The Determinants of the Price Impact of Block Trades: Further Evidence

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1 ABACUS, Vol. 43, No. 1, 2007 doi: /j x Blackwell Melbourne, Abacus ORIGINAL ABACUS EVIDENCE 2007 Accounting Publishing Australia OF ARTICLE DETERMINANTS Foundation, Asia Unviersity OF BLOCK of Sydney TRADE IMPACTS ALEX FRINO, ELVIS JARNECIC AND ANDREW LEPONE The Determinants of the Price Impact of Block Trades: Further Evidence This article extends previous literature which examines the determinants of the price impact of block trades on the Australian Stock Exchange. As previous literature suggests that liquidity exhibits intraday patterns, we introduce time of day dummy variables to explore time dependencies in price impact. Following theoretical developments in previous literature, the explanatory power of the bid ask spread, a lagged cumulative stock return variable and a refined measure of market returns are also examined. The model estimated explains approximately 29 per cent of the variation in price impact. Block trades executed in the first hour of trading experience the greatest price impact, while market conditions, lagged stock returns and bid ask spreads are positively related to price impact. The bid ask spread provides most of the explanatory power. This suggests that liquidity is the main driver of price impact. Key words: Australian Stock Exchange; Block trades; Determinants; Evidence; Pricing. Large or block transactions play a major role in trading on stock exchanges worldwide. Nearly half of the trades on the NYSE are made in blocks of 10,000 shares or more. Jain (2003) claims that institutional activity, which is predominantly made up of block transactions, accounts for over 70 per cent of all trading activity. The significant quantity of block trades has led to substantial research in the area. Most examines the price impact of block trades, where price impact is measured by comparing the transaction price to an unperturbed price that would have prevailed if the trade were not executed. 1 The causes of price impact are also addressed in the literature. In particular, this has centred on explaining the magnitude and variation in price impact. 1 See Holthausen et al. (1987, 1990), Choe et al. (1992), Chan and Lakonishok (1993, 1995, 1997), Keim and Madhavan (1995, 1996, 1997), Domowitz et al. (2001) and Chiyachantana et al. (2004) in the United States, Gemmill (1996) and Bozcuk and Lasfer (2005) in the United Kingdom and Aitken and Frino (1996a) in Australia. Alex Frino is a Professor and Elvis Jarnecic and Andrew Lepone are Lecturers in Discipline of Finance, The University of Sydney. This research has been funded by the Cooperative Research Centre for Technology Enabled Capital Markets. We thank two anonymous referees for several useful comments that have significantly improved this paper. We also would like to thank SIRCA for the provision of data and Teddy Oetomo, David Johnstone, Damien Moore and participants at the AFAANZ 2004 conference for useful suggestions. 94

2 EVIDENCE OF DETERMINANTS OF BLOCK TRADE IMPACTS The first study to explore the determinants of the price impact of large trades was Kraus and Stoll (1972). 2 They argue three factors can lead to price impact. First, price concessions are often required to find block transaction counterparties (i.e., find liquidity), and thus short-run liquidity effects could lead to price impact. Second, inelastic supply and demand curves, in that stocks are not perfect substitutes for each other, lead to price concessions and thus price impact. Finally, the information conveyed by large orders results in price adjustment or impact and thus new equilibrium stock prices. In their seminal paper, Kraus and Stoll regress the value of block trades on price impact and find that the size of trades is positively related to the magnitude of price impact. Several studies build on this original work to attempt to better explain price impact. Holthausen et al. (1987) use a firm-size adjusted volume variable and confirm that larger trades have the greatest price impact. 3 The impact of stock price volatility is examined by Chan and Lakonishok (1997), Domowitz et al. (2001), Conrad et al. (2001) and Chiyachantana et al. (2004). These studies find that volatility is positively related to price impact. They argue that elevated volatility is associated with greater dispersion in beliefs, with risk averse traders less inclined to participate in markets, thereby resulting in greater price concessions or price impact (Domowitz et al., 2001). Several studies, including Chan and Lakonishok (1995), Aitken and Frino (1996b) and Chiyachantana et al. (2004), also find substantial variability in price impact across brokers and money managers, arguing that this reflects the ability of trading desks to execute orders. A market return variable to control for underlying market movements is also examined by Aitken and Frino (1996b), Bonser-Neal et al. (1999) and Chiyachantana et al. (2004). These studies show that market returns are positively related to price impact. Aitken and Frino (1996b) and Gemmill (1996) include the bid ask spread to examine the effect of underlying stock liquidity on price impact and find that wider spreads lead to greater price impact. Stock price momentum variables, used by Jones and Lipson (1999) and Conrad et al. (2001) to examine the effect of past stock price performance, are not significantly related to price impact. Other variables relating to market structure and trading platforms are also examined. 