Spreads, Depths, and Quote Clustering on the NYSE And

Spreads, depths, and quote clustering on the NYSE and

Nasdaq: Evidence after the 1997 SEC rule changes

Kee H. Chung,a,* Bonnie F. Van Ness,b Robert A. Van Nessb

aState University of New York (SUNY) at Buffalo, Buffalo, NY 14260, USA

bKansas State University, Manhattan, KS 66506, USA

We are grateful to Jeffrey Bacidore, Tim McCormick, Thomas McInish, Robert Wood, and session participants at the 1999 SFA meetings for useful discussion and comments. We also thank Nasdaq for providing us with dealer quote data. All errors are ours.

*Corresponding Author: Kee H. Chung, The M&T Chair in Banking and Finance, Department of Finance and Managerial Economics, School of Management, SUNY at Buffalo, Buffalo, NY 14260.

Spreads, depths, and quote clustering on the NYSE and

Nasdaq: Evidence after the 1997 SEC rule changes

Abstract

This paper examines liquidity and quote clustering on the NYSE and Nasdaq using data after the two major market reforms─the 1997 SEC order handling rule change and the minimum tick size change. We find that Nasdaq-listed stocks exhibit wider spreads and smaller depths than NYSE-listed stocks. The average quoted, effective, and realized spreads of Nasdaq-listed stocks are about 33%, 40%, and 69% larger, respectively, than those of NYSE-listed stocks. The average depth of Nasdaq-listed stocks is about 57% less than the average depth of NYSE-listed stocks. We find that about 63% of the difference in quoted spreads between Nasdaq and NYSE stocks can be attributed to the differential use of even quotes (e.g., even-eighths and even-sixteenths) between Nasdaq market makers and NYSE specialists.

Key words: Liquidity; spreads; depths, quote clustering; collusion

JEL classification: G14; G18

1.Introduction

Numerous studies show that trading costs on Nasdaq are significantly greater than those on the NYSE. For example, Goldstein (1993), Christie and Schultz (1994), Huang and Stoll (1996), and Bessembinder and Kaufman (1997a, 1998) find that both the quoted and effective spreads of stocks traded on Nasdaq are wider than those of comparable stocks traded on the NYSE. In addition, Christie and Huang (1994) and Barclay (1997) find that spreads become narrower when stocks move from Nasdaq to the NYSE. Christie and Schultz (1994) maintain that Nasdaq dealers implicitly collude to set wider spreads than their NYSE counterparts based on their finding that stocks listed on Nasdaq exhibit fewer odd-eighth quotes than comparable stocks on the NYSE.

The public disclosure of Christie and Schultz's (1994) findings rekindled debates on the efficacy of the Nasdaq system. During the summer of 1994, numerous class-action lawsuits were filed in California, Illinois, and New York against Nasdaq market makers.[1] Prompted by renewed debates and also by legal action taken against Nasdaq market makers, both the Department of Justice (DOJ) and the Securities and Exchange Commission (SEC) undertook regulatory investigations into the issue. These investigations led to a series of reforms on Nasdaq. First, the DOJ investigation prompted market makers to curb the practice of avoiding odd-eighth quotes. Second, NASD Regulation Inc. was created to takeover the regulatory responsibilities of the National Association of Securities Dealers (NASD). Third, the SEC enacted sweeping changes in the order handling rules on Nasdaq.

On January 20, 1997, the phase-in of the new SEC order handling rules (OHR) began.[2] The first rule, known as the "Limit Order Display Rule," requires that limit orders be displayed in the Nasdaq BBO (i.e., best bid and offer) when they are better than quotes posted by market makers. This new rule allows the general public to compete directly with Nasdaq market makers in the quote-setting process. The second SEC rule, known as the "Quote Rule," requires market makers to publicly display their most competitive quotes. This rule allows the public access to superior quotes posted by market makers in Electronic Communication Networks (ECNs).[3] Under the new rule, if a dealer places a limit order into Instinet or another ECN, the price and quantity are incorporated in the ECN quote displayed on Nasdaq.

Barclay et al. (1999) examine the effect of these changes on Nasdaq trading costs for the first 100 stocks phased-in under the new rules. They find that quoted and effective spreads decline by about 30%, with the largest decline observed for the group of stocks with relatively wide spreads prior to the rule changes. They also find that approximately 60% of the total decline in trading costs for Nasdaq stocks between January 1994 and February 1997 arose prior to the introduction of the new SEC rules. They attribute this pre-reform decline in spreads to various government investigations and negative publicity directed at Nasdaq dealers ignited by the results in Christie and Schultz (1994).

On June 2, 1997, the minimum tick size on Nasdaq changed from $1/8 to $1/16 for stocks with a price greater than $10. A similar change occurred for NYSE stocks on June 24, 1997. Simaan, Weaver, and Whitcomb (1998) investigate the quotation behavior of Nasdaq market makers following the tick-size change. They find that Nasdaq market makers continue to avoid odd ticks, but traders entering orders on ECNs do not exhibit the same behavior. Their findings show that ECNs frequently establish the inside market quote and reduce trading costs for the public about 19% of the time.

