Electronic Communications Networks and Market Quality

Transcription

1 Electronic Communications Networks and Market Quality Michael J. Barclay Terrence Hendershott and D. Timothy McCormick First version: September 26, 2000 This version: January 10, 2001 Abstract We compare the execution quality of trades with market makers to trades executed on Electronic Communications Networks (ECNs). Average quoted, realized and effective spreads are smaller for ECN trades than for market-maker trades even though ECN trades are more informative than trades with market makers. Increased trading on ECNs also improves most measures of overall market quality. In the cross section, more ECN trading is associated with lower quoted, effective, and realized spreads, both overall and on trades with market makers. More ECN trading is also associated with less quoted depth. Barclay and Hendershott are at the Simon School of Business, University of Rochester, Rochester, NY, 14627, and can be reached at phone: 716/ , fax: 716/ and phone: 716/ , fax: 716/ McCormick is with Economic Research, NASD, Inc., 1801 K St., NW, Washington, D.C and can be reached at mccormit@nasd.com, phone: 202/ , fax: 202/ The views expressed herein are not intended to represent the views or official policy of The Nasdaq Stock Market, the NASD, Inc., or any NASD subsidiary. Any errors or omissions are the responsibility of the authors alone.

2 1 Introduction Technological innovations that enable high-speed, low-cost electronic trading systems are dramatically changing the structure of financial markets. Exchanges and markets around the world are merging or forming alliances to improve liquidity and reduce costs in the face of increased competition from each other and from these computerized trading systems. In the United States, Electronic Communications Networks (ECNs) are involved in more than a third of total Nasdaq trading volume. ECNs are now attempting to build market share in NYSE-listed issues. Who uses these new electronic trading systems and in what ways? What is their impact on the existing markets? This paper addresses these questions for the most widely used electronic markets, ECNs, in one of the world s largest stock markets, Nasdaq. ECN trading volume has grown rapidly over the past several years, transforming Nasdaq s operations. ECNs operational efficiency (e.g., Island, one of the largest ECNs, has only about 60 employees) promises lower costs in addition to improved limit order exposure, anonymity, and increased speed. We construct a model of trading with market makers and trading on ECNs that highlights the role of ECNs in facilitating customer-to-customer transactions. Although the model is simple, it generates a rich set of predictions about quoted, effective, and realized spreads on ECN and market-maker trades, the choice of trading venue by informed and uninformed traders, and the effect of ECN trading on overall market quality. We test these predictions using one month of data for all Nasdaq National Market (NNM) stocks. Investors choicesofwhethertotosendtheirorderstoamarketmakerortoanecnwilldepend on the total expected trading costs, including the implicit, explicit and opportunity costs. Because we do not have order submission data or a complete breakdown of commissions, we focus on the implicit costs of the trades as indicated by various measures of the bid-ask spread. Consistent with our model, we find that ECN trades have lower average effective spreads than trades with market makers. For small trades (1,000 shares or less), the lower effective spreads on ECNs result from lower quoted spreads at the time of the trade. Market makers give more price improvement (or size enhancement) on small trades than ECNs. For some institutional investors the choice of trading venue is dynamic. Institutions can examine the prices and depth offered by ECNs and by market makers and choose the venue that will provide 1

3 the best execution. For retail investors, however, the choice is generally static. Retail investors choose a broker, and the broker generally routes all orders to a particular market maker or ECN. Although effective spreads are lower for ECN trades than for market maker trades, our results suggestthatretailinvestorswillbebetteroff if their small orders are sent initially to a market maker. Best-execution rules ensure that small orders receive the best quoted price regardless of where that quote originates. Thus, the main factor affecting the quality of execution is price improvement, and market makers give more price improvement on small trades than ECNs. Medium and large trades have lower average effective spreads and receive more price improvement when they are executed on ECNs. However, institutions choose the trading venue for their medium and large trades dynamically, and they send these trades to an ECN only when the ECN is offering sufficient depth. Because ECNs generally offer less depth than market makers, it is likely that the better prices on ECNs for medium and large trades are available only if these trades are sent to the ECN dynamically. Our model also predicts that ECNs will attract a higher percentage of the informed orders than the uninformed orders. This is an unusual feature of ECNs. Generally, when a secondary market skims orders from the primary market, the secondary market skims the least informed and, consequently, most profitable orders. For example, regional exchanges pay for retail orders in NYSE stocks and thus skim these less-informed retail orders away from the NYSE. On Nasdaq, market makers have preferencing or internalization agreements that allow them to retain the less-informed retail orders. Because ECNs match customer orders without participating in the trades, the more informed orders to spill onto the ECNs. Consistent with our model, we find that ECN trades are more informative and have lower realized spreads than trades with market makers. Finally, we find that an increase in ECN trading activity improves most measures of market quality. In the cross section, we find that increased ECN trading improves market quality as measured by effective, realized, and time-weighted quoted spreads. These improvements occur in the overall market as well as for market-maker trades and quotes. There are several explanations for this result. First, ECNs facilitate customer-to-customer trades that occur at better prices than trades with intermediaries. Second, as noted above, ECNs attract a higher fraction of the informed orders than the uninformed orders. This reduces the adverse selection costs faced by market makers and, in a competitive market, reduces the spreads charged by market makers. Finally, the 2

