Back to the Futures: When Short Selling is Bannedfmaconferences.org/SanDiego/Papers/Back_to_the_futures_FMA2018.pdfBack to the Futures: When Short Selling is Banned . George Jianga,

Back to the Futures: When Short Selling is Banned

George Jianga, Yoshiki Shimizub, and Cuyler Strongc,

January 2018 aJiang is a professor in the Department of Finance and Management Sciences, in the Carson College of Business at Washington State University. Email: [email protected]. Phone: 509-335-4474 bShimizu is a Ph.D. Candidate in the Department of Finance and Management Sciences, in the Carson College of Business at Washington State University. Email: [email protected] Phone: 509-335-8000 cStrong is a Ph.D. Student in the Department of Finance and Management Science, in the Carson College of Business at Washington State University, Email: [email protected]. Phone: 509-335-1185 Abstract

mailto:[email protected]



Back to the Futures: When Short Selling is Banned

Abstract

Multiple articles have examined the impact that the short-sale ban in 2008 had on equity and derivatives markets, however little research has been done on the roles that single-stock futures played during the ban period. In this paper, we fill the gap in the literature by answering three research questions. First, did the trading volume of single-stock futures increase during the ban period? Second, what are the effects of single-stock futures on price discovery of the underlying stocks during the ban period? Third, what are the effects of single-stock futures on market quality of the underlying stocks during the ban period? We find that the number of single-stock futures listed increased by 55.5 percent, and single-stock futures trading volume increased significantly during the short sale ban period. Further examination shows that the single-stock futures’ contribution to price discovery of the underlying stock also increased significantly during the ban period. Moreover, we find that single-stock futures help mitigate the negative effects of the ban on market quality found by Boehmer et al. (2013).

JEL Classification: G01, G14

Keywords: Single-Stock Futures; Short Selling; Financial Crises; Price Discovery; Market Quality

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1. Introduction

On September 19, 2008, the SEC announced an “emergency plan” to temporary ban short

sales of 799 financial stocks. According to the SEC, the ban was “aimed at helping restore falling

stock prices that [had] shattered confidence in the financial markets.” On September 22, 2008,

198 non-financial stocks were added to the ban. These stocks included companies that had

financial subsidiaries, including General Electrics, Ford Motor Company, and General Motors.

The ban was in effect for 13 trading days and lifted on October 8, 2008. Much research has been

done on the effects that the ban had on financial markets. Boehmer et al. (2013) analyze short-

selling activities around the 2008 short sale ban and find that short-sell activities decreased by 77

percent during the ban. Battalio and Schultz (2011) and Grundy et al. (2012) examine the options

markets during the short sale ban, and find that liquidity in options markets dried up during the

ban period. Though a large body of literature examines the effect of the ban on stock and options

markets, less emphasis has been put on the single-stock futures market.

In 2002, the Nasdaq Liffe Markets and OneChicago Exchange started trading single-

stock futures (SSFs) The new futures markets were expected to be the next big thing in financial

markets. Once the SSFs market opened, growth came slowly, adding a few listings every couple

of months. In 2004, the Nasdaq Liffe Markets closed and assigned the remaining contracts to the

OneChicago Exchange. In August of 2008, there were only 517 stocks with SSFs listed on the

OneChicago Exchange. During the short sale ban period in 2008, there were 291 new

introductions, increasing the number of listings to 815, a 55.5 percent increase. Although the

overall size of the SSFs market is small in the United States, we argue that the importance of the

SSFs market was augmented during the short sale ban period, and that informed traders could

use SSFs as a viable substitute for short sales and to circumvent the short sale ban.

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In this paper, we analyze the stocks that had SSFs listed on the OneChicago Exchange

during the 2008 short sale ban period to answer three research questions. Our first research

question is: Did trading volume of SSFs on banned stocks increase during the short sale ban

period? Easley, O’Hara, and Srinivas (1998) develop a theoretical model, which predicts that

when short sales are constrained, informed traders will trade more in the options market. When

short-sales are restricted, a short seller could buy a put option as a substitute leading to increased

demand in options markets. Contrary to this belief, Battalio and Schultz (2011) and Grundy et al.

(2012) show trading volume in options markets decreased significantly during the 2008 short

sale ban. The reason for this is that in order for put option writers to hedge their positions, they

need to have an offsetting short. When short selling is banned, put option writers are not willing

to write the options, leading to a reduction in options trading volume. Danielsen et al (2009)

show that single-stock futures can be used as substitutes for short-selling. We extend the model

by Easley et al. by proposing SSFs as a potential alternative to short-selling when it is prohibited.

Grundy et al. make an attempt to examine the effect of the 2008 short sale ban on the SSFs

market. They find through preliminary examination that there is little evidence that short sellers

migrated to the SSFs market. Motivated by the theoretical model developed by Easley et al. and

finding by Grundy et al., we conduct a formal analysis of the effect of the 2008 short sale ban on

the trading volume of SSFs on banned stocks.

Our second research question is: What are the effects of SSFs on price discovery of

underlying banned stocks during the ban period? If the SSFs trading volume increased during the

ban period, it implies that informed and bearish investors may have migrated to the SSFs market

and that there was an increase in information passed through the SSFs market. Fung and Tse

(2008) and Kumar and Tse (2008) find that SSFs prices contribute to price discovery of

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underlying stocks by 33 percent in the Hong Kong Exchange and 28 percent in the National

Stock Exchange of India, respectively. Shastri et al. (2008) find that contribution of SSFs prices

to price discovery is 24 percent in the OneChicago Exchange. Given that bearish investors were

kicked out of the stock market and also that trading options was not an option to them, SSFs

prices of banned stocks potentially became more informative during the ban period. This

motivates us to examine the effects of SSFs on price discovery of underlying banned stocks

during the short sale ban period.

Our third research question is: What are the effects of SSFs on market quality of

underlying banned stocks during the ban period? Boehmer et al. (2013) study the stock market

quality during the 2008 short sale ban. They use a difference-in-difference approach to examine

the effect of the ban on market quality of stocks for which short selling was banned. They find

that market quality of banned stocks deteriorated dramatically during the ban, and the negative

effect of the ban on market quality was more distinct in banned stocks in the larger market cap

quartiles. As stated earlier, the number of SSFs listings increased by 55.5 percent during the ban

period. Of the 291 SSFs that were added at that time, 276 were stocks that were subject to the

ban. The number of SSFs listed for banned stocks rose from 64 to 342, a 534 percent increase

during the ban period. It can be inferred that the OneChicago Exchange increased the supply of

SSFs due to a sudden increase in demand for short positions in underlying banned stocks. It is of

our interest to examine whether the presence of SSFs lessen the effect of the ban on market

quality of underlying banned stocks.

We briefly summarize our findings and answers to the research questions as follows:

From the analyses of 64 banned stocks that had SSFs prior to the ban, we find that trading

volume of SSFs on these banned stocks increased significantly during the ban period.

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Subsequently, we construct a matched sample of non-banned stocks with similar size, volatility,

and liquidity to test whether SSFs trading volume increased significantly larger for banned

stocks than for non-banned stocks. We find that relative to their non-banned counterparts,

volume of SSFs on banned stocks increased significantly larger during the ban period. Next, we

show the results for our second research question that SSFs’ contribution to price discovery of

underlying banned stocks increased dramatically during the ban period. This suggests that during

the ban period, prices of SSFs became more informative than during non-ban periods. However,

our further investigation shows that SSFs’ contribution to price discovery increased more for

non-banned stocks. We attribute this result to the SEC’s Rule 204T, which became effective on

September 18, 2008, and made short selling more costly for all U.S. stocks. Lastly, we find that

market quality of banned stocks during the ban period was better when there was SSFs trading.

We find evidence that the presence of SSFs lessened the negative impact of the short sale ban on

stock market quality during the ban period.

The rest of the paper is organized as follows: Section 2 describes the sample and data we

employ in our analyses. Section 3 discusses our empirical designs. In Section 4, we provide main

empirical results. Section 5 concludes.

<FIGURE 1 HERE>

<TABLE 1 HERE>

2. Data

We obtain data from multiple sources. Data on new SSFs listing information are hand

collected from the press release page of the OneChicago Exchange. Data on SSFs quotes and

trading volume, available at the daily frequency, are from the Bloomberg terminal. Generally

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speaking, each SSFs contract has multiple expiration dates. For a SSFs contract underlying stock

i, we choose the one with the shortest time to expiration date. If there are less than 5 trading days

until the shortest maturity date, we choose the one with the second shortest time to expiration

date. Data on underlying stock price are from the Center for Research in Security Prices (CRSP).

