quote information. Basically, an order may involve a single trade, multiple trades, or no trade (i.e. no transaction taking place within the trading session and being automatically canceled after the close). To construct the feasible data structure, we first transpose the transaction data by each trade direction, stock, date, and order number. We then merge order-level and the transposed transaction data based on the same buy-sell indicators, security numbers, trading days, and order numbers.

After combining the order-level data and the transposed transaction data based on the same order identified codes, we merge the allied files with the simultaneous quote data based on the security numbers, trading days, and time records (hours, minutes,

seconds,

and

microseconds).

The

available

order-by-order,

transaction-by-transaction, and the best quote information allow us to analyze stock investors’ original order submission decisions and price contributions across different classifications of investors. Specifically, we are able to construct daily price contribution measures by order direction (buy and sell sides) for each stock and each type of investor. 4.3. Measurements 4.3.1. Weighted Price Contribution We incorporate the approach of Lee and Ready (1991) into Barclay and Warner

14

(1993) and O’Hara, Yao, and Ye (2014) to calculate WPC across investor types.9 Let superscript d represent the order direction (buy or sell) and let subscripts denote stocks and investor types, respectively. We also let subscript trading day and

the trade sequence. For all trades of stock

and

indicate the

by investor type

on

trading day , we modify Barclay and Warner’s (1993) method to calculate the price contribution by accommodating trade-initiated directions as follows:

=

where

∑

×

,

∑

(1)

denotes trader type ’s price contribution of buy- or sell-initiated trades

(superscript d) for stock

on trading day . The transitory trade return,

, denotes

the natural logarithm of the ratio of the executed price of trade

to the previous

executed price expressed as a percentage. The investor indicator,

, is a dummy

variable that takes the value one if trade sell-initiated trade for stock

on trading day

belongs to investor type ’s buy- or and zero otherwise.

To classify each trade into a buyer- or seller-initiated trade, we follow Lee and Ready (1991) by relying upon the quote test and the tick test (if necessary). Specifically, the quote test classifies a buy-sell paired trade as buyer- (seller-)initiated if the trade price is more (less) than the midpoint of the best bid and ask quotes. In case that the trade price equals to the midpoint quote, the tick test is applied and a trade is classified as buy- (sell-)initiated if the trade price is higher (lower) than the

9

The Lee and Ready (1991) approach is a common method for determining which counterparty drives a transaction more powerfully. Several previous studies use this method to identify the trade-initiator in a buy-sell paired transaction in order-driven markets (e.g., Aktas et al., 2007; Huang and Chou, 2007; Hung, Chen, and Wu, 2015; Lu and Wei, 2009). In TWSE, although we can directly use orderand trade-level data to explicitly identify trade directions, for the reason of inferring trade-initiated direction, we use the Lee and Ready (1991) approach. 15

executed price of the previous trade. If the current and previous trade prices are the same, then the trade is classified on a basis of the next previous trade and so on.

Under this call auction market, buy and sell orders are collected together and then executed at specific times. Thus, at a certain point, numerous different limit orders may be executed altogether at the same price. If there are different types of investors on the buy- or sell-initiated side, then the same transitory trade return is applied to each type of investors.

We next adopt O’Hara, Yao, and Ye’s (2014) method to calculate each stock’s WPC. The return-based weight,

, for stock

on trading day

is defined as

follows:

=∑

|∑ |∑

|

.

(2)

|

Following more recent papers, this study uses return-based WPC rather than price-change-based formulation.10 There are at least three reasons for employing a return-based weighting scheme to measure WPC by summing the product of the return-based weight and basic price contribution measure across individual stocks. First, we weigh each stock’s price contribution to mitigate the problem of heteroscedasticity, which may be severe for firms with small cumulative changes (O’Hara, Yao, and Ye, 2014). Second, TWSE adopts a multiple tick-size regime whereby the tick size increases with stock prices in a stepwise fashion. The absolute price changes in small-tick stocks are distinctly lower than those in large-tick stocks.

10

See, for example, Barclay and Hendershott (2008) and Wang and Yang (2015). O’Hara, Yao, and Ye (2014) calculate return-based WPCs. O’Hara, Yao, and Ye (2014) compute both price-change-based and return-based WPCs and obtain consistent results. 16

Third, price changes usually have a unit root, while returns tend to be stationary. A stationary series offers many convenient properties for financial analysis. Return measures also make the results comparable across stocks (Barclay and Hendershott, 2008). Thus, the return-based WPC has advantages over price-change-based WPC, even though illiquid or small stocks may be assigned relatively unreasonable weights. Overall, the return-based WPC is more appropriate than price-change-based WPC in TWSE. Accordingly, the weighted price contribution of trades by investor type trading day ,

on

, is computed as the product of the weight and the price

contribution as follows: = ∑!"#(

).

(3)

Suppose there are T days in the sample period. The average WPC of trades, , for trade-initiated direction d by investor type

= ∑&"#

/%.

is then defined as:

(4)

4.3.2. Order Aggressiveness and Trade Size We now turn to the volume-weighted aggressiveness and average trade size of each type of investor for each stock on a daily basis, sorted by the order direction. In an order-driven market, buyers and sellers display their bids and asks separately. TWSE discloses the five best prices and volumes of unexecuted bid and ask quotes. These price quotes are therefore public information that stock traders will incorporate when making their order decisions. The best bid price is the highest quoted bid that an investor is willing to pay for a particular security at a given time, whereas the best ask price is the lowest offer price that a seller is willing to sell a security. The midpoint of the best bid and ask quotes serves as a suitable benchmark to measure the level of 17

order aggressiveness for each order at the most recent time before an order submission. For each buy order, we first calculate the order-level aggressiveness as the difference in the buy price minus the prevailing midpoint of the highest bid and lowest ask quotes, divided by the midpoint expressed as a percentage (Hung, 2016):

'(* ) =

+ - , ./ , / ,

× 100%,

(5)

where '(* ) denotes investor type ’s aggressiveness of buy order 3 for stock *

on day ;

)

indicates the buy order price; and 4

)

is the midpoint of the best

bid and ask prices at the prevailing time.

For each sell order, the degree of order aggressiveness is similarly computed as the difference between the midpoint and the sell price, divided by the midpoint and expressed as a percentage.

'(

5

)

=

/ , .+6 , / ,

× 100%,

(6)

where '(5 ) denotes investor type ’s aggressiveness of sell order 3 for stock on day ;

5

)

indicates the sell order price.11

After calculating each order’s aggressiveness, we compute the volume-weighted aggressiveness for each stock and each trader type on a daily basis, sorted by order direction as follows:

11

The level of order aggressiveness depends on the difference between the order price and the midpoint of the best ask and bid quotes. Bid-ask spread is the difference between the best bid and the best ask quotes. Thus, stocks with larger spreads do not necessarily have a higher level of order aggressiveness. 18

= ∑; )"# :

'78(99

:

)

< , < , ,

= ∑=

where '78(99

)

,

(7)

(8)

is the volume-weighted aggressiveness by investor type

on an order-level basis; : )

)

,

on day ; '(

trade direction d and stock

>

'(

)

)

for

represents buy or sell aggressiveness

is the trade volume-based weight of each order; and

is the number of shares traded in order 3.

We finally consider two measures for the trade size, the number of shares traded, and the total value of trades, respectively (Alexander and Peterson, 2007; Barclay and Warner, 1993; Chakravarty, 2001; Chan and Fong, 2000; O’Hara, Yao, and Ye, 2014). The number of shares traded, %8?7@AB C , is measured by the natural logarithm of the number of average shares executed (in lots) by each stock and each type of investor for each order direction on a daily basis. The value of trades, %8?7@>?DE@ , is defined as the natural logarithm of the total value of trades (in NT$ million) by each stock and each type of investor for each order direction on a daily basis. 4.3.3. Firm Characteristics and Market Factors The price contribution may be related to firm characteristics and market factors. We consider the following factors in this study:

firm size, book-to-market ratio,

turnover rate, market return, and market turnover rate. Firm size, F G@ , is defined as the natural logarithm of the market capitalization measured for stock

at the end of

trading day ; book-to-market ratio, H4 , is calculated as the ratio of the book value at the end of the prior year to the market capitalization of common stocks outstanding on the trading day; and turnover rate, %E8 BI@8 , is calculated as the trading volume

19

divided by the shares outstanding on the trading day and expressed as a percentage. Market return, 43

@ , is defined as the rate of change of the closing Taiwan Stock

Exchange Capitalization Weighted Stock Index (TAIEX) on trading day

and

expressed as a percentage. Market turnover rate, 43 %B , is defined as the turnover rate of all common shares listed on TWSE on trading day

and expressed as a

percentage. 4.4. Analytical Models To examine the relationships between stock price contribution and investor types, we estimate the regression models using the Newey-West adjustment for the heteroscedasticity and autocorrelation in error terms (Newey and West, 1987). The model specification is as follows:

= J + L# MB8@ 9 LS %8?7@F G@

+ LN ME 7 + LO ' ℎ@8Q C + MB8@ 9

ME 7 × (LV '7@8(99 (L#X '7@8(99 L#R %E8 BI@8 ,

× (LT '7@8(99

+ LR '7@8(99 + + LU %8?7@F G@ ) +

+ LW %8?7@F G@ ) + ' ℎ@8Q C

+ L## %8?7@F G@ ) + L#N F G@ , .#

+ L#S 43

.#

+ L#O H4 ,

MB8@ 9

.#

+

@ + L#T 43 %B +

M Z@7 Q 7EC 8[ \]]@^ C + M Z@7 % _@ \]]@^ C + ` .

Recall that

×

(9)

indicates price contribution. The foreign investor dummy,

, is an indicator that equals one when the investor is a foreign investor and

zero otherwise. Similarly, ME 7

(' ℎ@8Q C ) is the dummy variable that equals

one when the investor is a mutual fund (other institution) and zero otherwise. All other variables are as defined in Section 4.3. We also control for the fixed industry and time effects in our baseline regression model.

20

Traditional wisdom suggests that firm size is closely related to stock returns (Banz, 1981; Fama and French, 1992). Barclay and Hendershott (2008) also show that firm size affects the observed price contribution significantly. The book-to-market ratio is well known to be related to stock returns (Fama and French, 1992, 1993). Stocks with higher book-to-market ratios are believed to be riskier and therefore yield higher returns. In addition, many existing studies show that stock turnover is a good proxy for various effects on stock returns. For example, it is related to liquidity (Brennan, Chordia, and Subrahmanyam, 1998; Chordia, Subrahmanyam, and Anshuman, 2001), the arrival of private information (He and Wang, 1995), and overconfidence of investors (Gervais and Odean, 2001; Lee and Swaminathan, 2000). As a consequence, we add firm size, book-to-market ratio, and turnover rate into the extended regression model. Market factors, such as market return and turnover rate, are also included as explanatory variables. Note that the three firm characteristics are all lagged one day in the regression model.