4 lower spreads available on ECNs increase competition and dissipate any quasi-rents on preferenced market-maker trades. Our results are related to the existing literature on multimarket trading. However, our results highlight important differences between ECNs and other trading venues, such as regional exchanges, that have skimmed order flow from primary markets. For example, Lee (1993) finds that execution costs on the regional exchanges are higher than execution costs on the NYSE and Easley, Keifer, and O Hara (1996) show that the Cincinnati Stock Exchange attracts mostly uninformed orders in NYSE stocks. In contrast, we find that trades on ECNs have lower effective and realized spreads and are more informative than trades with market makers. A major concern about multimarket trading is that it will reduce liquidity by fragmenting the order flow.(see, for example, Mendelson, 1987, Chowdry and Nanda, 1991, Grossman, 1992, Madhavan, 1995, and Hendershott and Mendelson, 2000). Empirical evidence on this effect is mixed (see, for example, Easley, Keifer, and O Hara, 1996, and Battalio, 1997). ECNs have the unusual feature, however, of attracting informed orders, something not found in any theoretical model of multimarket trading. Our model predicts that increased trading activity on ECNs will improve most measures of overall market quality, and this prediction is confirmed by the data. Our results suggest that ECN trading increases the level of competition among market makers, similar to the results in Battalio (1997) who shows that the entry of a third-market broker purchasing order flow in NYSE stocks reduces bid-ask spreads. Several previous papers have examined the effect of ECN quotes on market quality. For example, Barclay, Christie, Harris, Kandel, and Schultz (1999) analyze the impact of Nasdaq s new order handling rules and show that ECN quotes play an important role in reducing trading costs. Simaan, Weaver, and Whitcomb (1999) show that ECN quotes are more likely to be on an odd tick than market-maker quotes. Huang (2000) finds that ECN quote updates are more informative than market-maker quote updates. This paper, however, is the first to examine trading on ECNs. By examining ECN trades in addition to ECN quotes, we are able to focus on the demanders of liquidity, rather than the suppliers of liquidity, and we are able to calculate more direct measures of market quality such as effective and realized spreads and the information content of trades. We provide a detailed discussion of ECNs, including their history, uses, operations, and integration into Nasdaq, in the following section. We then present our model in Section 3, describe our 3

5 data in Section 4, and present our empirical results in Sections 5 and 6. Section 7 concludes. 2 Overview of ECNs ECNs are defined by the SEC as electronic trading systems that automatically match buy and sell orders at specified prices. In a recent report, the SEC describes ECNs as having become integral to the modern securities markets (SEC, 2000). Today, ECNs account for approximately 30 percent of total share volume and 40 percent of the dollar volume traded in Nasdaq securities. ECNs account for approximately 3 percent of total share and dollar volume in listed securities. In contrast, in 1993 ECNs accounted for only 13 percent of share volume in Nasdaq securities and only 1.4 percent of listed share volume (SEC, 2000). Competing ECNs offer different fee structures and levels of service and cater to different investor clienteles. However, all ECNs provide the same basic transaction services. ECN subscribers submit limit orders which are posted on the system for other subscribers to view. The ECN then matches contra-side orders for execution. In most cases, the buyer and seller remain anonymous, as the trade execution reports list only the ECN as the contra-side party. Subscribers may use additional features of the ECN, such as negotiation or reserve size, and may have access to the entire ECN order book that contains important real-time information about the depth of trading interest. When ECNs first developed, they served primarily as private trading vehicles for institutional investors and broker-dealers. The prices posted on ECNs by these professional traders often were better than the prices posted on Nasdaq. Because the ECNs were not integrated into the Nasdaq market, many investors, particularly retail investors, traded at prices inferior to those displayed by market makers and other subscribers on ECNs. Essentially this created a two-tiered market the traditional public market, and the new ECN market with better prices and limited access. In 1996, the SEC adopted new order-handling rules to integrate these markets. Before the adoption of the order-handling rules, market makers could post quotes in private ECNs that were better than the quotes they posted in the public market. This allowed market makers to segment their market, charging higher prices to retail customers and lower prices to more price-sensitive institutional investors. Under the new order-handling rules, market makers and specialists were required to reflect in their public quote any better prices that they placed on an ECN. The new 4

