# 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.

**K.H. Chung, C. Chuwonganant / Journal of Banking & Finance 31 (2007) 2770–2795 2777**

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.

**2786 K.H. Chung, C. Chuwonganant / Journal of Banking & Finance 31 (2007) 2770–2795**

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.

2790 K.H. Chung, C. Chuwonganant / Journal of Banking & Finance 31 (2007) 2770–2795

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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