Nasdaq Working Paper Series Nasdaq Working Paper Institutional Trading Costs on Nasdaq: Have They Been Decimated?

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1 Nasdaq Working Paper Series Nasdaq Working Paper Institutional Trading Costs on Nasdaq: Have They Been Decimated? Ingrid M. Werner 1 Nasdaq Economic Research and Ohio State University Abstract This paper uses unique order data to compare trading costs for institutional orders routed to Nasdaq sell-side dealers pre- and post-decimals. The results show that average execution-value-weighted trading costs have declined by 20 basis points compared to open mid-quotes, by 16 basis points compared to the quotes at order arrival (effective half-spreads), and by 1 basis point compared to mid-quotes at the close (realized halfspreads). The reduction in effective half-spreads represents a 22 percent reduction in average institutional trading costs, which corresponds to $69 million in total cost savings for one week s worth of institutional orders. The results are robust to controlling for order, stock, trading, and dealer characteristics. Moreover, fill rates have improved and duration has not increased significantly. The paper also documents differences in trading costs by order type: effective half-spreads for market orders are now 52 basis points and for marketable limit orders 42 basis points, while limit orders enjoy a 46 basis point spread gain on average. First Draft: June 6, 2002 Please do not quote without the author s permission. 1 Nasdaq Economic Research, 9513 Key West, Rockville, MD 20850, voice: (301) , Ingrid.Werner@nasdaq.com. The views expressed herein are not intended to represent the views of Nasdaq, Inc. My sincere thanks go to Jeff Smith who has provided invaluable help with understanding Nasdaq data and market structure. I am also indebted to Simon Wu for his help with order data, to the rest of Nasdaq Economic Research for their support and encouragement, and to the Nasdaq Stock Market Inc. for financial support. Comments from seminar participants at Georgetown University are also gratefully acknowledged. All remaining errors are my own. 1

2 I. Introduction Nasdaq converted its shares to decimals starting with a pilot on March 12, 2001, and all stocks were converted to decimals on April 9, Several studies have shown that quoted spreads for Nasdaq stocks have narrowed substantially after decimalization. 2 However, these studies also show that inside depth has all but dwindled. Thus, while decimalization seems to have benefited liquidity demanding retail-sized orders, it is a priori unclear what effect the tick-size reduction has had on liquidity demanding large institutional orders. Based on their initial experience with decimals, buy-side traders report that they face substantially higher trading costs and deteriorating liquidity following decimalization. 3 Yet, Smith and Wu (2001) found no empirical evidence of increases in Nasdaq institutional trading costs. 4 The primary goal of this paper is to use a more recent sample, after the dust has settled and traders have been able to adjust their trading strategies, to examine whether or not institutional trading costs have increased for broker-filled trades in Nasdaq stocks following decimalization. A second objective is to provide more recent estimates of Nasdaq institutional trading costs in a post-decimals environment. This paper provides the first comprehensive study of trading costs based on institutional orders released to Nasdaq sell-side dealers. Order data is used to identify institutional orders as those orders that reach sell-side desks explicitly marked Not Held or Worked. For comparability with the previous literature, only those orders that exceed 9,999 shares are included and the analysis is restricted to Nasdaq NNM stocks. Based on this measure, executed institutional volume represents 32 percent of total dollar volume in the main sample -- the week of November 26-30, This study is comprehensive: the primary sample covers more than 70 thousand orders totaling in excess of $72 billion dollars reaching 148 sell-side desks in over 2,000 different stocks. In addition, benchmark information on institutional trading costs before Nasdaq switched 2 See, e.g., Chakravarty, Wood, and Harris (2001), Chakravarty, Van Ness and Van Ness (2001), Chung, Van Ness and Van Ness (2001), and Bacidore, Battalio and Jennings (2001). 3 Survey Finds Trading Strategies Change as Decimalization Adversely Impacts Market Liquidity and Transparency, Midwood Perspectives, 7, October Harris, Smith, and Wu (2002) study changes in order-strategies on Nasdaq following decimalization. Bacidore, Battalio, Jennings, and Farkas (2001), and Chakravarty, Panchapagesan, and Wood (2001) find no evidence of increases in institutional trading costs on the NYSE following decimalization. 2

