Are Investors more Aggressive in Transparent Markets?

Transcription

1 Asia-Pacific Journal of Financial Studies (2008) v37 n2 pp Are Investors more Aggressive in Transparent Markets? Tai Ma ** National Sun Yat-sen University, Taiwan Yaling Lin Shih Chien University, Taiwan Hsiu-Kuei Chen Nan Jeon Institute of Technology, Taiwan Received 17 September 2007; Accepted 24 December 2007 Abstract This study examines the impact of increasing pre-trade transparency on the intraday order placement strategies of individual and institutional investors. The effect of pretrade transparency on market performance is also studied. We find that transparency affects order placement strategies. First, greater pre-trade transparency intensifies competition and thus aggressiveness in order placement, especially for institutional investors. Transparency also reduces and reduces extreme order placement by individual investors as they are better informed about how to place orders. Second, increasing transparency also changes trader order sizes. Institutional investors make smaller orders with increasing transparency, while individual investors make larger orders. Generally, greater transparency increases volatility, but not liquidity and efficiency. Finally, this study shows that the impact of transparency on market performance and the changes in order placement strategies are simultaneously determined. The analytical results are robust to different trading periods. Keywords: Pre-trade Transparency; Order Aggressiveness; Order Size; Market Performance; Intra-day Analysis * We thank the valuable comments from the participants of the 13th Conference on Securities and Financial Market, in Kaohsiung, Taiwan, and the First International Conference on Asia-Pacific Financial Markets in Seoul, South Korea. The research is funded by the National Science Council of the R.O.C., Taiwan. ** Corresponding Author. Address: 70 Lien Hai Rd, Kaohsiung, Taiwan R.O.C.; matai@finance.nsysu.edu.tw; Tel: ext 4810; Fax:

2 Are Investors more Aggressive in Transparent Markets? 1. Introduction 344 The recent trend in exchanges around the world has been to increase pre-trade transparency of limit order books (LOB). Intuitively, greater transparency of order flow should reduce information asymmetry of the uninformed, thus increasing market liquidity and information efficiency. However, the literature has not always found this to be the case. In fact, theoretical and empirical studies do not correspond in regard to the effect of pre-trade transparency. Whether or how pre-trade transparency affects the order placement strategies of various investors and market performance remains unclear. Will informed investors become more or less aggressive when more order flow information is disclosed? What about the uninformed investors? Without knowing the reactions of different market participants, it is difficult to evaluate the intricate effects of transparency enhancement or explain the results of changes in market performance. This study aims to analyze the influence of transparency on the order placement strategies of different market participants (namely, individual and institutional participants) and the impact on market performance. Order flow disclosure in the Taiwan Stock Exchange has gradually increased during recent years, providing a unique opportunity to empirically explore the effect of increasing pre-trade transparency. The related literature includes research on order strategies and the impact of transparency. Previous studies on limit order book has demonstrated that order submission strategies depend on limit order book information (e.g., Harris and Hasbrouck, 1996; Cao, Hansh and Wang, 2005; Al-Suhaibani and Kryzanowski, 2001). Furthermore, order flow information can explain the aggressiveness of the trading strategies (e.g., Biais, Hillion, and Spatt, 1995; Pascual and Veredas, 2004; Ranaldo, 2004). However, none of these studies articulates the differences in the strategies of each types of investors, 1) while each investor type may have different order submission strategies and respond differently to order exposure. Furthermore, most empirical studies on order strategies focus on either the relation between order book informa- 1) Although there are theoretical and empirical studies on the determinants of market versus limit orders for informed traders (e.g., Angel, 1994; Kaniel and Liu, 2006; Anand, Chakravarty and Martell, 2005; Beber and Caglio, 2005; Menkhoff and Schmeling, 2005), and on price formation and order placement decisions (e.g., Foucault, 1999; Foucault, Kadan, Kandel, 2003; Handa, Schwartz, Tiwari, 2003; Ma and Tsai, 2004) for informed and liquidity traders, empirical evidence regarding order placement decisions for institutional and individual investors is scant except Anand et al. (2005).

3 Asia-Pacific Journal of Financial Studies (2008) v37 n2 tion and order aggressiveness (e.g., Ranaldo, 2004; Griffiths, Smith, Turnbull and White, 2000), or on the relation between order book information and trade variables such as duration and quote revision (e.g., Irvine, Benston and Kandel, 2000; Coppejans and Domowitz, 2002; Harris and Panchapagesan, 2005). Indeed, only few studies have examined the impact of transparency on investor order placement strategies. The theoretical research on the impact of transparency is also inconclusive. Madhavan (1996) demonstrated that market transparency can increase price volatility and reduce market liquidity in a thin market. Pagano and Röell (1996) compared the price formation process in several stylized trading systems with different degrees of transparency, and found that overall greater transparency generates lower trading costs for uninformed traders, although not necessarily for all trade sizes. Using an experimental approach, Bloomfield and O Hara (2000) examined whether transparency accelerates price discovery. However, Flood, Huisman, Koedijk, and Mahieu (1999) found that increased pre-trade transparency slows price discovery in a multi-dealer market. Regarding empirical findings, Friedman (1993) demonstrated that displaying the entire book, as opposed to only the best bid and offer, reduces the market bid/ask spread, but does not significantly vary price information efficiency. Gerke, Arneth, Bosch, and Syha (1997) found lower volatility in the transparent setting but no difference in spreads. Furthermore, Madhavan, Porter and Weaver (2005) studied the effect of an increase in pre-trade transparency for the Toronto Stock Exchange and found that volatility and execution costs increase while liquidity decreases with increasing transparency. 2) Boehmer, Saar, and Yu (2005) studied the impact of increased order book transparency in NYSE and obtained results contrary to those obtained for Canadian market, finding that greater order flow transparency leads to increased liquidity and reduced trade execution cost. 3) Except for Boehmer, Saar and Yu (2005), the above studies on transparency focused mainly on market performance, without exploring the influence of transparency on investor order placement strategies. Even Boehmer, et al. (2005) only examined changes in average order size and cancellation rate, and did not clarify whether and how transparency affects the order patterns of institutional and individual inves- 2) On April 12, 1990, the Toronto Stock Exchange (TSE) provided real-time public dissemination of the best bid and offer and associated depth (bid and ask size) as well as the depth and limit order prices for up to four levels away from the inside market in both directions. 3) On January 24, 2002, the NYSE enabled traders off the exchange floor to observe depth in the limit order book in real time. 345

