How broker ability affects institutional trading costs

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1 Accounting and Finance 45 (2005) How broker ability affects institutional trading costs Carole Comerton-Forde, Christian Fernandez, Alex Frino, Teddy Oetomo Finance Discipline, School of Business, Faculty of Economics and Business, University of Sydney, Sydney, 2006, Australia Abstract The present paper shows that broker research and trade execution ability has a significant impact on the cost of institutional trading. The results reveal that there is significant variation in the ability of brokers to control execution costs. Trades executed by brokers with stronger research ability exhibit a higher permanent price impact, whereas those executed by brokers with better execution ability exhibit a lower temporary price impact. Brokers are also found to specialize on an industry level that gives rise to variation in ability within a brokerage house. Key words: Institutional trades; Transaction costs; Broker ability JEL classification: G20 doi: /j X x 1. Introduction The measurement of institutional trading costs and the identification of factors influencing them have attracted significant interest from practitioners and academic researchers alike. 1 This interest is motivated by the rapid growth in institutional The authors thank an anonymous institutional investor for providing the institutional trade data and the Securities Industry Research Centre of Asia Pacific (SIRCA) for providing the Australian Stock Exchange (ASX) Stock Exchange Automated Trading System (SEATS) data. We gratefully acknowledge Robert Faff (the Editor), David Gallagher, Vito Mollica, James Rydge, Kathryn Wong, Hui Zheng, Adrian Looi, Andrew Lepone, Michael Chan and two anonymous referees for their helpful comments and suggestions. We also thank Justin Bull and Nic McGilvray for programming assistance. Teddy Oetomo thanks the Capital Markets CRC for providing him with a PhD scholarship. Received 19 November 2003; accepted 29 July 2004 by Robert Faff (Editor). 1 See, for example, Berkowitz et al. (1988), Perold and Sirri (1993), Leinwener (1995), Chan and Lakonishok (1993, 1995, 1997) and Keim and Madhavan (1997).. Published by Blackwell Publishing.

2 352 Comerton-Forde et al. / Accounting and Finance 45 (2005) funds under management 2 and by the fact that trading costs are one of the key factors affecting the returns earned by institutional investors (see Fama, 1991; Lakonishok et al., 1992; Leinwener, 1995). Previous research has identified several variables that influence trading costs, through the price impact of institutional trades. These include trade size, firm capitalization and investment style. Larger trades are expected to incur higher execution costs as a result of the higher adverse selection costs (Kyle, 1985; Easley and O Hara, 1987). Firm capitalization is documented to be negatively correlated with execution costs as a result of the lower liquidity associated with smaller firms (Chan and Lakonishok, 1993, 1995). Finally, Keim and Madhavan (1997) document that, relative to active funds, index funds incur higher trading costs, induced by the funds higher demand for immediacy as they constantly aim to align their portfolios with the constituents of the index. In addition, prior studies have also conjectured that brokerage commissions affect costs, as brokers with better execution skills are expected to charge higher fees in return for lower execution costs. Keim and Madhavan (1997), and Domowitz et al. (2001) document a positive correlation between these variables, whereas Berkowitz et al. (1988) and Chan and Lakonishok (1995) find little evidence of a relationship. To date, most of the research examining trading costs has focused on major US markets (Chan and Lakonishok, 1995, 1997; Keim and Madhavan, 1996, 1997). However, the market microstructure published literature implies that market design influences liquidity and transaction costs. 3 For this reason, the results of US research might not be applicable to other markets with different market structures, such as the Australian Stock Exchange (ASX). Trading on the ASX is conducted by means of a fully automated order driven trading system. Unlike US markets, there are no official market makers or specialists. Instead, liquidity is provided by limit order traders. 4 The present paper, therefore, adds to the published literature by examining institutional trading costs on the ASX and the influence of factors identified in previous research on these costs. 2 In 1990, institutional activities represented 72 per cent of total trades executed on the New York Stock Exchange (NYSE) (Schwartz and Shapiro, 1992). Rainmaker Information (2001) estimated that $A651 of the $A714.46bn in assets under management in Australia are sourced from institutional investors, which represents a $A369bn increase over the past 5 years. 3 See Tinic and West (1974), Reinganum (1990), Blume and Goldstein (1992), Neal (1992), Lee (1993), Bessembinder and Kaufman (1996), Huang and Stoll (1996) for the effect of different market designs on liquidity and Keim and Madhavan (1996) for the effect of liquidity on execution costs. 4 There are some exceptions to this. Large trades that meet certain size requirements might be executed off-market. This is equivalent to the upstairs market on the NYSE. Brokers might execute these trades as agent or as principal. When trading as principal, brokers are essentially acting as unofficial market makers. Approximately 30 per cent of total ASX trading value is executed off-market. Brokers are not obligated to report when they are trading as principal, so it is not possible to determine what fraction of trading is done in this manner.