6 order-handling rules had a large and immediate impact on the securities markets trading costs fell dramatically, resulting in significant cost savings for investors (Barclay, Christie, Harris, Kandel, and Schultz, 1999). Today, almost any investor can trade through an ECN, including retail investors, institutional investors, market makers, and broker-dealers. The recent proliferation of new electronic markets led the SEC to consider how to incorporate these trading venues into the national market system. In December 1998, the SEC adopted Regulation ATS to establish a regulatory framework for alternative trading systems and to more fully integrate them into the national market system. The goals of Regulation ATS were to provide investors with access to the best prices, provide a complete audit trail and surveil trading on alternative trading systems, and reduce the potential for market disruption due to system outages. Once ECNs were fully integrated into the Nasdaq market, investors had to decide if and when they should be utilized. ECNs offer several potential benefits to investors. First, ECNs typically offer an advantage in the speed of execution. Traditional orders are sent first to a broker, either electronically or over the phone, who determines the market where they will be sent for execution. There are no SEC regulations concerning the time required to complete this task. Although trade executions are usually seamless and quick, they do take time. In fast-moving markets, investors using traditional brokers will not always receive the price they saw on their computer screen, or the price their broker quoted over the phone. By the time their orders reach the market, the price of the stock could be slightly or very different. The immediate execution offered by ECNs is one of their major selling points. 1 Second, ECNs sometimes offer better prices than Nasdaq s National Best Bid and Offer (NBBO) because they use finer tick sizes, and because not all ECN quotes are displayed in the Nasdaq NBBO. In the Nasdaq quote montage, ECN quotes are rounded to the nearest Nasdaq tick, but ECN trade prices are not. 2 Thus,tradesmayoccuronanECNatafractionofatickbelowtheNBBO.In addition, non-market makers (and ECNs with less than 5 percent of the volume in a stock) may choose to have their orders displayed only on the ECN and not included in the NBBO. Such limit 1 Some market makers, such as Knight Securities, offer automated execution that is as fast as ECNs and the price is guaranteed, whereas the limit order on the ECN may disappear before the trade can be executed. 2 Effective June 3, 1997, Nasdaq moved to a tick of one-sixteenth for stocks with a bid price of $10 or more, implying that ECN quotes are rounded to the nearest sixteenth of a dollar. Decimalization on Nasdaq will reduce rounding, but will not eliminate it because some ECNs already utilize a tick of less than one cent. 5

7 orders can execute only against orders originating on the same ECN. The potential benefits of trading on an ECN must be weighed against the costs.ecns charge fees for their services which are paid directly by subscribers and indirectly by nonsubscribers. 3 In addition, market makers sometimes execute orders at prices better than the NBBO. This practice, known as price improvement, is one dimension on which market makers compete with each other. 4 In addition, market makers sometimes execute orders larger than the inside quoted depth, a practice referred to as size enhancement. Institutional investors and broker-dealers who subscribe to an ECN can place orders directly with the ECN. These traders may make dynamic choices to route an order to an ECN or to a market maker based on current market conditions. For example, best execution (see Macey and O Hara, 1997, for more detailed discussion of best execution rules) does not require market makers tomatchtheroundedornondisplayedquotesonanecn.thus,customersmaygodirectlytothe ECN to get these better prices. If a subscriber sends a marketable order to an ECN and the ECN is not currently posting the best bid or offer, the ECN will send the order to another market for execution. Individual investors can access an ECN through their broker. Some individual investors choose a particular broker expressly because that broker routes their orders to a specific ECN.Other individual investors may or may not be aware of the market to which their broker routes their orders. Market makers can also route customer orders to an ECN. Market orders execute on an ECN when the ECN is posting the best bid or offer and the market maker does not want to match that price. Limit orders submitted to a market maker can be routed to an ECN in accordance with the new order-handling rules. 3 For subscribers these fees include a fixed component, the cost of purchasing the ECN terminal and line feed, and a per-share fee for execution. For non-subscribers an access fee of 1/4 to 2 cents per share is charged for orders that execute against a standing ECN order. During our sample period, this fee was paid by the intermediary routing the order to the ECN and was not charged directly to investors. 4 Because not all ECN orders are displayed in the NBBO, ECN trades sometimes occur at prices better than the NBBO. However, because ECNs simply match orders to buy with orders to sell, generally there is no opportunity for price improvement beyond the unrounded or undisplayed quote on an ECN. 6

8 3 A Model of Trading with Market Makers and on ECNs Before examining our data, we provide a simple single-trade model of trading with market makers and trading on ECNs. The model is intended to highlight the role of ECNs in facilitating customerto-customer transactions, and to generate a series of hypotheses to structure our empirical tests. There is a single risky asset with random future value, ṽ {V, V }. We assume that ṽ can take the value V or V with equal prior probability (cf. Easley and O Hara, 1987). Thus, the value of the security has a-priori mean E[ṽ] =(V + V )/2 and volatility σ =(V V )/2. There are two types of traders interested in buying or selling one unit of the asset. Traders of type 1 are informed with probability α 1, and submit their orders through brokers who have preferencing or internalization agreements with market makers (hereafter referred to as preferencing). Tradersoftype2areinformedwithprobabilityα 2, where α 1 < α 2, and can choose to send their orders to a market maker or to an ECN. 5 Informed traders know the true realization of ṽ and trade to profit from this information. Uninformed traders trade for exogenous liquidity reasons. We assume that an arriving trader is equally likely to buy or sell, and is of type 1 with probability (1 β) and type 2 with probability β. Market makers compete for type-2 orders with their quoted prices. This competition drives market makers bid and ask quotes to the expected value of the asset conditional on a type-2 order to sell or buy, respectively, and on all information publicly available at the time of the trade. Thus, market makers quote an ask price of a 2 = E[ṽ] +s 2 andabidpriceofb 2 = E[ṽ] s 2, where the half-spread, s 2 = α 2 σ, is the expected loss from trading with a type-2 trader. Because type-1 orders are preferenced, market makers can execute them at the NBBO or they can direct them to the venue currently offering the best quote. When they receive a preferenced order (from a type-1 trader), market makers will match ask prices greater than a 1 = E[ṽ] +s 1 or bid price less than b 1 = E[ṽ] s 1, where s 1 = α 1 σ <s We assume that market makers can identify type-1 and type-2 traders and preference the less informed (type-1) orders. This assumption does not imply that market makers can identify individual informed and uninformed orders, but rather that market makers can measure the average profitability of orders from various sources and discriminate between more profitable and less profitable sources. Battalio, Jennings, and Selway (2000) provide evidence that market makers can do this for Nasdaq stocks and Easley, Keifer, and O Hara (1996) show that the regional exchanges do this for NYSE listed stocks. 6 Some market makers choose to execute all internalized or preferenced orders automatically at the NBBO. Incorporating this autoexecution feature into our model would cause the market makers to knowingly lose money on some trades without qualitatively changing the results. 7