Intraday transaction data (trade and quotes) used to calculate the market quality measures are

obtained from the Trade and Quote (TAQ) database. The list of stocks that were subject to the

2008 short sale ban is obtained from the NYSE and NASDAQ websites.

Most of our analyses cover the sample period from August 1, 2008 to October 31, 2008.

We divide the sample periods into 3 sub-sample periods: Pre-ban (August 1, 2008 – September

18, 2008), ban (September 19, 2008 – October 8, 2008), and post-ban period (October 9, 208 -

October 31, 2008). Our treatment sample consists of 64 banned stocks with SSFs that had SSFs

quotes on OneChicago and stock data on CRSP throughout the sample period. In the same

manner, we require that control samples to have SSFs quotes and volume and stock data

available throughout the sample period to be considered for matching. To match a treatment

stock with a control stock, we select control stock i for treatment stock j that has the minimum

weighted sum of the absolute differences between the treatment and control matching variables,

Size, Volatility, and Liquidity.

3. Methodology

First, to examine the effect of the short sale ban on the trading volume of single-stock

futures of banned stocks during the short sale ban period, we estimate the following fixed effects

model for a daily panel of banned stocks with SSFs trading:

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𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑒𝑒𝑖𝑖,𝑡𝑡 = 𝛼𝛼 + 𝛽𝛽1𝐵𝐵𝐵𝐵𝐵𝐵 𝑝𝑝𝑒𝑒𝑝𝑝𝑝𝑝𝑉𝑉𝑝𝑝 + 𝛽𝛽2(𝐵𝐵𝐵𝐵𝐵𝐵 𝑝𝑝𝑒𝑒𝑝𝑝𝑝𝑝𝑉𝑉𝑝𝑝 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑒𝑒𝑝𝑝 𝑆𝑆𝑠𝑠𝑉𝑉𝑠𝑠𝑠𝑠)

+𝛽𝛽3𝑃𝑃𝑉𝑉𝑆𝑆𝑠𝑠𝑃𝑃𝐵𝐵𝐵𝐵 𝑝𝑝𝑒𝑒𝑝𝑝𝑝𝑝𝑉𝑉𝑝𝑝 + 𝛽𝛽4(𝑃𝑃𝑉𝑉𝑆𝑆𝑠𝑠𝑃𝑃𝐵𝐵𝐵𝐵 𝑝𝑝𝑒𝑒𝑝𝑝𝑝𝑝𝑉𝑉𝑝𝑝 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑒𝑒𝑝𝑝 𝑆𝑆𝑠𝑠𝑉𝑉𝑠𝑠𝑠𝑠) + 𝜃𝜃𝜃𝜃 + 𝜀𝜀𝑖𝑖,𝑡𝑡 , (1)

where 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑒𝑒𝑖𝑖,𝑡𝑡 is the daily trading volume of SSFs on underlying stock i on day t. Ban

period is an indicator variable that is equal to one during the short sale ban period (September 19

– October 8, 2008) for stocks subject to the ban, and zero otherwise. Postban period is an

indicator variable that is equal to one after the short sale ban period (i.e., after October 8, 2008),

and zero otherwise. Banned stock is an indicator variable that is equal to one for stocks that were

subject to the 2008 short sale ban, and zero otherwise. 𝜃𝜃 is a vector of firm-level characteristics

and other macroeconomic variable: Stock return (of underlying stock i), Stock volume (of

underlying stock i), and S&P500 Volatility Index (VIX).

Second, we follow Hasbrouck (1995) to measure the SSFs market’s contribution to price

discovery for the underlying security. The Hasbrouck’s price discovery methodology is used to

examine the price discovery of a single security traded in multiple markets. Prior research on

(single-stock) futures markets extend Hasbrouck’s methodology to analyze price discovery

between stock and SSFs markets (for example, Fung and Tse, 2008, Shastri et al. (2008), and

Kumar and Tse, 2009).

As in Shastri et al. (2008), consider the case where a single security trades in two

markets, stock and SSFs markets. Stock and SSFs prices underlying security i are denoted as Si,t

and Fi,t, respectively. Let mi,t represent the efficient price of underlying security i. Then stock

and SSFs prices share a common efficient price 𝑉𝑉𝑖𝑖,𝑡𝑡, such that:

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Si,t = mi,t + εS,i,t (2)

Fi,t = mi,t + εF,i,t , (3)

and the common efficient price, mi,t, follows a random walk:

mi,t = mi,t−1 + wi,t, (4)

where wt ~ i. i. d (0,σ2). We can represent stock price St as a function of SSFs price Ft

underlying security i as following:`

Si,t = �Fi,t + Dividend�e−rT, (5)

where t is the time to maturity, r is the risk-free rate, and Dividend is dividends to be paid on the

stock before the maturity date. Since we focus SSFs contracts with shortest time to maturity t

(for t > 5 trading days), t is small enough such that 𝑒𝑒−𝑟𝑟𝑟𝑟 will be close to 1. With 𝑒𝑒−𝑟𝑟𝑟𝑟 being

(almost) 1, Dividend is a constant:

Si,t = Fi,t + c (6)

Denote price vector for security i as: pi,t = �Si,tFi,t�. If Si,t and Fi,t are I(1) and there is a linear

combination of the processes Si,t = α + βFi,t + ϵi,t that is stationary, Si,t and Fi,t are said to be

co-integrated. Following the general vector error correction model (VECM) specification by

Hasbrouck (1995), pi,t can be expressed in terms of an error correction model of order N as:

∆pi,t = φ1∆pi,t−1 + φ2∆pi,t−2 + ⋯+ β�zi,t−1 − b� + ϵi,t, (7)

where zi,t−1 − b is an error correction term with zi,t−1 = Si,t−1 − Fi,t−1 and b = E�zi,t�. If there

is a disequilibrium of price relationship �Si,t−1 > Fi,t−1Si,t−1 < Fi,t−1

� , coefficient β corrects the error in last

8

period t-1 to adjust further towards the equilibrium value. The VECM allows for both short- and

long-run dynamics. Equation (7) can be expressed as a VMA model:

∆Pi,t = ϵi,t + ψ1ϵi,t−1 + ψ2ϵt−2 + ⋯ , (8)

where ϵi,t = �ϵi,S,tϵi,F,t

� and var�ϵi,t� = Ω. Since Si,t and Fi,t have the same underlying security,

𝜖𝜖𝑖𝑖,𝑆𝑆,𝑡𝑡 𝐵𝐵𝐵𝐵𝑝𝑝 𝜖𝜖𝑖𝑖,𝐹𝐹,𝑠𝑠 are likely to be correlated across two markets. Then Ω is not diagonal:

Ω = �𝑉𝑉𝐵𝐵𝑝𝑝�ϵI,S,t� 𝐶𝐶𝑉𝑉𝐶𝐶�ϵI,S,t, ϵI,F,t�

𝐶𝐶𝑉𝑉𝐶𝐶�ϵI,F,t, ϵI,S,t� 𝑉𝑉𝐵𝐵𝑝𝑝�ϵI,F,t�� , (9)

and thus the variance decomposition requires the Cholesky factorization. For covariance

matrix Ω, the lower triangle matrix 𝑆𝑆′ provides a Cholesky decomposition such that Ω =

𝑆𝑆′𝑆𝑆. The total variance of market innovations 𝜀𝜀𝑡𝑡 = �𝜀𝜀1,𝑡𝑡𝜀𝜀2,𝑡𝑡

� is: [𝜓𝜓]1𝛺𝛺[𝜓𝜓]′1 = [𝜓𝜓]1𝑆𝑆′𝑆𝑆[𝜓𝜓]′1, where

[𝜓𝜓]1denotes the first row of 𝜓𝜓, the row corresponding to 𝜀𝜀𝑡𝑡 = �𝜀𝜀𝑖𝑖,𝑆𝑆,𝑡𝑡𝜀𝜀𝑖𝑖,𝐹𝐹,,𝑡𝑡

�. The first element of

[𝜓𝜓]1𝑆𝑆′ is the portion of market innovation from the stock market, whereas the second element of

[𝜓𝜓]1𝑆𝑆′ is that from the SSFs market. Contribution of market j to price discovery for underlying

stock i is:

𝐶𝐶𝑉𝑉𝐵𝐵𝑠𝑠𝑝𝑝𝑝𝑝𝑃𝑃𝑉𝑉𝑠𝑠𝑝𝑝𝑉𝑉𝐵𝐵𝑖𝑖,𝑗𝑗 = �[𝜓𝜓𝑆𝑆′]2𝑗𝑗𝜓𝜓𝛺𝛺𝜓𝜓′ � , (10)

where [𝜓𝜓𝑆𝑆′]𝑗𝑗 is the j-th element of the row matrix 𝜓𝜓𝑆𝑆′ for j=S, F. From Equation (10), we

define the SSFs market’s contribution to price discovery of underlying stock i as:

𝑃𝑃𝐷𝐷𝑖𝑖,𝑡𝑡 = �[𝜓𝜓𝑆𝑆′]2𝐹𝐹𝜓𝜓𝛺𝛺𝜓𝜓′ � (11)

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Third, to examine the effect of the SSFs market on price discovery for underlying stocks

during the ban period, we conduct a differences-in-differences test. First of all, we examine how

the contribution by the SSFs market to price discovery changed during the ban period, relative to

the pre-ban period. The contribution of the SSFs market to price discovery for underlying stock i

during period t is measured by 𝑃𝑃𝐷𝐷𝑖𝑖,𝑡𝑡 as defined in equation (11). Secondly, we examine how the

SSFs market’s contribution to price discovery for banned stocks changed relative to stocks for

which short-selling was never banned during the ban period. We examine this by performing the

following differences-in-differences test:

𝑃𝑃𝐷𝐷𝑖𝑖,𝑡𝑡 = 𝛼𝛼 + 𝛽𝛽1𝐵𝐵𝐵𝐵𝐵𝐵 𝑃𝑃𝑒𝑒𝑝𝑝𝑝𝑝𝑉𝑉𝑝𝑝 + 𝛽𝛽2𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑒𝑒𝑝𝑝 𝑆𝑆𝑠𝑠𝑉𝑉𝑠𝑠𝑠𝑠 + 𝛽𝛽3(𝐵𝐵𝐵𝐵𝐵𝐵 × 𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝐵𝑒𝑒𝑝𝑝𝑆𝑆𝑠𝑠𝑉𝑉𝑠𝑠𝑠𝑠) + 𝜀𝜀𝑖𝑖,𝑡𝑡, (12)

where 𝑃𝑃𝐷𝐷𝑖𝑖,𝑡𝑡 is the SSFs market’s contribution to price discovery for underlying stock i, measured

over time period t for t= pre-ban, ban, and post-ban periods. Dummy variable Ban Period is

equal to 1 if an observation is during the ban period. Dummy variable Banned Stock is equal to 1

if underlying stock i was subject to the 2008 short sale ban. We match a group of treatment

sample with a group of control stocks by underlying stock’s market capitalization, idiosyncratic

volatility, and turnover. Market capitalization and idiosyncratic volatility are as of March 1, 2008

and turnover is averaged from January 2008 through July 2008. If an underlying stock is listed

on NASDAQ, turnover is divided by two. Our treatment group consists of 64 banned stocks that

had SSFs listed on the OneChicago exchange throughout the period from March 1, 2008 – June

30, 2009. We then create a matched control group of 64 unbanned stocks, matched by the

variables introduced earlier. Using the model (Equation 12), we make comparison of changes

over time in price discovery between treatment (banned) and control (unbanned) groups.

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After testing the difference in price discovery between banned and unbanned stocks

during the ban period, we turn our focus to examining how SSFs trading contributes to

improving market quality of underlying stocks over time. We follow Boehmer et al. (2013) to

test the market quality of the underlying stocks during the ban period. For a treatment group of

64 banned stocks with SSFs (as defined earlier), we create a matched control group of 64 banned

stocks that never had SSFs. We use a difference-in-difference approach to examine the effect of

SSFs trading on market quality of underlying stocks during the 2008 short sale ban period. Our

treatment sample consists of 64 banned stocks with SSFs listed and traded prior to the short sale

ban. We match the 64 treatment sample with a group of 64 banned stocks that never had SSFs.

Matching made by following the same matching criterion defined earlier in this section. Though

our primary focus is on the effect of SSFs trading on market quality of underlying banned stocks

during the ban period, we also create two other control groups: The control group 2 that consists

of 64 matched non-banned stocks that had SSFs listed and traded before the ban, and the control

group 3 that consists of 64 matched non-banned stocks that never had SSFs.

We estimate the following fixed effects model of a difference-in-difference test to

examine to what extent SSFs trading contributes to improving market quality of underlying

banned stocks:

𝑌𝑌𝑖𝑖,𝑡𝑡 = 𝛼𝛼 + 𝛽𝛽1𝐵𝐵𝐵𝐵𝐵𝐵 𝑝𝑝𝑒𝑒𝑝𝑝𝑝𝑝𝑉𝑉𝑝𝑝 + 𝛽𝛽2(𝐵𝐵𝐵𝐵𝐵𝐵 𝑝𝑝𝑒𝑒𝑝𝑝𝑝𝑝𝑉𝑉𝑝𝑝 × 𝑆𝑆𝑆𝑆𝑆𝑆) + β3𝑃𝑃𝑉𝑉𝑆𝑆𝑠𝑠𝑃𝑃𝐵𝐵𝐵𝐵 𝑝𝑝𝑒𝑒𝑝𝑝𝑝𝑝𝑉𝑉𝑝𝑝 +

𝛽𝛽4(𝑃𝑃𝑉𝑉𝑆𝑆𝑠𝑠𝑃𝑃𝐵𝐵𝐵𝐵 𝑝𝑝𝑒𝑒𝑝𝑝𝑝𝑝𝑉𝑉𝑝𝑝 × 𝑆𝑆𝑆𝑆𝑆𝑆) +𝜃𝜃𝜃𝜃𝑖𝑖,𝑡𝑡 + 𝜀𝜀𝑖𝑖,𝑡𝑡, (13)

where 𝑌𝑌𝑖𝑖,𝑡𝑡 is the measured market quality of stock i on day t. Dummy variables Ban period and

Banned stock are defined same as in Equation 11. SSF is a dummy variable set equal to 1 if stock

i had a SSFs listed on day t. 𝜃𝜃𝑖𝑖,𝑡𝑡 is a vector of firm-level control variables: market capitalization,

11

dollar trading volume, intraday value-weighted average price (VWAP), and proportional daily

range of stock prices (RVOL) of underlying stock i on day t as in Boehmer et al. (2013). We

calculate multiple measurements of market quality as in Boehmer et al. (2013):

𝑇𝑇ℎ𝑒𝑒 𝑄𝑄𝑉𝑉𝑉𝑉𝑠𝑠𝑒𝑒𝑝𝑝 𝑆𝑆𝑝𝑝𝑝𝑝𝑒𝑒𝐵𝐵𝑝𝑝𝑖𝑖,𝑡𝑡 �𝑅𝑅𝑄𝑄𝑆𝑆𝑖𝑖,𝑡𝑡� = 𝐴𝐴𝑠𝑠𝑘𝑘𝑖𝑖,𝑡𝑡−𝐵𝐵𝑖𝑖𝑑𝑑𝑖𝑖,𝑡𝑡𝑀𝑀𝑖𝑖,𝑡𝑡

, (14)

where 𝐴𝐴𝑆𝑆𝑠𝑠𝑖𝑖,𝑡𝑡 (𝐵𝐵𝑝𝑝𝑝𝑝𝑖𝑖,𝑡𝑡) is the ask (bid) price for stock i at trade time t, and 𝑀𝑀𝑖𝑖,𝑡𝑡 is the price

midpoint of the National Best Bid and Offer (NBBO) quotes for stock i at trade time t.

𝑇𝑇ℎ𝑒𝑒 𝐸𝐸𝐸𝐸𝐸𝐸𝑒𝑒𝑠𝑠𝑠𝑠𝑝𝑝𝐶𝐶𝑒𝑒 𝑆𝑆𝑝𝑝𝑝𝑝𝑒𝑒𝐵𝐵𝑝𝑝𝑖𝑖,𝑡𝑡 �𝑅𝑅𝐸𝐸𝑆𝑆𝑖𝑖,𝑡𝑡� =2�𝑃𝑃𝑖𝑖,𝑡𝑡 − 𝑀𝑀𝑖𝑖,𝑡𝑡�

𝑀𝑀𝑖𝑖,𝑡𝑡 , (15)

where 𝑃𝑃𝑖𝑖,𝑡𝑡 is the trade price per share for stock i at trade time t. 𝑀𝑀𝑖𝑖,𝑡𝑡 is the price midpoint of the

National Best Bid and Offer (NBBO) quotes for stock i at trade time t.