3 Comerton-Forde et al. / Accounting and Finance 45 (2005) There is no research examining the role that brokers play in helping institutions to manage their execution costs. Although previous research has examined the variation in execution costs across institutions, the contribution of broker ability to such variation is unknown. Although execution costs represent one factor influencing institutional investors performance, delivering low execution costs is one of the primary services offered by brokers. The present paper addresses the role that brokers play in managing execution costs using a unique institutional trade dataset originating from an institutional investor. By examining institutional trades from one institutional investor, the analyses in the present paper are not contaminated by any noise in execution costs attributed to variations in objectives across institutions and, therefore, the paper to better isolates the contribution of broker ability. 5 The present paper extends existing published literature by examining the impact of broker ability on the cost of institutional trading. The cost of institutional trades is expected to be related to brokers research ability. Trades executed by means of brokers with higher research ability are expected to convey greater information content and are expected to induce greater adjustment in the equilibrium price of a security (Krauss and Stoll, 1972; French and Roll, 1986; Easley and O Hara, 1987; Barclay et al., 1990). Therefore, trades executed by brokers with higher research ability and reputation are expected to induce greater permanent price impact. This conjecture is consistent with the finding of Stickel (1992) and Jackson (2005) that more reputable analysts forecasts induce larger security price returns. Permanent price impact is defined as the change in equilibrium price, measured by the difference between the pre- and post-trade benchmark prices (Holthausen et al., 1990). We hypothesize that the price impact of institutional trades also depends on broker s execution ability. Given the size of institutional trades, which create liquidity costs, these trades are often transacted away from equilibrium prices (Krauss and Stoll, 1972; Holthausen et al., 1990). Brokers with higher execution ability are expected to work orders to minimize the disparity between the traded and equilibrium prices. As a result, trades executed by means of brokers with higher execution skill are expected to induce lower temporary price impact. Holthausen et al. (1990) define temporary price impact as the price rebound following institutional trades as a result of liquidity costs. The remainder of the present paper is arranged as follows. Section 2 describes the dataset. Section 3 outlines the research design used in this paper. Section 4 reports the empirical results and Section 5 concludes the present paper. 5 We recognize that if the institution examined is atypical, the results in the present study can not be generalized and might not be applicable for the general market. However, given that the institution is one of the top 10 investment managers in Australia based on the value of total funds under management, we believe that it is highly unlikely that the examined institution is atypical.

4 354 Comerton-Forde et al. / Accounting and Finance 45 (2005) Data 2.1. Trade data The data used in the present paper were provided by an active institutional investor, which is ranked among the top 10 investment managers in Australia based on the value of total funds under management. 6 The original dataset consists of institutional trades ( purchases and sales) made by 41 different actively managed portfolios from 15 May 2001 to 15 May 2002, on the ASX. The dataset examined in the present paper contains individual trade, but not order level detail. The data contain details of the portfolio that made the trade, the stock traded, the execution date, trade direction, trade price, trade volume, the brokerage fee and the identity of the broker that executed the trade. The opening and closing price, the opening best bid and ask prices, the closing best bid and ask prices and stock information, such as market capitalization, share turnover and industry codes are provided by the Securities Industry Research Centre of Asia Pacific (SIRCA) Trade packages Given that institutional orders are, on average, large orders, brokers would typically break the orders into a series of mid-sized trades. 8 Chan and Lakonishok (1995) argue that resorting to an individual institutional trade as the basic unit for analysing the price impact of institutional trading is misleading. Unfortunately, our dataset does not identify whether a trade is part of a larger order. 9 Therefore, the methodology developed by Chan and Lakonishok (1995) is used, in which a sequence of trades is treated as the basic unit for analysing the price impact associated with institutional trades. A buy (sell) package is constructed by including a portfolio s successive purchases (sales) of shares in a particular company using the same broker. The package ends when the portfolio no longer purchases (sells) shares for 5 consecutive days. 10 In robustness tests, trade packages are also constructed based on 1, 3 and 6 Because of confidentiality issues, the institutional investor wishes to remain anonymous. 7 All stock information has been adjusted for dilutions. 8 For further information regarding execution strategies, see Barclay and Warner (1993), Chan and Lakonishok (1995), Brennan and Subrahmanyam (1998) and Chakravarty (2001). 9 Some of these trades might be executed in one trade through the off-market trading facility. However, our dataset does not distinguish between on-market and off-market trades. 10 Typically, institutional investors are reluctant to submit their orders to multiple brokers. Although it allows institutional investors to conceal the actual size of their orders from the market, such a strategy could result in competition among brokers, which translates to a higher overall execution cost.

5 Comerton-Forde et al. / Accounting and Finance 45 (2005) day gaps. Additionally, the analyses are also performed using the closing price 5 days after the completion of the trade package as the post-trade benchmark. The results are consistent with those reported in the present paper Summary statistics for packages A total of trade packages are examined in the present paper. To avoid small sample biases, portfolios and brokers with <50 trade packages, and securities that are traded <10 times throughout the sample period are excluded. Consistent with Keim and Madhavan (1997), trade packages with a duration of more than 21 days are also excluded to eliminate unrepresentative trades. 12 The final sample consists of trade packages representing packages of purchases and packages of sales made by 41 funds and executed by 45 brokerage firms. The sample captures a total volume of 2.43bn shares (1.19bn shares purchased and 1.24bn shares sold) representing a total value of $A9.42bn (purchases of $A4.71bn and sales of $A4.71bn). Table 1 describes the trade packages and shows that the average trade package consists of shares worth $A The average time taken to execute a trade package (duration) is 1.45 and 1.55 days for purchases and sales, respectively. Less than 10 per cent of the trade packages in the sample exhibit trade duration of 3 days or more. The average duration of trades examined in the present paper closely resembles Keim and Madhavan (1997), who find that the average completion rates are 1.80 and 1.65 days for purchases and sales, respectively. However, the sample of trade packages studied by Chan and Lakonishok (1995) is executed more patiently, with more than half of their sample taking at least 4 days to execute. This longer completion time is as a result of their sample consisting of larger trade packages, with an average size of $US1.167m for purchases and $US1.228m for sales. Such disparities are anticipated given that the average size of US funds is larger than that of Australian funds. 3. Research design Consistent with previous published literature, execution costs are calculated using an opening price, closing price and volume-weighted average price benchmark The results for robustness tests are not reported and are available upon request. 12 This results in the exclusion of <400 trade package, representing <1.5 per cent of the overall sample. 13 This method is comparable to that of Beebower and Priest (1980), Berkowitz et al. (1988) and Chan and Lakonishok (1995).

