Quote-based competition, market share, and execution quality in NASDAQ-listed securities

Quote-based competition, market share, and execution quality in NASDAQ-listed securities

Quote-based competition, market share, and execution quality in NASDAQ-listed securities Kee H. Chung a,*, Chairat Chuwonganant b a State University of New York (SUNY) at Buffalo, Department of Finance and Managerial Economics, Buffalo, NY 14260, United States b Department of Finance, College of Business Administration, Kansas State University, Manhattan, KS 66506-0503, United States Received 22 June 2006; accepted 2 January 2007 Available online 29 April 2007 Abstract We show that competitive quotes help increase dealer market share on NASDAQ, despite the fact that a large proportion of order flow is preferenced.

We find that decimal pricing and the introduc- tion of new trading platforms such as SuperSOES and SuperMontage have significantly changed the effect of quote aggressiveness on dealer market share. In particular, decimal pricing reduces (increases) the price (size) elasticity, SuperSOES increases the size elasticity, and SuperMontage increases both the size and price elasticity of dealer market share. We also show that market centers provide greater price improvements and faster executions when they post competitive quotes.  2007 Elsevier B.V. All rights reserved.

JEL classification: G18; G19 Keywords: Dealer market share; Quote aggressiveness; Order preferencing; Decimalization; SuperSOES; SuperMontage 1. Introduction In this study we examine the extent to which dealer market share in NASDAQ securi- ties is related to quote aggressiveness and how the effect of quote aggressiveness on market 0378-4266/$ - see front matter  2007 Elsevier B.V. All rights reserved. doi:10.1016/j.jbankfin.2007.01.023 * Corresponding author. Tel.: +1 716 645 3262; fax: +1 716 645 3823. E-mail addresses: keechung@buffalo.edu (K.H. Chung), cchuwong@yahoo.com (C. Chuwonganant).

Journal of Banking & Finance 31 (2007) 2770–2795 www.elsevier.com/locate/jbf

share has been affected by changes in trading environments such as decimalization, SuperSOES, and SuperMontage. Whether the elasticity of market share with respect to quote aggressiveness varies with trading environments is an important question because the answer to the question can help devise better trading environments and platforms. Prior research offers limited evidence concerning the relation between quote aggressive- ness and market share. Only recently have studies begun to shed some light on the direct empirical link between quote aggressiveness and market share. Blume and Goldstein (1997) find that non-NYSE market makers attract more order flow for NYSE stocks when they post the best available quotes.

Bessembinder (2003a) finds substantial quote-based competition for order flow in NYSE-listed stocks. Bessembinder also shows that off-NYSE liquidity providers post aggressive quotes when they are willing to give better-than-normal executions. However, the results of NYSE-listed securities may not be directly relevant for NASDAQ-listed securities because the nature of order flow competition between NAS- DAQ dealers is different from that between trading venues for NYSE-listed stocks. Klock and McCormick (2002) examine the effect of quote aggressiveness on dealer market share and find a positive relation between the two variables.

However, their results are based on limited data (i.e., seven months in 1996) before the introduction of the new order handling rules in 1997. Consequently, whether their results hold in the post market reform environ- ment is unclear. Chung et al. (2006) analyze the relation between dealer market share and quote aggressiveness using the cross-sectional regression method that is similar to the one employed in the present study. Their study also uses limited data (i.e., November 2000 and June 2001) and it does not examine how the effect of quote aggressiveness on market share has been affected by changes in trading environments.

Goldstein et al. (2005) analyze inter-market competition in NASDAQ-listed securities during the second quarter of 2003 using a sample of 100 stocks. They show that ECNs are capable of competing with NASDAQ on quotes, while AMEX and Chicago use non-price methods to attract trades. The authors also find that trading venues attract more orders when they quote aggressively on both sides. In this study, we analyze how quote aggressiveness affects dealer market share during the five-year period from 1999 through 2003 using a large sample of NASDAQ stocks. In particular, we investigate whether changes in trading environments such as decimal pricing, SuperSOES, and SuperMontage can explain changes in the price and size elasticity of dealer market share over time.

Considering the academic and regulatory debates on how these new protocols and trading platforms can affect market quality and investor wel- fare, the results of our study would be of great interest to both regulatory authorities and the general public. To the extent that the incentive to quote aggressively is determined by whether aggressive quotes attract more order flow and this incentive ultimately determines the inside spreads (i.e., execution costs), it is important to understand how different trad- ing protocols and platforms have changed the price and size elasticity of market share. Our results show that aggressive quotes help increase dealer market share, despite the fact that a large proportion of NASDAQ order flow is preferenced.1 We find that decimal 1 The rule of best execution is also likely to reduce both the effect of quote aggressiveness on market share and the dealer’s incentive to quote aggressively.

In deciding how to execute orders, brokers and dealers have a duty to seek the best execution that is reasonably available for customers’ orders. Best execution requires dealers to execute customer orders at prices that are equal to or better than the National Best Bid or Offer (NBBO), regardless of their own quotes. Under this rule, execution quality may be similar between dealers who quote aggressively and dealers who do not. Consequently, brokers may not have a strong incentive to send orders only to those dealers who post competitive quotes.

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pricing and the introduction of new trading platforms such as SuperSOES and SuperMon- tage have significantly changed the effectiveness of aggressive quotes in raising dealer mar- ket share. In particular, decimal pricing reduces (increases) the price (size) elasticity, SuperSOES increases the size elasticity, and SuperMontage increases both the size and price elasticity of dealer market share. We also show that market centers provide greater price improvements and faster executions when they post competitive quotes.

The paper is organized as follows. Section 2 describes data sources and presents descrip- tive statistics. Section 3 examines whether quote aggressiveness varies with the dealer Herfin- dahl index and dealer type. Section 4 investigates how decimal pricing and the introduction of new trading platforms, such as SuperMontage and SuperSOES, have affected the price and size elasticity of dealer market share. Section 5 examines the relation between quote aggres- siveness and execution quality. Section 6 provides a brief summary and concluding remarks. 2. Data sources and sample characteristics We obtain trade, inside quote, and dealer quote data for this study from the NAS- TRAQ Trade and Quote Data.

We obtain data on the number of shares outstanding from the CRSP database. Our study sample consists of 2004 stocks listed on NASDAQ from January 1999 to December 2003. We omit the following quotes and trades to minimize data error: quotes if either the ask or bid price is non-positive; quotes if either the ask or bid size is non-positive; quotes if the bid-ask spread is greater than $5 or less than or equal to zero2 ; before-the-open and after-the-close trades and quotes; trades if the price or volume is non-positive; trade price, pt, if j(pt  pt  1)/pt  1j > 0.5; ask quote, at, if j(at  at  1)/at  1j > 0.5; and bid quote, bt, if j(bt  bt  1)/bt  1j > 0.5.

We calculate monthly values of the following variables for each stock (stock i in month t): share price as measured by the mean value of quote midpoints, PRICE(i,t); the average daily number of trades, NTRADE(i,t); average dollar trade size, TSIZE(i,t); return volatility as measured by the standard deviation of daily quote midpoint returns, VOLATILITY(i,t); and market capitalization as measured by the market value of equity, MVE(i,t). For each stock, we then calculate the mean value of each variable using monthly time-series observations. Finally, we calculate the mean, standard deviation, and select per- centile values of each variable using cross-sectional observations.

Panel A of Table 1 shows the results. The average share price and market capitalization are $16.43 and $1188 million. The average daily number of trades and trade size are 718.7 and $9418, respectively. The average standard deviation of daily quote midpoint returns is 0.0068. The total number of market makers in our study sample is 378 and, of those, 13 are institutional brokers, five are wirehouses, and five are wholesalers.3 There are two 2 Shkilko et al. (forthcoming) show that zero and negative spreads are not rare on NASDAQ. 3 In this study we do not treat electronic communications networks (ECNs) as market makers for two reasons.

First, there are significant differences between ECNs and dealers in their quote and trade behavior. Second, Rule 605 market quality reports of two ECNs (i.e., INCA and INET) were alleged to contain material errors. On October 18, 2005, the Securities and Exchange Commission (SEC) alleged that from June 2001 through May 2004, INCA and INET repeatedly published Rule 605 reports that contained inaccurate order execution quality information. Pursuant to their settlement with the SEC, INCA and INET paid $700000 in civil penalties ($350000 each) and agreed to institute remedial undertakings. In addition, INCA and INET agreed to retain a third party regulatory auditor to conduct a comprehensive regulatory audit of the Rule 605 compliance program of INCA and INET by the end of 2006.

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Table 1 Descriptive statistics Variable Mean Standard deviation Percentile 1 5 25 50 75 95 99 Panel A: Descriptive statistics for stock characteristics Share price ($) 16.43 18.62 1.41 2.30 5.88 12.99 21.59 41.68 62.84 Number of trades 718.70 3,129.33 3.50 4.82 15.17 66.85 323.34 2505.37 13927.10 Trade size ($) 9418 7270 1221 1829 4195 7833 12489 22740 32453 Return volatility 0.0068 0.0055 0.0007 0.0012 0.0030 0.0053 0.0090 0.0175 0.0245 Market value of equity ( in thousands) 1188043 11060000 4843 10321 37824 113640 386847 2739400 12793200 Panel B: Descriptive statistics for dealer quote aggressiveness and market share PTINS(i,j) 0.2232 0.1745 0.0550 0.0560 0.0868 0.1642 0.3083 0.5896 0.7823 PTINSA(i,j) 0.0591 0.0628 0.0300 0.0302 0.0305 0.0345 0.0578 0.1728 0.3412 RELQS(i,j) 0.7459 0.6524 0.0870 0.1556 0.3450 0.5959 0.9680 1.7756 2.9464 MS(i,j) 0.1175 0.1221 0.0301 0.0308 0.0414 0.0728 0.1398 0.3665 0.6300 Panel A shows the descriptive statistics for our study sample of 2004 NASDAQ stocks and Panel B shows the descriptive statistics for dealer quote aggressiveness and market share.