𝑇𝑇ℎ𝑒𝑒 𝑄𝑄𝑉𝑉𝑉𝑉𝑠𝑠𝑒𝑒𝑝𝑝 𝑆𝑆𝑝𝑝𝑝𝑝𝑒𝑒𝐵𝐵𝑝𝑝𝑖𝑖,𝑡𝑡�𝑅𝑅𝑄𝑄𝑆𝑆𝑖𝑖,𝑡𝑡� =2�𝑁𝑁𝐵𝐵𝑂𝑂𝑖𝑖,𝑡𝑡 − 𝑁𝑁𝐵𝐵𝐵𝐵𝑖𝑖,𝑡𝑡�

𝑀𝑀𝑖𝑖,𝑡𝑡 , (16)

where 𝑁𝑁𝐵𝐵𝑂𝑂𝑖𝑖,𝑡𝑡 (𝑁𝑁𝐵𝐵𝐵𝐵𝑖𝑖,𝑡𝑡) is the National Best Offer (Bid) quote for stock i at trade time t.

𝑆𝑆𝑝𝑝𝐶𝐶𝑒𝑒 𝑀𝑀𝑝𝑝𝐵𝐵𝑉𝑉𝑠𝑠𝑒𝑒𝑆𝑆 𝑃𝑃𝑝𝑝𝑝𝑝𝑠𝑠𝑒𝑒 𝐼𝐼𝑉𝑉𝑝𝑝𝐵𝐵𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡 �𝑅𝑅𝑃𝑃𝐼𝐼5𝑖𝑖,𝑡𝑡� = 𝐷𝐷 �𝑀𝑀𝑖𝑖,𝑡𝑡+5min − 𝑀𝑀𝑖𝑖,𝑡𝑡

𝑀𝑀𝑖𝑖,𝑡𝑡� , (17)

where D is an indicator variable that is equal to +1 for buyer-initiated trades based on the Lee

and Ready (1991) algorithm. Similarly, D equals to -1 for seller-initiated trades.

12

4. Empirical Results

4.1 Did trading volume of SSFs on banned stocks increase during the 2008 short sale ban?

In this section, we attempt to answer our first research question: “Did trading volume of

SSFs on banned stocks increase during the 2008 short sale ban?” In doing so, it is worthwhile to

take a look at trading activities in the US single-stock futures market around the 2008-2009

financial crisis period. Figure 2 shows the daily total dollar volume for the single stock futures

market around the 2008-2009 financial crisis period. It is clear to see that there is dramatic

increase in the amount of trading in the OneChicago market during and immediately following

the initiation of the short-sell ban.

<FIGURE 2 HERE>

Figure 3 exhibits average single-stock futures trading volume per stock (SSFVS) of

banned stocks around the financial crisis period in 2008 and 2009, while Figure 4 plots average

SSFVS around the 2008 short sale ban period. We compute SSFVS by “summing all trades in all

SSF contracts on each individual stock on each day” (As in Grundy et al., 2012 pg.346). From

Figures 3 and 4, SSFs trading volume has increased sharply on September 19, 2008 when the ban

becomes effective, and it drops sharply after October 8, 2008 when the ban is lifted.

<FIGURE 3 HERE>

<FIGURE4 HERE>

Figures 3 and 4 also plot 75th and 90th percentiles of SSFVS. We see a similar

phenomenon that the 75th and 90th percentiles of daily trading volume of SSFs on banned stocks

increases when the ban becomes effective and declines when it is lifted. In answering our first

research question, “did trading volume of SSFs on banned stocks increase during the short sale

13

ban?” we analyze 458 US stocks with SSFs contracts listed and traded during the 2008 short sale

ban period. Table 2 summarizes our sample selection criterion. There are 523 stocks that had

SSFs listed on the OneChicago exchange prior to September 19, 2008. Of 523, 64 are stocks for

which short selling was banned during the ban period (September 19 – October 8, 2008). First,

we analyze 64 banned stocks with SSFs to examine whether trading volume of SSFs increased

for banned stocks during the ban period. Second, we analyze a full sample of 64 banned stocks

and 394 non-banned stocks to examine whether trading activity in the SSFs market differed for

banned stocks than for non-banned stocks during the ban period. Lastly, we create a matched

control group of 64 non-banned stocks with similar size, volatility, and liquidity to those of 64

treatment banned stocks, and then conduct a difference-in-difference test to examine whether

SSFs trading volume increased significantly more for banned stocks than for their matched non-

banned counterparts.

<TABLE 2 HERE>

Table 3 reports summary statistics of 458 sample stocks. From the preban period to ban

period, logged SSF trading volume for banned stocks increased from 0.498 to 0.592. After the

ban was lifted, the trading volume decreased to 0.455, suggesting that the 2008 short sale ban

induced bearish investors to trade SSFs on banned stocks. A similar phenomenon is observed for

non-banned stocks: SSFs trading volume increased from the preban period to ban period, and

then decreased after the ban was lifted.

<TABLE 3 HERE>

Table 4 reports regression results from a daily panel of 64 banned stocks with SSFs

traded before the short sale ban. The result from a univariate regression (model 1) suggest that

14

the short sale ban significantly increases SSFs trading volume for banned stocks. This effect of

the ban on SSFs trading volume holds after we include control variables in models 2, 3, and 4.

<TABLE 4 HERE>

In Table 5, we include the 394 non-banned stocks sample and then analyze a daily panel

of 458 stocks (64 banned and 394 non-banned) with SSFs using panel regressions with firm-

fixed effects. The short sale ban has a positive and significant effect on SSFs trading volume:

When the ban is in effect, it increases SSFs trading volume for both banned and non-banned

stocks. Contrary to our initial prediction that SSFs trading volume would increase more for

banned stocks than for non-banned stocks during the ban period, however, increment is not

significantly larger for banned stocks than for non-banned stocks during the ban period.

<TABLE 5 HERE>

In Table 6, we compare the 64 banned stocks with SSFs to the 64 matched control (non-

banned) stocks with SSFs by conducting a difference-in-difference test. Though we do not find

any significant effect of the ban on SSFs trading volume for banned stocks, it is now of interest

to examine whether this finding holds when we compare the 64 treatment sample with its

matched control sample with similar size, volatility, and liquidity. In Table 6, we estimate panel

regressions with firm-fixed effects. In Model 3, we find that the short sale ban has a significant

and positive effect on SSFs trading volume for banned stocks, compared to their matched control

sample with similar firm characteristics.

<TABLE 6 HERE>

From our analyses, we find that single-stock futures trading volume increased for both

banned and non-banned stocks during the 2008 short sale ban period, suggesting that the

15

presence of SSFs created a viable alternative to short selling. Further analyses are conducted to

examine to which extent SSFs trading volume on banned stocks increased relative to stocks that

were not subject to the ban. We did not find evidence that trading volume of SSFs on banned

stocks increased significantly larger than that for non-banned stocks. This suggests that the 2008

short sale ban had effects on trading of SSFs on both banned and non-banned stocks. We

attribute this to not only the short sale ban but also the Rule 204T, which became effective on

September 18, 2008 and made short selling more costly for all U.S. stocks. We subsequently use

a difference-in-difference approach to compare the effect of the ban on SSFs trading volume for

the 64 treatment (banned) stocks to that for the 64 matched non-banned stocks. We find that

SSFs trading volume increased significantly more for banned stocks than for the matched non-

banned counterparts during the ban period.

4.2 What are the effects of SSFs on price discovery of underlying banned stocks during the

short sale ban?

Table 7 reports descriptive statistics for the price discovery variable. Price discovery

variable 𝑃𝑃𝐷𝐷𝑖𝑖,𝑡𝑡 determines how much the SSFs trade contributes to price discovery of underlying

stock i, measured over period t. We have a full sample group of 346 banned stocks that had SSFs

listing on OneChicago during the ban period. We also create a sub sample of 64 banned stocks

that had had SSFs listed before the short sale ban became effective on September 19, 2008. Note

that we focus more on results on the latter sample group, as trading volume of SSFs on these 64

banned stocks represents almost all of the entire trading volume of SSFs on banned stocks.

16

Prior to the short sale ban period, the average contribution of SSFs to price discovery of

underlying banned stock is 28.5 percent. The average contribution increased by 14.4 percent, to

43.0 percent, during the ban period for the sub-sample banned stocks, and increased to 44.9

percent for the full sample. After the short sale ban is lifted, the average contribution decreases to

16.4 and 17.3 percent for full- and sub-sample banned stocks, respectively. From this result, we

find that SSFs contribution to price discovery of underlying banned stocks increased during the

short sale ban.

<TABLE7 HERE>

Next, it is of interest to examine to what extent contribution of SSFs to price discovery

for underlying banned stocks increased relative to non-banned stocks during the short sale ban

period. To examine this using a differences-in-differences test, we create a control group of 64

unbanned stocks with SSFs to match with our treatment group of 64 banned stocks with SSFs.