Overall, both academic research and anecdotal evidence[4] suggest that trading costs for Nasdaq issues have declined significantly since the public dissemination of the Christie-Schultz findings, and particularly since the implementation of the new SEC rules. Given the results of pre-reform studies (see, e.g., Huang and Stoll, 1996 and Bessembinder and Kaufman, 1997a) that trading costs on Nasdaq are significantly greater than those on the NYSE, it would be of great interest to both regulatory authorities and the general public to find out whether investors incur larger trading costs on Nasdaq than on the NYSE after the implementation of the new SEC rules and the new tick size.

We compare trading costs and depths between Nasdaq-listed and NYSE-listed stocks using data on 482 matched pairs of Nasdaq and NYSE stocks during the three-month period from February 1, 1998 to April 30, 1998.[5] Bessembinder (1999) also performs a post-reform comparison of execution costs between Nasdaq and NYSE stocks. Our study differs from his study in two important ways. First, while Bessembinder focuses only on the difference in spreads between Nasdaq and NYSE stocks, we examine their differences in depths as well as in spreads. We consider this important because the spread captures only one dimension of liquidity. As shown in Lee, Murklow, and Ready (1993), Harris (1994), Kavajecz (1996, 1999), and Goldstein and Kavajecz (2000), it is important that we consider both the price and quantity dimensions of dealer quotes to accurately measure liquidity. Second, while Bessembinder matches Nasdaq stocks with NYSE stocks on the basis of market capitalizations, our match is based on four stock attributes that are known to be highly correlated with spreads and depths. Specifically, we match each stock in our Nasdaq sample with a comparable NYSE stock on the basis of share price, number of trades, trade size, and return volatility. This enables us to accurately measure differences in spreads (depths) between NYSE and Nasdaq stocks after controlling for their attributes.

Our empirical results show that Nasdaq market makers post wider spreads than NYSE specialists, despite the fact that Nasdaq spreads have declined significantly during the last several years. We also find that Nasdaq market makers post significantly smaller depths than NYSE specialists. Our findings suggest that at least a part of the difference in spreads between Nasdaq and NYSE stocks can be attributed to the difference in quote clustering. Specifically, we find that about 63% of the difference between Nasdaq and NYSE quoted spreads is due to the differential use of even quotes between the two markets. Our results also show that the proportion of even-sixteenth quotes is significantly higher than the proportion of odd-sixteenths on both the NYSE and Nasdaq after the market reforms.

The paper is organized as follows. Section 2 describes our data and stock matching procedure. Section 3 explains our measures of trading costs and presents preliminary results on differential trading costs between Nasdaq and NYSE stocks. Section 4 presents a detailed analysis of the differential trading costs and depths. Section 5 examines the effect of quote clustering on trading costs. Section 6 analyzes the determinants of quote clustering, and Section 7 concludes.

2.Data source and sample selection

We obtain data for this study from the NYSE's Trade and Quote (TAQ) database. Additionally, Nasdaq provided dealer quote data for depth computation. We begin our sample selection by identifying Nasdaq stocks for which the new SEC rules were in effect as of June 30, 1997. This initial sample comprises 650 stocks that are included in the first 13 batches of Nasdaq stocks phased-in under the new SEC rules.[6] Of these 650 stocks, we find only 624 stocks on the list of stocks under the new rules posted on the NASD website.[7] Of these 624 stocks on the list, we are able to obtain data on 551 stocks from the TAQ database for our study period from February 1, 1998 to April 30, 1998. Because our study period starts at February 1, 1998, our choice of June 30 as the cutoff point ensures at least a seven-month assimilation period for the new rules.

Before we match our Nasdaq stocks with their counterparts on the NYSE, we precondition our data to minimize data errors. We omit trades and quotes if the TAQ database indicates that they are out of time sequence, involve an error, or involve a correction. We omit quotes if either the ask or bid price is equal to or less than zero. Similarly, we omit quotes if either the bid or ask depth is equal to or less than zero. We omit trades if the price or volume is equal to or less than zero. In addition, as in Huang and Stoll (1996), we omit the following to further minimize data errors:

1.quotes when the spread is greater than $4 or less than zero;

2.before-the-open and after-the-close trades and quotes;

3.trade price, pt, when (pt - pt-1)/pt-1 > 0.10;

4.ask quote, at, when (at - at-1)/at-1 > 0.10;

5.bid quote, bt, when (bt - bt-1)/bt-1 > 0.10.

We match each stock in the Nasdaq sample with its NYSE counterpart on the basis of four stock attributes─share price, number of trades, trade size, and return volatility─that are believed to determine the inter-stock difference in spreads and depths.[8] Our matching procedure differs from those used by Huang and Stoll (1996), Bessembinder and Kaufman (1997a, 1997b), and Bessembinder (1999). Huang and Stoll (1996) match stocks based on the two-digit industry code and firm characteristics identified by Fama and French (1992) as correlated with expected stock returns (i.e., share price, leverage, market value of equity, and the ratio of book to market value of equity). Bessembinder and Kaufman (1997a, 1997b) and Bessembinder (1999) match stocks using only market capitalizations. In contrast, we match Nasdaq and NYSE stocks on the basis of stock attributes that are strongly associated with spreads and depths. The main goal of the present study is to obtain a matching sample of Nasdaq and NYSE stocks that are similar in these attributes and to test for a difference in spreads and depths. To the extent that our matched samples of Nasdaq and NYSE stocks have similar attributes, the difference (if any) in spreads and depths between the two groups must be due to reasons other than the attributes.