We calculate monthly values of the following variables for each stock (stock i in month t): share price as measured by the mean value of quote midpoints, the average daily number of trades, average dollar trade size, return volatility as measured by the standard deviation of daily quote midpoint returns, and market capitalization as measured by the market value of equity. For each stock, we then calculate the mean value of each variable using monthly time-series observations. Finally, we calculate each variable’s mean, standard deviation, and select percentile values using cross-sectional observations.

We measure dealer J’s price aggressiveness for stock i in month t by the percentage of dealer time at the inside, PTINS(i,j,t) and the percentage of dealer time at the inside alone, PTINSA(i,j,t). We measure size aggressiveness by the relative magnitude of a dealer’s quoted depth in relation to the average quoted depth of all dealers at the inside for a given stock, RELQS(i,j,t). Dealer J’s market share in stock i during month t, MS(i,j,t), is measured by V(i,j,t)/RjV(i,j,t), where V(i,j,t) is stock i’s volume accounted for by dealer j during month t. To calculate the descriptive statistics of these variables, we first calculate the mean value of PTINS(i,j,t), PTINSA(i,j,t), and RELQS(i,j,t) using monthly time-series observations of each variable.

We then calculate the mean, standard deviation, and select percentile values of each variable using dealer-stock observations.

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dimensions of quote aggressiveness: price and size. We measure price aggressiveness (of dealer j in stock i during month t) by both the percentage of dealer time at the inside market, PTINS(i,j,t) and the percentage of dealer time at the inside market alone, PTINSA(i,j,t). We obtain PTINS(i,j,t) by dividing dealer j’s time at the inside ask, inside bid, or both for stock i by the total trading hours in month t. Likewise, we obtain PTINSA(i,j,t) by dividing dealer j’s time at the inside ask alone, inside bid alone, or both for stock i by the total trading hours in month t.

We measure size aggressiveness by the relative magnitude of a dealer’s quoted depth in relation to the average quoted depth of all dealers at the inside for a given stock, RELQS(i,j,t).

Dealer J’s market share in stock i during month t, MS(i,j,t), is measured by V(i,j,t)/ RjV(i,j,t), where V(i,j,t) is stock i’s volume accounted for by dealer j during month t. Because NASDAQTrader.com maintains Market Participant (MP) Monthly Share Vol- ume data only for the most recent 12 months, we obtain the data that are required to cal- culate MS(i,j,t) directly from NASDAQ.4 NASDAQ obtains and compiles the share volume data from trade reports that are directly entered into NASDAQ Market Center Trade Reporting Service (ACT).5 Although there have been few changes in data construc- tion and format in recent years, they may not materially affect our results because we measure the price and size elasticity of dealer market share using monthly cross-sectional data.

To calculate the descriptive statistics of these variables, we first calculate the mean value of PTINS(i,j,t), PTINSA(i,j,t), RELQS(i,j,t), and MS(i,j,t) using monthly time-series observations of each variable. We then calculate the mean, standard deviation, and select percentile values of each variable using dealer-stock observations. Panel B of Table 1 shows the results. Dealers are at the inside market 22.3% of time on average, and they are alone at the inside market only 5.9% of time. These results are in line with the finding of previous studies that although dealers are required to quote on both sides, they tend to post competitive quotes (i.e., inside market quotes) on only one side.

Chan et al. (1995) show that NASDAQ dealers rarely (0.7% of the total trading time) post quotes that lie at both the inside bid and inside ask, except when the width of the inside spread is unusu- ally large. Chung and Zhao (2004) show that the percentage of time during which the mar- ket maker’s quotes are at either the inside bid or the inside ask is only 24.5%. Chung and 4 We thank Tim McCormick for providing the data. Although we exclude ECNs from the list of market makers, we measure dealer market share using all trades, including trades executed on ECNs. See Fink et al. (2006) for a detailed description of difficulties associated with measuring ECN market shares.

5 NASDAQ Market Center for NASDAQ Trading automatically reports trades to ACT and identifies the member that provides the liquidity as the reporting member. ECN trade reporting varies because each ECN has different policies on reporting obligation. There are two general policies that ECN employs: (1) ECN requires its NASD member subscribers to report trades executed in the ECN’s system, which means the subscriber is identified as the reporting member and credited with the trade; (2) ECN reports the trades executed in its system, identifies itself as the reporting member, and therefore, is credited with the volume.

These varying policies result in different volume credited to the market maker versus the ECN in the daily volume reports and Monthly Volume Summaries reports. Beginning with April 1, 2003 data, volume reported from all MPIDs assigned to a single NASDAQ broker/dealer organization is aggregated under a single MPID in the NASDAQ Broker/Dealer Data volume reports and monthly share volume reports. For example, if a NASDAQ broker/dealer firm is using a second or third MPID to facilitate SuperMontage order management, the volume activity reported for these related MPIDs is aggregated under the firm’s primary MPID.

Visit http://www.nasdaqtrader.com/trader/ tradingdata/generalvolumecalc.stm for a detailed description of NASDAQ trading volume data. 2774 K.H. Chung, C. Chuwonganant / Journal of Banking & Finance 31 (2007) 2770–2795

Zhao also show that market makers rarely (2.4% of time) post competitive prices on both sides of the quote. 3. Dealer Herfindahl-index and dealer quote aggressiveness Although prior research sheds light on whether the distribution of market share across dealers in a given stock (i.e., the Herfindahl-index of a stock) has an impact on dealer com- petition, there has been no prior research concerning whether the distribution of trading volume across stocks for a given dealer has any impact on dealer quotation behavior. We believe this is an interesting issue because dealers who focus on fewer stocks may not only post aggressive prices and sizes, but also be more effective in raising their market shares through those quotes.

To examine whether dealers who make markets in a small number of stocks tend to quote more aggressively than those who make markets in a large number of stocks, we calculate the Herfindahl-index of each dealer. The Herfindahl-index of dealer j is defined as: H-INDEX(j) = Ri[100V(i,j)/RiV(i,j)]2 , where V(i,j) is stock i’s dol- lar volume executed by dealer j. The dealer Herfindahl-index measures whether the deal- er’s trading is concentrated on a few stocks or dispersed across many stocks. A dealer with a high Herfindahl-index makes markets in a small number of stocks, whereas a dealer with a low Herfindahl-index makes markets in many stocks.

To shed light on whether dealers exhibit different quotation behaviors depending on whether they have a concentrated or dispersed market making business, we estimate the following regression model: QAðj; tÞ ¼ a0 þ a1H-INDEXðj; tÞ þ a2DUMIBðjÞ þ a3DUMWHðjÞ þ a4DUMWSðjÞ þ eðj; tÞ; ð1Þ where QA(j,t) denotes dealer J’s average quote aggressiveness (across stocks) measured by PTINS(j,t), PTINSA(j,t), and RELQS(j,t). H-INDEX(j,t) denotes the Herfindahl-index of dealer j. To control for dealer types, we include three indicator variables (DUMIB(j), DUMWH(j), and DUMWS(j)), which equal one for institutional brokers, wirehouses, and wholesalers, respectively, and zero otherwise.

We estimate the above model for each month and calculate the mean a coefficients across months and the z-statistics. We obtain the z-statistic by adding individual regres- sion t-statistics across months and then dividing the sum by the square root of the number of regression coefficients. The results (see Table 2) show that all three measures of quote aggressiveness (i.e., PTINS, PTINSA, and RELQS) are positively and significantly related the dealer Herfindahl-index after controlling for dealer types. This indicates that dealers who have a concentrated market making business quote more aggressively.

The results also show that wholesalers, wirehouses, and institutional brokers post more aggressive price (PTINS) and size quotes (RELQS) than regional firms. Among wholesalers, wire- houses, and institutional brokers, wholesalers quote most aggressively. 4. Quote aggressiveness and dealer market share In this section we first estimate the price and size elasticity of dealer market share with respect to quote aggressiveness using cross-sectional data in each month. We then examine whether aggressive price quotes are less effective in raising dealer market share when the tick size is smaller by comparing the price elasticity of dealer market share before and after K.H.

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decimalization. Similarly, we examine whether the introduction of new trading platforms, such as SuperMontage and SuperSOES, has affected the price and size elasticity of dealer market share. Boehmer et al. (2007) analyze whether market share is related to execution quality mea- sures reported under the Securities and Exchange Commission (SEC)’s Rule 605 (formerly Rule 11Ac1-5). While Boehmer, Jennings, and Wei examine whether market share is related to the post-trade execution quality measures (i.e., effective spreads and execution speeds), our study focuses on whether market share is related to the pre-trade market qual- ity measure (i.e., quote aggressiveness).

This distinction has important implications for research design because most market participants can easily observe pre-trade market quality when they make order routing decisions, whereas they can only find out post-trade execution quality with some time delay. Hence, a reasonable conjecture would be that there is a contemporaneous relation between market share and quote aggressiveness (as proposed in the present study), but a lagged one between market share and execution qual- ity (as shown in Boehmer et al., 2007).