9 Traders also have the opportunity to trade on an ECN. The provision of liquidity on an ECN is exogenous to the model, and comes from patient investors who are willing to supply, rather than demand, liquidity or from market makers who wish to rebalance their inventories or trade on an ECN for other exogenous reasons. We assume that when a trader arrives, there is an existing limit order that betters the market maker s quote with probability 2λ. The limit order is posted on the ECN with probability q and with a market maker with probability 1 q. 7 The limit order is either an order to buy or an order to sell with equal probability, and is independent of the subsequent market order. Thus, when a trader arrives, there is a limit order on the side of the market where he wants to trade with probability λ and that limit order resides on the ECN with probability q and with a market maker with probability 1 q. ECN limit orders, market maker quotes, and market makers display of customer limit orders comprise the quote montage. When there is a limit order to buy or sell, the best bid price or ask price is given by b E = E[ṽ] γ or a E = E[ṽ] +γ, respectively, where γ follows an arbitrary distribution on [0,s 2 ] with cumulative distribution function F (γ). A type-2 trade will always be sent to an ECN when the ECN is establishing the best bid or offer. Because they are preferenced, however, type-1 traders will be sent to an ECN only when γ <s 1. Thus, the probability that a type-i tradeoccursonanecnisλqf(s i ), the probability that the next trade occurs with a market maker is P (M) =qλ((1 β)(1 F (s 1 ))) + (1 q)λ +(1 λ), and the probability the next trade occurs on an ECN is P (E) =1 P (M) =qλ(β +(1 β)f (s 1 )). This model produces the following testable predictions about trades with market makers and trades on ECNs: 8 Hypothesis 1: Effective half-spreads for ECN trades (E[s E E ])arelowerthaneffective halfspreads for market-maker trades (E[s E M ]). The lower effective half-spreads for ECN trades are due to narrower quoted half-spreads at the time of ECN trades than at the time of market maker trades. Define the effective half-spread as the transaction price minus the expected value of the asset 7 In our model the only way that a limit order can execute against a less-informed type-1 order is if it is placed with a market maker. However, in practice there are a number of reasons why placing a limit order on an ECN is advantageous. Limit orders are displayed immediately on an ECN, while market makers have 30 seconds before they must display a limit order (and the S.E.C. has found that market makers do not always meet this requirement). ECNs also allow greater anonymity for limit orders and some ECNs pay for limit orders. 8 The model s results are sufficiently easy to verify that we omit some of the analysis. 8

10 conditional on public information (or minus 1 times this quantity for sell orders). Then E[s E E] = E[γ]β + E[γ γ <s 1](1 β)f (s 1 ) β +(1 β)f (s 1 ) < E[γ s 1 < γ <s 2 ]qλ((1 β)(1 F (s 1 ))) + E[γ](1 q)λ + s 2 (1 λ) qλ((1 β)(1 F (s 1 ))) + (1 q)λ +(1 λ) = E[s E M]. The average effective half-spread is lower for ECN trades than for market-maker trades because trades occur on an ECN only when a limit order on an ECN is establishes a price that market makers are unwilling to match. Because these ECN limit orders are included in the quote montage, this implies that the average quoted spread will be lower at the time of an ECN trade than at the time of a market-maker trade. Hypothesis 2: market-maker trades. ECN trades are more informative and have smaller realized spreads than Define the information content of a trade (IC) as the change in expected value of the asset that is caused by a trade. Then E[IC E ] = σ α 2β + α 1 (1 β)f (s 1 ) β +(1 β)f (s 1 ) > σ α 1qλ((1 β)(1 F (s 1 ))) + (α 1 (1 β)+α 2 β)(1 q)λ +(1 λ) qλ((1 β)(1 F (s 1 ))) + (1 q)λ +(1 λ) = E[IC M ]. Because market makers preference type-1 trades, a higher percentage of type-2 trades go to an ECN than type-1 trades. This reduces the informativeness of market maker trades in relation to ECN trades. Define the realized half-spread (E[s R ]) as the transaction price minus the stock s expected value immediately after the trade (or minus 1 times this amount for sell orders). Then E[s R E]=E[s E E] E[IC E ] < 0 <E[s E M] E[IC M ]=E[s R M]. Market-maker trades have positive realized spreads due to the quasi-rents earned on preferenced type-1 trades. Because ECN trades include only type-1 orders that market makers choose not to preference and type-2 trades at prices inside the market makers quotes, the average realized spread 9

