Single Stock Futures_Evidence From the Indian Securities Market

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Single-stock futures: Evidence from the Indiansecurities market

Umesh Kumar a, Yiuman Tse b,

a SUNY Canton, School of Business & Liberal Arts, Canton, NY 13617, USAb University of Texas at San Antonio, Department of Finance, San Antonio, TX 78249, USA

a r t i c l e i n f o a b s t r a c t

Article history:

Received 27 January 2007

Accepted 4 June 2009

Available online 31 August 2009

Although single-stock futures (SSFs) are useful multi-purpose stock

derivatives, they have not received much attention in developed

markets. We analyze SSFs in the Indian market to understand their

contribution in price leadership. The findings indicate that trades in

the stock market contribute more to price discovery than trades in the

SSF market (72% and 28%, respectively),while quotes in the SSFmarket

are more price innovative than quotes in the stock market (39% and

61%, respectively). Our analysis suggests that while stock and SSF trade

returns have predictiveability for eachother, in the caseof quotes, only

SSF quotes have predictive ability for stock and SSF returns.

2009 Elsevier Inc. All rights reserved.

JEL classification:

G11G14

Keywords:

Single-stock futures

Price discovery

Information share

1. Introduction

Single-stock futures (SSFs) represent a significant development in stock-related derivatives. It is of

academic interest as to why SSFs, as a derivative product, have not gained widespread acceptance in most

markets, particularly in developed markets. We analyze the Indian securities market for evidence about therole of SSFs and their effectiveness in terms of price discovery, information share, and stock returns.

SSFs traded on the National Stock Exchange of India (NSE) have grown substantially since their

inception in 2001. As to why other markets have struggled to generate interest among investors for SSFs,

see, Fung and Tse (2008), among others, for a review of SSFs traded in a number of international exchanges.

A single-stock futures contract provides a way to take advantage of arbitrage, speculative, and hedging

opportunities, while reducing trading pressures on the underlying markets. Without futures contracts on

individual stocks, arbitrageurs and investors must trade in the underlying assets, or trade options and

index products.

Global Finance Journal 20 (2009) 220234

Corresponding author. Department of Finance, College of Business, University of Texas at San Antonio, San Antonio, TX 78249,USA. Tel.: +1 210 458 2503; fax: +1 210 4580 2515.

E-mail address: [email protected] (Y. Tse).

1044-0283/$ see front matter 2009 Elsevier Inc. All rights reserved.

doi:10.1016/j.gfj.2009.06.004

Contents lists available at ScienceDirect

Global Finance Journal

j o u r n a l h o m e p a g e : w w w. e l s e v i e r . c o m / l o c a t e / g f j
mailto:[email protected]://dx.doi.org/10.1016/j.gfj.2009.06.004http://www.sciencedirect.com/science/journal/10440283http://www.sciencedirect.com/science/journal/10440283http://dx.doi.org/10.1016/j.gfj.2009.06.004mailto:[email protected]


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Manaster and Rendleman (1982) show that stock option prices contain additional information com-

pared to stock prices at the closing price level. This difference occurs due to the trading costs, the absence

of tick rule governing short sales of options, and the lower margin requirements of options. The SSF has

similar characteristics in terms of trading opportunities. It is a linear and efficient instrument for short-

selling a stock. It further enables a cleaner hedge relative to options with potential tax advantages. In this

context, it becomes imperative to explore whether SSFs contain additional information in trades or quotes

as do option prices.

The US typically has the most vibrant markets for stocks and derivative products. Passage of the

Commodity Futures and Modernization Act of 2000 made SSFs legal in the US by repealing the Shad

Johnson Accord. On November 8, 2002, two exchanges, OneChicago and the Nasdaq Liffe Market (NQLX),

started SSF trading. Single-stock futures offer a cheap andflexible way to gain equity market exposure for a

wide range of purposes, such as hedging, speculation, andfinancial engineering, yet SSF trading is relatively

infrequent in the US, the largest and the most sophisticated securities market in the world.

Research so far has concentrated on developed and mature markets for SSF trading. We look at the

Indian market, where we find remarkable progress in SSF trading. Since their launch in November 2001,

SSFs have shown incredible progress, making the NSE the most vibrant SSF market in the world. In 2004,

the NSE traded more than 25 million SSF contracts. SSF trading is also successful in the JohannesburgStock Exchange and Eurex. Markets in the developed economies such as Sweden, Denmark, Spain, Italy,

Greece, Australia, USA, UK, Euronext.liffe, Hong Kong, and Bulgaria have SSF trading but that trading is

not significant. The Futures Industry Association (July/August 2006) reports the NSE as the 13th-largest

derivatives exchange by volume, and the NSE has the largest trading volume in SSFs worldwide, making

it the largest global exchange for single-stock futures. In the first four months of 2006, the worldwide

volume in SSFs was 84.5million contracts. India's NSE, the largest global exchange for single stock futures,

contributed 35.4million contracts in total volume.

