Specialist: The firm or the individual?

Journal of Economics and Business 57 (2005) 555–575

Specialist: The firm or the individual? Empirical evidence from the options markets夽 Amber Anand ∗ College of Business, University of Central Florida, Orlando, FL 32816, USA Received 10 August 2004; received in revised form 21 January 2005; accepted 26 May 2005

Abstract This paper investigates the role of the individual specialist vis-`a-vis that of the specialist firm on the quality of markets. While previous studies have not denied the importance of the individual, they have focused exclusively on the performance of the specialist firm. This study is the first empirical test of the specialist as an individual and his influence on market quality. By implication, it tests whether the firm is the appropriate level of analysis. Within specialist firms, we find significant differences in quoting behavior while the evidence on execution quality is mixed. Some firms are able to design an effective mechanism that enforces uniformity in goals of the members of the firm. Considering that exchanges are unable to impose such uniform performance, these firms appear to have better incentive or penalty systems in place. However, the existence of other firms where significant differences in execution quality exist, presents a challenge to policy makers, as differences in execution quality within a firm indicate that the disclosure of market quality needs to be at the post-level, not just at the firm level. © 2005 Published by Elsevier Inc. JEL classification: G20 Keywords: Specialist; Options market microstructure

1. Introduction This paper investigates the role of the individual specialist vis-`a-vis that of the specialist firm on the quality of markets. The structure of the specialist system relies on an individual for maintaining a market in a particular security. This individual stands ready to trade, ensures smooth markets and 夽 This paper has benefited from the comments of Brian Hatch, Terry Martell, Gideon Saar, Ravi Shukla, Dan Weaver,

Li Wei and seminar participants at the FMA 2002 meetings. ∗ Tel.: +1 407 823 3690. E-mail address: [email protected]. 0148-6195/$ – see front matter © 2005 Published by Elsevier Inc. doi:10.1016/j.jeconbus.2005.05.002

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acts as an agent to all incoming limit orders in the specialist book. Predictably, the performance of the specialist becomes critical to the execution costs paid by the investors. In this study, we test for differences in execution costs in securities handled by different specialists within the same specialist firm. While previous studies have not denied the importance of the individual, they have focused exclusively on the performance of the specialist firm.1 This study is the first empirical test of the specialist as an individual and his influence on market quality. By implication, it tests whether the firm is the appropriate level of analysis. The role of individual specialists assumes additional significance in light of the SEC (2000) study on payment for order flow arrangements in options markets, documenting calls for disclosure of market quality statistics at the post-level. The study discusses that specific specialist post-performance is an important criteria for order routing firms.2 In their survey, broker-dealers indicate their preference for information on post-performance over firm performance. The study also notes the failure of certain firms to provide such information. If we find substantial differences in execution quality within a specialist firm then the issue assumes greater significance. However, if we find uniformity in performance within a firm, then specialist firms need not always provide specialist post-performance data. The SEC (2000) study also points out the reliance of the exchange on individual specialists to compete for order flow. Thus, order routing firms and exchanges are obviously interested in the individual specialist as the unit of analysis. The SEC interest is evident in deciding disclosure standards for execution quality by specialist firms—firm level or post-level. Furthermore, recent studies emphasize the significance of the individual in the market making process, from both the inventory control, and the asymmetric information points of view. Naik and Yadav (2003) find that inventories are managed at the individual dealer level on the London Stock Exchange market making firms. This would suggest that firms allow dealers considerable latitude in setting their inventory levels and responding to imbalances, thus making it appropriate that the analysis of inventory effects on market quality be conducted at the individual level. Battalio, Ellul, and Jennings (2004) find that relationships between individual specialists and floor brokers influence the quality of executions that floor brokers obtain on the NYSE. Saar (2001) links investor uncertainty to transactions costs and investor welfare. Specifically, Saar (2001) predicts that the performance of a specialist would depend on the specialist’s ability to resolve short-term uncertainty about investor preferences. Thus, expert market makers would be better able to predict short term variations in order flow, hence have lower order imbalances and be able to provide better market quality in lower bid-ask spreads. This intuition is similar to Madhavan and Smidt (1993) where quotes reflect the specialist’s estimate of future (short-term) order imbalances. Then, the shocks to order imbalance will be lower the better the specialist is at forecasting order imbalances. We combine the results from Saar (2001) and Madhavan and Smidt (1993) in hypothesizing that stocks handled by expert specialist would be prone to lower shocks to order imbalances. Further, according to Saar (2001) this ability to predict order flow imbalances should also result in better market quality for the stocks handled by these specialists. We then expect to see such specialists offer lower transactions costs to investors. The question then is, why have previous empirical studies ignored the role of the individual specialist. The predominant reason is the lack of data identifying the post a security trades on. As Corwin (1999) observes, “Ideally, this analysis would address performance differences across 1

Cao, Choe, and Hatheway (1997), Corwin (1999), Coughenour and Deli (2002), Hatch and Johnson (2002), and Corwin (2004) study various aspects of NYSE specialist firms. 2 Individuals are specific to posts, and are used interchangeably in the paper.

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both specialist firms and individual specialists. However, the data sources employed in this study provide only firm-level specialist identification.” We focus our study on the individual options specialists on the Chicago Board of Options Exchange (CBOE) and the Pacific Stock Exchange (PCX). Options markets provide a unique laboratory for this study. The availability of data on the specific posts various options trade on, allows us to focus on the individual specialist to identify differences in execution quality within a specialist firm. Also, the presence of specialist firms on multiple exchanges (for example, Spear Leeds acting as specialist on both the CBOE and the PCX) allows us to compare the performance of the firm on the two exchanges, giving us a confirmatory test of intra-firm differences. It is important to discuss the differences between the firm, posts and individuals here. Each specialist post is assigned certain option classes which trade exclusively on those locations within the exchange. A post can be handled by one or two individual specialists. However, these individuals are specific to the particular post. This level of detail in the data, then, is sufficient for our study since our objective here is not to isolate the performance of a particular individual, but to differentiate between individual specialist performance. Since there is no overlap of one individual on two different posts, we can safely draw conclusions about market quality differences within specialist firms. Our study makes a significant contribution to the existing literature on specialist firms. For instance, if the individual specialist dominates any firm effects then the results of previous studies might have to be interpreted cautiously. On the other hand, if the firm is the primary determinant of specialist behavior and performance, then the study provides a missing link which further strengthens previous results. In this case it would serve to validate the results documented by previous studies. Another possible application of our study is to the understanding of specialist firm acquisitions (Hatch and Johnson (2002) study specialist firm acquisitions on the NYSE). If the individual specialist remains at his post after the firm is acquired, then efficiency gains can occur only if the firm level effects dominate the individual influence on market quality.3 We follow the advice of Corwin (1999) and conduct an exhaustive analysis of specialist firms as well as individuals in the two options exchanges. Towards that end, we first demonstrate that differences in specialist firm performance documented on NYSE (Cao et al., 1997; Corwin, 1999) also exist for our sample. More importantly, for the first time in the literature, we then test our hypotheses regarding differences in individual specialist performance within a firm. We find that, within specialist firms, quoted spreads differ significantly among individual specialists. Since our study focuses only on multiple listed options, and quoted spreads induce order routing firms to route order flow to a particular exchange, this result indicates that even within a firm, individual specialists tend to compete for order flow differently. The differences among individual specialists within a firm are markedly less pronounced for effective and realized spreads, and the price impact of trades. The combined results point to differences in execution quality in some firms, and not in others. Thus, some firms are able to design an effective mechanism that enforces uniformity in goals of the members of the firm. Considering that exchanges are unable to impose such uniform performance, these firms appear to have better incentive or penalty systems in place. However, the existence of other firms where differences exist presents a challenge to policy makers, as differences in execution quality within a firm indicate that the disclosure of market quality needs to be at the post-level, not just at the firm level. 3

Conversations with exchange officials suggest that specialists frequently remain at their posts after the acquisition. Coughenour and Deli (2002) document that all 18 specialists at Merrill Lynch Specialists moved over to JJC Specialist Corporation after Merrill Lynch Specialists was acquired by JJC Specialist Corporation.