We finally test whether the current price contribution and investors’ order aggressiveness and trade size affect future stock returns. We now incorporate all different types of traders in a single equation with the Newey-West procedure. The model is specified as follows:

@

( , ab)

= J + L# M(99 + LN 4(99 + LO '(99 + LR MF G@ + LS 4F G@ + LT 'F G@ + LU M 4(99 + LW ' L## 4 L#R H4 ,

× '(99 + L#X M

× 4F G@ + L#N ' .#

× M(99 + LV 4

+ L#S %E8 BI@8 ,

×

× MF G@ +

× 'F G@ + L#O F G@ , .#

+ L#T 43

@

( , ab)

+

+

M Z@7 Q 7EC 8[ \]]@^ C + M Z@7 % _@ \]]@^ C + ` (

21

.#

, ab) .(10)

Note that

@

( , ab)

indicates stock i’s return over the period from day

to

+ c,

computed as the natural logarithm of the ratio of the ending value of the holding period to the beginning value of the holding period and expressed as a percentage. The ending value is the closing price adjusted by ex-right, ex-dividend, and capital reduction. Holding periods include the current trading day (c = 0), one week, two weeks, one month, three months, six months, and one year. The current trading day, c = 0, is referred to as the trading event day. M(99

denotes foreign investors’

volume-weighted order aggressiveness for trade direction 7 and stock day . 4(99

and '(99

on trading

are volume-weighted order aggressiveness for mutual

funds and other institutions, respectively. MF G@

stands for foreign investors’

average trade size, measured by the natural logarithm of the total trade value divided by the number of orders; 4F G@

and 'F G@

are mutual funds’ and other

institutions’ average trade sizes, respectively. Market return, 43

@

( , ab) ,

as the rate of change of the closing TAIEX over the period from day

to

is defined + c and

expressed as a percentage. 5.

Empirical Results Table 1 presents the descriptive statistics of daily returns, turnover, and firm

characteristics over our sample period from July 2009 to May 2015. Number of firms (days) is the number of non-overlapping firms (trading days) over our sample period; number of firm-day obs. denotes the number of all firm-day observations. In total there are 845 firms over 1,468 trading days, for a total of 1,126,024 firm-day observations. Table 1 shows that the averages of daily market returns and daily turnover rates are 0.03% and 0.42% per day, respectively. The annualized turnover rate is about 105.84%, showing that TWSE is an active market. A negative average market return and larger standard deviation appear in 2011. 22

For individual stocks, the results present that the mean and standard deviation of firm-day stock returns are respectively 0.03% and 2.14%, while the market standard deviation is only 0.98%. The mean turnover is about 0.63% per day; the highest turnover rate occurs in 2009 and the lowest is in 2015. The average market capitalization is NT$28.61 billion per firm with a book-to-market ratio of 0.61. The standard deviation of market capitalization is up to NT$118 billion, since our sample covers most stocks listed on TWSE, including many small and large firms and therefore spanning a wide range.

Table 2 reports the descriptive statistics for the limit orders. Panels A and B present buy and sell orders, respectively. The results show that the average number of buy (sell) orders is about 550 (521) - that is, on average, stock traders submit more buy orders than sell orders for each stock on the daily basis. The buy and sell order aggressiveness levels are 0.70% and 1.07%, respectively. Sell aggressiveness is higher than buy aggressiveness. Order aggressiveness is positive for both order directions.12 In other words, stock traders tend to submit more aggressive orders, relative to the prevailing midpoint quotation, upon observing the displayed price information, suggesting that quote data are valued and greatly influences stock traders’ order decisions. Moreover, sellers behave more aggressively than buyers.

The average number of buy (sell) shares traded is about 3,001 (3,055) lots on a daily basis, and the average value of trades is around NT$105 (107) million. The differences in the number of orders and the number of shares traded indicate

12

By definition, order aggressiveness is not necessarily positive. The larger the value is, the more aggressive the investors are, irrespective of order direction. Some passive investors may display negative measures of order aggressiveness as they place buy (sell) orders with prices less (more) than the prevailing midpoint of the bid and ask quotes. 23

asymmetric order submission behavior between buyers and sellers. Our findings are consistent with Chou and Wang (2009), Griffiths et al. (2000), Hung (2016), and Hung, Chen, and Wu (2015). In brief, stock traders place more buy orders with a smaller value traded, while they place fewer sell orders with a larger value traded. Moreover, stock traders behave more aggressively when selling than when buying. 5.1. Weighted Price Contribution across Investor Types Table 3 provides descriptive statistics of WPC for various types of investors. Panels A and B show buy and sell orders, respectively. The results in Panel A indicate individuals account for the largest proportion (about 81%) of WPC, echoing the role of dominant participants, while institutions comprise less than 20% of WPC. This disproportional WPC is associated with the call auction trading mechanism in TWSE. A call auction system is different from the continuous trading mechanism (which is adopted in U.S. markets). Continuous trading involves the immediate execution of orders upon their reception. Call auction collects orders and executes them all at once within a predetermined interval. Thus, at the same transaction time, there are different types of investors simultaneously contributing to the transitory trade return. Since individuals are the dominant participants, they account for the largest proportion of WPC. Similar to TWSE, individuals account for about 70% of the total transaction amount in the Korea Stock Exchange (Bae, Min, and Jung, 2011). Kang and Ryu (2010) analyze price contribution of trades in the KOSPI futures market and find that domestic individuals make up of 81.37% of WPC. Our results are in agreement with Kang and Ryu (2010) and may be generalized to all emerging financial markets. In a retail investor-dominated emerging market, such as Taiwan, China, and South Korea, the roles of foreign and domestic institutional investors in influencing WPC are very different with those in well-developed Western markets in which most existing 24

literature has targeted (e.g., NYSE and NASDAQ). Our findings provide a better understanding of WPC in emerging markets.

Among institutional investors, foreign investors account for 10.94%, followed by other institutions at 6.99%. Mutual funds only account for 1.56%. In TWSE, trading by individuals make up about 67% of order or trade value; foreign investors contribute about 18%; whereas mutual funds account only for less than 2%. WPC per order (per lot) is measured by the total WPC divided by the number of orders (total shares in lots) across investor types, measured in basis points. Although mutual funds comprise less than 2% of total WPC, their WPC per lot (0.0065bp) and per order (0.0614bp) are the largest among various investor types. By contrast, foreign investors have the lowest WPC per lot (0.0031bp) and per order (0.0161bp), which are even lower than individuals have (0.0043bp per lot and 0.0219bp per order, respectively). The results are in accordance with foreign investors’ splitting order behavior (Chakravarty, 2001; Chan and Lakonishok, 1995).

Domestic other institutions have the largest average trade value per order, about NT$480,430, followed by mutual funds (about NT$468,160) and foreign investors (NT$232,140). One possible reason is that non-professional domestic institutions (i.e., corporations doing business unrelated to securities) seldom engage in strategic transactions, such as order splitting or high frequency trading. Although individuals are dominant participants in TWSE, their average trade value per order is the lowest at only NT$190,210. Note that foreign investors’ average trade value is less than that of domestic institutions, which is expected as foreign investors tend to split their large orders into smaller ones to camouflage their private information and minimize possible price impacts (Chakravarty, 2001; Chan and Lakonishok, 1995). Foreign

25

investors include qualified foreign institutional investor (QFIIs) and generalized foreign individual investors (GFIIs), in which the former accounts for the largest proportion of this investor category. Specifically, QFIIs consist of foreign banks, insurance companies, fund management institutions, securities firms, and other investment institutions (Chen, Weng, and Chien, 2018). Most of these juristic investors pursue long-run performance goals and prefer program trading based on specific trading rules, such as momentum behavior (Badrinath and Wahal, 2002; Liao, Chou, and Chiu, 2013) or bilateral trading strategies between spot and futures markets (Clark, Qiao, and Wong, 2016). For example, Liao, Chou, and Chiu (2013) find that foreign investors are inclined to conduct momentum behavior that is anchored by their prior foreign ownership.

Although mutual funds comprise a small portion of trades in TWSE, they display the greatest order aggressiveness among all investor types. On average, mutual funds’ buy order aggressiveness is 2.39%, followed by foreign investors at 1.28%. Mutual funds tend to conduct the most aggressive order submission behavior, regardless of buy or sell side. Trading strategies by mutual fund managers may be related to their shortening of the trading process or risk reduction (Giambona and Golec, 2010), or personal career concerns or portfolio disclosure considerations (Agarwal, Gay, and Ling, 2014; He, Ng, and Wang, 2004; Ling and Arias, 2013; Morey and O’Neal, 2006; Ng and Wang, 2004). Mutual funds seem to have short- or medium-term performance goals as compared to foreign investors, since they are obliged to disclose their portfolio holdings at the end of each quarter, and thus they trade more aggressively than other investors. Clearly, different types of investors exhibit their own trading behavior.

26

Panel B (sell order) of Table 3 presents similar patterns to Panel A (buy order). Overall, different types of investors have different order submission styles and thus dissimilar WPCs. Individuals account for the largest proportion of order and trade value (more than 67%) and provide more than 80% of WPC, but on average they have the smallest average trade value per order and less aggressive order submissions. Among professional institutions, foreign investors consist of 17% of trade value and around 9% of WPC; however, their order aggressiveness and average trade value are smaller than those of mutual funds. Mutual funds make up the smallest proportion of order and trade value (less than 2%) and the lowest percentage of WPC (about 2%), but they have the most aggressive order submissions and larger average trade value per order than foreign investors. 5.2. Investor Types, Order Submission, and Price Contribution Foreign investors in TWSE mainly consist of foreign mutual funds and financial institutions, such as Capital Fund Management (CFM), Fidelity Investments Inc., and Jardine Fleming Asset Management, which possessed large capital funds. Their investment strategies highly value corporate fundamentals and thus focus on mediumand long-term performances. Domestic mutual funds care about both corporate fundamentals and the impacts of immediate news events. They need to disclose their portfolio holdings at the end of each quarter and therefore typically use more aggressive orders to improve medium-term performance. Other institutions include a small number of dealers and most non-financial corporations. They are not professional institutions and lack clear investment strategies. Finally, individuals are generally regarded as less sophisticated investors.

To examine whether different investors prefer dissimilar order submission 27

strategies, we extract one thousandth of all submitted orders over the sample period by the random sampling without replacement. Table 4 documents the frequency distribution of order aggressiveness, size, and value across investor types. Panel A reports the frequency distribution of order aggressiveness, Panel B the frequency distribution of order lot size, and Panel C the frequency distribution of trade value. The results indicate that domestic institutions prefer to submit more aggressive orders than foreign investors, while individuals behave the least aggressively. Domestic institutions are inclined to place larger orders and generate greater trade value. Foreign investors’ one-lot orders account for almost 53% of their total orders, which is greater than that of individuals (43.24%). Regarding trade value, the frequency of smallest orders for foreign investors (23.68%) is larger than that for individuals (20.08%). Clearly, different investors tend to choose different order aggressiveness and trade size.

