Individual Trades, Institutional Trades and Intraday Futures Price ...

Individual Trades, Institutional Trades and Intraday Futures Price Behavior: Evidence from the Taiwan Futures Exchange By Robin K. Chou, George H. K. Wang, Yun-Yi Wang and Johan Bjursell

Abstract This paper examines the price, liquidity and information effects of large institutional trades versus individual trades on three futures contracts traded on the Taiwan Futures Exchange. Our unique intraday data set identifies institutional traders and individual traders and the direction of buy and sell orders. Several interesting results are obtained. We find, for the entire sample period, most buyer-initiated large trades have larger permanent price effects than seller-initiated large trades and vice versa for liquidity effects. These results are consistent with previous findings on large trades of futures contracts on the Chicago Mercantile Exchange and on institutional trades in equity markets. However, we find that the permanent price effects of large sells are larger than the effects of large purchases in bearish markets and the reverse pattern is found for bullish markets. These results are consistent with both the current economic condition hypothesis and the momentum trading hypothesis which are used to explain the asymmetry between price impacts, information and liquidity effects of large buys and sells. Further, the magnitude of price impacts of large trades are inversely related to the liquidity of individual futures contracts. Finally, we provide new empirical results showing that the asymmetric patterns between price impacts of large buys and sells holds for individual traders as well as for institutional traders. Keywords: Large Trades; Trader Types; Total Price Effects; Liquidity Effects; Information effects; Futures Price Behavior. JEL classification codes: G10 ___________________________ Robin K. Chou is Professor and Chairman, Department of Finance, School of Management, National Central University, Jhongli, Taiwan. George H. K. Wang is Research Professor of Finance, School of Management, George Mason University, Fairfax, VA. Yun-Yi Wang is a Ph. D. Candidate at Department of Finance, National Central University, Jhongli, Taiwan. Johan Bjursell is a Ph. D. Candidate, Department of Computational and Data Science, George Mason University, Fairfax, VA.

Individual Trades, Institutional Trades and Intraday Futures Price Behavior: Evidence from the Taiwan Futures Exchange Abstract This paper examines the price, liquidity and information effects of large institutional trades versus individual trades on three futures contracts traded on the Taiwan Futures Exchange. Our unique intraday data set identifies institutional traders and individual traders and the direction of buy and sell orders. Several interesting results are obtained. We find, for the entire sample period, most buyer-initiated large trades have larger permanent price effects than seller-initiated large trades and vice versa for liquidity effects. These results are consistent with previous findings on large trades of futures contracts on the Chicago Mercantile Exchange and on institutional trades in equity markets. However, we find that the permanent price effects of large sells are larger than the effects of large purchases in bearish markets and the reverse pattern is found for bullish markets. These results are consistent with both the current economic condition hypothesis and the momentum trading hypothesis which are used to explain the asymmetry between price impacts, information and liquidity effects of large buys and sells. Further, the magnitude of price impacts of large trades are inversely related to the liquidity of individual futures contracts. Finally, we provide new empirical results showing that the asymmetric patterns between price impacts of large buys and sells holds for individual traders as well as for institutional traders.

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Individual Trades, Institutional Trades and Intraday Futures Price Behavior: Evidence from the Taiwan Futures Exchange 1

Introduction Futures market participants are interested in the market impact costs for different

order types and trade sizes, because they are a major part of the implicit trading costs. Trading costs affect their decisions regarding the implementation of alterative trading strategies, which determines the performance of investment returns. Futures exchanges and regulators are interested in measuring the price impacts of various trade sizes because levels of transaction cost are used as one of the criteria to judge market quality. However, to the best of our knowledge, there is no empirical literature on examining the price impact of large trades initiated by different types of traders. This paper seeks to fill this gap. Most of the previous literature on the price impacts of large (block) trades is mainly concentrated in the equity markets. For example, existing literature on the price impacts of block trades in equity markets includes Kraus and Stoll (1972), Holthausen, Leftwich and Mayers (1987, 1990), Gemmill (1996), Keim and Madhavan (1996) and others. Chan and Lakonishok (1993, 1995) investigated the price impacts of institutional trades on equity prices. They found a general pattern that the purchase of a block trade in equity markets is accompanied by an increase in its price, which continue to rise after the block trade. Block sales are associated with an initial drop in price, but then are followed by a strong price reversal.

These studies provided empirical evidence that block

purchases have larger total price and permanent price impacts than block sales. One possible reason proposed by Scholes (1972) and Shleifer (1986) for the price changes around block trades is the imperfect substitution for a particular stock. A buyer 2

faces an upward supply curve and a seller faces a downward demand curve. Thus, a premium has to be offered by the buyer or seller of a block trade to attract the opposite side of the desired trade. If supply is more inelastic than demand, than the permanent price effect of buys would be larger than for sells. As a consequence, liquidity effects (price reversals) of buys would be smaller than sells. Chan and Lakonishok (1993) and Keim and Madhavan (1996) suggest that the asymmetric price response between block purchases and sales is due to differences in the information content of buys and sells.

They suggest that the creation of new long

positions is more likely the result of new private information (firm-specific information). On the other hand, sales of institutional trades are mostly due to liquidity–motivated reasons. For instance, the decision to sell a particular stock may be driven by failure to meet the objectives of the mutual fund or it may be due to portfolio asset reallocation. Chan and Lakonishok (1993) also suggest that differences in price and liquidity effects of block and institutional trades may be due to differences in short–run liquidity costs. Large traders are more willing to accommodate large sales by purchasing shares and holding them in inventory, for which they are compensated by short-run price concessions. However, most block traders are less willing to do short selling in order to meet the needs of the block buys because they are concerned that prices are likely to rise after the block purchase. Saar (2001) proposes a theoretical model to explain the previous empirical evidence found in equity markets that the permanent (information) effect of buys is greater than that of sells.

His model demonstrates how the trading strategy of

institutional portfolio managers generates a difference in information content between

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buys and sells.1 According to the theoretical model, the history of past price performance influences the shape of asymmetry between the permanent effects of buys and sells after a block trade. For example, the model predicts that the information effect of buys is greater than that of sells following a long period of price declines and the information effect of sells is greater than that of buys following a long period of price run ups. Using the characteristics of institutional trading in international stocks from 37 countries for the periods 1997 to 1998 and 2001, Chiyachantana et al. (2004) find that the current economic condition is a major determinant of the asymmetry between the price impacts of institutional buys and sells. They show that in bullish markets, the total price impact of buys is greater than that of sells and the asymmetry pattern of total price impact is reversed in the bearish markets. They suggest that all previous studies on US equity markets employed data consisting of more bullish market periods, thus leading to the conclusion that the price impact of large buys is greater than for large sells. The basic reasoning behind their hypothesis is that the price impact of a large trade is a function of market liquidity on the opposite side. Institutional investors pay for demanding liquidity when selling into falling markets and when buying into rising markets. Conversely, institutional traders effectively provide liquidity when trading against price trends in the market, and thus, face lower price impacts in this situation. Frino and Oetomo (2005) were the first to provide empirical evidence of the market price impact of trading packages in futures markets. They characterize the market price effect, and information and liquidity price effects incurred in executing packages of trades in four main futures markets (SPI 200, BAB, 3-year Bond and 10-year Bond 1

Saar (2001, p. 1154) presents an excellent discussion on four basic assumptions of mutual fund managers on their investment and trading strategy. He derived his theoretical model based on these assumptions.

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futures) traded on the Sydney Futures Exchange using intraday data from July 1, 2000 to June 30, 2003. They document three interesting results: (1) the market impacts incurred executing trade packages in stock index futures and interest-rate futures are significantly smaller than the price impact costs documented previously in US equity markets; (2) there is little evidence of asymmetry between the price impacts of purchases and sells, which is contrary to the findings in US equity markets; and (3) the liquidity price impacts (costs) are the major portion of price impact costs in the Sydney futures markets, and there is little information price impacts. Using Computer Trade Reconstruction (CTR) data from January 2001 to December 2004, Frino et al. (2007) examine the price impacts of outside customer large trades on five futures contracts traded on the Chicago Mercantile Exchange (CME). They find for the whole sample period that the price impact of buyer–initiated large trades and seller-initiated large trades are consistent with the empirical results found in equity markets. They also find that the current economic condition hypothesis proposed by Chiyachantana et al. (2004) is the major determinant of asymmetry between large buys and large sells in bullish and bearish markets. Furthermore, they find that there are information effects of large buys and sells in futures contracted on the CME. Their results are in contrast to the previous results found in Australian futures markets. Our paper extends the previous work in the price impact of large trades in futures markets in several important ways. First, using a unique intraday data set from the Taiwan Futures Exchange (TAIFEX), which includes three futures contracts by types of traders, we document the empirical patterns of total, liquidity and permanent price effects by institutional trades

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versus individual trades. These three contracts are: (1) the Taiwan Stock Exchange (TSE) Index futures (FITX); 2 (2) the TSE Electronic Sector Index futures (FITE); and (3) the TSE Finance Sector Index futures (FITF). The institutional traders include domestic institutions, futures proprietary firms and foreign institutional traders.

In Taiwan,

individual traders play a very important role in trading activity; in particular, 75% to 79% of total trades were executed by individual traders in these three futures contracts during the sample period from January 2004 to December 2006. To the best of our knowledge, there is no empirical literature on the price impacts of large individual trades in either equity or in futures markets. We provide the first empirical results of the price impacts of large trades by individual traders in the literature and these results will allow us to examine whether there are differences in permanent and liquidity effects of large trades due to different types of traders. Second, the CME futures contracts are traded in the open–outcry system with locals, while the Taiwan futures contracts are traded in an electronic limit order market without market makers.

Thus, we can observe whether our empirical results are

consistent with the empirical results documented by the previous paper on the CME futures markets.

These empirical results will provide some evidence of whether

differences in trading systems affect the patterns of liquidity and permanent effects of large trades in futures markets. Third, the intraday data with trader types allow us to perform a direct test on whether the current economic condition hypothesis suggested by Chiyachantana et al. (2004) is a major determinant of asymmetry between price, liquidity and information 2

The Taiwan Stock Exchange (TSE) index futures contract is based on the TSE Capitalization Weighted Stock Index (TAIEX), a value-weighted broad market index of all firms listed on the TSE.

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effects of block buys and sells by institutional versus large individual traders in the Taiwan futures market. Chiyachantana et al. (2004) only confirmed this hypothesis on market (total) price impact effects for institutional trades with a daily data set. The paper is organized in four sections. Section 2 discusses the institutional feature of TAIFEX and the data. The empirical methodology is presented in section 3. Section 4 reports the empirical results. Section 5 presents a summary and conclusions. 2

Institutional Descriptions of TAIFEX and the Data TAIFEX is operated under an automated auction system from 8:45 am to 1:45 pm,

Monday through Friday (excluding public holidays).

Investors, through the help of

brokers, submit orders to the automated trading system. There are no designated market makers. The automatic trading system sets a single transaction price that will clear the largest number of buy and sell orders periodically. The buy (sell) orders with higher (lower) limit prices than the set transaction price will be executed at the transaction price. The price limits on TAIFEX are 7% of the previous day’s close. TAIFEX was operated under an automated batch-call system before July 29, 2002, and after that it was transformed to a continuous auction system. Our samples include three stock index futures contracts traded on the TAIFEX, including the TSE Index futures, the TSE Electronic Sector Index futures, and the TSE Finance Sector Index futures. The contract size for these three futures are, respectively, the index value of TAIEX × 200 New Taiwan Dollars (NT$), the index value of the TSE Electronic Sector Index × NT$4,000, and the index value of the TSE Finance Sector Index × NT$1000. The detailed contract specifications of these contracts are in Appendix A. Our sample period covers from January 1, 2004 to December, 2006.