11 for ECN trades is negative. Because ECN trades have lower effective spreads and higher information content than market maker trades, the realized spread for ECN trades must be less than the realized spread for market-maker trades. In this model, limit orders placed on an ECN are likely to be hit by an informed trader and thus generate in a negative expected realized spread. Although the supply of limit orders is exogenous to the model, it is important to note that the limit orders are not irrational. For a patient (uninformed) trader who wants to buy shares, the alternatives are to demand liquidity and pay the current spread, or supply liquidity through a limit order and receive the (negative) realized spread for ECN trades if the order is filled. 9 If a limit order is optimal, its expected execution cost must be less than the trader s expected cost using a market order. This condition is satisfied in our model. The model also produces the following predictions about the effect of the overall level of ECN activity on market quality. We derive the comparative statics results with respect to P (E), i.e., the relative frequency of trades executed on an ECN. Hypothesis 3: The average effective half-spread, the average effective half-spread for marketmaker trades, the average quoted spread, and the average quoted spread at the time of market-maker trades are decreasing in ECN activity. Hypothesis 2 demonstrates that effective spreads are smaller for ECN trades than for market maker trades. Therefore, increasing P (E) lowers the average effective spread. An increase in ECN trading also lowers the effective spread for market-maker trades for two reasons. First, ECNs attract a higher percentage of informed trades than uninformed trades. Therefore, the more ECN trades, the lower the average information content of market-maker trades. Since market makers face less adverse selection, their effective spreads decline. Second, market makers are earning quasi-rents on their preferenced type-1 orders. An increase in ECN activity dissipates some of these quasi-rents. Greater ECN activity increases the likelihood of an ECN limit order when a type-1 order arrives. The ECN limit order narrows the NBBO and causes preferenced order to be executed at a better price. 9 In practice market makers face inventory and order processing costs, in addition to adverse selection costs. These additional costs will increase market-maker spreads which, in turn, will increases ECN spreads. Consequently, realized spreads on ECN trades may be positive, yet these trades will still be unprofitable for a market maker. 10

12 Hypothesis 4: The average realized spread, the average realized spread for market-maker trades, and the information content of market-maker trades are decreasing in ECN activity. Because realized spreads are lower on ECN trades than on market-maker trades (Hypothesis 3), an increase in ECN activity lowers the average realized spread. Because ECN trades are more informative than market-maker trades (Hypothesis 3), but the average information content of a trade is unaffected by ECN activity, increasing ECN activity reduces the average information content of market-maker trades. The dissipation of the quasi-rents on type-1 trades further reduces the realized spreads for market-maker trades. Before testing these predictions in Sections 5 and 6, we describe our data. 4 Data Our data set provides the details of all trades and quotes for all Nasdaq National Market (NNM) stocks during the normal market trading hours of 9:30a.m. and 4:00p.m. EST for the month of June These represent almost 50 million trades for about $1.5 trillion. The data are grouped into categories by stock, trade size, counter parties, trade initiator, and trading venue. Tradesize categories are defined as small (1,000 shares or less), medium (1,001 to 10,000 shares), and large (10,000 shares or more). Because some ECNs, e.g., Instinet and Bloomberg, have trading desks to facilitate large trades, large ECN trades are more like the upstairs market for NYSE trades (see Keim and Madhavan, 1996, and Madhavan and Cheng, 1997) and quite unlike the smaller anonymous ECN trades. Thus, we include the large trades for completeness, but focus less attention on them. The bulk of our analysis uses data obtained from detailed trade, quote, and clearing databases maintained by the NASD, Inc. The database identifies the unrounded trade price and volume, and includes a number of unique features that allow us to accurately classify trades. The buyer and seller for each trade (the counter parties) are identified as registered market makers for the traded stock or investors. The trading venue is defined as ECN if the trade is executed by an ECN, routed to an ECN for execution, or routed from an ECN for execution. The ECNs included in the analysis are Archipelago, Attain, Bloomberg TradeBook, Brass Utility LLC, Instinet, Island, Market XT, NexTrade, Redibook, and Strike Technologies LLC. All other trades are classified as market-maker 11

13 trades. 10 Trades are matched with quotes using execution times and the following algorithm that has been found to perform well for the Nasdaq market. For SelectNet, SOES, and ACES trades, we match the trade with the inside quote one second or more before the trade execution time. Because SelectNet, SOES, and ACES are electronic trading systems run by Nasdaq, the execution times are very reliable. For other trades, we match the trade with the inside quote three seconds or more before the trade report time. Trades are classified as buyer-initiated if the trade price was greater than the bid-ask midpoint and seller-initiated if the trade price was less than the bid-ask midpoint (Lee and Ready, 1991). Midpoint trades are evenly distributed between the buyer- and seller-initiated categories. For each category of trades, we have the following data items: the number of trades the total dollar volume the average effective half-spread the average realized half-spread the average quoted half-spread the average information content where the effective half-spread is defined as the absolute value of the transaction price minus the bid-ask midpoint at the time of the trade, the realized half-spread is the appropriately signed transaction price minus the bid-ask midpoint 5 minutes after the trade, the quoted half-spread is bid minus the ask divided by two, and the information content of the trade is the appropriately signed change in the bid-ask midpoint in the 5 minutes after the trade. 11 The spread and informationcontent variables are measured as a percentage of the bid-ask midpoint. There are over 4,000 stocks listed on the NNM. The highest-volume stocks are among the largest and most active stocks in the world, while many of the lowest-volume stocks have small 10 Clearing data is used to identify the parties to each trade, to determine whether an ECN was involved, and to correct for any missing data in the trade database. Additional details on this process are available from the authors. 11 Huang and Stoll (1996) use this measure for similar purposes and refer to it as the adverse selection component of the realized spread. 12