Table 8 shows descriptive statistics for matching variables, Market cap, Idiosyncratic volatility,

and Turnover of both treatment and control groups. Size reported as the log of market

capitalization is very similar for both groups, with a difference in means of -.003. Volatility

reported as idiosyncratic volatility has essentially no difference between the means. Liquidity

reported as turnover is much higher for the treatment group, however this would be expected

because the treatment group is comprised of mostly financial firms, which were under a severe

financial distress during the 2008-2009 financial crisis period. This implies that each treatment

sample is well matched with a control sample with similar size, volatility, and liquidity.

<TABLE 8 HERE>

17

Table 9 makes comparison of the SSFs’ contribution to price discovery between the two

groups. We predict that the effect will be stronger in the treatment group of banned stocks for

which shorting was prohibited during the ban period, and hence that SSFs would not contribute

to price discovery of unbanned stocks as much as that of banned stocks. Contrary to our

prediction, Table 5 shows that SSFs’ contribution to price discovery increases dramatically for

matched unbanned stocks as well. This implies that the 2008 short sale regulations; the short sale

ban which was intended to protect financial stocks from falling stock price, and Rule 204T which

applied to all stock, had effects on both banned and unbanned stocks, especially in the context of

price discovery.

<TABLE 9 HERE>

4.2.1 What are the effects of the ban on price discovery contribution of single-stock futures

when controlling for options?

We test to see if our results are robust when taking into account options trading. First we

separate the stocks with single-stock futures into two groups those with options trading and those

without options trading, we then test for price discovery contribution in each of the groups. We

find that when using the full sample of stocks with singles stock futures at the end of the short-

sale ban, we have 151 stocks with single-stock futures and options and 198 stocks with single-

stock futures and no options that have valid results. In Table 10 we find that the price discovery

contribution increases to 44% in both cases during the ban period.

<TABLE 10 HERE>

18

We then calculate the price discovery contributions of the spot, future and options price

together. Chakravarty, Gulen and Mayhew (2004) find the option market contribution to price

discovery to be about 17%, in this case we would expect to find similar results. Consistent with

our previous findings, in Table 11, we find that the futures price contribution increases from 25%

in the pre-ban period to 30% during the ban, while the spot, and option price discovery

contribution decrease with the subsample of stocks with single stock futures prior to the ban.

<TABLE 11 HERE>

4.3 What are the effects of SSFs on market quality of underlying banned stocks during the

short sale ban?

Figure 5 shows different measures of market quality from August 1, 2008 to October 31,

2008, for the banned stocks with single-stock futures and each of the three control groups. We

see that for almost all measures, prior to the ban, all groups have similar market quality. We also

see a spike for most measures at the beginning of the ban, while the non-banned stocks quickly

revert, but not quite to pre-ban levels. We also see that banned stocks with single-stock futures

have lower relative quoted spreads and relative effective spreads throughout the entire ban

period, compared to banned-stocks without single stock futures. While these stocks also have

lower 5 minute price impact and realized spreads for most of the ban period, compared to

banned-stocks without single stock futures. We also see that all measure, banned stock market

quality improves in the post ban period but does not go back to pre-ban levels, while for non-

banned stocks market quality stays at the banned period level, which is worse than the pre-ban

19

level. We can also see that non-banned stocks with singles-stock futures have consistently better

market quality.

<FIGURE 5 Here>

Table 12 shows the descriptive statistics of the market quality measures, for banned

stocks with single-stock futures and banned stocks without single-stock futures. We can see that

in the pre ban period the stocks with single-stock futures have similar market quality measures.

However, in general market quality is slightly better for those with single stock futures. During

the ban period the market quality gets worse for both sample but is much worse for those without

single stock futures.

Table 13 reports the difference in difference regression results for banned stocks, with the

market quality measures as the dependent variables. The quoted spread for stocks with single-

stock futures during the ban period is 0.33 basis point lower than for stocks without futures. The

5 minute price impact for stocks with futures during the ban is 0.209 basis points lower than for

stocks without futures.

<TABLE 13 HERE>

Due to the illiquidity of the futures market, there are futures contracts that do not trade

every day, to adjust for this we use SSF trade as a dummy variable if there is trading volume for

the future of a particular stock. We use SSF trade instead of SSF and rerun the previous

regression. Table 14 reports the difference in difference results with stock market quality

measures as the dependent variables and using SSF trade as one of the independent variables.

Table 14 shows that the interaction term SSF trade*Ban Period is negative and significant for all

market quality measures. We can see that the market quality of the underlying banned stocks is

20

better when there is single-stock futures trading compared to banned stocks without single-stock

futures. Although market quality decreased for all stocks during the short-sale ban, stocks with

single-stock futures had better market quality during the ban than those without single-stock

future.

<TABLE 14 HERE>

5. Conclusion

We investigate the impact that the 2008 short-sale ban had on the single stock-futures

market, and what effect single-stock futures had on the underlying equities during the ban period.

We suggest that SSFs are a viable alternative for bearish investors when short-selling is

restricted. We report a 55.5% increase in the number of stocks with single stock futures available

to trade on the OneChicago Exchange. We also find a significant increase in volume in the single

stock futures market, implying that the SSFs market provided a potential venue for investors to

trade when short selling of designated financial and non-financial stocks was prohibited.

We also find that SSF’s contribution to price discovery of the underlying security

increased across all stocks, although the price discovery increased more for non-banned stocks

than for banned stocks. Rule 204T, restricting naked short-selling, was announced by the SEC

one day before the short-sale ban on financial stocks was initiated, this increased the importance

of SSFs role in information transfer for all stocks, however this importance was augmented for

those stocks affected by the short-sale ban.

Finally, the presence of single-stock futures lessened the negative impact of the short-sale

ban on stock market quality reported by Boehmer et al. (2013). Although the single-stock futures

21

market is much smaller in comparison to options and stock markets, they are an essential part of

financial markets, especially when short selling is constrained.

22

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Tables and Figures

Figure 1: New single-stock futures listings on the OneChicago Exchange around the 2008 short sale ban

This figure plots the number of new single-stock futures (SSFs) listings on the OneChicago Exchange by listing month (Panel A) and listing date (Panel B). The “All stocks” group consists of all US stocks whose SSFs contracts were introduced in 2008. The “Banned stocks” group consists of financial and non-financial stocks that were subject to the 2008 short sale ban.

Panel A: New single-stock futures listings on the OneChicago Exchange in 2008 (by listing month)

Panel B: New single-stock futures listings on the OneChicago Exchange around the 2008 short sale ban (by listing date)

193

3659

119

75

248 1

99

255

27

2 11 110 3 8 0

94

244

0

50

100

150

200

250

300

Before2008

Feb-08 Mar-08 Apr-08 May-08 Jun-08 Jul-08 Aug-08 Sep-08 Oct-08

All stocks Banned stocks

515

2574

2575 91

6424

7024

72 85

0

100

200

300

400

500

600

Before9/19/2008

9/26/2008 9/29/2008 10/1/2008 10/2/2008 10/8/2008


Figure 2: Daily dollar trading volume of single-stock futures on the OneChicago Exchange around the 2008-2009 financial crisis period

This figure plots daily total dollar trading volume of SSFs contracts on the OneChicago Exchange during the 01/01/2008 – 01/01/2009 period. The shaded area is the 2008 short sale ban period: September 18, 2008 – October 8, 2008. Total dollar volume is reported in millions.

Figure 3: Average daily single-stock futures trading volume per stock (SSFVS) during the 2008 – 2009 period

This figure plots the daily single-stock futures trading volume per stock (SSFVS) from 03/01/2008 to 06/30/2009. We follow Grundy et al. (2012) to compute SSFVS by “summing all trades in all SSF contracts on each individual stock on each day.” Panel A plots the average daily SSFVS, Panel B plots the third quartile SSFVS, and Panel C plots the 90th percentile SSFVS. Each panel uses a different scale on the vertical axis. The solid (dashed) line represents banned (nonbanned) stocks.

Panel A: Daily single-stock futures volume per stock (average)

Figure 3 (cont.):

Panel B: Daily single-stock futures volume per stock (3rd quartile)

Figure 3 (cont.):

Panel C: Daily single-stock futures volume per stock (90th percentile)

Figure 4:

Average daily single-stock futures trading volume per stock (SSFVS) around the 2008 short sale ban

This figure plots the daily single-stock futures trading volume per stock (SSFVS) around the 2008 short sale ban period. The ban became effective on 09/18/2008 and was lifted on 10/08/2008. We follow Grundy et al. (2012) to compute SSFVS by “summing all trades in all SSF contracts on each individual stock on each day.” Panel A plots the average daily SSFVS, Panel B plots the third quartile SSFVS, and Panel C plots the 90th percentile SSFVS. Each panel uses a different scale on the vertical axis. The solid (dashed) line represents banned (nonbanned) stocks.

