Single Stock Futures as a Substitute for Short Sales: Evidence from Microstructure Data

Transcription

1 Journal of Business Finance & Accounting Journal of Business Finance & Accounting, 36(9) & (10), , November/December 2009, X doi: /j x Single Stock Futures as a Substitute for Short Sales: Evidence from Microstructure Data Bartley R. Danielsen, Robert A. Van Ness and Richard S. Warr* Abstract: We examine how the introduction of single-stock futures impacts short sale costs and short interest levels in the underlying spot market. We find that short selling in the underling securities declines, after futures are introduced, the cost of borrowing stock for short sales declines and the available unborrowed supply of lendable shares increases. These results are consistent with futures exchanges providing a low-cost substitute market for establishing short positions. Microstructure evidence also suggests that the lower cost and greater ease of short selling via futures markets draws informed traders from the spot market. Keywords: stock futures, microstructure, short sales 1. INTRODUCTION Single stock futures were first introduced in Australia and Hong Kong in the mid 1990s, and since then have become available on numerous other markets including those in the UK and the US. While research on single stock futures has examined their effect on volatility and price discovery in the underlying market, there has been little work on their potential impact on short selling in the cash market. In this paper we examine the impact of the introduction of single stock futures on the short selling and trading characteristics of the underlying stocks. Because of the size and liquidity of US stock markets, the introduction of single stock futures there represents a unique opportunity to test their impact on short sale constraints for the underlying securities. While our study examines only US securities, the results are reflective of spot-futures market interactions in a more general context, and in other countries. A single stock futures contract allows a buyer (or seller) to commit to buy (or sell) a specific stock at a pre-determined price and on a pre-determined date. In the United *The first and third authors are from the College of Management, North Carolina State University. The second author is from the University of Mississippi School of Business. They wish to thank the editor and the anonymous referee for their comments on earlier drafts of this paper as well as session participants at the 2007 Financial Management Association Annual Meeting. The usual disclaimer applies. (Paper received October 2007, revised version accepted May 2009, Online publication September 2009) Address for correspondence: Richard S. Warr, College of Management, Box 7229, North Carolina State University, Raleigh, NC , USA. rswarr@ncsu.edu Journal compilation C 2009 Blackwell Publishing Ltd, 9600 Garsington Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA. 1273

2 1274 DANIELSEN, VAN NESS AND WARR States, single stock futures contracts first began trading on November 8, 2002, on two exchanges; OneChicago and NQLX. Prior to the introduction of single stock futures, index futures contracts were the only equity futures available to investors in the United States. In general, futures contracts allow investors to take leveraged positions in stocks at a lower cost than could be obtained by purchasing the stock in the spot market. Such positions may be attractive when stocks are difficult to short and may alleviate short sale constraints on these stocks by providing an alternative mechanism for creating short positions. Previous work by Danielsen and Sorescu (2001) suggests that other derivative (option) introductions may relax short sale constraints. However, futures exchanges contend that futures contracts are significantly superior products for establishing short positions due to the potential for financial leverage and the linear payoff structure of the securities. Exchanges argue, in particular, that futures contracts provide a lowcost alternative to short sales in the spot market because the relatively opaque security lending market can be circumvented. The aim of this paper is to test whether spot market short selling costs and activities change in response to futures introductions. In light of recent political efforts to reign in the use of futures trading by speculators, understanding the impact of futures on short sale constraints is important to both investors and regulators. The ability of investors to utilize an alternative venue to short a stock may improve price discovery and create better opportunities to trade on information and to manage risk. Also, from a regulator s perspective, limitations and controls on short selling in the cash market may be less effective if short selling can be undertaken via a futures contract. We also examine changes in the market microstructure characteristics of stocks with newly listed single-stock futures. To the extent that short sellers are informed traders, as opposed to noise traders, the availability of highly leveraged, low-cost futures contracts may cause short sellers, as well as other informed traders, to migrate from the spot market to the futures market. An exodus of informed traders can be expected to lead to tighter spreads, reduced intra-day volatility, and lower trade sizes. We may also observe reduced volatility in the spot market if the newly added futures contracts serve to complete the market, as posited by Weller and Yano (1987). An alternative hypothesis, as suggested by Chau et al. (2008), is that the presence of cheaply traded derivatives may allow for leveraged positions that destabilize the spot market due to the activities of speculative investors. This speculative trading may lead to increasing stock market volatility, and spreads may widen upon the introduction of the futures contract as market makers respond to greater volatility of the underlying stock. To test these hypotheses, we use a method similar to that of Kumar et al. (1998) who examine the impact of options contracts on a range of microstructure measures. Our paper contributes to the literature in the following ways. First, we examine the effect of futures introductions on the level of short selling in the underlying stock and find that short selling declines after the introduction of the futures. Furthermore, we find that the cost of shorting also declines and the availability of shares in the stock-lending market increases. All these findings are consistent with futures contracts ameliorating short sales constraints by providing an alternative venue for short selling. This is a new, previously undocumented, result. This result also contributes to the literature started by Ross (1976) that argues that derivatives may complete markets. Our second contribution is that we find significant reductions in spreads, intra-day volatility and trading volume. The decline in spot market trading volume results from