6 356 Comerton-Forde et al. / Accounting and Finance 45 (2005) Table 1 Description of packages Total Purchases Sales Total unit (shares) Mean % % Median % % Sum Total value ($A) Mean % % Median % % Sum Duration (days) Mean % % Median % % Number of packages (46.88%) (53.02%) This table provides a description of the packages constructed based on the 5 day trading gap methodology introduced by Chan and Lakonishok (1995). A purchase (sale) package denotes successive purchases (sales) by the same manager by means of the same broker on the same stock with a break of <5days between successive trades. Total unit is the size of the packages in terms of shares traded. Total value represents the dollar value of packages. Duration the number of days required to complete a trade package. Specifically, execution costs are calculated as follows: Open to Trade = θ Trade to Close = θ Open to Close = θ Trade to VWAP = θ (Price Opening Price), (1) Opening Price (Price Closing Price), (2) Closing Price (Closing Price Opening Price), (3) Opening Price (Price VWAP). (4) VWAP θ is a dummy variable that takes the value of 1 for purchases and 1 for sales. Price is defined as the volume-weighted average gross unit price of all the trades in the

7 Comerton-Forde et al. / Accounting and Finance 45 (2005) package. VWAP is the volume-weighted average price of all trades on the day(s) a trade package is executed. Opening and Closing Price represent the first and last traded price during normal trading hours on the day a trade package is executed. Following Chiyachantana et al. (2004), all four measures of execution costs used in the present paper are adjusted for market returns to control for market conditions. The SFE SPI 200 (Sydney Futures Exchange Share Price Index), which represents the futures index on the Standard and Poor s (S&P)/ASX 200, is used to measure market returns. 14 Although it is recognized that futures contracts omit dividends, the preference for SFE SPI 200 over the conventional measure of market return for the S&P/ASX 200 is driven by the higher liquidity of the SFE SPI 200 futures index that reduces potential biases induced by non-synchronous trading. The SPI returns are calculated using the return of the nearest to maturity contract. The contract is assumed to be rolled over to the next nearest to maturity contract at expiry. To perform a joint test of the relationships between brokerage commission, trade complexity, trade duration, firm size, bid ask spread, broker ability and execution costs, a model similar to that of Chan and Lakonishok (1995) is estimated: r i = α + βc i + 6 δ j S i,j + 6 γ j D i,j + χ i BAS i + π i DUR i + 41 κ j PortfolioID i,j + 45 φ j BROK i,j + ε i. (5) The cost of institutional trades, r i, represents one of the four measures of execution costs: Open to Trade, Trade to Close, Open to Close and Trade to VWAP. The variables C i, S i,j and D i,j are, respectively, the dollar brokerage commission cost, a dummy variable for the stock s classification by market capitalization and a dummy variable for the package s classification by relative package size. The relative trade size, which represents a measure of trade complexity, is computed as the ratio between the size of the trade package and the average number of shares traded 20 days before the start of the trade package. The market capitalization and relative trade size are classified into six groups: <10th percentile, 10th 25th percentile, 25th 50th percentile, 50th 75th percentile, 75th 90th percentile and >90th percentile. A dummy variable is assigned to each group, with the smallest group (<10th percentile) serving as the benchmark. This approach is used to maintain consistency with the methodology of Chan and Lakonishok (1995) and to account for the nonlinear relationships between execution costs, market capitalization and relative trade size. The variable BAS i represents the time-weighted relative bid ask spread. Following Chan and Lakonishok (1997), the variable DUR i represents the number of days required to complete the trade package. 14 The S&P/ASX 200 index is the most frequently used benchmark for fund managers in Australia.