3 over to decimals, February 1-8, 2001, is provided. During this period, there were more than 70 thousand orders totaling in excess of $143 billion reaching 127 sell-side desks. The data includes previously unexplored information on order type. 5 While all sample orders are discretionary, it turns out that the order-type designation matters tremendously for fill rates and trading costs. Therefore, separate estimates of execution quality are provided for un-priced (market) orders and priced (marketable limit, and limit) orders. There is a substantial literature on execution costs for block trades, i.e., trades in excess of 9,999 shares, as identified in trade data (e.g., Kraus and Stoll (1972), Scholes (1972), Mikkelson and Partch (1985), Holthausen, Leftwich, and Mayers (1987, 1990), and Madhavan and Cheng (1997)). However, block trades are at best a proxy for institutional trading since institutional orders may be broken up into trades smaller than 10,000 shares. Retail trades and proprietary trades by sell-side dealers may exceed 10,000 shares, further complicating the picture. Perhaps more importantly, studies of institutional trading costs based on block executions cannot pinpoint the pre-execution benchmark price, e.g., the quotes at order arrival (or order release), nor can they measure execution costs for orders that receive multiple fills. This might explain why for example Bacidore, Battalio, Jennings, and Farkas (2001) who use trade data do not find an increase in trading costs for institutional sized orders on the NYSE following decimalization. Several authors have studied institutional execution costs based on order data from, e.g., Plexus Group and SEI (e.g., Chan and Lakonishok (1993, 1995, 1997), Keim and Madhavan (1995, 1996), Jones and Lipson (1999, 2001), and Conrad, Johnson, and Wahal (2001)). This approach is a significant improvement compared to identifying institutional trading based on executions. With this kind of data, it is possible both to identify the benchmark price when the order was sent from the portfolio manager to the buy-side trader. It is also possible to measure execution quality for orders that receive multiple fills and for orders that are split across multiple venues, e.g., brokers, crossing networks, and ECNs. However, these studies jointly study the execution quality delivered by different trading venues, and the decisions of the buy-side trader on when and where to route the order. This blurring of the picture might explain why for example 5 Werner (2002) estimates trading costs for different order types on the NYSE using trade data. 3

4 Chakravarty, Panchapagesan, and Wood (2001) do not find an increase in institutional trading costs on the NYSE following decimalization. This paper tests whether institutional orders that are routed to Nasdaq sell-side dealers face significantly higher trading costs following decimalization. Despite the reported deteriorating climate for institutional trades in the Nasdaq market, univariate tests show that trading costs for institutional market orders have decreased substantially across the board. For example, effective half-spreads have decreased from 74 to 52 basis points (or 29 percent) on an execution-value-weighted basis. The picture for marketable limit orders is more mixed with reduced effective half-spreads (42 compared to 49 basis points) but increased realized spreads (22 compared to 9 basis points). As expected, limit orders have experienced an increase in effective half-spreads (i.e., a reduction in the spread-gain) from 64 to 46 basis points (or 27 percent). However, it is quite likely that other features of the trading environment have changed between the pre- and post-decimals periods. For example, the average order size was larger in the pre-decimals period. It is also possible that more of the institutional trading is concentrated in the most liquid stocks following decimalization. Such changes can be controlled for in a pooled time series, cross section regression analysis. The regressions control for order characteristics such as the order size relative to the average daily trading volume, for stock characteristics such as volatility, liquidity, and price, and for market conditions such as other institutional orders in the stock during the same day when evaluating execution quality. In addition, unique data on institutional market share and institutional focus are used to account for differences across sell-side dealers. After controlling for order, stock, and dealer characteristics, the estimated institutional trading cost reduction remains both economically and statistically significant. For market (marketable limit) orders the cost reduction ranges from 60 (88) percent for the easiest orders to 42 (20) percent for the most difficult orders. Estimated trading costs for market orders fall across liquidity rankings as well. The largest reduction in estimated trading costs occurs for most liquid stocks for market orders (53 percent), and for the least liquid stocks for marketable limit orders (46 percent). There is no significant change in pre-and post-decimalization trading costs (reduction in rewards for providing liquidity) for limit orders after controlling for order, stock, and dealer characteristics. 4

5 This paper also documents systematic variation in institutional trading costs across various dimensions. For example, are institutional orders concentrated in the most liquid stocks? How much more expensive is it to execute a very difficult order (large relative to average daily dollar volume) compared to an easy order (small relative to average daily dollar volume)? It is well known that quoted spreads for less liquid stocks are wider, is it also true that institutional execution costs are significantly higher for less liquid stocks? Do institutional trading costs vary systematically with market conditions? For example, to what extent is the price impact of a particular order affected by other orders in the same stock during the trading day? Do characteristics of sell-side dealers and their trading strategies significantly affect institutional trading costs? Do dealers with a large institutional market share deliver significantly lower execution costs, or is it the institutional focus of the sell-side firm that helps lower trading costs? There are considerable systematic differences in order arrival patterns across order types. Limit orders are more likely to arrive in the morning than later in the day. Market orders are also relatively more pervasive for easy trades in highly liquid stocks. By contrast, the proportion of marketable limits and limit orders increase as orderdifficulty increases and stock liquidity deteriorates. There are also systematic differences across order types in standard execution quality measures. For example, as expected, fill rates are significantly higher for market orders (84 percent) and marketable limits (79 percent) than for limit orders (49 percent). There are also clear differences in execution costs as estimated based on the mid-quotes at order arrival: Liquidity-demanding market orders have effective half-spreads of 52 basis points, while liquidity-supplying limit orders have effective half-spreads of 46 basis points on an execution value-weighted basis. Failure of taking the order type into account would result in a weighted average effective half-spread across order types. In this sample such an average estimated effective half-spread benchmarked on the midquotes at order arrival would be roughly 43 basis points. 6 If the opening mid-quotes instead are used as the benchmark price, average estimated trading costs would be 6 The figure is based on weighing together the fraction of executed value in each order type and accounting for the buy and sell proportions: *(52.34) *(42.02) *(-46.46). 5