Studies show that SSF trading improves market efficiency. Ang and Cheng (2005) find that SSFs have a

stabilizing influence on a market. SSFs, with lower trading costs and higher leverage provide better relief

for arbitrageurs than for speculators. In a study of stock futures trading in Australia, Lee and Tong (1998)

conclude that SSF trading offers many of the benefits associated with derivatives trading without in-creasing volatility or instability in the market. A coincidental increase in volume in the underlying stock

market has made stock brokers less wary of losing market share and profits to the SSF market.

Our research investigates the success of SSFs in the Indian market and analyzes price discovery

mechanics. We examine the most comprehensive sample of stocks and stock futures available over a

12 month period (252 trading days). We also examine the information linkage between the SSFs and their

stocks.

Our findings suggest that trades in the stock market perform better in terms of price discovery and

information share than do trades in the SSF market. This result is contrary to previous findings asserting

that derivative products account for more price discovery and information share. However, both the stock

and SSF trade returns have predictive ability for each other. Our quote analysis suggests that quotes on the

SSF market lead quotes from the stock market in contributing to price discovery, and that SSF quotes havepredictive ability for both stock and SSF trade returns. Both markets are mutually dependent and neither

market simply free-rides on the other. More than 93% of the contracts traded in the SSF market are for

single-contract trades.1 This suggests the healthy participation of retail investors in the SSF market.

One plausible reason for the success of SSFs on the NSE could be the absence of an efficient or active

stock-lending mechanism in the equity market. A competing hypothesis is that the first two of the three

markets (the equity market and the stock-lending market) appear to act as hidden markets for the third

the SSF market. In the case of India, the equity and SSF markets came first, and so the SSF market may be

seen as a supplement to the stock-lending market. In the case of the US, the equity and the stock-lending

markets developed first, so together they act as a complement to the SSF market. This hypothesis further

theorizes that even if the third market is introduced later on, it will not necessarily develop or, expand as

the other two markets would continue to offer a hidden market. That may explain the lackluster responseto SSFs in the US or other developed markets that have vibrant stock-lending markets.

1 Sometimes, institutional investors can place single-contract trades to conceal their identities.

221U. Kumar, Y. Tse / Global Finance Journal 20 (2009) 220234


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The remainder of this study is organized as follows: in Section 2, we discuss the development of the

SSF market, Section 3 describes the data construction and methodology, Section 4 contains the empirical

results, and the final section concludes the paper.

2. Development of the single-stock futures market

Single-stock futures can be used as a substitute for equities for investment or speculation, as a leveraging

instrument for hedging or speculation, or as a tool for price discovery in underlying stocks. Because they

are inexpensive to trade, they have considerable appeal to retail investors as a way to manage their stock

portfolios.2 SSFs have a downside, as their leverage can amplify any losses, and they do not provide share-

holder rights.

Formalized exchange trading of SSFs started in the late 1980s in Sweden, and they have recently

become legal in the U.S. Now, more than 20 exchanges offer SSF products worldwide, but, by most

estimates, the volume of SSF trading remains at less than 1% of total financial-derivatives trading. SSFs have

shown sluggish growth in most exchanges, even though they are sound and useful instruments, are well

regulated, are offered by renowned exchanges, and provide flexible instruments to achieve cost-effective

hedging and portfolio rebalancing. Consequently, the instrument has not altered the dynamics of equityinvesting.

2.1. SSF market in the US

Studies demonstrate that derivative products such as SSFs boost the trading volume in the underlying

assets, enhance their liquidity, and make the whole market more efficient. The average daily turnover of

SSFs in the U.S. is around 10,000 contracts. It constitutes only about 1% of the market for futures linked to

the Standard & Poor's 500 stock index. This size of turnover is insufficient for a critical level of liquidity that

is essential to narrowing bid-ask spreads. Recently institutional investors and other sophisticated traders

have shown enthusiasm in the SSF market, but retail investors are wary and circumspect in dealing with

single-stock futures. Commenting on this poor response from retail investors, Jones and Brooks (2005)state that single-stock futures prices in the US often have little relation to the prices of their underlying

stocks. Theirfindings imply that many hedging or large speculative trades may be difficult to execute in the

current SSF market. Perhaps this situation makes institutional investors reluctant to utilize this medium.