4.1. Regression results We note that dealer quotes frequently reflect limit orders from customers rather than the dealer’s trading interest. Because our main focus is to examine whether aggressive quotes (regardless of their origins) lead to larger market share, it is unimportant to distin- guish whose interests are reflected in dealer quotes. Because our variable measurement interval (i.e., a month) is a reasonably long period, we conjecture that a dealer’s market Table 2 Effects of H-index and dealer types on quote aggressiveness PTINS(j,t) PTINSA(j,t) RELQS(j,t) Intercept 0.1139** 0.0192** 0.6971** (132.90) (69.90) (149.99) H-INDEX(j,t) 0.0255** 0.0046** 0.4699** (8.09) (4.53) (27.52) DUMIB(j) 0.0195** 0.0068** 0.0081** (5.15) (5.23) (3.35) DUMWH(j) 0.0352** 0.0071** 0.0932** (6.01) (3.64) (4.19) DUMWS(j) 0.1351** 0.0311** 0.2991** (23.87) (18.85) (5.17) Mean R2 0.052 0.040 0.058 Mean sample size 295 295 295 This table reports the results of the following regression model: QAðj; tÞ ¼ a0 þ a1H-INDEXðj; tÞ þ a2DUMIBðjÞ þ a3DUMWHðjÞ þ a4DUMWSðjÞ þ eðj; tÞ; where QA(j,t) denotes dealer J’s average quote aggressiveness (across stocks) measured by PTINS(j,t), PTIN- SA(j,t), and RELQS(j,t).

H-INDEX(j,t) denotes the Herfindahl-index of dealer j. To control for dealer types, we include three indicator variables (DUMIB(j), DUMWH(j), and DUMWS(j)), which equal one for institutional brokers, wirehouses, and wholesalers, respectively, and zero otherwise. We estimate the above model for each month and calculate the mean a coefficients across months and the z-statistics. We obtain the z-statistic by adding individual regression t-statistics across months and then dividing the sum by the square root of the number of regression coefficients. Numbers in parenthesis are z-statistics.

** Significant at the 1% level. 2776 K.H. Chung, C. Chuwonganant / Journal of Banking & Finance 31 (2007) 2770–2795

share in month t is determined largely by his quotes in month t, rather than by quotes dur- ing the previous months. We estimate the following regression models for each stock using the monthly cross-sec- tional (across dealers) data: logðMSði; j; tÞÞ ¼ k0it þ k1it logðPTINSði; j; tÞÞ þ k2it logðRELQSði; j; tÞÞ þ e1ði; j; tÞ ð2Þ and logðMSði; j; tÞÞ ¼ w0it þ w1it logðPTINSAði; j; tÞÞ þ w2it logðRELQSði; j; tÞÞ þ e2ði; j; tÞ: ð3Þ Our focus here is to look at how inter-dealer differences in market share for a given stock can be explained by inter-dealer differences in quote aggressiveness during each month.

Panel A of Table 3 reports the standard error-weighted mean values of regression coefficients and the t-statistics. The method assigns smaller weights to the regression coef- ficients that are less meaningful. Panel B presents the equal-weighted mean values of regression coefficients and the z-statistics.

Table 3 shows that a 1% increase in PTINS results in a 0.59–0.62% increase in dealer mar- ket share, depending on averaging methods. Similarly, a 1% increase in PTINSA results in a 0.56% increase in dealer market share. The table also shows that a 1% increase in RELQS results in a 0.41–0.56% increase in dealer market share, depending on model specifications or averaging methods. The t- and z-statistics indicate that the results are all statistically sig- nificant at the 1% level. These results indicate that dealers who quote aggressively have a lar- ger market share than dealers who quote less aggressively during a given month.

Overall, our results indicate that aggressive quotes help increase dealer market share, despite the fact that a large proportion of NASDAQ volume is either internalized or rou- ted through payment for order flow agreements.6 Our results are consistent with the find- ing of Bessembinder (2003a) that there is substantial quote-based competition for order flow in NYSE-listed stocks. Although orders are expected to execute at best available prices whether or not the order-receiving dealer is at the inside market, brokers send more orders to those dealers who post competitive quotes.

A possible explanation for this result is that dealers may provide greater price and/or size improvements as well as speedier executions when they post competitive quotes. 6 Order preferencing reduces the effect of quote aggressiveness on dealer market share. To the extent that preferenced orders are captive orders, they are less likely to be affected by quote aggressiveness than unpreferenced orders. Godek (1996) holds that order preferencing weakens the linkage between quote aggressiveness and order flow. Bloomfield and O’Hara (1998) show that order preferencing can significantly degrade market performance if preferenced orders are a large share of the market or are received by virtually all market makers.

Chung et al. (2004) show that a large proportion of order flow on NASDAQ is preferenced. They find wide variation in the percentage of internalized volumes across stocks, ranging from zero to almost 100%, with a mean value of around 25%. They also show that the non-inside volume accounts for more than 50% of the total volume during both the pre- and post-decimalization periods. Chung, Chuwonganant, and McCormick estimate that over 75% of NASDAQ trading volume is either internalized or routed via payment for order flow arrangements. Therefore, it is unclear whether aggressive quotes result in a larger market share on NASDAQ.

Benveniste et al. (1992), Easley et al. (1996), Battalio et al. (1997, 2001a,b), Securities and Exchange Commission (1997), Battalio and Holden (2001) and Peterson and Sirri (2003) also examine the effect of order preferencing on market quality.

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For instance, Chung et al. (2004) show that orders receive greater price improvements when they are executed by dealers who are at the inside market. Bessembinder (2003a) shows that off-NYSE liquidity providers post aggressive quotes when they are prepared to give better-than-normal trade executions, and order routing responds systematically to these quotes. In a similar vein, dealers on NASDAQ may also post competitive quotes when they are willing to give better-than-normal trade executions.

We perform empirical investigation of this issue later in the paper.

4.2. Decimal pricing and the price and size elasticity of dealer market share The NASDAQ Stock Market began its decimal test phase with 14 securities on March 12, 2001, followed by another 197 securities on March 26, 2001. All remaining NASDAQ securities converted to decimal trading on April 9, 2001. We conjecture that decimal pric- ing reduces the effect of price aggressiveness on dealer market share for at least two rea- sons. In general, the price priority rule becomes less consequential when tick size is smaller because traders can easily step in front of other traders without incurring a large cost (Harris, 1994, 1997).

As a result, the fact that the dealer is at the inside market is less likely to result in an increase in his market share during the post-decimal period (since Table 3 Elasticity of dealer market share with respect to price and size aggressiveness log(MS(i,j,t)) t-Statistic log(MS(i,j,t)) t-Statistic Panel A: Results based on the standard error-weighted mean value of regression coefficients log(PTINS(i,j,t)) 0.5939** 292.40 log(PTINSA(i,j,t)) 0.5581** 291.65 log(RELQS(i,j,t)) 0.5496** 156.58 0.4093** 113.66 log(MS(i,j,t)) z-Statistic log(MS(i,j,t)) z-Statistic Panel B: Results based on the equal-weighted mean value of regression coefficients log(PTINS(i,j,t)) 0.6223** 866.81 log(PTINSA(i,j,t)) 0.5605** 946.93 log(RELQS(i,j,t)) 0.5569** 282.89 0.4142** 213.02 This table reports the results of the following regression models: logðMSði; j; tÞÞ ¼ k0it þ k1it logðPTINSði; j; tÞÞ þ k2it logðRELQSði; j; tÞÞ þ e1ði; j; tÞ and logðMSði; j; tÞÞ ¼ w0it þ w1it logðPTINSAði; j; tÞÞ þ w2it logðRELQSði; j; tÞÞ þ e2ði; j; tÞ; where MS(i,j,t) is dealer J’s market share in stock i [i.e., V(i,j)/RjV(i,j), where V(i,j) is stock i’s volume accounted for by dealer j] for time t, PTINS(i,j,t) is the percentage of time during which dealer j is at the inside for stock i for time t, RELQS(i,j,t) is the ratio of the average depth of dealer J’s inside market quotes to the average depth of all dealers’ inside market quotes for stock i for time t, PTINSA(i,j,t) is the percentage of time during which dealer j is at the inside alone for stock i for time t, and e1(i,j,t) and e2(i,j,t) are the error terms.

We estimate the above regres- sion models for each stock using the monthly cross-sectional (across dealers) data. Panel A reports the standard error-weighted mean values of regression coefficients and the t-statistics, where the reciprocal of the standard error of the estimated coefficient is used as weight. The method assigns smaller weights to the regression coeffi- cients that are less meaningful (i.e., smaller t-statistics). Panel B shows the equal-weighted mean values of regres- sion coefficients and the z-statistics. We obtain the z-statistic by adding individual regression t-statistics and then dividing the sum by the square root of the number of regression coefficients.

** Significant at the 1% level. 2778 K.H. Chung, C. Chuwonganant / Journal of Banking & Finance 31 (2007) 2770–2795

other traders can more easily step in front of him), relative to the pre-decimal period. In addition, price elasticity is likely to be smaller after decimalization because there are fewer shares available at each price point under decimal pricing than under $1/16 pricing.7 To test this conjecture, we compare the price elasticity of dealer market share in the periods before and after decimalization. We consider the period from January 2001 to February 2001 as the pre-decimal period, and the period from May 2001 to June 2001 as the post-decimal period.8 We report the standard error-weighted and equal-weighted mean values of the price elasticity of market share in Panel A of Table 4.

The results show that the mean values of price elasticity during the post-decimal period are significantly smaller than the corre- sponding values during the pre-decimal period, regardless of whether we use PTINS or PTINSA as the measure of price aggressiveness. For example, the mean value of k1it esti- mates for the pre-decimal period is 0.7416 (0.7629), while the corresponding value for the post-decimal period is 0.6607 (0.6801) according to the standard error-weighted (equal- weighted) results. Similarly, the mean value of w1it estimates for the pre-decimal period is 0.6643 (0.6658), while the corresponding value for the post-decimal period is 0.6136 (0.611) according to the standard error-weighted (equal-weighted) results.