3 SINGLE STOCK FUTURES AS A SUBSTITUTE FOR SHORT SALES 1275 smaller average trade size following the introduction of single-stock futures contracts. We interpret our results as providing evidence that informed traders migrate to the futures markets, resulting in lower trading costs for liquidity traders in the spot markets. This result reinforces previous work that finds that single stock futures at worst have little effect on the underlying market (Chau et al., 2008) and at best actually improve the quality of the underlying market (Shastri et al., 2008). Our results are inconsistent with single stock futures destabilizing the spot market. The remaining sections of our study are structured as follows: Section 2 discusses the previous literature and hypothesis development. Section 3 describes the data and method. Section 4 presents the results and analysis, and Section 5 concludes. (i) Related Literature 2. RELATED LITERATURE AND HYPOTHESIS DEVELOPMENT Single stock futures are derivative securities in that their value is derived from an underlying stock. These futures contracts were first introduced in Australia in May Hong Kong began trading the securities in March 1995, and more recently, single stock futures were introduced in the UK in January In November 2002, single stock futures finally began trading in the United States. Although single stock futures were not traded until the 1990s, as far back to Ross (1976) and Hakansson (1982) theorists have proposed that stock values might be influenced by the trading in their own derivatives. Specifically, derivatives lead to more complete markets and improve trading efficiencies in the underlying security. Looking at stock options, John et al. (1993) show that the lower cost of options, and their ability to avoid short sale constraints, makes them attractive to speculators in the spot market. As informed investors move to the options market, fewer informed investors remain in the spot market, and the market maker reduces his spreads. The thesis of John et al. is also valid for the introduction of a futures market as well. Research to date finds that the introduction of single stock futures ranges from having a benign effect to a positive effect on the underlying stock market. For example, several papers find that the introduction of single stock futures coincides with a reduction in the volatility of the underlying stock. Lien and Yang (2003) find that futures dampen price volatility in Australia, while McKenzie et al. (2001) find that futures lead to a reduction in the systematic risk of the underlying stock. Dennis and Sim (1999) find that futures introduction have very little effect on cash market volatility. Lee and Tong (1998) find that the trading volume in the cash market increases following futures introductions, but that there is no change in the volatility of the underlying stock. Ang and Cheng (2005b), show that after single stock futures introductions in the US, the number of large daily stock returns declines for stocks that have single-stock futures contracts trading, and that this reduction is correlated with the volume of the futures contract. Overall these studies support the earlier work of Weller and Yano (1987) and Detemple and Jorion (1990) who find that the introduction of a derivative security can result in lower spot asset volatility. In addition to a reduction in volatility, there is also evidence that single stock futures improve the quality of the underlying market through greater pricing efficiency and lower trading costs. For example, Ang and Cheng (2005a) find evidence consistent with the presence of futures contracts leading to more precise pricing in the spot market.

4 1276 DANIELSEN, VAN NESS AND WARR They argue that futures provide a low-cost means by which arbitrageurs can profit from short term spot mispricing. Shastri et al. (2008) also find an improvement in market quality in the US after futures introductions. An important feature of single stock futures is their usefulness in establishing short positions in a stock. Although much attention has been given to the impact of short selling in recent literature (for example, Diether et al., 2009; Blau et al., 2009; and Christophe et al., 2009), there is little if any work on the effect of single stock futures on the cost of shorting. There are good reasons to believe that single stock futures may impact the cost of short selling. Miller (1977) speculated that derivatives (more specifically put options) might relax short-sale constraints. Figlewski and Webb (1993) and Danielsen and Sorescu (2001) find evidence consistent with the hypothesis that options relax short-sale constraints. Marketing materials circulated by exchanges regarding the benefits of single-stock futures frequently note that single-stock futures have lower margin requirements than spot market trades. Specifically, initial margin requirements for single stock futures in the US are around 20%, compared to 50% in the cash market (Dutt and Wein, 2003). 1 Short sales, in particular, are easier and cheaper to initiate via futures, and maintaining a short stock position via futures is also less risky and less costly. Futures also have advantages over options in establishing short positions. Option payoffs are non-linear in their payoffs, but futures yield payoffs that are linear in the stock price. An investor must also coordinate writing a put and buying a call with identical strike prices and expirations in order to establish the same position as a short single-stock futures contract. In fact, brokerage firms tout single-stock futures as superior hedging vehicles for some purposes because delta hedging is not required. A long-short strategy that seeks to short one oil company while owning a competing oil company derives no benefit from non-linear payoffs if the short position requires frequent rebalancing. Thus, introduction of single-stock futures is expected to lead to a substitute and competing market for short selling that will relax short-sale constraints. (ii) Testable Hypotheses Our main hypothesis can be stated as follows: single-stock futures contracts serve as a substitute for short selling and security lending in the underlying stock market. To the extent that short sellers are informed traders, the creation of a substitute market should reduce the presence of informed traders in the underlying market and lead to lower spreads and reduced volatility. Assuming that informed traders trade in above-average trade sizes before the introduction of the derivative, trade sizes should also decline following single stock futures introductions. The foregoing hypothesis generates specific testable implications. First, we should observe declines in short selling, a decline in the cost of shorting, and an increase in the supply of shares available for shorting after futures are introduced. Second, we should observe declines in spreads and trade size as informed traders migrate to futures. Third, we should expect to see a cross sectional relation between the change in short sale constraints and the change in the microstructure variables. 1 The margin requirement for index futures in the US is even lower and ranges from 2% to 10% (Chatrath et al., 2001).