6 roughly 67 basis points. Benchmarking on the closing mid-quote would result in an average estimated trading cost of roughly 2 basis points. Moreover, there is considerable variation in trading costs across order difficulty and stock liquidity. For example, estimated trading costs (order arrival benchmark) for market orders range from 39 basis points for top 100 stocks to 122 basis points for stocks ranked below 500. The quintile of easiest orders have an average trading cost of 18 basis points, while those in the most difficult quintile cost 105 basis points on average. These numbers are comparable to those found by Chakravarty, Panchapagesan, and Wood (2001) for NYSE stocks post decimals using Plexus data. 7 The institutional trading environment is described in Section II. Section III describes the data and provides summary statistics. Results on fill rates and duration for institutional orders are reported in Section IV. Trading costs based on different benchmarks are reported in Section V. The effect of decimalization on trading costs is examined in Section VI. Section VII concludes. II. Institutional Trading on Nasdaq Institutional trading in Nasdaq stocks takes place in many different venues, e.g., discretionary trading by sell-side dealers, anonymous crossing networks like ITG s POSIT, and matches of orders sent by the institution directly to an ECN like Instinet or Island. Any given order may of course be routed to multiple trading venues. In a recent study based on late 1990s data, Conrad, Johnson, and Wahal (2001) find that 64 percent of all orders are filled by discretionary sell-side dealers (brokers), 18 percent are filled by ECNs, 13 percent are filled in multiple mechanisms, and 5 percent are filled in day crosses. Granted, ECN volume has increased tremendously in recent years, but since orders that receive broker fills are considerably larger than ECN orders on average, sellside dealers still manage the bulk of institutional share volume for Nasdaq stocks. 7 Chakravarty et al (2001) divide all NYSE stocks into three groups by market capitalization, and choose the top 50 stocks in each group. Hence, their sample is skewed towards larger firms than ours. They find trading costs of 20 basis points for large capitalization stocks, 50 basis points for medium capitalization stocks, and 120 basis points for small capitalization stocks. 6

7 This paper focuses entirely on a subset of all institutional orders, namely those that are discretionary, broker-filled institutional orders. These are orders released by the buy-side trader to the sell-side dealer, and they are explicitly marked as Not Held or Worked orders. While the order that the buy-side dealer has received from a portfolio manager might be released over multiple days (e.g., Keim and Madhavan (1995, 1997)), the orders that sell-side dealers actually see are almost exclusively either marked explicitly as DAY orders or have a cancellation for the unexecuted part before the end of the day. Hence, orders are not followed across days in the empirical work. The buy-side trader is effectively hiring the sell-side dealer to execute the order and it is understood that the sell-side dealer is to use his discretion in executing the order. The order instructions include order-size, time in force, and for limit orders a price. Additional instructions might have been verbally communicated to the dealer but are not captured in the data. In deciding whether to use a market order or a limit order, a buy-side trader trades-off between price, probability of execution, and adverse selection (Cohen, Maier, Schwartz, and Whitcomb (1981)). In the context of institutional orders, however, the link between the order instructions and executions are much looser than in a central limit order book. By submitting a market order, the buy-side trader does not necessarily mean that the dealer is to buy the shares at any cost. There is typically an implicit understanding that there should be additional communication if the market turns out to be unexpectedly soft or strong so that the order may be updated. Priced orders are also different from orders submitted to an electronic limit order book in that they do not automatically execute if the market price reaches the limit price. In fact, priced orders do not even necessarily execute at the price limit. They often get price improvement. Hence, perhaps a more useful way of thinking of order-type selection in this context is that it serves to reduce the space of trading strategies that the sell-side dealer can rely on. The cost of restricting the trading strategies by imposing a price limit is that it makes the order more difficult to deal with and we would expect fill rates to suffer as a result. The benefit is that it encourages the sell-side dealer to trade the order quietly in order not to disturb the market price. 7

