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

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 Ness^b
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 evidence⁴ 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, p_t, when (p_t - p_t-1)/p_t-1 > 0.10;

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

5. bid quote, b_t, when (b_t - b_t-1)/b_t-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 = [(Y_k^N - Y_k^Y)/{(Y_k^N + Y_k^Y)/2}]²,

where Y_k 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 spread_it = (A_it - B_it)/M_it,

where A_it is the posted ask price for stock i at time t, B_it is the posted bid price for stock i at time t, and M_it is the mean of A_it and B_it.

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 spread_it = 2D_it(P_it - M_it)/M_it,

where P_it is the transaction price for security i at time t, M_it is the midpoint of the most recently posted bid and ask quotes for security i, and D_it 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 spread_it = 2D_it(P_it - P_it+30)/M_it,

where P_it+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

We regress the spread against the four stock attributes to assess whether these attributes are important determinants of the cross-sectional variation in the spread for our sample of stocks. We use the reciprocal of share price, number of trades, and trade size in the regressions because a close inspection of plots between spreads and these variables suggests such a functional form (see Fig. 1).¹⁵ We present the regression results in Table 2. The results show that both Nasdaq and NYSE spreads are strongly related to the four stock attributes in the predicted manner. All three measures of trading costs (i.e., the quoted, effective, and realized spreads) are negatively related to share price, number of trades, trade size, and positively to return volatility. These variables explain about 91 to 96% of the cross-sectional variation in NYSE spreads and about 57 to 68% of the variation in Nasdaq spreads. When we perform similar regression analyses with the depth, we find that the depth is also significantly related to these stock attributes.

We also run regressions using the differences in the variables (i.e., spreads and four stock attributes) between our Nasdaq and NYSE stocks. The results of these regressions show whether there exists any difference in spreads between our Nasdaq and NYSE stocks, after controlling for their differences in share price, number of trades, trade size, and risk. The regression results, reported in Table 2, indicate that there is a significant difference in quoted spreads between our NYSE and Nasdaq stocks. The highly significant and positive intercept suggests that the average quoted spread of Nasdaq stocks is larger than the average quoted spread of NYSE stocks. Similarly, we find that both the effective and realized spreads are larger for our Nasdaq stocks. When we replicate our analysis with the quoted depth, we find that the intercept is highly significant and negative, indicating that the average quoted depth of Nasdaq stocks is smaller than the average quoted depth of NYSE stocks.¹⁶ The differential spread (depth) cannot be attributed to differences in stock attributes because we control for these differences in our regression. In the next section, we present a detailed analysis of differential execution costs and depths between the two markets.
4. Comparison of spreads and depths between Nasdaq and NYSE stocks

4.1. Spreads and depths

In Table 3 we report the average quoted, effective, and realized spreads for our entire sample of Nasdaq-listed stocks and for each quartile based on share price, number of trades, trade size, and return volatility. We report the average spreads for our NYSE sample in the same format. The results show that mean differences (0.24%, 0.21%, and 0.29%) in the quoted, effective, and realized spreads between Nasdaq and NYSE stocks (see the last row of Table 3) are almost identical to corresponding regression intercepts (0.25%, 0.22%, and 0.30%) in Table 2. This result suggests that our matched samples of Nasdaq and NYSE stocks are very similar in price, number of trades, trade size, and return volatility, and the differences in spreads between Nasdaq and NYSE stocks presented in Table 3 are not due to differences in the stock attributes. Overall, these results indicate that we have a good matched sample of Nasdaq and NYSE stocks.

The results show that quoted spreads of Nasdaq-listed stocks are wider than those of NYSE-listed stocks across all quartiles of share price, number of trades, trade size, and return volatility. The results of paired comparison t-tests show that the differences are statistically significant in most cases. The difference in quoted spreads between the two markets is particularly large for stocks with a smaller number of trades. For the whole sample, the average Nasdaq quoted spread (0.9641%) is about 33% larger than the average NYSE quoted spread (0.7228%). Similarly, we find that Nasdaq-listed stocks have, on average, wider effective spreads than NYSE-listed stocks. The average effective spread for Nasdaq stocks (0.7442%) is about 40% larger than the average effective spread for NYSE stocks (0.5335%). We find that effective spreads are significantly wider for Nasdaq stocks across all quartiles of share price, number of trades, trade size, and return volatility. The difference in effective spreads between the two markets is particularly large for less-active stocks.

In Table 4 we report the average quoted depth for our entire sample of Nasdaq-listed stocks and for each quartile based on share price, number of trades, trade size, and return volatility. Similarly, the table shows the average depth for our NYSE sample in the same format. The results show that the quoted depth of Nasdaq-listed stocks is significantly smaller than the quoted depth of NYSE-listed stocks. The average depth of Nasdaq-listed stocks (4,983 shares) is only about 43% of the average depth of NYSE-listed stocks (11,698 shares). We observe the similar pattern across all quartiles of share price, number of trades, trade size, and return volatility. The results of paired comparison t-tests show that the differences are all statistically significant.