4 Are Investors more Aggressive in Transparent Markets? tors, and how these patterns in turn affect market performance. While previous studies have examined the influence of transparency on market performance, this study explores the effect of increasing transparency on investors order strategies (i.e., order aggressiveness and order size) and the resulting changes in market performance. This study contributes to the literature by integrating the issues of pre-trade transparency with market performance and intraday order placement strategies for institutional and individual investors, thus enhancing understanding of fundamental issues in making transparency policy. The simultaneous equations approach is used to incorporate the possible endogeneity among variables. Since July 1, 2002, the Taiwan Stock Exchange has adopted a series of measures to enhance market transparency. First, the volume of the best bid/ask limit order is disclosed along with the price, whereas previously only the best price was disclosed. Moreover, beginning in 2003, the top five quotes in the book together with the accompanying depth at each price are also disclosed. These external shocks provide us with a unique opportunity to study the impact of increasing pre-trade transparency. To compare the impact of different degrees of transparency, this study examines three two-month periods: the least transparent period, the partially transparent period, and the most transparent period. For intraday analysis, each day is further divided into nine half-hour intervals. The order placement strategies are measured based on the degree of order aggressiveness and order size. Regarding investors order aggressiveness, this study finds that the percentage of the most aggressive orders made by institutional investors increases with increasing pre-trade transparency, while that by individual investors decreases. Additionally, for individual investors, the most conservative orders also decline significantly. Furthermore, both institutional and individual investors increase inside-quote orders as order flow transparency enhances. The results indicate that greater pre-trade transparency intensifies competition in order placement strategies, especially for institutional investors, and simultaneously it reduces individual investors extreme order placement. Compared to institutional investors, individual investors seem to be more patient with increasing transparency, suggesting that liquidity providers are mainly individuals. The impact of increasing transparency on order submissions differs between opening and closing interval. During market opening, most orders placed are the very conservative orders, regardless of the level of transparency. However, at the closing 346

5 Asia-Pacific Journal of Financial Studies (2008) v37 n2 interval, the aggressiveness of most favored orders decreases with increasing transparency. Increased transparency has little impact on order strategies at market opening, indicating that investors maintain a robust conservative attitude at market opening in all stages. The other aspect of order placement strategies examined in this study is order size. Interestingly, we find that institutional and individual traders have opposite responses to transparency in terms of order size. As market transparency increases, the proportion of institutional orders increases while their order size decreases slightly. This phenomenon may occur because, to conceal their trading, institutional investors submit smaller orders as transparency of order flow information increases. Contrary to that of institutional investors, the order size of individual investors increases significantly in transparent markets, suggesting that individual investors are more confident in submitting larger orders as information transparency increases. The intraday analysis of order placement strategies shows that the intraday patterns of the more aggressive orders are U-shaped, while conservative orders are inversely J-shaped. The intraday patterns of order types change little with the level of transparency, except for those of institutional investors at the opening and closing intervals. The intraday results reveal the complexity of the order exposure effect on institutional order strategies, which is more sensitive at the opening and closing intervals. Regarding the impact of transparency on market quality, we find that volatility increases with increasing transparency but that liquidity and efficiency do not change significantly. This finding is consistent with the evidence found by Madhavan, Porter and Weaver (2005) for the Toronto Stock Exchange, but different from the result found for the NYSE by Boehmer et al. (2005). Our findings provide partial support for Madhavan (1996) who proposed that, unless the market is very large, higher transparency may result in higher volatility and lower liquidity. However, we do not find that efficiency improves monotonically with the level of transparency. In fact, market efficiency improves in the partial transparent market, but not in the most transparent market. Finally, to decipher the transparency impact, this study integrates the relationships among market qualities, transparency level, and investor order placement strategies, by using the simultaneous equations approach. The results indicate that transparency affects investor order placement strategies and market performance, 347