8 358 Comerton-Forde et al. / Accounting and Finance 45 (2005) The dataset used in the present paper only captures trades executed by one institutional investor and, therefore, the analysis is not influenced by variations in objectives across institutions. However, some variations are expected to exist between the portfolios offered by the institution. Accordingly, a set of dummy variables that identify the portfolio where the trade originated, PortfolioID i,j, is included to control for such variation. Finally, BROK i,j denotes a set of broker dummy variables, where the variable BROK i,j takes on a value of 1 if trade i is executed by broker j and 0 otherwise. The average execution costs of each portfolio and broker are compared to those of the whole sample. Those with average execution costs closest to those of the whole sample are used as the benchmarks. The benchmarks are computed independently for each measure of execution costs as well as for institutional purchases and sales. The benchmark selection methodology is applied to ensure that the benchmarks represent typical portfolios and brokers. To examine the contribution of each of the variables to the full model, the adjusted R 2 of the model when excluding each of the determinants is reported. Each reported adjusted R 2 is then compared to that of the full model using an F-test. To investigate the source of variation in broker ability, each brokerage house is ranked based on φ j, which represents the broker dummy s coefficient. For the permanent price effect (measured by Open to Close), the magnitude of φ j is ranked from highest to lowest, with the highest values representing brokers with higher research ability. Conversely, for the temporary price effect (measured by Trade to Close), the magnitude of φ j is ranked from lowest to highest, with the lowest values representing brokers with higher execution ability. To conform to the industry measure of execution ability, the Trade to VWAP measure is used as an additional gauge. The rankings of brokers generated earlier are compared to rankings reported in the 2001 Reuters Survey (Reuters, 2001). The survey ranks brokerage houses on their research, execution and sales and is based on responses from 52 fund management groups that account for $US96bn invested in Australian and New Zealand equities (Reuters 2001). A further ranking service provided by the East Coles Survey is also used to ensure the robustness of the results. 15 Ideally, broker ranking would be included in the joint test analysis illustrated by equation (5), replacing the broker dummy variables. However, not all brokers in our sample are reported in the Reuters and East Coles Survey rankings as these surveys only report the top 18 and 10 brokers, respectively. Consequently, the Pearson correlation between the rankings generated by our sample and those stated by the 2001 Reuters Survey and East Coles Survey are reported. Potentially, variations in broker ability might persist not only across, but also within brokerage houses. This is induced by brokerage houses tendency to assign 15 Given the nature of the present paper, which examines institutional trades, fund management group rankings reported in the 2001 Reuters Survey are used rather than company rankings. The 2002 East Coles Survey is based on the participation of 42 institutions, which represents more than $A170bn in funds under management across 32 sectors. The East Coles Survey is published annually in Business Review Weekly (BRW).

9 Comerton-Forde et al. / Accounting and Finance 45 (2005) Table 2 Descriptive statistics of the execution costs and the determinants Open to Trade Trade to Close Open to Close Trade to VWAP Panel A: Purchases Principal weighted Mean th percentile th percentile Median th percentile th percentile Standard deviation Panel B: Sales Principal weighted Mean th percentile th percentile Median th percentile th percentile Standard deviation The principal-weighted measure weighs the magnitude of execution costs for each trade package by trade size. Four different measures of execution costs are reported. Open to Trade captures the pre-execution benchmark. This measure is defined as the difference between the trade price and the opening price of the first day of the trade in the package. Trade to Close, a measure of post-execution cost, is defined as the difference between the trade price and the close price on the last day of the package. Open to Close is measured as the difference between the closing price of the last day of the package and the opening price of the first day of the package. The same day benchmark, Trade to VWAP, is measured as the difference between the trade price and the volume-weighted average price during the period of the trade package. responsibility for research and trading on different stocks (typically by industry) to different research and trading teams. Because these teams have different execution and research skills, disparities exist in the research and trading ability within a brokerage house. To test this conjecture, equation (5) is estimated separately on trade packages executed by each brokerage house. The broker dummy variables, BROK i,j, are replaced with TopRank i,j. The variable TopRank i,j represents a dummy variable that takes the value of 1 if the trade package is executed in a stock from the industry where the broker is ranked first by the 2001 Reuters Survey or the East Coles Survey and 0 otherwise. The TopRank i variable captures the variation in ability within a brokerage house Sensitivity analysis is conducted using the top 3 brokers as the measure for the TopRank variable. These results are consistent and are, therefore, not reported.

10 360 Comerton-Forde et al. / Accounting and Finance 45 (2005) Table 3 Correlation between trade complexity and execution costs Complexity <10th 10th 25th 25th 50th 50th 75th 75th 90th >90th percentile percentile percentile percentile percentile percentile Panel A: Purchases Open to Trade Trade to Close Open to Close Trade to VWAP Panel B: Sales Open to Trade Trade to Close Open to Close Trade to VWAP Complexity is measured as the ratio between the volume of the package and the average daily trading volume of the stock. The packages are partitioned into six different classes. The <10th percentile class represents packages with the lowest trade complexity, whereas the >90th percentile class denotes packages with the highest trade complexity. 4. Results The results reported in Table 2 document principal-weighted execution costs associated with purchases (sales), measured by Open to Trade and Trade to Close, of 0.34 (0.16) and 0.26 (0.12) per cent, respectively. The execution cost estimates are somewhat smaller than those documented by Chan and Lakonishok (1995), which report purchase (sales) pre- and post-execution costs at 0.88 (0.44) and 0.21 (0.22), respectively. Their higher execution costs are attributed to the larger average size of packages in their sample. Additionally, Chan and Lakonishok (1995) examine the price impact of trades executed by institutional investors in the US market, which uses different market structures to the ASX. Prior published literature has shown that markets that use different market structures exhibit different levels of liquidity. 17 Given the variation in liquidity levels, the magnitude of execution costs of trades that are executed in markets with different market structure are expected to vary (Keim and Madhavan, 1996). Table 3 presents the results of univariate analysis. Trade complexity is found to be positively correlated with execution costs. The pre-execution cost benchmark (Open to Trade) for purchases (sales) increases from 0.03 (0.14) per cent for the smallest packages to 0.28 (0.30) per cent for the largest. Trade duration is found to be positively correlated with trade complexity. The smallest purchase (sale) packages exhibit an 17 See Tinic and West (1974), Reinganum (1990), Blume and Goldstein (1992), Neal (1992), Lee (1993), Bessembinder and Kaufman (1996) and Huang and Stoll (1996).

