trades versus individual trades on three futures contracts traded on the Taiwan ... Robin K. Chou is Professor and Chairman, Department of Finance, School of.
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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5
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.
20
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
Percentage of Total Volume ---
---
Daily Trading Volume Mean
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
Daily Dollar Value (106) Mean
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
Percentage of Total Volume ---
---
Daily Trading Volume Mean
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
Daily Dollar Value (106) Mean
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
Median Permanent 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
Standard Error Median Permanent Effect
28
Table 3 (continue): 1 Buy
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 Median Permanent Effect
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
Panel A: All Traders Total Price Effect Volume Weighted
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 Permanent Effect
Median
Panel B: Individual Traders Total Price Effect Volume Weighted
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
Median Permanent Effect
30
Table 4 (continue): 1 Buy
2 Sell
Buy
Sell
Panel C: Domestic Institution Traders Total Price Effect Volume Weighted
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
Panel D: Proprietary Firm Traders Total Price Effect Volume Weighted
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
Standard Error Median Permanent Effect
31
Table 4 (continue): 1 Buy
2 Sell
Buy
Sell
Panel E: Foreign Traders Total Price Effect Volume Weighted
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
Panel A: All Traders Total Price Effect Volume Weighted
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 Permanent Effect
Median
Panel B: Individual Traders Total Price Effect Volume Weighted
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
Median Permanent Effect
33
Table 5 (continue): 1 Buy
2 Sell
Buy
Sell
Panel C: Domestic Institution Traders Total Price Effect Volume Weighted
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
Standard Error Median Permanent Effect
Panel D: Proprietary Firm Traders Total Price Effect Volume Weighted
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
Standard Error Median Permanent Effect
34
Table 5 (continue): 1 Buy
2 Sell
Buy
Sell
Panel E: Foreign Traders Total Price Effect Volume Weighted
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
Panel A: All Traders Total Price Effect Volume Weighted
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
Median Permanent Effect
Panel B: Individual Traders Total Price Effect Volume Weighted
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
Standard Error Median Permanent Effect
38
Table 8 (continue): Bull (21 Months) 1 Buy
Bear (15 Months) 2
Sell
Buy
1 Sell
2
Buy
Sell
Buy
Sell
Panel C: Domestic Institution Traders Total Price Effect Volume Weighted
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
Standard Error Median Permanent Effect
Panel D: Proprietary Firm Traders Total Price Effect Volume Weighted
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
Median Permanent Effect
39
Table 8 (continue): Bull (21 Months) 1 Buy
Bear (15 Months) 2
Sell
Buy
1 Sell
2
Buy
Sell
Buy
Sell
Panel E: Foreign Traders Total Price Effect Volume Weighted
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
Standard Error Median 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 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
Panel A: All Traders Total Price Effect Volume Weighted
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
Median Permanent Effect
Panel B: Individual Traders Total Price Effect Volume Weighted
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
Standard Error Median Permanent Effect
41
Table 9 (continue): Bull (20 Months) 1 Buy
Bear (16 Months) 2
Sell
Buy
1 Sell
2
Buy
Sell
Buy
Sell
Panel C: Domestic Institution Traders Total Price Effect Volume Weighted
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
Panel D: Proprietary Firm Traders Total Price Effect Volume Weighted
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
Median Permanent Effect
42
Table 9 (continue): Bull (20 Months) 1 Buy
Bear (16 Months) 2
Sell
Buy
1 Sell
Buy
2 Sell
Buy
Sell
Panel E: Foreign Traders Total Price Effect Volume Weighted
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
Standard Error Median Permanent 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
Panel A: All Traders Total Price Effect Volume Weighted
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
Median Permanent Effect
Panel B: Individual Traders Total Price Effect Volume Weighted
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
Standard Error Median Permanent Effect
44
Table 10 (continue): Bull (18 Months) 1 Buy
Bear (18 Months) 2
Sell
Buy
1 Sell
Buy
2 Sell
Buy
Sell
Panel C: Domestic Institution Traders Total Price Effect Volume Weighted
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
Standard Error Median Permanent Effect
Panel D: Proprietary Firm Traders Total Price Effect Volume Weighted
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
Median Permanent Effect
45
Table 10 (continue): Bull (18 Months) 1 Buy
Bear (18 Months) 2
Sell
Buy
1 Sell
2
Buy
Sell
Buy
Sell
Panel E: Foreign Traders Total Price Effect Volume Weighted
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
Standard Error Median Permanent 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
Panel A: All Traders Total Price Effect Volume Weighted
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
Panel B: Individual Traders Total Price Effect Volume Weighted
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
Table 11 (continue): 1 Buy
2 Sell
Buy
Sell
Panel C: Domestic Institution Traders Total Price Effect Volume Weighted
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
Panel D: Proprietary Firm Traders Total Price Effect Volume Weighted
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
Table 11 (continue): 1 Buy
2 Sell
Buy
Sell
Panel E: Foreign Traders Total Price Effect Volume Weighted
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
Panel A: All Traders Total Price Effect Volume Weighted
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
Panel B: Individual Traders Total Price Effect Volume Weighted
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
Table 12 (continue): Bull (21 Months) 1 Buy
Bear (15 Months) 2
Sell
Buy
1 Sell
Buy
2 Sell
Buy
Sell
Panel C: Domestic Institution Traders Total Price Effect Volume Weighted
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
Panel D: Proprietary Firm Traders Total Price Effect Volume Weighted
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
Median Permanent Effect
51
Table 12 (continue): Bull (21 Months) 1 Buy
Bear (15 Months) 2
Sell
Buy
1 Sell
2
Buy
Sell
Buy
Sell
Panel E: Foreign Traders Total Price Effect Volume Weighted
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