6 Are Investors more Aggressive in Transparent Markets? and that the relationship between order placement strategies and market performance is simultaneously determined. That is, investor response to increased transparency may influence market quality, in turn causing changes in placement strategies. Specifically, greater transparency intensifies order aggressiveness, while reducing the order size of institutional investors and increasing that of individual investors. Transparency increases volatility, but it has little effect on liquidity and efficiency. More aggressive orders lead to higher volatility and lower liquidity. Generally, the trading size of individual investors negatively influences market performance. On the other hand, market performance may influence order strategies. For example, institutional investors place smaller orders as market quality deteriorates, while individual investors are less sensitive to market conditions. Additionally, all investors will be more aggressive with better market efficiency. The findings of this study present a clear message: transparency does not always improve market quality, though it will certainly change the order placement strategies of investors. As we find individual and institutional traders react oppositely to increased order flow information in terms of their order sizes, it would be interesting for future research to study whether there is an optimum level of order flow transparency exists in markets with different investor structures. 2. Market Background and Data 2.1 Market background Trading on the Taiwan Stock Exchange Corporation (TSEC) begins at 9:00 a.m. and closes at 1:30 p.m. Orders can be entered half an hour before the trading session starts. The Fully Automated Securities Trading (FAST) system of TSEC is a frequent call auction mechanism, where orders are accumulated and cleared every 45 seconds or so at a price that maximizes the trading volume. Since July 2002, the auction period at the close has been extended to 5 minutes. 4) Orders are executed according to a price and time priority principle. All traders can observe the transaction prices and volumes as well as the order flow information on a real time basis. There is a 7% 4) That is, the system accumulates orders for 5 minutes (from 1:26 p.m. to 1:30 p.m.) before the closing call auction. 348

7 Asia-Pacific Journal of Financial Studies (2008) v37 n2 daily price movement limit for all stocks. The market is generally quite liquid, ranking among the world s top ten security markets in terms of market capitalization and the fifth in turnover ratios. Individual investors account for about 70%~80% of the trading on the TSEC, but institutional traders are usually the price leaders. 2.2 Sample and Data The sample comprises 50 stocks with various trading intensities drawn from firms listed on the Taiwan Stock Exchange from February 2002 to June ) To compare the influence of different levels of transparency, the sample period covers three stages of increasing transparency, including two months for each stage. 6) This study defines February and June of 2002 as the first stage, the least transparent stage when only the prices of the best bid and ask quotes are disclosed; moreover, July and December of 2002 are defined as the second stage, the partially transparent period during which the quote and volume of the best bid/ask are disclosed; and March and June of 2003 are defined as the third stage, the most transparent period during which the top five prices in the book are revealed, together with information on the depth at each price. The intraday data set contains the complete order book and all trades executed from February 2002 to June 2003 during the trading session. The total number of orders is 36,718,742, and the number of trades is 2,301,096. The records of each order and trade include information on the price, size, direction, investor type (institutional or individual), and the time-stamped to the nearest one hundredth of a second. In intraday analysis, trading time is divided into nine, half-hour intervals, the first running from 9:01 a.m. to 9:30 a.m., and the last comprising the thirty minutes before the closing call. Table 1 shows summary statistics regarding order characteristics under various transparency stages. The average daily order quantity in, so called, the opaque, partially transparent and most transparent stages are 94,627, 131,267 and 148,695 thousands of shares, respectively, while average 5) The stocks are classified into ten deciles based on turnover ratios and we randomly select five samples from each decile. 6) The sample periods cover three different transparent stages. To control the market conditions, for each stage, periods with similar market index level are selected, including February, June, July, and December of 2002, and March and June of

8 Are Investors more Aggressive in Transparent Markets? Table 1. Summary statistics for order characteristics by investors type in three transparency stages This table lists summary statistics regarding order characteristics for the entire market, institutional and individual investors during various transparent stages. The appellations include daily order quantity, daily number of orders, average order size and trading value by type of investors. The order quantity is in thousand shares. The daily number of orders is the average number of orders per day. The average order size is the daily order quantity divided by the daily number of orders. first stage entire market institutional investors individual investors daily order daily number average daily order daily number average daily order daily number average Mean 94,627 10, , ,349 10, Median 16,505 2, , ,763 2, Std.Dev 209,020 22, ,296 1, ,956 21, Max 840,596 82, ,357 4, ,803 80, Min 1, second stage entire market institutional investors individual investors daily order daily number average daily order daily number average daily order daily number average Mean 131,267 13, , ,221 13, Median 20,091 2, , ,767 2, Std.Dev 306,358 30, ,143 1, ,868 29, Max 1,244, , ,231 5, ,100, , Min 1, , third stage entire market institutional investors individual investors daily order daily number average daily order daily number average daily order daily number average Mean 148,965 12, , ,732 11, Median 22,803 2, , ,316 2, Std.Dev 331,641 26, ,519 1, ,509 24, Max 1,256,388 97, ,907 5, ,481 93, Min 2, ,

9 Asia-Pacific Journal of Financial Studies (2008) v37 n2 daily numbers of orders in the three stages are 10,775, 13,722 and 12,165. The average order size in the opaque, partially, and most transparent stages are 8.53, 8.76, and 9.82 thousands of shares. It seems that investors are more active in the transparent market, in which the order quantity, the number of orders and average order size are all increasing. 3. Methodology This section analyzes the influence of transparency on order placement strategies and market performance, and then utilizes the two-stage least squares (2SLS) approach to explore interactions between order strategies and market performance. 3.1 Measuring Order Aggressiveness Studies on order placement strategies usually focus on limit orders versus market orders. This study conducts a more detailed distinction of order strategies based on order aggressiveness, order size, and investor type, and analyzes changes in each order type over the study period. Orders are classified into six categories according to limit price aggressiveness. The entire time series of order flow is divided into buying and selling sub-samples. The aggressiveness classification is based on orders submitted at time t, and the state of order book immediately before, i.e., at t-1. Category 6 (C6) is the most aggressive, and category 1 (C1) is the most conservative. For instance, in the case of buying orders, the order price that equals the daily maximum price limit is the most aggressive order. 7) If the order price is less than the maximum price limit and higher than the best ask then the order is in category 5. The price of category 4 orders is less than or equal to the best ask and higher than the best bid. If the order price equals to the best bid then it is in category 3. If the order price is less than the best bid and higher than or equals to the best bid minus two ticks then it is in category 2. 8) Category 1 is the most passive, in that the order price is less than the best bid minus two ticks and higher than or equals to the minimum price limit. The category for selling orders is determined similarly. Meanwhile, the categories of order 7) The trading mechanisms in Taiwan allow investors to submit limit orders only, though some very aggressive investors submit ceiling buy orders or floor sell orders. 8) The Tick is the smallest possible movement in the price of a security. The tick is NT$0.01 for prices less than NT$10, NT$0.05 for prices between NT$10~50, NT$0.1 for prices between NT$50~100, NT$0.5 for those between NT$100~500, NT$1 for those between NT$500~1000, and NT$5 for those higher than NT$