Panel A: Daily single-stock futures volume per stock (average) around the 2008 short sale ban

Figure 4: (cont.)

Panel B: Daily single-stock futures volume per stock (3rd quartile) around the 2008 short sale ban

Figure 4: (cont.)

Panel C: Daily single-stock futures volume per stock (90th percentile) around the 2008 short sale ban

Figure 5: Average market quality measures around the 2008 short sale ban

This figure plots average market quality measures around the 2008 short sale ban. The sample period covers from 08/01/2008 – 10/31/2008. Panel A reports the average relative quoted spread, Panel B reports the average relative effective spread, Panel C reports average realized spread, and Panel D reports average 5-minute price impact. The ‘Treatment’ line represents the average market quality measure of 64 banned-stocks that had SSFs listed prior to the ban. The “Control 1” line represents the average market quality of 64 banned-stocks that never had SSFs listed during the sample period. The “Control 2 (Control 3)” lines represent the average market quality of 64 non-banned stocks that had (never had) SSFs listed during the sample period. Relative quoted spread is time-weighted, and the other market quality measures are equal-weighted. Each panel uses a different scale on the vertical axis.

Panel A: Average relative quoted spread

Figure 5: (cont.)

Panel B: Average relative effective spread

Figure 5: (cont.)

Panel C: Average relative realized spread

Panel 5: (cont.)

Panel D: Average 5-minute price impact

Table 1: New single-stock futures listings on the OneChicago Exchange around the 2008 financial crisis time

This table exhibits the number of new single-stock futures (SSFs) listings around the 2008 financial crisis time. Prior to 2008, there were a total of 193 SSFs listings. Panel A reports the number of new SSFs listings in 2008 by month. Panel B reports the number of new SSFs listings during the 2008 short sale ban period (09/19/2008 – 10/08/2008). Column “All stocks” reports new listings of SSFs on both banned and non-banned stocks, whereas column “Banned stocks” reports those on stocks that were subject to the ban.

Panel A: New single-stock futures listings in 2008 by month


Month New SSFs Listings

Cumulative SSFs listings Month New SSFs

Listings Cumulative SSFs listing

Before 2008 195 195 Before 2008 29 29 Feb-08 36 231 Feb-08 2 31 Mar-08 59 290 Mar-08 11 42 Apr-08 119 409 Apr-08 11 53

May-08 75 484 May-08 0 53 Jun-08 24 508 Jun-08 3 56 Jul-08 8 516 Jul-08 8 64

Aug-08 1 517 Aug-08 0 64 Sep-08 99 616 Sep-08 94 158 Oct-08 255 871 Oct-08 244 402

Panel B: New single-stock futures listings around the 2008 short sale ban period


Day New SSFs Listings

Cumulative SSFs listings Month New SSFs

Listings Cumulative SSFs listing

Before 9/19/2008 515 515 Before 9/19/2008 64 64 9/26/2008 25 540 9/26/2008 24 88 9/29/2008 74 614 9/29/2008 70 158 10/1/2008 25 639 10/1/2008 24 182 10/2/2008 75 714 10/2/2008 72 254 10/8/2008 91 805 10/8/2008 85 339

Table 2: Sample selection and filtering

The sample period is from August 1, 2008 to October 31, 2008. The 2008 short sale ban was initiated on September 19, 2008, and was lifted on October 8, 2008. To be included in our sample, stocks must have single-stock futures (SSFs) listed and traded on the OneChicago Exchange prior to September 19, 2008 and have SSFs data available on the Bloomberg terminal throughout the sample period. We also exclude stocks with data unavailable on CRSP from our analyses. We obtained a list of SSFs listings from the press release page of the OneChicago Exchange (https://www.onechicago.com/).

Sample selection

Full sample Sub sample Banned +

non-banned stocks

Banned stocks Non-

banned stocks

Number of stocks with SSFs traded prior to September 19, 2008 523 64 459 (Less) Number of stocks for which SSFs data are not available on the Bloomberg terminal

-58 - -58

(Less) Number of stocks for which data are not available on CRSP

-7

- -7

Total number of sample stocks in our sample 458 64 394

https://www.onechicago.com/

Table 3: Descriptive statistics

Panel A of this table reports summary statistics of SSF trading volume, Stock return, Stock volume, and VIX. The sample consists of 458 U.S. stocks that had had single-stock futures (SSFs) listed before the 2008 short sale ban was initiated on September 19, 2008. 64 stocks are banned stocks that had SSFs listed prior to the ban. 394 stocks are stocks for which short selling was never banned and that had SSFs listed before the ban. The sample period is from August 1, 2008 to October 31, 2008. The preban period is August 1, 2008 – September 18, 2008; the ban period is September 19, 2008 – October 8, 2008; and the postban period is October 9, 2008 – October 31, 2008. SSF Trading volume is logged trading volume of SSFs contract on underlying stock i on day t. Stock return is daily return of underlying stock i on day t. Stock volume is daily trading volume of underlying stock i on day t. VIX is the S&P500 Volatility Index. Panel B reports the time-series average of the cross-sectional correlations between variables.

Panel A: Summary statistics

Full sample Sub sample Banned + non-banned stocks Banned stocks Non-banned stocks Preban Ban Postban Preban Ban Postban Preban Ban Postban SSF trading volume (logged)

0.368 0.430 0.368 0.498 0.592 0.455

0.348 0.403 0.354

Stock return -0.001 -0.017 0.000 0.001 -0.015 0.000 -0.001 -0.018 -0.001 Stock volume (in Million) 7.017 8.873 9.876 16.780 19.363 18.960 5.449 7.175 8.373 VIX 22.9 41.7 65.5 22.9 41.7 65.5 22.9 41.7 65.5 Number of stocks 458 64 394

Panel B: Correlations between variables

SSF trading

volume Ban

period Post ban period

Stock return

Stock volume VIX

SSF trading volume 1.000 Ban period 0.023* 1.000 Post ban period -0.009 -0.310* 1.000 Stock return -0.005 -0.113* 0.036* 1.000 Stock volume 0.194* 0.018* 0.050* -0.020* 1.000 VIX 0.011 0.010* 0.861* -0.090* 0.080* 1.000

* indicates that significance at the 5 percent.

Table 4: The effect of the 2008 short sale ban on trading volume of SSFs on banned stocks

This table reports firm-fixed effects regression results from a daily panel of 64 banned stocks that had SSFs listed prior to the 2008 short sale ban. The dependent variable is logged daily SSFs trading volume of stock i. Firm-level control variables are Stock return and Stock volume. VIX is the S&P500 Volatility Index. Ban period is an indicator variable that is equal to one if an observation is during the ban period (September 19, 2008 – October 8, 2008), and zero otherwise. Postban period is an indicator variable that is equal to one if an observation is after the ban period (after October 8, 2009), and zero otherwise. Significance is computed using Newey-West (1987) standard errors. Standard errors are reported in parentheses. ***, **, and * indicate p<0.01, p<0.05, and p<0.1, respectively.

Dependent variable: SSFs trading volume (1) (2) (3) (4)

Ban period 0.107** 0.0946** 0.0808* 0.0988** (0.0457) (0.0479) (0.0479) (0.0460) Post ban period -0.0358 -0.0492 (0.0426) (0.0416) Stock return 0.0227 0.0167 (0.178) (0.180) Stock volume 0.00548*** 0.00547*** (0.00173) (0.00172) VIX -0.000364 (0.000931) Observations 4,112 4,112 4,112 4,112 Adjusted R-squared 0.422 0.422 0.440 0.439

Table 5: The effect of the 2008 short sale ban on trading volume of SSFs on banned and non-banned stocks

This table reports firm-fixed effects regression results from a daily panel of 458 stocks- 64 banned and 394 non-banned stocks that had SSFs listed prior to the 2008 short sale ban. The dependent variable is logged daily SSFs trading volume of stock i. Banned stock is an indicator variable that is equal to one if a stock was subject to the ban, and zero otherwise. Firm-level control variables are Stock return and Stock volume. VIX is the S&P500 Volatility Index. Ban period is an indicator variable that is equal to one if an observation is during the ban period (September 19, 2008 – October 8, 2008), and zero otherwise. Postban period is an indicator variable that is equal to one if an observation is after the ban period (after October 8, 2009), and zero otherwise. Significance is computed using Newey-West (1987) standard errors. Standard errors are reported in parentheses. ***, **, and * indicate p<0.01, p<0.05, and p<0.1, respectively. The coefficient on VIX is multiplied by 103.