14 market capitalizations and trade infrequently. Because of the large heterogeneity across stocks, we analyze 4 separate dollar-volume categories. The volume categories are defined by ranking all stocks traded on the Nasdaq National Market by total dollar trading volume in June The high-volume category includes firms with dollar volume ranks from 1 to 200, the medium-volume category includes firms ranked from 201 to 1,000, the low-volume category includes firms ranked from 1,001 to 2,000, and the inactive category includes all remaining NNM firms. Because over 80 percent of Nasdaq trading volume is in the high-volume stocks, this category is of primary interest. The medium and low-volume categories are also of some interest. The inactive category is included primarily for completeness. Table 1 provides the following descriptive statistics for stocks by volume category: daily dollar trading volume, market capitalization, trade size, price, the standard deviation of daily returns (measured by the close-to-close bid-ask average), the time-weighted percentage quoted spread, the number of market makers, 12 and the percentage of volume traded on ECNs. Table 1: Descriptive Statistics by Dollar Volume Category. Volume categories are determined by ranking all firms traded on the Nasdaq National Market by total dollar trading volume in June The categories are defined as high volume (ranks 1 to 200), medium volume (ranks 201 to 1,000), low volume (ranks 1,001 to 2,000), and inactive (all remaining NNM firms). Descriptive statistics are calculated for each security and then averaged across securities. Data: All firms traded on the Nasdaq National Market in June Volume Categories High Medium Low Inactive All Daily Trading Volume ($million) Market Cap ($billion) Average Trade Size ($000) Share Price Daily Return Std Dev (%) Quoted Half-Spread (%) Number of Market Makers Dollar ECN Volume (%) 33% 26% 22% 15% 20% Not surprisingly the higher-volume categories have higher average market capitalization, trade size, and price, as well as lower quoted spreads and more market makers. The skewness of the distribution of trading volume is seen in the fact that over 80 percent of the total trading volume is 12 On a daily basis, we identify market makers in each security from the individual market participant quote data. Any firm that has at least one valid quote during the day is classified as a market maker in that security. 13

15 represented by the top 200 stocks. Volatility is higher in the higher-volume categories, a property of NNM stocks in and out of our sample period. The percentage of trading that occurs on ECNs is 20 percent for the average Nasdaq stock and increases with volume category, from 15 percent in the inactive category to 33 percent in the high-volume category. This increase is likely due to a liquidity externality (Mendelson, 1982). Because the inactive stocks trade infrequently, once or twice a day on average, we will limit our discussion of them. Our primary focus in this paper is the effect of ECNs on public orders that are demanding liquidity. We call these investor-initiated trades. Investor-initiated trades correspond directly with the liquidity demanders in our model, and the cost of these trades provides a natural metric for assessing market quality. We classify trades as investor-initiated or market-maker initiated using the tick test described above. A trade is classified as investor-initiated if the buyer is an investor and the trade is buyer-initiated, or if the seller is an investor and the trade is seller-initiated. All other trades are classified as market-maker-initiated. Table 2 reports the percentage of volume that is investor-initiated and market-maker-initiated by trading venue. Table 2: Percentage Dollar Trading Volume by Trade Initiator and Trading Venue. Trades are classified as ECN if either the buyer or seller submitted their order through an ECN. All other trades are classified as market-maker (MM) trades. The trade initiator is defined as the buyer if the transaction price is greater than the bid-ask midpoint, and the seller if the transaction price is less than the bid-ask midpoint. Data: All Nasdaq National Market trades in June Initiator Venue MM Investor All ECN 14.4% 21.0% 35.4% MM 22.9% 41.7% 64.6% All 37.3% 62.7% 100.0% Table 2 shows that approximately 63 percent of the total NNM dollar trading volume is investorinitiated. Of the 37 percent of Nasdaq volume that is initiated by market makers, slightly less than half represents market-maker-to-market-maker trading, and the rest is with investors. 13 About 35 percent of the total dollar trading in NNM stocks occurs on ECNs and the rest occurs with market makers. The fraction of investor-initiated ECN trades (59 percent) is slightly less than the fraction 13 Some investor-initiated trades will be misclassified when market makers offer price improvement beyond the bid-ask midpoint, or when market makers act as intermediaries in riskless principal trades. 14