Overall, our results suggest that the effect of competitive price quotes on dealer market share is weaker under the smaller tick size scheme because such scheme reduces the importance of price priority and results in smaller guaranteed depths.9 Although decimal pricing reduced the price elasticity of market share, it increased the size elasticity of market share. Panel B of Table 4 shows that the size elasticity of market share after decimal pricing is significantly larger than the corresponding figure during the pre-decimal period. Bessembinder (2003b) finds that quotation sizes decreased dramati- cally after decimalization for stocks in all capitalization groups.

He finds that the mean NBBO quote size decreased by 26.1% (from 1031 shares to 762 shares), and the greatest percentage reductions in quote sizes are for large capitalization stocks. Hence, decimal pricing led to smaller displayed depths on average, and aggressive size quotes became more effective in raising market shares than under fractional pricing. This result should not be surprising because the extent to which traders wanted to buy or sell larger sizes than quoted sizes is probably greater under decimal pricing because of smaller quoted depths. Although the above results show that both the price and size elasticity of dealer market share differs significantly between the pre- and post-decimal periods, the different elasticity 7 See, e.g., Goldstein and Kavajecz (2000), Bacidore et al.

(2003), Bessembinder (2003b) and Chakravarty et al. (2004). In particular, Goldstein and Kavajecz (2000) investigate the impact of reducing the minimum tick size on the liquidity of the market. They analyze both spreads and depths for periods before and after the NYSE’s change from eighths to sixteenths. The authors find that depth declined throughout the entire limit order book after the change.

8 The SEC initially mandated Rule 605 from April 2001, which coincides with the timing of decimal pricing. Our results are not likely to be materially contaminated by this concurrent event because our study concerns the relation between market share and pre-trade market quality (i.e., quote aggressiveness), not post-trade execution quality. 9 There may be alternative interpretations of this result. For example, to the extent that large tick sizes can be viewed as restriction that is imposed on the marketplace, reducing tick sizes can be viewed as eliminating that restriction. When a restriction is eliminated, then the market should be more responsive to both prices and quantities.

Hence, decimalization could be expected to increase both price elasticity and size elasticity. From this perspective, our price elasticity result may be viewed as an empirical anomaly that runs counter to theory. We thank the referee for this point.

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Table 4 Comparison of the price and size elasticity before and after decimal pricing Standard error-weighted mean values Equal-weighted mean values Before After Difference (after  before) t-Statistica Before After Difference (after  before) t-Statistica Panel A: Comparison of the price elasticity of dealer market share before and after decimalization Price elasticity estimated from model (2): k1 0.7416 0.6607 0.0809** 10.83 0.7629 0.6801 0.0828** 9.54 Price elasticity estimated from model (3): w1 0.6643 0.6136 0.0507** 7.82 0.6658 0.6110 0.0548** 7.18 Panel B: Comparison of the size elasticity of dealer market share before and after decimalization Size elasticity estimated from model (2): k2 0.5786 0.6421 0.0635** 5.26 0.5780 0.6505 0.0720** 5.81 Size elasticity estimated from model (3): w2 0.4417 0.5575 0.1158** 8.18 0.4359 0.5609 0.1250** 7.66 Standard error-weighted regression results Non-weighted regression results Price elasticity Size elasticity Price elasticity Size elasticity k1 w1 k2 w2 k1 w1 k2 w2 Panel C: Regression coefficients on the indicator variable representing the post-decimal period Regression coefficient on POSTDECIMAL: b1 0.0890** 0.0688** 0.1064** 0.1187** 0.0815** 0.0709** 0.1093** 0.1317** (7.29) (8.77) (5.95) (9.50) (7.14) (8.64) (5.75) (9.37) R2 0.041 0.143 0.050 0.042 0.023 0.053 0.033 0.021 Number of observations 6424 6424 6424 6424 6424 6424 6424 6424 In this table, we compare the price and size elasticity of dealer market share before and after decimal pricing.

Panel A compares the price elasticity before and after decimalization. Panel B compares the size elasticity before and after decimalization. We consider the two-month period from January 2001 to February 2001 as the pre-decimal period and May 2001 to June 2001 as the post-decimal period. We report the standard error-weighted and equal-weighted mean values. We calculate the standard error-weighted mean values of elasticity estimates from the first- pass regression using the reciprocal of the standard error of the estimated coefficient as weight. The method assigns smaller weights to the first-pass regression coefficients that are less meaningful (i.e., smaller t-statistics).

Panel C shows the estimate of b1 from the following regression model: k1it; k2it; w1it; or w2it ¼ b0 þ b1POSTDECIMAL þ b2 logðPRICEði; tÞÞ þ b3 logðNTRADEði; tÞÞ þ b4 logðTSIZEði; tÞÞ þ b5VOLATILITYði; tÞ þ b6 logðMVEði; tÞÞ þ b7H-INDEXði; tÞ þ eði; tÞ; where k1it, k2it, w1it, and w2it are the estimated coefficients from the first-pass regressions (i.e., regression models (2) and (3)), POSTDECIMAL is an indicator variable which equals one for the post-decimal period and zero otherwise, PRICE(i,t) is the average quote midpoint of stock i in month t, NTRADE(i,t) is the average daily number of transactions of stock i in month t, TSIZE(i,t) is the average dollar trade size of stock i in month t, VOLATILITY(i,t) is the standard deviation of stock i’s daily quote midpoint returns in month t, MVE(i,t) is the market value of equity of stock i in month t, H-INDEX(i,t) is the Herfindahl-index of stock i in month t, and e(i,t) is the error term.

We show the results of the weighted and non-weighted regres- sions. In the weighted regression, we use the reciprocal of standard error from the first-pass regression as weight. Numbers in parenthesis are the t-statistics. a The t-statistic testing the equality of mean values between before and after periods. ** Significant at the 1% level.

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estimates between the two periods may result from changes in stock attributes rather than decimal pricing per se. To examine this possibility, we estimate the following regression model: k1it; k2it; w1it; or w2it ¼ b0 þ b1 POSTDECIMAL þ b2 logðPRICEði; tÞÞ þ b3 logðNTRADEði; tÞÞ þ b4 logðTSIZEði; tÞÞ þ b5VOLATILITYði; tÞ þ b6 logðMVEði; tÞÞ þ b7H-INDEXði; tÞ þ eði; tÞ; ð4Þ where k1it, k2it, w1it, and w2it are the estimated coefficients from the first-pass regressions (i.e., regression models (2) and (3)), POSTDECIMAL is an indicator variable which equals one for the post-decimal period and zero otherwise, PRICE(i,t) is the average quote midpoint of stock i in month t, NTRADE(i,t) is the average daily number of transactions of stock i in month t, TSIZE(i,t) is the average dollar trade size of stock i in month t, VOLATILITY(i,t) is the standard deviation of stock i’s daily quote midpoint returns in month t, MVE(i,t) is the market value of equity of stock i in month t, H-INDEX(i,t) is the Herfindahl-index of stock i in month t, and e(i,t) is the error term.

Panel C of Table 4 shows the estimates of b1 from the weighted and non-weighted regres- sions. In the weighted regression, we use the reciprocal of standard error from the first-pass regression as weight. This method assigns smaller weights to the less meaningful first-pass regression coefficients (i.e., smaller t-statistics). In contrast, in the non-weighted regression, the estimated coefficients from the first-pass regressions have the same weight, regardless of their statistical significance. We employ both approaches to assess the robustness of our results to different estimation methods. Numbers in parenthesis are the t-statistics.

The first four columns show the results of the weighted regressions and the next four columns show the results of the non-weighted regressions. The results show that the esti- mated coefficients on POSTDECIMAL are significantly negative in the price elasticity regression (i.e., when the dependent variable is k1it or w1it) and significantly positive in the size elasticity regression (i.e., when the dependent variable is k2it or w2it) from both the weighted and non-weighted regressions. These results indicate that the observed changes in both the price and size elasticity shown in Panel A and B cannot be attributed to concurrent changes in stock attributes.

To shed additional light on the effect of decimalization, consider a dealer firm that increases its size quote aggressiveness (RELQS) from 0.3450 to 0.9680, which corresponds to a move from the 25th percentile position to the 75th percentile position (see Panel B of Table 1) among dealers in our sample. Let us also assume that the dealer’s initial market share was 0.05. Panel B of Table 4 shows that the mean size elasticity estimated from regression model (3) is 0.4417 and 0.5575, respectively, during the pre- and post-decimal period. These figures indicate that the dealer’s market share during the pre-decimal period would increase by 0.0399 [=0.4417 · {(0.9680  0.3450)/0.3450} · 0.05] with more aggres- sive size quotes.

This is equivalent to a 79.8% (=0.0399/0.05) increase in market share and the dealer’s new market share would be 0.0999 (=0.05 + 0.0399). In contrast, the dealer’s market share during the post-decimal period would increase by 0.0503 [=0.5575 · {(0.9680  0.3450)/0.3450} · 0.05] to 0.1003 (=0.05 + 0.0503) with more aggressive size quotes. In relative term, this is equivalent to a 100.6% (=0.0503/0.05) increase in market share, which is 20.8% (=100.6%  79.8%) greater than the corresponding figure for the pre-decimal period.

K.H. Chung, C. Chuwonganant / Journal of Banking & Finance 31 (2007) 2770–2795 2781

4.3. SuperSOES and the size elasticity of dealer market share The NASDAQ Stock Market implemented the final phase of its automated order exe- cution system, SuperSOES, on July 30, 2001. From this date forward, orders for all 3500+ NASDAQ National Market (NNM) securities were automatically executed using Super- SOES.10 SuperSOES is a revised version of the Small Order Execution System (SOES), NASDAQ’s auto-execution system for small agency orders. SuperSOES accepted orders of up to 999999 shares at a time, allowing traders to execute large orders in a single trans- action.