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The dataset is obtained from the TAIFEX and contains the detailed history of order flows, order book, transaction data and the identity of the traders. For each order, the dataset reports the date and time of arrival of the order, its direction (buy or sell), the quantity demanded or offered, and most importantly for our purposes the identification of traders. The trader code enables us to categorize four types of traders: individual traders, domestic institutional traders, future proprietary firms, and foreign institutional traders. Because of the availability of detailed transaction costs on each type of trader, we can examine the price impact of different types of traders on the market. From Table 1, we can see that individual traders execute the largest percentage of total volume. For example, the trading activity of individual trades account for 79.10% of total trading volume in FITF , 77.5% of trading volume in FITE and 75.1% of trading volume in FITX Domestic institutional traders including corporations and governmentowned firms account for only 1.92% of total trading volume in FITX and 0.83% of total trading volume in FITF. Future proprietary firms are different from futures broker in that they trade futures and options for their own accounts. In other words, they trade on their own accounts to make profits, instead of earning commissions. The trading activity of futures proprietary firms is ranked second in term of percentage of total trading volume in FITX and FITE markets. The futures proprietary firms are subject to fewer regulations. For example, the unsettled positions of individuals and domestic intuitional investors on TAIEX index futures are restricted by the TAIFEX to 2,000 and 4,000 contracts, respectively, while futures proprietary firms are not subject to such limitations. The trading activity of foreign institutional traders account for about 11% in the FITF market

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and about 6% and 10 % of total trading volume in the FITX and FITE markets, respectively. 3

Empirical Methodology The empirical procedures to measure total, liquidity and permanent price effects

of buyer and seller-initiated large trade transactions by order size within a single day are described as follows: Total Price effect = ln( PT / Pp ,T ) X 100 ,

(1)

Liquidity (Temporary) effect = ln( PT / PT , a ) X 100 ,

(2)

Information (Permanent) effect = ln( PT , a / Pp ,T ) X 100 ,

(3)

where PT denotes the price of either a buyer or seller-initiated large trade transaction. Pp,T is the benchmark market price prior to the large trade transaction. It represents the equilibrium price of the contract absent any information about the incoming large trade. PT,a is the benchmark (equilibrium) price after the large trade transaction. To analyze the price effects after either a buyer or seller-initiated large trade (in order to measure the price reversal effect following the large trade), we calculate the liquidity effect as the log difference between PT and PT,a. Thus we expect that the liquidity effect will be negative for the seller-initiated trades and positive for the buyer-initiated trades. A negative sign for the liquidity effect of buyer-initiated trades suggests that the price further increases after a buyer-initiated trade. Similarly, a positive sign for the liquidity effect of seller-initiated trades indicates that the price decreases even further after a sell trade. measures the difference between lnPT,a and lnPp,T.

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The permanent price effect This difference reflects the

information content of a large trade. All measures are in percentage terms. Based on these definitions, the total price effect is equal to the sum of the liquidity price effect and the permanent price effect.3 We also calculate the volume-weighted total, liquidity and information (permanent) price effects for buyer and seller-initiated large trades to take account of the volume effect and to minimize data noise.4 The volume weights for buyer-initiated trades are given by the volume of the ith buyer-initiated trade divided by the total volume for the complete data period of all buyer-initiated trades that belong to the corresponding trade size class. The weights for the seller-initiated trades are obtained in a similar way. To measure the price effects of a large trade, we need to select benchmark prices before and after a large trade. It is agreed in general that the selection of benchmark prices depends on the timing of a trader’s decision to trade. We use the day's opening price as the benchmark price before a large trade and the day's closing price as the benchmark price after a large trade (see Chan and Lakonishok (1993)). The selection of these benchmark prices rests on the implicit assumption that traders usually decide to trade before the opening of the trading session.5 To enhance the robustness of our results, we also use as benchmark prices those prices calculated from the mean of the prices traded at 15 minutes before and the mean of the prices traded at 15 minutes after a large trade. 3

There are two ways to decompose total price effects into permanent (information) and liquidity effects. We follow the procedure used by Holthausen et al. (1987) and Gemmill (1996). The other procedure, used by Chan and Lakonishok (1993), produces the result that the sum of the total price effects and the liquidity effects equals the permanent price effects. In this case, the expected sign for the liquidity effect of buys is negative and the sign for the liquidity effects of sells is positive. 4

Further discussion on the advantages of using volume-weighted average price as a less noisy estimate of unobservable equilibrium price is referred to Ting (2006). 5

Further discussion on the pros and cons regarding the selection of alternative benchmarks is referred to Collins and Fabozzi (1991) and Harris (2003, Chapter 21).

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Since there is no consensus to what constitutes a large transaction (block trade) in futures markets, we construct trade size class based on the empirical distribution of the intraday trading volume for the whole sample period reported in Table 2. We define the two trade size classes as follows: trade size 1 includes trades with size up to but not including the 95th percentile; trade size 2 includes trades with size greater than the 95th percentile. 6 The two trade size classes allow us to test the hypothesis that the price, liquidity and information (permanent) effects are positively correlated with trade size in futures markets. Furthermore, the definition of trade size 2 uses the same criteria as the threshold of block trades established by the CME and the Commodity Futures Trading Commission (CFTC) in the US futures markets. 4 4.1

Empirical Results The Total Price, Liquidity and Information Effects of Large Trades Table 2 presents descriptive statistics of the three contracts used in our analysis.

FITX is the most active contract in term of total trading frequency and mean daily trading volume and the least active contract is FITF. In term of trading frequency, we find that most active trading occurred in the size 1 class. This is true for all three contracts. However, in term of mean daily trading volume and daily dollar value, about 31 percent of buys and sells of FITX occurred in the size 2 class, which is equal to or above the 95th percentile of their corresponding empirical distributions of trading volume. About 23 percent of daily trading volume and daily dollar value of FITE and FITF fall into the size 2 class. This shows the importance of larges trades on the TAIFEX. 6

The 95th percentiles of empirical distribution for FITX, FITE and FITF are equal to eight, four and four contracts respectively.

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Tables 3 to 5 present three measures of the average total price, liquidity and permanent (information) effects by trader types incurred in executing two trade size classes in FITX, FITE and FITF, respectively. We use daily opening and closing prices as the benchmark prices in estimating the three measures of price effects. In each table, Panel A reports the three measures for all traders and Panel B for individual traders, Panel C for domestic institutions, Panel D for proprietary firms and Panel E for foreign institutional traders. Several interesting findings are summarized below. First, we find the total price effect for size 2 (the largest size class) for all traders in FITX (Panel A, Table 3), FITE (Panel A, Table 4) and FITF (Panel A, Table 5) are 0.1266 percent, 0.2196 percent and 0.3049 percent for buy trades and -0.1297 percent, 0.1172 and -0.1027 percent for sell trades, respectively. We observe that the total price impacts of buy trades are larger than the corresponding total price impacts of sell trades for FITE and FITF contracts. These results are consistent with previous results found in equity markets and in CME futures markets showing an asymmetry between buys and sells. It is interesting to compare our findings with previous research. In the CME futures markets, Frino et al. (2007) report that purchases and sales in the largest size class incur an average total price impact of 0.0952 percent and -0.0812 percent, respectively, for transactions executed in the S&P 500 index futures market. In equity markets, Keim and Madhavan (1997) report that the total price impact of institutional transactions are 0.34 percent for purchases and -0.31 percent for sales executed on the New York Stock Exchange (NYSE). Thus, it is clear that the magnitude of total price impacts of buys and sells of the three contracts in our study are larger than the corresponding total price

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impacts documented in previous studies of the S&P 500 index futures market, but lower than the corresponding price impacts documented in previous studies for equity markets. These results are consistent with the perception that total price impacts are inversely related to the market liquidity. Second, we find large purchases are associated with positive information (permanent) effects and large sales with negative information (permanent) price effects for most trades reported in Tables 3 to 5. The magnitude of information effect for buys is larger than for sells, indicating that large purchases convey more information than large sales. For the liquidity effects, large purchases and sales are often associated with price continuations or with weak reversals. Our results are inconsistent with previous results found in equity and CME futures markets for the entire sample period. Third, in Panels B to E of Tables 3 to 5, we report three measures of total price, liquidity and information (permanent) effects of large purchases and sales by individual traders, domestic institution traders, proprietary firm traders and foreign institutional traders. For example, in the Electronic Sector Index futures market (see Table 4), the total price impacts for size 2 class by individual traders, domestic institutional traders, proprietary firm traders and foreign institutional traders are 0.2178 percent, 0.0.1544 percent, 0.2469 percent and 0.1877 percent for buy trades and -0.1054 percent, -0.0918, and -0.1224 and -1574 for sell trades. The pattern of total price effects of buy trades being larger than the corresponding sell trades holds true for all types of traders in FITF as well, but this pattern does not hold for all types of traders in FITX. The patterns of liquidity and information effects of purchases and sales for all trades also carry over to those patterns for each type of trader.

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In general, we find that the total price and information effects of large purchases and sales are largest for proprietary firms; next for individual traders; followed by domestic institutional traders and foreign institutional traders. These results may be due to the differences in trading time horizon versus price preference by type of traders and trading intensity. Proprietary firm traders may be information-motivated traders who prefer to execute their trades in a short time horizon. Foreign institution traders in Taiwan are more likely value-motivated traders who perceive valuation errors and have price preference rather than short time horizon to execute their trades (see Madhavan, Treynor and Wagner (2007)). Thus, foreign traders are patient traders and often use limit orders. Individual traders in Taiwan are composed of day traders, liquidity traders and informed trader, thus, they are generally inpatient traders and they have time preference than price preference. Thus, individual traders are impatient traders and tend to use market orders. Thus, individual traders and proprietary firm traders often experienced higher price impact costs of large trades in comparison to the price impact costs of foreign institutional traders’ large trades. Fourth, from Tables 3 to 5, we observe that total price, liquidity and information effects are positively related to trade size and there exist differences in the magnitudes of the liquidity and information effects by type of traders. To formally test these observed relationships, we perform the one–way analysis of variance by the following regression models: Si = β 0 + β1 D2 + ei ,

(4)

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where Si, i = 1, 2, 3 represents the total price, liquidity and information effects, respectively.

D2 equals 1 if trade size falls into the second trade size class and 0,

otherwise. To further test the differences in the price effects of large trades originated from different types of traders, the following equation is estimated: Si = β 0 + β1Ddomestic + β 2 D proprietary + β 3 D foreign + ei , (5)

(5)

where Si, i = 1, 2 represents the information and liquidity effects in size 2 class (large trade class), respectively. Ddomestic (Dproprietary, Dforeign) is a dummy variable that equals 1 if trade is initiated by domestic institutional traders (proprietary firm traders, foreign institutional traders) in size 2 class and 0, otherwise. OLS is used to estimate the parameters of equations (1) and (2) and the White procedure is used to calculate the heteroscedasticity consistent standard errors. From our regression results reported in Table 6, we observe that, in the total price effect equations, the coefficients of the size 2 class dummy variable are positive (negative) and are statistically significant for buys (sells), respectively, for all three contracts. We also find similar regression results for the information effects for all three contracts.

Our

regression results support the hypothesis that there are relationships between liquidity effect and trade sizes for buys and sells in all three contracts except in the sell side of FITE. In short, our results are consistent with the hypothesis that the total price effects and information contents of trades are positively related to trade size. The tests of equality on liquidity and information effects by four trader types are reported in Table 7. We find that the coefficients of the dummy variables for domestic institutional traders are negative and statistically significant at the five percent level for

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large buy trades in FITE and FITF and negative and significant for the large sell trades in FITX and FITF. These results confirm that the information effects of domestic institution trades are less than those of individual trades in most buy and sell transactions. The coefficients of the proprietary trader dummy variables are positive for buys and negative for sells and they are all significant at the five percent level or better. These results confirm that the information effects of large trades initiated by proprietary firms are larger than the information effect of large trades initiated by individuals in all three contracts. Our regression results do not find a consistent ranking relationship among different types of traders for liquidity effects. 4.2

The Determinant of Asymmetry between Price Impacts of Purchases and Sales Chiyachantana et al. (2004) suggest that current economic conditions are a

primary determinant of asymmetry between price impacts of buys and sells of institutional trades.

To test the validity of this hypothesis for understanding the

asymmetry of price impacts of large buy and sell trades by types of traders in TAIFEX, we stratify our whole sample into bullish markets when the average return of the month is positive and bearish markets when the average return of the month is negative. Tables 8 to 10 present the total price, liquidity and information effects of large trades by type of traders for FITX, FITE, and FITF, respectively. Several interesting results from Table 8 to 10 are summarized below. First, during bullish markets, large buys have larger price and information effects than sells, while sells have strong liquidity effects (price reversals) and large buys are often associated with continuations after the execution of a large trade.

In bearish

markets, we find reverse asymmetry between the price and information effects of buys

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and sells. Furthermore, large purchases are often associated with price reversals and large sells are associated with price continuations. These results hold true for individual traders and institutional traders in all three contacts. Time series behavior of monthly average of price impacts of buys and sells for all trades during bullish and bearish markets are plotted in Figure 1 and these plots clearly demonstrate these phenomena. Second, our empirical results confirm the current economic condition hypothesis suggested by Chiyachantana et al. (2004), who propose that the market condition is a key determinant of asymmetry between total price and information effects of purchases and sells. These results are consistent with previous results of all trades found in the CME. The most important new finding in this paper is that the patterns of total price, liquidity and information effects of large individual trades share the same patterns of large institutional (block) trades found in the equity markets.