We measure share price by the mean value of the midpoints of quoted bid and ask prices and return volatility by the standard deviation of daily returns calculated from the daily closing midpoints of bid and ask prices. We recognize that the reported number of trades on Nasdaq is not directly comparable to that on the NYSE because there are many inter-dealer trades on Nasdaq.[9] Because inter-dealer trades exaggerate the reported volume, Nasdaq volume tends to be larger than the NYSE volume. We measure the number of trades and trade size for NYSE-listed stocks using transactions on both the NYSE and other markets (i.e., regional and over-the-counter markets) to counterbalance the effect of inter-dealer trades on the reported volume of Nasdaq-listed stocks. Note that trades and quotes for Nasdaq-listed stocks originate mostly from the Nasdaq market whereas many trades and quotes for NYSE-listed stocks reflect activity at a regional stock exchange or the NASD over-the-counter market. Bessembinder (1999) reports that approximately one-third of the trades for NYSE-listed stocks are executed off the NYSE. Because the recommended adjustment factor for Nasdaq volume that will neutralize the effect of inter-dealer trades is about 30 to 50% (see, e.g., Atkins and Dyl, 1997), our volume counting scheme appears reasonable. We measure trade size by the average dollar transaction during the study period.

To obtain a matched sample of Nasdaq and NYSE stocks, we first calculate the following composite match score (CMS) for each Nasdaq stock in our sample against each of the 2,912 NYSE stocks in the TAQ database:[10]

(1)CMS = [(YkN - YkY)/{(YkN + YkY)/2}]2,

where Yk represents one of the four stock attributes, the superscripts, N and Y, refer to Nasdaq and NYSE, respectively, and  denotes the summation over k = 1 to 4. Then, for each Nasdaq stock, we pick the NYSE stock with the smallest score. This procedure results in 551 pairs of Nasdaq and NYSE stocks. A close inspection of the stock attributes of our matched sample shows that differences in one or more stock attributes between Nasdaq and NYSE stocks become considerable when the composite match score exceeds three. Hence, to ensure the quality of our matched sample, we include only those pairs (482 pairs) with a composite match score of less than three in our study sample.[11]

We report summary statistics of our matched sample in Table 1. The average price of our Nasdaq sample is $29.92 and the corresponding figure for our NYSE sample is $30.02. The average number of transactions and trade size for the Nasdaq sample are 20,643 and $41,707, respectively, and the corresponding figures for the NYSE sample are 19,066 and $44,982. The mean values of the standard deviation of daily returns for our Nasdaq and NYSE stocks are 0.0319 and 0.0284, respectively. Overall, our matched sample of Nasdaq and NYSE stocks are similar in price, number of trades, trade size, and return volatility.

3.Measures of trading costs and depths

We use three measures of trading costs in this study: quoted spread, effective spread, and realized spread.[12] The quoted spread is calculated as

(2)Quoted spreadit = (Ait - Bit)/Mit,

where Ait is the posted ask price for stock i at time t, Bit is the posted bid price for stock i at time t, and Mit is the mean of Ait and Bit.

To more accurately measure trading costs when trades occur at prices inside the posted bid and ask quotes, we calculate the effective spread using the following formula:

(3)Effective spreadit = 2Dit(Pit - Mit)/Mit,

where Pit is the transaction price for security i at time t, Mit is the midpoint of the most recently posted bid and ask quotes for security i, and Dit is a binary variable which equals one for customer buy orders and negative one for customer sell orders.[13] The effective spread measures the actual execution cost paid by the trader.

We calculate the realized spread using the following formula:

(4)Realized spreadit = 2Dit(Pit - Pit+30)/Mit,

where Pit+30 denotes the first transaction price observed at least 30 minutes after the trade for which the realized spread is measured and the other variables are the same as defined above. The realized spread measures the average price reversal after a trade (or market-making revenue net of losses to better informed traders). For each stock, we calculate the time-weighted average quoted, effective, and realized spreads using all the time-series observations during the three-month study period.

For each NYSE stock, we calculate the time-weighted average depth during the study period using data from the TAQ. The Nasdaq quotes in the TAQ database contain only the Best Bid and Offer (BBO) for Nasdaq NMS issues. For stocks with more than one market maker, the TAQ database reports only the depth of the market maker who quotes the largest size at the BBO. For this reason, the size field for Nasdaq quotes in the TAQ database is not representative of the market depth. To correctly measure the aggregate depth for each Nasdaq stock, we acquire the market maker quote data from Nasdaq, which include the spread and depth quotes of each and every market maker. To obtain the aggregate depth, we first sum the depth at each BBO across market makers. We then calculate the time-weighted average of this aggregate depth for each Nasdaq stock during the three-month period.[14]