4 While all of these models extend the original study by Kraus and Stoll (1972), each of the variables has limited explanatory power, with overall models explaining only a small portion (generally less than 5 per cent) of the variation in price impact. This article extends previous research by including time of day dummy variables to explore time dependencies in price impact. Several studies show that proxies for liquidity exhibit particular patterns across the trading day. McInish and Wood (1992) document a U-shaped intraday pattern in spreads, with spreads reaching a 2 See also Scholes (1972) and Ball and Finn (1989) for the first ASX based study. 3 Barclay and Warner (1993), Brown et al. (1998) and Chakravarty (2001) show that order size conveys information, which in turn could lead to variation in the price impact of trades. 4 See Chan and Lakonishok (1997) for a comparison of the NYSE and Nasdaq, and Keim and Madhavan (1996) for a comparison of trading in upstairs and downstairs markets. 95

3 ABACUS maximum at the close of trading. 5 Lee et al. (1993) show that wider spreads and increased volume (especially towards the close of trading) lead to a reduction in quoted depth. As liquidity affects the price impact of block trades, it is possible that there are time dependencies in price impact. This article also uses refined measures for two variables examined in previous studies. The market return variable has previously been calculated over the entire day that a block trade is executed. A large rise in the market index in the last half hour of trading is not likely to affect a block trade executed in the first half hour of the trading day. 6 We thus calculate market returns over the same time interval that the price impact is measured. A theoretical study by Saar (2001) shows that the trading intensity of a portfolio manager is dependent on the price performance of a stock, and thus lagged cumulative returns will affect the magnitude of price impact. Previous studies measure this variable over one to two trading days prior to the block trade. As portfolio managers trading decisions are formed over longer horizons (Chan and Lakonishok, 1995), unlike previous research, we test lagged cumulative returns over the five trading days prior to the block trade. The bid ask spread variable is included to examine the role of liquidity on price impact. Aitken and Frino (1996b) examine whether the bid ask spread explains variation in the price impact of packages of transactions executed by a broker, and find that the spread is not a significant explanatory variable. 7 Gemmill (1996) examines the ability of the bid ask spread to explain the price impact of large transactions executed off-market on the London Stock Exchange, finding very little explanatory power for the price impact of off-market trades. Our work includes the bid ask spread to further examine the effect of liquidity on the price impact of single block trades which are executed on-market. In addition to this primary motivation, analysing factors that lead to price adjustment surrounding block trades has implications for the reporting of financial positions. According to the Financial Accounting Standards Board (FASB 157), the quoted price should not be adjusted due to the size of the trade, even if the market s normal daily trading volume is insufficient to handle the size of the trade, resulting in price impact. Thus understanding the determinants of price impact (and being able to control the level of price impact) has important financial reporting implications. DATA AND METHOD The available data for this study contain complete records describing all trades and orders submitted to the ASX. Each trade and order record contains a date and time stamp (to the nearest hundredth of a second) as well as price and 5 Aitken et al. (1995) show that volumes tend to be higher at the commencement of trading, then fall throughout the day, before rising towards the end of the day and peaking at the close of trading. 6 See Harris (1989). 7 Aitken and Frino (1996b) define trade packages as successive purchases/sales by the same broker in the same stock on the same day. 96