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We also contribute to the options literature in this study. In the Saar (2001) model, the bid–ask spread arises as a result of uncertainty about the investor demand for the security. Thus, according to the model, the specialist with a larger share of the order flow would be better able to reduce adverse selection. We find that this is indeed the case for our sample in that trades handled by the dominant specialist (50% or more market share in an option class) have a significantly lower price impact. Wang (2000), Mayhew (2002) and De Fontnouvelle, Fishe, and Harris (2003) document that multiple listing of options is associated with significantly improved market quality. Battalio, Hatch, and Jennings (2004) document differences in effective spreads based on the number of listing venues. We extend their analysis to other measures of market quality. Additional listing venues raise issues of the benefits of increased competition versus the costs of fragmentation.4 We find that spreads are significantly lower for options listed on all four exchanges, even though adverse selection is higher for these options. This would indicate that the reduction in profits dominates the increase in adverse selection costs. In Section 2, we review the relevant literature. Section 3 provides an institutional background. Section 4 discusses the data used in the analysis. Section 5 outlines the methodology and discusses the results. Section 6 concludes. 2. Literature Saar (2001) emphasizes the importance of the “human factor” in financial markets. He proposes a model where uncertainty about the demand for a security causes the bid-ask spread to arise. According to his model, “. . . stocks traded by expert market makers should have smaller spreads . . .” Here again we have an acknowledgement of the central role the individual plays in the market quality of a security. Battalio, Ellul et al. (2004) examine the impact of relationships between individual specialists and floor brokers on execution quality on the NYSE. They use the context of physical relocations of NYSE specialists on the floor and find that floor brokers with established relationships with the specialists obtain lower trading costs than floor brokers with no prior relationship. For our analysis, these results underscore the importance of individual interactions in determining market quality. Hasbrouck and Sofianos (1993) find that specialists are better at short-term investments than long-term. Madhavan and Smidt (1993) find evidence that specialists profit from their position on the floor by anticipating future order imbalances. Thus, the specialists’ principal function lies in their expertise at assessing the short-term demand for the security by investors. These arguments have two implications for our analysis. First, we would expect differences among individual specialists based on their ability to resolve demand uncertainty. The better a particular specialist is at predicting order flow, the lower the bid-ask spread would be. This is specific to the individual and not to the firm. Second, in case of competing markets (and consequently, competing specialists) we would expect the specialist with the largest market share in a security to be better able to resolve the demand uncertainty associated with the security. The inventory control literature centers on a dealer’s deviation from the optimal diversified portfolio due to his function of standing ready to buy and sell. Theoretical models include those by Ho and Stoll (1983), Ho and Macris (1985), and O’Hara and Oldfield (1986). Empirical studies

4 Boehmer and Boehmer (2003) provide an excellent review of the literature on the empirical evidence on the fragmentation versus competition effects of additional listing of securities.

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(Hasbrouck & Sofianos, 1993; Madhavan & Smidt, 1993) find validation for dealer’s inventory affecting his quotes. The inventory control exercised by a dealer is likely to be a function of the available capital and his expertise at predicting future (short-term) direction of order flow. While the capital provided by the firm and the risk aversion are likely to be uniform for the individual specialists within a firm, the expertise is specific to the individual. Further, Naik and Yadav (2003) document that inventory control is decentralized to the individual markets makers by firms on the LSE. All the theoretical studies discussed above emphasize the significance of the individual specialist. Yet, we are aware of no empirical study which focuses on the individual specialist. Cao et al. (1997) and Corwin (1999), focusing their analysis at the firm level, document differences in the trading behavior of the NYSE specialist firms. This result implies that competition, NYSE’s monitoring mechanism, incentive or penalty systems fail to achieve homogeneity in firms’ execution performance.5 The relevant question this raises for our study is whether the firm is more successful at creating a system that imposes uniform behavior across its members (the individual specialists). Studies of the specialist system in options markets have concentrated on the differences in the specialist and the market maker system. Neal (1992) compares the execution costs of options trading on the CBOE and AMEX. Mayhew (2002) compares execution quality of options traded by specialists and market makers on CBOE. Anand and Weaver (2002) examine the impact of superimposing the specialist system on the market-maker system on the CBOE. While these studies document the benefits of the specialist system, none of them study specialist units, firms or individuals. Thus, our analysis also extends the studies of specialist firms on NYSE to options markets. 3. Institutional background The five options exchanges, the Chicago Board Options Exchange (CBOE), the American Stock Exchange (AMEX), the Pacific Stock Exchange (PCX), the Philadelphia Stock Exchange (PHLX) and the International Securities exchange (ISE), all operate on a specialist system.6 Due to data constraints, we focus our analysis on specialists on CBOE and PCX only. Our sample is discussed in the next section. The designated primary market-maker (DPM) on the CBOE has rights and obligations similar to the NYSE specialist. Anand and Weaver (2002) provide a detailed description of the DPM program on the CBOE. The lead market-maker (LMM) program on PCX closely resembles the DPM program on CBOE. SEC approved the LMM program on 17 November 1990. The program was expanded floor-wide around the same time that the DPM system was expanded on CBOE. The PCX transferred all option classes to LMMs by 31 August 1999.7 The Options Allocation Committee on PCX is responsible for allocating option classes to LMMs. The LMMs have rights and obligations very similar to the DPMs on the CBOE.8 The guaranteed participation provision is slightly different as LMMs on PCX are guaranteed 50% participation in transactions at their bid or offer. This is in contrast to 40% at CBOE. The PCX uses the participation right as an 5

The allocation of stocks based on performance or reallocation based on poor performance might constitute the incentive system in place for the specialist firms. Corwin (2004) analyzes the allocation of stocks to specialist firms on the NYSE. 6 During our sample period, the International Stock Exchange had not started trading. 7 “Pacific Exchange Expand Lead Market Maker Program,” Pacific Exchange Press Release, 30 August 1999. 8 PCX Rule 6.82.