These results suggest that

previous theories (for example, see Saar (2001)) derived from the trading strategy of institutional trading is not appropriate to explain the asymmetry between purchase and sales of large individual trades because the trading strategy of individual traders are not likely to follow the assumed trading strategy of institutional traders. On the other hand, the current economic hypothesis derived from the implications of the liquidity provisions of large traders can be used to explain asymmetry between purchases and sells of all types of traders. Furthermore, our results are consistent with the hypothesis that traders in these futures markets engage in herding trading behavior; further research in this area is necessary. Third, there is no clear asymmetry pattern of total price impact and information effects of large buys and sells in the FITX market for the entire sample period (see Table

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3). However, there are strong asymmetry patterns of large buys and sells in bullish versus bearish markets in FITX (see Table 8). These empirical results provide evidence that an asymmetrical pattern of large buys and sells in entire sample period depends on the number of bullish versus bearish periods in the entire sample period and the magnitudes of large buys versus large sells in bullish versus bearish markets. 4.3

Robustness Tests To test the robustness of our empirical results to the choice of benchmarks, all

results presented in Tables 3 to 5 and Tables 8 to 10 are re-calculated with new intraday benchmarks. We choose the mean transaction prices traded fifteen minutes before and after a large trade as the new benchmarks. Table 11 reports empirical results based on the whole sample period for FITX by types of traders and Table 12 presents empirical results based on the bullish and bearish markets for FITX by types of traders.7 In general, the empirical results based on the new intraday benchmarks are consistent with the empirical results based on our opening and closing prices of a large trade as the benchmarks. It is worthwhile to note that the magnitude of price, liquidity and information effects based on the new benchmarks are smaller than the corresponding effects based on the open and close benchmarks. The results suggest that the magnitude of these three measures of a large trade is a function of the length of time between the benchmarks chosen. However the general patterns of price, liquidity and information effects remain the same.

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To save space, we do not include all tables reporting empirical results based on the whole sample period and based on bullish and bearish markets by trader types for FITE and FITF. The patterns of these results using the new benchmarks are similar to the patterns of results based on the daily open and close benchmark prices and these results are available upon request from the authors.

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Summary and Conclusions This paper uses a unique intraday data set from January 2004 to December 2006

to estimate the total price, liquidity and information effects of large institutional trades versus large individual trades on three index futures contracts traded on TAIFEX. They are: (1) the TSE Index futures; (2) the TSE Electronic Sector Index futures and (3) the TSE Finance Sector Index futures. Our paper provides the first empirical results in the academic literature to show the price impacts of large individual trades versus institutional trades. We have obtained several interesting results. First, we find, for the entire sample period, buyer-initiated large trades have larger permanent price effects than seller-initiated large trades and vice versa for liquidity effects. These results are consistent with previous findings on large trades of futures contracts traded on the CME and on institutional trades in equity markets. Our results confirm that the difference in trading mechanisms in futures markets does not affect the patterns of price liquidity and information effects of large trades. Second, there is no clear asymmetric pattern of total price impact and information effects of large buys and sells in the FITX market for the entire sample period. However, there are strong asymmetric patterns of large buys and sells in bullish versus bearish markets in FITX. These empirical results provide evidence that an asymmetric pattern of large buys and sells in the entire sample period depends on the number of bullish versus bearish periods in the entire sample period and the magnitudes of large buys versus large sells in bullish versus bearish markets. Third, stratifying our sample into bullish and bearish markets as suggested by the current economic condition hypothesis, we observe that the price and permanent effects

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of large sales are larger than the effects of large purchases by all types of traders in bearish markets, with the reverse pattern found by all types of traders in bullish markets. These results demonstrate that large individual trades share the same asymmetric patterns of purchases and sales as institutional traders.

These results have two important

implications. First, previous theories (for example, see Saar (2001)) derived from the trading strategy of institutional trading is not appropriate to explain the asymmetry between purchases and sales of large individual trades because the trading strategy of individual traders are not likely to follow the assumed trading strategy of institutional traders. On the other hand, the current economic condition hypothesis derived from the implications of the liquidity provisions of large traders can be used to explain asymmetry between purchases and sales of all type of traders. Second, the common assumption often made in financial literature (see Barber, Odean and Zhu (2005) and Diagler and Wiley (1999)) that individual traders do not carry information in equity or futures markets is inappropriate on TAIFEX. Fourth, our regression analysis demonstrates that most of the total price effects and information effects have positive significant relationships with trade sizes during the whole sample period. These results are consistent with those found on the CME, but it is in contrast to the findings on the Australian Futures Exchange. Finally, as expected, the magnitude of price impacts of large trades are inversely related to the liquidity of individual futures contracts. For example, the price impacts of large trades for FITX are the smallest among these three contracts, because FITX is the most actively traded index future on the Taiwan futures market.

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References Barber, M., Odean, T and Zhu, N. (2005) “Do Noise Traders Move Markets?” Working paper, Graduate School of Management, UC-Davis. Chan, L.K. C. and Lakonishok, J. (1993) “Institutional Trades and Intra-Day Stock Price Behavior,” Journal of Financial Economics, 33, 173-200. Chan, L.K. C. and Lakonishok, J. (1995) “The Behavior of Stock Prices around Institutional Trades.” Journal of Finance, 504, 1147-1174. Chiyachantana, C., Jain, P., Jiang, C., and Wood, R. (2004) “International Evidence on Institutional Trading Behavior and Price Impact,” Journal of Finance, 59, 869-898. Collins, B. M. and Fabozzi, F. J. (1991) “A Methodology for Measuring Transaction Costs,” Financial Analysts Journal, March-April, 27-44. Diagler, R.T. and Wiley, M. K. (1999) “The Impact of Trader Type on the Futures Volatility-Volume Relation,” Journal of Finance, 54, 2297-2316, Frino, A. and Oetomo, T. (2005) “Slippage in Futures Markets: Evidence from the Sydney Futures Exchange,” Journal of Futures Markets, 25:12, 1129-1146. Frino, A., Bjursell, J., Wang, G.H. K, and Lepone, A. (2007) “Large Trades and Intraday Futures Price Behavior,” paper presented at Financial Management Association October 18, Orlando, Florida. Gemmill, G. (1996) “Transparency and Liquidity: A Study of Block Trade on the London Stock Exchange under Different Publication Rules,” Journal of Finance, 51, 17651790. Harris, L. (2003) Trading and Exchanges: Market Microstructure for Practitioners. Oxford University Press, New York, New York. Holthausen, R., Leftwich, R., and Mayers, D. (1987) “The Effects of Large Block Transactions on Security Prices: A Cross Sectional Analysis,” Journal of Financial Economics, 19, 237-268. Holthausen, R., Leftwich, R., and Mayers, D. (1990) “Large Block Transactions, the Speed of Response and Temporary and Permanent Stock Price Effects,” Journal of Financial Economics, 26, 71-95. Keim, D. B. and Madhavan, A. (1996) “The Upstairs Markets for Large-Block Transactions: Analysis and Measurement of Price Effects,” Review of Financial Studies, 9, 1-36. Keim, D. B. and Madhavan, A. (1997) “Transaction Costs and Investment Style: An Inter-exchange Analysis of Institutional Equity Trades,” Journal of Financial Economics, 46, 265-292. Kraus A. and Stoll, H. R. (1972) “Price Impacts of Block Trading on the New York Stock Exchange,” Journal of Finance, 27, 569-588. Madhavan, A., Treynor, J. L., and Wagner, W. H. (2007) “Execution of Portfolio Decisions,” Chapter 10 p637-681 in Managing Investment Portfolios: A Dynamic

21

Process edited by Maginn, J. L., Tuttle, D.L., McLeavey, D. W. and Pinto, J. E. John Wiley& Sons, Hoboken, New Jersey. Saar, G. (2001) “Price Impact Asymmetry of Block Trades: An Institutional Trading Explanation,” Review of Financial Studies, 14, 1153-1181. Scholes, M. (1972) “The Market for Securities: Substitution versus Price Pressure and the Effects of Information on Share Price,” Journal of Business, 45, 179-211. Shleifer, A. (1986) “Do Demand Curves for Stocks Slope Down?” Journal of Finance, 41, 579-590. Ting, C. (2006) “Which Daily Price is Less Noisy?” Financial Management, 35, 81-95. White, A. (1980) “A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test of Heteroskedasticity,” Econometrica, 48, 350-371.

22

Figure 1 Panel A: FITX

Panel B: FITE

23

Figure 1 (continued) Panel C: FITF

The figure plots the monthly mean values of the price effect (in percentage) for trades in the futures contracts FITX, FITE and FITF. The total price effect, liquidity effect and information effect are estimated by, Total Price effect = ln( PT / Pp ,T ) X 100 Liquidity (Temporary) effect = ln( PT / PT ,a ) X 100 Information (Permanent) effect = ln( PT ,a / Pp ,T ) X 100 where PT denotes the price of the transaction for which the price impact is estimated; P T,b and PT,a are the benchmark prices prior to and after the transaction of interest. The daily opening and closing prices are used as benchmark prices. The left column plots the monthly estimates for buys and sells in bullish markets while the column to the right plots the estimates for bearish markets. For each month, the market is classified as bullish if the monthly price return is positive and bearish otherwise. The price return is computed as the log difference between the first and the last trade price of the month. Furthermore, these estimates are for transactions with a trade size larger than the 95 th percentile based on the percentiles of the empirical trade size distribution. The estimates for the sells are multiplied by -1 so that they can be compared to the buys.

24

Table 1: Descriptive statistics by trader type categories.

FITX FITE FITF

Percentage of Total Volume per Trade Group Domestic Proprietary Foreign Individual Institution Firm Institution 75.07 1.92 17.35 5.66 77.49 1.07 11.58 9.86 79.10 0.83 9.49 10.58

Total Daily Average Volume 30,061.29 4790.85 4415.32

This table presents descriptive statistics for three futures contracts: (1) Taiwan Stock Exchange Index futures (FITX); (2) Electronic Sector Index futures (FITE); and (3) Financial Sector Index futures (FITF). The percentage of the total volume is tabulated for the four types of traders, which are individual, domestic institution, proprietary firm and foreign traders.

25

Table 2: Descriptive statistics per contract by trade size categories. All

1

Buy

Sell

Buy

2 Sell

Buy

Sell

Panel A: Taiwan Stock Exchange Index Futures (FITX) Total Trading Frequency 4,771,419

4,954,678

4,528,914

4,700,151

242,505

254,527

69.30

69.26

30.70

30.74

Percentage of Total Volume ---

---

Daily Trading Volume Mean

14,766.37

15,294.92

10,233.71

10,592.92

4,532.66

4,702.01

Median

14,068.50

14,612.50

9,654.00

9,909.00

4,245.50

4,304.50

Daily Dollar Value (106) Mean

18,754.93

19,405.64

12,987.86

13,430.46

5,767.07

5,975.19

Median

17,681.73

18,058.81

12,177.13

12,403.58

5,235.05

5,409.41

Panel B: Electronic Sector Index Futures (FITE) Total Trading Frequency 1,141,785

1,200,421

1,073,095

1,131,403

68,690

69,018

76.24

77.64

23.76

22.36


---


2,353.13

2,437.72

1,794.04

1,892.75

559.09

544.97

Median

2,217.00

2,271.50

1,699.50

1,756.50

489.00

477.50


2,460.57

2,541.59

1,879.11

1,977.51

581.46

564.07

Median

2,307.11

2,301.60

1,757.20

1,790.08

510.01

493.17

Panel C: Financial Sector Index Futures (FITF) Total Trading Frequency 1,062,206

1,099,658

998,724

1,036,489

63,482

63,169

75.69

76.83

24.31

23.17


---


2,184.32

2,231.00

1,653.36

1,714.15

530.96

516.84

Median

1,571.50

1,640.00

1,186.00

1,231.50

356.00

365.50


2,138.10

2,181.87

1,616.57

1,673.96

521.52

507.90

Median

1,499.81

1,560.02

1,130.46

1,170.26

333.27

339.46

This table contains sample characteristics of the trades in the futures contracts: FITX, FITE and FITF. The trades are categorized into two groups based on the 95 th percentile of the empirical trade size distribution, which is equivalent to eight contracts for FITX, four for FITE, and four for FITF. Transactions with a trade size less than the 95th percentile belong to category 1 and transactions with a trade size larger than or equal to the 95th percentile are classified as 2. Total trading frequency denotes the total number of trades for the complete period in thousands; Percentage of total volume is the percentage of trades per category computed for buys and sells separately; Daily trading volume reports the mean and median of the total daily volumes; and Daily dollar value reports the mean and median of the total daily dollar value in millions where the dollar value is computed per transaction by multiplying the price by the contract multiplier and the trade size.