We

now

analyze

the

relationships

between

investor

types,

order

aggressiveness/trade size, and stock price contribution with our benchmark regression model. To control specific firm characteristics, we follow O’Hara, Yao, and Ye (2014) and use price contribution as the dependent variable in Equation (9). Table 5 presents the regression results for the price contribution controlling for firm characteristics and market factors. Panels A and B present the cases for buy and sell orders, respectively. Panel A shows that all coefficients on investor dummies are significantly negative, i.e., all institutional buy orders have a smaller effect on price contribution than individuals.

The null hypothesis of the difference test (ME 7 − MB8@ 9 ) states that mutual fund and foreign investors provide equal price contribution. Similar interpretations apply to the difference tests (ME 7 − ' ℎ@8Q C ) and (' ℎ@8Q C −

28

MB8@ 9 ). We find that all the differences are significantly negative, suggesting that mutual funds provide smaller price contribution than other institutions, which in turn provide smaller price contribution than foreign investors.

In Models 1-5 of Panel A, order aggressiveness and trade size (both number of shares and trade value) are positively related to price contribution. In other words, on average, more aggressive buy orders have greater price contribution, and larger trade sizes also result in greater price contribution. Our results are in accordance with Griffiths et al. (2000), showing that aggressive orders have stronger price impacts.

To allow the order aggressiveness of different investors to have different impacts, we add the interaction terms between investor type dummies and order aggressiveness into Model 6. It is found that the coefficient on '87@8(99 now becomes significantly negative. More importantly, the coefficient on each interaction term is significantly positive. As a result, the effect of order aggressiveness on price contribution varies across different types of investors. For example, the coefficients for '87@8(99 and ME 7 × '87@8(99 are -0.58 and 3.60, respectively, and hence a 1% increase in mutual funds’ buy order aggressiveness increases the stock price contribution by 3.02% (3.60 – 0.58 = 3.02). Similar calculations show that a 1% increase in the order aggressiveness of other institutions increases the stock price contribution by 3.55%, whereas a 1% increase in the order aggressiveness of foreign investors increases the stock price contribution by 0.39%.

Model 7 incorporates the interaction terms between investor type dummies and the trade value into the regression model. We find that the coefficient on %8?7@>?DE@ is significantly positive and the coefficient on the interaction term of foreign investors (mutual funds) is significantly positive (negative). The results 29

indicate that the effect of trade value on price contribution varies across different types of investors. For example, the coefficients for %8?7@>?DE@ and MB8@ 9 × %8?7@>?DE@ are 6.11 and 3.17, respectively, indicating that a 1% increase in foreign investors’ trade value leads to a 9.28% (6.11 + 3.17 = 9.28) increase in stock price contribution. Similarly, as mutual funds’ trade value increases by 1%, stock price contribution increases by 4.53% (6.11 – 1.58 = 4.53). The evidence conforms to Chan and Fong (2000) and Easley and O’Hara (1987), showing that large size trades have stronger price impacts than smaller size trades.

Model 8 encompasses Models 6 and 7 by including both types of interaction variables into the regression equation. The estimation results reveal consistent findings with those in Models 6 and 7. Both order aggressiveness and trade size influence the effects of investor types on price contribution. Moreover, trade size has a much stronger impact on price contribution than order aggressiveness. In Panel B of Table 5, we present regression results for the price contribution of sell orders and demonstrate similar patterns to those in Panel A. It is found that the effects of trade size on price contribution for sell orders are stronger than those for buy orders.

In sum, regardless of the order direction, institutional investors exhibit a smaller price contribution than individuals in TWSE. The orders of mutual funds display greater sensitivity of price contribution to order aggressiveness than those of foreign investors. On the other hand, the orders of foreign investors show greater effects of trade value on price contribution than those of mutual funds. Relatively speaking, trade size has a much stronger impact on price contribution than order aggressiveness, regardless of investor types. Although foreign investors have much smaller trade value per order than domestic institutions, their trade size causes the greatest impact

30

on price contribution in TWSE.

Since TWSE has imposed daily price limits on all listed stocks, to rule out interferences of the upper and lower limits on the price contribution and investor order aggressiveness, we exclude firm-day observations with price limit hits and re-run Equation (9). The main results do not change. To save space, the results are not reported, but are available upon request.

To be more persuasive, we re-define an alternative order aggressiveness measure. Our original order aggressiveness uses a benchmark of the changeable midpoint of bid and ask quotes according to the market quotation conditions. We now adopt an alternative benchmark with a fixed weighted average traded price for a given stock at each trading day. We define order-level buy aggressiveness relative to the daily weighted average traded price by the difference of the buy price minus the daily weighted average traded price of all investors’ orders, divided by the weighted average traded price expressed as a percentage. By contrast, order-level sell aggressiveness is computed as the difference of the weighted average traded price minus the sell price, divided by the weighted average traded price as a percentage. The higher the measure is, the more aggressive the order will be, regardless of order direction or measures used. The main difference between the two aggressiveness measures is the benchmark. The original aggressiveness measure uses a variable benchmark according to the market quotation conditions, while the alternative measure adopts a fixed benchmark of the weighted average traded price for a given stock at the same trading day. Accordingly, the higher (lower) the buy (sell) price is, the higher the alternative aggressiveness measure will be. However, our original aggressiveness measure may not necessarily turn out that way. We re-run Equation (9)

31

by replacing the original aggressiveness measure with the alternative one. The main results do not change. To save the space, the results are not reported, but are available upon request.

Table 6 further presents regression results for stock traders’ weighted price contribution with Newey-West heteroscedasticity and autocorrelation consistent estimators (Newey and West, 1987). Panels A and B present buy and sell orders, respectively. The dependent variable is the weighted price contribution; the independent variables include trader dummy variables, order aggressiveness measures, trade size, and market factors. We also control fixed time effects. The empirical results still agree with our previous findings. Institutional investors relatively have a smaller weighted price contribution than individuals in TWSE, regardless of the order direction. Both order aggressiveness and trade size positively correlate to weighted price contribution. Trade size has a stronger impact on weighted price contribution than does order aggressiveness. 5.3. Price Contribution, Order Submission, and Future Stock Returns This section tests whether order aggressiveness, order size, and the current price contribution are related to future stock returns. To eliminate the effects of possible return outliers, we winsorize the holding period returns at the top and bottom 1%.13 Table 7 presents regression results for the stock returns of buy orders across various investor types and different holding periods with Panels A to D corresponding to foreign investors, mutual funds, other institutions, and individuals, respectively.

13

Winsorization is often adopted to address concerns for outliers in the finance literature; for example, see Musumeci and Peterson (2011) and Kirch and Terra (2012). 32

Panel A of Table 7 shows that the order size of foreign investors is positively related to future stock returns, while the order aggressiveness of foreign investors is negatively related to future stock returns. All the coefficients for the interaction term × '87@8F G@ are significantly positive. The coefficients for the interaction terms × '87@8(99 are significantly positive when the holding period is not greater than one month. Thus, foreign investors’ larger size orders perform better than smaller size orders, and the differences enlarge as the length of the holding period increases. The dominance of larger orders over smaller orders also becomes more obvious when the length of holding period increases. Overall, foreign investors’ large buy orders seem to be related to informed trading, particular for those orders with larger price contribution.

The

negative

relationship

between

foreign

investors’

order

aggressiveness and future stock returns is likely due to the fact that larger order size is generally accompanied by low aggressiveness (Hung, 2016). Price contribution does mitigate the negative effect of order aggressiveness on the stock return to some extent, particularly when the holding period is not greater than one month.

Panels B and C of Table 7 document that the order sizes of domestic institutions produce similar effects on stock returns to that of foreign investors. Specifically, the coefficients on '87@8F G@ and

× '87@8F G@ for mutual funds and other

institutions are almost significantly positive, either remaining constant or increasing with the length of holding period. The results reveal that domestic institutions’ large buy orders are informed trading. '87@8(99 is significantly negative for mutual funds across all holding periods; however, for other institutions, it is significantly positive when the holding period is not more than two weeks and significantly negative positive when the holding period is equal to or greater than three months. On the other hand, the coefficients on

× '87@8(99 are almost significantly negative 33

for mutual funds and other institutions. Overall, regardless of the price contribution level, the order aggressiveness of domestic institutions produces negative returns.

Panel D of Table 7 shows a very different pattern from Panels A to C. The coefficients on interaction terms,

× '87@8(99 and

× '87@8F G@ , for

individuals are all significantly negative (except for the one-day case). Moreover, the negative influence tends to accelerate with the length of the holding period. This pattern differs from institutional trading behavior. In contrary to institutions, individuals’ aggressive or larger size buy orders with larger price contribution suffer from lower future returns, after controlling other factors.

Table 8 presents regression results for stock returns of sell orders across various investor types and different holding periods. Panel A shows that, for foreign investors, the coefficients on

× '87@8F G@ are significantly negative for one-day,

one-week, or one-month returns - that is, the large sell orders by foreign investors constitute informed trading, but the expected profits only last for one month after the trade. The results confirm an asymmetric effect between buy and sell orders (Chou and Wang, 2009; Griffiths et al., 2000; Hung, 2016; Hung, Chen, and Wu, 2015; Keim and Madhavan, 1996). Foreign investors’ large buying has a positive effect on stock returns lasting at least for one year (see Panel A of Table 7), while their large selling has a negative influence on stock returns just for not more than one month. Moreover, in the presence of larger price contribution, large sell trades lead to worse stock returns, indicating foreign investors have made correct decisions for selling stocks early and reducing losses. In short, foreign investors’ large sell orders perform better, but only for a short-term period.

Panel B of Table 8 shows the coefficient on 34

× '87@8F G@ for mutual funds

is negatively related to the current day and future three-month performance. The coefficient on '87@8F G@ is significantly positive up to two weeks at much smaller magnitudes. Similar results apply to other institutions in Panel C except that the coefficient on '87@8F G@ is much larger. Panel D displays a different pattern from Panels A-C. The coefficients on '87@8(99 and

× '87@8(99 are all

significantly negative. Thus, individuals’ more aggressive sell orders perform well.

For further examination, we focus only on price contribution, order aggressiveness, and order size of different types of institutional investors and include them in a single model. In Table 9, we display the regression results for buy orders in Panel A and for sell orders in Panel B. Panel A shows that order size tends to be significantly positively associated with stock returns, particularly for foreign investors. All coefficients on M(99 and 4(99 are significantly negative, whereas all the coefficients on MF G@, 4F G@, and 'F G@ are significantly positive up to one month after the trade. Large orders involve private information, but they generally coincide with lower aggressiveness (Hung, 2016; Lo and Sapp, 2010). Thus, all estimates for order aggressiveness are negative. All the interaction terms of price contribution and order size are significantly positive over a one-year period. This finding confirms that institutional buy order size is a good predictor of future stock returns.

Panel B of Table 9 demonstrates that all coefficients for the interaction term between sell price contribution and order size are significantly negative only in a short period. The main findings remain in line with our previous results. There is an asymmetric effect between buy and sell orders. The impacts of price contribution and order size on stock returns are more persistent for buy orders than for sell orders.