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incentive. If the LMM shows signs of losing market share the Options Allocation Committee can reduce the participation rate to 40%. If, on further review, the committee is satisfied with the LMM’s performance, the participation rights are restored. The Options Allocation Committee is also responsible for monitoring LMM performance through market quality reviews.9 If the committee is not satisfied with a LMM’s performance the option classes could be reallocated.10 Thus, the institutional framework in options markets recognizes the significance of specialists in attracting order flow, and attempts to regulate their behavior through various incentives, monitoring mechanisms and the use of allocations and reallocations. Are the exchanges successful at creating homogeneity in specialist performance through these mechanisms? We answer this question in subsequent sections. 4. Data Both, CBOE and PCX, conducted their operations based on a specialist system during our sample period. Options markets are characterized by identical securities trading on multiple exchanges. At the same time, there are specialist firms that operate on multiple exchanges. This provides a unique opportunity to study the competitiveness of specialist firms as well as test for differences in specialist firm behavior across exchanges. CBOE and PCX provided information on the specialist firm responsible for each option class in October 1999. There were 51 specialist units on CBOE and 32 on the PCX. Eight of these operated on both exchanges. One firm was not included since it was not handling any of the sample option classes. This gives us a total of 74 specialist firms on the two exchanges in October 1999. Further, the exchanges also provided data on the post at which each option class trades, giving us a unique view within a specialist firm. We use this data to test for performance differences among individual specialists in specialist firms. We use options price reporting authority (OPRA) data to construct our performance measures. The OPRA is the disseminator of options price and quote data for all options markets. Thus, we have time stamped data on all trades and quotes generated on all options exchanges for October 1999.11 OPRA did not provide data on quoted size during our sample period. We restrict our study to normal trading hours for options markets (9.30 a.m.–4.02 p.m.). To include an option class in our sample we require that the option be multiple listed through out the sample period. This is to avoid any confounding effects of actual or potential multiple listing of single listed options. We also require at least 20 trades in the sample period (average of a trade per day). This leaves us with 559 multiple listed option classes that form our sample. We eliminate option series with less than 7 days to maturity and then identify the most liquid series in an option class for each day in the sample. Thus, each option class is represented by only one series per day. This selection criterion allows us to develop better controls for our tests, as on each day we can measure the moneyness and time to maturity for the particular series. Also, the selection criterion gives equal weight to each option class. We filter the data for obvious data errors. Prices, bids and asks equal to zero are eliminated. Trades and quotes are also identified as errors if a particular trade is more than four standard deviations away from the average price 9

PCX Rule 6.82(e)(4). Role of Options Allocation Committee is outlined in Rule 11.10(b). PCX Rule 6.82(f)(A). 11 We do not have data identifying specialist trades or quotes. We consider all quotes and trades emanating from an exchange to be associated with the performance of the specialist handling the option on the particular exchange. This is consistent with studies of specialist firms on the NYSE. 10

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for the particular day. For quotes, the bid and ask prices are analyzed separately. The bid price of each quote is compared with the mean of the bid prices, and is marked as a possible error if it is more than four standard deviations away from the mean for the day. Ask prices are similarly analyzed. This methodology is adopted since focusing on the spreads for filtering can, in some instances, miss certain errors.12 We construct National Best Bid and Offer (BBO) quotes as the highest bid and the lowest ask at any time during the trading period.13 For BBO quotes, if the market is crossed, then that quote is not included in the analysis. We also construct measures of moneyness and time to maturity of the option series on each day. Moneyness is calculated as the stock price divided by the exercise price for call options and the exercise price divided by the stock price for put options. Time to maturity is the number of days remaining till the expiration of the option series. We use the CRSP database to obtain shares outstanding of the underlying stock for each options class. TAQ data is used to collect information on the price and the spread of the underlying stock. Data on total volume traded in an option class is taken from the Options Clearing Corporation web site (http://www.theocc.com). 5. Results Our tests focus on differences in market quality of options handled by specialists, firms as well as individuals. We measure market quality by quoted, effective and realized spreads. Since both the CBOE and PCX emphasize the role of the specialist in attracting order flow, we also focus on the competitiveness of the specialist. Theoretical models of the specialist suggest the central role of the specialist in reducing adverse selection in the market, or in predicting demand uncertainty. We capture differences in adverse selection costs for the specialist by analyzing the price impact of the trades handled by each specialist. A better performing specialist would be characterized by a lower price impact. We first analyze whether differences documented among NYSE specialist firms also exist in the options markets. Next, we analyze specialist posts within a firm. This is done in two ways—first, by analyzing different posts of the same firm on the same exchange, and second, by comparing the performance of the same firm on the two exchanges.14 Results are presented first for quoting behavior (quoted spreads), then for execution quality (effective and realized spreads) and finally, for adverse selection (price impact). 5.1. Specialist firm level differences 5.1.1. Quoted spreads We analyze the quoting behavior of specialists by focusing on time weighted quoted spreads as a measure of market quality. Quoted spreads are measured as the difference between the

12

For example, if we allow spreads of less than say US$ 2 as “normal,” then that does not differentiate between a quote of US$ 20 and 21, and another of US$ 120 and 121. If the two occur on the same day, it is likely that one of them is a data entry error. We find that our data filters eliminate all instances of unreasonably wide spreads. 13 Hansch and Hatheway (2001) find that the constructed best bid and offer quote in options markets provides a good measure of the actual BBO quotes. 14 We identify eight firms that act as specialists on CBOE as well as PCX. On CBOE, these firms handle 30% of the option classes in our sample, while on the PCX the proportion is 38%.

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Table 1A Performance measures: descriptive statistics (CBOE) Number of specialist firms Options on number of underlying stocks per specialist firm Average quoted $ spread Average quoted % spread Average effective $ spread Average effective % spread Average realized $ spread Average realized % spread Average price impact ($)

Mean

Median

Minimum

Maximum

S.D.

51

9.4

8.0

2.0

44.0

7.1

51 51 51 51 51 51 51

0.26 16.5 0.19 11.1 0.14 7.9 −0.07

0.25 15.8 0.18 11.2 0.13 8.2 −0.07

0.17 6.1 0.13 5.9 0.06 2.2 −0.16

0.44 27.2 0.41 16.5 0.35 12.5 −0.03

0.05 3.9 0.05 2.1 0.05 2.2 0.02

This table presents the descriptive statistics for the performance measures used in the study. Quoted spreads are measured as the difference between the offer and the bid prices of the option series. The spreads are then weighted by the duration of time they are in effect to arrive at a time weighted daily average for each option series. Effective spreads are measured as: ESt = 200|pt − mt |/mt , where ESt is the percentage effective spread for trade t, pt is the price at which the trade occurred, mt is the midpoint of the prevailing quote. For our analysis, we use volume weighted effective spreads for each day for each options series in the sample. Realized spreads are measured as: RSt = 200It (pt − mt+5 )/mt , where RSt is the realized spread for trade t, It is +1 for buy orders and −1 for sell orders, mt is the midpoint of the prevailing quote and mt+5 is the midpoint of the quote 5 min after the trade. For our analysis, we use volume weighted realized spreads for each day for each options series in the sample. Price impact is measured as: PIt = 200It (mt+5 − mt ), where PIt is the price impact for trade t, It is +1 for buy orders and −1 for sell orders, mt is the midpoint of the prevailing quote and mt+5 is the midpoint of the quote 5 min after the trade. The measures are calculated for each specialist firm on the CBOE. Each measure is averaged cross-sectionally for each specialist firm and then across specialist firms to arrive at the results reported below. The results for each specialist firm (on each measure) are available from the author.

offer and the bid prices of the option series, weighted by the duration of time they are in effect. Tables 1A and 1B present summary statistics on quoted spreads by specialist firms on the CBOE and the PCX. Tables 1A and 1B show a wide dispersion in the performance of the specialist firms. Quoted percentage spreads range from 6.1% to 27.2% of the quote midpoint. However, we need to control for security specific characteristics before drawing any conclusions on differences between specialist firms.

Table 1B Performance measures: descriptive statistics (PCX)

Options on number of underlying stocks per specialist firm Average quoted $ spread Average quoted % spread Average effective $ spread Average effective % spread Average realized $ spread Average realized % spread Average price impact ($)

Number of specialist firms

Mean

Median

31

11.5

9.0

31 31 31 31 31 31 31

0.29 17.7 0.18 10.4 0.12 6.9 −0.11

0.28 17.3 0.18 9.9 0.12 7.0 −0.10

Minimum

Maximum

S.D.