10 Are Investors more Aggressive in Transparent Markets? aggressiveness are summarized below: Aggressiveness of buying orders: C6:order pricet = the maximum price limit C5:the best askt-1 < order pricet < maximum price limit C4:the best bid t-1 < order pricet <= the best ask t-1 C3:order pricet = the best bid t-1 C2:best bid t-1 minus two ticks <= order pricet < the best bid t-1 C1:the minimum price limit <= order pricet < best bid t-1 minus two ticks Aggressiveness of selling orders: C6:order pricet = the minimum price limit C5:the minimum price limit < order pricet < the best bid t-1 C4:the best bid t-1 <= order pricet < the best ask t-1 C3:order pricet = the best ask t-1 C2:the best ask t-1 < order pricet <= best ask t-1 plus two ticks C1:best ask t-1 plus two ticks < order pricet <= the maximum price limit For each order aggressiveness category, we calculate the percentage of the number of orders made by institutional(individual)investors relative to all investors during each trading interval. For example, the percentage of C6 orders made by institutional investors during interval j ( INST C6, j ) is calculated as follows: INST C6 INST C6, j = 6 INST 6 Ci + Ci i= 1 i= 1 INDIV (1) INST where Ci and Ci INDIV represent the number of orders in category i made by institutional and individual investors, respectively. Furthermore, to analyze the order placement pattern of institutional and individual investors, proportion of orders by investor type, 9) number of shares, number of 9) For example, the proportion of orders placed by institutional investors is the institutional investor order quantity (number of shares) divided by all investors quantity (institutional and individual investor number of shares). The denominator does not include cancelled and revised orders. 352

11 Asia-Pacific Journal of Financial Studies (2008) v37 n2 orders, average order size and average value per order are calculated for each time interval. 3.2 Measuring Market Performance The main point this study attempts to make is to show that the impact of increasing order flow transparency on market performance depends on how different types of investors adjust their order placement strategies in response to the change in transparency, which in turn affects market liquidity, volatility and efficiency. We define trading strategies by two dimensions: order aggressiveness and order size. The following analyses measure volatility by the standard deviation of threeminute returns and measure liquidity by the reciprocal of the effective spread. 10) Efficiency is measured by the reciprocal of 1-MEC. MEC is the market efficient coefficient, defined as the variance ratio of three-minute to one-minute returns. 11) Therefore, higher values of liquidity and efficiency measures represent better market quality. Additionally, efficiency is also measured by the absolute value of the first order autocorrelation of three-minute returns and runs test. 12) 3.3 Interaction Between Order Placement Strategies and Market Performance To clarify the empirical interaction of order placement strategies and market performance, the two stage least squares (2SLS) approach is used. The endogenous variables include market performance (i.e., volatility, liquidity and efficiency) and order placement strategies (i.e., weighted order aggressiveness and the order size of institutional and individual investors). Meanwhile, the exogenous variables are transparency dummy (i.e., three transparency stages), market factors (i.e., transaction prices 10) The results of effective and quoted spread are similar, and the following analysis shows the results of the percentage effective spread. 11) * V( R ), where V * ( R ) and V( r * ) denote the variances of the three-minute returns and one-minute MEC = T * 3 V( rt ) T t returns, respectively. The market is more efficient if MEC is close to 1. (see Lo and MacKinlay, 1988; Chow and Denning, 1993; Wright, 2000 for examples of this measure). 12) Since the results of MEC, first order autocorrelation and run test are similar, the results are presented based on MEC to save space. 353

12 Are Investors more Aggressive in Transparent Markets? and volumes), firm characteristics (i.e., market capitalization, profitability, and ownership structure), 13) and trading activity variables (i.e., turnover ratios and margin trading). 14) The empirical models are described as follows: Performance 2 = β0 + β1 i T i+ β2 Aggressive ++ β3 InsOrd + β4 IndOrd + β5 Price + β6 Volume + β7 Board + β8 Turnover + ε (2) 1 i= 1 InsOrd 2 = λ0 + λ1 i T i+ λ2 Aggressive + λ3 IndOrd + λ4 Price + λ5 Volume + λ6 Board + λ7 Size + λ8 Performance + ε (3) 2 i= 1 IndOrd 2 = γ 0 + γ1 i T i+ γ2 Aggressive + γ3 InsOrd + γ4 Price + γ5 Volume + γ6 Margin + γ7 Size + γ8 Performance + ε (4) 3 i= 1 Aggressive 2 = η0 + η1 i T i + η2 InsOrd + η3 IndOrd + + η4 Price + η5 Volume + η6 M arg in + η7 NI + η8 Performance + ε (5) 4 i= 1 Where Performance includes volatility, liquidity and efficiency. Volatility is the standard deviation of three-minute returns: liquidity is the reciprocal of proportional effective spread: and efficiency is the reciprocal of 1-MEC (market efficient coefficient). Therefore, higher values of these measures indicate higher volatility, liquidity, and efficiency. Moreover, T1 and T2 are the partially transparent and the most transparent stage dummy variables; Aggressive denotes weighted aggressiveness; 15) InsOrd and IndOrd are the order sizes of institutional and individual investors, respectively; Price and Volume are transaction prices and volumes, respectively; Board denotes director shareholdings; Turnover represents turnover ratio; Size is market capitalization; Margin denotes margin trading ratio; and NI represents net profit margin. 16) 13) Profitability reflects firm performance and ownership structure influences the level of information asymmetry. Both may affect investors incentives to trade and further influence market performance. 14) The turnover ratio captures trading activity, which is known to influence price performance. Margin trading reflects investor s risk attitude, which may influence their order aggressiveness and therefore market performance. 15) Weighted aggressiveness (WA) = 6 wi i, where w i represents the proportion of orders for order category i= 1 i at a given interval and i = 1~6. Higher WA thus indicates more aggressive order strategies. 16) As pointed out by the referee, in the simultaneous regression volume and turnover ratio might be endogenous. However, as the result of 2SLS indicates that volume and turnover are insignificant, the extent of endogeneity may be trivial in this case, and they are treated as control variables in a cross sectional analysis. 354