5 SINGLE STOCK FUTURES AS A SUBSTITUTE FOR SHORT SALES DATA AND METHOD Single stock futures were introduced on two exchanges in the United States; OneChicago LLC and NQLX. OneChicago is a joint venture created by the Chicago Mercantile Exchange Inc. (CME), the Chicago Board Options Exchange (CBOE) and the Chicago Board of Trade (CBOT). NQLX is a fully electronic exchange formed as a joint venture between the NASDAQ Stock Market and the London International Financial Futures and Options Exchange (LIFFE). NQLX initially listed single stock futures on the largest US companies through LIFFE s electronic trading platform, LIFFE CONNECT. We examine futures listing events on NASDAQ and NYSE listed stocks (excluding exchange traded funds) from November 2002 through the end of Over this period, 111 unique firms had single-stock futures listed. Forty of these firms were listed by either OneChicago or NQLX, but not by both. Five of the companies were listed by both futures exchanges on the same day. 2 The remaining firms were listed by both futures exchanges, but on different days. These sequential listings occurred for 66 firms. For the 66 stocks that had sequential futures listings on the exchanges, the time between listings varies. It is typically at least a month apart, and many of these occur more than 90 days apart. A few stocks have more than a year pass before the second exchange lists a futures contract on the firm. In our analysis we examine only initial listings, including the five simultaneous listings. We do not investigate short sale activities around date of subsequent listings on a second exchange. (i) Short Interest Data We obtain short interest data from two sources. We use publicly available monthly short interest data published by the NYSE and NASDAQ. These data are for trades that settle by the 15th day of each month. We scale the monthly short interest by CRSP shares outstanding to generate a monthly relative short interest variable. Because overall market relative short interest varies month to month, we then scale the firm level relative short interest measure by the relative short interest for the entire market. We measure relative short interest over the 12 months before and after the futures listing event. This short interest data is monthly, while the futures listings can be on any trading day of the month, therefore we only use short interest data in the month prior to and after the listing month to avoid any overlap. In Section 3(ii) we discuss the daily share borrowing data that provides a finer measure of short selling activity. Using this data we compute the variable relative short interest (RSI) as the total monthly short interest for the stock as a percentage of the shares outstanding, scaled by the market-wide average percentage short interest. It is a measure of what proportion of the stock is shorted at any point compared to the average level of short interest in the market. The formula for the relative short interest for stock i, in month t is as follows: / ShortInterest it Shares RSI it = it n ShortInterest /, (1) jt j =1 Shares jt 2 The stocks that listed on two exchanges on the same day are General Electric, Metro Goldwyn Mayer, Exxon Mobil, Microsoft and Oracle.