1.0

34.0

7.3

0.20 9.7 0.09 6.2 −0.01 −0.6 −0.20

0.57 27.2 0.28 14.7 0.25 12.1 −0.04

0.07 3.3 0.04 1.8 0.05 2.3 0.04

The measures are calculated for each specialist firm on the PCX. The rest as in the legend of Table 1A.

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To control for security specific characteristics, we estimate the following regression equation: Si,j = β0 + β1 Money + β2 TtMty + β3 Vlm + β4 MktCap + β5 Usprd + β6 Ustd + β7 Conc + β8 Ex4 +

n−1
i=1

γi D i +

n−1
i=1

θi (Di Money) +

n−1


λi (Di Ustd) + ε

(1)

i=1

where Si,j is the time weighted quoted percentage spread for the day for series i and specialist j. Money is the moneyness of the option series i, TtMty the time to maturity of the options series i, Vlm the total volume in the option class, MktCap the market capitalization of the underlying stock, Usprd the mean percentage spread on the underlying stock on the day, Ustd the standard deviation of daily return of the underlying stock for the month of October, Conc a dummy which takes the value 1 if the particular exchange has 50% or more of the market share in the particular option class. Ex4 is a dummy that equals 1 if the option is traded on 4 exchanges. D1 − Dn−1 are dummies for the specialist firms. For the CBOE, there are 50 such dummies and for the PCX, 30. Wang (2000) and Neal (1987) show that moneyness and time to maturity of the option are important determinants of the option spread and need to be controlled for. Volume and market capitalization measure the liquidity of the security and are frequently used in microstructure literature as determinants of the bid-ask spread and proxies for the information environment of the firm. The spread of the underlying is used since it is argued that options market makers use the underlying markets to hedge their positions (Cho & Engle, 1999). In this case, the higher the spread of the underlying stock, the higher the hedging costs are likely to be and hence the higher the option spread will be. We also control for market dominance by a specialist in an option class through a dummy variable (Conc). If the practice of routing orders based on market share (SEC, 2000) leads to a decrease in competitive behavior then we should see a positive association between market dominance and spreads. On the other hand, if the dominant specialist is better able to resolve uncertainty in the Saar (2001) framework due to a better view of the market, he should be able to quote lower spreads. In this case, market dominance is a result of competitive advantage, and we would expect to see a negative association between quoted spreads and dominance. The dummy variable, Ex4, examines the differences between multiple listed options. Order flow fragmentation could cause each specialist to have an incomplete view of the market and hence quote higher spreads. The opposing force here would be the added competition from another listing venue. Recall that our sample consists of multiple listed options only. Therefore, our benchmark options are those listed on two or three exchanges only. The specialist dummies D1 − Dn−1 test for differences in specialist firm performance on the spread measure after controlling for option and market specific factors in the regression. We also allow for differences in specialists’ attitude towards options characteristics such as moneyness of the option and the volatility of the underlying stock, by including interaction terms (specialist dummies with moneyness and volatility, respectively) in the regression. Table 2A shows the results for quoted percentage spreads for CBOE and PCX. The hypothesis that the specialist dummies are jointly equal to zero is rejected for both CBOE and PCX at the 1% level of significance. The hypothesis that the interaction dummies are jointly equal to zero across specialist firms is rejected at the 1% level as well, indicating that specialist firms differ in the impact that moneyness and volatility of the options have on their quotes. This is consistent with the results of studies of specialist firms on the NYSE. Table 2B tests for the influence of specialist firm characteristics on market quality. Following the arguments of Corwin (1999) and

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Table 2A Quoting behavior: differences in specialist firm performance Quoted spreads PCX

CBOE

Intercept Moneyness Time to maturity Volume Market capitalization Mean percentage spread (underlying) Volatility Conc Listing on four exchanges—dummy

−0.29 0.41 (6.43* ) −0.001 (−31.61* ) −0.04 (−6.19* ) −0.0001 (−2.95* ) 6.54 (23.58* ) −0.03 (−0.68) −0.02 (−5.02* ) −0.01 (−3.10* )

−0.50 (−5.69* ) 0.65 (7.81* ) −0.001 (−36.79* ) −0.05 (−6.09* ) −0.000003 (−0.06) 6.07 (27.96* ) −0.13 (−1.61) 0.005 (1.70*** ) −0.02 (−5.12* )

Adjusted R2 F-test: specialist dummies = 0 F-test: specialist dummies × moneyness = 0 F-test: specialist dummies × volatility = 0

0.38 12.32* 14.29* 10.34*

0.37 9.02* 12.82* 5.00*

(−3.96* )

This table summarizes the results of control regressions for quoted percentage spreads. The regression equation estimated is Si,j = β0 + β1 Money + β2 TtMty + β3 Vlm + β4 MktCap + β5 Usprd + β6 Ustd + β7 Conc + β8 Ex4 + n−1 n−1 n−1 i=1 γi Di + i=1 θi (Di Money) + i=1 λi (Di Ustdi ) + ε, where Si,j is the relevant dependent variable. Vlm is the natural log of the total volume in the option class, MktCap the market capitalization of the underlying stock, Usprd the mean percentage spread on the underlying stock on the day, Ustd the standard deviation of daily return of the underlying stock for the month of October, Conc a dummy which takes the value 1 if the particular exchange has 50% or more of the market share in the particular option class. Ex4 is a dummy that equals 1 if the option is traded on 4 exchanges. D1 − Dn−1 are dummies for the specialist firms. For the CBOE, there are 50 such dummies and for the PCX, 30. Separate results are presented for CBOE and PCX. The parameter estimates for Vlm and MktCap are multiplied by 1,000,000 for ease of presentation. * Significance at 1% level. *** Significance at 10% level.

Coughenour and Deli (2002), we create proxies for specialist firm size and organizational form. We proxy for size using two variables. The dummy variable Mult is 1 if the specialist firm operates on both CBOE and PCX, the variable Propvlm is the proportion of total volume on the exchange that is allocated to the particular specialist firm. We also include an interaction variable MultProp which is the product of Mult and Propvlm. We expect firms present on both exchanges to be bigger than ones that operate on only one, firms that handle a higher proportion of volume to be bigger than ones that handle a lower proportion of volume, and firms that operate on multiple exchanges as well as handle high volumes to be the biggest firms in options markets. We classify closely held firms as the ones where the name of the individual specialist occurs in the firm name.15 The dummy variable Closeheld is 1 for closely held specialist firms and 0 otherwise. Then, the regression equation estimated is Si,j = β0 + β1 Money + β2 TtMty + β3 Vlm + β4 MktCap + β5 Usprd + β6 Ustd + β7 Closeheld + β8 Propvlm + β9 Mult + β10 Multprop + β11 Conc + β12 Ex4 + ε

15

The firms identified as closely held are listed in Appendix A.