In summary, the trading behavior of individuals is very different from that of 35

institutions. There exist asymmetric effects between buy and sell orders. For buy orders, institutional large buy orders contain trading information and are positively related to future stock returns. In contrast, individual buy order size is negatively related to future returns. On the sell side, larger orders by foreign investors and mutual funds are negatively related to future short-term stock returns, indicating that stock prices fall after these trades, but bounce back later. On average, stocks with institutional large selling recently experience falling future stock returns. Thus, professional institutions (foreign investors and mutual funds) seem to conduct more informed trades than individuals. Our results are consistent with Barber et al. (2009), Hung (2016), Lee et al. (2004), and Wang and Chiao (2008). For example, we agree with Lee et al. (2004) that large institutional investors conduct the most informed trades, thus supporting extended evidence with price contribution effects. 5.4. Robustness Checks We perform some robustness checks to address issues related to the sample period, tick size, and idiosyncratic risk. We re-estimate the regression model (10) under different data designs. To save space, we do not report the details, but the empirical results are available upon request. Essentially, we split the entire sample period into two subsample spans to test for constancy of the coefficients. The first subsample period is from July 2009 to May 2012, and the second is from June 2012 to May 2015. The main results are the same, albeit the adjusted

N

for the second

subsample period seems to be lower compared to the others. We also separate the full sample period into four subsample periods based on the matching intervals of 5, 10, 15, and 20 seconds. The main results still hold.

The tick size defines the minimum incremental price change. Based on the wide 36

numerical range of stock prices, TWSE adopts a multiple-tick-size rule.14 A larger tick size may motivate stock traders to place more aggressive orders in pursuit of higher profit margins (O’Hara, Saar, and Zhong, 2019). We classify stocks with prices lower than NT$100 as low-tick sizes and those with prices higher than or equal NT$100 as high-tick sizes. The main results are still in accordance with our previous findings. Moreover, the order size effect seems to be stronger for high-tick stocks, suggesting institutional investors using large orders to buy high-price stocks have better performance.

We proceed to consider the effect of idiosyncratic risk. We define idiosyncratic volatility as the standard deviation of the residuals from the estimated market model based upon the rolling-window analysis over the prior quarter. Stocks with idiosyncratic volatility less than the synchronous median are regarded as low idiosyncratic risk stocks, whereas those with idiosyncratic volatility more than the synchronous median are classified as high idiosyncratic risk stocks. The results show that the order size effects of buy orders are especially stronger for high idiosyncratic risk stocks; however, those of sell orders disappear. It seems that institutional investors using large orders to buy stocks with idiosyncratic risk perform better. 6.

Conclusions Our empirical results document several new findings not available in the current

literature. Different types of investors have various order submission behaviors and thus provide distinct levels of WPC. Individuals account for the largest proportion of

14

See the multiple tick sizes from the website of the Taiwan Stock Exchange Corporation: http://www.twse.com.tw/en/products/trading_rules/mechanism01.php#6. 37

order and trade value (more than 67%) and about 80% of WPC, but their average trade value per order is the smallest and their order aggressiveness is the lowest. Institutions comprise less than 20% of WPC. However, the average trade value per order and order aggressiveness of institutions are much higher than those of individuals. Among professional institutions, foreign investors make up about 17% of the trade value and around 10% of WPC. Their trade value per order and order aggressiveness are smaller than domestic institutions’. Mutual funds make up the smallest proportion of order and trade value (less than 2%) and the lowest percentage of WPC (about 2%), but they have the most aggressive order submission behavior and a larger trade value per order than foreign investors.

Regarding the sensitivities of the price contribution to investor types and order submission behavior, we find that institutional investors provide smaller price contributions than individuals in TWSE, regardless of the order direction. Among institutions, foreign investors have larger price contribution than domestic institutions (mutual funds and other institutions), after controlling for other factors. Although foreign investors have much smaller trade value per order than domestic institutions, their trade size produces the greatest impact on price contribution in TWSE. By comparison, trade size has a much stronger impact on price contribution than order aggressiveness, regardless of investor types.

Finally, we test whether investors’ order aggressiveness and size, along with current price contribution, affect future stock returns. For buy orders, institutional large orders, particularly those with large price contribution, perform better than smaller orders. Individual buy order size is negatively related to future returns, even though they have a larger current price contribution. Regarding sell orders, stocks of

38

institutional large sell orders experience falling future returns. Overall, compared to individuals, institutional investors’ trading is more likely to be information-based. Their large order size is more closely related to future stock performance than order aggressiveness effects. Our empirical results at least partially reconfirm that institutional investors are more informed, which runs in accordance with existing literature using Taiwanese data (e.g., Barber et al., 2009; Gao and Lin, 2015; Lee et al., 2004) and well-developed market data (e.g., Edelen, Ince, and Kadlec, 2016; Hendershott and Schürhoff, 2015; Kaniel, Saar, and Titman, 2008).

39

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Table 1 Descriptive Statistics of Returns, Turnover, and Firm Characteristics This table shows the descriptive statistics of daily returns, turnover, and firm characteristics over our sample period from July 2009 to May 2015. Number of firms (days) is the number of non-overlapping firms (trading days) over our sample period; number of firm-day obs. denotes the number of all firm-day observations over our sample period. Mean and Std. dev. indicate the sample mean and standard deviation of the total firm-day observations, respectively. Q1 and Q3 indicate the number that divides the data into the first and third quartiles, respectively. Market return is defined as the average rate of change of the closing TAIEX for each trading day and expressed as a percentage; market turnover is calculated by the average of the daily turnover rates of all common shares listed on the TWSE and expressed as a percentage. For individual stocks, return is calculated as the average natural logarithm of the ratio of the closing price on the trading day to the previous closing price and expressed as a percentage; turnover rate is calculated as the average number of shares traded, divided by the number of shares outstanding for each stock and each trading day and expressed as a percentage; market capitalization is computed as the average closing price multiplied by shares outstanding of individual stocks for each trading day (in NT$ billion); and the book-to-market ratio is calculated as the average ratio of the book value at the end of the prior year to the market capitalization of individual stocks on the current trading day.

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Variables All 2009 Number of firms 845 741 Number of days 1,468 131 Number of firm-day obs. 1,126,024 94,695 Market return (%) Mean 0.03 0.18 Std. dev. 0.98 1.07 Q1 -0.45 -0.32 Q3 0.58 0.92 Market turnover (%) Mean 0.42 0.68 Std. dev. 0.15 0.13 Q1 0.31 0.56 Q3 0.50 0.78 Return (%) Mean 0.03 0.30 Std. dev. 2.14 2.53 Q1 -0.94 -1.03 1.35 Q3 0.87 Turnover (%) Mean 0.63 1.09 1.56 Std. dev. 1.10 Q1 0.11 0.23 1.30 Q3 0.68 Market capitalization (NT$ billion) Mean 28.61 25.94 Std. dev. 118.00 94.05 2.40 Q1 2.63 Q3 15.08 15.11 Book-to-market ratio Mean 0.61 0.73 Std. dev. 0.71 1.10 0.27 Q1 0.25 Q3 0.76 0.83

2010 763 251 184,819

2011 778 247 186,085

2012 786 250 192,723

2013 806 246 192,281

2014 816 248 198,467

2015 815 95 76,954

0.04 1.04 -0.49 0.69

-0.10 1.37 -0.76 0.61

0.03 0.98 -0.48 0.63

0.05 0.73 -0.39 0.51

0.03 0.69 -0.30 0.45

0.04 0.70 -0.33 0.53

0.56 0.14 0.45 0.64

0.44 0.13 0.36 0.50

0.34 0.12 0.26 0.39

0.34 0.06 0.30 0.37

0.34 0.07 0.29 0.39

0.33 0.06 0.29 0.35

0.03 2.30 -1.05 1.01

-0.14 2.42 -1.25 0.95

0.05 2.09 -0.93 0.91

0.08 1.86 -0.77 0.75

0.02 1.90 -0.83 0.72

-0.02 1.72 -0.78 0.61

0.89 1.37 0.19 0.99

0.58 0.95 0.12 0.64

0.46 0.82 0.08 0.48

0.51 0.92 0.09 0.52

0.57 1.07 0.09 0.58

0.45 0.83 0.08 0.47

27.97 98.22 2.97 16.36

28.45 106.01 2.60 15.33

26.05 107.51 2.31 13.69

28.36 121.73 2.53 14.43

31.44 140.81 2.84 15.41

33.46 159.14 2.79 15.71

0.56 0.68 0.23 0.67

0.59 0.59 0.25 0.73

0.72 0.83 0.28 0.89

0.63 0.64 0.26 0.81

0.54 0.53 0.23 0.71

0.54 0.54 0.23 0.70

45

Table 2 Descriptive Statistics of Orders This table presents the descriptive statistics of orders. Panels A and B present buy and sell orders, respectively. The number of orders indicates the average number of buy or sell orders submitted by all investors for each stock on a daily basis. Order aggressiveness denotes the average volume-weighted aggressiveness of all investors for each stock on a daily basis and expressed as a percentage. In more detail, for each buy order, we first calculate the level of order aggressiveness as the difference of the buy price minus the prevailing midpoint of the highest bid and lowest ask quotes, divided by the midpoint quote and expressed as a percentage. Conversely, for each sell order, the degree of order aggressiveness is computed as the difference of the midpoint price minus the sell price, divided by the midpoint quote and expressed as a percentage. The formulae state that the higher the value is, the more aggressive the order will be, irrespective of order direction. We then compute the average volume-weighted aggressiveness for all investors on a daily basis, sorted by order direction for each stock. The number of shares traded is measured by the average number of shares (in lots) executed by all investors for each stock on a daily basis. The value of trades is computed as the average value of the sum of execution prices multiplied by shares executed by all investors for each stock on a daily basis (in NT$ million). Number of Obs.

Mean

Std. Dev.