4 EVIDENCE OF DETERMINANTS OF BLOCK TRADE IMPACTS volume fields. Intraday data for the Share Price Index (SPI) futures traded on the Sydney Futures Exchange is also available, and contains trade and quote records with fields similar to that available from the ASX. The SPI is used to avoid non-synchronicity problems associated with the underlying index (see Stoll and Whaley, 1990). The data extends from 1 January 1992 to 31 December As the ASX does not employ a standard definition for block trades, block trades are defined as the largest 1 per cent of on-market transactions for each stock, in each calendar year, over the sample period. Trades in which market orders execute against a series of limit orders are combined and treated as one transaction for the purposes of identifying block trades. 8 These trades are classified as block purchases if they execute against standing limit ask(s) orders or block sales if they execute against standing limit bid(s) orders. 9 Several trades are excluded from the analysis. The ASX employs a complex opening auction. Each stock has a random opening time (within a 30 second interval), with all overlapping buy and sell orders matched at a price determined (by SEATS) using a computer algorithm. To avoid any randomness introduced by this process, all trades executed in the opening auction are excluded from the analysis. To ensure that a clean sample of block trades is examined, trades that execute near the open or close of trading are removed. Specifically, block trades that execute within the first five trades or the last five trades on the day are excluded. 10 Also excluded are block trades that execute within five trades of another block trade. 11 Table 1 profiles the transactions examined here. The overall sample consists of 80,400 block purchases and 86,576 block sales. The mean size of purchases is 163,007 shares ($631,429), while the mean size of sales is 177,864 shares ($621,979). This indicates that the sample is made up of a similar number of purchases and sales, and they are of similar average size. However, there is substantial difference between the maximum values for purchases and sales. This difference in extreme values is tested later by removing the largest and smallest 1 per cent of trades. In order to examine variation in the magnitude of the price impact of block trades, the following regression is estimated: Price Impact = a 0 + a 1 ln(size) + a 2 Volatility + a 3 ln(turnover) + a 4 BAS + a 5 Market Return + a 6 Lagged Return + a 7 Time 1 + a 8 Time 2 + ε (1) 8 The price of the final limit order is used in the calculation of price effects for aggregated trades. To test the robustness of this, the first limit order price and a volume weighted limit order price are also used throughout the analysis, leading to consistent results. These results are available upon request. 9 Off-market transactions are excluded from the analysis because it is not possible to determine accurately the time at which they occur, and because they do not involve the submission of buy and sell orders and therefore cannot unambiguously be categorized as purchase or sale transactions. 10 Quotes that are not observed after the completion of the market opening process are also removed. 11 The cleaning procedure is similar to that used in Aitken and Frino (1996a) and Frino et al. (2005). 97

5 ABACUS Table 1 SUMMARY STATISTICS OF TRADE SIZE Number of observations Mean Median Minimum Maximum SD Panel A: Share volume Purchases 80, , ,000 1,000 44,000, ,801 Sales 86, , , ,878, ,198 Panel B: Dollar value Purchases 80, , ,500 4, ,800,800 1,174,954 Sales 86, , ,200 4, ,077,730 1,512,895 This table reports the number of observations, the mean, median, minimum, maximum and standard deviation of share volume (Panel A) and dollar value (Panel B) for block trade size. The sample consists of the largest 1 per cent of trades, in each stock, in each calendar year, on the ASX, for the period of 1 January 1992 to 31 December Transactions are classified into purchases and sales using a quote based rule. To ensure consistency and comparability with prior literature, Price Impact represents one of the three following measures. 12 The total effect is calculated as the percentage return from five trades prior to the block trade to the block trade. The temporary effect is calculated as the percentage return from the block trade to five trades after the block trade. The permanent effect is calculated as the percentage return from five trades prior to the block trade to five trades after the block trade. 13 As several studies have shown that transaction price data could contain microstructural biases, we replace transaction prices with quotes in existence immediately prior to each transaction in the calculation of price effects. 14 Size is the number of shares traded in each block transaction. Various measures of size, including the number of shares traded, the dollar value of the trade, the natural logarithm of the dollar value of the trade, the volume relative to average daily trading volume and dollar value relative to average daily trading value, are tested. Overall the natural logarithm of the number of shares traded provides the best fit for the model. Larger sized trades are expected to be associated with greater price impact Block Quote Quote 5 Total Effect = Quote 5 Quote + 5 Block Quote Temporary Effect = Block Quote Quote +5 Quote 5 Permanent Effect = Quote 5 13 The use of five-trade benchmarks is consistent with Holthausen et al. (1990) and Gemmill (1996). 14 See Harris (1989), Foerster et al. (1990), Lease et al. (1991), Bhardwaj and Brooks (1992), Cox and Peterson (1994) and Frino et al. (2005) regarding the use of quote rather than transaction price data. 15 All alternative measures of volume provide consistent results. 98