13 Asia-Pacific Journal of Financial Studies (2008) v37 n2 4. Analysis of Results To explore the effect of increasing transparency on order strategies and market performance, this study first examines the distribution of order types and the intraday pattern of each order category under various transparent stages, then conducts performance analysis in three stages, and finally discusses the results of the 2SLS approach. 4.1 Intraday order placement strategies under varying transparency levels The summary statistics of the order distribution at each 30 minute trading interval in three transparency stages for institutional and individual investors are listed in Tables 2 and 3, respectively. Orders are classified into six categories based on aggressiveness level. The largest order type at a given interval is marked with #. Table 2 lists the order type distribution for institutional investors. In the partially transparent market (the second stage), the orders most frequently placed are the best bid/ask category (C3), averaging about one fourth of the total institutional orders. In the most transparent market (the third stage), orders are primarily concentrated on C3 or C4. Notably, C4 becomes increasingly favored as the level of transparency increases during stages 2 and 3. That is, as the level of transparency increases, institutional traders become increasingly aggressive in order submission during the intraday trading sessions, 17) suggesting a rat race effect as transparency increases. 18) At the closing interval, the top order choice for institutional traders is the most aggressive order, C6, in stage one. However, as transparency increases, the proportion of C6 at the closing interval declines significantly, and C3 becomes the top order choice. On the other hand, at the beginning interval (interval 1), the top order type is C1 for all stages. This result suggests that institutional traders would submit more conservative orders when the market opens to extract information and more aggressive orders 17) The intraday trading sessions refer to periods after the market s opening session and before the market s closing session. 18) Foster and Viswanathan (1996) and Back, Cao, Willard (2000) raised the issue of rat race, which refers to the increase in trade competition among informed traders when information correlation is high. 355

14 Are Investors more Aggressive in Transparent Markets? Table 2. Distribution of order types by institutional investors This table illustrates the distribution of institutional order submission for each 30 minute trading interval in the three stages. Orders are classified into six categories based on level of aggressiveness. For example, interval 1 indicates the trading interval between 9:01 am and 9:30 am, and C6 represents category 6 orders, i.e. the most aggressive orders. The table lists the percentage of each order category for institutional investors relative to all investors(institutional and individual investors)at a given interval. The largest order type in a given interval is marked by #. Additionally, weighted aggressiveness (WA) is indicated by calculating w i, where w i 6 represents the proportion of orders for order category i at a given i= 1 i interval and i =1~6. Higher WA thus indicates more aggressive order strategies. first stage C6 C5 C4 C3 C2 C1 WA interval % 0.29% 0.52% 0.85% 0.84% 1.10% # 3.08 interval % 0.27% 0.62% 1.21% # 0.84% 1.02% 3.21 interval % 0.31% 0.92% 1.45% # 0.92% 1.05% 3.35 interval % 0.38% 0.96% 1.67% # 1.11% 1.10% 3.37 interval % 0.37% 1.23% 1.75% # 1.03% 1.06% 3.45 interval % 0.42% 1.41% 2.17% # 1.13% 1.08% 3.43 interval % 0.38% 1.48% 1.80% # 1.15% 1.14% 3.49 interval % # 0.38% 1.25% 1.67% 1.16% 1.10% 3.52 interval % # 0.37% 0.79% 0.98% 0.75% 0.74% 3.76 daily 1.24% 0.34% 0.89% 1.34% 0.94% 1.03% 3.41 second stage C6 C5 C4 C3 C2 C1 WA interval % 0.32% 0.69% 0.94% 0.87% 1.02% # 3.05 interval % 0.25% 0.97% 1.33% # 0.91% 0.96% 3.19 interval % 0.28% 1.15% 1.55% # 0.89% 0.91% 3.28 interval % 0.36% 1.36% 1.66% # 1.01% 0.89% 3.37 interval % 0.39% 1.62% 1.85% # 0.99% 0.90% 3.39 interval % 0.39% 1.66% 2.11% # 1.11% 1.03% 3.37 interval % 0.43% 1.80% 2.06% # 1.19% 0.99% 3.47 interval % 0.42% 1.68% 1.82% # 1.05% 0.92% 3.48 interval % 045% 1.05% 1.25% # 0.83% 0.63% 3.57 daily 0.96% 0.36% 1.21% 1.52% 097% 0.91% 3.35 third stage C6 C5 C4 C3 C2 C1 WA interval % 0.43% 1.13% 1.42% 1.25% 1.53% # 3.03 interval % 0.43% 1.61% 2.15% # 1.21% 1.12% 3.27 interval % 0.55% 1.80% 2.44% # 1.23% 1.16% 3.35 interval % 0.63% 2.32% 2.78% # 1.30% 1.05% 3.46 interval % 0.69% 2.34% 2.67% # 1.39% 1.00% 3.51 interval % 0.78% 3.31% # 2.86% 1.40% 1.15% 3.55 interval % 0.82% 3.39% # 3.14% 1.46% 1.08% 3.62 interval % 0.86% 3.07% # 3.03% 1.38% 1.13% 3.61 interval % 0.67% 1.74% 1.86% # 1.15% 0.82% 3.64 daily 1.33% 0.56% 1.97% 2.37% 1.27% 1.16%