Q1

Number of orders

1,108,316

549.75

1,054.46

62.00

194.00

592.00

Order aggressiveness (%)

1,105,351

0.70

0.78

0.24

0.62

1.06

Number of shares traded (lots)

1,108,316

3,000.73

7,906.22

214.00

734.00

2,516.00

Value of trades (NT$ million)

1,108,316

104.57

325.31

4.09

17.80

76.12

Number of orders

1,108,316

521.39

997.52

59.00

181.00

553.00

Order aggressiveness (%)

1,105,385

1.07

0.82

0.60

1.02

1.48

Number of shares traded (lots)

1,108,316

3,054.89

8,070.43

218.00

747.00

2,558.00

Value of trades (NT$ million)

1,108,316

106.68

331.01

4.16

18.10

77.50

Variables

Median

Q3

Panel A: Buy Orders

Panel B: Sell Orders

46

Table 3 Weighted Price Contribution and Trading Summary across Investor Types This table shows the weighted price contribution (WPC) and trading summary across investor types. Panels A and B present buy and sell orders, respectively. We incorporate the approach of Lee and Ready (1991) into Barclay and Warner (1993) and O’Hara, Yao, and Ye (2014) to calculate WPC across investor types. Here, % of WPC indicates the percentage of the total weighted price contribution across investor types. WPC per order is measured by the total weighted price contribution divided by the number of orders across investor types, measured by basis points. The number of total orders denotes the number of total buy or sell orders for all stocks by each type of investor over our sample period, measured in millions of times; % of orders is the percentage of the number of orders across investor types. Total value of orders is the sum of the product of the order price and the number of shares submitted, measured by NT$ trillion; % of value of orders is the percentage of the order value across investor types; and mean value of orders is computed as the total order value divided by the number of orders, measured by NT$ thousand. Similarly, total value of trades is the average value of the sum of the product of the trade price and the number of shares traded, measured by NT$ trillion; % of value of trades is the percentage of the trade value executed by each type of investor; and mean value of trades is calculated as the total trade value divided by the number of orders, measured by NT$ thousand. WPC

Orders

% of WPC

WPC per Lot (bp)

WPC per Order (bp)

10.94

0.0031

0.0161

Mutual funds

1.56

0.0065

Other institutions

6.99

0.0027

Investor Types

Number of Total Orders (million times)

Trades Mean Order Mean Value Aggressive-ne (NT$ thousand) ss (%)

Total Value

% of

Mean Value

(NT$ trillion)

Value

(NT$ thousand)

20.55

17.73

232.14

2.39

1.55

1.34

468.16

0.73

14.63

12.62

480.43

% of Orders

Total Value (NT$ trillion)

% of Value

88.52

14.53

20.91

17.71

236.23

1.28

0.0614

3.32

0.54

1.57

1.33

473.39

0.0300

30.44

5.00

14.89

12.61

489.26

Panel A: Buy Orders Foreign investors

Individuals All investors

80.51

0.0043

0.0219

480.75

78.90

79.22

67.08

164.79

0.64

77.69

67.04

161.60

100.00

0.0043

0.0236

609.30

100.00

118.10

100.00

193.83

0.70

115.89

100.00

190.21

Panel B: Sell Orders Foreign investors

9.29

0.0026

0.0118

99.89

15.78

21.78

17.24

218.09

1.30

21.98

17.24

220.08

Mutual funds

1.89

0.0079

0.0701

3.43

0.54

1.63

1.29

475.33

2.60

1.64

1.29

479.20

Other institutions

6.01

0.0023

0.0235

32.59

5.15

15.28

12.10

468.85

0.88

15.30

12.00

469.29

Individuals All investors

82.81

0.0042

0.0215

489.70

77.36

85.89

68.00

175.39

1.07

86.84

68.12

177.33

100.00

0.0042

0.0227

633.02

100.00

126.31

100.00

199.53

1.06

127.49

100.00

201.40

47

Table 4 Frequency Distribution of Order Aggressiveness and Size Categories across Investor Types This table presents the frequency distribution of order aggressiveness and size categories across investor types. To examine whether different investors prefer dissimilar order submission decisions, we first extract one thousandth of all submitted orders over the sample period by the random sampling without replacement. Panel A shows the frequency distribution of order aggressiveness categories across investor types. For each buy order, we first calculate the level of order aggressiveness as the difference of the buy price minus the prevailing midpoint of the highest bid and lowest ask quotes, divided by the midpoint quote and expressed as a percentage. Conversely, for each sell order, the degree of order aggressiveness is computed as the difference of the midpoint price minus the sell price, divided by the midpoint quote and expressed as a percentage. The formula states that the higher the value is, the more aggressive the order will be, irrespective of order direction. We then classify all sampling order samples based on order aggressiveness quintiles and investor types and count the number of orders in each crossed classification. Panel B shows the frequency distribution of order lot size groups across investor types. The order lot size indicates the number of lots of each order. Finally, Panel C reports the frequency distribution of trade value categories across investor types. The trade value denotes the value of the components of trades executed for each order (in NT$ thousand). We categorize all sampling order samples based on trade value quintiles and investor types and count the number of orders in each crossed classification. Numbers in parentheses indicate the percentage of order aggressiveness or size categories for each type of investor.

48

All Foreign Mutual Other Individuals Investors Investors Funds Institutions Panel A: Frequency Distribution of Order Aggressiveness Categories across Investor Types Aggressive 220,674 21,990 3,618 17,297 177,769 (20.00) (12.66) (27.15) (28.82) (20.76) Quintile 2 220,677 48,006 3,722 16,009 152,940 (20.00) (27.63) (27.93) (26.67) (17.86) Quintile 3 220,737 30,778 2,321 11,187 176,451 (20.00) (17.72) (17.42) (18.64) (20.60) Quintile 4 220,807 51,993 2,508 9,286 157,020 (20.01) (29.93) (18.82) (15.47) (18.33) Passive 220,668 20,950 1,157 6,246 192,315 (20.00) (12.06) (8.68) (10.41) (22.45) Total 1,103,563 173,717 13,326 60,025 856,495 (100.00) (100.00) (100.00) (100.00) (100.00) Panel B: Frequency Distribution of Order Lot Size across Investor Types 1 lot 485,298 92,056 4,156 18,761 370,325 (43.98) (52.99) (31.19) (31.26) (43.24) 2-4 lots 288,186 45,763 3,504 15,482 223,437 (26.11) (26.34) (26.29) (25.79) (26.09) 5-10 lots 238,182 20,423 3,310 14,986 199,463 (21.58) (11.76) (24.84) (24.97) (23.29) 11-20 lots 47,153 8,233 1,103 4,616 33,201 (4.27) (4.74) (8.28) (7.69) (3.88) 21-50 lots 32,679 5,046 965 4,395 22,273 (2.96) (2.90) (7.24) (7.32) (2.60) >50 lots 12,065 2,196 288 1,785 7,796 (1.09) (1.26) (2.16) (2.97) (0.91) Total 1,103,563 173,717 13,326 60,025 856,495 (100.00) (100.00) (100.00) (100.00) (100.00) Panel C: Frequency Distribution of Trade Value across Investor Types Smallest 220,811 41,133 1,120 6,571 171,987 (20.01) (23.68) (8.40) (10.95) (20.08) Quintile 2 220,532 34,101 1,668 7,655 177,108 (19.98) (19.63) (12.52) (12.75) (20.68) Quintile 3 220,862 34,833 2,252 9,751 174,026 (20.01) (20.05) (16.90) (16.24) (20.32) Quintile 4 220,571 30,231 2,871 12,838 174,631 (19.99) (17.40) (21.54) (21.39) (20.39) Largest 220,787 33,419 5,415 23,210 158,743 (20.01) (19.24) (40.63) (38.67) (18.53) Total 1,103,563 173,717 13,326 60,025 856,495 (100.00) (100.00) (100.00) (100.00) (100.00) Categories

49

Table 5 Regression Results for Price Contribution Controlling for Firm Characteristics and Market Factors This table presents regression results for stock traders’ price contribution, controlling for firm characteristics and market factors. Panels A and B present buy and sell orders, respectively. We estimate the regression models using the Newey-West procedure with appropriate adjustment for heteroscedasticity and autocorrelation of residuals (Newey and West, 1987) as follows: = J + L# MB8@ 9 ME 7

+ LN ME 7

× (LV '7@8(99

L#R %E8 BI@8 ,

.#

+ LO ' ℎ@8Q C

+ LR '7@8(99

+ LW %8?7@F G@ ) + ' ℎ@8Q C

+ L#S 43

+ LS %8?7@F G@

× (L#X '7@8(99

+ MB8@ 9

× (LT '7@8(99

+ L## %8?7@F G@ ) + L#N F G@ ,

.#

+ LU %8?7@F G@ ) + + L#O H4 ,

.#

+

@ + L#T 43 %B + M Z@7 Q 7EC 8[ \]]@^ C + M Z@7 % _@ \]]@^ C + ` .

We incorporate the approach of Lee and Ready (1991) into Barclay and Warner (1993) and O’Hara, Yao, and Ye (2014) to calculate trader type ’s price contribution, , for order direction d, stock i, and trading day . A foreign investor dummy, MB8@ 9 , is an indicator that equals one when the trader is a foreign investor and zero otherwise. Similarly, ME 7 (' ℎ@8Q C ) is a dummy variable that equals one when the investor is a mutual fund (other institution) and zero otherwise. Order aggressiveness, '7@8(99 , denotes the volume-weighted aggressiveness of buy or sell orders for each stock and each type of investor on a daily basis. For each buy order, we first calculate the level of order aggressiveness as the difference of the buy price minus the prevailing midpoint of the highest bid and lowest ask quotes, divided by the midpoint price and expressed as a percentage. Conversely, for each sell order, the degree of order aggressiveness is computed as the difference of the midpoint quote minus the sell price, divided by the midpoint quote and expressed as a percentage. We then compute the volume-weighted aggressiveness for each type of investor on a daily basis, sorted by order direction for each stock. Trade size, %8?7@F G@ , is respectively measured by the number of shares traded and the total value of trades. The number of shares traded, %8?7@AB C , is measured by the natural logarithm of the number of total shares executed (in lots) by each type of investor for each order direction and each stock on a daily basis. The value of trades, %8?7@>?DE@ , is defined as the natural logarithm of the total value of trades (in NT$ million) by each type of investor for each order direction and each stock on a daily basis. We also add the interaction terms of investor dummies and order aggressiveness/trade size into the regression models. As to firm characteristics, firm size, F G@ , .# , is defined as the natural logarithm of the market capitalization (in NT$ million) measured on the previous day; the book-to-market ratio, H4 , .# , is calculated as the ratio of the book value at the end of the prior year to the market capitalization of common shares outstanding on the previous day; and the turnover rate, %E8 BI@8 , .# , is calculated as the number of shares traded divided by the shares outstanding on the previous day and expressed as a percentage. Market return, 43 @ , is defined as the rate of change of the closing TAIEX on trading day and expressed as a percentage; the market turnover rate, 43 %B , is defined as the turnover rates of all common shares listed on TWSE on trading day and expressed as a percentage. The difference test for ME 7 − MB8@ 9 is under the null hypothesis that mutual funds’ price contribution is indifferent to foreign investors’; the difference test for ME 7 − ' ℎ@8Q C is under the null hypothesis that mutual funds’ price contribution is indifferent to other institutions’. Similarly, ' ℎ@8Q C − MB8@ 9 is for the null hypothesis that other institutions’ price contribution is indifferent to foreign investors’. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

50

Independent Variables Panel A: Buy Orders Q @8^@f MB8@ 9 ME 7 ' ℎ@8Q C '87@8(99 %8?7@AB C %8?7@>?DE@ MB8@ 9 × '87@8(99 MB8@ 9 × %8?7@>?DE@ ME 7 × '87@8(99 ME 7 × %8?7@>?DE@ ' ℎ@8Q C × '87@8(99 ' ℎ@8Q C × %8?7@>?DE@ F G@ H4 %E8 BI@8 43 @ 43 %B Fixed Industry Effects Fixed Time Effects Adjusted N Number of Obs. Difference Tests ME 7 − MB8@ 9 ME 7 − ' ℎ@8Q C ' ℎ@8Q C − MB8@ 9