6 EVIDENCE OF DETERMINANTS OF BLOCK TRADE IMPACTS Volatility is the standard deviation of trade to trade returns on the trading day prior to the block trade. 16 An increase in volatility increases the risk to market participants, resulting in possible counterparties to block transactions demanding greater compensation. We thus expect that volatility will be positively related to price impact. Turnover is the (natural logarithm of) total dollar value of on-market stock turnover on the trading day prior to the block trade, and is a proxy for liquidity. It is anticipated that turnover will be negatively related to price impact. BAS represents the proportional bid ask spread immediately prior to the block order being released to the market, and is another proxy for liquidity. 17 Trades executed when liquidity is high and spreads tight are expected to have lower price impact, and thus a positive relationship to exist between BAS and price impact. Market Return represents the return on the SPI futures contract. While Aitken and Frino (1996b) and Bonser-Neal et al. (1999) calculate a market return on the day of the block trade, we use a more refined measure of market return. In particular, market return represents the percentage return on the SPI from five trades prior to the block trade to the block trade for the total effect, the return from the block trade to five trades after the block trade for the temporary effect, and the return from five trades prior to the block trade to five trades after the block trade for the permanent effect. A positive relationship is expected to exist between market return and price impact. The Lagged Return variable is calculated as the cumulative daily return to the stock on the five trading days prior to the block trade. As articulated earlier, Saar (2001) proposes that momentum in the stock price affects the price impact of block trades. Thus a positive (negative) relationship is expected to exist between the cumulative stock return and the price impact for block sales (purchases). That is, negative coefficients should exist for both block purchases and sales for this variable. Finally, to examine any time dependencies in price impact, we include time of day dummy variables. The trading day is divided into three segments, with Time 1 taking the value of one if the block trade occurs in the first hour of trading (up to 11 a.m.) and Time 2 taking the value of one if the block trade occurs between 11 a.m. and 3 p.m. As spreads tend to be wider and depth tends to be lower at the end of the day, 18 we expect that the price impact of block trades executed in the final hour of trading will be greater than the price impact of all other block trades (negative coefficients for both Time 1 and Time 2 ). 19 REGRESSION RESULTS Table 2 presents the results for both purchase and sale transactions. The dependent variables total, temporary and permanent effects are calculated 16 We also calculate volatility using quote midpoints with qualitatively similar results. 17 BAS = (Ask Bid)/(Ask + Bid)/2 18 See McInish and Wood (1992) and Lee et al. (1993). 19 For our sample of block trades, we find that approximately 25 per cent of block trades are executed in the final hour of trading, while the first hour of trading includes about 20 per cent of block trades. 99

7 100 Table 2 REGRESSION RESULTS: DETERMINANTS OF THE PRICE IMPACT OF BLOCK TRADES Total effect Temporary effect Permanent effect Purchases Sales Purchases Sales Purchases Sales Panel A: Price impact estimates Mean Return *** *** *** *** *** *** Panel B: Regression results Intercept 1.129*** *** *** *** 1.856*** 1.653*** Size *** *** *** *** *** *** Volatility *** ** *** ** *** *** Turnover *** ** *** *** *** *** BAS *** *** *** *** *** *** Market Return *** *** *** *** *** *** Lagged Return *** *** * *** *** *** Time *** *** * *** *** *** Time Adjusted R ABACUS *** indicates statistical significance at the level, ** indicates statistical significance at the 0.01 level, * indicates statistical significance at the 0.05 level. This table presents the price impact estimates (Panel A) and regression results (Panel B) of the following model: Price Impact = a 0 + a 1 ln(size) + a 2 Volatility + a 3 ln(turnover) + a 4 BAS + a 5 Market Return + a 6 Lagged Return + a 7 Time 1 + a 8 Time 2 + ε, where Price Impact is one of total, temporary and permanent effects (in percent), Size is the natural logarithm of the number of shares traded, Volatility is the standard deviation of trade to trade returns on the trading day prior to the block trade, Turnover is the natural logarithm of total stock turnover on the trading day prior to the block trade, BAS is the proportional (to the quote midpoint) bid ask spread immediately prior to the block trade, Market Return is the return on the SPI futures contract over the same time horizon as the dependent variable, Lagged Return is the cumulative stock return over the five trading days prior to the block, Time 1 takes the value of 1 if the trade occurs in the first hour of the trading day and Time 2 takes the value of 1 if the trade occurs between 11 a.m. and 3 p.m.