15 Asia-Pacific Journal of Financial Studies (2008) v37 n2 as market close approaches. Although traders become more aggressive during intraday trading sessions when pre-trade transparency increases, the most aggressive and costly C6 order declines sharply especially in the closing interval. Table 3 lists the distribution of order types by individual investors. The pattern differs from Table 2. Compared to institutional traders, the top choice of individual traders is one-step less aggressive in stage 3. The largest order type concentrates on C3, differing from the concentration of institutional traders on. Similar to institutional traders, at the opening interval orders are more conservative (except in the least transparent stage), while at the closing interval they are more aggressive. However, unlike institutional investors who prefer C3 orders at the close, the orders of individual traders are dichotomous at the close, with both C6 and C3 orders being common. Compared with institutional traders, individual traders are more patient in general, but less patient at the closing interval. Tables 2 and 3 show that the impact of increasing transparency on order submissions differs between the opening and closing intervals. At the opening interval most orders placed are quite conservative orders (C1), regardless of the degree of transparency. However, at the closing interval the most favored orders change from very aggressive (C6) to fair (C3), as transparency increases from stage one to stage three. Enhanced transparency impacts order placement strategies near market close more than near market opening, which suggests that investors submit more conservative orders at the start of the trading day to gain information from the market. As more information is revealed during the course of the day and as pre-trade transparency increases, the need to submit a very aggressive and expensive orders before the end of trading to ensure execution is largely reduced. Tables 4 and 5 list the results of nonparametric Wilcoxon sign tests for the differences in order aggressiveness between the stages of various transparency levels. The percentage of the most aggressive orders (C6) placed by institutional investors increases significantly with pre-trade transparency, while that placed by individual investors decreases. Additionally, not only the percentage of the most aggressive orders but also that of the most conservative orders (C1) for individual investors declines significantly, indicating that they reduce extreme order placement as market transparency enhances. Furthermore, both institutional and individual investors place increased orders after the secondary aggressive order grade (C5~C3), especially for inside-quote orders (C4), as order flow transparency enhances. The study results 357

16 Are Investors more Aggressive in Transparent Markets? Table 3. Distribution of order types by individual investors This table illustrates the distribution of individual order submission for each 30 minute trading interval in the three stages. Orders are classified into six categories based on level of aggressiveness. For example, nterval 1 indicates the trading interval between 9:01 am and 9:30 am, and C6 represents category 6 orders, i.e. the most aggressive orders. The table lists the percentage of each order category for individual investors relative to all investors(institutional and individual investors)at a given interval. The largest order type in a given interval is marked by #. Additionally, weighted aggressiveness (WA) is indicated by calculating 6 w i 1 i i, where w represents the proportion of orders for order category i at a given interval and i i =1~6. Higher WA thus indicates more aggressive order strategies. first stage C6 C5 C4 C3 C2 C1 WA interval % 6.29% 10.96% 16.25% 16.35% 24.67% # 3.22 interval % 5.98% 12.23% 20.07% 17.63% 20.78% # 3.21 interval % 5.31% 12.95% 21.80% # 16.43% 19.83% 3.22 interval % 5.24% 12.68% 21.91% # 17.34% 18.58% 3.23 interval % 5.55% 12.88% 22.51% # 16.59% 17.49% 3.29 interval % 5.68% 12.96% 22.67% # 16.80% 16.63% 3.29 interval % 5.28% 13.08% 22.85% # 16.77% 15.96% 3.33 interval % 5.69% 14.34% 22.34% # 15.77% 15.08% 3.41 interval % # 8.18% 13.27% 18.50% 13.68% 12.75% 3.80 daily 20.61% 6.15% 12.53% 19.97% 16.26% 18.70% 3.35 second stage C6 C5 C4 C3 C2 C1 WA interval % 8.26% 14.29% 19.41% 16.06% 21.35% # 3.22 interval % 6.39% 16.46% 22.03% # 17.00% 19.26% 3.16 interval % 6.06% 16.28% 23.02% # 16.92% 18.80% 3.15 interval % 6.57% 16.92% 22.72% # 16.54% 17.67% 3.19 interval % 6.09% 17.73% 23.52% # 15.81% 16.77% 3.22 interval % 5.57% 18.04% 22.76% # 16.12% 16.61% 3.22 interval % 6.23% 17.67% 23.64% # 15.54% 15.50% 3.26 interval % 6.50% 18.22% 23.37% # 15.42% 14.80% 3.32 interval % 9.96% 16.82% 20.53% # 14.71% 13.42% 3.56 daily 15.00% 7.24% 16.45% 21.74% 16.02% 17.62% 3.30 third stage C6 C5 C4 C3 C2 C1 WA interval % 7.35% 12.95% 18.66% 16.25% 23.17% # 3.11 interval % 6.24% 14.86% 21.26% # 16.50% 19.59% 3.15 interval % 6.21% 15.34% 22.21% # 15.98% 18.30% 3.18 interval % 5.94% 16.09% 22.19% # 16.14% 17.23% 3.27 interval % 6.30% 15.96% 21.64% # 15.60% 16.73% 3.24 interval % 6.19% 16.19% 22.73% # 15.41% 15.93% 3.30 interval % 5.96% 16.64% 23.11% # 15.14% 14.32% 3.28 interval % 7.05% 17.49% 23.17% # 14.31% 13.26% 3.45 interval % 9.37% 16.17% 20.07% # 14.24% 12.81% 3.58 daily 14.96% 6.65% 15.43% 21.51% 15.56% 17.23% 3.33 = 358