11 Comerton-Forde et al. / Accounting and Finance 45 (2005) average duration of 1.35 (1.58) days, whereas the largest purchase (sale) packages exhibit an average duration of 2.07 (1.91) days. As expected, this indicates that larger packages, which are more difficult to trade, tend to be completed over a longer time period. The regression results reported in Table 4 are consistent with those of Chan and Lakonishok (1995). Execution costs are positively correlated with relative trade size. This confirms that packages with higher trade complexity are more difficult to trade and incur higher costs. Firm capitalization is found to be negatively correlated with execution costs, indicating that trades in more liquid stocks tend to have lower costs as a result of lower execution difficulty associated with them. The correlation between brokerage commissions and cost of institutional trading reported in Table 4 provides mixed evidence. This is because higher brokerage commissions could indicate better execution and, therefore, lower market impact costs, but higher commissions also directly translate to higher transaction costs. Additionally, Chan and Lakonishok (1995) argue that the weak association between execution costs and commission expenses might be induced by several unobserved components of commissions, such as soft-dollar services. The results reported in Table 4 for trade duration are also mixed. This is expected as longer trade duration could represent greater trade complexity as well as a more patient trading strategy. Trade duration is found to be negatively correlated with execution costs measured by both Trade to Close and Trade to VWAP, suggesting that trade packages that are transacted more patiently tend to incur lower liquidity costs. Reflecting higher complexity, trade duration is found to be positively correlated with the total price effect costs, measured by Open to Trade. The positive correlation documented between trade duration and permanent price effect costs, measured by Open to Close, indicates that more informed trades are harder to execute and, therefore, require more time to complete. 18 With the exception of purchases measured by Trade to Close and Open to Close, bid ask spreads are found to be positively correlated with execution cost. Table 4 also illustrates that broker identity is one of the most significant determinants of execution costs. The high explanatory power exhibited by the broker dummy variables shows that the magnitude of execution costs varies significantly depending on which brokerage house executes the trade. The significance of broker dummy variables could indicate the presence of important variations in ability across brokerage houses. Indeed, the contribution of the broker dummy variables is found to be larger than that of the portfolio identification dummy variables. Additionally, with the exception of price impact of institutional purchases measured by Open to Trade and Trade to VWAP, the portfolio identification dummy variables are not statistically significant. This finding is at odds with the results documented by Chan and Lakonishok (1995), which diagnose portfolio identification as one of the primary determinants of institutional cost of trading. This finding, however, is anticipated, as 18 However, if the information is short-lived, trade duration is expected to be negatively correlated with the degree of informativeness of a trade.

12 Table 4 Regression estimates of the model: r i =α + βc i + δ j S i,j + γ j D i,j + χ i BAS i + π i DUR i + κ j PortfolioID i,j + φ j BROK i,j + ε i Open to Trade Trade to Close Purchases Sales Purchases Sales Full model Excluding brokerage 9.55 (0.00) 5.53 (0.00) 9.38 (18.26) 4.76 (11.05) Excluding complexity 9.18 (10.30) 5.41 (3.50) 8.95 (15.94) 4.45 (10.74) Excluding market capitalization 9.48 (1.99) 4.60 (25.45) 9.23 (7.97) 4.83 (0.32) Excluding duration 6.31 (459.95) 4.95 (79.50) 8.24 (180.92) 4.12 (97.86) Excluding BAS 9.54 (1.66) 5.50 (4.77) 9.51 (0.00) 4.77 (9.47) Excluding broker effects 4.75 (15.52) 3.17 (7.32) 3.04 (20.93) 2.11 (8.42) Excluding portfolio identification 8.45 (3.82) 5.11 (1.40) 8.96 (1.91) 4.35 (1.62) Intercept 0.62 (2.29) 0.97 (4.31) 0.46 (2.06) 0.38 (2.08) Brokerage (0.80) ( 0.68) ( 3.79) (3.32) Complexity 2 (least) 0.27 (4.55) 0.16 (3.00) 0.15 (3.07) 0.07 (1.67) (6.31) 0.14 (2.57) 0.21 (4.15) 0.16 (3.65) (5.21) 0.06 (1.05) 0.35 (6.12) 0.29 (5.78) (4.51) 0.01 (0.09) 0.44 (6.64) 0.36 (6.00) 6 (most) 0.39 (4.03) 0.03 (0.35) 0.57 (7.45) 0.37 (5.62) 362 Comerton-Forde et al. / Accounting and Finance 45 (2005)

13 Table 4 (continued) Open to Trade Trade to Close Purchases Sales Purchases Sales Market capitalization 2 (smallest) 0.18 ( 0.94) 0.08 (0.53) 0.33 ( 2.20) 0.14 ( 1.18) ( 1.27) 0.53 ( 3.85) 0.47 ( 3.14) 0.09 ( 0.83) ( 0.59) 0.80 ( 5.65) 0.38 ( 2.48) 0.09 ( 0.81) ( 1.06) 0.69 ( 4.68) 0.39 ( 2.42) 0.09 ( 0.76) 6 (largest) 0.18 ( 0.89) 0.68 ( 4.60) 0.27 ( 1.66) 0.10 ( 0.79) Duration 0.17 (18.92) 0.06 (8.57) 0.09 ( 11.91) 0.06 ( 9.45) BAS 0.03 (1.05) 0.03 (2.28) 0.68 ( 0.34) 3.63 (3.10) Broker effects 10th percentile th percentile Median th percentile th percentile Number of significant broker dummy Portfolio identification 10th percentile th percentile Median th percentile th percentile Number of significant portfolio dummy Comerton-Forde et al. / Accounting and Finance 45 (2005)