16 of investor-initiated market-maker trades (65 percent). ECNs can be used to demand liquidity or to supply liquidity. Limit orders posted on an ECN are supplying liquidity and can be hit by orders originating on or off the ECN. Marketable limit orders submitted to an ECN are demanding liquidity. These orders are routed off of the ECN for execution when the ECN is not posting the best bid or ask. Although our data allow us to determine when an ECN was used in a trade, we cannot determine whether the ECN was demanding or supplying liquidity. 5 Characteristics of ECN and Market-Maker Trades The relative magnitude of trading with market makers and trading on ECNs depends on both the stock and trade characteristics. Table 3 provides the percentage of trading volume on ECNs by volume category and trade size for investor-initiated trades. As seen in Table 3, the percentage of ECN volume is increasing in the stocks total trading volume. For the high-dollar-volume category, 36.6 percent of the total dollar volume is traded on an ECN; for the inactive volume category, only 11.5percentoftotaldollarvolumeistradedonanECN. Table3: PercentageDollarVolumeonECNsbyVolumeCategoryandTradeSize. Trades are classified as ECN if either the buyer or seller submitted their order through an ECN. Volume categories are determined by ranking all firms traded on the Nasdaq National Market by total dollar trading volume in June The categories are defined as high volume (ranks 1 to 200), medium volume (ranks 201 to 1,000), low volume (ranks 1,001 to 2,000), and inactive (all remaining NNM firms). Trade-size categories are defined by the number of shares in the trade. Percentage dollar volume on ECNs is calculated stock by stock and then averaged across stocks. Data: All investor-initiated Nasdaq National Market trades in June Trade-Size Volume Categories Categories High Medium Low Inactive All 1-1, % 34.1% 23.0% 13.8% 44.9% 1,001-10, % 13.0% 13.2% 11.6% 19.4% 10, % 2.2% 1.8% 5.0% 2.4% All 36.6% 17.8% 14.7% 11.5% 33.5% Table 3 also shows that ECN trades are smaller, on average, than market maker trades. For the most active stocks, 51.3 percent of the volume from small trades (1,000 shares or less) occurs on 15

17 an ECN. This percentage declines to 21.5 percent for medium-size trades (1,001 to 10,000 shares), and to 2.4 percent for large trades (more than 10,000 shares). Some ECNs (Instinet, for example) facilitate the negotiation of large trades in a fashion similar to the upstairs market on the New York Stock Exchange. Because some large ECN trades are negotiated, they represent a trading process that is fundamentally different from the open limit order book that characterizes smaller ECN trades. 5.1 Effective Spreads Table 4 reports average percentage effective half-spreads for investor-initiated trades by volume category, trade size, and trading venue (ECN or market maker). Our model predicts that effective half-spreads will be lower for ECN trades than for market-maker trades (Hypothesis 1). The results in Table 4 indicate that this is generally true. ECN trades have lower effective half-spreads than market-maker trades in every volume category and every trade-size category except for small trades in the high-volume category. Table4: PercentageEffective Half-Spreads by Trading Venue, Volume Category, and Trade Size. The effective half-spread is defined as 100 times the absolute value of the transaction price minus the bid-ask midpoint divided by the bid-ask midpoint. Trades are classified as ECN if either the buyer or seller submitted their order through an ECN. All other trades are classified as market-maker (MM) trades. Volume categories are determined by ranking all Nasdaq National Market firms by total dollar trading volume in June The categories are defined as high volume (ranks 1 to 200), medium volume (ranks 201 to 1,000), low volume (ranks 1,001 to 2,000), and inactive (all remaining NNM firms). Trade-size categories are defined by the number of shares traded. The average effective half-spread is calculated stock by stock and then averaged across stocks. Data: All investor-initiated Nasdaq National Market trades in June Volume Categories and Venues Trade-Size High Medium Low Inactive All Categories MM ECN MM ECN MM ECN MM ECN MM ECN 1-1, ,001-10, , All Effective half-spreads for the high-volume Nasdaq stocks are quite small, averaging between 12 and 25 basis points depending on trade size and trading venue. Consequently, there is not much 16

18 room for large absolute differences in effective spreads between ECN and market-maker trades for these stocks. However, even for this high-volume category, the differences in effective spreads are economically material. For medium-size trades, the market-maker effective spreads are more than 50 percent larger than the ECN effective spreads. In the lower-volume categories, spreads widen and the absolute difference between ECN and market-maker effective spreads increases, although the percentage difference declines. An additional pattern that emerges from Table 4 is that for each volume category, the difference betweenecnandmarket-makereffective spreads increases with trade size. Due to both higher inventory costs and higher adverse selection costs, most microstructure models predict that larger trades with intermediaries will be more expensive than smaller trades. Finding a natural counter party for these trades on an ECN generates the greatest cost savings. The univariate statistics do not control for other characteristics of the trade or stock that affect bid-ask spreads. We control for these characteristics in Table 5 by regressing the percentage effectivehalf-spreadontrade-sizedummies,anecndummyinteractedwiththetrade-sizedummies, and stock characteristics including stock-return standard deviation, price per share, log market capitalization, log total dollar trading volume, and number of market makers. Regressions are reported separately for each volume category. To estimate these regressions, we aggregate the data described in Section 4 by trade-size category and trading venue, resulting in 6 observations per stock. 14 Since our data have a panel structure (a cross section of stocks with multiple observations per stock), the standard assumptions underlying ordinary-least-squares regressions are unlikely to be satisfied. In particular, the error terms from the regression are likely to be correlated across the observations for a given stock. We correct for this dependence with a variance components model. Consider the regression y ij = β 0 x ij + α i + ε ij where y ij is the average effective half-spread for stock i in (trade-size and trading-venue) category j, β is a vector of regression coefficients, x ij is a vector of exogenous variables including trade-size and trading-venue dummy variables and firm characteristics, and α i and ε ij are random 14 All of the regressions in this section are estimated using this categorized data. 17