17 Asia-Pacific Journal of Financial Studies (2008) v37 n2 Table 4. Differences in institutional investor order aggressiveness between various transparency levels This table lists the differences between various periods in order submission by institutional investors for given trading intervals. Orders are classified into six categories based on level of aggressiveness. For example, interval 1 indicates the trading interval between 9:01 am and 9:30 am, and C6 represents the category 6 orders, i.e. the most aggressive orders. The second column indicates the two stages under comparison, e.g., 2-1 indicates stage 2 minus stage 1. For example, at interval 1, the percentage of C6 in the second stage minus the percentage of C6 in the first stage is -0.16%; that is, C6 decreases from stage 1 to stage 2 for the first trade interval. The final column represents the differences in weighted aggressiveness between various transparent stages; that is, the positive value represents increased weighted aggressiveness and investors submitting more aggressive orders. C6 C5 C4 C3 C2 C1 WA interval % 0.03% 0.18%** 0.17% 0.03% -0.16% % 0.14% 0.61%*** 0.68% 0.41% 0.32% % *** 0.11% 0.44% *** 0.51% 0.38% 0.48% interval % -0.02% 0.35% *** 0.13% 0.07% -0.07% % 0.16% 0.99%*** 0.94% 0.37% 0.10% ** % ** 0.18%* 0.64%*** 0.82% 0.30% 0.17% *** interval % -0.03% 0.23% ** 0.11% -0.03% -0.14% % 0.24% 0.88%*** 1.00% 0.31% 0.11% % *** 0.27% 0.64% ** 0.89% 0.34% 0.25% interval % -0.03% 0.40% *** -0.01% 0.10% -0.21% % 0.25% 1.36%*** 1.12% 0.19% -0.05% *** % *** 0.28% 0.96% *** 1.12% 0.29% 0.16% *** interval % 0.01% 0.39%** 0.10% -0.04% -0.16% % 0.31%** 1.12%*** 0.92% 0.36% -0.06% % *** 0.30% 0.72% ** 0.82% 0.40% 0.10% ** interval % -0.03% 0.24% -0.06% -0.02% -0.06% % ** 0.36% 1.45% *** 1.14% 0.27% 0.07% *** % *** 0.39%** 1.21%*** 1.20%*** 0.29% 0.12% *** interval % 0.06% 0.32% 0.26% 0.03% -0.15% % ** 0.44% 1.66% *** 1.59%*** 0.31% -0.06% *** % *** 0.39%*** 1.34%*** 1.33% 0.28% 0.09% interval % 0.03% 0.43%*** 0.15% -0.11% -0.18% % ** 0.48% 1.81% *** 1.36%*** 0.22% 0.03% % *** 0.44%** 1.38%*** 1.21% 0.34% 0.20% ** 359

18 Are Investors more Aggressive in Transparent Markets? interval % ** 0.07% 0.26% * 0.27% 0.08% -0.11% ** % 0.29% 0.95%*** 0.87% 0.40% ** 0.08% ** % *** 0.22% 0.69% *** 0.60% 0.32% 0.18% daily % *** 0.03% 0.32% ** 0.19% 0.03% -0.12% % 0.23% 1.07%*** 1.04%*** 0.33%* 0.13% % *** 0.20% 0.75% *** 0.85%*** 0.30%*** 0.25% ** Note) * indicates significant at 10%, ** indicates significant at the 5%, and ** indicates significant at the 1%. Table 5. Differences in individual investor order aggressiveness between various transparency levels This table lists the differences between various periods in order submission by individual investors for given trading intervals. Orders are classified into six categories based on level of aggressiveness. For example, interval 1 indicates the trading interval between 9:01 am and 9:30 am, and C6 represents the category 6 orders, i.e. the most aggressive orders. The second column indicates the two stages under comparison, e.g., 2-1 indicates stage 2 minus stage 1. For example, at interval 1, the percentage of C6 in the second stage minus the percentage of C6 in the first stage is -0.16%; that is, C6 decreases from stage 1 to stage 2 for the first trade interval. The final column represents the differences in weighted aggressiveness between various transparent stages; that is, the positive value represents increased weighted aggressiveness and investors submitting more aggressive orders. C6 C5 C4 C3 C2 C1 WA interval % *** 1.98%*** 3.33%** 3.17%*** -0.29% -3.32% *** % *** 1.07% 1.99% *** 2.41%*** -0.09% -1.50% % ** -0.91%** -1.34% -0.76% 0.19% 1.82% ** interval % *** 0.41% 4.22% *** 1.95%*** -0.63% -1.52% * % *** 0.25% 2.63% *** 1.19%** -1.13% -1.19% % -0.16% -1.59%*** -0.77% -0.50% 0.33% interval % *** 0.75%** 3.32%** 1.22%** 0.49% -1.03% % *** 0.89% 2.38% *** 0.41% -0.45% -1.53% ** % 0.14% -0.94% -0.80% -0.94% -0.50% ** interval % *** 1.33% 4.25% *** 0.82% -0.79% -0.91% % *** 0.69% 3.41% *** 0.28% -1.19% ** -1.35% % -0.64% -0.83% -0.53% -0.40% -0.44% interval % *** 0.54% 4.85% ** 1.01% -0.78% -0.72% % *** 0.75% 3.09% *** -0.87% -0.99% -0.76% % 0.21% -1.76% -1.88% -0.22% -0.04% interval % *** -0.11% 5.08% ** 0.09% -0.67% -0.02%