8 The orders typically will reach a sell-side dealer via the firm s sales desk, but some buy-side firms also have direct order routing capabilities. Once the order has arrived, it is up to the sell-side dealer to devise an appropriate strategy for executing the order. There are three main ways of filling the order: (i) working the order by displaying the order (or pieces thereof) in the dealer s quotes and by sending the order (or orderpieces) to ECNs (no capital commitment) or to crossing networks; (ii) acting as a dealer and filling the order out of inventory; and (iii) finding a natural cross with another institution that has an opposite trading interest. The dealer also has to decide whether to trade the order all at once, or to split the order into multiple fills. The dealer s explicit compensation is the commission, which until January 2002 (see, Sofianos (2001), and Werner (2002)) was included in the execution price. Dealers may also gain spread revenue to the extent that they commit capital in the process of executing the order. III. Data and Summary Statistics The primary sample is all orders in Nasdaq NNM stocks during one week, November 26-30, 2002, that are marked Not Held or Worked that in aggregate exceed 9,999 shares. 8 This sample week was chosen as a relatively calm period away from earnings season (see Figure 1). It was purposely chosen to be a recent period rather than a period immediately following the tick-size reduction (April 9, 2001) since part of the objective is to characterize current institutional trading costs. To examine any change in trading costs compared to the pre-decimals period, a benchmark sample, February 1-8, 2001, is also inlcuded. Again, this period is away from earnings-season, but was characterized by falling stock prices (see Figure 1). For both samples, orders that were submitted after 16:00:59 are excluded. 9 To ensure that benchmark prices for evaluating execution quality exist, observations without valid opening and closing quotes for the 8 Orders are retrieved from the Order Audit Trail System (OATS). The screen on order exceeding 9,999 shares eliminates less than 10 percent of share volume in NH and WRK orders. While some of the eliminated orders originate from institutions, some also come from wealthy private individuals who submit note held orders. 9 We also exclude a small number of proprietary intra-firm orders and a small number of market orders submitted to ITG s crosses that do not execute. 8

9 trading day, and observations where there were no other trades in the stock following the arrival of the institutional order are excluded. The population of orders comes from three different files that are part of the Order Audit Trail System (OATS): new orders, cancel and replace orders, and order executions. A unique order identifier links the orders to OATS executions and cancellations. Executions and/or explicit cancellations exist in the data for 87.6 percent of all orders, and virtually all the remaining orders are DAY orders that would automatically be cancelled at the end of the trading day. Unfortunately, the OATS data does not capture the execution price. Hence, to evaluate execution quality it is necessary to develop an algorithm to match OATS execution reports with trade reports that appear in Nasdaq s audit-trail, Automated confirmation Transactions (ACT). 10 The algorithm matches between 92 and 95 percent of all OATS executions. For orders where OATS and ACT do not match, trading costs cannot be calculated. It is important to clarify how our data relates to the data used in other studies of execution quality for large (institutional) orders. Figure 2 attempts to outline how an order might be routed and traded. Consider a Portfolio Manager that decides to buy 250,000 shares of say Intel Corporation. He releases the order to a Buy-Side Trader at t 0. The Buy-Side Trader decides on a strategy to fill the order, which includes how to split the order over time and across trading venues. (It is essentially this decision that is evaluated in Plexus Group data.) In this example, the Buy-Side Trader sends a market buy order for 50,000 shares to Dealer 1, and the order arrives at t Dealer 1 devises a trading strategy based on the order instructions, which include order type, time in force, whether the order is held or not held/worked, but may also include other information not captured in the OATS data. Dealer 1 might use a combination of capital commitment, displaying the order in his quotes, finding natural crosses, sending the order to an ECN, or to a crossing network like POSIT, to fill the 50,000 share buy order. These individual prints, in our example 5,000 shares, 15,000 shares, and 5,000 shares, will appear in ACT data as well as in commonly available academic databases such as Nastraq and TAQ. Partway through filling the orders, Dealer 1 executes a print-back to the customer that is 10 We develop an algorithm that matches market maker identifier, ticker, order identifier, execution time, size, and trade direction. Multiple matching rounds are necessary. 11 The order is typically communicated to the sell-side dealer from the sell-side dealer firm s sales desk. 9

10 priced at the volume-weighted-average price of the executions so far plus a commission. In other words, institutional trading on Nasdaq is net during our sample period. While there are no hard numbers on commissions for institutional orders on Nasdaq, industry professionals suggest that they range from 2 to 5 cents a share (see Sofianos (2001)). These print-backs are also included in ACT/Nastraq/TAQ data. After having filled the 50,000 share order, Dealer 1 instructs the sales desk to inform the Buy-Side Trader that his order is filled and at what price. The Buy-Side Trader in the example responds by releasing an additional order to buy 100,000 shares of Intel Corporation to Dealer 1 at t 2. He might have simultaneously been sending pieces of the order to one or more other dealers, to ECNs, and/or to crossing networks. As he evaluates how the order is getting filled, he might communicate the progress to the Portfolio Manager. In the example, the communication that the order is getting filled at good prices prompts the Portfolio Manager to release and additional 250,000 shares to buy at t 3. Meanwhile, Dealer 1 has filled 50,000 shares of his 100,000- share order for a total of 100,000 shares. The Buy-Side Trader contacts Dealer 1 at t 4 to cancel the remaining part of the old order (50,000 shares) and replace it with a 250,000- share buy order. This example describes a typical, albeit stylized, scenario for how large orders are dynamically updated over time. In studies using Plexus Group data, each part of the Portfolio Manager s order would be compared to the original order-release time, t 0. The strategy used in this paper is instead to compare each order that the sell-side dealer sees to the quotes at order arrival. In other words, the first 50,000 shares (the two 25,000- share print-backs) are compared to the quotes at t 1. The second 50,000 shares (one 50,000-share print-back) are compared to the quotes at t 2. Any executions that take place on the last 250,000-share order are compared to the quotes at t 4 and so forth. Note that studies using exclusively trade data, i.e., Nastraq or TAQ, would compare each execution to the quotes at execution. Moreover, studies that focus on trades larger than 9,999 shares would pick up the 15,000-share print followed by three 25,000-share prints etc. On the one hand, this method misses any execution that is part of an order exceeding 9,999 shares but where the print itself is smaller. On the other hand, using this method would double-count part of the volume because of the practice of periodic print-backs to 10