26

Table 3: Taiwan Stock Exchange Index Futures (FITX): Price effect (in percentage) using open and close benchmark prices. 1 Buy

2 Sell

Buy

Sell

Panel A: All Traders Total Price Effect Volume Weighted

0.0586

-0.0707

0.1266

-0.1297

Mean

0.0533

-0.0604

0.1065

-0.1126

Standard Error

0.0004

0.0004

0.0017

0.0017

Median

0.0530

-0.0268

0.1157

-0.0707

Volume Weighted

0.0003

0.0194

-0.0340

0.0104

Mean

0.0050

0.0199

-0.0253

0.0116

Standard Error

0.0005

0.0005

0.0018

0.0018

-0.0570

-0.0519

-0.0661

-0.0501

Volume Weighted

0.0583

-0.0900

0.1606

-0.1402

Mean

0.0483

-0.0802

0.1318

-0.1242

Standard Error

0.0007

0.0007

0.0026

0.0026

0.1129

0.0485

0.2158

-0.0154

Liquidity Effect

Median Permanent Effect

Median

Panel B: Individual Traders Total Price Effect Volume Weighted

0.0632

-0.0682

0.1243

-0.1205

Mean

0.0559

-0.0559

0.1092

-0.1049

Standard Error

0.0005

0.0005

0.0021

0.0021

Median

0.0513

-0.0169

0.1114

-0.0653

Volume Weighted

0.0016

0.0175

-0.0364

0.0130

Mean

0.0071

0.0165

-0.0297

0.0128

Standard Error

0.0005

0.0005

0.0023

0.0023

-0.0519

-0.0557

-0.0682

-0.0478

Volume Weighted

0.0616

-0.0857

0.1607

-0.1335

Mean

0.0488

-0.0724

0.1389

-0.1177

Standard Error

0.0007

0.0007

0.0033

0.0033

Median

0.1097

0.0511

0.2090

-0.0145

Liquidity Effect


27

Table 3 (continue): 1 Buy

2 Sell

Buy

Sell

Panel C: Domestic Institution Traders Total Price Effect Volume Weighted

0.0689

-0.1616

0.1313

-0.1995

Mean

0.0643

-0.1548

0.1164

-0.1901

Standard Error

0.0041

0.0041

0.0095

0.0084

Median

0.0836

-0.1102

0.1393

-0.1538

-0.0049

0.0332

-0.0360

-0.0335

0.0033

0.0413

-0.0336

-0.0275

Liquidity Effect Volume Weighted Mean

0.0045

0.0051

0.0099

0.0105

-0.0604

-0.0366

-0.0696

-0.0756

Volume Weighted

0.0738

-0.1948

0.1673

-0.1660

Mean

0.0610

-0.1960

0.1499

-0.1626

Standard Error

0.0064

0.0067

0.0144

0.0139

Median

0.1438

-0.1232

0.2400

-0.0832

Standard Error Median Permanent Effect

Panel D: Proprietary Firm Traders Total Price Effect Volume Weighted

0.0726

-0.0817

0.1771

-0.1672

Mean

0.0711

-0.0847

0.1438

-0.1442

Standard Error

0.0012

0.0013

0.0035

0.0036

Median

0.0762

-0.0346

0.1467

-0.0880

Volume Weighted

-0.0141

0.0231

-0.0435

0.0160

Mean

-0.0143

0.0313

-0.0303

0.0235

0.0014

0.0014

0.0036

0.0037

-0.0775

-0.0480

-0.0671

-0.0492

Volume Weighted

0.0867

-0.1048

0.2206

-0.1832

Mean

0.0854

-0.1160

0.1741

-0.1677

Standard Error

0.0019

0.0020

0.0053

0.0054

Median

0.1656

0.0160

0.2525

-0.0474

Liquidity Effect


28


2 Sell

Buy

Sell

Panel E: Foreign Traders Total Price Effect Volume Weighted

-0.0744

-0.0491

0.0128

-0.0772

Mean

-0.0609

-0.0681

-0.0250

-0.0545

Standard Error

0.0025

0.0023

0.0070

0.0056

Median

0.0166

-0.0511

0.0574

-0.0356

Volume Weighted

0.0265

0.0410

0.0077

-0.0054

Mean

0.0207

0.0665

0.0271

-0.0178

Liquidity Effect

0.0023

0.0026

0.0062

0.0063

-0.0631

-0.0165

-0.0327

-0.0789

Volume Weighted

-0.1009

-0.0901

0.0051

-0.0718

Mean

-0.0816

-0.1346

-0.0521

-0.0368


Standard Error

0.0036

0.0036

0.0101

0.0087

Median

0.0926

-0.0530

0.1300

0.0573

This table contains estimates of the price effect (in percentage) for trades in the futures contract FITX. Panel A presents the price effects based on all trades; panels B through E tabulate the results for four categories of traders which are individual, domestic institutional, proprietary firm and foreign traders The total price effect, liquidity effect and information effect are estimated by, Total Price effect = ln( PT / Pp ,T ) X 100 Liquidity (Temporary) effect = ln( PT / PT ,a ) X 100 Information (Permanent) effect = ln( PT ,a / Pp ,T ) X 100 where PT denotes the price of the transaction for which the price impact is estimated; P T,b and PT,a are the benchmark prices prior to and after the transaction of interest. The daily opening and closing prices are used as benchmark prices. The price effects are computed for purchases and sales, separately. Furthermore, the trades are categorized into two groups based on the 95 th percentile of the empirical trade size distribution, which is equivalent to eight contracts for FITX. Transactions with a trade size less than the 95th percentile belong to category 1 and transactions with a trade size larger than or equal to the 95 th percentile are classified as 2. The estimates labeled Volume Weighted are volume weighted averages price effects. Proportion denotes the fraction of the estimates of price effects that are positive for purchases and the fraction of the estimates that are negative for sales.

29

Table 4: Electronic Sector Index Futures (FITE): Price effect (in percentage) using open and close benchmark prices. 1 Buy

2 Sell

Buy

Sell


0.1083

-0.0457

0.2196

-0.1172

Mean

0.1040

-0.0383

0.1991

-0.0997

Standard Error

0.0010

0.0010

0.0037

0.0039

Median

0.0940

0.0000

0.1840

-0.0663

Volume Weighted

0.0306

0.0485

-0.0296

0.0439

Mean

0.0331

0.0493

-0.0174

0.0447

Standard Error

0.0012

0.0012

0.0044

0.0045

-0.0405

-0.0357

-0.0617

-0.0228

Volume Weighted

0.0777

-0.0942

0.2492

-0.1611

Mean

0.0709

-0.0876

0.2165

-0.1444

Standard Error

0.0016

0.0016

0.0060

0.0061

0.1125

-0.0336

0.2275

-0.1180

Liquidity Effect


Median


0.1086

-0.0340

0.2178

-0.1054

Mean

0.1035

-0.0277

0.2062

-0.0847

Standard Error

0.0010

0.0010

0.0047

0.0051

Median

0.0917

0.0000

0.1880

-0.0431

Volume Weighted

0.0200

0.0531

-0.0246

0.0513

Mean

0.0230

0.0521

-0.0189

0.0521

Standard Error

0.0013

0.0013

0.0057

0.0058

-0.0452

-0.0348

-0.0644

-0.0211

Volume Weighted

0.0886

-0.0871

0.2424

-0.1567

Mean

0.0806

-0.0797

0.2252

-0.1368

Standard Error

0.0017

0.0017

0.0077

0.0081

Median

0.1212

0.0000

0.2551

-0.0807

Liquidity Effect


30


2 Sell

Buy

Sell


0.0485

-0.1237

0.1544

-0.0918

Mean

0.0444

-0.1183

0.1575

-0.1043

Standard Error

0.0112

0.0105

0.0291

0.0248

Median

0.0488

-0.0744

0.1826

-0.0436

Volume Weighted

0.1657

0.0492

0.1708

0.0056

Mean

0.1796

0.0566

0.1488

0.0212

Standard Error

0.0157

0.0110

0.0370

0.0260

Median

0.0204

-0.0179

0.0000

-0.0448

Volume Weighted

-0.1172

-0.1729

-0.0164

-0.0975

Mean

-0.1353

-0.1749

0.0087

-0.1255

0.0202

0.0154

0.0503

0.0377

-0.0716

-0.1265

0.2052

0.0173

Liquidity Effect

Permanent Effect

Standard Error Median


0.0977

-0.0548

0.2469

-0.1224

Mean

0.0997

-0.0528

0.1977

-0.1053

Standard Error

0.0037

0.0037

0.0072

0.0075

Median

0.1061

-0.0461

0.1880

-0.0916

Volume Weighted

-0.0063

0.0747

-0.0555

0.0152

Mean

-0.0055

0.0747

-0.0464

0.0268

0.0043

0.0042

0.0083

0.0089

-0.0496

-0.0181

-0.0818

-0.0170

Volume Weighted

0.1040

-0.1295

0.3023

-0.1376

Mean

0.1052

-0.1275

0.2442

-0.1321

Standard Error

0.0057

0.0057

0.0113

0.0119

Median

0.1044

-0.0853

0.2489

-0.1265

Liquidity Effect


31


2 Sell

Buy

Sell


0.1204

-0.1537

0.1877

-0.1574

Mean

0.1186

-0.1524

0.1768

-0.1555

Standard Error

0.0041

0.0037

0.0106

0.0099

Median

0.1199

-0.1092

0.1729

-0.1239

Volume Weighted

0.1577

-0.0256

-0.0227

0.0651

Mean

0.1709

-0.0116

0.0157

0.0423

Standard Error

0.0047

0.0040

0.0121

0.0115

Median

0.0213

-0.0720

-0.0325

-0.0461

Volume Weighted

-0.0373

-0.1281

0.2104

-0.2225

Mean

-0.0524

-0.1409

0.1611

-0.1978

Liquidity Effect

Permanent Effect

Standard Error

0.0066

0.0056

0.0170

0.0156

Median

0.0173

-0.1283

0.1319

-0.1558

This table contains estimates of the price effect (in percentage) for trades in the futures contract FITE. Panel A presents the price effects based on all trades; panels B through E tabulate the results for four categories of traders which are individual, domestic institutional, proprietary firm and foreign traders. The total price effect, liquidity effect and information effect are estimated by, Total Price effect = ln( PT / Pp ,T ) X 100 Liquidity (Temporary) effect = ln( PT / PT ,a ) X 100 Information (Permanent) effect = ln( PT ,a / Pp ,T ) X 100 where PT denotes the price of the transaction for which the price impact is estimated; P T,b and PT,a are the benchmark prices prior to and after the transaction of interest. The daily opening and closing prices are used as benchmark prices.. The price effects are computed for purchases and sales, separately. Furthermore, the trades are categorized into two groups based on the 95 th percentile of the empirical trade size distribution, which is equivalent to four contracts for FITE. Transactions with a trade size less than the 95 th percentile belong to category 1 and transactions with a trade size larger than or equal to the 95 th percentile are classified as 2. The estimates labeled Volume Weighted are volume weighted averages price effects. Proportion denotes the fraction of the estimates of price effects that are positive for purchases and the fraction of the estimates that are negative for sales.

32

Table 5: Financial Sector Index Futures (FITF): Price effect (in percentage) using open and close benchmark prices. 1 Buy