8 EVIDENCE OF DETERMINANTS OF BLOCK TRADE IMPACTS using contemporaneous quotes. 20 As block purchases occur at the ask quote, and block sales at the bid quote, the price effects are calculated using ask quotes for purchases and bid quotes for sales. 21 Panel A presents price impact estimates. Block purchases have a total effect of per cent, while the total effect for block sales is per cent. The continuation following these trades averages per cent for purchases and per cent for sales. The overall permanent effect for block purchases is per cent, while the overall permanent effect for block sales is per cent. Panel B of Table 2 reports the estimated coefficients for the explanatory variables. The volume coefficient in all cases is significantly different from zero, with larger volumes associated with greater price impact, as expected. The volatility coefficient is significantly positive for buys and significantly negative for sales. Increased volatility leads to greater price impact for both block purchases and block sales, consistent with previous studies including Chan and Lakonishok (1997) and Chiyachantana et al. (2004). The turnover coefficient is significantly negative for purchases, and significantly positive for sales, indicating that increased liquidity reduces the price impact of block trades. The BAS coefficient is significantly positive for block purchases, and significantly negative for block sales, consistent with both Aitken and Frino (1996b) and Gemmill (1996). Wider spreads lead to greater price impact. The market return coefficient is significantly positive for both block purchases and block sales. A positive market return leads to larger price impact for block purchases and smaller price impact for block sales. This is consistent with the findings of both Aitken and Frino (1996b) and Bonser-Neal et al. (1999). The lagged return coefficient is significantly positive for block purchases and significantly negative for block sales. Larger price run-ups lead to larger price impact for both block purchases and block sales, contrary to the prediction by Saar (2001). The coefficients for the time of day dummy variables indicate that for both block purchases and sales, block transactions executed in the first hour of trading are associated with the largest price impact compared to block trades in the final hour of trading. Block transactions executed in the middle of the trading day experience price impact not significantly different from block transactions executed at the end of the trading day. Contrary to the hypothesis that block trades in the final hour of the trading day have the greatest price impact, we find that block trades executed in the first hour of trading experience the greatest price impact. Panel B of Table 2 also reports R-squared estimates. The R-squared values range from a low of 9.00 per cent with the temporary effect for block sales, to a high of per cent with the permanent effect for block purchases. The model 20 Bessembinder (2003) and Peterson and Sirri (2003) show that execution cost estimates are least biased when measured using contemporaneous bid ask quotes. 21 We also estimate the regressions using price impact calculated with transaction prices. The results from this are qualitatively similar, and are available upon request. 101

9 ABACUS Table 3 INCREMENTAL EXPLANATORY POWER CONTRIBUTED BY DETERMINANTS OF THE PRICE IMPACT OF BLOCK TRADES Total effect Temporary effect Permanent effect Purchases Sales Purchases Sales Purchases Sales Full Model Size 17.08*** 12.17*** 15.36** 8.82*** 28.20*** 18.32*** Volatility 17.08*** 12.25** 14.64*** 8.99** 28.02*** 19.54*** Turnover 17.17*** 12.25** 15.18*** 8.98*** 28.19*** 19.41*** BAS 6.97*** 3.51*** 7.83*** 2.84*** 12.73*** 6.48*** Market Return 16.73*** 11.79*** 15.32*** 8.92*** 28.03*** 18.94*** Lagged Return 17.12*** 12.18*** 15.35*** 8.94*** 28.48*** 19.43*** Time-of-Day 17.10*** 11.86*** 15.34*** 8.94*** 28.45*** 19.22*** *** indicates statistical significance at the level, ** indicates statistical significance at the 0.01 level, * indicates statistical significance at the 0.05 level. This table presents the adjusted R-squared values of the following model: Price Impact = a 0 + a 1 ln(size) + a 2 Volatility + a 3 ln(turnover) + a 4 BAS + a 5 Market Return + a 6 Lagged Return + a 7 Time 1 + a 8 Time 2 + ε, where Price Impact is