Ban period 0.0622*** 0.0593*** 0.0409** 0.0490*** (0.0155) (0.0162) (0.0174) (0.0166) Ban period*Banned stock 0.0356 0.0452 (0.0505) (0.0484) Post ban period -0.00854 -0.0237 (0.0162) (0.0182) Post ban period*Banned stock -0.0284 (0.0446) Stock return -0.0661 -0.0676 (0.0880) (0.0892) Stock volume 0.00663*** 0.00655*** (0.00180) (0.00177) VIX -0.00424 (0.000383) Observations 29,491 29,491 29,480 29,480 Adjusted R-squared 0.389 0.389 0.395 0.395

Table 6: Difference-in-difference regression results

This table reports firm-fixed effects regression results from a difference-in-difference test using a daily panel of 64 treatment (banned) stocks with SSFs and their matched control (non-banned stocks with SSFs) counterparts. We use firm’s size (market capitalization), volatility (idiosyncratic volatility), and liquidity (turnover) to match the 64 treatment sample with the 64 control sample. The dependent variable is logged daily SSFs trading volume of stock i. Banned stock is an indicator variable that is equal to one if a stock was subject to the ban, and zero otherwise. Firm-level control variables are Stock return and Stock volume. VIX is the S&P500 Volatility Index. Ban period is an indicator variable that is equal to one if an observation is during the ban period (September 19, 2008 – October 8, 2008), and zero otherwise. Postban period is an indicator variable that is equal to one if an observation is after the ban period (after October 8, 2009), and zero otherwise. Significance is computed using Newey-West (1987) standard errors. Standard errors are reported in parentheses. ***, **, and * indicate p<0.01, p<0.05, and p<0.1, respectively. In Model 4, the coefficient on Ban period is multiplied by 103.


Ban period 0.0549* 0.0565* -0.00931 0.00258 (0.0314) (0.0328) (0.0432) (0.0447) Ban period*Banned stock 0.104* 0.0795 (0.0623) (0.0649) Post ban period 0.00491 0.0250 (0.0344) (0.0549) Post ban period*Banned stock -0.0750 (0.0682) Stock return -0.00388 -0.00908 (0.161) (0.159) Stock volume 0.00575*** 0.00577*** (0.00176) (0.00177) VIX 0.000249 (0.000763) Observations 7,978 7,978 7,967 7,967 Adjusted R-squared 0.414 0.414 0.424 0.424

Table 7: Contribution of single-stock futures price to price discovery around the 2008 short sale ban period

This table reports descriptive statistics for contribution/information share of single-stock futures (SSFs) price to price discovery of the underlying stock, using the vector error correction model by Hasbrouck (1995). Full sample consists of all SSFs contracts on banned-stocks listed on the OneChicago Exchange during the period from March 1, 2008 to June 30, 2009. Subsample consists of 64 banned stocks that had had SSFs listed before the 2008 short sale ban was initiated on September 19, 2008. *** indicates that difference in means is significant at the 1 percent level.

Banned stocks with SSFs Full sample Subsample Preban period (March 1, 2008 - September 18, 2008) Number of observations 61 61 Mean 0.285 0.285 Maximum 0.998 0.998 Minimum <0.0001 <0.0001 Ban period (September 19, 2008 - October 8, 2008) Number of observations 352 60 Mean 0.449 0.430 Maximum 0.998 0.989 Minimum <0.0001 <0.0001 Postban period (January 1, 2009 - June 30, 2009) Number of observations 356 57 Mean 0.164 0.173 Maximum 0.974 0.998 Minimum <0.0001 <0.0001 Difference (Ban period - Preban period) 0.1638*** 0.1442*** t-value 3.56 2.70 Difference (Postban period - Ban period) -0.2847*** -0.2562*** t-value -13.74 -4.97

Table 8: Descriptive statistics for matching variables

This table reports descriptive statistics for matching variables. We match a group of treatment stocks with a group of control stocks by underlying stock's size (market capitalization), volatility (idiosyncratic volatility), and liquidity (turnover). Market capitalization and idiosyncratic volatility are as of March 1, 2008 and turnover is averaged over the period from January 2008 to July 2008. Idiosyncratic volatility of underlying stock i on day t is calculated over the previous 22 trading days as in Ang et al. (2009). If an underlying stock is listed on NASDAQ, turnover is divided by two. Our treatment group consists of 64 banned stocks that had SSFs listed and traded on the OneChicago exchange throughout the period from March 1, 2008 to June 30, 2009. Similarily, our matched control group consists of 64 non-banned stocks with SSFs listed and traded throughout the March, 2008 - June 30, 2009 period. *** indicates that difference in means is significant at the 1 percent.

Group Matching variable Mean Sum Minimum Maximum

Treatment Size (log of market capitalization)

16.041 1010.6 12.16 19.62

Control Size (log of market capitalization)

16.038 1010.4 12.15 19.35

Difference in means -0.003 t-value -0.010

Treatment Volatility (idiosyncratic volatility)

0.022 1.375 0.007 0.083

Control Volatility (idiosyncratic volatility)

0.022 1.394 0.006 0.089

Difference in means 0.000 t-value 0.130

Treatment Liquidity (turnover) 0.019 1.193 0.001 0.076

Control Liquidity (turnover) 0.013 0.826 0.001 0.074 Difference in means -0.006

t-value -2.35***

Table 9: The effect of the 2008 short sale ban on SSFs’ contribution to price discovery of banned and non-banned stocks

This table reports the cross-sectional average of the price discovery variable. The price discovery variable is the contribution/information share of single-stock futures (SSFs) prices to price discovery of underlying stocks, using the vector error correction model by Hasbrouck (1995). The "Diff" column reports the difference in the price discovery variable between preban and ban periods within the group (treatment or control). The "Diff-Diff" column reports the difference in the cross-sectional average between treatment and control groups. Our treatment group consists of 64 banned stocks that had had SSFs listed and traded before the 2008 short sale ban was initiated. Our control group consists of 64 non-banned stocks that had SSFs prior to the ban. Matching was made based on underlying stock's size (market capitalization), volatility (idiosyncratic volatility), and liquidity (turnover). Preban period is from March 1, 2008 to September 18, 2008. Ban period is from September 19, 2008 to October 8, 2008. Postban period is from January 1, 2009 to June 30, 2009. Panel A (B) reports difference in SSFs' contribution to price discovery between preban and pan periods (ban and postban periods). Standard errors are reported in the parentheses. *** indicates significance at the 1 percent level.

Panel A: Difference in SSFs' contribution to price discovery between preban and ban periods Treatment (Banned stocks) Control (Non-banned stocks) Diff-Diff Preban Ban Diff Preban Ban Diff

Average SSFs' contribution to

price discovery 0.285 0.429 0.144 0.151 0.449 0.298 -0.153**

(0.0533) (0.0488) (0.0723) Panel B: Difference in SSFs' contribution to price discovery between ban and postban periods Treatment (Banned stocks) Control (Non-banned stocks) Diff-Diff Preban Ban Diff Preban Ban Diff

Average SSFs' contribution to

price discovery 0.429 0.173 -0.256*** 0.449 0.181 -0.268*** 0.0114

(0.0516) (0.0538) (0.0745)

Table 10: SSF Price Discovery contribution of stocks with and without options This table reports the summary statistics for the contribution of single-stock futures(SSFs), with and without options on the underlying stock, to price discovery. The full-sample covers all SSFs contracts on banned-stocks listed on OneChicago during the sample period. The sub-sample covers 48 banned-stocks whose SSFs contracts were listed on OneChicago before the 2008 short sale ban was initiated on September 19, 2008.

Panel A: Pre-ban period (March 1, 2008-September 18, 2008)

Full Sample Subsample (banned stocks with SSFs prior to the

ban)

SSFs with Options SSFs without

Options


Options N 54 N 7 N 54 N 7 Mean 0.284 Mean 0.183 Mean 0.284 Mean 0.183 Median 0.179 Median 0.219 Median 0.179 Median 0.219 Max 0.999 Max 0.404 Max 0.999 Max 0.404 Min <0.001 Min <0.001 Min <0.001 Min <0.001

Panel B: Ban-period (September 19, 2008- October 31, 2008)


ban)


Options



Panel C: Post-ban period (January 1, 2008- June 30, 2009)


ban)


Options



Table 11: Contribution to price discovery by single-stock futures and options prices

This table reports summary statistics for contribution of single-stock futures (SSFs), spot, and options prices to price discovery of the underlying stock. The full sample covers all SSFs contracts on banned stocks listed on the OneChicago Exchange during the sample period (March 1, 2008 – June 30, 2009). The subsample covers 64 banned stocks whose SSFs contracts were listed on the OneChicago Exchange before the 2008 short sale ban was initiated on September 19, 2008. Panels A, B, and C report summary statistics over the preban (March 1, 2008 –September 18, 2008), ban (September 19, 2008 – October 31, 2008), and postban period (January 1, 2008 – June 30, 2008), respectively.