6 1278 DANIELSEN, VAN NESS AND WARR where n is the total number of all stocks trading in month t. Short interest is measured over the 12 months before and 12 months after the futures listing. (ii) Daily Security Borrowing Data We have obtained short-stock rebate data from a broker who makes markets as a securities lender. These market-making activities consist, in part, of borrowing shares from mutual funds and re-lending the shares to hedge funds that short the stock. The mutual funds cannot lend directly to the hedge funds, which typically have C credit ratings, so the broker intermediates stock loans and earns a spread. The data we are using details the stock borrowing activities of the broker/market maker. This daily data begins in March 2001 and contains the average short-stock rebate and fee associated with the borrowed shares. The short-stock rebate is the rate of interest that the borrower earns on his posted cash collateral. Normally this rate is the general collateral rate which is close to the short-term treasury rate. Occasionally, when a stock becomes hard to borrow, the rebate rate will be lower. This provides additional compensation to the lender for lending hard-to-borrow securities. The borrowing fee is the short-term treasury rate net of the rebate. Simply stated, the fee is the amount of interest foregone by the borrower assuming that the collateral is invested in short-term treasury securities. This variable, the borrowing fee, is the cost of borrowing shares: the rental price. Although the borrowing fee is not a price determined in a centralized market, the securities lending market is competitive and the market-maker asserts that the borrowing fees for the firms in this study are similar across market makers. One shortcoming of the borrowing fee data is that, although the data is reported daily, the fee is reported only when a transaction occurs on that day. Further, we cannot impute a fee on the basis of bid-ask prices because we do not have the information. Thus, we have many missing observations in the borrowing fee data and for any individual firm, there are discontinuities. Because we are interested in observing changes in the market around the futures introduction, we do not interpolate the data on the basis of nearby trades, as doing so would artificially smooth the data. We measure the borrowing fee over the 25 days before and the 25 days after the futures listings event. The final short interest variable in our data is a measure of street availability at the close of trading each day. We refer to this variable, which is also scaled by CRSP shares outstanding, as Share Availability, and it is the aggregate shares reported to be available by approximately 30 large institutions. We believe that this is a unique measure of the magnitude of short-sale constraints in the market in that it is a broad measure of available supply. Stocks with greater share availability should be less costly to borrow. As evidence of this relationship, we observe that Share Availability is negatively correlated with the marginal cost of shorting shares. The Spearman rank correlation is approximately Like the Borrowing Fee data, Share Availability has missing observations. The variable is not reported for stocks on any day for which the market maker had neither a new borrowing transaction nor a day-end open position. Again, we do not interpolate the data to generate values for the missing data. If futures introductions relax short-sale constraints, the nature of the relaxation might be evidenced in a decline in the borrowing fee for borrowing shares in the securities lending market. A relaxation of short sales constraints should also result in an increase in the shares available for borrowing.

7 SINGLE STOCK FUTURES AS A SUBSTITUTE FOR SHORT SALES 1279 (iii) Stock Trading Data Our liquidity and trading cost measures for the stocks underlying the futures are computed using data obtained from the New York Stock Exchange TAQ (trade and quote) database. We precondition the TAQ data in several ways; we omit trades and quotes if they are flagged as out of time sequence or involve either an error or a correction. We omit quotes if either the ask or the bid price is equal to or less than zero, and we omit trades if the price or volume is not greater 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; and (5) bid quote, b t when (b t b t 1 )/b t 1 > We collect trades and quotes data for all the stocks in our sample for the 25 days before and the 25 days after the initial trading date of the single stock futures. We consolidate the data on a daily basis in order to compute the microstructure statistics as follows. The quoted Percentage Spread is defined as the difference in the ask price and the bid price, for each firm, divided by the midpoint of the spread: Percentage Spread = Ask Price Bid Price, (2) Midpoint where the midpoint of the spread is defined as the mean of the ask price and the bid price, for each firm: (Ask Price + Bid Price) Midpoint =. (3) 2 Percentage Effective Spread reflects the fact that trades occur inside the bid and ask quotes, we compute the Percentage Effective Spread as: Percentage Effective Spread = 2D(Trade Price Midpoint). (4) Midpoint Trade Price is the transaction price, and D is a binary variable which equals +1 for a customer buy order and 1 for customer sell orders (as in Lee and Ready, 1991). There exist structural differences between NYSE and NASDAQ that lead to differences in costs and trading activity across the two exchanges. Bessembinder (1999 and 2003), Chung et al. (2002), Bessembinder and Kaufman (1997), Christie and Huang (1994) and Huang and Stoll (1996) show that NASDAQ has higher trading costs than NYSE stocks. Additionally, Atkins and Dyl (1997) show that NASDAQ tends to double count trading activity (the number of trades and volume) relative to the NYSE. Given these findings, we separate our results by the exchange in which the underlying stock trades (the NYSE or NASDAQ). (iv) Summary Statistics Table 1 presents summary statistics of the 111 stocks that comprise our sample (a full listing of the stocks is provided in the Appendix). In Panel A, we break out the listings by stock market of the underlying stock and by futures exchange. Because five