one of total, temporary and permanent effects (in percent), Size is the natural logarithm of the number of shares traded, Volatility is the standard deviation of trade to trade returns on the trading day prior to the block trade, Turnover is the natural logarithm of total stock turnover on the trading day prior to the block trade, BAS is the proportional (to the quote midpoint) bid ask spread immediately prior to the block trade, Market Return is the return on the SPI futures contract over the same time horizon as the dependent variable, Lagged Return is the cumulative stock return over the five trading days prior to the block, Time 1 takes the value of 1 if the trade occurs in the first hour of the trading day and Time 2 takes the value of 1 if the trade occurs between 11 a.m. and 3 p.m. From the full model, one variable at a time is removed, the regression re-estimated and the R-squared recalculated. The statistical significance refers to the F-test from Greene (2003), which tests if the removal of a variable significantly affects the R-squared value. estimated in this study explains more variation in the price impact of block trades than the best models from previous research. Following Chan and Lakonishok (1993), to test the proportion of variation in price impact explained by each variable, one variable at a time is removed from the full model and the regression re-estimated. To determine if the removal of a variable has reduced the fit of the model, we use the F-test described by Greene (2003) which compares the R-squared values from the full and alternate models. The results are presented in Table 3. The statistical significance of all values are significant at the 1 per cent level or better. Each explanatory variable adds predictive power to the model. When the BAS variable is removed, the R-squared value drops substantially (for example, the R-squared falls from to 6.97 with the total effect for purchases). The bid ask spread is the most significant factor in explaining variation in price impact. When the market return variable is removed, the R-squared value drops 102

10 EVIDENCE OF DETERMINANTS OF BLOCK TRADE IMPACTS significantly, especially for block sales. The underlying market conditions, as shown by Chiyachantana et al. (2004), are important in explaining variation in price impact. 22 TESTS OF ROBUSTNESS A number of tests are conducted to ascertain the robustness of the results presented in Table To further explore the role that the cumulative stock return has on price impact, the lagged return variable is recalculated as (a) the return to the stock on the trading day prior to the block trade, (b) the cumulative return on the two trading days prior to the block trade and (c) the cumulative return on the ten trading days prior to the block trade. To further investigate time dependencies in price impact, six dummy variables for each hour of the trading day are included. The results from these alternative regressions are consistent with earlier findings. The lagged return coefficients indicate that larger price run-ups lead to larger price impact for both block purchases and block sales. The argument proposed by Saar (2001) does not hold. Block trades executed in the first hour of the trading day are associated with the greatest price impact vis-à-vis block trades executed in the final hour of trading. Trades executed in the second hour of trading have price impact slightly larger than the price impact of block trades executed in the final hour of trading. Block trades executed between midday and 3 p.m. have price effects insignificantly different from block trades executed at the end of the trading day. Second, to explore if the choice of pre- and post-trade benchmarks unduly affect results, the five-trade benchmarks are replaced with the quotes in existence ten trades before and after the block trade, and the opening and closing quotes on the day of the block trade. The results are similar to the original findings reported in Table 2. Although the R-squared values are slightly reduced, the coefficient estimates remain relatively unchanged, both in terms of direction and statistical significance. Third, to explore how sensitive results are to extreme observations, we exclude the largest and smallest 1 per cent of the block trade sample and then re-estimate all regressions. To remove very small block