ابعاد کیفیت اجرا: شواهد تازه برای بازارهای سهام آمریکا

I analyze market-order execution quality using order-based data reported in accordance with Securities and Exchange Commission Rule 11Ac1-5. These data facilitate a comprehensive investigation of multiple dimensions of execution quality, including measures of costs and speed, for large samples of common stocks on Nasdaq and the NYSE. The evidence is consistent with competitive equity markets. Overall execution costs on Nasdaq exceed those on the NYSE, but orders execute faster. This relationship reverses for larger orders exceeding 1,999 shares. The apparent trade-off between costs and speed suggests that inferring execution quality from costs alone could be problematical. It also illustrates the need for models of trader behavior that can accommodate multiple dimensions of execution quality.

The economic importance of order execution quality in equity markets has generated substantial attention from financial economists. Yet, data limitations have largely confined analysis to a single dimension of execution quality, the out-of-pocket costs of completing an order, and have required approximate algorithms to estimate these costs.1 While costs are probably the single most important component of execution quality, the recent proliferation of alternative trading systems, automated trading algorithms, and online trading suggests that the speed of order executions is also important to traders. For example, Boehmer et al. (2004) find that markets generally receive more order flow when either execution costs decline or execution speed increases. (Blume, 2001, also argues that execution speed is important. He cites a May 2000 survey in which 58% of online traders rate speed as more important than a favorable price.) This study uses novel data that eliminate the need for approximations and allow a simultaneous analysis of two dimensions of execution quality, costs and speed. I compare market-order executions on the two dominant U.S. equity markets, the New York Stock Exchange and the Nasdaq Stock Market. Reports published in accordance with Securities and Exchange Commission (SEC) Rule 11Ac1-5 (Dash 5) allow a comparison based on actual orders. Since fall 2001, the rule has required U.S. market centers to report various standardized measures of execution quality for orders below 10,000 shares in nearly all publicly traded securities. Compared with the traditional approach of estimating execution costs from trade reports, order-based analysis does not require approximate algorithms to determine trade direction and the timing of benchmark quotes. Moreover, Rule 11Ac1-5 makes the average order execution speed, or the period between order receipt and execution, publicly available.2 The only other order-based study that compares execution quality on Nasdaq and the NYSE is SEC (2001).3 For orders below 5,000 shares during a five-day period in June 2000, it finds that NYSE execution costs are below Nasdaq costs (except for the smallest orders in the largest stocks, where the cost differential vanishes), but Nasdaq orders generally execute faster. I extend this analysis in two ways. First, I use a large sample of 2,001 common stocks and examine a longer period, from November 2001 through December 2003, which encompasses recent structural changes in equity markets. Second, I add orders between 5,000 and 9,999 shares and show that the SEC result is driven by small orders below 2,000 shares. These orders execute at lower cost on the NYSE, but substantially faster on Nasdaq. This result reverses for larger orders between 2,000 and 9,999 shares. These execute more cheaply on Nasdaq, but faster on the NYSE. Thus, the difference between execution costs on Nasdaq and the NYSE is inversely related to the difference in execution speed. Moreover, the difference in costs decreases monotonically with order size, while the difference in speed increases monotonically with order size. I show that this apparent trade-off between costs and speed is robust over time and insensitive to the econometric specification. I attribute these findings to differences in how informed traders submit orders. Because dealer systems are not anonymous, informed Nasdaq traders prefer to split their orders and submit them simultaneously to different execution venues. On the NYSE, splitting is more problematic because orders are executed sequentially. Therefore, informed traders prefer to submit large orders directly to the specialist. Because market makers have no incentive to offer better than quoted prices to informed traders, small orders on Nasdaq and large orders on the NYSE are generally executed against published quotes and public limit orders. As a result, these orders receive no price improvement and execution is relatively fast. In contrast, market makers can benefit from trading with uninformed orders (or, alternatively, from offering the uninformed order to a preferred client wishing to trade in the other direction). Because of price priority, this requires that they offer a better price than the published quote. Thus, large Nasdaq orders and small NYSE orders tend to execute at lower cost. Because improving the quote takes time, these orders also execute slower. I provide additional evidence that is consistent with the variation in order informativeness and price improvement implied by these arguments. Execution costs in U.S. equity markets have been a frequent academic research topic. Studies generally fall into one of two categories: analysis of the same stocks trading in different markets, or analysis of different stocks across markets. In the former approach, researchers study firms that either trade in multiple locations (see, for example, Lee, 1993; and Easley et al., 1996 and Easley et al., 1996) or have switched trading venues (see, for example, Christie and Huang, 1994; Barclay, 1997). In the latter approach, researchers match firms by characteristics that control for ex ante differences in execution costs (see, for example, Huang and Stoll, 1996; Bessembinder and Kaufman, 1997a and Bessembinder and Kaufman, 1997b; Bessembinder, 1999; SEC, 2001). Execution costs are typically estimated from publicly available trade-based data. The drawback is that order size, order direction, and order arrival time are not observable and must be estimated using approximation methods (see Bessembinder, 2003a, for a summary). Stock or period-specific systematic biases could affect the estimates, and comparisons across markets could be misleading if trade report delays differ across markets (see Bessembinder, 1999). Moreover, these studies were completed before substantial changes in the structure of U.S. equity markets. The move to decimalization was completed in April 2001. SEC Rule 11Ac1-5 was implemented between July and October 2001. Since January 2002, the NYSE has publicly displayed limit orders. Nasdaq's SuperMontage quotation and execution system was launched in October 2002, and the NYSE automated specialist quotations in March 2003. These structural changes could have had substantial effects on relative execution costs. Evidence presented by Bessembinder (1999) and Weston (2000), for example, shows that changes in the Nasdaq order-handling rules in 1997 narrowed execution cost differences between Nasdaq and the NYSE.4 Most of these studies show higher execution costs on Nasdaq than on the NYSE. To better compare my estimates with these results, I present aggregate estimates across all order sizes up to 9,999 shares. Executions are still significantly more costly (but faster) on Nasdaq. While execution costs decline on both markets during the 26-month sample period, I show that a significant difference persists throughout the period. These results suggest that recent changes in the structure of U.S. equity markets have not dramatically changed the relative costs of trading on Nasdaq and the NYSE. Execution speed is an important component of execution quality for professional traders but has been largely ignored in the academic literature. Other things equal, traders prefer faster executions for a variety of reasons. First, prices could be decision-relevant. Because they fluctuate over time, a longer wait impairs a trader's ability to react when price moves quickly. This is crucial for automated trading strategies if they include order submission or cancellation instructions that are conditional on the execution of another order. Second, slower execution could increase uncertainty about the execution price. While the realized execution price could be better or worse than the expected execution price, the increased uncertainty is undesirable for risk-averse traders. Alternatively, for orders that are not sent to automatic execution systems, a longer wait could make traders more apt to perceive adverse selection or front running to their disadvantage, whether justified or not. This would occur, for example, if executions are fast when prices move in the trader's favor and take longer when prices move against them. Third, traders could prefer faster resolution of uncertainty to slower resolution, even if prices are not decision-relevant and independent of their risk aversion. (Kreps and Porteus, 1978, analyze a dynamic choice model in which intertemporal preferences are distinct from risk aversion. Ross, 1989, shows how such a preference for early resolution can be reconciled with informationally efficient markets.) The trade-off between execution speed and out-of-pocket costs depends on individual trader characteristics and is difficult to assess without data on individual order submission strategies. Therefore, it is not possible to determine, say, what execution delay traders are willing to incur in exchange for a certain reduction in effective spreads. While no previous authors develop systematic evidence or a theoretical model of the relationship between execution cost and execution speed, Battalio et al. (2003) and Boehmer et al. (2004) show that both costs and speed are important. For a proprietary sample of small retail orders submitted in March 1999, Battalio, Hatch, and Jennings show that Trimark Securities Inc. executes orders at higher costs, but faster than the NYSE. They argue that additional dimensions of execution quality, beyond out-of-pocket execution costs, could be relevant in comparing different execution venues. Boehmer et al. analyze whether order-routing decisions in NYSE-listed securities depend on past execution quality. They find that a market center receives more order flow when either its reported execution cost declines or its execution speed increases. I go beyond these indicative results and provide systematic evidence, using orders submitted to all NYSE and Nasdaq market centers, of a negative relationship between costs and speed that is robust to different samples and methods. I also provide a rationale for this relationship that relies on differences across markets in how informed traders submit orders. The remainder of this paper is organized as follows. In Section 2, I discuss data sources and provide a detailed description of sample selection and empirical methodology. In Section 3, I first present results on execution quality differentials and show how they change with order size. After showing that the results are robust over time, I discuss institutional differences between Nasdaq and the NYSE that affect how informed traders submit their orders and how they are filled. I present additional evidence consistent with these arguments and discuss how they can explain the observed trade-off between execution costs and execution speed. Section 4 concludes.