SuperSOES also provided a more robust trading platform that could handle increased trading volumes and reduced dual liability for market makers, encouraging the display and execution of larger orders.

SuperSOES was a significant change in how market participants interact with NAS- DAQ to obtain quotes and execute trades. SuperSOES made accessing liquidity more effi- cient, allowing participants to access large blocks of shares automatically in one order. Previously, numerous trades had been required to achieve the same result. SuperSOES could access several quotes with one order as well as access more size (up to 999999 shares), whereas SelectNet accessed one participant per order and the prior SOES share limit was 1000 shares for automatic execution. Thus, SuperSOES significantly reduced the number of orders for a given volume of activity.

Based on these considerations, we conjecture that thesize elasticity of market share after the implementation of SuperSOES is greater than the corresponding figure before the implementation.11 Panel A of Table 5 shows the size elasticity of dealer market share in the periods before and after SuperSOES implementation. We consider the two-month period from May 2001 to June 2001 as the pre-SuperSOES period and August 2001 to September 2001 as the post-SuperSOES period. According to both the standard error- weighted and equal-weighted results, the mean values of size elasticity during the post- SuperSOES period are significantly greater than the corresponding values during the pre-SuperSOES period, regardless of different model specifications.

These results are con- sistent with our expectation that the introduction of SuperSOES increased the impact of size aggressiveness on dealer market share.

To assess the economic significance of SuperSOES, consider a dealer firm that increases its size quote aggressiveness (RELQS) from 0.3450 to 0.9680. Let us also assume that the dealer’s initial market share was 0.05. Panel A of Table 5 shows that the mean size elas- ticity estimated from regression model (2) is 0.6421 and 0.9834, respectively, during the pre- and post-SuperSOES period. These figures indicate that the dealer’s market share during the pre-SuperSOES period would increase by 0.058 [=0.6421 · {(0.9680  0.3450)/0.3450} · 0.05] with more aggressive size quotes. This is equivalent to a 116% (=0.058/0.05) increase in market share and the dealer’s new market share would be 0.108 (=0.05 + 0.058).

In contrast, the dealer’s market share during the post-SuperSOES 10 The first pilot in the conversion to the SuperSOES trading system was launched on Monday, July 9, 2001, when NASDAQ implemented 18 pilot SuperSOES securities and two test stocks. The second pilot conversion of additional 80 NASDAQ stocks was launched on July 16, 2001.

11 The main difference between SOES and SuperSOES is that the latter allowed market participants to access large blocks of shares automatically in one order, compared with the pre-SuperSOES environment. Hence, we do not examine the effect of SuperSOES on price elasticity. 2782 K.H. Chung, C. Chuwonganant / Journal of Banking & Finance 31 (2007) 2770–2795

Table 5 Comparison of the size elasticity before and after SuperSOES implementation Standard error-weighted mean values Equal-weighted mean values Before After Difference (after  before) t-Statistica Before After Difference (after  before) t-Statistica Panel A.

Comparison of the size elasticity of dealer market share before and after the implementation of SuperSOES Size elasticity estimated from model (2): k2 0.6421 0.9834 0.3413** 13.97 0.6505 0.9822 0.3317** 14.06 Size elasticity estimated from model (3): w2 0.5575 0.7551 0.1976** 7.58 0.5609 0.7774 0.2165** 8.68 Panel B. Comparison of the size elasticity of dealer market share before and after Island’s departure from NASDAQ Size elasticity estimated from model (2): k2 0.5716 0.4057 0.1659** 7.47 0.5761 0.4119 0.1642** 9.02 Size elasticity estimated from model (3): w2 0.3785 0.2059 0.1726** 13.21 0.3961 0.2222 0.1739** 11.54 Standard error-weighted regression results Non-weighted regression results Size elasticity Size elasticity k2 w2 k2 w2 Panel C: Regression coefficients on the indicator variable representing the post-SuperSOES period Regression coefficient on POSTSUPERSOES: b1 0.3483** 0.2050** 0.3388** 0.2246** (15.26) (9.51) (13.63) (9.60) R2 0.099 0.069 0.061 0.044 Number of observations 6424 6424 6424 6424 Panel D: Regression coefficients on the indicator variable representing Island’s departure from NASDAQ Regression coefficient on POSTISLAND: b1 0.1684** 0.1687 0.1646** 0.1698 (9.28) (10.32) (8.29) (9.43) R2 0.009 0.021 0.006 0.013 Number of observations 6424 6424 6424 6424 Panel A compares the size elasticity of dealer market share in the periods before and after SuperSOES imple- mentation.

We consider the two-month period from May 2001 to June 2001 as the period before SuperSOES and August 2001 to September 2001 as the period after SuperSOES. Panel B compares the size elasticity of dealer market share before and after Island ECN’s departure from NASDAQ in August 2002. We consider the two- month period from June 2002 to July 2002 as the pre-Island departure period and August 2002 to September 2002 as the post-Island departure period. We report the standard error-weighted and equal-weighted mean values. We calculate the standard error-weighted mean values of elasticity estimates from the first-pass regression using the reciprocal of the standard error of the estimated coefficient as weight.

The method assigns smaller weights to the first-pass regression coefficients that are less meaningful (i.e., smaller t-statistics). Panels C and D show the estimates of b1 from the following regression model: k2it or w2it ¼ b0 þ b1POSTSUPERSOESðor POSTISLANDÞ þ b2 logðPRICEði; tÞÞ þ b3 logðNTRADEði; tÞÞ þ b4 logðTSIZEði; tÞÞ þ b5VOLATILITYði; tÞ þ b6 logðMVEði; tÞÞ þ b7H-INDEXði; tÞ þ eði; tÞ; where k2it and w2it are the estimated coefficients from the first-pass regressions (i.e., regression models (2) and (3)), POSTSUPERSOES is an indicator variable representing the post-SuperSOES period and POSTISLAND is an (continued on next page) K.H.

Chung, C. Chuwonganant / Journal of Banking & Finance 31 (2007) 2770–2795 2783

period would increase by 0.0888 [=0.9834 · {(0.9680  0.3450)/0.3450} · 0.05] to 0.1388 (=0.05 + 0.0888) with more aggressive size quotes. In relative term, this is equivalent to a 178% (=0.0888/0.05) increase in market share, which is 62% (=178%  116%) greater than the corresponding figure for the pre-SuperSOES period. The Island ECN left NASDAQ in early August 2002 and started posting their quotes on the Cincinnati Stock Exchange. This fragmented the markets because market partici- pants could not access the Cincinnati quotes (Island) through SuperSOES. Island traders were the most aggressive and active traders, so losing these hyperactive traders could have lowered dealers’ incentives to post quotes.

More importantly, the absence of these hyper- active traders would diminish the effect of size quotes on market share.12 To examine whether the market segmentation prompted by the departure of Island from NASDAQ led to a decrease in the size elasticity of market share, we compare the mean size elasticity before and after the market segmentation. We consider the two-month period from June 2002 to July 2002 as the period before market segmentation, and August 2002 to September 2002 as the period after market segmentation. Panel B of Table 5 shows the results. The mean size elasticity after the market segmentation is significantly smaller than the corresponding figure before the market segmentation.

These results are not sur- prising, given the large market share of Island and Instinet in NASDAQ securities and support our conjecture that the effectiveness of size quotes in raising market share is weaker in segmented markets.

To examine whether the different size elasticity estimates between the pre- and post- SuperSOES periods indeed result from SuperSOES rather than from concurrent changes in stock attributes, we estimate the following regression model: k2it or w22it ¼ b0 þ b1POSTSUPERSOES þ b2 logðPRICEði; tÞÞ þ b3 logðNTRADEði; tÞÞ þ b4 logðTSIZEði; tÞÞ þ b5VOLATILITYði; tÞ þ b6 logðMVEði; tÞÞ þ b7H-INDEXði; tÞ þ eði; tÞ; ð5Þ where POSTSUPERSOES is an indicator variable which equals one for the post-Super- SOES period and zero otherwise, and all other variables are the same as previously de- Table 5 (continued) indicator variable representing the post-Island departure period, PRICE(i,t) is the average quote midpoint of stock i in month t, NTRADE(i,t) is the average daily number of transactions of stock i in month t, TSIZE(i,t) is the average dollar trade size of stock i in month t, VOLATILITY(i,t) is the standard deviation of stock i’s daily quote midpoint returns in month t, MVE(i,t) is the market value of equity of stock i in month t, H-INDEX(i,t) is the Herfindahl-index of stock i in month t, and e(i,t) is the error term.

We show the results of the weighted and non-weighted regressions. In the weighted regression, we use the reciprocal of standard error from the first-pass regression as weight. Numbers in parenthesis are the t-statistics.

a The t-statistic testing the equality of mean values between before and after periods. ** Significant at the 1% level. 12 On September 23, 2002, the SEC required Island to comply with Regulation ATS’ display requirements in several exchange-traded funds (ETFs). Instead of participating in existing linkages, Island stopped displaying its automated limit order book. Herdershott and Jones (2005) show that when Island stopped displaying the book, the ETF market worsened in terms of price discovery and trading costs.

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fined. Panel C of Table 5 shows that the estimates of b1 are significantly positive, indicat- ing that the greater size elasticity during the post-SuperSOES period is not due to concur- rent changes in stock attributes. When we replicate the above regression analysis with a dummy variable representing the post-Island departure period, we find (see Panel D) that the estimates of b1 are significantly negative, indicating that the size elasticity of dealer market share declined significantly after Island’s departure from NASDAQ, after control- ling for the effects of changes in stock attributes.

4.4.