trades from the sample, we also impose a minimum restriction of $10,000 on the block trade sample, and again re-estimate regressions. Under both alternatives, results are similar. The direction and statistical significance of all coefficient estimates remain unchanged and R-squared values exhibit minimal variation. Finally, to test how sensitive results are to sample filters, each filter is removed. In particular, all block trades, irrespective of their closeness to the open or close of trading, or to other block trades, are included and the regressions re-estimated To explore if our large sample sizes are driving significance in results, we calculate sample size adjusted t-statistics as outlined in Connolly (1995). All conclusions based on these adjusted t-statistics are consistent with the original conclusions. All tests of robustness results are available upon request. 103

11 ABACUS Also, defining block trades as the largest 1 per cent of trades can be considered ambiguous. To explore the impact that our sampling procedure has, we include the largest 2 per cent and the largest 5 per cent of trades as alternative definitions, and re-estimate regressions. The coefficients for all explanatory variables remain in the same direction (with consistent statistical significance) as found in Table 2. R-squared values show minimal variation with these changes. SUMMARY The price impact of block trades is an extensively studied area, with many attempts to explain the variation in the level of price impact. Variables relating to the size of the block trade, liquidity, volatility, market returns and brokers execution ability are examined. However, each of the variables provides limited explanatory power. This article uses a sample of block trades executed on the ASX to examine the determinants of price impact. We include time of day dummy variables to explore time dependencies in price impact. We use more refined measures for market return and lagged cumulative stock return variables. We also examine the role of liquidity on the price impact of block trades by including a bid ask spread variable. The model estimated explains approximately 29 per cent of the variation in price impact. The bid ask spread is the most significant factor in explaining variation in price impact. Block trades executed in the first hour of trading experience the greatest price impact. Market returns and lagged stock returns are positively related to price impact. Future research could further investigate time dependencies in price impact. In particular, examining whether block trades in the first hour of trading occur late in the hour after buyers/sellers have had time to observe and interpret information revealed during the market opening process, is an important future research avenue. references Aitken, M. J., P. Brown, H. Izan, A. Kua and T. Walter, An Intraday Analysis of the Probability of Trading on the ASX at the Asking Price, Australian Journal of Management, Vol. 20, No. 2, Aitken, M. J., and A. Frino, Asymmetry in Stock Returns Following Block Trades on the ASX: A Note, Abacus, Vol. 32, No. 1, 1996a., Execution Costs Associated With Institutional Trades on the ASX, Pacific-Basin Finance Journal, Vol. 4, No. 1, 1996b. Ball, R., and F. J. Finn, The Effect of Block Transactions on Share Prices: Australian Evidence, Journal of Banking and Finance, Vol. 13, No. 3, Barclay, M. J., and J. B. Warner, Stealth Trading: Which Traders Trades Move Stock Prices?, Journal of Financial Economics, Vol. 34, No. 3, Bessembinder, H., Issues in Assessing Trade Execution Costs, Journal of Financial Markets, Vol. 6, No. 3, Bhardwaj, R. K., and L. D. Brooks, The January Anomaly: Effects of Low Share Price, Transaction Costs and Bid Ask Bias, Journal of Finance, Vol. 47, No. 2, Bosner-Neal, C., D. Linnan and R. Neal, Emerging Market Transaction Costs: Evidence From Indonesia, Pacific-Basin Finance Journal, Vol. 7, No. 2,