2 Sell

Buy

Sell


0.1872

-0.0150

0.3049

-0.1027

Mean

0.1819

-0.0078

0.2799

-0.0744

Standard Error

0.0012

0.0012

0.0050

0.0052

Median

0.1390

0.0000

0.2578

-0.0675

Volume Weighted

0.0339

0.0813

-0.0159

0.0672

Mean

0.0354

0.0812

-0.0036

0.0720

Standard Error

0.0013

0.0014

0.0052

0.0050

-0.0629

-0.0572

-0.0864

-0.0212

Volume Weighted

0.1533

-0.0963

0.3208

-0.1699

Mean

0.1465

-0.0890

0.2835

-0.1464

Standard Error

0.0019

0.0019

0.0077

0.0076

0.2305

0.0572

0.4154

-0.0463

Liquidity Effect


Median


0.2053

-0.0057

0.3634

-0.0855

Mean

0.1973

0.0014

0.3301

-0.0583

Standard Error

0.0013

0.0014

0.0061

0.0066

Median

0.1466

0.0000

0.2821

-0.0397

Volume Weighted

0.0217

0.0912

-0.0375

0.1115

Mean

0.0247

0.0887

-0.0233

0.1102

Standard Error

0.0015

0.0015

0.0064

0.0064

-0.0706

-0.0583

-0.0993

0.0000

Volume Weighted

0.1836

-0.0969

0.4008

-0.1969

Mean

0.1726

-0.0873

0.3534

-0.1685

Standard Error

0.0021

0.0021

0.0095

0.0098

Median

0.2812

0.0753

0.4773

-0.0463

Liquidity Effect


33


2 Sell

Buy

Sell


0.1626

-0.0169

0.0616

-0.2604

Mean

0.1581

0.0155

0.1152

-0.2475

Standard Error

0.0192

0.0150

0.0497

0.0337

Median

0.1624

-0.0194

0.2823

-0.1811

Volume Weighted

0.0302

0.0720

0.1860

0.0761

Mean

0.0367

0.0750

0.1271

0.0718

Liquidity Effect

0.0182

0.0166

0.0411

0.0370

-0.0736

-0.0418

0.0000

0.0000

Volume Weighted

0.1324

-0.0888

-0.1244

-0.3365

Mean

0.1214

-0.0594

-0.0119

-0.3194

Standard Error

0.0281

0.0240

0.0741

0.0541

Median

0.3512

-0.0209

0.3180

-0.1850



0.1675

-0.0067

0.2950

-0.1708

Mean

0.1719

-0.0131

0.2583

-0.1248

Standard Error

0.0045

0.0045

0.0103

0.0111

Median

0.1491

-0.0209

0.2183

-0.1074

Volume Weighted

-0.0415

0.0295

-0.0676

-0.0053

Mean

-0.0432

0.0333

-0.0539

0.0031

0.0044

0.0042

0.0105

0.0097

-0.0812

-0.0218

-0.0982

-0.0225

Volume Weighted

0.2090

-0.0362

0.3626

-0.1656

Mean

0.2151

-0.0463

0.3121

-0.1279

Standard Error

0.0065

0.0064

0.0154

0.0152

Median

0.2827

0.0209

0.3529

-0.0956

Liquidity Effect


34


2 Sell

Buy

Sell


0.0648

-0.1250

0.0923

-0.0835

Mean

0.0553

-0.1252

0.0960

-0.0806

Standard Error

0.0040

0.0038

0.0141

0.0117

Median

0.0784

-0.1641

0.2015

-0.1243

Volume Weighted

0.1755

0.0200

0.1173

-0.0493

Mean

0.1786

0.0270

0.1230

-0.0472

Standard Error

0.0043

0.0044

0.0139

0.0116

Median

0.0000

-0.0624

-0.0206

-0.0922

Volume Weighted

-0.1107

-0.1450

-0.0250

-0.0342

Mean

-0.1232

-0.1522

-0.0271

-0.0334

Liquidity Effect

Permanent Effect

Standard Error

0.0064

0.0059

0.0215

0.0169

Median

0.0381

-0.0407

0.1304

-0.0209

This table contains estimates of the price effect (in percentage) for trades in the futures contract FITF. Panel A presents the price effects based on all trades; panels B through E tabulate the results for four categories of traders which are individual, domestic institutional, proprietary firm and foreign traders. The total price effect, liquidity effect and information effect are estimated by, Total Price effect = ln( PT / Pp ,T ) X 100 Liquidity (Temporary) effect = ln( PT / PT ,a ) X 100 Information (Permanent) effect = ln( PT ,a / Pp ,T ) X 100 where PT denotes the price of the transaction for which the price impact is estimated; P T,b and PT,a are the benchmark prices prior to and after the transaction of interest. The daily opening and closing prices are used as benchmark prices. The price effects are computed for purchases and sales, separately. Furthermore, the trades are categorized into two groups based on the 95 th percentile of the empirical trade size distribution, which is equivalent to four contracts for FITF. Transactions with a trade size less than the 95 th percentile belong to category 1 and transactions with a trade size larger than or equal to the 95 th percentile are classified as 2. The estimates labeled Volume Weighted are volume weighted averages price effects. Proportion denotes the fraction of the estimates of price effects that are positive for purchases and the fraction of the estimates that are negative for sales.

35

Table 6: Regression analysis on price impacts and trade sizes. Total Price Effect

Liquidity Effect

Information Effect

Buy

Buy

Buy

Sell

Sell

Sell

Panel A: Taiwan Stock Exchange Index Futures ( FITX) Intercept

0.0533 (122.48)

-0.0604 (-139.71)

0.0050 (10.43)

0.0199 (41.08)

0.0483 (71.27)

-0.0802 (-118.87)

D2

0.0532 (27.55)

-0.0523 (-27.41)

-0.0304 (-14.16)

-0.0083 (-3.87)

0.0836 (27.80)

-0.0440 (-14.77)

Adj R2 F statistic

0.0002 758.74

0.0002 751.09

0.0000 200.50

0.0000 15.01

0.0002 772.92

0.0000 218.12

Panel B: Electronic Sector Index Futures ( FITE) Intercept

0.1040 (108.43)

-0.0383 (-39.53)

0.0331 (28.24)

0.0493 (42.58)

0.0709 (45.42)

-0.0876 (-56.23)

D2

0.0951 (24.33)

-0.0614 (-15.19)

-0.0505 (-10.57)

-0.0046 (-0.94)

0.1457 (22.90)

-0.0569 (-8.75)

Adj R2 F statistic

0.0005 592.13

0.0002 230.64

0.0001 111.81

0.0000 0.89

0.0005 524.36

0.0001 76.56

Panel C: :Financial Sector Index Futures( FITF) Intercept

0.1819 (147.67)

-0.0078 (-6.24)

0.0354 (26.42)

0.0812 (60.40)

0.1465 (76.39)

-0.0890 (-46.20)

D2

0.0980 (19.45)

-0.0667 (-12.83)

-0.0390 (-7.11)

-0.0093 (-1.65)

0.1370 (17.46)

-0.0574 (-7.14)

Adj R2 F statistic

0.0004 378.49

0.0001 164.54

0.0000 50.61

0.0000 2.72

0.0003 304.83

0.0000 51.01

This table presents the results from a regression analysis to test the hypotheses that the total price, liquidity and information effects for trades in the futures contracts FITX, FITE and FITF are positively related to the trade size classes. The regression model is specified as follows, S i = β 0 + β1 D 2 + ei

where Si, i=1, 2, 3, represents the total price, liquidity and information effects, respectively. D2 is a dummy variable that is equal to one if the trade size falls into the second trade size class and zero, otherwise. The trades are categorized into two groups based on the 95 th percentile of the empirical trade size distribution, which is equivalent to eights contracts for FITX, four for FITE, and four for FITF. Transactions with a trade size less than the 95th percentile belong to category 1 and transactions with a trade size larger than or equal to the 95th percentile are classified as 2. OLS is used to estimate the parameters of the model. The tstatistic is reported in parentheses for each estimate.

36

Table 7: Regression analysis on testing equality of information and liquidity effects by trader types. FITX Buy

FITE Sell

Buy

FITF Sell

Buy

Sell

Panel A: Information Effect Intercept

0.1389 (42.66)

-0.1177 (-36.30)

0.2252 (29.35)

-0.1368 (-17.60)

0.3534 (37.90)

-0.1685 (-18.53)

Ddomestic

0.0110 (0.71)

-0.0450 (-2.93)

-0.2165 (-4.53)

0.0113 (0.26)

-0.3653 (-5.60)

-0.1509 (-2.44)

Dproprietary

0.0351 (5.53)

-0.0500 (-7.91)

0.0190 (1.31)

0.0047 (0.30)

-0.0413 (-1.92)

0.0406 (1.90)

-0.1911 -19.55

0.0809 8.27

-0.0641 -3.73

-0.0610 -3.41

-0.3805 -17.53

0.1351 5.98

0.0019 154.9700

0.0006 54.8700

0.0005 12.9100

0.0001 4.3200

0.0051 110.1900

0.0007 14.8800

Dforeign Adj R2 F statistic

Panel B: Liquidity Effect Intercept

-0.0297 (-13.20)

0.0128 (5.61)

-0.0189 (-3.36)

0.0521 (9.25)

-0.0233 (-3.71)

0.1102 (18.38)

Ddomestic

-0.0038 (-0.36)

-0.0403 (-3.73)

0.1677 (4.79)

-0.0309 (-0.97)

0.1504 (3.43)

-0.0383 (-0.94)

Dproprietary

-0.0006 (-0.13)

0.0107 (2.41)

-0.0275 (-2.58)

-0.0253 (-2.25)

-0.0306 (-2.12)

-0.1071 (-7.59)

0.0569 8.42

-0.0306 -4.44

0.0346 2.75

-0.0098 -0.75

0.1463 10.03

-0.1574 -10.57

0.0003 24.6600

0.0002 14.7400

0.0006 14.0400

0.0000 1.9100

0.0019 42.1100

0.0023 48.6900

Dforeign Adj R2 F statistic

This table presents the results from a regression analysis to test the hypothesis that the information and liquidity effects for trades in the futures contracts FITX, FITE and FITF are different for four categories of traders which are individual, domestic institutional, proprietary firm and foreign traders. The regression model is specified as follows, S i = β 0 + β1Ddomestic + β 2 Dproprietary + β 3 Dforeign + ei

where Si, i=1, 2, represents the information and liquidity effects, respectively. D domestic (Dproprietary, Dforeign) is a dummy variable that is equal to one if the trade is initiated by a domestic (proprietary firm, foreign) trader and zero, otherwise. The results are for trades with a trade size that is above the 95 th percentile of the empirical trade size distribution, which is equivalent to eight contracts for FITX, four for FITE, and four for FITF.

37

Table 8: Taiwan Stock Exchange Index Futures (FITX): Price effect (in percentage) in bull and bear markets using open and close benchmark prices. Bull (21 Months) 1 Buy