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

98.66 *** -56.88 *** -65.00 *** -61.27 *** 1.00 ***

117.78 *** -40.15 *** -41.82 *** -46.08 ***

154.10 *** -38.51 *** -40.75 *** -44.69 ***

118.95 *** -41.06 *** -43.28 *** -46.10 *** 1.47 *** 6.63 ***

155.94 *** -39.42 *** -42.25 *** -44.67 *** 1.55 ***

156.47 *** -38.85 *** -46.49 *** -47.64 *** -0.58 ***

158.67 *** -55.65 *** -36.44 *** -47.79 *** 1.51 ***

158.56 *** -53.90 *** -39.76 *** -49.88 *** 0.46 ***

7.26 ***

7.42 *** 0.97 ***

6.11 ***

6.24 *** -0.07 3.03 *** 1.62 *** -1.24 *** 3.04 *** 0.27 *** -10.36 *** 2.89 *** -3.61 *** -0.77 *** -12.21 *** Yes Yes 0.70 2,264,223 14.14 *** 10.12 *** 4.02 ***

6.54 *** 7.15 ***

3.17 *** 3.60 *** -1.58 *** 4.13 *** -2.61 *** 1.83 *** -0.77 *** -0.48 *** -3.95 *** Yes Yes 0.63 2,264,223

-8.25 *** -2.52 *** -3.34 *** -0.62 *** -12.08 *** Yes Yes 0.68 2,269,705

-10.15 *** 4.15 *** -3.81 *** -0.68 *** -12.98 *** Yes Yes 0.69 2,269,705

-8.46 *** -2.67 *** -3.47 *** -0.67 *** -11.92 *** Yes Yes 0.69 2,264,223

-10.41 *** 4.10 *** -3.96 *** -0.74 *** -12.83 *** Yes Yes 0.69 2,264,223

-10.43 *** 4.14 *** -3.84 *** -0.65 *** -12.84 *** Yes Yes 0.70 2,264,223

0.31 *** -10.37 *** 2.82 *** -3.66 *** -0.81 *** -12.27 *** Yes Yes 0.70 2,264,223

-8.13 *** -3.73 *** -4.40 ***

-1.67 *** 4.26 *** -5.93 ***

-2.24 *** 3.94 *** -6.18 ***

-2.22 *** 2.83 *** -5.05 ***

-2.83 *** 2.42 *** -5.25 ***

-7.64 *** 1.15 *** -8.79 ***

19.21 *** 11.36 *** 7.85 ***

51

Independent Variables Panel B: Sell Orders Q @8^@f MB8@ 9 ME 7 ' ℎ@8Q C '87@8(99 %8?7@AB C %8?7@>?DE@ MB8@ 9 × '87@8(99 MB8@ 9 × %8?7@>?DE@ ME 7 × '87@8(99 ME 7 × %8?7@>?DE@ ' ℎ@8Q C × '87@8(99 ' ℎ@8Q C × %8?7@>?DE@ F G@ H4 %E8 BI@8 43 @ 43 %B Fixed Industry Effects Fixed Time Effects Adjusted N Number of Obs. Difference Tests ME 7 − MB8@ 9 ME 7 − ' ℎ@8Q C ' ℎ@8Q C − MB8@ 9

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

97.22 *** -62.99 *** -68.27 *** -66.56 *** 0.44 ***

114.99 *** -47.70 *** -47.21 *** -52.35 ***

146.75 *** -46.32 *** -46.34 *** -51.19 ***

115.11 *** -47.82 *** -47.89 *** -51.93 *** 1.02 *** 5.93 ***

147.62 *** -46.39 *** -47.02 *** -50.67 *** 1.12 ***

149.25 *** -44.04 *** -50.44 *** -52.49 *** 0.79 ***

150.25 *** -64.82 *** -44.59 *** -57.53 *** 1.03 ***

150.85 *** -62.20 *** -48.83 *** -59.76 *** 1.50 ***

6.43 ***

6.62 *** -1.16 ***

4.92 ***

4.95 *** -1.85 *** 3.56 *** 0.36 *** -0.40 *** 1.51 *** 1.11 *** -9.07 *** 1.89 *** -3.26 *** 0.72 *** -6.45 *** Yes Yes 0.77 2,280,078 13.38 *** 10.93 *** 2.44 ***

5.82 *** 6.29 ***

3.55 *** 1.97 *** -1.10 *** 2.28 *** -2.14 *** 1.59 *** -0.64 *** 0.53 *** 0.04 Yes Yes 0.71 2,280,078

-7.27 *** -2.20 *** -3.08 *** 0.64 *** -6.71 *** Yes Yes 0.75 2,285,344

-8.89 *** 3.75 *** -3.47 *** 0.60 *** -7.42 *** Yes Yes 0.76 2,285,344

-7.42 *** -2.36 *** -3.19 *** 0.66 *** -6.84 *** Yes Yes 0.75 2,280,078

-9.10 *** 3.70 *** -3.60 *** 0.63 *** -7.58 *** Yes Yes 0.76 2,280,078

-9.32 *** 3.67 *** -3.60 *** 0.61 *** -7.67 *** Yes Yes 0.76 2,280,078

0.91 *** -8.92 *** 2.03 *** -3.27 *** 0.72 *** -6.54 *** Yes Yes 0.76 2,280,078

-5.28 *** -1.71 *** -3.57 ***

0.49 *** 5.14 *** -4.64 ***

-0.02 4.85 *** -4.87 ***

-0.06 4.04 *** -4.10 ***

-0.63 *** 3.65 *** -4.28 ***

-6.40 *** 2.04 *** -8.44 ***

20.24 *** 12.94 *** 7.29 ***

52

Table 6 Regression Results for Weighted Price Contribution This table presents regression results for stock traders’ weighted price contribution controlling for market factors. Panels A and B present buy and sell orders, respectively. We estimate the regression model as follows: = J + L# MB8@ 9

+ LN ME 7 + LO ' ℎ@8Q C

+ LR '7@8(99 + LS %8?7@>?DE@ + MB8@ 9

ME 7 × gLV '7@8(99 + LW %8?7@>?DE@ h + ' ℎ@8Q C L#R

, .#

× gLT '7@8(99 + LU %8?7@>?DE@ h +

× gL#X '7@8(99 + L## %8?7@>?DE@ h + L#N 43

@ + L#O 43 %B +

+ M Z@7 % _@ \]]@^ C + ` .

We calculate trader type ’s weighted price contribution, , by return-weighted price contribution for order direction d on trading day . A foreign investor dummy, MB8@ 9 , is an indicator that equals one when the trader is a foreign investor and zero otherwise. Similarly, ME 7 (' ℎ@8Q C ) is the dummy variable that equals one when the investor is a mutual fund (other institution) and zero otherwise. Order aggressiveness, '7@8(99 , denotes the traded value-weighted aggressiveness of buy or sell orders for each type of investor on a daily basis. We use two variables to measure order-level aggressiveness: one is order aggressiveness relative to the prevalent midpoint of bid and ask quotes; the other is order aggressiveness relative to the daily weighted average traded price. For each buy order, we first calculate the level of order aggressiveness as the difference of the buy price minus the prevailing midpoint of the highest bid and lowest ask quotes, divided by the midpoint price and expressed as a percentage. Conversely, for each sell order, the degree of order aggressiveness is computed as the difference of the midpoint quote minus the sell price, divided by the midpoint quote and expressed as a percentage. We then compute the value-weighted aggressiveness for each type of investor on a daily basis, sorted by order direction. '87@8(991 and '87@8(992 indicate the value-weighted aggressiveness measures based on the prevalent midpoint of bid and ask quotes and the daily weighted average traded price, respectively. The value of trades, %8?7@>?DE@ , is defined as the natural logarithm of the total value of trades (in NT$ billion) of total stocks by each type of investor for each order direction on a daily basis. We also add the interaction terms of investor dummies and order aggressiveness/trade value into the regression models. Market return, 43 @ , is defined as the rate of change of the closing TAIEX on trading day and expressed as a percentage; the market turnover rate, 43 %B , is defined as the turnover rates of all common shares listed on the TWSE on trading day and expressed as a percentage. is , .# indicates a one-day lagged weighted price contribution. The difference test for ME 7 − MB8@ 9 under the null hypothesis that mutual funds’ price contribution is indifferent to foreign investors’; the difference test for ME 7 − ' ℎ@8Q C is under the null hypothesis that mutual funds’ price contribution is indifferent to other institutions’. Similarly, ' ℎ@8Q C − MB8@ 9 is for the null hypothesis that other institutions’ price contribution is indifferent to foreign investors’. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

53

Independent Variables

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Panel A: Buy Orders 64.48 ***

64.33 ***

50.96 ***

48.68 ***

48.50 ***

12.45 **

11.95 **

MB8@ 9

-55.77 ***

-55.43 ***

-49.43 ***

-49.93 ***

-49.56 ***

-27.11 ***

-25.77 ***

ME 7

-64.97 ***

-64.81 ***

-46.60 ***

-46.03 ***

-45.81 ***

***

***

***

***

***

Q @8^@f

' ℎ@8Q C

-58.61

-58.42

-51.50

1.66 ***

'87@8(991

-50.95

2.06 *** 1.52 ***

'87@8(992

-50.75

-19.76

0.19 ***

4.91 ***

4.93 ***

7.75 *** 13.21 ***

-7.16 ***

MB8@ 9 × '87@8(992 MB8@ 9 × %8?7@>?DE@

0.01

***

-4.85 ***

ME 7 × '87@8(992 ME 7 × %8?7@>?DE@

-0.01 ***

' ℎ@8Q C × '87@8(991

-7.37 *** 0.01 ***

' ℎ@8Q C × %8?7@>?DE@ 0.10

( .#)

Fixed Time Effects Adjusted

N

-0.01 *** -6.80 ***

' ℎ@8Q C × '87@8(992

43 %B

0.01 ***

-5.13 ***

ME 7 × '87@8(991

@

13.27 ***

-6.68 ***

MB8@ 9 × '87@8(991

43

-19.64 ***

7.98 *** 1.89 ***

4.78 ***

%8?7@>?DE@

-0.09

0.16

-0.16 -6.47

-0.12 ***

-6.66

-0.04 ***

-6.76

-0.24 ***

-7.74

0.01 *** -0.08

***

-7.86 ***

0.74

0.68

0.13 ***

0.13 ***

0.13 ***

0.12 ***

0.13 ***

0.10 ***

0.10 ***

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.84

0.84

0.84

0.84

0.84

0.84

0.84

5,828

5,840

5,840

5,828

5,840

5,828

5,840

ME 7 − MB8@ 9

-9.20 ***

-9.38 ***

2.84 **

3.90 ***

3.75 ***

27.02 **

25.96 **

ME 7 − ' ℎ@8Q C

-6.36 ***

-6.39 ***

4.90 ***

4.92 ***

4.93 ***

19.67 ***

19.84 ***

' ℎ@8Q C − MB8@ 9

-2.84 ***

-2.99 ***

-2.06 ***

7.35 ***

6.12 ***

Number of Obs. Difference Tests

54

-1.02 *

-1.18 **

Independent Variables

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Panel B: Sell Orders 72.07 ***