SuperMontage and the price and size elasticity of dealer market share NASDAQ completed the rollout of SuperMontage on December 2, 2002, transitioning the last of its 3950 stocks onto its advanced order display and execution system. Super- Montage is a fully integrated order display and execution system for the trading of NAS- DAQ-listed securities. The trading platform enables investors to get better prices and helps brokers better serve their customers by providing more transparency. The launch of SuperMontage was a significant development in the evolution of trading NASDAQ securities. SuperMontage provides the ability to list multiple quotes and orders for the same security and creates a central trading book.

Furthermore, SuperMontage enables market makers to input all or part of their buy or sell interest, by name or anon- ymously. These buy and sell interests are available to view at five price levels, not just the best price. Since the implementation of decimalization, there are fewer shares available at each price point. However, SuperMontage makes it easier for market participants to access depth of trading interest because of the system’s five levels of displayed depth. The NASDAQ Stock Market President Rick Ketchum made the following comments on the Securities and Exchange Commission’s Report on the Comparison of Order Exe- cutions Across Equity Market Structures: ‘‘Nasdaq’s proposed SuperMontage market structure will lead directly to better exe- cution quality for investors by providing improved access to available liquidity (automatic execution); greater transparency and depth, making more liquidity visible and available to all users; and a stronger center of gravity, greatly enhancing order interaction.

Combined with greater quote competition, SuperMontage will provide better prices for investors’’.

To examine how SuperMontage affected the price and size elasticity of dealer market share, we compare the price and size elasticity of dealer market share in the periods before and after the introduction of SuperMontage. We consider the two-month period from August 2002 to September 2002 as the pre-SuperMontage period and December 2002 to January 2003 as the post-SuperMontage period.13 Panel A of Table 6 shows that the mean values of price elasticity during the post-Super- Montage period are significantly greater than the corresponding values during the pre- SuperMontage period, regardless of whether we use PTINS or PTINSA as the measure of price aggressiveness.

Similarly, Panel B shows that the size elasticity during the post- SuperMontage period is significantly greater than the corresponding value during the 13 We define August 2002 to September 2002 as the pre-SuperMontage period because NASDAQ began implementing SuperMontage on October 14, 2002 with five securities. K.H. Chung, C. Chuwonganant / Journal of Banking & Finance 31 (2007) 2770–2795 2785

Table 6 Comparison of the price and size elasticity before and after SuperMontage implementation Standard error-weighted mean values Equal-weighted mean values Before After Difference (after  before) t-Statistica Before After Difference (after  before) t-Statistica Panel A: Comparison of the price elasticity of dealer market share before and after the introduction of SuperMontage Price elasticity estimated from model (2): k1 0.4984 0.5630 0.0646** 10.15 0.4961 0.5615 0.0654** 8.94 Price elasticity estimated from model (3): w1 0.6082 0.6466 0.0384** 7.01 0.5899 0.6308 0.0409** 5.58 Panel B: Comparison of the size elasticity of dealer market share before and after the introduction of SuperMontage Size elasticity estimated from model (2): k2 0.4057 0.4828 0.0771** 7.84 0.4119 0.4938 0.0819** 6.93 Size elasticity estimated from model (3): w2 0.2059 0.2524 0.0465** 5.89 0.2222 0.2709 0.0487** 4.74 Standard error-weighted regression results Non-weighted regression results Price elasticity Size elasticity Price elasticity Size elasticity k1 w1 k2 w2 k1 w1 k2 w2 Panel C: Regression coefficients on the indicator variable representing the post-SuperMontage period Regression coefficient on 0.0655** 0.0304** 0.0898** 0.0611** 0.0655** 0.0269** 0.0981** 0.0698** POSTSUPERMONTAGE: b1 (8.38) (6.31) (5.57) (5.48) (7.79) (5.66) (5.63) (5.57) R2 0.054 0.291 0.022 0.049 0.031 0.146 0.013 0.031 Number of observations 6424 6424 6424 6424 6424 6424 6424 6424 Panels A and B compare the price and size elasticity of dealer market share before and after the introduction of SuperMontage.

We consider the two-month period from August 2002 to September 2002 as the period before SuperMontage and December 2002 to January 2003 as the period after SuperMontage. We report the standard error-weighted and equal-weighted mean values. We calculate the standard error-weighted mean values of elasticity estimates from the first-pass regression using the reciprocal of the standard error of the estimated coefficient as weight. The method assigns smaller weights to the first-pass regression coefficients that are less meaningful (i.e., smaller t-statistics). Panel C shows the estimate of b1 from the following regression model: k1it; k2it; w1it; or w2it ¼ b0 þ b1POSTSUPERMONTAGE þ b2 logðPRICEði; tÞÞ þ b3 logðNTRADEði; tÞÞ þ b4 logðTSIZEði; tÞÞ þ b5VOLATILITYði; tÞ þ b6 logðMVEði; tÞÞ þ b7H-INDEXði; tÞ þ eði; tÞ; where k1it, k2it, w1it, and w2it are the estimated coefficients from the first-pass regressions (i.e., regression models (2) and (3)), POSTSUPERMONTAGE is an indicator variable which equals one for the post-SuperMontage period and zero otherwise, PRICE(i,t) is the average quote midpoint of stock i in month t, NTRADE(i,t) is the average daily number of transactions of stock i in month t, TSIZE(i,t) is the average dollar trade size of stock i in month t, VOLATILITY(i,t) is the standard deviation of stock i’s daily quote midpoint returns in month t, MVE(i,t) is the market value of equity of stock i in month t, H-INDEX(i,t) is the Herfindahl-index of stock i in month t, and e(i,t) is the error term.

We show the results of the weighted and non-weighted regressions. In the weighted regression, we use the reciprocal of standard error from the first-pass regression as weight. Numbers in parenthesis are the t-statistics. a The t-statistic testing the equality of mean values between before and after periods. ** Significant at the 1% level.

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pre-SuperMontage period. These results suggest that the effect of competitive quotes on dealer market share has increased after the introduction of SuperMontage. Consider a dealer firm that increases its price quote aggressiveness (PTINS) from 0.0868 to 0.3083, which corresponds to a move from the 25th percentile position to the 75th per- centile position (see Panel B of Table 1). Let us also assume that the dealer’s initial market share was 0.04. Panel A of Table 6 shows that the mean size elasticity estimated from regression model (2) is 0.4984 and 0.5630, respectively, during the pre- and post-Super- Montage period.

Hence the dealer’s market share during the pre-SuperMontage period would increase by 0.0509 [=0.4984 · {(0.3083  0.0868)/0.0868} · 0.04] with more aggres- sive price quotes. This is equivalent to a 127% (=0.0509/0.04) increase in market share and the dealer’s new market share would be 0.0909 (=0.04 + 0.0509). In contrast, the dealer’s market share during the post-SuperMonatage period would increase by 0.0574 [=0.5630 · {(0.3083  0.0868)/(0.0868} · 0.04)] to 0.0974 (=0.04 + 0.0574) with more aggressive price quotes. In relative term, this is equivalent to a 143% (=0.0574/0.04) increase in market share, which is 16% (=143%  127%) greater than the corresponding figure for the pre-SuperMontage period.

To examine whether the changes in price and size elasticity are driven by the differences in stock attributes between the pre- and post-SuperMontage periods, we estimate the fol- lowing regression model: k1it; k2it; w1it; or w2it ¼ b0 þ b1POSTSUPERMONTAGE þ b2 logðPRICEði; tÞÞ þ b3 logðNTRADEði; tÞÞ þ b4 logðTSIZEði; tÞÞ þ b5VOLATILITYði; tÞ þ b6 logðMVEði; tÞÞ þ b7H-INDEXði; tÞ þ eði; tÞ; ð6Þ where POSTSUPERMONTAGE is an indicator variable which equals one for the post-SuperMontage period and zero otherwise, and all other variables are the same as pre- viously defined. Panel C of Table 6 shows that the estimates of b1 are all significantly positive, indicating that the increases in the size and price elasticity are not due to con- current changes in stock attributes.

On the whole, our results suggest that the implementation of SuperMontage signifi- cantly increased the value of competitive price and size quotes in attracting order flow. To the extent that the larger price and size elasticity of market share gives dealers greater incentives to post competitive quotes, our results indicate that the implementation of SuperMontage will most likely promote greater competition among market participants, resulting in narrower spreads and larger depths. 5. Quote aggressiveness and execution quality We show in Section 4.1 that brokers send more orders to dealers who post competitive quotes despite the fact that orders are expected to execute at best available prices, whether or not the order-receiving dealer is at the inside market.

A possible explanation is that dealers who quote competitively may provide larger price improvements and/or faster exe- cutions. In this section, we analyze the relation between quote aggressiveness and various measures of execution quality.

The SEC adopted Rule 605 on November 15, 2000 to improve public disclosure of exe- cution quality. Rule 605 requires market centers to make monthly electronic disclosure of K.H. Chung, C. Chuwonganant / Journal of Banking & Finance 31 (2007) 2770–2795 2787

basic information regarding execution quality on a stock-by-stock basis.14 To facilitate comparison across market centers, the rule adopts basic measures of execution quality (such as the number of shares cancelled prior to execution, execution speed, and realized spreads) and sets forth specific instructions on how they are to be calculated.

For each security, the statistical information is categorized by five order types (i.e., market orders, marketable limit orders, inside-the-quote limit orders, at-the-quote limit orders, and near- the-quote limit orders) and four order size groups (i.e., 100–499 shares, 500–1999 shares, 2000–4999 shares, and 5000–9999 shares).15 For market orders and marketable limit orders, the rule requires market centers to report effective spreads, as well as various sta- tistics related to price improvement.16 The SEC requires all market centers to post monthly order-execution reports on their websites under designated file names.17 Although the original compliance date of Rule 605 was April 2, 2001, the actual com- pliance dates were extended several times at the request of the NYSE and NASDAQ, and also because of the market closure following the September 11th tragedy.