8 1280 DANIELSEN, VAN NESS AND WARR Table 1 Distribution of Listings Across Futures and Stock Exchanges, and Summary Statistics Panel A: Distribution of Listings ONE NQLX Total NASDAQ NYSE Total Panel B: Summary Statistics Mean Standard Deviation Minimum Maximum Market Value ($US Millions) 49,348 67,557 1, ,589 Borrowing Fees Share Availability Relative Short Interest Percentage Spread Percentage Effective Spread Standard Deviation of Quote Midpoint Volume 7,651 10, ,413 Number of Trades 8,258 10, ,739 Trade Size 1,347 1, ,474 Notes: Panel A shows the distribution of the single stock futures listings across stock exchanges and futures exchanges. Note that only first listings of a stock are reported. Panel B presents the summary statistics of the major variables. These are measured on the listing day for market value and the microstructure variables (percentage spread, effective spread, volume, number of trades, trade size and standard deviation of the quote midpoint, on the listing month for the relative short interest, share availability and borrowing fee variables. firms are listed simultaneously on OneChicago and NQLX, the table aggregates to 116 listings. Panel B presents summary statistics for the sample. Overall the stocks are large, with a mean market value of nearly $50 billion, however, there is quite a range in size with the smallest stock having a market value of just $1.4 billion. Looking at the short interest data, the borrowing fees are on average low, being only 20 basis points, but reach a maximum of 175 basis points. Clearly, based on these fees, none of the stocks is terribly short sale constrained. On average there is around 11.7% of the stock s shares outstanding availability for lending in the share borrowing market. Relative short interest compares the short interest of the stock with that of the average stock in the market. For the average stock in the sample, the relative short interest is around 1.57 times that of the average stock in the market. So it would be fair to conclude that these stocks are a little more heavily shorted than those in the market in general. The average percentage spread is 1/10 th of a percent of the stock price. Thus these stocks are on average very liquid. 3 The percentage effective spread numbers are similar, although smaller as one would expect given that they are based on trade prices. Consistent with 3 It is worth noting that the highest percentage spread is only about 0.5% of the stock price, thus the $4 screen is likely to screen out TAQ data errors, rather than abnormal actual trades.

9 SINGLE STOCK FUTURES AS A SUBSTITUTE FOR SHORT SALES 1281 the spreads being narrow, we observe quite high trading volumes for the sample stocks. With an average of million shares traded per day. Average trade size is around 1,350 shares. A cursory glance at the names of the stocks (provided in the Appendix) reveals that these stocks are generally very well known high profile companies. Clearly the futures markets carefully selected these companies to ensure healthy demand for their single stock futures products on their initial listings. Given the fairly large size of the firms chosen for listing, it would be unreasonable to say that these firms are representative of the overall market. However, the goal of the futures exchange is to list contracts that will generate trading volume for the exchange and not to create a broad market sample along the lines of a broad index. Therefore, we should recognize some endogeneity in the sample selection process. 4. RESULTS Our first tests examine the impact of the futures introductions on short-sale-constraintrelated attributes of the underlying stock. Specifically, we examine the following three metrics: the cost of borrowing shares in the security lending market, the available supply of shares in the security lending market that have not been borrowed already, and the level of short interest outstanding in the stock. (i) Borrowing Fees, Share Availability, and Relative Short Interest In Table 2 we present an examination of the changes in Borrowing Fees, Share Availability, and Relative Short Interest. If single-stock futures provide an alternative market for short sellers, we expect that shorting in the underlying market will decline and the cost of borrowing the stock will also decline. Share availability for new borrowing should increase. Panel A presents the mean and median fees associated with borrowing stocks for the 25 trading days before and 25 days after the futures contract listing. There is a statistically significant decline in the borrowing fee for both the mean and median which is an economically significant decrease of around 14% of the pre-listing borrowing cost. Panel B presents changes in the number of shares available for borrowing in the spot market. We observe a statistically significant increase in the shares that are available. This increase is about 6.5% of the pre-futures level. Finally, Panel C presents the changes in mean and median relative short interest of the listed stocks. Both the mean and median levels of relative short interest show a significant decline following the futures introduction. The results of our short interest tests can be summarized as follows. After futures are introduced, the cost of shorting declines, the share availability increases and the relative short interest declines. These results are consistent with short-sales activities migrating from the spot market to the futures market because the single stock futures market provides a substitute shorting venue. (ii) Changes in Spreads for Futures Contract Listings Investors that engage in shorting are typically characterized as being more informed as they are taking greater risks and face higher trading costs than investors in long