71.94 ***

56.54 ***

51.51 ***

50.83 ***

17.04 ***

18.40 ***

MB8@ 9

-63.55 ***

-63.48 ***

-56.27 ***

-55.72 ***

-55.48 ***

-34.53 ***

-34.59 ***

ME 7

-72.14 ***

-72.27 ***

-50.66 ***

-48.56 ***

-48.21 ***

***

***

***

***

***

Q @8^@f

' ℎ@8Q C

-66.42

-66.39

-58.44

2.43 ***

'87@8(991

-56.44

3.08 *** 2.45 ***

'87@8(992

-56.18

-26.91

-5.18 ***

6.10 ***

6.23 ***

7.74 *** 13.95 ***

-5.59 ***

MB8@ 9 × '87@8(992 MB8@ 9 × %8?7@>?DE@

0.01

***

-4.90 ***

ME 7 × '87@8(992 ME 7 × %8?7@>?DE@

-0.01 ***

' ℎ@8Q C × '87@8(991

-4.42 ** 0.01 ***

' ℎ@8Q C × %8?7@>?DE@ -0.07 -0.40

( .#)

Fixed Time Effects Adjusted

N

-0.01 *** -4.43 **

' ℎ@8Q C × '87@8(992

43 %B

0.01 **

-4.81 ***

ME 7 × '87@8(991

@

13.33 ***

-4.68 ***

MB8@ 9 × '87@8(991

43

-28.13 ***

7.35 *** 3.17 ***

5.82 ***

%8?7@>?DE@

-2.62

-0.20 -0.57

0.10 -7.86

0.07 ***

-8.38

-0.10 ***

-8.78

0.12 ***

-8.94

0.01 *** -0.05

***

-9.15 ***

0.05 ***

0.05 ***

0.04 **

0.03 **

0.03 **

0.02

0.02

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.83

0.83

0.83

0.83

0.83

0.83

0.83

5,828

5,840

5,840

5,828

5,840

5,828

5,840

ME 7 − MB8@ 9

-8.59 ***

-8.79 ***

5.62 ***

7.16 ***

7.28 ***

31.90 **

29.41 ***

ME 7 − ' ℎ@8Q C

-5.72 ***

-5.88 ***

7.79 ***

7.88 ***

7.97 ***

24.28 ***

22.95 ***

' ℎ@8Q C − MB8@ 9

-2.87 ***

-2.91 ***

-2.17 ***

7.62 ***

6.46 ***

Number of Obs. Difference Tests

55

-0.72

-0.69

Table 7 Regression Results for Stock Returns of Buy Orders This table presents regression results for stock returns of buy orders across investor types. Panels A-D present buy orders placed by foreign investors, mutual funds, other institutions, and individuals, respectively. We estimate the regression model as follows: @

( , a c)

= J + L# '87@8(99* + LN '87@8F G@ * + LO LR

*

LV 43

× '87@8F G@ * + LS F G@ , @

( , ac)

.#

*

+ LT H4 ,

× '7@8(99* +

.#

+ LU %E8 BI@8 ,

.#

+

+ M Z@7 Q 7EC 8[ \]]@^ C + M Z@7 % _@ \]]@^ C + ` (

, ac) .

We state here that stock return, @ ( , ab) , indicates stock i’s return over the period from day to + c, computed as the natural logarithm of the ratio of the ending value of the holding period to the closing price on the previous day and expressed as a percentage. Holding periods include the current trading day, one week, two weeks, one month, three months, six months, and one year. Buy order aggressiveness, '87@8(99* , denotes the volume-weighted aggressiveness of buy orders for each stock and each type of investor on a daily basis. For each buy order, we first calculate the level of order aggressiveness as the difference of the buy price minus the prevailing midpoint of the highest bid and lowest ask quotes, divided by the midpoint quote and expressed as a percentage. We then compute the volume-weighted aggressiveness of buy orders for each stock and each type of investor on a daily basis. Buy order size, '87@8F G@ * , is measured by the natural logarithm of the total trade value of buy orders divided by the number of buy orders for each stock and each type of investor on a daily * basis. Buy price contribution, , is trader type ’s price contribution of buy orders for stock on trading day . As to control variables, firm size, F G@ , .# , is defined as the natural logarithm of the market capitalization (in NT$ million) measured on the previous day; the book-to-market ratio, H4 , .# , is calculated as the ratio of the book value at the end of the prior year to the market capitalization of common stocks outstanding on the previous day; and the turnover rate, %E8 BI@8 , .# , is calculated as the number of shares traded divided by the shares outstanding at the end of the previous day. Market return, 43 @ ( , ab) , is defined as the rate of change of the closing TAIEX over the period from day to + c and expressed as a percentage. *** ** , , and * indicate significance at the 1%, 5%, and 10% levels, respectively.

56

Independent Variables

Holding Periods 2 Weeks 1 Month 3 Months

1 Day 1 Week 6 Months 1 Year Panel A: Foreign Investors Q @8^@f 0.23 *** 0.64 *** 0.67 *** 1.20 *** 2.83 *** 3.09 *** 9.97 *** *** *** *** *** *** *** '87@8(99 -0.16 -0.13 -0.13 -0.12 -0.18 -0.29 -0.33 *** '87@8F G@ 0.41 *** 0.43 *** 0.39 *** 0.43 *** 0.47 *** 0.47 *** 0.22 *** *** *** *** *** × '87@8(99 (× 100) 0.16 0.05 0.11 0.13 0.05 0.02 -0.09 × '87@8F G@ (× 100) 0.05 *** 0.12 *** 0.11 *** 0.16 *** 0.52 *** 1.03 *** 1.62 *** Controls Yes Yes Yes Yes Yes Yes Yes -value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 Adjusted N 0.25 0.24 0.24 0.26 0.27 0.24 0.24 Number of Obs. 599,377 599,377 599,377 599,377 599,377 599,377 599,377 Panel B: Mutual Funds Q @8^@f 0.12 1.30 *** 1.95 *** 4.77 *** 9.41 *** 13.90 *** 21.03 *** *** *** *** *** *** *** '87@8(99 -0.08 -0.14 -0.17 -0.18 -0.18 -0.14 -0.21 *** '87@8F G@ 0.13 *** 0.11 *** 0.12 *** 0.13 *** 0.12 ** 0.11 -0.03 *** *** *** *** × '87@8(99 (× 100) -0.15 -0.23 -0.22 -0.33 -0.25 * -0.56 *** -0.45 *** *** *** *** *** *** × '87@8F G@ (× 100) 0.24 0.40 0.45 0.54 0.35 0.81 1.05 *** Controls Yes Yes Yes Yes Yes Yes Yes -value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 N Adjusted 0.26 0.27 0.26 0.27 0.24 0.21 0.21 Number of Obs. 69,652 69,652 69,652 69,652 69,652 69,652 69,652 Panel C: Other Institutions Q @8^@f 0.32 *** 0.87 *** 1.22 *** 2.17 *** 4.20 *** 5.05 *** 10.01 *** '87@8(99 0.05 *** 0.02 *** 0.02 *** 0.00 -0.07 *** -0.11 *** -0.19 *** *** *** *** *** *** *** '87@8F G@ 0.18 0.19 0.20 0.27 0.30 0.30 0.36 *** × '87@8(99 (× 100) -0.13 *** -0.18 *** -0.25 *** -0.26 *** -0.19 *** -0.33 *** -0.45 *** *** *** *** *** *** *** × '87@8F G@ (× 100) 0.12 0.18 0.24 0.30 0.50 0.88 1.47 *** Controls Yes Yes Yes Yes Yes Yes Yes -value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 Adjusted N 0.22 0.24 0.24 0.25 0.24 0.23 0.24 Number of Obs. 440,345 440,345 440,345 440,345 440,345 440,345 440,345 Panel D: Individuals Q @8^@f -1.37 *** -1.58 *** -2.08 *** -2.80 *** -2.44 *** -2.51 *** 8.09 *** '87@8(99 0.59 *** 0.64 *** 0.71 *** 0.84 *** 0.88 *** 0.94 *** 1.75 *** *** *** *** *** *** *** '87@8F G@ 1.20 1.47 1.70 2.13 2.87 3.62 3.58 *** × '87@8(99 (× 100) 0.21 *** -0.05 ** -0.24 *** -0.65 *** -1.36 *** -1.88 *** -3.27 *** *** *** *** *** *** *** × '87@8F G@ (× 100) -0.15 -0.19 -0.22 -0.25 -0.46 -0.85 -1.19 *** Controls Yes Yes Yes Yes Yes Yes Yes -value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 Adjusted N 0.31 0.23 0.23 0.25 0.26 0.25 0.26 Number of Obs. 937,427 937,427 937,427 937,427 937,427 937,427 937,427

57

Table 8 Regression Results for Stock Returns of Sell Orders This table presents regression results for stock returns of sell orders across investor types. Panels A-D present sell orders placed by foreign investors, mutual funds, other institutions, and individuals, respectively. We estimate the regression model as follows: @

( , a c)

= J + L# '87@8(99 5 + LN '87@8F G@ 5 + LO LR

j

LV 43

× '87@8F G@ j + LS F G@ , @

( , ac)

.#

5

+ LT H4 ,

× '7@8(99 5 + .#

+ LU %E8 BI@8 ,

.#

+

+ M Z@7 Q 7EC 8[ \]]@^ C + M Z@7 % _@ \]]@^ C + ` (

, ac) .