On the whole, our empirical findings indicate that Nasdaq dealers post larger spreads and smaller depths than their counterparts on the NYSE. Hence, despite legal actions taken against Nasdaq dealers and a series of subsequent market reforms, including the new order handling rules and the tick-size reduction, the overall level of liquidity on Nasdaq is still significantly lower than the level of liquidity on the NYSE. Since we obtain these results using a matched sample of NYSE and Nasdaq stocks, it is unlikely that the results are driven by differential characteristics between the two groups of stocks. Rather, the results are likely driven by institutional differences and/or market structure differences. We provide our conjecture on these issues later in the paper.
4.2 Price improvement

The larger difference (40%) in effective spreads between Nasdaq-listed and NYSE-listed stocks, relative to the difference (33%) in quoted spreads, suggests that traders receive better price improvement on the NYSE. To confirm this, we show (see Table 5) the proportion of trades that occur at prices inside the posted bid and ask quotes for our sample of Nasdaq and NYSE stocks. The table shows that, on average, 32% of trades occur at prices inside NYSE specialist quotes, while the corresponding figure is only 26% for the Nasdaq sample.

Differential price improvement rates between the NYSE and Nasdaq may largely be attributed to differences in their market structures. On the NYSE, the specialist can generate price improvement for a market order by agreeing to take the order at a price better than the standing quote. The standing quote may not be the specialist's quote. If it is a public limit order, the specialist can trade with the incoming order only by bettering the public limit order. Floor traders on the NYSE can also provide price improvement for market orders. Instead of entering a limit order, an investor can hire a floor broker to work the order. The floor trader can wait by the specialist post and compete with the specialist for incoming orders. Without revealing his presence to traders off the exchange floor, the floor trader can capture the incoming order flow by bettering the posted quotes.

We find that the likelihood of price improvement is higher for larger trades on both the NYSE and Nasdaq. For example, only 22% of smallest Nasdaq trades (Q1) receive price improvement, compared to 31% of largest Nasdaq trades (Q4). Similarly, 27% of smallest NYSE trades receive price improvement, compared to 37% of largest NYSE trades. The low rate of price improvement for small Nasdaq trades may reflect that these trades are likely to be retail orders that are subject to order preferencing agreements or that are entered into Nasdaq's Small Order Execution System (SOES). The high rate of price improvement for large trades may indicate that these trades are likely to be institutional orders for which prices inside the quote can be negotiated.

Our empirical results also show that stocks with fewer trades have higher price improvement rates on both Nasdaq and the NYSE. For example, for our Nasdaq sample, the average price improvement rate for the lowest volume (i.e., smallest number of trades) quartile is 32%, while the corresponding figure is only 19% for the largest volume quartile. Similarly, the average price improvement rate for the lowest volume quartile in the NYSE sample is 36%, while the corresponding figure for the largest volume quartile is only 29%. The low rate of price improvement for high-volume stocks may be due to the fact that quoted spreads for high-volume stocks are more likely to reflect the trading interest of limit order traders than the interest of specialists (see Chung, Van Ness, and Van Ness, 1999). Hence, the reservation price of the specialist is likely to be higher than the quoted ask price and lower than the quoted bid price. As a result, there is little incentive for the specialist to offer price improvement. The low rate of price improvement for high-volume stocks may also partially reflect the fact that quoted spreads for high-volume stocks are much narrower than low-volume stocks, leaving less room for improvement.

We find that the likelihood of price improvement is higher for stocks with higher price and/or lower return volatility on both the NYSE and Nasdaq. The high rate of price improvement for low volatility stocks may reflect that NYSE specialists and Nasdaq dealers perceive these stocks as having less adverse selection problems and thus are more willing to offer price improvement. Similarly, NYSE specialists and Nasdaq dealers may perceive low-price stocks as having significant adverse selection problems. Typically, low-price stocks are stocks of small, emerging companies, while high-price stocks are stocks of large, mature companies. These observations may partially explain the low rate of price improvement for low-price stocks.

4.3 Price impact

The effective spread can be decomposed into two parts: price impact and the realized spread. Price impact measures the average information content of trades or the market maker's losses to better informed traders. Realized spreads measure price reversals after trades or, alternatively, market making revenue net of losses to better informed traders. Table 3 shows that Nasdaq stocks have, on average, wider realized spreads than NYSE stocks. The average realized spread on Nasdaq (0.7193%) is about 69% larger than the average realized spread on the NYSE (0.4253%). We find that realized spreads are significantly wider for Nasdaq stocks across all quartiles of share price, number of trades, trade size, and return volatility. The difference in realized spreads between the two markets is particularly large for stocks with a small number of trades.

Table 6 reports measures of price impact on each market. We find that the average price impact (0.1082%) for NYSE-listed stocks is significantly greater than the corresponding figure (0.0248%) for Nasdaq-listed stocks. We find similar results across all quartiles of share price, number of trades, trade size, and return volatility.¹⁷ Within each market, we find that price impact decreases with share price, number of trades, and trade size. We find that price impact increases with return volatility on both the NYSE and Nasdaq.