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Table 2B Quoting behavior: specialist firm characteristics Quoted spreads PCX Intercept Moneyness Time to maturity Volume Market capitalization Mean percentage spread (underlying) Volatility Closeheld Multiple trading specialist Proportion of total volume Mult × proportion of volume Conc Listing on four exchanges—dummy Adjusted R2

CBOE

−0.23 0.37 (44.71* ) −0.001 (−26.51* ) −0.06 (−7.66* ) 0.00005 (1.02) 6.78 (26.10* ) −0.002 (−0.56) 0.03 (6.23* ) 0.31 (5.84* ) −0.01 (−1.89*** ) 0.25 (2.30** ) −0.02 (−5.72* ) −0.01 (−1.88*** ) (−23.34* )

0.31

−0.21 (−25.88* ) 0.36 (52.03* ) −0.001 (−30.03* ) −0.06 (−8.74* ) 0.0001 (1.81** ) 6.07 (28.68* ) −0.003 (−1.46) −0.02 (−3.98* ) −0.01 (−1.08) 0.05 (0.24) 0.21 (1.02) 0.00 (0.39) −0.02 (−6.83* ) 0.30

This table tests for the influence of specialist firm characteristics on market quality. The regression equation estimated is Si,j = β0 + β1 Money + β2 TtMty + β3 Vlm + β4 MktCap + β5 Usprd + β6 Ustd + β7 Closeheld + β8 Propvlm + β9 Mult + β10 Multprop + β11 Conc + β12 Ex4 + ε, where Si,j is the relevant dependent variable for the day for series i and specialist j. Money is the moneyness of the option series i, TtMty the time to maturity of the options series i, Vlm the total volume in the option class, MktCap the market capitalization of the underlying stock, Usprd the mean percentage spread on the underlying stock on the day, Ustd the standard deviation of daily return of the underlying stock for the month of October, Closeheld is 1 for closely held specialist firms and 0 otherwise. Mult is 1 if the specialist firm operates on both CBOE and PCX and 0 otherwise, Propvlm is the proportion of total volume on the exchange that is allocated to the particular specialist firm. MultProp is an interaction term which is the product of Mult and Propvlm. Conc is a dummy which takes the value 1 if the particular exchange has 50% or more of the market share in the particular option class. Ex4 is a dummy that equals 1 if the option is traded on four exchanges. Separate results are presented for CBOE and PCX. The parameter estimates for Vlm and MktCap are multiplied by 1,000,000 for ease of presentation. * Significance at 1% level. ** Significance at 5% level. *** Significance at 10% level.

The security specific control variables are the same as in Eq. (1). We find that, closely held firms have significantly higher spreads on PCX while they are significantly lower on the CBOE. The dominant specialist quotes significantly lower spreads on the PCX. We also test the hypothesis that the number of listing venues influences the spreads. Here we find that the spreads for options listed on four exchanges are significantly lower than those listed on two or three exchanges. The coefficients for proxies of the size of the specialist firm do not provide any clear results. 5.1.2. Effective spreads Quoted spreads measure transaction costs if trades occur at the bid and ask price. However, trades often occur inside the spread. Hence, we use effective spreads to measure transaction costs. Effective percentage spreads are measured as ESt = 200|pt − mt |/mt

(3)

where ESt is the percentage effective spread for trade t, pt the price at which the trade occurred and mt the midpoint of the prevailing quote. We use volume weighted effective spreads for our

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566

analysis: VWES =




(ESt Sizet )/DailyVolume

(4)

where VWES is the volume weighted effective spread on the option series for the particular day, ESt the effective spread of the trade, Sizet the number of contracts transacted in the particular trade and DailyVolume is the total volume traded in the option series on the day. Tables 1A and 1B report the summary statistics on average effective spreads for specialist firms on the CBOE and the PCX. Effective spreads range from 5.9% to 16.5% of the quote midpoint across specialist firms. We first estimate Eq. (1) for effective spreads. Table 3A reports the results. The difference in specialist firms on this measure is highly significant for CBOE, while only the difference in the intercept term is significant for PCX. The results of estimation of Eq. (2) for effective spreads are reported in Table 3B. The results are again mixed for our proxies for size of specialist firms. We find no difference in execution costs as measured by effective spreads among closely and widely held firms. Also, options listed on four exchanges have significantly lower effective spreads than those listed on two or three exchanges.16 5.1.3. Realized spreads We also analyze realized spreads, which measure execution costs of trades after accounting for adverse selection and provide an approximation of dealer revenues. Realized spreads are measured as RSt = 200It (pt − mt+5 )/mt

(5)

where RSt is the realized spread for trade t, It is +1 for buy orders and −1 for sell orders and mt+5 is the midpoint of the quote 5 min after the trade. Similar to our analysis of effective spreads, we analyze volume weighted realized spreads, calculated as
VWRS = (RSt Sizet )/DailyVolume (6) where VWRS is the volume weighted realized spread on the option series for the particular day, RSt the realized spread of the trade, Sizet the number of shares transacted in the particular trade and DailyVolume is the total volume traded in the option series on the day.17 The summary statistics for average realized spreads for specialist firms are reported in Tables 1A and 1B. Realized spreads are lower than effective spreads and range from −0.6% to 12.5% of the midpoint of the quote. Estimates of Eq. (1) (Table 3A) add further weight to results of earlier studies. Specialist firms differ from each other on this dimension of market quality as well. Eq. (2) estimates for realized spreads (Table 3B) do not differ significantly between closely and widely held firms. We find that realized spreads, and hence dealer revenues adjusted for adverse selection costs, are lower for option classes listed on four exchanges vis-`a-vis those listed on two or three exchanges. This is consistent with increased competition lowering dealer

16 On PCX, we find that the dominant market dummy is negative and significant for quoted spreads, while it is positive and insignificant for effective spreads. These results can also be interpreted as evidence of cream skimming by the market. We thank the referee for pointing this out. 17 The measurement of realized spreads using quotes 5 min after the trade, and the emphasis on volume weighted effective and realized spreads is consistent with SEC (2001) final rule on disclosure of order execution and routing practices, as well as with the methodology adopted in Hansch and Hatheway (2001).

Table 3A Execution quality: differences in specialist firm performance

Intercept Moneyness Time to maturity Volume Market capitalization Mean percentage spread (underlying) Volatility Conc Listing on four exchanges—dummy Adjusted R2 F-test: specialist dummies = 0 F-test: specialist dummies × moneyness = 0 F-test: specialist dummies × volatility = 0 See the legend of Table 2A. * Significance at 1% level. ** Significance at 5% level. *** Significance at 10% level.

Realized spreads

Price impact

PCX

CBOE

PCX

CBOE

PCX

CBOE

−0.05 (−0.37) 0.11 (0.85) −0.0005 (−11.11* ) −0.01 (−1.56) −0.00003 (−0.55) 3.69 (8.26* ) 0.04 (0.49) 0.01 (0.98) −0.01 (−1.78*** )

−0.22 0.30 (3.45* ) −0.0006 (−21.36* ) −0.02 (−2.78* ) 0.000004 (0.09) 3.65 (15.20* ) 0.001 (0.01) 0.01 (3.98* ) −0.01 (−3.10* )

−0.12 (−0.70) 0.16 (1.02) −0.0003 (−5.88* ) −0.01 (−1.04) −0.00003 (−0.50) 2.49 (4.76* ) 0.03 (0.26) 0.01 (1.09) −0.02 (−2.39** )

−0.10 (−0.88) 0.15 (1.41) −0.0004 (−13.40* ) −0.03 (−2.58** ) 0.000003 (0.05) 2.71 (9.67* ) −0.06 (−0.60) 0.01 (2.62* ) −0.01 (−2.66* )

0.28 (1.20) −0.14 (−0.65) 0.0001 (0.73) 0.01 (0.88) −0.0002 (−2.59** ) −2.43 (−3.33* ) −0.02 (−0.13) −0.03 (−2.82* ) 0.01 (1.27)

0.02 (0.14) 0.08 (0.56) 0.0002 (5.38* ) 0.004 (0.31) −0.0001 (−0.99) −1.43 (−3.66* ) 0.06 (0.43) −0.02 (−3.74* ) 0.00 (−0.01)

(−2.47** )

0.10 1.38*** 1.32

0.18 4.17* 5.27*

0.05 1.36*** 1.36***

0.10 3.14* 3.65*

0.03 1.24 1.34

0.04 1.85* 1.65*

1.08

1.85*

1.13

2.12*

1.90*

2.58*

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567

568

Table 3B Execution quality: specialist firm characteristics

PCX Intercept Moneyness Time to maturity Volume Market capitalization Mean percentage spread (underlying) Volatility Closeheld Multiple trading specialist Proportion of total volume Mult × proportion of volume Conc Listing on four exchanges—dummy Adjusted R2 See the legend of Table 2B. * Significance at 1% level. ** Significance at 5% level. *** Significance at 10% level.