11 the customer. 12 It also mixes net trades (including commission) and trades that do not include a commission together arbitrarily. Returning now to the two samples of institutional orders. Executed institutional volume represents 32.1 percent of total dollar volume in the primary sample, and there are such orders for over 2,000 different stocks. In the benchmark sample, executed institutional volume represents a bit less, 28.3 percent of total dollar volume. Each order release that reaches a sell-side dealer is treated as a separate order in the empirical analysis. 13 Hence, the focus is on execution quality delivered by the sell-side dealer based on the information that the buy-side trader is sharing with the dealer by releasing an order. Obviously, a sell-side dealer cannot condition his execution strategy on order information that the buy-side dealer has not yet released. Table 1 describes the two samples. Total volume is the dollar value of shares released, where the price is the mid-quote at order arrival. Executed volume is the dollar value of shares executed, evaluated at the same benchmark. The primary sample includes 70,313 orders, representing a total value of more than $72 billion and an executed value of almost $43 billion. Institutional orders are large -- the average (median) total value of orders is $1.028 million ($444 thousand). The average (median) executed value is smaller, $610 ($227) thousand, reflecting the fact that fill rates are below one hundred percent. The average total value of an order as a fraction of average daily dollar volume in October 2001 ($ADV) is 20.4 percent. Table 1 also breaks down the statistics by order type, order difficulty, defined as the dollar value of the order divided by $ADV, and by liquidity, defined by ranking sample stocks based on October 2001 dollar volume and sorting them into six groups. Two order types are represented in the data: market orders and limit orders. 14 Based on the quotes at order arrival, it is possible to further divide limit orders into marketable limits, i.e. a buy (sell) order with a limit price at or exceeding (below) the inside offer (bid), and straight limit orders (the rest). Market orders constitute the majority of the sample, representing percent of all orders, and percent of total dollar volume 12 Note that after January 2002, the practice of printing back trades in this fashion is gradually changing as more and more sell-side firms are moving to commission-based instead of net trading. 13 Orders may be dynamically updated as described in our example (see Werner (2002)). 14 There are also stop loss orders in the data, but these are excluded from the analysis. 11

12 (see also Table 2). It follows that market orders are on average larger than marketable limits, and straight limit orders. The average (median) market order is worth $1.228 million ($547 thousand), while the average (median) straight limit order is worth $748 ($318) thousand. Marketable limits are only slightly larger than straight limits on average. The average total value as a fraction of $ADV ranges from 10 basis points for the easiest trades to 92.9 percent for the most difficult trades. Hence, the order-size distribution is extremely skewed. Interestingly, the average (median) dollar value of an order does not vary nearly as much, suggesting that many of the more difficult trades are in low priced, less liquid stocks. This conjecture is confirmed by looking at the order distribution across stock liquidity groupings. There is heavy concentration of orders in the top 100 stocks, but also stocks below 500 in the liquidity rankings have a large number of orders. Note, however, that average (median) executed order size declines as we move from the most liquid to the less liquid stocks. Descriptive statistics for the pre-decimals benchmark are summarized in Panel B at the bottom of Table 1. There are 70,820 orders in that sample for a total value of $143 billion. Hence, average total order size was almost twice as large during the pre-decimals sample, $2.019 million. The difference across sample periods in median total order size, and average and total order size based on executed volume is much smaller. For example, the average executed size pre-decimals is $785 thousand compared to the average executed size of $610 thousand post-decimals. Despite the much larger orders by dollar value, the average total value of an order as a fraction of average daily dollar volume in January 2001 ($ADV) is lower than in the primary sample, percent. Hence, it is not clear that the orders on average were more difficult during the predecimalization benchmark period. Information regarding the direction of trades and order types are reported in Table 2. The post-decimals sample orders are roughly balanced between institutional buys and sells, however buy orders are slightly more likely to get executed. Interestingly, only 6.7 (7.3) percent of all orders (total order value) are short sales. More than half of all orders and more than two-thirds of executed value are market orders. In terms of submitted orders, marketable limits and limit orders each represent slightly more than one-fifth of 12