Bear (15 Months) 2

Sell

Buy

1 Sell

2

Buy

Sell

Buy

Sell


0.1296

0.0446

0.2024

-0.0244

-0.0211

-0.1948

0.0144

-0.2687

Mean

0.1203

0.0586

0.1851

-0.0075

-0.0172

-0.1813

-0.0054

-0.2493

Standard Error

0.0004

0.0004

0.0017

0.0018

0.0008

0.0008

0.0034

0.0031

Median

0.0841

0.0295

0.1540

-0.0265

0.0159

-0.0961

0.0609

-0.1410

Volume Weighted

-0.1185

-0.1163

-0.1235

-0.0935

0.1336

0.1655

0.0983

0.1475

Mean

-0.1172

-0.1209

-0.1219

-0.0988

0.1336

0.1629

0.1122

0.1551

Liquidity Effect

Standard Error

0.0004

0.0004

0.0018

0.0018

0.0009

0.0009

0.0036

0.0035

-0.1193

-0.1262

-0.1256

-0.1189

0.0439

0.0644

0.0517

0.0689

Volume Weighted

0.2481

0.1610

0.3258

0.0691

-0.1547

-0.3604

-0.0839

-0.4163

Mean

0.2376

0.1795

0.3070

0.0912

-0.1508

-0.3443

-0.1177

-0.4044

Standard Error

0.0006

0.0006

0.0025

0.0025

0.0012

0.0012

0.0052

0.0049

Median

0.2525

0.1685

0.3172

0.1068

-0.0832

-0.2601

0.0341

-0.2698



0.1309

0.0481

0.1926

-0.0152

-0.0103

-0.1897

0.0256

-0.2577

Mean

0.1202

0.0638

0.1802

-0.0043

-0.0101

-0.1751

0.0102

-0.2344

Standard Error

0.0005

0.0005

0.0021

0.0022

0.0008

0.0008

0.0041

0.0040

Median

0.0832

0.0312

0.1496

-0.0163

0.0154

-0.0901

0.0585

-0.1375

Volume Weighted

-0.1134

-0.1175

-0.1230

-0.0906

0.1266

0.1584

0.0883

0.1481

Mean

-0.1119

-0.1229

-0.1231

-0.0968

0.1290

0.1554

0.1005

0.1539

0.0005

0.0005

0.0022

0.0022

0.0010

0.0009

0.0045

0.0043

-0.1151

-0.1279

-0.1255

-0.1164

0.0351

0.0550

0.0413

0.0665

Volume Weighted

0.2442

0.1655

0.3156

0.0753

-0.1369

-0.3481

-0.0627

-0.4058

Mean

0.2321

0.1866

0.3033

0.0925

-0.1391

-0.3305

-0.0903

-0.3883

Standard Error

0.0007

0.0007

0.0031

0.0031

0.0013

0.0013

0.0064

0.0062

Median

0.2423

0.1713

0.3099

0.0999

-0.0333

-0.2367

0.0511

-0.2656

Liquidity Effect


38

Table 8 (continue): Bull (21 Months) 1 Buy

Bear (15 Months) 2

Sell

Buy

1 Sell

2

Buy

Sell

Buy

Sell


0.1338

-0.0780

0.2001

-0.1467

-0.0144

-0.2620

0.0193

-0.2782

Mean

0.1187

-0.0537

0.1934

-0.1375

-0.0009

-0.2651

-0.0063

-0.2684

Standard Error

0.0040

0.0041

0.0101

0.0094

0.0076

0.0071

0.0183

0.0153

Median

0.0986

-0.0516

0.1845

-0.1318

0.0687

-0.1915

0.0799

-0.1879

Volume Weighted

-0.1347

-0.0979

-0.1033

-0.1067

0.1619

0.1908

0.0736

0.0754

Mean

-0.1215

-0.1069

-0.1046

-0.1065

0.1531

0.2030

0.0796

0.0903

0.0043

0.0043

0.0103

0.0102

0.0083

0.0095

0.0198

0.0213

-0.1318

-0.1200

-0.1240

-0.1381

0.0813

0.1305

0.0499

0.0333

Volume Weighted

0.2685

0.0198

0.3034

-0.0400

-0.1763

-0.4528

-0.0542

-0.3536

Mean

0.2402

0.0531

0.2979

-0.0310

-0.1541

-0.4680

-0.0859

-0.3587

Standard Error

0.0060

0.0059

0.0145

0.0137

0.0120

0.0122

0.0289

0.0275

Median

0.2653

0.0921

0.3400

-0.0454

-0.1003

-0.3811

0.0341

-0.2698

Liquidity Effect



0.1440

0.0367

0.2617

-0.0480

-0.0222

-0.2285

0.0434

-0.3315

Mean

0.1327

0.0392

0.2284

-0.0189

-0.0057

-0.2264

0.0162

-0.3126

Standard Error

0.0012

0.0013

0.0036

0.0037

0.0023

0.0023

0.0069

0.0065

Median

0.0980

0.0162

0.1970

-0.0316

0.0470

-0.1296

0.0794

-0.1762

Volume Weighted

-0.1362

-0.1196

-0.1350

-0.1125

0.1482

0.1999

0.1013

0.1929

Mean

-0.1419

-0.1203

-0.1253

-0.1074

0.1451

0.2044

0.1131

0.1994

Liquidity Effect

Standard Error

0.0012

0.0013

0.0036

0.0037

0.0026

0.0026

0.0072

0.0071

-0.1420

-0.1204

-0.1331

-0.1204

0.0499

0.0921

0.0608

0.0996

Volume Weighted

0.2802

0.1563

0.3966

0.0646

-0.1703

-0.4284

-0.0579

-0.5245

Mean

0.2746

0.1596

0.3537

0.0886

-0.1508

-0.4308

-0.0969

-0.5120

Standard Error

0.0017

0.0018

0.0051

0.0052

0.0036

0.0037

0.0106

0.0103

Median

0.2653

0.1656

0.3766

0.1068

-0.0145

-0.2990

0.0514

-0.3489


39


Bear (15 Months) 2

Sell

Buy

1 Sell

2

Buy

Sell

Buy

Sell


0.0522

0.0489

0.1156

0.0181

-0.2128

-0.1646

-0.1356

-0.1947

Mean

0.0817

0.0286

0.0932

0.0541

-0.2105

-0.1803

-0.1872

-0.1864

Standard Error

0.0022

0.0024

0.0061

0.0066

0.0045

0.0040

0.0143

0.0094

Median

0.0615

-0.0154

0.0815

0.0000

-0.0438

-0.1008

0.0152

-0.0832

Volume Weighted

-0.1369

-0.0863

-0.1039

-0.0601

0.2050

0.1912

0.1689

0.0619

Mean

-0.1493

-0.0750

-0.1092

-0.0855

0.1992

0.2308

0.2142

0.0645

0.0022

0.0025

0.0061

0.0069

0.0041

0.0048

0.0118

0.0111

-0.1522

-0.1134

-0.1075

-0.1306

0.1010

0.1531

0.1315

0.0322

Volume Weighted

0.1891

0.1353

0.2195

0.0782

-0.4178

-0.3558

-0.3045

-0.2567

Mean

0.2310

0.1036

0.2024

0.1397

-0.4096

-0.4111

-0.4014

-0.2508

Standard Error

0.0031

0.0034

0.0086

0.0094

0.0065

0.0066

0.0203

0.0152

Median

0.2555

0.1656

0.2423

0.1842

-0.2627

-0.3811

-0.1921

-0.1442

Liquidity Effect


This table contains estimates of the price effect (in percentage) for trades in the futures contract FITX. Panel A presents the price effects based on all trades; panels B through E tabulate the results for four categories of traders which are individual, domestic institutional, proprietary firm and foreign traders. The total price effect, liquidity effect and information effect are estimated by, Total Price effect = ln( PT / Pp ,T ) X 100 Liquidity (Temporary) effect = ln( PT / PT ,a ) X 100 Information (Permanent) effect = ln( PT ,a / Pp ,T ) X 100 where PT denotes the price of the transaction for which the price impact is estimated; P T,b and PT,a are the benchmark prices prior to and after the transaction of interest. The daily opening and closing prices are used as benchmark prices. The price effects are computed for purchases and sales in bullish and bearish markets, respectively. For each contract, the market is classified per month as bullish if the monthly price return is positive and bearish otherwise. The price return is computed as the log difference between the first and the last trade price of the month. The number of bullish and bearish months is noted in parentheses in the table panels. Furthermore, the trades are categorized into two groups based on the 95 th percentile of the empirical trade size distribution, which is equivalent to eight contracts for FITX. Transactions with a trade size less than the 95th percentile belong to category 1 and transactions with a trade size larger than or equal to the 95th percentile are classified as 2. The estimates labeled Volume Weighted are volume weighted averages price effects. Proportion denotes the fraction of the estimates of price effects that are positive for purchases and the fraction of the estimates that are negative for sales.

40

Table 9: Electronic Sector Index Futures (FITE): Price effect (in percentage) in bull and bear markets using open and close benchmark prices. Bull (20 Months) 1 Buy

Bear (16 Months) 2

Sell

Buy

1 Sell

Buy

2 Sell

Buy

Sell


0.1674

0.0578

0.3002

-0.0132

0.0453

-0.1512

0.1141

-0.2384

Mean

0.1603

0.0671

0.2796

0.0010

0.0460

-0.1424

0.0945

-0.2196

Standard Error

0.0010

0.0010

0.0042

0.0042

0.0016

0.0016

0.0065

0.0068

Median

0.1319

0.0488

0.2287

-0.0174

0.0497

-0.0747

0.1114

-0.1301

Volume Weighted

-0.1203

-0.1122

-0.1561

-0.0882

0.1912

0.2123

0.1363

0.1979

Mean

-0.1178

-0.1130

-0.1531

-0.0951

0.1881

0.2094

0.1588

0.2113

Liquidity Effect

Standard Error

0.0012

0.0011

0.0045

0.0046

0.0020

0.0020

0.0081

0.0079

-0.0996

-0.0988

-0.1200

-0.0884

0.0445

0.0570

0.0426

0.0704

Volume Weighted

0.2877

0.1700

0.4562

0.0750

-0.1459

-0.3634

-0.0222

-0.4363

Mean

0.2781

0.1802

0.4326

0.0961

-0.1420

-0.3518

-0.0643

-0.4308

Standard Error

0.0016

0.0016

0.0063

0.0064

0.0027

0.0026

0.0108

0.0109

Median

0.2052

0.1510

0.3448

0.0325

-0.1265

-0.2212

-0.0342

-0.2508



0.1674

0.0718

0.2948

0.0063

0.0469

-0.1389

0.1233

-0.2271

Mean

0.1597

0.0803

0.2837

0.0207

0.0466

-0.1316

0.1100

-0.2034

Standard Error

0.0011

0.0011

0.0054

0.0054

0.0017

0.0017

0.0081

0.0090

Median

0.1304

0.0620

0.2325

0.0000

0.0470

-0.0673

0.1241

-0.1051

Volume Weighted

-0.1232

-0.1114

-0.1488

-0.0829

0.1701

0.2161

0.1279

0.1974

Mean

-0.1211

-0.1136

-0.1501

-0.0901

0.1693

0.2115

0.1441

0.2122

0.0013

0.0013

0.0059

0.0060

0.0022

0.0022

0.0103

0.0103

-0.1002

-0.0983

-0.1195

-0.0820

0.0341

0.0565

0.0192

0.0467

Volume Weighted

0.2907

0.1832

0.4437

0.0892

-0.1232

-0.3550

-0.0046

-0.4245

Mean

0.2808

0.1939

0.4338

0.1108

-0.1227

-0.3431

-0.0341

-0.4156

Standard Error

0.0017

0.0017

0.0082

0.0083

0.0028

0.0028

0.0137

0.0143

Median

0.2130

0.1582

0.3461

0.0458

-0.0807

-0.2201

0.0192

-0.2234

Liquidity Effect


41


Bear (16 Months) 2

Sell

Buy

1 Sell

2

Buy

Sell

Buy

Sell


0.1491

-0.0438

0.2883

0.0173

-0.0541

-0.2073

-0.0026

-0.2087

Mean

0.1532

-0.0242

0.2776

0.0164

-0.0597

-0.2097

0.0141

-0.2414

Standard Error

0.0134

0.0116

0.0358

0.0280

0.0176

0.0173

0.0466

0.0419

Median

0.0757

0.0369

0.2256

0.0192

0.0000

-0.2162

0.0699

-0.1375

Volume Weighted

0.0494

-0.1311

0.0082

-0.0855

0.2843

0.2380

0.3613

0.1032

Mean

0.0478

-0.1252

-0.0340

-0.0930

0.3057

0.2333

0.3672

0.1511

Standard Error

0.0155

0.0121

0.0352

0.0310

0.0267

0.0179

0.0682

0.0425

Median

0.0000

-0.1303

-0.0658

-0.0805

0.0615

0.1612

0.1282

0.0639

Volume Weighted

0.0997

0.0874

0.2801

0.1028

-0.3384

-0.4452

-0.3638

-0.3119

Mean

0.1054

0.1009

0.3116

0.1094

-0.3654

-0.4430

-0.3531

-0.3924

Standard Error

0.0203

0.0165

0.0519

0.0422

0.0340

0.0253

0.0888

0.0630

Median

0.0975

0.1212

0.3313

0.1886

-0.2701

-0.3594

-0.2418

-0.2752

Liquidity Effect

Permanent Effect


0.1662

0.0051

0.3576

-0.0467

0.0121

-0.1246

0.0886

-0.2206

Mean

0.1632

0.0075

0.3067

-0.0423

0.0237

-0.1208

0.0464

-0.1874

Standard Error

0.0040

0.0042

0.0083

0.0088

0.0064

0.0062

0.0124

0.0127

Median

0.1480

-0.0158

0.2696

-0.0663

0.0448

-0.0881

0.0635

-0.1335

Volume Weighted

-0.1200

-0.1005

-0.1632

-0.1076

0.1359

0.2786

0.0985

0.1748

Mean

-0.1168

-0.0985

-0.1575

-0.1125

0.1275

0.2703

0.1077

0.2080

Liquidity Effect

Standard Error

0.0043

0.0043

0.0089

0.0099

0.0077

0.0074

0.0152

0.0156

-0.1046

-0.1002

-0.1367

-0.0978

0.0388

0.1347

0.0353

0.1273

Volume Weighted

0.2861

0.1057

0.5208

0.0608

-0.1238

-0.4032

-0.0099

-0.3954

Mean

0.2800

0.1060

0.4641

0.0702

-0.1037

-0.3911

-0.0613

-0.3954

Standard Error

0.0060

0.0059

0.0124

0.0132

0.0102

0.0100

0.0202

0.0209

Median

0.2010

0.0668

0.3516

0.0000

-0.1284

-0.2417

-0.0453

-0.2417


42


Bear (16 Months) 2

Sell

Buy

1 Sell

Buy

2 Sell

Buy

Sell


0.1694

-0.0194

0.2258

-0.0336

0.0677

-0.3168

0.1327

-0.3179

Mean

0.1662

-0.0210

0.2226

-0.0178

0.0692

-0.3110

0.1118

-0.3406

Standard Error

0.0037

0.0038

0.0111

0.0113

0.0074

0.0067

0.0202

0.0172

Median

0.1385

-0.0360

0.1778

-0.0433

0.0892

-0.2007

0.1542

-0.2426

Volume Weighted

-0.1079

-0.1284

-0.1809

-0.0772

0.4431

0.0991

0.2059

0.2497

Mean

-0.0954

-0.1176

-0.1693

-0.0906

0.4467

0.1164

0.2780

0.2209

0.0042

0.0039

0.0114

0.0114

0.0084

0.0075

0.0237

0.0219

-0.0935

-0.1040

-0.0982

-0.1050

0.2077

-0.0215

0.1314

0.0886

Volume Weighted

0.2773

0.1090

0.4067

0.0436

-0.3754

-0.4159

-0.0732

-0.5675

Mean

0.2616

0.0967

0.3919

0.0727

-0.3775

-0.4274

-0.1662

-0.5615

Standard Error

0.0055

0.0057

0.0165

0.0164

0.0119

0.0100

0.0330

0.0284

Median

0.1573

0.0458

0.2275

0.0173

-0.2201

-0.2701

-0.1265

-0.3617

Liquidity Effect


This table contains estimates of the price effect (in percentage) for trades in the futures contract FITE. Panel A presents the price effects based on all trades; panels B through E tabulate the results for four categories of traders which are individual, domestic institutional, proprietary firm and foreign traders. The total price effect, liquidity effect and information effect are estimated by, Total Price effect = ln( PT / Pp ,T ) X 100 Liquidity (Temporary) effect = ln( PT / PT ,a ) X 100 Information (Permanent) effect = ln( PT ,a / Pp ,T ) X 100 where PT denotes the price of the transaction for which the price impact is estimated; P T,b and PT,a are the benchmark prices prior to and after the transaction of interest. The daily opening and closing prices are used as benchmark prices. The price effects are computed for purchases and sales in bullish and bearish markets, respectively. For each contract, the market is classified per month as bullish if the monthly price return is positive and bearish otherwise. The price return is computed as the log difference between the first and the last trade price of the month. The number of bullish and bearish months is noted in parentheses in the table panels. Furthermore, the trades are categorized into two groups based on the 95 th percentile of the empirical trade size distribution, which is equivalent to four contracts for FITE. Transactions with a trade size less than the 95th percentile belong to category 1 and transactions with a trade size larger than or equal to the 95th percentile are classified as 2. The estimates labeled Volume Weighted are volume weighted averages price effects. Proportion denotes the fraction of the estimates of price effects that are positive for purchases and the fraction of the estimates that are negative for sales.