−0.13 0.22 (14.13* ) −0.0005 (−10.01* ) −0.02 (−2.40** ) 0.0001 (0.95) 3.68 (9.16* ) −0.01 (−1.06) 0.01 (1.20) 0.01 (0.09*** ) −0.01 (−1.35) 0.24 (1.70*** ) 0.01 (1.19) −0.01 (−1.29) (−7.46* )

0.08

Realized spreads CBOE

PCX

−0.12 0.21 (28.85* ) −0.001 (−18.92* ) −0.03 (−3.80* ) 0.00001 (0.32) 3.50 (15.58* ) −0.002 (−0.86) −0.01 (−1.64) −0.001 (−0.13) 0.12 (0.62) 0.12 (0.60) 0.01 (3.81* ) −0.02 (−4.76* )

−0.08 0.15 (8.43* ) −0.0003 (−5.03* ) −0.01 (−1.57) 0.00003 (0.57) 2.57 (5.49* ) −0.003 (−0.57) 0.01 (0.77) −0.05 (−0.62) −0.01 (−1.63) 0.12 (0.70) 0.00 (0.63) −0.01 (−1.97** )

(−13.50* )

0.14

CBOE (−4.19* )

0.03

Price impact

−0.11 0.18 (20.43* ) −0.0004 (−11.89* ) −0.02 (−3.14* ) 0.0000001 (0.00) 2.64 (10.17* ) 0.0001 (0.02) −0.002 (−0.48) −0.002 (−0.41) −0.05 (−0.22) 0.19 (0.83) 0.01 (3.27* ) −0.02 (−4.51* ) (−10.51* )

0.08

PCX

CBOE (8.14* )

0.23 −0.11 (−4.20* ) 0.00002 (0.30) 0.01 (0.84) −0.0002 (−2.68* ) −1.95 (−2.98* ) 0.02 (2.64* ) −0.01 (−1.18) −0.24 (−2.03** ) −0.01 (−0.58) 0.20 (0.88) −0.03 (−3.65* ) 0.02 (2.51** ) 0.02

0.13 (9.28* ) −0.05 (−4.38* ) 0.0002 (4.78* ) 0.001 (0.12) −0.00005 (−0.75) −1.49 (−4.11* ) −0.0005 (−0.13) 0.01 (1.01) 0.01 (1.10) 0.35 (1.15) −0.45 (−1.39) −0.02 (−4.35* ) 0.01 (2.41** ) 0.01

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569

revenues and also indicates that options listed on two or three exchanges do not achieve the full benefits of competition. 5.1.4. Price impact We measure a specialist’s ability to reduce adverse selection by the price impact of trades in option classes he is responsible for. Price impact is measured as PIt = 200It (mt+5 − mt )

(7)

Summary statistics on price impact for specialist firms on CBOE and PCX are given in Tables 1A and 1B. We find that, for most firms, the average price impact is negative. Therefore, on average specialists seem to perform adequately at managing adverse selection in the market. Eq. (1) (Table 3A) estimates for price impact show significant variation among specialist firms on the CBOE. However, on PCX the only significant difference is in specialists attitude towards volatility. Results from estimating Eq. (2) for price impact on CBOE and PCX are summarized in Table 3B. Consistent with our expectations from the Saar (2001) model, we find that option classes where a particular specialist has the dominant market share tend to have significantly lower adverse selection costs on that exchange, as measured by the price impact of trades. This result holds for both the CBOE and the PCX. We find that the corporate form of the specialist firm is not a significant determinant of price impact. This result is inconsistent with Coughenour and Deli (2002), who find that closely held firms have significantly lower adverse selection costs. To summarize the results so far, we find evidence that specialist firms differ significantly in the market quality offered. This is consistent with the results of Cao et al. (1997) and Corwin (1999), and is the first such result outside the context of the NYSE. 5.2. Differences in specialist posts within specialist firms 5.2.1. Quoted spreads Having established that specialist firms do exhibit markedly different market quality characteristics, we now turn to the analysis of differences among specialist posts within a specialist firm. Tables 4 and 5 present the results.18 We identify seven specialist firms that operate more than one post on PCX and two that operate more than one post on CBOE. For each firm with multiple posts on the floor of the exchange, we include n − 1 dummy variables for the posts if there are n posts in the firm. The regression equation estimated is Si,j = β0 + β1 Money + β2 TtMty + β3 Vlm + β4 MktCap + β5 Usprd + β6 Ustd + β7 Ex4 +

n−1
i=1

γi D i +

n−1
i=1

θi (Di Money) +

n−1


λi (Di Ustdi ) + ε

(8)

i=1

The regression equation is estimated for each firm on each exchange. If a specialist firm has three or more posts, then we test that the dummy variables are jointly equal to zero to evaluate differences in specialist post-performance. Table 4 reports the results for PCX and Table 5 for CBOE. Five of the seven specialist firms with multiple posts on PCX show significantly different quoted spreads between specialist posts

18

For economy, only test-statistics of interest are reported. Complete results are available from the author.

570

Table 4 PCX: differences within specialist firms

Postdummy Susquehanna 5.70* Group 1 4.49* DRZ Derivatives −0.26 Letco LMM −0.96 MJT Securities 0.26 Omega −4.10* Oppenheimer 3.00*

Effective spreads

Realized spreads

Postmoneyness*

PostPostvolatility* dummy

Postmoneyness*

PostPostvolatility* dummy

7.74* −4.79* −0.30 0.91 −0.31 4.03* −3.93*

6.08* −2.32** −0.17 −0.97 −2.87* 1.15

15.62* −0.90 0.33 1.99** 0.18 1.39 −1.78***

0.12 −0.01 −0.97 1.98** −0.84 −0.26

16.58* 0.83 0.10 −1.94*** −0.35 −0.98 1.64

13.73* 0.93 −0.36 0.27 −0.48 −0.61 0.46

Price Impact

Postmoneyness*

PostPostvolatility* dummy

Postmoneyness*

Postvolatility*

13.64* −0.74 0.19 −0.27 0.36 0.97 −0.54

0.74 −1.01 0.43 0.98 −0.56 −0.27

0.13 −0.71 1.42 −1.24 0.05 0.31 −0.22

0.25 0.73 0.80 −0.92 −0.18 −0.04

0.13 0.60 −1.43 1.17 0.01 −0.61 0.28

This table report the results of the test for differences in performance across specialist posts within a specialist firm. The regression equation estimated is Si,j = β0 + β1 Money +  n−1 n−1 β2 TtMty + β3 Vlm + β4 MktCap + β5 Usprd + β6 Ustd + β7 Ex4 + n−1 i=1 γi Di + i=1 θi (Di Money) + i=1 λi (Di Ustdi ) + ε, where Si,j is the relevant dependant variable for the day for series i and specialist j. Money is the moneyness of the option series i, TtMty the time to maturity of the options series i, Vlm the total volume in the option class, MktCap the market capitalization of the underlying stock, Usprd the mean percentage spread on the underlying stock on the day, Ustd the standard deviation of daily return of the underlying stock for the month of October. Ex4 is a dummy that equals 1 if the option is traded on four exchanges. D1 − Dn−1 are dummies for the specialist posts within the specialist firms. Separate results are presented for each specialist firm that is identified to have multiple specialist posts on the floor of the exchange. For economy, only test statistics for variables of interest are reported. The test statistic for Susquehanna comes from an F-test as Susquehanna has three posts. All other test statistics are based on t-tests. Complete results are available from the author. * Significance at 1% level. ** Significance at 5% level. *** Significance at 10% level.