19 Asia-Pacific Journal of Financial Studies (2008) v37 n % *** 0.51% 3.23% *** 0.06% -1.39% * -0.70% * % ** 0.62% -1.85%** -0.03% -0.72% ** -0.68% interval % *** 0.95% 4.59% 0.80% -1.23% * -0.46% * % *** 0.68% 3.56% *** 0.26% -1.63% ** -1.64%** % -0.27% -1.03% -0.54% -0.40% -1.17%** interval % *** 0.81% 3.89% *** 1.03% -0.34% -0.28% ** % *** 1.36%*** 3.15%*** 0.83% -1.46% *** -1.83%** ** % 0.55% -0.73% -0.20% -1.11%** -1.54%** interval % *** 1.78%** 3.55%** 2.02%** 1.03% 0.68% *** % *** 1.19%** 2.90%*** 1.06% 0.56% 0.06% *** % -0.59% -0.65% -0.96% -0.47% -0.62% ** daily % *** 1.09%* 3.92%*** 1.77%** -0.25% -1.08% % *** 0.51% 2.89% *** 1.54% -0.70% -1.47% % -0.58%** -1.02%** -0.23% -0.46% -0.39% Note) * indicates significant at 10%, ** indicates significant at the 5%, and *** indicates significant at the 1%. Table 6. Differences in the order placement pattern between various transparency levels by investor types The table lists the differences in order placement pattern between different transparency stages. The proportion of institutional investor orders is the institutional investor order quantity (number of shares) divided by the order quantity of all investors (institutional and individual investor numbers of shares). The proportion of individual investor orders is defined according to the same logic. The order quantity represents the number of shares. Furthermore, the number of orders represents the orders submitted by investors during specific time intervals. Moreover, the average order size is the total order quantity divided by the total number of orders during that interval. Summarized, statistics are listed for both institutional and individual investors in the three transparency stages. difference Institutional Investors orders relative to all institutional investors institutional investors average daily order quantity (thousand shares) average daily number of orders average order size (thousand shares) Individual investors orders relative to all average daily order quantity (thousand shares) average daily number of orders average order size (thousand shares) interval % % 935 * 97** %*** 681** 10*** %*** %*** *** %*** * interval % ** -0.41%

20 Are Investors more Aggressive in Transparent Markets? %** 342 5** -8.80* -2.68%*** * %*** 387* 7 ** % *** * interval % % 393 ** 25* %** 374 5** % ** %*** ** % *** ** interval % % 586 *** 29** %** 189 5** -8.09* -2.94%** %*** *** %*** interval % % 493 * * %** 210* 4 * % ** * %** 231* 6 ** % ** * interval % * 0.02% 319 ** 21* %** 99 3** % ** ** %*** ** % *** ** interval % % 245 *** %*** * -7.19** -4.09%*** 1095** ** %*** 260* 7 ** -1.60* -3.61%*** ** interval % % 316 ** %*** 254** 7 * % *** ** %*** 283** 9 *** %*** ** interval % 31-3 * % %*** 603** %*** ** %*** 572** 7 *** %*** * daily % ** %** 4160** 255* 0.45 * % 3015 * 50** -6.00* -0.63% ** %** 3091** 69*** %** ** Note) * indicates significant at 10%, ** indicates significant at the 5%, and *** indicates significant at the 1%. 362

21 Asia-Pacific Journal of Financial Studies (2008) v37 n2 indicate that greater pre-trade transparency intensifies competition in order placement strategies, especially for institutional investors. Compared to institutional investors, individual investors seem to be more patient with increasing transparency, which suggests that liquidity providers are mainly individuals. Figure 1. The intraday patterns for each order type under various transparency levels for institutional investors The figure illustrates the intraday patterns of each order type for institutional investors. Orders are classified into six categories based on level of aggressiveness. The trading time is divided into nine 30-minute trading intervals. For example, interval 1 indicates the trading interval between 9:01 am and 9:30 am, and C1 represents the category 1 orders, i.e. the most aggressive orders. Moreover, the vertical axis indicates the proportion of orders placed in each interval for a given order category. C6( %) 25.00% 20.00% 15.00% The intraday pattern of C6 orders C5( %) 25.00% 20.00% 15.00% The intraday pattern of C5 orders 10.00% 10.00% 5.00% 5.00% 0.00% interval 0.00% interval 1st stage 2nd stage 3rd stage 1st stage 2nd stage 3rd stage The intraday pattern of C4 orders The intraday pattern of C3 orders 20.00% C4( %) 20.00% C3( %) 15.00% 15.00% 10.00% 10.00% 5.00% 5.00% 0.00% interval 0.00% interval 1st stage 2nd stage 3rd stage 1st stage 2nd stage 3rd stage 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% The intraday pattern of C2 orders C2( %) interval The intraday pattern of C1 orders C1( %) 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% interval st stage 2nd stage 3rd stage 1st stage 2nd stage 3rd stage 363