43

Table 10: Financial Sector Index Futures (FITF): Price effect (in percentage) in bull and bear markets using open and close benchmark prices. Bull (18 Months) 1 Buy

Bear (18 Months) 2

Sell

Buy

1 Sell

Buy

2 Sell

Buy

Sell


0.3290

0.1810

0.4606

0.1009

0.0753

-0.1623

0.1598

-0.2707

Mean

0.3213

0.1895

0.4368

0.1198

0.0750

-0.1531

0.1357

-0.2361

Standard Error

0.0014

0.0014

0.0054

0.0056

0.0019

0.0019

0.0081

0.0081

Median

0.2335

0.1197

0.3554

0.0678

0.0570

-0.1062

0.1578

-0.1867

Volume Weighted

-0.1656

-0.1422

-0.1981

-0.1028

0.1914

0.2493

0.1539

0.2075

Mean

-0.1688

-0.1472

-0.1884

-0.1107

0.1919

0.2495

0.1665

0.2240

Liquidity Effect

Standard Error

0.0014

0.0014

0.0052

0.0051

0.0021

0.0021

0.0086

0.0080

-0.1430

-0.1382

-0.1451

-0.1072

0.0191

0.0226

0.0000

0.0526

Volume Weighted

0.4947

0.3231

0.6587

0.2037

-0.1161

-0.4116

0.0060

-0.4782

Mean

0.4901

0.3367

0.6252

0.2305

-0.1168

-0.4025

-0.0308

-0.4601

Standard Error

0.0020

0.0020

0.0079

0.0080

0.0030

0.0029

0.0126

0.0120

Median

0.4924

0.3741

0.5649

0.2160

0.0205

-0.2045

0.1700

-0.2897



0.3307

0.2159

0.4924

0.1558

0.1098

-0.1667

0.2463

-0.2806

Mean

0.3209

0.2225

0.4645

0.1705

0.1052

-0.1564

0.2096

-0.2449

Standard Error

0.0016

0.0015

0.0067

0.0071

0.0020

0.0020

0.0098

0.0103

Median

0.2310

0.1460

0.3733

0.1074

0.0663

-0.1018

0.1833

-0.1828

Volume Weighted

-0.1712

-0.1539

-0.2263

-0.1087

0.1685

0.2692

0.1338

0.2896

Mean

-0.1712

-0.1592

-0.2097

-0.1125

0.1706

0.2657

0.1437

0.2918

0.0015

0.0015

0.0066

0.0064

0.0023

0.0023

0.0106

0.0103

-0.1455

-0.1450

-0.1658

-0.1060

0.0000

0.0231

-0.0190

0.0807

Volume Weighted

0.5019

0.3698

0.7187

0.2645

-0.0587

-0.4358

0.1125

-0.5702

Mean

0.4920

0.3817

0.6742

0.2830

-0.0654

-0.4221

0.0659

-0.5367

Standard Error

0.0023

0.0022

0.0098

0.0100

0.0032

0.0032

0.0154

0.0153

Median

0.4880

0.4329

0.6137

0.3088

0.0714

-0.2091

0.2200

-0.3013

Liquidity Effect


44


Bear (18 Months) 2

Sell

Buy

1 Sell

Buy

2 Sell

Buy

Sell


0.2787

0.0954

0.3828

0.0129

0.0760

-0.0934

-0.1615

-0.4462

Mean

0.2823

0.1267

0.3853

0.0038

0.0671

-0.0559

-0.0949

-0.4221

Standard Error

0.0192

0.0188

0.0483

0.0472

0.0300

0.0215

0.0788

0.0453

Median

0.2516

0.0842

0.3859

0.0191

0.0855

-0.1173

0.1881

-0.3193

Volume Weighted

-0.2520

-0.1337

-0.2094

-0.2063

0.2407

0.2122

0.4607

0.2681

Mean

-0.2589

-0.1421

-0.2134

-0.1865

0.2535

0.2144

0.3918

0.2513

0.0184

0.0188

0.0444

0.0438

0.0278

0.0243

0.0619

0.0536

-0.2162

-0.1191

-0.1521

-0.1030

0.0408

0.0198

0.1615

0.0439

Volume Weighted

0.5307

0.2291

0.5923

0.2191

-0.1647

-0.3056

-0.6222

-0.7144

Mean

0.5412

0.2688

0.5988

0.1902

-0.1864

-0.2703

-0.4867

-0.6734

Standard Error

0.0273

0.0268

0.0736

0.0652

0.0436

0.0350

0.1142

0.0764

Median

0.6252

0.1871

0.6874

0.1074

0.1061

-0.1517

0.0753

-0.4978

Liquidity Effect



0.2766

0.1144

0.4142

0.0025

0.0610

-0.1158

0.1689

-0.3241

Mean

0.2738

0.1097

0.3723

0.0418

0.0758

-0.1212

0.1382

-0.2705

Standard Error

0.0051

0.0049

0.0123

0.0127

0.0072

0.0072

0.0164

0.0173

Median

0.2222

0.0648

0.3011

0.0000

0.0767

-0.1167

0.1229

-0.1909

Volume Weighted

-0.1733

-0.0869

-0.1760

-0.0754

0.0872

0.1343

0.0470

0.0567

Mean

-0.1749

-0.0802

-0.1688

-0.0840

0.0810

0.1332

0.0672

0.0793

Liquidity Effect

Standard Error

0.0049

0.0048

0.0116

0.0117

0.0071

0.0067

0.0177

0.0149

-0.1573

-0.0950

-0.1647

-0.0917

0.0000

0.0438

0.0000

0.0231

Volume Weighted

0.4499

0.2013

0.5902

0.0779

-0.0262

-0.2501

0.1219

-0.3808

Mean

0.4486

0.1899

0.5411

0.1258

-0.0053

-0.2544

0.0710

-0.3498

Standard Error

0.0073

0.0073

0.0174

0.0182

0.0105

0.0101

0.0254

0.0232

Median

0.4880

0.2180

0.5271

0.1071

0.0606

-0.1815

0.1096

-0.2382


45


Bear (18 Months) 2

Sell

Buy

1 Sell

2

Buy

Sell

Buy

Sell


0.3551

-0.0902

0.3920

-0.0151

-0.1976

-0.1575

-0.1843

-0.1415

Mean

0.3588

-0.1048

0.3910

-0.0301

-0.2064

-0.1444

-0.1735

-0.1256

Standard Error

0.0046

0.0042

0.0137

0.0129

0.0062

0.0063

0.0233

0.0190

Median

0.2652

-0.1487

0.3391

-0.0590

-0.0958

-0.1757

0.0884

-0.1892

Volume Weighted

-0.1179

-0.0785

-0.1072

-0.1005

0.4408

0.1121

0.3244

-0.0058

Mean

-0.1410

-0.0725

-0.1152

-0.1242

0.4541

0.1214

0.3406

0.0214

0.0041

0.0046

0.0121

0.0124

0.0070

0.0073

0.0239

0.0188

-0.1191

-0.1225

-0.0835

-0.1395

0.1842

0.0000

0.0814

0.0000

Volume Weighted

0.4730

-0.0117

0.4992

0.0854

-0.6385

-0.2696

-0.5087

-0.1357

Mean

0.4998

-0.0323

0.5062

0.0941

-0.6605

-0.2658

-0.5141

-0.1471

Standard Error

0.0067

0.0064

0.0198

0.0181

0.0099

0.0098

0.0357

0.0274

Median

0.4880

0.0215

0.4126

0.0381

-0.3152

-0.2035

-0.0410

-0.1959

Liquidity Effect


This table contains estimates of the price effect (in percentage) for trades in the futures contract FITF. Panel A presents the price effects based on all trades; panels B through E tabulate the results for four categories of traders which are individual, domestic institutional, proprietary firm and foreign traders. The total price effect, liquidity effect and information effect are estimated by, Total Price effect = ln( PT / Pp ,T ) X 100 Liquidity (Temporary) effect = ln( PT / PT ,a ) X 100 Information (Permanent) effect = ln( PT ,a / Pp ,T ) X 100 where PT denotes the price of the transaction for which the price impact is estimated; P T,b and PT,a are the benchmark prices prior to and after the transaction of interest. The daily opening and closing prices are used as benchmark prices. The price effects are computed for purchases and sales in bullish and bearish markets, respectively. For each contract, the market is classified per month as bullish if the monthly price return is positive and bearish otherwise. The price return is computed as the log difference between the first and the last trade price of the month. The number of bullish and bearish months is noted in parentheses in the table panels. Furthermore, the trades are categorized into two groups based on the 95 th percentile of the empirical trade size distribution, which is equivalent to four contracts for FITF. Transactions with a trade size less than the 95th percentile belong to category 1 and transactions with a trade size larger than or equal to the 95th percentile are classified as 2. The estimates labeled Volume Weighted are volume weighted averages price effects. Proportion denotes the fraction of the estimates of price effects that are positive for purchases and the fraction of the estimates that are negative for sales.

46

Table 11: Taiwan Stock Exchanges Index Futures (FITX): Price effect (in percentage) using 15-minute prices as benchmark prices. 1 Buy