19 disturbances where α i represents factors that vary across firms but are constant for a given firm and ε ij represents factors that differ across firms and across observations for a given firm. Thevariancecomponentsmodelcanbeestimatedusingafixed-effects or a random-effects regression. Under the assumption that α i and x ij are uncorrelated, the random-effects estimates aremoreefficient. However, the fixed-effects estimates are unbiased when α i and x ij are correlated, while the random-effects estimates are not. Hausman (1978) provides a chi-square test of this assumption that also serves as a specification test for correlated omitted variables. The Hausman test is based on the difference between the fixed-effects and random-effects coefficients. When this difference is large (in relation to their respective standard errors) it is likely that the model is misspecified. In our regressions, the fixed-effects and random-effects coefficients are nearly identical and the Hausman statistic is close to zero. Thus, we report coefficients from the more efficient random-effects regressions. However, statistical inferences from the fixed-effects regressions are the same. The variables of interest in these regressions are the ECN dummy interacted with the trade-size dummies (ECN1 through ECN3). The coefficients on these variables indicate the average difference in percentage effective half-spreads between ECN and market-maker trades, after controlling for trade size and other variables that have been shown to affect bid-ask spreads. The results in Table 5 confirm the univariate statistics in Table 4. For high-volume Nasdaq stocks, there is no significant difference in effective spreads between ECN and market-maker trades. ECNs have lower effective half-spreads than market makers for medium and large trades, however, and the difference is statistically significant and economically material. The 6.7 basis point and 9.0 basis point reduction in effective spreads for medium and large ECN trades, respectively, represent a discount of about 35 percent from the market-maker effective spread. In the medium-volume category, ECN trades have significantly lower effective spreads than market-maker trades for all trade sizes. ECN trades generally have lower average effective spreads for the low-volume and inactive categories as well. However, the difference is statistically significant only in the large trade-size category. 18

20 Table 5: Random-Effects Regressions of the Percentage Effective Half-Spread on ECN Indicators, Trade-Size Dummy Variables, and Firm Characteristics. The effective halfspread is defined as 100 times the absolute value of the transaction price minus the bid-ask midpoint divided by the bid-ask midpoint. Size1, Size2 and Size3 are trade-size dummy variables that take the value 1 for trade sizes 1,000 shares or less, 1,001 to 10,000 shares, and greater than 10,000 shares, respectively. ECN1, ECN2, and ECN3 are the trade-size dummies interacted with an ECN dummy. Volume categories are determined by ranking all Nasdaq National Market firms by total dollar trading volume in June The categories are defined as high volume (ranks 1 to 200), medium volume (ranks 201 to 1,000), low volume (ranks 1,001 to 2,000), and inactive (all remaining NNM firms). Trade-size categories are defined by the number of shares traded. Coefficient estimates are given with the corresponding t-statistics in parentheses beneath. Data: All investor-initiated Nasdaq National Market trades in June Independent Volume Category Variable High Medium Low Inactive ECN (.39) (-3.40) (-.10) (-.20) ECN (-11.11) (-9.48) (-1.23) (.77) ECN (-13.96) (-13.20) (-7.69) (-3.48) Size (5.99) (18.71) (23.83) (39.22) Size (6.49) (18.87) (23.65) (38.79) Size (6.91) (18.87) (24.29) (39.40) Sigma (4.07) (15.48) (17.13) (15.82) Price (-2.19) (-2.86) (-.54) (9.19) Log Mkt Cap (-3.54) (-4.28) (-9.34) (-7.53) Log $ Volume (-2.01) (-12.37) (-13.91) (-7.53) MM Count (-1.12) (-.01) (3.25) (3.04) 19

21 5.2 Quoted Spreads and Price Improvement The previous section documents that, other things being equal, effective spreads are lower for ECN trades than for market-maker trades. The lower effective spreads for ECN trades could result from either lower quoted spreads at the time of ECN trades, greater price improvement, 15 or some combinationofthetwo. Ourmodelpredictsthatthelower effective spreads for ECN trades can be explained by narrower quoted spreads at the time of ECN trades. We test this prediction in this section as well as measuring the price improvement on ECN and marker-maker trades. Table 6 reports random-effects regressions of the percentage quoted half-spread on trade-size dummies, an ECN dummy interacted with the trade-size dummies, and stock characteristics. Regressions are reported separately for each volume category. As with the effective-spread regressions, the variables of interest in these regressions are the ECN dummy interacted with the trade-size dummies (ECN1 through ECN3). The coefficients on these variables indicate the average difference in percentage quoted half-spreads at the time of ECN and market-maker trades, after controlling for trade size and other variables that have been shown to affect bid-ask spreads. As predicted, quoted spreads are narrower at the time of ECN trades than at the time of market-maker trades for every volume category and every trade-size category, and the differences are statistically significant at the 0.01 level. Quoted spreads for the most active Nasdaq stocks are quite small, averaging 10 or 11 basis points. Nevertheless, quoted spreads for these stocks are approximately 10 to 15 percent lower (between 1.2 and 1.9 basis points) when trades occur on ECNs than when trades occur with market makers. For the medium and low-volume stocks, the quoted spreads are larger, and thus the absolute differences are larger, but quoted spreads remain about 10 to 15 percent lower when ECN trades occur. 15 Price improvement is defined as the percentage quoted half-spread minus the percentage effective half-spread. 20