14 Table 4 (continued) Open to Trade Trade to Close Purchases Sales Purchases Sales Full model Excluding brokerage 4.14 (12.55) 10.69(0.00) 9.73(0.00) 2.97 (15.50) Excluding complexity 3.98 (6.90) 10.33(10.35) 9.00(20.97) 2.77 (8.37) Excluding market capitalization 3.84 (10.67) 9.87(23.58) 9.28(12.98) 3.04 (1.24) Excluding duration 3.45 (105.08) 7.66(435.52) 9.34(56.57) 3.01 (10.85) Excluding BAS 4.20 (4.71) 10.58(15.81) 9.72(1.66) 3.09 (0.00) Excluding broker effects 2.22 (6.15) 8.08(8.55) 1.76(25.88) 1.22 (5.65) Excluding portfolio identification 3.65 (1.92) 10.16(1.86) 8.90(2.89) 2.61 (1.55) Intercept 1.28 (3.98) 0.56(1.84) 0.16( 0.98) 0.30 (2.10) Brokerage (3.19) (0.08) (0.81) (3.67) Complexity 2 (least) 0.23 (3.26) 0.20(2.82) 0.25(6.78) 0.00 (0.03) (4.33) 0.07(0.90) 0.31(8.09) 0.04 (1.11) (2.43) 0.14( 1.66) 0.34(7.80) 0.12 (3.04) (2.01) 0.30( 3.02) 0.40(8.07) 0.18 (3.79) 6 (most) 0.07 (0.62) 0.45( 4.04) 0.47(8.14) 0.27 (5.13) 364 Comerton-Forde et al. / Accounting and Finance 45 (2005)

15 Table 4 (continued) Open to Trade Trade to Close Purchases Sales Purchases Sales Market capitalization 2 (smallest) 0.29 ( 1.30) 0.01 (0.04) 0.12 ( 1.02) 0.08 ( 0.95) ( 1.13) 0.71 ( 3.85) 0.17 ( 1.56) 0.09 ( 1.03) ( 1.56) 1.14 ( 5.98) 0.11 ( 0.92) 0.11 ( 1.30) ( 2.70) 1.16 ( 5.91) 0.11 ( 0.90) 0.07 ( 0.78) 6 (largest) 0.47 ( 1.99) 1.30 ( 6.51) 0.04 (0.34) 0.08 ( 0.91) Duration 0.10 (9.02) 0.21 (21.64) 0.04 ( 6.67) 0.01 ( 3.19) BAS 6.00 ( 2.01) 7.68 (4.09) 2.04 (1.17) 0.32 (0.34) Broker effects 10th percentile th percentile Median th percentile th percentile Number of significant broker dummy Portfolio identification 10th percentile th percentile Median th percentile th percentile Number of significant portfolio dummy Execution costs are the cost measures (in %) from the Open to Trade, Trade to Close, Open to Close and Trade to VWAP. C denotes the dollar value of brokerage commissions. S and D are dummy variables capturing the firm size and trade complexity variables. BAS denotes the time-weighted bid ask spread. PortfolioID denotes the dummy variables for portfolio identification. BROK is a set of dummy variables representing different brokerage houses. The values inside the brackets next to the coefficients represent t-values. The adjusted R 2 is reported with the result from the F-test, which compares the adjusted R 2 of the model relative to that of the full model. The values inside the brackets next to the adjusted R 2 represent the F-values. Comerton-Forde et al. / Accounting and Finance 45 (2005)

16 366 Comerton-Forde et al. / Accounting and Finance 45 (2005) our sample controls for variations in the objectives across institutional investors by only considering trades by one institutional investor. Therefore, the portfolio identification dummy variables capture only the variation in objective across portfolios offered by the same institution. This variation is expected to be less pronounced than that documented by previous research. The significance of broker dummy variables in explaining the magnitude of institutional cost of trading is somewhat anticipated. Research and execution represent the primary benchmarks for measuring the performance of brokerage houses. The results in Table 4 clearly show that variation in ability persists across brokerage houses, which translates into disparities in price impact of institutional trades executed by different brokerage houses. However, the results in Table 4 do not discriminate between the research and execution ability that persist across brokerage houses. To examine the variation in ability across brokerage houses, the broker rankings based on research and execution ability reported by the 2001 Reuters Survey are used as a proxy for brokers ability in these areas. Panels A and B in Table 5 report the top 10 brokers by research and execution ability according to the 2001 Reuters Survey. As previously discussed, trades executed by brokers with higher research ability are expected to convey greater information content and are, therefore, anticipated to exhibit higher permanent price impact. The brokers research and execution rankings are compared to the magnitude of permanent and temporary price impact of institutional trades executed by each broker in our sample. The broker rankings based on research and execution ability reported by the 2001 Reuters Survey are used as the primary proxy for brokers abilities. The East Coles Survey is used as an additional proxy for brokers research ability. Unfortunately, the East Coles Survey does not report brokers ranking based on execution ability. Therefore, a Trade to VWAP measure is used as an additional measure of execution costs to ensure the robustness of the results for brokers execution ability. 19 Panel A in Table 5 reports comparisons between the permanent costs, based on the Open to Close measure, and the broker ranking based on research ability. Panel A reports the top 10 brokers by research ability across the whole market as reported by the 2001 Reuters Survey and the East Coles Survey. Although the results reported in Table 5 (Panel A) show that the magnitude of permanent price effects exhibited by a broker is positively correlated with the broker s research ranking, the results are not statistically significant. As reported in Table 5 (Panel B), the magnitude of execution costs, measured by means of both Trade to Close and Trade to VWAP, are positively correlated with the brokers execution ability ranking. However, the results are only statistically significant for institutional purchases and not for institutional sales. Potentially, the less pronounced correlation between brokers research ability and the permanent price impact costs of trades executed by the brokers indicates that institutional investors do not constantly channel their trades to the brokers that provide them with the recommendations. This is consistent with the fact that many institutional investors use a panel of brokers for trade execution and typically allocate a 19 The Trade to VWAP is widely used as a measure of execution costs by the industry.