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Table 5 CBOE: differences within specialist firms Quoted spreads

Effective spreads

Realized spreads

Price impact

Susquehanna F-test: post-dummies = 0 F-test: post-dummies × moneyness = 0 F-test: post-dummies × volatility = 0

9.17* 11.27* 4.56*

3.78* 3.93* 1.22

4.23* 4.39* 1.10

0.62 1.05 1.40

Botta F-test: post-dummies = 0 F-test: post-dummies × moneyness = 0 F-test: post-dummies × volatility = 0

12.97* 12.21* 2.70**

8.11* 7.90* 4.68*

2.72** 2.73** 2.68**

0.60 0.63 0.97

This table report the results of the test for differences in performance across specialist posts within a specialist firm. The regression equation estimated is Si,j = β0 + β1 Money + β2 TtMty + β3 Vlm + β4 MktCap + β5 Usprd + β6 Ustd +  n−1 n−1 β7 Ex4 + n−1 i=1 γi Di + i=1 θi (Di Money) + i=1 λi (Di Ustdi ) + ε, where Si,j is the relevant dependant variable for the day for series i and specialist j. Money is the moneyness of the option series i, TtMty the time to maturity of the options series i, Vlm the total volume in the option class, MktCap the market capitalization of the underlying stock, Usprd the mean percentage spread on the underlying stock on the day, Ustd the standard deviation of daily return of the underlying stock for the month of October. Ex4 is a dummy that equals 1 if the option is traded on four exchanges. D1 − Dn−1 are dummies for the specialist posts within the specialist firms. Separate results are presented for each specialist firm that is identified to have multiple specialist posts on the floor of the exchange. For economy, only test statistics for variables of interest are reported. Complete results are available from the author. * Significance at 1% level. ** Significance at 5% level.

(either through differences in the intercept term or differences in the interaction terms). Both the firms on the CBOE have significantly different quoted spreads across specialist posts. Thus, there is evidence that specialist performance varies due to individual specialist influence as far as quoted spreads are concerned. Table 6 presents the results of tests for differences within specialist firms across exchanges.19 We identify eight firms that operate as specialists on both CBOE and PCX. If the firm is able to impose uniformity across its specialists, then we should expect to see similar performance across the two exchanges. Conversely, if the individual specialists operate differently then we will witness differential performance by the firm on the two exchanges. This test has the advantage of eliminating any confounding effects that might creep into earlier tests due to options being traded by relief specialists.20 On the other hand, this test suffers from the disadvantage that differences in exchange regulations might influence the results. However, we do not find any substantial differences in regulations of specialists in the two exchanges.21 The regression equation estimated for this test is Si,j = β0 + β1 Money + β2 TtMty + β3 Vlm + β4 MktCap + β5 Usprd + β6 Ustd + β7 CBOE + β8 CBOE × Money + β9 CBOE × Ustd + β10 Ex4 + ε

19

(9)

For economy, only test-statistics for the variables of interest are reported in the table. Complete results are available from the author. 20 Corwin (1999) notes the practice of relieving specialists on break or during absences. 21 Apart from the guaranteed participation rule which guarantees a higher proportion of the order flow to the specialist on the PCX.

572

Table 6 Differences in specialist firm performance across exchanges

Postdummy Botta 3.20* Group 1 −5.92* Letco LMM 0.19 Spear −0.37 Susquehanna 1.98** TFM 0.85 Timberwolf 4.53* Wolverine −1.50

Effective spreads

Realized spreads

Price impact

Postmoneyness*

Postvolatility*

Postdummy

Postmoneyness*

Postvolatility*

Postdummy

Postmoneyness*

Postvolatility*

Postdummy

Postmoneyness*

Postvolatility*

−1.93*** 6.04* 1.13 0.05 −2.25** −0.96 −4.34* 1.04

−4.47* 1.56 −3.51* 2.11** 1.09 4.39* −1.18 −2.03**

−0.51 −1.64 −0.99 0.58 0.64 −0.84 1.19 −1.66***

0.51 1.87*** 1.38 −0.65 −0.52 1.16 −1.08 1.65

0.78 0.95 −1.42 −0.45 0.25 1.23 −0.88 −0.66

−0.78 −0.58 −0.27 −1.52 0.48 −1.03 0.69 −0.92

1.24 0.68 0.45 1.53 −0.47 1.30 −0.53 1.09

−0.22 0.68 −0.67 0.05 0.56 1.35 −0.19 −0.93

−0.77 −0.08 0.71 −0.38 −1.71*** 0.58 −0.11 −0.95

0.78 −0.42 −1.07 0.29 1.52 −0.79 −0.53 0.84

−1.37 0.40 1.72*** −1.21 −1.46 −0.63 1.29 −0.86

This table presents the results of tests for differences within specialist firms across exchanges. The regression equation estimated is Si,j = β0 + β1 Money + β2 TtMty + β3 Vlm + β4 MktCap + β5 Usprd + β6 Ustd + β7 CBOE + β8 CBOE × Money + β9 CBOE × Ustd + β10 Ex4 + ε, where Si,j is the relevant dependent variable for the day for series i and specialist j. Money is the moneyness of the option series i, TtMty the time to maturity of the options series i, Vlm the total volume in the option class, MktCap the market capitalization of the underlying stock, Usprd the mean percentage spread on the underlying stock on the day, Ustd the standard deviation of daily return of the underlying stock for the month of October. CBOE, equals 1 for observations on the CBOE and 0 otherwise. CBOE × Money and CBOE × Ustd are interaction terms of variables mentioned above. Ex4 is a dummy that equals 1 if the option is traded on four exchanges. Separate results are presented for each specialist firm that is identified to have multiple specialist posts on the floor of the exchange. For economy, only test statistics for variables of interest are reported. * Significance at 1% level. ** Significance at 5% level. *** Significance at 10% level.