13 all orders but they only represent 15 percent and 8 percent of executed orders respectively. Hence, limit order fill rates are low (see Section IV). In terms of order arrivals, 12 percent of all orders arrive pre-open, and another 25 percent arrive in the first hour of the trading day. Order arrivals are otherwise u-shaped over the course of the trading day as would be predicted by for example the model of strategic trading by an informed trader developed by Admati and Pfleiderer (1988). The pre-decimals benchmark sample has very similar characteristics. There are two exceptions. First, total order value of institutional sell orders is larger reflecting the general down-wards trend in prices (Figure 1). Note, however, that the imbalance between buys and sells in terms of number of orders, and more importantly, executed dollar volume is much smaller. Second, some very large orders must have arrived during the middle of the day during the pre-decimals sample (35.91 percent of total dollar volume). Interestingly, it seems that these large orders were never executed (11.10 percent of executed dollar volume arrive in this time-slot). Table 3 reports information on differences across order types in order arrival times, difficulty, and stock liquidity for the post-decimals sample. The results for the pre-decimals sample (not reported) are very similar. It reports percentages of total order values, i.e., the value of the entire order based on the mid-quotes at order arrival. The demand for immediacy is likely to increase, i.e., the buy-side trader is likely to get more and more anxious to get the order filled, as the close approaches. Indeed, the table shows that limit orders are more likely to arrive in the morning than later in the day while the pattern for market orders is the reverse. As mentioned above, putting a price limit on the order is a way of narrowing down the trading strategies that the dealer might engage in when executing the order. To do so is relatively more important when dealing with difficult orders in less liquid stocks where prices might move considerably as a result of the order. Hence, limit orders should be favored in such situations. These predictions are borne out in the data. Market orders are also relatively more pervasive for easy trades in highly liquid stocks. By contrast, the proportion of marketable limits and limit orders increase as order-difficulty increases and stock liquidity deteriorates. During the sample period, 148 sell-side dealers report large Not Held and Worked orders. Institutional trading is highly concentrated: The top five (ten) sell-side 13

14 dealers ranked by institutional market share together represent 41 (67) percent of executed institutional volume; and more than 90 percent of institutional volume is traded by the top 25 sell-side dealers. Moreover, sell-side firms vary tremendously in their institutional focus, defined here as institutional volume divided by total customer volume. Institutional trading represents more than 90 percent of all customer share volume for more than 30 sell-side dealers in our sample. Many of these sell-side firms also have a high institutional market share. However, there are also many niche players who have a small overall institutional market share, but that specialize in for example low and midcap stocks or particular sectors, e.g., regional banks. IV. Fill Rates and Duration for Institutional Orders The most basic measure of execution quality is whether or not the order got filled. This would seem like a trivial thing to measure, were it not for the fact that many orders get cancelled and replaced and/or cancelled during their life as described in Figure 2. The question then becomes, should the fill rate be expressed relative to the part of the order that was not cancelled or in terms of the entire original order? The problem is that it is not known why the order was cancelled. It could have been cancelled because the sell-side dealer did not provide timely fills or the price was not satisfactory, or it could have been cancelled because the buy-side trader changed his trading strategy as in Figure 2. Fortunately, it is possible to link such cancel and replace orders to the original order in the data. Since the cancel and replace order is direct evidence that the buy-side trader changes the order, but leaves the execution with the same dealer, it seems unreasonable to view this as a non-fill. Therefore, the portion of orders that are subsequently cancelled and replaced when calculating fill rates below (50,000 shares of the 100,000-share order in Figure 2) are excluded from the analysis. Of course, it is also possible that orders that are simply cancelled are also subsequently replaced by a new, but not linked, order. Hence, this measure is likely to underestimate the true fill rate for institutional orders in Nasdaq stocks. Fill rates, fill times, and number of market orders are reported in Panel A of Table 4. Recall that a 100 percent fill rate should not be expected even if they are designated as 14

15 market orders. The average (median) fill rate is 83.7 (100) percent, and it is decreasing in difficulty and increasing in liquidity as expected. The average fill rate ranges from 88.5 percent (easy orders) to 68.7 percent (difficult orders) across difficulty quintiles and from 72.6 percent (>500 stocks) to 87.8 percent (1-100 stocks) across liquidity groupings. It takes on average one hour (87 minutes) between order arrival and the last execution (last execution or cancellation) of the order. The average order for which there are execution records receives 3.2 fills. As expected, fill times for market orders increase significantly in difficulty and decrease significantly in liquidity. While the number of fills grows as trades get more difficult, there is no significant change in the number of fills as we move across liquidity groupings. Note that fills here are the so-called print-backs to the customer. The sell-side dealer might have executed many more trades with the street in his efforts to fill the order. Similar figures for marketable limits and for limit orders are reported in Panels B and C of Table 4. Since putting a price limit on the order makes it more difficult for the sell-side dealer to fill the order, fill rates should be lower for priced orders. The average fill rate for marketable limits is 78.6 percent while the average fill rate for limit orders is as low as 48.7 percent. Note that this in part is because limit orders are more frequently used both for difficult trades and when trading less liquid stocks. There is no noticeable difference in fill times between marketable limits and market orders, but it is clear that limit orders take considerably longer to execute. The average fill time until the last execution or cancellation is 190 minutes for limit orders compared to 88 minutes for marketable limits. The number of fills ( print-backs ) is actually lower for priced orders than for market orders, which is natural since market orders on average are substantially larger than limit orders in the sample. V. Trading Costs for Institutional Orders The second dimension of execution quality explored in this paper is trading costs. Studies based on institutional trading data typically calculate trading costs by compare the volume-weighted average execution price (VWAP) to a pre- or post-execution benchmark price (e.g., Chan and Lakonishok (1995, 1997), Keim and Madhavan (1997), Jones and Lipson (1999, 2001), Chakravarty et al (2001)). Thus, first VWAP for the 15