2 Sell

Buy

Sell


0.0431

-0.0608

0.0869

-0.1037

Mean

0.0392

-0.0563

0.0729

-0.0888

Standard Error

0.0002

0.0002

0.0006

0.0006

Median

0.0461

-0.0352

0.0770

-0.0722

Liquidity Effect Volume Weighted

-0.0002

0.0011

-0.0060

0.0002

Mean

0.0007

0.0010

-0.0055

0.0020

Standard Error

0.0001

0.0001

0.0006

0.0006

Median

0.0000

-0.0131

0.0000

-0.0141

Volume Weighted

0.0433

-0.0619

0.0929

-0.1039

Mean

0.0385

-0.0573

0.0785

-0.0908

Standard Error

0.0002

0.0002

0.0008

0.0009

0.0419

-0.0318

0.0715

-0.0649

Permanent Effect

Median


0.0434

-0.0602

0.0826

-0.1026

Mean

0.0387

-0.0546

0.0690

-0.0855

Standard Error

0.0002

0.0002

0.0008

0.0008

Median

0.0466

-0.0348

0.0732

-0.0682

-0.0005

0.0012

-0.0105

0.0048

Mean

0.0007

0.0005

-0.0099

0.0059

Standard Error

0.0002

0.0002

0.0007

0.0007

Median

0.0000

0.0000

0.0000

0.0000

Volume Weighted

0.0439

-0.0613

0.0932

-0.1074

Mean

0.0380

-0.0551

0.0789

-0.0914

Standard Error

0.0002

0.0002

0.0010

0.0011

Median

0.0423

-0.0315

0.0708

-0.0650

Liquidity Effect Volume Weighted

Permanent Effect

47


2 Sell

Buy

Sell


0.0775

-0.1227

0.1135

-0.1522

Mean

0.0709

-0.1203

0.1088

-0.1461

Standard Error

0.0015

0.0015

0.0034

0.0034

Median

0.0627

-0.0846

0.1004

-0.1269

Volume Weighted

0.0096

0.0194

0.0064

-0.0036

Mean

0.0108

0.0257

0.0069

0.0009

Standard Error

0.0013

0.0026

0.0028

0.0045

Median

0.0000

-0.0156

0.0150

-0.0272

Volume Weighted

0.0679

-0.1421

0.1070

-0.1486

Mean

0.0601

-0.1460

0.1019

-0.1470

Standard Error

0.0020

0.0031

0.0045

0.0057

Median

0.0532

-0.0742

0.0871

-0.1082

Liquidity Effect

Permanent Effect


0.0502

-0.0658

0.1099

-0.1191

Mean

0.0509

-0.0685

0.0936

-0.1055

Standard Error

0.0004

0.0004

0.0012

0.0013

Median

0.0506

-0.0456

0.0944

-0.0869

Volume Weighted

-0.0049

0.0030

-0.0054

-0.0011

Mean

Liquidity Effect

-0.0058

0.0052

-0.0041

0.0008

Standard Error

0.0004

0.0004

0.0011

0.0012

Median

0.0000

0.0000

0.0000

-0.0149

Volume Weighted

0.0551

-0.0688

0.1153

-0.1180

Mean

0.0567

-0.0737

0.0977

-0.1064

Standard Error

0.0006

0.0006

0.0017

0.0017

Median

0.0512

-0.0423

0.0871

-0.0800

Permanent Effect

48


2 Sell

Buy

Sell


0.0030

-0.0337

0.0492

-0.0557

Mean

0.0047

-0.0389

0.0320

-0.0454

Standard Error

0.0007

0.0008

0.0022

0.0019

Median

0.0151

-0.0163

0.0458

-0.0351

Volume Weighted

0.0192

-0.0137

0.0196

-0.0268

Mean

0.0178

-0.0101

0.0205

-0.0265

Standard Error

0.0007

0.0008

0.0021

0.0020

Median

0.0144

-0.0169

0.0152

-0.0316

Volume Weighted

-0.0162

-0.0200

0.0295

-0.0288

Mean

-0.0131

-0.0288

0.0114

-0.0189

Standard Error

0.0010

0.0011

0.0031

0.0028

Median

0.0000

0.0000

0.0186

-0.0143

Liquidity Effect

Permanent Effect

This table contains estimates of the price effect (in percentage) for trades in the futures contract FITX. Panel A presents the price effects based on all trades; panels B through E tabulate the results for four categories of traders which are individual, domestic institutional, proprietary firm and foreign traders. The total price effect, liquidity effect and information effect are estimated by, Total Price effect = ln( Pb / Pp ,b ) X 100 Liquidity (Temporary) effect = ln( Pb / Pp, a ) X 100 Information (Permanent) effect = ln( Pp , a / Pp ,b ) X 100 where Pb denotes the price of the transaction for which the price impact is estimated; P p,b and Pp,a are the benchmark prices where Pp,b (Pp,a) is the first trade that is executed at least fifteen minutes prior (subsequent) to the trade Pb. During the opening and closing fifteen minutes of trading, the opening and closing prices are used. The price effects are computed for purchases and sales, separately. Furthermore, the trades are categorized into two groups based on the 95th percentile of the empirical trade size distribution, which is equivalent to eight contracts for FITX. Transactions with a trade size less than the 95 th percentile belong to category 1 and transactions with a trade size larger than or equal to the 95 th percentile are classified as 2. The estimates labeled Volume Weighted are volume weighted averages price effects. Proportion denotes the fraction of the estimates of price effects that are positive for purchases and the fraction of the estimates that are negative for sales.

49

Table 12: Taiwan Stock Exchanges Index Futures (FITX): Price effect (in percentage) in bull and bear markets using 15-minute prices as benchmark prices. Bull (21 Months) 1 Buy

Bear (15 Months) 2

Sell

Buy

1 Sell

Buy

2 Sell

Buy

Sell


0.0457

-0.0289

0.0936

-0.0770

0.0401

-0.0952

0.0770

-0.1389

Mean

0.0400

-0.0222

0.0803

-0.0628

0.0384

-0.0909

0.0624

-0.1226

Standard Error

0.0002

0.0002

0.0007

0.0007

0.0003

0.0003

0.0011

0.0012

Median

0.0454

-0.0168

0.0786

-0.0597

0.0475

-0.0648

0.0708

-0.0960

Volume Weighted

-0.0085

-0.0113

-0.0136

-0.0084

0.0091

0.0145

0.0051

0.0115

Mean

-0.0077

-0.0125

-0.0128

-0.0071

0.0096

0.0147

0.0049

0.0137

Liquidity Effect

Standard Error

0.0001

0.0001

0.0006

0.0006

0.0003

0.0003

0.0011

0.0011

Median

0.0000

-0.0150

0.0000

-0.0151

0.0000

0.0000

0.0000

0.0000

Volume Weighted

0.0542

-0.0176

0.1072

-0.0686

0.0310

-0.1097

0.0718

-0.1504

Mean

0.0477

-0.0097

0.0931

-0.0557

0.0288

-0.1056

0.0575

-0.1364

Standard Error

0.0002

0.0002

0.0009

0.0009

0.0004

0.0004

0.0016

0.0016

Median

0.0453

-0.0141

0.0801

-0.0480

0.0339

-0.0628

0.0616

-0.0915

Permanent Effect


0.0466

-0.0283

0.0883

-0.0742

0.0398

-0.0935

0.0744

-0.1398

Mean

0.0402

-0.0207

0.0755

-0.0597

0.0372

-0.0884

0.0601

-0.1187

Standard Error

0.0002

0.0002

0.0008

0.0008

0.0003

0.0003

0.0014

0.0015

Median

0.0463

-0.0167

0.0754

-0.0540

0.0474

-0.0634

0.0695

-0.0913

Volume Weighted

-0.0078

-0.0117

-0.0169

-0.0033

0.0074

0.0146

-0.0013

0.0153

Mean

Liquidity Effect

-0.0068

-0.0133

-0.0158

-0.0029

0.0085

0.0143

-0.0015

0.0173

Standard Error

0.0002

0.0002

0.0007

0.0007

0.0003

0.0003

0.0013

0.0013

Median

0.0000

-0.0151

0.0000

-0.0137

0.0000

0.0000

0.0000

0.0000

Volume Weighted

0.0545

-0.0166

0.1053

-0.0709

0.0324

-0.1081

0.0757

-0.1551

Mean

0.0471

-0.0074

0.0913

-0.0569

0.0287

-0.1026

0.0616

-0.1359

Standard Error

0.0002

0.0002

0.0011

0.0011

0.0004

0.0004

0.0020

0.0020

Median

0.0456

-0.0138

0.0783

-0.0480

0.0342

-0.0618

0.0629

-0.0918

Permanent Effect

50


Bear (15 Months) 2

Sell

Buy

1 Sell

Buy

2 Sell

Buy

Sell


0.0756

-0.0850

0.1251

-0.1349

0.0798

-0.1679

0.0945

-0.1780

Mean

0.0638

-0.0735

0.1203

-0.1250

0.0795

-0.1714

0.0904

-0.1776

Standard Error

0.0016

0.0017

0.0039

0.0038

0.0025

0.0025

0.0061

0.0061

Median

0.0509

-0.0510

0.0986

-0.1138

0.0802

-0.1367

0.1043

-0.1532

Volume Weighted

-0.0097

-0.0078

-0.0060

-0.0184

0.0344

0.0522

0.0266

0.0185

Mean

0.0196

Liquidity Effect

-0.0067

-0.0085

-0.0066

-0.0116

0.0319

0.0630

0.0285

Standard Error

0.0013

0.0015

0.0032

0.0036

0.0024

0.0052

0.0053

0.0098

Median

0.0000

-0.0152

0.0000

-0.0170

0.0169

-0.0165

0.0171

-0.0313

Volume Weighted

0.0854

-0.0772

0.1311

-0.1165

0.0454

-0.2200

0.0679

-0.1964

Mean

0.0705

-0.0650

0.1269

-0.1134

0.0476

-0.2344

0.0620

-0.1971

Standard Error

0.0021

0.0022

0.0051

0.0053

0.0036

0.0059

0.0083

0.0119

Median

0.0529

-0.0450

0.0933

-0.0988

0.0539

-0.1243

0.0821

-0.1188

Permanent Effect


0.0467

-0.0318

0.1155

-0.0891

0.0548

-0.1080

0.1011

-0.1606

Mean

0.0433

-0.0310

0.0987

-0.0730

0.0604

-0.1114

0.0859

-0.1493

Standard Error

0.0004

0.0005

0.0014

0.0014

0.0007

0.0008

0.0023

0.0022

Median

0.0462

-0.0278

0.0980

-0.0684

0.0620

-0.0762

0.0867

-0.1195

Volume Weighted

-0.0142

-0.0074

-0.0128

-0.0094

0.0075

0.0159

0.0063

0.0103

Mean

-0.0154

-0.0063

-0.0114

-0.0078

0.0063

0.0184

0.0068

0.0125

Liquidity Effect

Standard Error

0.0004

0.0004

0.0012

0.0012

0.0007

0.0008

0.0021

0.0022

-0.0136

-0.0131

0.0000

-0.0155

0.0000

0.0000

0.0000

0.0000

Volume Weighted

0.0610

-0.0244

0.1283

-0.0796

0.0473

-0.1239

0.0948

-0.1709

Mean

0.0587

-0.0247

0.1100

-0.0651

0.0541

-0.1297

0.0790

-0.1618

Standard Error

0.0006

0.0006

0.0018

0.0019

0.0011

0.0011

0.0032

0.0031

Median

0.0511

-0.0165

0.0971

-0.0547

0.0514

-0.0756

0.0732

-0.1161


51


Bear (15 Months) 2

Sell

Buy

1 Sell

2

Buy

Sell

Buy

Sell


0.0151

-0.0113

0.0617

-0.0436

-0.0103

-0.0601

0.0311

-0.0705

Mean

0.0174

-0.0141

0.0492

-0.0315

-0.0086

-0.0677

0.0082

-0.0622

Standard Error

0.0007

0.0008

0.0021

0.0023

0.0012

0.0013

0.0043

0.0032

Median

0.0162

0.0000

0.0503

-0.0330

0.0000

-0.0350

0.0317

-0.0466

Volume Weighted

0.0020

-0.0207

0.0052

-0.0377

0.0379

-0.0055

0.0405

-0.0135

Mean

0.0002

-0.0162

0.0044

-0.0378

0.0362

-0.0031

0.0426

-0.0127

Standard Error

0.0007

0.0008

0.0021

0.0023

0.0013

0.0014

0.0039

0.0034

Median

0.0000

-0.0168

0.0000

-0.0329

0.0172

-0.0173

0.0175

-0.0292

Volume Weighted

0.0131

0.0094

0.0565

-0.0059

-0.0482

-0.0547

-0.0094

-0.0570

Mean

0.0172

0.0020

0.0448

0.0064

-0.0449

-0.0647

-0.0344

-0.0495

Standard Error

0.0010

0.0011

0.0030

0.0032

0.0018

0.0020

0.0060

0.0047

Median

0.0151

0.0145

0.0336

0.0000

-0.0186

-0.0166

0.0000

-0.0167

Liquidity Effect

Permanent Effect

This table contains estimates of the price effect (in percentage) for trades in the futures contract FITX. Panel A presents the price effects based on all trades; panels B through E tabulate the results for four categories of traders which are individual, domestic institutional, proprietary firm and foreign traders. . The total price effect, liquidity effect and information effect are estimated by, Total Price effect = ln( PT / Pp ,T ) X 100 Liquidity (Temporary) effect = ln( PT / PT ,a ) X 100 Information (Permanent) effect = ln( PT , a / Pp ,T ) X 100 where PT denotes the price of the transaction for which the price impact is estimated; P p, T and PT, a are the benchmark prices where Pp, T (PT a) is the average price of the thirteenth to fifteenth trades prior (subsequent) to the trade PT. During the opening and closing fifteen minutes of trading, the opening and closing prices are used. The price effects are computed for purchases and sales in bullish and bearish markets, respectively. For each contract, the market is classified per month as bullish if the monthly price return is positive and bearish otherwise. The price return is computed as the log difference between the first and the last trade price of the month. The number of bullish and bearish months is noted in parentheses in the table panels. Furthermore, the trades are categorized into two groups based on the 95th percentile of the empirical trade size distribution, which is equivalent to eight contracts for FITX. Transactions with a trade size less than the 95th percentile belong to category 1 and transactions with a trade size larger than or equal to the 95th percentile are classified as 2. The estimates labeled Volume Weighted are volume weighted averages price effects. Proportion denotes the fraction of the estimates of price effects that are positive for purchases and the fraction of the estimates that are negative for sales.

52

Appendix A. TAIFEX Index Futures Contract Specifications Taiwan Stock Exchange Index Futures (FITX) Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX)

Electronic Sector Index Futures (FITE) Taiwan Stock Exchange Electronic Sector Index

Financial Sector Index Futures (FITF) Taiwan Stock Exchange Finance Sector Index

Contract Size

NT$200 x Index

NT$4,000 x Index

NT$1,000 x Index

Minimum Price Fluctuation Delivery Months

One index point (NT$200)

Last Trading Day

The third Wednesday of the delivery month of each contract

Trading Hours

08:45AM-1:45PM Taiwan time Monday through Friday of the regular business days of the Taiwan Stock Exchange

Daily Price Limit

+/- 7% of previous day's settlement price

Settlement

Cash settlement

Underlying Index

0.05 index point 0.2 index point (NT$200) (NT$200) Spot month, the next calendar month, and the next three quarter months

Position Limit Individual

2,500 contracts

400 contracts

300 contracts

Institutional

5,000 contracts

1,000 contracts

1,000 contracts

Exemption

Institutional investors may apply for an exemption from the above limit on trading accounts for hedging purpose. Exemptions are allowed for Future Proprietary Firms and omnibus accounts.

53