10 1282 DANIELSEN, VAN NESS AND WARR Table 2 Changes in Short Interest Pre Post Difference P-Value Panel A: Borrowing Fees Mean < Median < Panel B: Share Availability Mean < Median < Panel C: Relative Short Interest Mean Median < Notes: This table examines the change in the cost of short selling and the level of short interest in the stocks underlying single stock futures. In both panels we measure the pre and post futures level of the variable of interest where the first futures contract listing is used as the event date. Panel A presents the cost of short selling proxied by the fee paid to borrow the shares. Panel B presents the shares available in the share lending market as a fraction of the shares outstanding in the stock. The variables in Panels A and B are measured over the 25 trading days before and 25 trading days after the futures introduction. Panel C presents the level of short selling computed as the total monthly short interest for the stock as a percentage of the shares outstanding, scaled by the average short interest divided by shares outstanding for all stocks in the market, i.e. The relative short interest for stock i, in month t, / ShortInterest it Shares it RSI it = / nj=1 ShortInterest jt Shares jt where n is the total number of stocks trading in month t. Panel C variables are measured over the 12 months before and 12 months after the futures contract listing. In each panel paired t-tests are used to examine the difference in pre and post futures listing means, and non-parametric sign rank tests are used to examine the difference in the pre and post futures listings medians.,, statistically significant at the 10%, 5% and 1% levels respectively. positions (Diamond and Verrecchia, 1987). Rationally, short sellers will only bear these risks and costs if they have confidence that their positions are appropriate based upon private information supporting their trades. If informed short sellers migrate from the spot market to the futures market, market makers in the spot market should face fewer informed traders. We therefore conjecture that we should observe a decline in spreads in the spot market as well as a decline in traded volume and volatility. The following tests examine the trading properties of the underlying stocks. In Table 3 we examine the trading costs of the underlying stocks for the 51 day window surrounding the introduction of the futures contract. We test for differences in the spread variables for each firm for 25 days prior to the listing compared to the 25 days after the listing. In Panel A, we present the changes in the percentage quoted spread for stocks. As is standard in this type of study, we separate the listings between those where the underlying stock trades on the NYSE and those where the underlying stock trades on NASDAQ. The first row in Panel A shows the change in spreads for firms regardless of whether the single stock future has been initiated on the ONE or NQLX exchange. For NASDAQ stocks, there is no significant reduction in spreads; however for NYSE stocks the reduction is statistically and economically significant. We speculate that the lack

11 SINGLE STOCK FUTURES AS A SUBSTITUTE FOR SHORT SALES 1283 Table 3 Changes in Spreads: Initial Listings NASDAQ NYSE Option Mkt Obs Pre Post Diff t-stat Obs Pre Post Diff t-stat Panel A: Percentage Quoted Spread Both Both (ex DL) ONE NQLX Panel B: Percentage Effective Spread Both Both (ex DL) ONE NQLX Notes: Changes in quoted percentage spread (Panel A) and effective percentage spread (Panel B) are presented for all the listings of futures contracts. The tests are broken out by futures exchange. Both represents listings on both NQLX and OneChicago, Both (ex DL) represents listings on both exchanges, excluding dual listings of firms that listed simultaneously on both exchanges. The quoted Percentage Spread is defined as the difference in the ask price and the bid price, for each firm, divided by the midpoint of the spread, i.e. Percentage Spread = Ask Price Bid Price Midpoint where the Midpoint of the spread is defined as the mean of the ask price and the bid price, for each firm, i.e. (Ask Price + Bid Price) Midpoint =. 2 To measure spreads when trades occur inside the bid and ask quotes, we compute the Percentage Effective Spread as: Percentage Effective Spread = 2D(Trade Price Midpoint) Midpoint Trade Price is the transaction price, and D is a binary variable which equals +1 for a customer buy order and 1 for customer sell orders. In both panels, the variables are measured over the 25 days pre and 25 days post the listing of the futures contract. Statistical significance tests are performed using a two sided t-test.,, statistically significant at the 10%, 5% and 1% levels respectively..

12 1284 DANIELSEN, VAN NESS AND WARR of statistical significance for NASDAQ stocks results from having fewer observations in the NASDAQ sample. However, we cannot rule out the possibility that the differing structure of these markets is contributing to this finding. Out of our sample, five stocks are listed simultaneously on both OneChicago and NQLX. Therefore, the first row of this table contains duplicate observations for these five stocks as we have recorded the effect of each of these five twice. In the second row of the table, we exclude the second observation of each of these five stocks and find the results are broadly unchanged. 4 The third and fourth rows examine the changes after separating the data by futures exchange. Again, we find no significant change for NASDAQ listed stocks, but for the NYSE stocks, futures listings on either exchange appear to be followed by reductions in spreads in the spot market. In Panel B we examine the changes in effective spread, as opposed to quoted spreads, around the futures contract listings. Given that effective spreads are computed using the actual trade price, they are smaller than quoted spreads when the trade is negotiated and the trader receives some form of price improvement from the market maker. Such price improvement is only likely to take place when the market maker receives beneficial information about the transaction such as assurance that the counterparty is uninformed. The effective spread is therefore, perhaps, a better measure of the aggregate cost to the market of asymmetric information than the quoted spread even if the quoted spread is a better measure of the level of asymmetric information. We find a significant change in effective spreads NASDAQ stocks, across the combined sample, and segregated by futures market. The change in effective spreads is a little greater than half a percent of the stock price. For NYSE stocks we also find significant changes in the percentage effective spread. These results provide evidence consistent with the hypothesis that the futures introductions resulted in fewer informed traders trading in the spot market and a general improvement in bid-ask spreads for the spot asset. The hypothesis that the informed traders migrated from the spot market is also supported by a greater incidence of price improvement in the underlying market. The market maker is more likely to offer price improvement (trading within the spread) if he or she thinks that the trader is uninformed, consistent with fewer informed trades taking place in that market. We find evidence for greater price improvement by observing that the effective spread (which measures two times the distance from the trade price to the quote) narrows more than the quoted spread (which is just the difference between ask and bid). (iii) Changes in Trading Volume, Trade Size and Number of Trades In Table 4 we examine changes in the stock trading volume and the components of volume around the futures contract listing. Panel A shows the overall level of share volume for the 25 days before and after the listing for NASDAQ and NYSE stocks. For both futures exchanges and for both stock markets there is a significant reduction in the share volume after the listing of a futures contract. The decreases in volume are quite dramatic and economically significant. These results are consistent with a significant proportion of the trading activity migrating away from the spot market to the futures market. 4 Note that the duplicate observations for the five stocks are identical for both futures exchanges, as we are measuring the variable on the stock exchange where the stock is listed.