16 executions that sell-side dealers print back to their customers are calculated. However, a reference point is also needed for the VWAP. The reference point should ideally be the true value of the security where the true value is unperturbed by the presence of the order itself. 15 Since the true value of the security is unobservable, and any one measure can be criticized, this paper provides a number of different benchmarks. The VWAP is compared to: the opening mid-quote (pre-execution benchmark); the quotes at order arrival (pre-execution benchmark, commonly referred to as the effective half-spread); the VWAP of all trades following order arrival (to avoid gaming issues); and the closing mid-quotes (post-execution benchmark, commonly referred to as realized half-spread). Note that liquidity-demanding orders (market orders and marketable limits) are expected to have positive trading costs, while liquidity-supplying orders (limit orders) should have negative trading costs. A troubling issue with institutional order data is how to deal with implementation shortfall (see, Perold (1988)), i.e., any unexecuted part of an order. One way of capturing the opportunity cost of not executing the entire order during the trading day is to assume that the remaining portion of the order gets a fill at the closing quotes. In other words, if 25,000 shares of a 250,000-share buy (sell) order is not executed by the end of the day it is assumed that it is filled at the closing ask (bid). Such a measure is provided in the empirical analysis below, but the reader is cautioned that it is unlikely that sufficient depth is available at the closing quotes to accommodate institutional-sized order. Hence, this measure is likely to underestimate the full cost due to implementation shortfall. That is, of course, provided that the buy-side institution indeed meant for the sell-side dealer to fill the entire order. As Figure 2 shows, it is often the case that the buy-side trader adapts his trading strategy during the trading day, and he might change the order, or cancel it entirely. If this is the case, it is not reasonable to penalize the sell-side dealer for implementation shortfall. Hence, the discussion will focus on trading cost measures without implementation shortfall. Trading costs for different order types, market orders, marketable limits, and limit orders, are tabulated for the primary sample in Panels A-C in Table 5. The results are 15 See Keim and Madhavan (1997) for a discussion on the importance of choosing the appropriate benchmark. 16

17 further broken down by difficulty and stock liquidity. All estimates are execution-valueweighted, and they are expressed in basis points. Hence, the total cost for institutional trades can be estimated by multiplying executed institutional dollar volume by these trading cost measures. Note that no filters for outliers were applied when calculating the trading costs in these tables. Since there are a number of outliers, execution-valueweighted medians are also reported. 16 Trading costs for market orders vary tremendously depending on which benchmark is used to estimate trading costs. Average effective half-spreads (VWAP relative to mid-quote at OA) for market orders are basis points. The implementation shortfall correction raises execution costs, but the change is very small in magnitude. 17 Market orders execute at prices that on average are statistically insignificantly different from the VWAP of trades following order arrival (VWAP(+)). Market orders are informative on average -- the realized half-spread (VWAP relative to mid-quote at close) for is 9.82 basis points. Hence, on average the mid-quote moves more than 60 basis points between order arrival and the close, and more than 100 basis points between open and the close. As expected, median trading costs measures are considerably smaller in magnitude. The way to read these numbers is that half the dollar value traded in market orders had an effective half spread of less than basis points. Table 5 also breaks down the numbers by difficulty and liquidity. Effective halfspreads for easy (Q1) orders are as low as basis points on average, while they are as high as basis points for difficult orders (Q5). Market orders in the top 100 stocks by liquidity have an average effective half-spread of basis points, and trading costs increase as liquidity drops as expected. Effective half-spreads reach basis points for stocks ranked below 500. As expected, the most difficult trades move price more. Realized half-spreads range from basis points for easy trades to basis points on average for the quintile of most difficult trades. Since realized spreads are estimated based on closing prices, these price impacts are more likely to be due to the information content of the trades than a short-run liquidity effect. Note also that market 16 An alternative approach would be to Winsorize the data, and we resort to that in the regressions in Section VI. 17 Note that this calculation only captures orders that have a partial fill or complete fill. Orders that receive no fill at all are excluded. 17