17 Table 5 Brokers ranking by research and execution ability for whole market Panel A: Research ability Ranking by 2001 Broker Ranking based on Ranking by East Broker Ranking based on Reuters Survey Open to Close measure Coles Survey Open to Close measure Purchases Sales Purchases Sales 1 Broker A Broker F Broker B Broker D Broker C Broker A Broker D Broker G Broker E Broker H BrokerF BrokerE Broker G Broker C Broker H Broker B Broker I Broker I Broker J Broker K 13 9 Correlation 0.38 (0.28) 0.40 (0.25) Correlation 0.56 (0.07) 0.41 (0.24) Comerton-Forde et al. / Accounting and Finance 45 (2005)

18 Table 5 (continued) Panel B: Execution ability Ranking by 2001 Broker Ranking based on Ranking by 2001 Broker Ranking based on Reuters Survey Trade to Close measure Reuters Survey Trade to VWAP measure Purchases Sales Purchases Sales 1 BrokerF BrokerF BrokerA BrokerA BrokerB BrokerB BrokerC BrokerC BrokerE BrokerE Broker D Broker D BrokerH BrokerH BrokerI BrokerI BrokerG BrokerG Broker L Broker L 4 10 Correlation 0.66 (0.04) 0.59 (0.07) 0.52 (0.013) 0.50 (0.15) Panel A reports the top 10 brokerage houses by research ability as reported by the 2001 Reuters Survey and the East Coles Survey. The rankings are compared with the permanent price impact ranking generated from our sample. This ranking is derived from the coefficients of the broker dummy variables of the Open to Close analysis reported in Table 4. The highest ranking represents the broker with the largest coefficients. Panel B reports the top 10 brokerage houses by execution ability as reported by the 2001 Reuters Survey. The rankings are compared with the execution costs generated from our sample. Two measures of execution costs are used, Trade to Closeand Trade to VWAP. The Pearson correlations are reported. The numbers inside the brackets located next to the Pearson correlation represent the p-values. 368 Comerton-Forde et al. / Accounting and Finance 45 (2005)

19 Comerton-Forde et al. / Accounting and Finance 45 (2005) specified proportion of their trading to each broker on the panel. This approach might reflect active fund managers attempts to restrict excessive information leakage regarding their portfolio holding. This conjecture is supported by the more pronounced correlation documented between execution ability and execution costs (measured by means of both Trade to Close and Trade to VWAP) with research ability and permanent impact costs. Unfortunately, the dataset used in the present paper does not allow such a hypothesis to be formally tested. Alternatively, the weak correlations documented could indicate that research and execution abilities might not be constant within a brokerage house. This is because research and trading teams are often responsible for different stocks. Given the similarities in characteristics of stocks within the same industry, brokers often organize their research and trading into industry-based teams. Consequently, brokerage specialization might be documented at an industry level. To test for this conjecture, trade packages are examined independently for each brokerage house using the following regression: r i = α + βc i δ j S i,j + γ j D i,j + χ i BAS i + π i DUR i + 41 κ j PortfolioID i,j + η i TopRank i + ε i. (6) The broker specialization test outlined by equation (6) closely resembles the joint test analysis outlined by equation (5). To account for the variation in research and trading ability within a brokerage house, the broker dummy variables from equation (5) are replaced by the TopRank i dummy variable. This variable identifies the best-ranked broker in research and trading for each industry. Given that trades executed by brokers with higher research ability are anticipated to convey greater information content and, therefore, to induce higher permanent price impact, the variable TopRank i is expected to be positively correlated with the permanent price impact measure. In contrast, trades executed by brokers with higher execution ability are expected to exhibit lower temporary price impact and the variable TopRank i is conjectured to be negatively correlated with the temporary price impact measure. Table 6 reports the top broker by research and execution ability for each of the industries examined as stated in the 2001 Reuters Survey and the East Coles Survey. Table 7 reports the average value of the coefficients outlined in equation (6) for brokers that are listed in Table 6, weighted by the number of observations. 20 The t-values reported in Table 7 are aggregated based on the methodology of Christie (1990). These results are consistent with the conjectured hypothesis. The TopRank i 20 The disaggregated results on broker by broker analysis are not reported and are available upon request.

20 370 Comerton-Forde et al. / Accounting and Finance 45 (2005) Table 6 Brokers ranking by research and execution ability by industry Panel A: Broker research ranking by industry (2001 Reuters Survey) Metals and minings Paper and forest product Transportation Construction and engineering Construction materials Industrials Retailing Utilities Banks Diversified financials Insurance Oil and gas Telecommunication services Food, beverages and tobacco Hotels, restaurants and leisure Media Gold, precious metals and minerals Health care Real estate investment trust Panel B: Broker research ranking by industry (East Coles Survey) Energy Transport Paper Building materials Construction and engineering Infrastructure Telecommunication Banks Property trust Health care Financial Industrial Tourism Insurance Media Metals Retail Food Gold Panel C: Broker execution by industry (2001 East Coles Survey) Banks/financials Industrials Resources Telecommunications Small capitalization Media Broker C Broker C Broker C Broker A Broker A Broker A Broker A Broker A Broker G Broker B Broker B Broker B Broker B Broker F Broker F Broker F Broker D Broker D Broker D Broker I Broker C Broker E Broker A Broker A Broker A Broker A Broker G Broker G Broker H Broker H Broker H Broker B Broker B Broker F Broker F Broker F Broker F Broker D Broker B Broker F Broker A Broker I Broker D Broker F Panel A reports the top brokers by research ability for each industry according to the 2001 Reuters Survey. Panel B reports the top brokers by research ability for each industry according to the East Coles Survey. Panel C reports the top brokers by execution ability according to the 2001 Reuters Survey.