I provide the first comprehensive analysis of market-order execution quality in the post-decimals environment, taking advantage of new order-based data made available through SEC Rule 11Ac1-5. The rule requires individual market centers to publish monthly reports that provide standardized measures of execution quality for orders below 10,000 shares. The sample period, November 2001 through December 2003, covers or follows several important changes relating to equity trading, including decimalization, the implementation of Rule 11Ac1-5, the public display of limit orders on the NYSE, the introduction of Nasdaq's SuperMontage quotation and execution system, and the automation of quotations on the NYSE. To assess differences in execution quality between Nasdaq and the NYSE, I use three different methodologies: (1) a matched-sample analysis, (2) a regression analysis using 227 matched pairs, and (3) a comprehensive regression analysis of 1,001 NYSE and 1,000 Nasdaq stocks. Both regression models use standard control variables to adjust for differences in ex ante execution quality. Throughout the analysis, the three approaches produce virtually identical results. Overall, market-order executions are significantly faster, but more expensive on Nasdaq in terms of effective spreads, whether measured in dollars or relative to share price. The cost differential cannot be explained by more informed order flow. I further show that the cost and speed differentials reverse for larger order sizes. Specifically, executions of orders exceeding 1,999 shares are cheaper on Nasdaq, but also slower than on the NYSE. In contrast, executions of smaller orders are cheaper, but slower on the NYSE. These results suggest a trade-off between execution costs and execution speed that changes with order size. Costs appear to be negatively related to speed in a systematic fashion that persists over time. This negative relationship affects the way researchers, regulators, and market professionals can measure and interpret execution quality and how issuers make listing decisions. While this trade-off is well understood conceptually, its mechanics remain unclear on both a theoretical and a practical level. Presumably, the trade-off depends on trader preferences, order characteristics, and market conditions. I rationalize the observed negative relationship between costs and speed based on institutional differences between Nasdaq and the NYSE. Large NYSE market orders are likely to come from informed traders. The alternatives, submitting a small order or delegating execution to a floor broker, are too slow if short-lived private information is involved. As a result, the specialist executes large NYSE market orders against published quotes or limit orders. In contrast, small NYSE orders are unlikely to be informed and the specialist has incentives to improve price. This takes longer but makes execution of small NYSE orders relatively cheap. On Nasdaq, large orders are uncommon on ECNs and dealer systems are not anonymous. Therefore, informed traders benefit from splitting their orders and submitting them simultaneously to several execution venues. This makes small Nasdaq orders relatively expensive and fast compared with small NYSE orders. Moreover, it allows uninformed traders to help market makers identify them by submitting large orders to dealer systems. Market makers have incentives to improve price on these orders, which slows execution speed but leads to lower execution costs compared with large NYSE orders. In the absence of publicly available data on the speed of order execution, researchers have traditionally suggested that lower out-of-pocket costs imply a higher-quality execution. Given the negative cost-speed relationship I have shown, and because slow execution is costly for many traders, this inference could have to be qualified. My analysis illustrates the importance of further research to explain trader preferences and competition between markets. Better understanding the trade-off between execution costs and speed would allow a more precise measurement of execution quality and provide valuable guidance for appropriate regulation and theoretical models of market design and trader behavior.