It is pertinent to understand whether theSSF market hasanything to do with thebias shown by investors

due to unfamiliarity with the products, or the long side of the stock market, or some other considerations.

It is also important to know whether the challenges facing SSF market are due just to investors' indifference

or the result of some form of regulatory initiatives.

2.2. Market design and structure in India

India has a modern securities market, with 5600 firms listed on the two major stock exchanges. Theexchanges are electronic and they have a T+2 rolling settlement system. The National Stock Exchange

(NSE) is the largest stock exchange in India. It is the 3rd-largest stock exchange in the world in terms of

number of trades, after the NYSE and Nasdaq. Measured by the number of futures and options traded in

2004, NSE ranked as the 17th-largest derivatives exchange in the world, and the 10th-largest futures

exchange. It contributes to almost all derivatives transactions in India. The value of equity derivatives

trading is more than two times the value of equity trading on the NSE.

The NSE has three market segments (Wholesale Debt Market (WDM) segment, Capital Market (Equity)

segment, and Derivatives segment). The derivatives trading system provides fully-automated, screen-

based trading for all kind of derivative products. It supports an anonymous order-driven market, which

operates on a strict price time priority. Trading terminals for the derivative segment are available in more

than 300 cities across the country, and trading can be accomplished by investors through the Internet.

2 As the SSFs are linear pay-off products, even retail investors can estimate the proceeds based on their risk appetites.

222 U. Kumar, Y. Tse / Global Finance Journal 20 (2009) 220234


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India introduced SSFs on November 9, 2001. Prior to June 2001, there was no trading of derivatives of

any kind, and trading of equities was done on an accounting period settlement basis. Accounting-period

trading was akin to weekly futures for the equities. However, in accounting-period trading, trades deal

with a physically-deliverable asset (unlike stock or index futures, which are notional). The trades remain

outstanding and are settled by actual deliveries on the settlement date. When SSFs were introduced,

market participants were doubtful of their success in India, because even the U.S. did not have SSF trading.

In India, retail investors are dominant in SSF trading, including the proprietary trading of small

brokerage houses. The top ten member firms account for just 21% of total SSF trading, a sharp contrast

to most mature markets where the top ten member firms might account for more than 60%.3 Only a

small segment of SSF trading is institutional, and of that small amount, almost all comes from foreign

institutional investors, who use SSF trades to carry out their hedging and portfolio-rebalancing activities.

The lack of institutional trading can affect the information content in SSF trades because institutional

investors are more sophisticated and have greater resources.4 A pricepressure hypothesis implies that

institutional trades influence price formation in a market more than do trades by individuals.

Despite a short history of derivative trading in India, in the first two months of 2005 NSE conducted 35

times more trading in SSF contracts than did OneChicago. This paper looks at plausible reasons for the

success of SSFs in India. The practice of badla and accounting-period trading has been credited to an extentfor this success. The badla was a quasi-derivative product and conceptually close to futures contracts, since

it was used to defer settlement in the equity market. Individual investors used the badla market before

the introduction of the SSF market. These investors had years of practice trading something akin to equity

futures. So, when the badla products ceased to be available in June 2001 and SSFs were introduced in

November 2001, these investors easily migrated to single-stock futures.

However, other features that have made the SSF market successful are as follows: (a) It is an order driven

market having similar characteristics to the equity market, implying that individual investors familiar with

the equity market system can easily understand the execution of trades. (b) Individual investors generally

put 1530% of the total value of an SSF trade on margin with their broker. (c) The SSF contract size is

affordable since its value is just above Rs 0.2 million (approximately USD 4500). Thus, the affordability

and familiarity of the products have made it easier for individual investors to participate heavily in the SSFmarket.5

3. Data construction and methodology

3.1. Data source

This study employs data from high-frequency stocks and their SSFs, obtained from the National Stock

Exchange of India (NSE) for the period January 2004 through December 2004, a total of 252 trading days.

All derivatives trading in India is overwhelmingly concentrated in the NSE. We choose only NSE trade data

for stocks, since its stock market segment contributes almost 2/3 of total trading volume in India. The data

are in two segments (trade data and snapshots of limit order books). The trade data contain the detailsof all trades that took place in the exchange for the stocks and SSFs. The snapshots of limit order book

for stocks were taken at four different times during the day. In the case of SSFs, the snapshots of the limit

order book were taken at five different times. The limit order book contains all limit orders entering

the NSE trading system (right to trade against them, without any obligation) and they are free options

which anyone can exploit. The order book snapshot time for stocks is 11 A.M., 12 noon, 1 P.M., and 2 P.M.