18 orders in less liquid stocks move prices relatively more in the direction of the trade. In fact, the realized half-spread is as low as basis points on average for stocks in the below 500 category which means that the mid-quote moved on average 136 basis points in the direction of market orders. Trading costs for our sample of marketable limit orders are reported in Panel B of Table 5. Recall that buy (sell) limit orders are classified as marketable if the price limit is equal to or exceeds (falls below) the inside offer (bid) at the time of order arrival. Average effective half-spreads are roughly 10 basis points lower both for marketable limit orders than for market order in Panel A. So, why would not all buy-side traders use marketable limits? The answer is presumably that the fill rate for marketable limits is on average 5 percentage points lower for marketable limits than for market orders (Table 4). Marketable limits execute at prices that are basis points higher than the VWAP of trades following order arrival, VWAP(+). The realized half-spread for marketable limit sells is significantly positive at basis points on average. Thus, marketable limit orders move prices less on average than market orders basis points compared to 60 basis points. Panel C of Table 5 reports the results for the remaining order type in the sample, regular limit orders. Recall from Table 4 that the fill rate for these orders is less than 50 percent, and trading costs only capture orders that have at least a partial execution. As expected, effective half-spreads are negative for limit orders. The average effective halfspread is basis points as expected limit orders gain the spread on average. Limit orders also execute at prices that are insignificantly different from other trades following order arrival (VWAP(+)) on average. However, prices tend to move against the limit orders on average, resulting in a positive average realized half-spreads of basis points. Before moving on to testing whether institutional trading costs have increased following decimalization, it is useful to compare the levels of trading costs for institutional orders executed by Nasdaq sell-side dealers with trading costs from previous studies. It is challenging to find comparable data on trading costs in the literature, so we need to proceed with great caution. Not only do the samples vary tremendously, the definitions of trading costs, and the benchmarks chosen vary from study to study. 18

19 Moreover, no study has distinguished between order types in their analysis. For comparison, the value-weighted average total trading cost based on the mid-quotes at order arrival would be 43 basis points for the post-decimals sample. 18 If the opening mid-quotes instead are used as the benchmark price, average estimated trading costs would be roughly 67 basis points. Benchmarking on the closing mid-quote would result in an average estimated trading cost of roughly 2 basis points. Finally, orders on average execute 4 basis points above the VWAP of all trades following order arrival. In their study of the reduction of the tick-size back in 1997, Jones and Lipson (2001) find total trading costs (commissions plus market impact) of 85.4 basis points for NYSE stocks post-sixteenth. Conrad, Johnson, and Wahal (2001) find total trading costs of 56.4 basis points for broker filled (NYSE+Nasdaq) orders in 1998:Q1. The Plexus Group gathered more recent data to compare trading costs for Nasdaq and the NYSE before decimalization. They find total trading costs of 97 (179) basis points for Nasdaq large (mid) capitalization stocks in 2000:Q4. The same study finds trading costs of 66 (108) basis points for NYSE large (mid) cap stocks. Hence, the estimated institutional trading costs in this study are significantly lower than those found in earlier samples. In part, this is because trading costs are based on the order-releases to the sell-side dealer instead of the order-releases to the buy-side trading desks. In part, it is a result of overall improvements in liquidity in the markets after decimalization. VI. Institutional Trading Costs and Decimalization In the previous section, it was shown that trading costs for institutional orders on Nasdaq are lower or comparable to those found in studies of institutional trading costs from previous time periods and other markets (NYSE). The question remains whether these costs have increased compared to the pre-decimals period as reported by Nasdaq buy-side traders. To examine this issue, this paper first provides univariate tests of differences in fill rates, durations, and trading costs across the two samples. Since univariate tests do not permit controls for other factors that might differ across the two 18 The figure is based on weighing together the fraction of executed value in each order type and accounting for the buy and sell proportions: *(52.34) *(42.02) *(-46.46). 19

20 samples aside from the tick-size, the univariate tests are further complemented with regression analysis of trading costs. Table 6 reports the results for tests of differences in average and median: fill rates; durations; and number of fills ( print-backs ) across the two samples. In addition, the table reports average and (un-weighted) median fill rates etc. in the pre-decimals period. Recall that the post-decimals sample is November 26-30, 2001, and the pre-decimals sample is February 1-8, There is no significant change in fill rates or durations (time to fill) between the samples for market orders. Interestingly, fill rates for marketable limit orders and limit orders are significantly higher during the post-decimals period than in the pre-decimals period. The duration of marketable limit is not significantly different between the two samples, while limit orders take significantly longer to fill in the post-decimals sample. However, based on the second measure of duration, time to the last fill or cancel, the durations both of marketable limits and limit orders are significantly lower in the post-decimals sample. There is a significant decline in the number of fills for all order types in the post-decimals sample compared to the predecimals sample. Hence, decimalization does not seem to be associated with a decrease in fill rates and an increase in duration as might have been expected. Univarate tests for differences in trading costs between the two samples are reported in Table 7. In addition, the table reports the average and median trading costs for the pre-decimals period. Panel A summarizes the results for market orders. Postdecimals institutional trading costs are significantly lower than pre-decimals trading costs for all benchmarks based on a t-test. For example, effective half-spreads have decreased by basis points (29 percent) on an execution-value-weighted basis using the quotes at order arrival as a benchmark. There has been a basis point (21 percent) reduction using open quotes as a benchmark, and a 9.01 basis points (1,112 percent) reduction using closing quotes as a benchmark. Also the non-parametric tests show that trading costs based for the post-decimals period are significantly lower based on preexecution benchmarks, but trading costs based on post-execution benchmarks while lower are less significant. The results for marketable limit orders are reported in Panel B. The picture for these orders is more mixed as should be expected since they are a hybrid between market 20