21 Table 7 Brokers ranking by research and execution ability (partition by broker) Top brokers by research ability Top brokers by research ability Top brokers by execution ability Top brokers by execution ability based on ranking provided based on ranking provided (measured by Trade to Close) (measured by Trade to VWAP) by 2001 Reuters Survey by East Coles Survey based on raking provided by based on raking provided by 2001 Reuters Survey 2001 Reuters Survey Purchases Sales Purchases Sales Purchases Sales Purchases Sales R Intercept 0.38 (0.16) 0.05 ( 1.14) 0.42 (0.19) 0.36 ( 0.29) 0.73 (1.19) 0.08 ( 0.91) 0.22 ( 0.26) 0.04 ( 0.48) Brokerage (0.38) (0.52) (1.09) (0.55) ( 2.70) ( 0.44) (1.15) ( 0.22) Market capitalization 2 (smallest) 1.04 (0.28) 0.01 (0.01) 0.06 ( 0.31) 0.02 ( 0.80) 1.06 ( 1.52) 1.18 ( 1.94) 0.23 (0.39) 0.12 ( 0.34) ( 0.35) 0.34 ( 0.19) 1.22 ( 0.99) 0.38 ( 0.73) 0.85 ( 1.25) 0.86 ( 2.38) 0.07 ( 0.12) 0.02 (0.11) ( 0.10) 0.38 ( 0.13) 1.40 ( 1.44) 0.53 ( 2.01) 1.21 ( 1.75) 0.92 ( 3.97) 0.13 ( 0.25) 0.15 ( 0.95) ( 0.43) 0.51 ( 1.18) 1.70 ( 1.96) 0.62 ( 2.16) 1.62 ( 2.35) 0.80 ( 4.71) 0.19 ( 0.33) 0.13 ( 1.79) 6 (largest) 0.45 ( 0.20) 0.52 ( 0.90) 1.89 ( 2.07) 0.69 ( 2.44) 1.40 ( 1.92) 1.04 ( 4.85) 0.24 ( 0.45) 0.27 ( 0.52) Complexity 2 (smallest) 0.22 (1.00) 0.40 (1.38) 0.19 (0.97) 0.33 (1.21) 0.19 (1.22) 0.15 (1.12) 0.36 (2.57) 0.02 (0.54) (1.31) 0.31 (1.11) 0.53 (2.33) 0.48 (1.90) 0.31 (1.97) 0.25 (1.86) 0.50 (3.52) 0.05 (0.93) (0.72) 0.25 (0.99) 0.70 (2.72) 0.56 (2.01) 0.45 (2.61) 0.33 (2.13) 0.61 (3.95) 0.14 (1.31) (0.76) 0.22 (0.89) 0.93 (2.92) 0.56 (1.76) 0.63 (3.10) 0.34 (1.63) 0.59 (3.32) 0.15 (1.07) 6 (largest) 0.54 (0.83) 0.11 (0.65) 1.09 (2.88) 0.47 (1.39) 0.79 (3.11) 0.52 (2.01) 0.59 (2.70) 0.25 (1.60) BAS 0.04 ( 0.52) 0.00 (0.66) 0.09 ( 0.27) 0.06 (1.17) 0.09 ( 1.83) 0.03 (0.76) 0.01 (0.72) 0.06 ( 0.94) Duration 0.07 (2.12) 0.20 (5.61) 0.02 (1.28) 0.21 (6.13) 0.15 ( 5.16) 0.03 (1.48) 0.04 ( 2.02) 0.05 (3.02) TopRank 0.45 (2.67) 0.48 (2.61) 1.41 (4.97) 0.64 (2.16) 0.37 ( 3.12) 0.44 ( 3.01) 0.41 ( 3.21) 0.16 ( 2.66) Portfolio identification 10th percentile th percentile Mean th percentile th percentile The following equation is analysed for each of the broker listed in Table 6 separately: r i = α + βc i + δ j S i,j + γ j D i,j + χ i BAS i + π i DUR i + κ j PortfolioID i,j + η i TopRank i + ε i. Execution costs are the cost measures (in %) from the Open to Trade, Trade to Close, Open to Close and Trade to VWAP. C denotes the dollar value of brokerage commissions. S and D are dummy variables capturing the firm size and trade complexity variables. BAS denotes the time-weighted bid ask spread. PortfolioID denotes the dummy variables for portfolio identification. TopRank is a dummy variable that takes the value of 1 if the trade package is executed on a stock from the industry where the broker is ranked as the top broker and 0 otherwise. The regression analysis is performed on each broker separately. The reportedcoefficientsrepresenttheaveragevalueof thecoefficients,weightedbythenumberofobservations. Thenumberinbracketsrepresents thet-values. The t-valuesareadjustedusing themethodologyofchristie (1990). Comerton-Forde et al. / Accounting and Finance 45 (2005)