Note that @ ( , ab) indicates stock i’s return over the period from day to + c, computed as the natural logarithm of the ratio of the ending value of the holding period to the closing price on the previous day and expressed as a percentage. Holding periods include the current trading day, one week, two weeks, one month, three months, six months, and one year. Sell order aggressiveness, '87@8(99 5 , denotes the volume-weighted aggressiveness of sell orders for each stock and each type of investor on a daily basis. For each sell order, we first calculate the level of order aggressiveness as the difference between the midpoint of the highest bid and lowest ask quotes and the sell price, divided by the midpoint quote and expressed as a percentage. We then compute the volume-weighted aggressiveness of sell orders for each stock and each type of investor on a daily basis. Sell order size, '87@8F G@ 5 , is measured by the natural logarithm of the total trade value of sell orders divided by the number of sell orders for each stock and each type of investor on a daily basis. Sell price 5 contribution, , is trader type ’s price contribution of sell orders for stock on trading day . As to control variables, firm size, F G@ , .# , is defined as the natural logarithm of the market capitalization (in NT$ million) measured on the previous day; the book-to-market ratio, H4 , .# , is calculated as the ratio of the book value at the end of the prior year to the market capitalization of common stocks outstanding on the previous day; and the turnover rate, %E8 BI@8 , .# , is calculated as the number of shares traded divided by the shares outstanding at the end of the previous day. Market return, 43 @ ( , ab) , is defined as the rate of change of the closing TAIEX over the period from day to + c and expressed as a percentage. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

58

Independent Variables

Holding Periods 2 Weeks 1 Month 3 Months

1 Day 1 Week 6 Months 1 Year Panel A: Foreign Investors Q @8^@f -0.49 *** -0.19 *** -0.18 * 0.23 * 1.71 *** 1.58 *** 7.85 *** *** *** *** ** *** '87@8(99 0.08 0.05 0.04 0.02 0.00 -0.06 -0.15 *** '87@8F G@ -0.01 *** -0.02 ** -0.02 0.02 0.15 *** 0.20 *** 0.04 ** ** ** × '87@8(99 (× 100) -0.02 -0.05 -0.07 0.06 -0.01 -0.09 -0.11 × '87@8F G@ (× 100) -0.18 *** -0.16 *** -0.15 *** -0.14 *** 0.16 *** 0.52 *** 0.87 *** Controls Yes Yes Yes Yes Yes Yes Yes -value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 Adjusted N 0.23 0.24 0.24 0.26 0.27 0.24 0.24 Number of Obs. 615,982 615,982 615,982 615,982 615,982 615,982 615,982 Panel B: Mutual Funds Q @8^@f 0.84 *** 2.06 *** 3.17 *** 6.38 *** 10.39 *** 14.91 *** 23.59 *** *** *** *** *** *** *** '87@8(99 0.10 0.13 0.14 0.15 0.19 0.20 0.15 *** '87@8F G@ 0.06 *** 0.05 ** 0.05 * 0.01 0.08 0.00 -0.41 *** *** × '87@8(99 (× 100) 0.23 0.08 0.10 -0.03 -0.08 -0.32 -0.34 *** *** *** *** ** × '87@8F G@ (× 100) -0.45 -0.38 -0.44 -0.43 -0.22 -0.04 -0.03 Controls Yes Yes Yes Yes Yes Yes Yes -value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 N Adjusted 0.28 0.27 0.26 0.27 0.25 0.21 0.21 Number of Obs. 71,195 71,195 71,195 71,195 71,195 71,195 71,195 Panel C: Other Institutions Q @8^@f -0.05 0.31 *** 0.66 *** 1.49 *** 3.62 *** 4.47 *** 10.17 *** '87@8(99 -0.06 *** -0.06 *** -0.06 *** -0.06 *** -0.09 *** -0.11 *** -0.21 *** *** *** *** *** *** ** '87@8F G@ 0.10 0.15 0.18 0.23 0.18 0.08 0.01 × '87@8(99 (× 100) 0.19 *** 0.16 *** 0.08 *** 0.00 -0.21 *** -0.44 *** -0.70 *** × '87@8F G@ (× 100) -0.20 *** -0.10 *** -0.04 *** 0.08 *** 0.48 *** 0.99 *** 1.75 *** Controls Yes Yes Yes Yes Yes Yes Yes -value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 Adjusted N 0.23 0.25 0.24 0.25 0.24 0.23 0.24 Number of Obs. 446,346 446,346 446,346 446,346 446,346 446,346 446,346 Panel D: Individuals Q @8^@f 0.46 *** 0.27 *** -0.05 0.17 1.24 *** 1.56 *** 12.77 *** '87@8(99 -0.39 *** -0.43 *** -0.48 *** -0.56 *** -0.87 *** -1.07 *** -1.16 *** *** *** *** *** *** '87@8F G@ -0.86 -0.70 -0.64 -0.59 -0.06 0.23 -0.44 *** × '87@8(99 (× 100) -0.40 *** -0.53 *** -0.57 *** -0.68 *** -0.83 *** -1.21 *** -1.87 *** *** *** *** *** *** × '87@8F G@ (× 100) 0.29 0.32 0.32 0.29 0.00 -0.28 -0.50 *** Controls Yes Yes Yes Yes Yes Yes Yes -value <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 Adjusted N 0.28 0.23 0.23 0.25 0.26 0.24 0.26 Number of Obs. 927,595 927,595 927,595 927,595 927,595 927,595 927,595

59

Table 9 Regression Results for Stock Returns This table presents extended regression results for stock returns. Panels A and B present the buy and sell orders, respectively. We estimate the regression model with the Newey-West procedure as follows: @

( , a c)

= J + L# M(99 + LN 4(99 + LO '(99 + LR MF G@ + LS 4F G@ + LT 'F G@ + LU M L## 4

× M(99 + LV 4

× 4(99 + LW '

× 4F G@ + L#N '

L#S %E8 BI@8 ,

.#

+ L#T 43

M Z@7 % _@ \]]@^tC + ` (

× '(99 + L#X M

× 'F G@ + L#O F G@ , @

( , a c)

.#

+ L#R H4 ,

.#

× MF G@ + +

+ M Z@7 Q 7EC 8[ \]]@^ C +

, a c) .

We state here that stock return, @ ( , ab) , indicates stock i’s return over the period from day to + c, computed as the natural logarithm of the ratio of the ending value of the holding period to the closing price on the previous day and expressed as a percentage. Holding periods include the current trading day, one week, two weeks, one month, three months, six months, and one year. M(99 denotes foreign investors’ volume-weighted order aggressiveness for trade direction 7 and stock on trading day . 4(99 and '(99 are respectively mutual funds’ and other institutions’ volume-weighted order aggressiveness. MF G@ stands for foreign investors’ order size for trade direction 7 and stock on trading day , measured by the natural logarithm of the total trade value divided by the number of orders. Likewise, 4F G@ and 'F G@ are indicates foreign investors’ price mutual funds’ and other institutions’ order size, respectively. M contribution for trade direction 7 and stock on trading day . Similarly, 4 and ' are respectively mutual funds’ and other institutions’ price contribution. As to control variables, firm size, F G@ , .# , is defined as the natural logarithm of the market capitalization (in NT$ million) measured on the previous day; the book-to-market ratio, H4 , .# , is calculated as the ratio of the book value at the end of the prior year to the market capitalization of common stocks outstanding on the previous day; and the turnover rate, %E8 BI@8 , .# , is calculated as the number of shares traded divided by the shares outstanding at the end of the previous day. Market return, 43 @ ( , ab) , is defined as the rate of change of the closing TAIEX over the period from day to + c and expressed as a percentage. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively.

60

Independent Variables

Holding Periods 1 Day

1 Week

2 Weeks

1 Month

3 Months

6 Months

1 Year

Panel A: Buy Orders 0.83 ***

2.17 ***

2.75 ***

5.97 ***

11.25 ***

17.55 ***

25.99 ***

M(99

-0.29 ***

-0.23 ***

-0.26 ***

-0.31 ***

-0.40 ***

-0.47 ***

-0.51 ***

4(99

-0.08 ***

-0.12 ***

-0.13 ***

-0.15 ***

-0.13 ***

-0.09 **

-0.12 **

'(99

0.07 ***

MF G@

0.40

***

4F G@

0.07 ***

'F G@

0.09

***

M

0.06 **

Q @8^@f

× M(99(× 100)

***

0.06 **

0.04 0.36

***

0.05 ** 0.09

***

0.07

0.37

***

0.53

0.00 ***

0.09

0.02

***

*

0.07

0.13

0.13

0.11 0.13

***

0.14 ***

× 4(99(× 100)

-0.23

'

× '(99(× 100)

-0.38 ***

-0.47 ***

-0.57 ***

-0.64 ***

M

× MF G@(× 100)

0.32 ***

0.36 ***

0.43 ***

0.63 ***

4

× 4F G@(× 100)

0.41 ***

0.66 ***

0.77 ***

'

× 'F G@(× 100)

0.40 ***

0.48 ***

0.53 ***

Controls Adjusted

N

Number of Obs.

0.84

0.07 *

4

-0.39

0.75

0.04 ***

0.08 ***

0.08 ***

0.06

-0.38

-0.49

-0.69

-0.01 ***

-0.10 0.73 ***

0.23 ***

-0.57 **

-1.01

0.68 ***

0.17 ***

-1.04 **

-1.04 ***

-1.24 ***

1.11 ***

1.69 ***

2.90 ***

0.86 ***

0.87 ***

1.41 ***

2.23 ***

0.62 ***

0.85 ***

1.47 ***

2.50 ***

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.33

0.30

0.29

0.29

0.25

0.21

0.22

61,747

60,582

60,582

60,582

60,582

60,582

60,572

Panel B: Sell Orders -1.01 ***

Q @8^@f M(99 4(99

0.56

1.40 ***

4.66 ***

8.76 ***

14.29 ***

24.42 ***

0.11 ***

0.07 ***

0.07 ***

0.05

0.01

-0.08

-0.07

***

***

***

0.09

-0.06 ***

'(99 MF G@

-0.01 ***

0.11

-0.07 ***

0.12

-0.09 ***

0.14

***

-0.08 *

0.19

***

-0.16 *

-0.09

0.02

0.05

0.07

0.31

0.01

0.02

-0.02

0.01

0.21

***

**

0.45

**

0.19 *** -0.12 0.28 -0.44 ***

4F G@

0.05

'F G@

0.08 ***

0.07 ***

0.10 **

0.15 ***

0.26 ***

0.25 *

M

0.29 ***

0.20 ***

0.24 **

0.33 **

0.35

0.47

-0.10

0.21

**

0.03

0.01

-0.26

0.08

0.18

*

0.01

-0.38

-0.47

-1.20 **

4

× M(99(× 100) × 4(99(× 100)

0.33

***

0.37

***

0.15

**

0.28

***

-0.07

'

× '(99(× 100)

M

× MF G@(× 100)

-0.52 ***

-0.40 ***

-0.39 ***

-0.30 ***

-0.01

4

× 4F G@(× 100)

-0.66 ***

-0.60 ***

-0.69 ***

-0.63 ***

-0.38 **

***

***

***

'

× 'F G@(× 100) Controls Adjusted

N

Number of Obs.

-0.46

-0.34

-0.27

-0.11

0.35

0.52 *** ***

-0.14 0.77

0.38 *

1.27 *** -0.10

***

1.77 ***

Yes

Yes

Yes

Yes

Yes

Yes

Yes

0.33

0.30

0.28

0.29

0.25

0.21

0.21

63,214

61,913

61,913

61,913

61,913

61,913

61,904

61

Highlights

Journal: International Review of Economics & Finance Title: Whose Trades Move Stock Prices? Evidence from the Taiwan Stock Exchange Manuscript Number: IREF_2018_201_R2

•

Individuals account for the overwhelming majority of the weighted price contribution.

•

Mutual funds’ weighted price contribution per order and order aggressiveness are the highest.

•

Order aggressiveness and trade value are positively related to price contribution.

•

Professional institutions’ current price contribution is related to future stock returns.