Panel A: Preban period (March 1, 2008 – September 18, 2008)

Full sample Subample Variable N Mean Median Maximum Minimum Variable N Mean Median Maximum Minimum Futures 47 0.252 0.119 0.956 <0.001 Futures 48 0.250 0.127 0.956 <0.001

Spot 47 0.566 0.639 1.000 0.003 Spot 48 0.570 0.640 1.000 0.003 Options 47 0.181 0.125 0.952 <0.001 Options 48 0.180 0.117 0.952 <0.001

Panel B: Ban period (September 18, 2008 – October 31, 2008)

Full sample Subample Variable N Mean Median Maximum Minimum Variable N Mean Median Maximum Minimum Futures 67 0.300 0.154 0.999 0.000 Futures 38 0.281 0.142 0.956 0.000

Spot 67 0.520 0.556 1.000 0.001 Spot 38 0.497 0.495 1.000 0.003 Options 67 0.180 0.070 0.810 0.000 Options 38 0.222 0.129 0.775 0.000

Panel C: Postban period (January 1, 2008 – June 30, 2008)

Full sample Subample Variable N Mean Median Maximum Minimum Variable N Mean Median Maximum Minimum Futures 102 0.247 0.176 0.997 0.000 Futures 42 0.228 0.167 0.812 <0.001

Spot 102 0.607 0.662 1.000 0.002 Spot 42 0.614 0.637 1.000 0.002 Options 102 0.146 0.068 0.775 0.000 Options 42 0.158 0.113 0.659 <0.001

Table 12: Descriptive statistics for market quality variables

This table reports descriptive statistics of market quality variables and other firm-level control variables of treatment and matched control stocks. The treatment sample consists of 64 banned stocks that had single-stock futures (SSFs) listed prior to the ban period (September 19, 2008 - October 8, 2008). The sample period is from August 1, 2008 to October 31, 2008, during which the preban period is August 1, 2008 - September 18; the ban period is September 19, 2008 - October 8, 2008; and the postban period is October 9, 2008 - October 31, 2008. The control sample consists of 64 stocks that were also subject to the ban but had no SSFs. Matching is made based on underlying stock's size (market capitalization), volatility (idiosyncratic volatility), and liquidity (turnover). Relative quoted spread is time-weighted. Relative effective spread, realized spread, and 5-minute price impact are equal-weighted. Intraday price range is the difference between the highest and lowest prices for each underlying stock on each day, divided by its intraday value-weighted average daily price (VWAP) as in Boehmer et al. (2013). Market quality variables are reported in basis points. Market cap, dollar trading volume, and VWAP are reported in logged values.

Variables Treatment: Banned stocks

with SSFs Control: Banned stocks

without SSFs Preban Ban Postban Preban Ban Postban

Relative quoted spread (bps) 10.01 20.14 21.55 11.44 24.14 26.57 Relative effective spread (bps) 7.15 13.49 14.02 8.38 17.32 17.08 Realized spread (bps) 2.54 6.52 7.10 2.77 6.59 8.78 5-minute price impact (bps) 11.51 22.29 24.75 13.62 28.55 30.52 Intraday price range 0.07 0.14 0.14 0.06 0.14 0.15 Market cap (logged) 22.61 25.51 22.17 22.52 22.35 22.02 Dollar trading volume (logged) 18.44 18.30 18.23 18.08 17.90 17.83 Value-weighted average price (logged)

3.18 3.08 2.72

3.23 3.09 2.75

Table 13: The effect of single-stock futures listings on market quality of underlying stocks during the 2008 short sale ban

This table reports results from firm-fixed effects regressions, using a daily panel of 64 matched stock pairs. The sample consists of 64*2=128 U.S. stocks that were subject to the 2008 short sale ban. The treatment sample includes 64 banned stocks that had had SSFs listed and traded before the short sale ban was initiated on September 19, 2008. We then match the treatment stocks with 64 matching control stocks that were subject to the ban but never had SSFs listed during the sample period. Our sample period covers August 1, 2008 – October 31, 2008. The dependent variable in each regression is the relative quoted spread, relative effective spread, relative realized spread, and 5-minute price impact. Ban (Postban) period is an indicator variable that is equal to one if the observation is made during (after) the ban period: September 19, 2008 – October 8, 2008, and zero otherwise. SSF is an indicator variable that is equal to one if sample stock had a SSFs contract listed, and zero otherwise. MCap is the logged market capitalization. DVOL is the logged daily trading volume. VWAP is the intraday value-weighted average price. RVOL is the intraday price range, divided by VWAP. Significance is computed using the Newey-West (1987) standard errors. Coefficients and standard errors are multiplied by 103. ***, **, and * indicate p<0.01, p<0.05, and p<0.1, respectively.

(1) (2) (3) (4) VARIABLES Quoted Spread Effective Spread Realized Spread 5 Min Price Impact Ban period 1.03*** 0.709*** 0.240*** 0.496*** (0.139) (0.101) (0.00806) (0.00938) Ban period*SSF -0.336** -0.174 0.00494 -0.209** (0.152) (0.116) (0.00923) (0.103) Post ban period 0.760*** 0.407*** 0.271*** 0.113 (0.135) (0.104) (0.00738) (0.00895) Post ban period*SSF

-0.311** -0.301** -0.117 -0.151**

(0.142) (0.122) (0.00866) (0.00759) MCap 0.557 0.998*** 0.508 0.544* (0.388) (0.367) (0.318) (0.315) DVOL -0.300*** -0.261*** -0.179*** -0.00528 (0.00624) (0.00590) (0.00424) (0.00457) VWAP -1.79*** -2.41*** -0.968*** -1.51*** (0.432) (0.391) (0.346) (0.327) RVOL 3.08*** 4.28*** 0.569 3.45*** (0.509) (0.483) (0.381) (0.480) Observations 7,562 7,562 7,562 7,562 Adjusted R-squared

0.757 0.744 0.269 0.627

Table 14: The effect of single-stock futures trading on market quality of underlying stocks during the 2008 short sale ban

This table reports results from firm-fixed effects regressions, using a daily panel of 64 matched stock pairs. The sample, dependent variables, and control variables are same as those used in analyses in Table 13. We add an indicator variable, SSF trade, which is equal to one if stock i had SSFs trade on day t, and zero otherwise. Significance is computed using the Newey-West (1987) standard errors. Coefficients and standard errors are multiplied by 103. ***, **, and * indicate p<0.01, p<0.05, and p<0.1, respectively.

(1) (2) (3) (4) VARIABLES Quoted Spread Effective Spread Realized Spread 5 Min Price Impact Ban period 1.03*** 0.710*** 0.241*** 0.496*** (0.139) (0.101) (0.008) (0.0094)

SSF trade 0.221*** 0.184*** 0.101* 0.007805 (0.00577) (0.00504) (0.005215) (0.00505)

Ban period*SSF trade -0.508*** -0.484*** -0.153* -0.354*** (0.105) (0.00932) (0.00865) (0.00940)

Post ban period 0.757*** 0.404*** 0.270*** 0.112 (0.135) (0.104) (0.00738) (0.00894)

Post ban period*SSF trade -0.373*** -0.186* -0.00984 -0.121 (0.116) (0.00964) (0.00778) (0.00928)

Ban period*SSF -0.259* -0.100 0.00722 -0.154 (0.000156) (0.000121) (9.78e-05) (0.000106)

Post ban period*SSF -0.257* -0.274** -0.103 -0.133* (0.147) (0.125) (0.00889) (0.00788)

MCap 0.525 0.973*** 0.497 0.529* (0.386) (0.367) (0.318) (0.315)

DVOL -0.295*** -0.257*** -0.178*** -0.00488 (0.00624) (0.00590) (0.00427) (0.00459)

VWAP -1.77*** -2.40*** -0.962*** -1.50*** (0.430) (0.391) (0.346) (0.327)

RVOL 3.05*** 4.26*** 0.556 3.43*** (0.508) (0.483) (0.382) (0.480)

Observations 7,562 7,562 7,562 7,562 Adjusted R-squared 0.758 0.745 0.269 0.628

Documents

Back to the Futures: When Short Selling is Bannedfmaconferences.org/SanDiego/Papers/Back_to_the_futures_FMA2018.pdfBack to the Futures: When Short Selling is Banned . George Jianga,