13 SINGLE STOCK FUTURES AS A SUBSTITUTE FOR SHORT SALES 1285 Table 4 Changes in Trading Activity NASDAQ NYSE Option Mkt Obs Pre Post Diff t-stat Obs Pre Post Diff t-stat Panel A: Mean Daily Volume (thousands of shares) Both 44 14, , , , , Both (ex DL) 42 13, , , , , ONE 24 11, , , , , NQLX 20 18, , , , , Panel B: Mean Daily Trade Size Both 44 20, , , , , Both (ex DL) 42 18, , , , , ONE 24 18, , , , , NQLX 20 21, , , , , Panel C: Daily Number of Trades Both , , Both (ex DL) , , ONE , , NQLX , , Notes: Changes in volume (Panel A) trade size (Panel B) and number of trades (Panel C) are presented for all the listings of futures contracts. The tests are broken out by futures exchange. Both represents listings on both NQLX and OneChicago, Both (ex DL) represents listings on both exchanges, excluding dual listings of firms that listed simultaneously on both exchanges. In all panels, the variables are measured over the 25 days pre and 25 days post the listing of the futures contract. Statistical significance tests are performed using a two sided t-test. Two sided T-tests are presented.,, statistically significant at the 10%, 5% and 1% levels respectively.

14 1286 DANIELSEN, VAN NESS AND WARR In Panels B and C we decompose the volume measure into the number of trades and the trade size. For NASDAQ stocks we observe a decline in the average trade size as presented in Panel B. The declines are consistent across futures listing exchanges at statistically significant levels. Reductions in trade size for NYSE listed firms are not statistically significant. As previously discussed, we speculate that differing structures of the markets may be a factor in the differing statistical significance for NYSE and NASDAQ stocks in Panel B. In Panel C we observe a statistically significant decline in the number of trades for both NASDAQ and NYSE stocks. The decline in the overall share volume in the spot market is consistent with informed traders migrating from the spot market to the futures market. Furthermore, the decline in the average trade size is also consistent with this migration if we assume that, on average, informed traders will tend to trade larger share lots. This assumption makes sense if informed traders seek to capitalize on their costly information production by trading large numbers of shares. (iv) Changes in Volatility Table 5 presents the effect of the futures listing on the intra day volatility of the listed stock. The intraday volatility is measured as the standard deviation of the quote midpoint and provides a daily measure of price volatility. For NASDAQ stocks, the listing event is associated with a significant drop in volatility. A similar drop is observed for NYSE stocks. These results are consistent with more informed traders moving to the futures contract and reducing volatility of the underlying stock. Furthermore, these results are consistent with earlier work that posits that the creation of a derivatives market will result in greater stability in the underlying spot market (see, for example, Weller and Yano, 1987; Turnovsky and Campbell, 1985; and Detemple and Jorion, 1990). (v) Cross Sectional Regressions So far we have shown that when single stock futures contracts are created the short sale constraints on the listed stocks decline, and the liquidity measures for the underlying stocks improve. These results are consistent with the hypothesis that some informed traders (who engage in shorting) are migrating to the futures contract. However, if there is a direct relation between the change in short sale constraints and the microstructure effects, we should observe a cross sectional relation where those stocks that have greater relaxation in their short sale costs also have greater improvement in their liquidity. Table 6 presents correlations between the major variables used in the cross sectional regressions which follow. We measure the changes in all the variables as the percentage change of the post listing variable compared to the pre-listing variable. With the exception of the relative short interest variable, we measure all the variables over the same time windows as in the prior tables, namely 25 days before and 25 days after the listing. 5 As relative short interest is only reported on the 15 th of each month, it lacks the fine granularity of the other variables. Recognizing this, and in order to best match the measurement period for the other variables, we measure the relative short interest 5 Our results are qualitatively the same if we use +/ 40 or 50 day windows.