Thus, the first monthly reports for NASDAQ National Market Securities (for execution quality in Octo- ber 2001) were made available to the public by November 30, 2001. Hence, we collect exe- cution quality data for our study sample of NASDAQ securities from October 2001 through December 2003 from the website (www.tagaudit.com) of Transaction Auditing Group (TAG).18 We measure dealer J’s execution quality for stock i in month t (EQ(i,j,t)) by the follow- ing six variables: (1) price improvement rate (PIR(i,j,t)) – the ratio of the cumulative num- ber of shares of covered orders executed with price improvement to the cumulative number of shares of covered orders executed at the receiving market center; (2) price improvement size (PIS(i,j,t)) – the share-weighted average amount per share that prices were improved for shares executed with price improvement; (3) order execution time (OET(i,j,t)) – the 14 Rule 605 applied only to securities that are designated as National Market System (NMS) securities under Exchange Act Rule 11Aa2-1.

This designation includes exchange-listed equities as well as equities included in the National Market tier of NASDAQ. Originally it did not apply to NASDAQ SmallCap securities, Over-the- Counter (OTC) Bulletin Board securities, and exchange-listed options. Subsequently, NASDAQ SmallCap securities were also subject to the rule for the December 2001 reporting period and all subsequent monthly reports.

15 Orders for 10000 shares or more are exempt from Rule 605. 16 The SEC did not include size improvement statistics in Rule 605, primarily because of its desire to minimize as much as possible the complexity and quantity of statistics to be disclosed. The size associated with the consolidated best bid or offer (BBO) may not provide a useful basis on which to compare execution quality among market centers. For example, consolidated size varies substantially between NASDAQ and listed securities. For listed securities, the quoted size nearly always reflects the quotes of the primary exchanges and generally is much larger than the size associated with the public quotes for NASDAQ securities.

17 Several recent studies analyze SEC 605 data. Bessembinder (2003c) examines the role of, and methods of correcting for, selection biases in market quality comparisons. Lipson (2004) examines competition among six market centers for NYSE-listed stocks using Rule 605 data. Boehmer (2005) performs a post-decimal comparison of market quality between NYSE and NASDAQ securities. He et al. (2006) compare effective and realized spreads for marketable orders in NYSE-listed and NASDAQ stocks. Boehmer et al. (2007) investigate whether brokers/ traders use Rule 605 data in order routing decisions. Zhao and Chung (forthcoming) analyze the effect of Rule 605 on trading costs.

18 The Rule 605 execution quality data for NYSE stocks are available from the Wharton Research Data Services (WRDS). However, the WRDS do not include execution quality data for NASDAQ securities. Consequently, we downloaded execution quality data for each stock and market center from this website. See Boehmer et al. (2007) for a detailed discussion of problems associated with Rule 605 data. 2788 K.H. Chung, C. Chuwonganant / Journal of Banking & Finance 31 (2007) 2770–2795

share-weighted average period of time from order receipt to order execution for shares executed with price improvement; (4) fill rate (FRATE(i,j,t)) – the ratio of the cumulative number of shares of covered orders executed at the receiving market center to the cumu- lative number of shares of covered orders; (5) the realized spread for execution of covered orders (RSPRD(i,j,t)) – twice the difference between the execution price of an order and the midpoint of the consolidated BBO as it stands five minutes after the time of order exe- cution; and (6) the effective spread for executions of covered orders (ESPRD(i,j,t)) – twice the difference between the execution price of an order with the midpoint of the consoli- dated BBO at the time of order receipt.19 Table 7 shows the descriptive statistics of the six execution quality measures provided by 135 market centers for our study sample of 2004 stocks.20 We first calculate the mean value of each variable using its monthly time-series observations.

We then calculate each variable’s the mean, standard deviation, and select percentile values using dealer-stock observations. The results show that small orders received price improvement more fre- quently than large orders. The mean price improvement rate (PIR) for the smallest order size group (100–499 shares) is 0.1763, whereas the corresponding figure for the largest order size group (5000–9999 shares) is only 0.0962. On average, about 17% of shares received price improvements at the receiving market center. Although smaller orders received price improvement more frequently, they tend to receive smaller price improve- ments.

For shares executed with price improvement, the smallest order size group received an average price improvement of 1.57 cents, whereas the largest order size group received an average price improvement of 2.05 cents.

Not surprisingly, large orders took more time to execute than small orders. The share- weighted average duration of time from order arrival to order execution is about 15 s for the smallest order size group, whereas the corresponding figure for the largest order size group is nearly 62 s. On average, it took 26 s to execute across all order size groups. The receiving market centers executed 94% of all incoming orders (in shares) and routed a greater proportion of large orders to other market centers than of small orders. The mean realized spread for the smallest order size group is 9.11 cents, whereas the corre- sponding figure for the largest order size group is almost zero.

This result reflects that lar- ger trades typically exert greater price impacts and thus market makers generate smaller revenues from such trades. The mean effective spread for the smallest order size group is 11.3 cents, whereas the corresponding figure for the largest order size group is 13.4 cents. To examine whether execution quality is related to quote aggressiveness, we estimate the following regression model using cross-sectional data for each month and for each order size group: EQði; j; tÞ ¼ d0 þ d1 logðQAði; j; tÞÞ þ Control variables þ eði; j; tÞ; ð7Þ where EQ(i,j,t) denotes dealer J’s execution quality for stock i in month t, QA(i,j,t) de- notes dealer J’s quote aggressiveness for stock i in month t measured by PTINS(i,j,t), PTINSA(i,j,t), and RELQS(i,j,t), and e(i,j,t) is the error term.

We estimate the above model using execution quality for market orders and marketable limit orders because information on price improvement is available only for these orders. We include the 19 The realized spread measures the net compensation to liquidity providers. See Huang and Stoll (1997). 20 When we merge the NASTRAQ data and the SEC 605 data downloaded from the website of Transaction Auditing Group, we find 135 matching dealer IDs.

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following stock and dealer attributes as control variables in the regression model: SPRD(i,t), PRICE(i,t), NTRADE(i,t), TSIZE(i,t), VOLATILITY(i,t), MVE(i,t), H-IN- DEX(i,t), DUMIB(j), DUMWH(j), DUMWS(j), H-INDEX(j,t), and SIZE(j,t). Table 7 Descriptive statistics for execution quality measures Order size (shares) Mean Standard deviation Percentile 5 25 50 75 95 PIR(i,j) 100–499 0.1763 0.3081 0 0.0087 0.1023 0.2705 0.8399 500–1999 0.1461 0.2535 0 0.0062 0.0718 0.2005 0.7086 2000–4999 0.1023 0.1968 0 0.0049 0.0631 0.1265 0.5917 5000–9999 0.0962 0.2053 0 0.0031 0.0405 0.0915 0.5276 All categories 0.1699 0.2686 0 0.0052 0.0134 0.2094 0.7994 PIS(i,j) 100–499 0.0157 0.0421 0.0007 0.0023 0.0083 0.0155 0.0531 500–1999 0.0171 0.0390 0.0008 0.0026 0.0092 0.0175 0.0578 2000–4999 0.0181 0.0379 0.0008 0.0020 0.0095 0.0182 0.0659 5000–9999 0.0205 0.0393 0.0009 0.0028 0.0098 0.0215 0.0787 All categories 0.0168 0.0352 0.0008 0.0025 0.0099 0.0177 0.0542 OET(i,j) 100–499 14.56 79.94 0 0.10 0.32 3.32 50.88 500–1999 26.05 100.35 0 0.19 2.61 17.36 96.07 2000–4999 38.30 119.83 0 0.44 8.11 28.37 156.32 5000–9999 62.27 164.99 0 0.93 13.54 44.45 300.53 All categories 25.83 92.75 0 0.24 4.17 20.30 92.63 FRATE(i,j) 100–499 0.9726 0.1051 0.8105 0.9831 1 1 1 500–1999 0.9344 0.1508 0.5957 0.9566 1 1 1 2000–4999 0.8846 0.2169 0.3351 0.8603 1 1 1 5000–9999 0.8359 0.2739 0.1698 0.7457 1 1 1 All categories 0.9370 0.1676 0.5414 0.9968 1 1 1 RSPRD(i,j) 100–499 0.0911 0.1663 0.0598 0.0175 0.0555 0.1256 0.3590 500–1999 0.0549 0.1634 0.1236 0.0011 0.0335 0.0912 0.3014 2000–4999 0.0206 0.1751 0.2202 0.0294 0.0177 0.0727 0.2624 5000–9999 0.0016 0.1769 0.2555 0.0511 0.0073 0.0599 0.2369 All categories 0.0456 0.1652 0.1434 0.0055 0.0277 0.0822 0.2837 ESPRD(i,j) 100–499 0.1130 0.1701 0.0111 0.0299 0.0619 0.1302 0.3735 500–1999 0.1148 0.1714 0.0109 0.0309 0.0628 0.1305 0.3825 2000–4999 0.1295 0.1939 0.0107 0.0330 0.0689 0.1455 0.4454 5000–9999 0.1340 0.2088 0.0102 0.0321 0.0701 0.1487 0.4694 All categories 0.1004 0.1673 0.0098 0.0235 0.0497 0.1099 0.3502 We measure execution quality of dealer j for stock i in month t (EQ(i,j,t)) by the following six variables: (1) price improvement rate (PIR(i,j,t)) – the ratio of the cumulative number of shares of covered orders executed with price improvement to the cumulative number of shares of covered orders executed at the receiving market center; (2) price improvement size (PIS(i,j,t)) – the share-weighted average amount per share that prices were improved for shares executed with price improvement; (3) order execution time (OET(i,j,t)) – the share-weighted average period of time from the time of order receipt to the time of order execution for shares executed with price improvement; (4) fill rate (FRATE(i,j,t)) – the ratio of the cumulative number of shares of covered orders executed at the receiving market center to the cumulative number of shares of covered orders; (5) the realized spread for executions of covered orders (RSPRD(i,j,t)); and (6) the effective spread for executions of covered orders (ESPRD(i,j,t)).