12 EVIDENCE OF DETERMINANTS OF BLOCK TRADE IMPACTS Bozcuk, A., and M. A. Lasfer, The Information Content of Institutional Trades on the London Stock Exchange, Journal of Financial and Quantitative Analysis, Vol. 40, No. 3, Brown, P., N. Thomson and D. Walsh, Characteristics of the Order Flow Through an Electronic Open Limit Order Book, Working Paper, University of Western Australia, Chakravarty, S., Stealth Trading: Which Traders Trades Move Stock Prices?, Journal of Financial Economics, Vol. 61, No. 2, Chan, L., and J. Lakonishok, Institutional Trades and Intraday Stock Price Behaviour, Journal of Financial Economics, Vol. 33, No. 2, 1993., The Behaviour of Stock Prices Around Institutional Trades, Journal of Finance, Vol. 50, No. 4, 1995., Institutional Equity Trading Costs: NYSE Versus Nasdaq, Journal of Finance, Vol. 52, No. 2, Chiyachantana, C. N., P. K. Jain, C. Jiang and R. A. Wood, International Evidence on Institutional Trading Behaviour and Price Impact, Journal of Finance, Vol. 59, No. 2, Choe, H., T. H. McInish and R. A. Wood, Market Microstructure Effects on the Measurement of the Impact of Block Trades, Working Paper, University of Memphis, Connolly, R. A., An Examination of the Robustness of the Weekend Effect, Journal of Financial and Quantitative Analysis, Vol. 24, No. 2, Conrad, J., K. Johnson and S. Wahal, Institutional Trading and Soft Dollars, Journal of Finance, Vol. 56, No. 1, Cox, D., and D. Peterson, Stock Returns Following One-Day Declines: Evidence on Short-Term Reversal and Long-Term Performance, Journal of Finance, March Domowitz, I., J. Glen and A. Madhavan, Liquidity, Volatility and Equity Trading Costs Across Countries and Over Time, International Finance, Vol. 4, No. 2, Foerster, S. D., D. B. Keim and D. C. Porter, Intraday Spreads, Returns and Variances: Tests of the Informed Trader Hypothesis, Unpublished Working Paper, University of Western Ontario, Frino, A., E. Jarnecic, D. Johnstone and A. Lepone, Bid Ask Bounce and the Measurement of Price Behaviour Around Block Trades on the Australian Stock Exchange, Pacific-Basin Finance Journal, Vol. 13, No. 3, Gemmill, G., Transparency and Liquidity: A Study of Block Trades in the London Stock Exchange Under Different Publication Rules, Journal of Finance, Vol. 51, No. 5, Greene, W. H., Econometric Analysis, 5th ed., Pearson Education, Harris, L., A Day-End Transaction Price Anomaly, Journal of Financial and Quantitative Analysis, Vol. 24, No. 1, Holthausen, R., R. Leftwich and D. Mayers, The Effect of Large Block Transactions on Security Prices: A Cross-Sectional Analysis, Journal of Financial Economics, Vol. 19, No. 2, 1987., Large Block Transactions, the Speed of Response, and Temporary and Permanent Stock-Price Effects, Journal of Financial Economics, Vol. 26, No. 1, Jain, P. K., Discussion of Equity Trading by Institutional Investors: Evidence on Order Submission Strategies by Naes and Skjeltorp, Journal of Banking and Finance, Vol. 27, No. 9, Jones, C. M., and M. L. Lipson, Execution Costs of Institutional Equity Orders, Journal of Financial Intermediation, Vol. 8, No. 3, Keim, D. B., and A. Madhavan, The Anatomy of the Trading Process: Empirical Evidence on the Behaviour of Institutional Traders, Journal of Financial Economics, Vol. 25, No. 3, 1995., The Upstairs Market for Large-Block Transactions: Analysis and Measurement of Price Effects, Review of Financial Studies, Vol. 9, No. 1, 1996., Transaction Costs and Investment Style: An Inter-Exchange Analysis of Institutional Equity Trades, Journal of Financial Economics, Vol. 46, No. 3, Kraus, A., and H. R. Stoll, Price Impact of Block Trading on the New York Stock Exchange, Journal of Finance, Vol. 27, No. 3,

13 ABACUS Lease, R., R. Masulis and J. Page, An Investigation of Market Microstructure Impacts on Event Study Returns, Journal of Finance, Vol. 46, No. 4, Lee, C. M. C., B. Mucklow and M. J. Ready, Spread, Depths, and the Impact of Earnings Information: An Intraday Analysis, Review of Financial Studies, Vol. 6, No. 2, McInish, T. M., and R. A. Wood, An Analysis of Intraday Patterns in Bid/Ask Spreads for NYSE Stocks, Journal of Finance, Vol. 47, No. 2, Peterson, M., and E. Sirri, Order Submission Strategy and the Curious Case of Marketable Limit Orders, Journal of Financial and Quantitative Analysis, Vol. 37, No. 2, Saar, G., Price Impact Asymmetry of Block Trades: An Institutional Trading Explanation, Review of Financial Studies, Vol. 14, No. 4, Scholes, M., The Market for Securities: Substitution Versus Price Pressure and the Effects of Information on Share Prices, Journal of Business, Vol. 45, No. 2, Stoll, H. R., and R. E. Whaley, The Dynamics of Stock Index and Stock Index Futures Returns, Journal of Financial and Quantitative Analysis, Vol. 25, No. 4,

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