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The only new variables in this regression are the dummy variable CBOE, which equals 1 for observations on the CBOE and 0 otherwise, and this dummy variable interacted with Moneyness and Volatility. The equation is estimated for each specialist firm that acts as specialist on both exchanges. Table 6 shows that at least one of the three variables of interest is significantly different from zero for all of the eight specialist firms. Thus, the results suggest differences in quoted spreads within specialist firms. 5.2.2. Effective spreads Table 4 shows that three of the seven firms with multiple posts on PCX show some differences in effective spreads. In two of these firms, differences are significant at the 5% level.22 On CBOE, specialist posts differ significantly in both specialist firms with multiple posts (Table 5). Interestingly, Susquehanna shows significant differences on both the PCX and the CBOE. Table 6 reports that, of the eight specialist firms which operate on CBOE as well as PCX, only two differ significantly across exchanges on the basis of effective spreads (and that too only at the 10% level). The evidence thus indicates that although, for most firms effective firms tend to be uniform, some firms show significant variation in posts within the firm. 5.2.3. Realized spreads We do not find any significant differences in realized spreads for specialist posts within specialists firms on the PCX, except for one firm, Susquehanna (Table 4). On the CBOE (Table 5) posts within both firms show significant differences in realized spreads. Table 6 shows that none of the eight specialist firms (that trade on the two exchanges) differ on CBOE vis-`a-vis the PCX. Therefore, while realized spreads show significant variation across specialist firms, the measures are largely similar within specialist firms. 5.2.4. Price impact Estimates from Eq. (8) for PCX (Table 4) and CBOE (Table 5) show no evidence of variation in price impact of trades within specialist firms. Table 6 reports the results of Eq. (9). There is again little evidence of differences within firms across exchanges. Thus, we do not find evidence of intra-firm differences for price impact. This could indicate a role for the firm in building floor relationships. 6. Conclusions, limitations and further research Theoretical models of dealer behavior have focused on the role of the individual dealer in setting spreads and providing executions to traders. The SEC (2000) study on payment for order flow arrangements in options markets emphasizes the importance of specific specialist postperformance for order routing firms. However, no empirical study has focused on the individual specialist as the unit of analysis. In this paper we look inside a specialist firm at the individuals who actually make markets on the floor. The question answered is whether specialist firms are able to influence individual behavior such that we observe uniformity in performance across the individuals making markets. Previous studies have already documented that differences exist across specialist firms, indicating that exchanges are not able to impose uniformity on the firms. Our findings in this study are highly pertinent for the results of these studies. We also contribute

22

The parameter for Post × Ustd could not be calculated for one firm (Oppenheimer) in this equation.

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to the debate on the level of disclosure that needs to be mandated by the SEC. Our results indicate that firm level disclosure of market quality might be sufficient. In addition, we also provide the first analysis of specialist firms outside the NYSE. Thus, we test the results of earlier studies with different securities and on two separate exchanges providing the first “out of sample” test of their results. We find that significant differences in performance exist between specialist firms on the two options exchanges analyzed. This result is consistent with the literature. However, within a specialist firm, we find that while quoting characteristics differ significantly across specialist posts within a firm, the differences are markedly less where execution quality is concerned. These results indicate that while specialists (within the firm) compete for order flow differently (through their quoted spreads), their executions tend to be less different. Since the execution quality criteria analyzed here include adverse selection and dealer revenues (realized spreads), we conclude that some specialist firms are able to create penalty/incentive systems that create uniformity in the revenues dealers earn. The uniformity in managing adverse selection across specialist posts indicates a role for firm reputation in information sharing relationships on the floor. Some firms, however, consistently display differences within the firm even where execution quality is concerned. This presents a challenge to policy makers, as differences in execution quality within a firm indicate that the disclosure of market quality needs to be at the post-level, not just at firm level. In the options markets, we contribute to the literature by analyzing the performance of the “dominant” specialist and the significance of the number of listing venues on different measures of market quality. We find that the “dominant” specialists manage the adverse selection problem better than other specialists do. These results are consistent with the predictions in Saar (2001). Our results also show that option classes that are listed on four exchanges have significantly lower spreads than those that are listed on only two or three exchanges. This indicates that benefits of competition are not fully realized for even those options that are multiple listed on two or three exchanges. There are limitations to our analysis that are important to note here. In this study, we find evidence that specialist firms are able to create systems that impose uniformity among the specialists. However, we have no understanding of what these systems are. A study that sheds light into the internal processes of specialist firms and their relation to market quality would be invaluable.

Appendix A Closely held specialist firms on CBOE and PCX CBOE

PCX

Boyle & Snyder Trading, LLC Brown Trading Group Felt Trading Johnson Trading, JV KFT DPM Saliba Partners Samuelson Trading Corp. Schwartz Trading Group, LLC ZH Partners

Oppenheimer, Noonan, Weiss Craig A. Resnick David Post&Partners/Beemac Trading Armstrong&Kurnik, LLC J Squared&Associates Cooney, Donahue, Zeigler Berger Barnett Inv. Partners, CLP Kovell Trading, LLC Chin Options

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References Anand, A., & Weaver, D. G. (2002). The value of the specialist: Empirical evidence from the CBOE, Working paper, Syracuse University. Battalio, R., Ellul, A., & Jennings, R. (2004). Reputation effects in trading on the New York Stock Exchange, Working paper, Indiana University. Battalio, R., Hatch, B., & Jennings, R. (2004). Does a national market system exist for U.S. exchange-listed equity options? An analysis of multiple-traded equity options. Journal of Finance, 59, 933–962. Boehmer, B., & Boehmer, E. (2003). Trading your neighbor’s ETFs: Competition or fragmentation? Journal of Banking and Finance, 27, 1667–1703. Cao, C., Choe, H., & Hatheway, F. (1997). Does the specialist matter? Differential execution costs and inter-security subsidization on the New York Stock Exchange. Journal of Finance, 52, 1615–1640. Cho, Y., & Engle, R. (1999). Modeling the impacts of market activity on bid-ask spreads in the option market, NBER Working paper 7331. Corwin, S. (1999). Differences in trading behavior across NYSE specialist firms. Journal of Finance, 54, 721–746. Corwin, S. (2004). Specialist performance and new listing allocations on the NYSE: An empirical analysis. Journal of Financial Markets, 7, 27–51. Coughenour, J., & Deli, D. (2002). Liquidity provision and the organizational form of NYSE specialists firms. Journal of Finance, 57, 841–869. De Fontnouvelle, P., Fishe, R., & Harris, J. (1999). The behavior of bid-ask spreads and volume in options markets during the competition for listings in 1999. Journal of Finance, 58, 2437–2464. Hansch, O., & Hatheway, F. (2001). Measuring execution quality in the listed option market, Working paper, Penn State University. Hasbrouck, J., & Sofianos, G. (1993). The trades of market makers: An empirical analysis of NYSE specialists. Journal of Finance, 48, 1565–1593. Hatch, B. C., & Johnson, S. A. (2002). The impact of specialist firm acquisitions on market quality. Journal of Financial Economics, 66, 139–167. Ho, T., & Stoll, H. R. (1983). The dynamics of dealer markets under competition. Journal of Finance, 28, 1053–1074. Ho, T. S. Y., & Macris, R. G. (1985). Dealer market structure and performance. In Y. Amihud, T. S. Y. Ho, & R. A. Schwartz (Eds.), Market making and the changing structure of the securities industries. Lexington Books. Madhavan, A., & Smidt, S. (1993). An analysis of changes in specialist inventories and quotations. Journal of Finance, 48, 1595–1628. Mayhew, S. (2002). Competition, market structure and bid-ask spreads in stock option markets. Journal of Finance, 57, 931–958. Naik, N. Y., & Yadav, P. K. (2003). Do dealer firms manage inventory on a stock-by-stock or a portfolio basis? Journal of Financial Economics, 69, 325–353. Neal, R. (1987). Potential competition and actual competition in equity options. Journal of Finance, 42(3), 511–531. Neal, R. (1992). A comparison of transaction costs between competitive market maker and specialist market structures. Journal of Business, 65(3), 317–334. O’Hara, M., & Oldfield, G. S. (1986). The microeconomics of market making. Journal of Financial and Quantitative Analysis, 21, 361–376. Saar, G. (2001). Investor uncertainty and order flow information, Working paper, New York University. U.S. Securities and Exchange Commission (SEC) (2000). Special Study: Payment for order flow and internalization in the options markets. U.S. Securities and Exchange Commission (SEC) (2001). Final Rule: Disclosure of order execution and routing practices, Release No. 34-43591. Wang, E. (2000). Competition among exchanges: Does multiple listing affect trading costs on options markets? Ph.D. Dissertation, University of Chicago, Department of Economics.