We first calculate the mean value of each variable using its monthly observations. We then calculate the mean, standard deviation, and select percentile values of each variable using dealer-stock observations.

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Table 8 Quote aggressiveness and execution quality EQ(i,j,t) Order size (shares) Quote aggressiveness measures PTINS(i,j,t) PTINSA(i,j,t) RELQS(i,j,t) PIR(i,j,t) 100–499 0.0131** 0.0169** 0.0085** (17.81) (28.07) (4.93) 500–1999 0.0290** 0.0243** 0.0127** (46.63) (47.42) (6.68) 2000–4999 0.0311** 0.0262** 0.0171** (45.98) (45.96) (5.58) 5000–9999 0.0460** 0.0303** 0.0276** (53.12) (41.80) (6.59) All categories 0.0329** 0.0315** 0.0160** (63.27) (73.49) (7.09) PIS(i,j,t) 100–499 0.0045** 0.0035** 0.0041** (36.05) (34.52) (13.30) 500–1999 0.0067** 0.0055** 0.0114** (46.98) (47.43) (32.27) 2000–4999 0.0059** 0.0053** 0.0065** (34.72) (38.10) (13.25) 5000–9999 0.0058** 0.0043** 0.0035** (26.88) (25.74) (8.46) All categories 0.0061** 0.0058** 0.0122** (52.02) (61.07) (44.33) OET(i,j,t) 100–499 0.0733** 0.0834** 0.1775** (37.53) (54.89) (43.89) 500–1999 0.1178** 0.1192** 0.2188** (67.68) (85.54) (64.91) 2000–4999 0.1033** 0.1141** 0.2815** (56.21) (78.64) (78.05) 5000–9999 0.0743** 0.1003** 0.2824** (29.95) (52.82) (62.33) All categories 0.0506** 0.0599** 0.1040** (32.58) (48.39) (38.49) FRATE(i,j,t) 100–499 0.0086** 0.0063** 0.0115** (34.87) (32.19) (18.46) 500–1999 0.0058** 0.0049** 0.0093** (18.55) (20.15) (6.72) 2000–4999 0.0053** 0.0062** 0.0047** (10.76) (17.28) (4.45) 5000–9999 0.0013** 0.0106** 0.0137** (6.30) (19.59) (12.55) All categories 0.0024** 0.0057** 0.0050** (7.62) (6.74) (5.46) RSPRD(i,j,t) 100–499 0.0003** 0.0007** 0.0043** (5.14) (12.63) (23.01) 500–1,999 0.0012** 0.0013** 0.0054** (11.58) (15.73) (24.75) 2000–4999 0.0024** 0.0016** 0.0087** (13.00) (11.28) (25.25) 5000–9999 0.0027** 0.0013** 0.1327** (13.97) (9.17) (28.29) (continued on next page) K.H.

Chung, C. Chuwonganant / Journal of Banking & Finance 31 (2007) 2770–2795 2791

SPRD(i,t) is the average quoted dollar spread of stock i in month t, SIZE(j,t) is the size of dealer j in month t, and all other variables are the same as defined in regression model (1) in Section 3 and regression model (4) in Section 4.2. Table 8 shows the equal-weighted mean values of regression coefficients and the z-sta- tistics.21 The price improvement rate (PIR), the price improvement size (PIS), and the fill rate (FRATE) are positively and significantly related to all three measures (PTINS, PTINSA, and RELQS) of quote aggressiveness for all order sizes. The order execution time is negatively and significantly related to quote aggressiveness measures.

These results support the idea that market centers tend to provide better executions (in terms of greater price improvements, faster executions, and higher fill rates) when they are posting compet- itive quotes. We find that the realized and effective spreads are negatively and significantly Table 8 (continued) EQ(i,j,t) Order size (shares) Quote aggressiveness measures PTINS(i,j,t) PTINSA(i,j,t) RELQS(i,j,t) All categories 0.0018** 0.0017** 0.0080** (19.92) (23.77) (40.34) ESPRD(i,j,t) 100–499 0.0039** 0.0046** 0.0180** (20.13) (31.74) (43.94) 500–1999 0.0058** 0.0061** 0.0252** (28.19) (38.65) (57.63) 2000–4999 0.0120** 0.0132** 0.0402** (38.65) (54.40) (62.27) 5000–9999 0.0162** 0.0149** 0.0516** (36.24) (42.78) (60.17) All categories 0.0011** 0.0022** 0.0057** (7.95) (16.63) (18.92) We estimate the following regression model using cross-sectional data for each month and for each order size group to examine the relation between execution quality and dealer quote aggressive: EQ(i,j,t) = d0 + d1log(QA(i,j,t)) + Control variables + e(i,j,t); where EQ(i,j,t) denotes dealer j’s execution quality for stock i in month t, QA(i,j,t) denotes dealer j’s quote aggressiveness for stock i in month t measured by PTINS(i,j,t), PTINSA(i,j,t), and RELQS(i,j,t), and e(i,j,t) is the error term.

We estimate the above model using execution quality for market orders and marketable limit orders because information on price improvement is available only for these orders. We measure execution quality of dealer j for stock i in month t (EQ(i,j,t)) by the following six variables: (1) price improvement rate (PIR(i,j,t)) – the ratio of the cumulative number of shares of covered orders executed with price improvement to the cumulative number of shares of covered orders executed at the receiving market center; (2) price improvement size (PIS(i,j,t)) – the share-weighted average amount per share that prices were improved for shares executed with price improvement; (3) order execution time (OET(i,j,t)) – the share-weighted average period of time from the time of order receipt to the time of order execution for shares executed with price improvement; (4) fill rate (FRATE(i,j,t)) – the ratio of the cumulative number of shares of covered orders executed at the receiving market center to the cumulative number of shares of covered orders; (5) the realized spread for executions of covered orders (RSPRD(i,j,t)); and (6) the effective spread for executions of covered orders (ESPRD(i,j,t)).

We include, for each stock, the quote midpoint, daily number of transactions, dollar trade size, standard deviation of daily quote midpoint returns, spread, market value of equity and Her- findahl-index and, for each dealer, the dealer type indicator variables and dealer size as control variables in the regressions. The table reports the equal-weighted mean values of regression coefficients and the z-statistics. We obtain the z-statistic by adding individual regression t-statistics and then dividing the sum by the square root of the number of regression coefficients.

** Significant at the 1% level. 21 We obtain similar results from the standard error-weighted mean values of regression coefficients. 2792 K.H. Chung, C. Chuwonganant / Journal of Banking & Finance 31 (2007) 2770–2795

related to quote aggressiveness measures. Hence, market centers posting aggressive quotes are more likely to provide better liquidity in terms of smaller execution costs. Combined with the results in Section 4.1, our results are in line with the finding of Bessembinder (2003a) that liquidity providers post aggressive quotes when they are prepared to give bet- ter-than-normal trade executions, and order routing responds to these quotes.

6. Summary and concluding remarks Prior research shows that a large proportion of NASDAQ order flow is preferenced. There is also a significant body of analytical and experimental literature showing the adverse effect of order preferencing on market quality. Studies show that order preferenc- ing reduces the dealer’s incentive to quote aggressively because preferenced orders are cap- tive orders that are unlikely to be affected by quote aggressiveness. Because the inside market spread is determined by the dealers’ incentive to quote aggressively, traders are likely to pay larger spreads when a substantial proportion of order flow is preferenced.

The rule of best execution, which requires dealers to execute orders at the best available quotes regardless of their own quotes, further weakens the brokers’ incentive to send orders to only those dealers who quote competitively. These considerations suggest that the relation between quote aggressiveness and dealer market share may be weak or non-existent on NASDAQ. In the present study, we address this issue using a large sample of NASDAQ stocks.

We find that quote aggressiveness plays a significant role in determining dealer market share. Our results show that dealers who quote competitively have larger market shares on NASDAQ. Because all NASDAQ dealers implicitly guarantee executions at the national best bid and offer or better, the above result suggests that dealers who post competitive quotes may offer greater price improvements and/or speedier executions than those dealers who do not post competitive quotes. Our empirical results confirm this conjecture: we find that the size and likelihood of price improvements, execution speeds, and fill rates are all positively related to quote aggressiveness.

We also find that market centers posting aggres- sive quotes provide better liquidity in terms of smaller effective and realized spreads. Over- all, these results suggest that market makers post aggressive quotes when they are willing to offer price improvements and/or speedier executions, and traders and brokers make order routing decisions accordingly.

Most important, we show that changes in trading environments, such as the implemen- tation of decimal pricing and the introduction of new trading platforms, have exerted a significant impact on the value of quote aggressiveness in raising dealer market share. In particular, we find that decimal pricing reduces (increases) the price (size) elasticity, SuperSOES increases the size elasticity, and SuperMontage increases both the size and price elasticity of dealer market shares. We consider these findings important because they help assess the efficacy of these regulatory or platform changes, at least in the context of whether they increase or decrease the dealers’ incentives to post competitive quotes.

Acknowledgements The paper benefited greatly from the valuable comments of two anonymous referees. The authors also thank the editor, Raymond Fishe, Jing Jiang, Kaunyoung Lee, Li Lin, Tim K.H. Chung, C. Chuwonganant / Journal of Banking & Finance 31 (2007) 2770–2795 2793

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