The order book snapshot time for SSFs is 11 A.M., 12 noon, 1 P.M., 2 P.M., and 3 P.M. The normal market

3 NSE monthly derivates update (December 2004) reports that the top five members make 12% valuewise contribution while the

next five have 9%.4

By examining the information content of institutional trades on the London Stock Exchange, Bozcuk and Lasfer (2005) foundthat the type of investors, the combination of the size of the trade, and investors' resulting level of ownership are the major

determinants of price impact. Institutional and individual investors observe news or price movements in different ways, process

such information differently, and thus trade accordingly.5 Presently, the SSF market in South Africa (Johannesburg Stock Exchange) has been doing well and credit goes to the affordability

and participation of retail investors; the contract price in SSF market is sufficiently low to encourage participation by retail investors.



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operation time for stock and SSF markets in the NSE is synchronized, with trading starting at 9:55 A.M. and

closing at 3:30 P.M.

The snapshots obtained are the pictures of the complete limit order book at a given point in time. The

limit order book is an electronic order matching embedded in all orders present at any point in time.

Though the snapshots data provide a picture at a few time points during the day, many plausible answers

related to liquidity, market impact, and bid-ask spread can be obtained accurately. The snapshots are rich in

terms of quote characteristics, such as order ID number, ticker symbol, quantity, price, timestamp, buy or

sell, type of order (day order, good till date, cancel, immediate/cancel), quantity flags (minimum fill, all or

none, disclosed quantity), quantity disclosed, and priceflags (at the open price, market price, and stop loss

order). We mainly use price, timestamp, and buy or sell data for our study. The buy or sell indicator allows

for calculation of spread and mid quote price. Timestamp and price facilitate the construction of desired

data series for stocks and their SSFs in price discovery analysis.6

The exchange selects SSF stocks from the top 500 stocks based on average daily market capitalization

and daily traded value for the previous six months. We restrict our sample to only those SSFs that have

daily trading volume above 1000 contracts. Based on this criterion, we initially selected 40 SSFs. These SSFs

and their stocks are the most liquid and actively-traded securities on the NSE. We found that some of these

firms have merged, changed their name or split, and sometimes their trading volume became too low tomatch our criterion. We therefore eliminated such SSFs from our sample. In other cases where we could

obtain the desired data series from the raw data we were forced to drop those SSFs from our final sample.

The resulting sample for our study is comprised of 30 stocks and their SSFs. These 30 SSF contracts

contribute 8085% of total trading volume. Similarly, their stocks represent 8590% of total stock trading

volume. These stocks are all constituents of the primary index of the exchange (the S&P Nifty Index).

Our dataset is more comprehensive and larger than the datasets used in many previous studies, and the

integrity of the data is strong, since the data are obtained directly from the exchange.

3.2. Data preparation

The trade data contain the details of all trades which occurred at the exchange for the stocks and SSFson a daily basis. All trades are time stamped. We sort the trade data based on the time stamp and choose

the last trade from each minute of trades. The quote data are obtained from the limit order book snapshot

that lists all outstanding orders of all securities at the time the snapshot is recorded. The orders are time-

stamped and identified as buy or sell orders. We merge these snapshots on a daily basis for each stock

separately. The merged snapshot data are sorted based on the time stamp. We follow this procedure for

stock and SSF quotes separately. Order Type indicates buy (B) or sell (S) orders. From these daily files, we

select the highest bid quote and lowest ask quote for every minute for both stocks and SSFs. We calculate

the mid-quote after averaging the bid and ask quotes for each minute. Hence the data generated are similar

for both stocks and SSFs. We omit the outliers, if any, from trade and quotes to avoid contamination of

the data series. A trade price is an outlier, if the SSF price is 5% above or below the stock price. A quote price

is treated as an outlier, if the SSF midquote is 5% above or below the stock midquote. Both filters removeless than 3% of all observations from the final data. Following this procedure, we obtain 2,429,656 and

1,648,650 minute-to-minute observations for trades and quote files, respectively.

3.3. Understanding regulatory implications and institutional trading

One of the tasks of this paper is to develop an understanding of regulatory initiatives toward SSFs. We

pay particular attention to July 2004, since there was a regulatory intervention in that month. New

regulations increased the exposure and position limits for Foreign Institutional Investors (FIIs) in

derivative products. We scrutinize whether such regulatory changes facilitate SSF trading by impacting

price discovery activities. Institutional investors, including FIIs, were allowed to trade in SSFs in 2002, but

faced a restrictive burden in terms of limits to position and open interest. In July 2004, the capital marketregulator eased some restrictions on FIIs for position limits on derivative products, including doubling the

market-wide position limit for SSFs.