15 SINGLE STOCK FUTURES AS A SUBSTITUTE FOR SHORT SALES 1287 Table 5 Changes in Volatility NASDAQ NYSE Option Mkt Obs Pre Post Diff t-stat Obs Pre Post Diff t-stat Both Both (ex DL) ONE NQLX Notes: Changes in the standard deviation of the quote midpoint are presented for all the listings of futures contracts. The tests are broken out by futures exchange. Both represents listings on both NQLX and OneChicago, Both (ex DL) represents listings on both exchanges, excluding dual listings of firms that listed simultaneously on both exchanges. In all panels, the variables are measured over the 25 days pre and 25 days post the listing of the futures contract. Statistical significance tests are performed using a two sided t-test. Two sided t-tests are presented.,, statistically significant at the 10%, 5% and 1% levels respectively.

16 1288 DANIELSEN, VAN NESS AND WARR Table 6 Correlation Matrix of Changes in Key Variables % Quoted % Effective Number Mean Std Dev Borrowing Share Spread Spread Volume of Trades Trade Size of Quote Fee Availability % Effective Spread (<0.001) Volume (0.227) (0.003) Number of Trades (0.004) (0.737) (<0.001) Mean Trade Size (0.001) (<0.001) (<0.001) (0.140) Std Dev of Quote (0.195) (0.034) (0.001) (0.001) (0.029) Borrowing Fee (0.107) (0.043) (0.098) (0.823) (0.022) (0.572) Share Availability (0.009) (0.011) (0.038) (0.274) (0.048) (0.847) (0.196) Rel. Short Interest (0.786) (0.795) (0.603) (0.314) (0.959) (0.184) (0.598) (0.005) Notes: All variables in this table are computed as percentage changes where the variable is measured as the post-listing value/pre-listing value 1. With the exception of Relative Short Interest, the pre-listing values of all the variables are measured over the 25 days before the listing, and the post listing values are measured over the 25 days after the listing. The Relative Short Interest variable is measured over the two months pre and two months post. This variable is only reported monthly.,, statistically significant at the 10%, 5% and 1% levels respectively.

17 SINGLE STOCK FUTURES AS A SUBSTITUTE FOR SHORT SALES 1289 variable over the two months before and the two months after the listing. To address the coarseness of the Relative Short Interest data we also consider using wider measurement windows. However, an important tradeoff is faced when expanding the measurement period. While the two-month window is the most consistent time-frame match with the other variables being considered, a longer measurement period may reduce noise in the relative short interest variable, at the cost of consistency with the variables. Changes in spreads (both quoted and effective) are significantly negatively correlated with the change in share availability, consistent with liquidity improving as short sale constraints decline. In addition, the change in borrowing fee is positively correlated with the change in effective spread again consistent with the hypothesis that short selling is undertaken by informed traders. Table 7 presents the results of cross sectional regressions of the changes in the liquidity variables on the changes in the short sale measures. The first regression in Table 7 is the change in percentage quoted spread on the three short sale variables. We find a significant and negative relation between the change in spread and the change in share availability. Thus an increase in the shares available for borrowing is correlated with the reduction in spreads. In the second regression, the dependent variable is the change in percentage effective spread. The change in percentage effective spread is positively correlated with the borrowing fee and negatively correlated with change in the supply of shares available for borrowing. The relation to the borrowing fee indicates that as short sales constraints are relaxed and the cost of borrowing declines, the effective spread also declines. Table 7 Regressions of the Change in Short Interest Variables on the Change in Liquidity Variables (1) (2) (3) (4) (5) Percentage Percentage Mean Quoted Effective Number Trade Spread Spread Volume of Trades Size Intercept (3.11) (6.73) (1.62) (2.94) (1.11) Borrowing Fee (1.62) (1.96) (1.44) (0.43) (1.79) Share Availability (3.46) (3.40) (3.16) (0.90) (2.55) Relative Short Interest (0.54) (0.47) (0.01) (0.83) (0.59) Observations R-squared Notes: All variables in this table are computed as percentage changes where the variable is measured as the post-listing value/pre-listing value 1. With the exception of Relative Short Interest, the pre-listing values of all the variables are measured over the 25 days before the listing, and the post listing values are measured over the 25 days after the listing. The Relative Short Interest variable is measured over the two months pre and two months post. This variable is only reported monthly. We report t-statistics in parenthesis that are robust to heteroskedasticity using White s (1980) correction.,, statistically significant at the 10%, 5% and 1% levels respectively.