6 See Chakrabarty and Jain (2005) for a detailed description of the data.



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3.4. Price discovery and information share between the SSF and stock markets

One implicit assumption in the studies of price discovery is that markets share at least one common

driving force, leading to the use of common-factor models in such studies. These models derive their results

from the same economic rationales. To investigate price discovery and information share between and SSF

and stock markets, we use two popular common-factor models, from Hasbrouck (1995) and Gonzalo &

Granger (1995). Baillie, Booth, Tse & Zabotina et al. (2002), De Jong (2002), and Lehmann (2002) establish

the relationship between these two models. Harris, McInish, & Wood, (2002) and Hasbrouck (2002)

enumerate the differences between the two models. Booth, Lin, Ji-Chai, Martikainen & Tse, (2002) use both

models to examine the Finnish upstairs and downstairs stock markets. A number of other studies have also

used the information-share and/or permanent-transitory models (see, e.g., Brockman & Tse, 1995; Ding,

Harris, deB, Lau & McInish, 1999l; Tse & Erenburg, 2003).

Both models use the vector error correction model (VECM) as their basis, but they differ in their price

discovery mechanisms. The Hasbrouck (1995) model defines price discovery in terms of the variance of

the innovations to the common factor, and measures each market's relative contribution to this variance.

This contribution is called the market's information share. The Gonzalo & Granger (1995) model, however,

focuses on the error correction process and the components of the common factor. This process involvesonly permanent (as opposed to transitory) shocks that result in a disequilibrium. The Gonzalo and Granger

model measures each market's contribution to the common factor, where contribution is defined as a

function of the market's error correction coefficients.

Thefeature that distinguishes themodels from each other is that theHasbrouck (1995) model decomposes

the variance of the implicit efficient price. Relying on the premise that price volatility reflects the flow of

information, it attributes a greater share of efficient price discovery to the market that contributes the greatest

share to this volatility. By contrast, the Gonzalo & Granger (1995) model approach decomposes the common

factor itself. In doing so, the Gonzalo and Granger model ignores the correlation among the markets and

attributes the leading role solely to the market that adjusts least to the price movements in other markets.

In markets affected by the same information flow (i.e. with similar volatility), these two models produce

consistent results, i.e. a market with the greatest contribution to the price discovery has the largest loading onthe common factor.

Both information-share and permanent-transitory models are derived from a vector error correction

model (VECM) in the following form:

Xt = Xt1 + k

i = 1iXt1 + t 1

Where Xt= {Xit} is an n 1 vector of cointegrated prices. and s are n n matrices of parameters, andt is an n 1 vector of serially-uncorrelated residuals with a covariancecovariance matrix = {ij}. Thelong run relation matrix has a reduced rank of r < n and can be decomposed as =, where andare n r matrices. The matrix consists of the cointegrating vectors and is the error correction (orequilibrium adjustment) matrix. If r = n1 and is spanned by the differentials of each pair of priceseries, then all Xit are driven by one common factor. This is the case for stock and stock futures prices.

Hasbrouck (1995) transforms the VECM into an integrated form of a vector moving average (VMA):

Xt = Jk

t= 1 + *Lt 2

whereJ(1,,1) is a column vector of ones,= (1,,n) isa row vector,and*is a matrix of polynomials

in the lag operator, L.

The Hasbrouck (1995) model defines a market's contribution to price discovery as its informationshare the market's proportion of the variance of the efficient price innovation. By contrast, the Gonzalo

and Granger (1995) model decomposes the common factor into a linear combination of the prices. An

advantage of the Gonzalo and Granger model is that the common-factor estimates are exactly identified,

as they do not depend on the ordering of the variables.



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Baillie et al. (2002) and De Jong (2002) show that the information-share and permanent-transitory

models provide similar results if the contemporaneous cross-equation residuals are uncorrelated. If there is

a strong correlation among the contemporaneous cross-equation residuals, differences in the results from

the two models can be substantial.

Hasbrouck (1995) points out that the information share estimates will depend on the ordering of

variables in the Cholesky factorization, if the price innovations are correlated. Martens, Kofman and Vorst

(1998), Baillie et al. (2002), Booth et al. (2002), and Huang (2002) also report a substantial difference in

their Hasbrouck upper and lower bounds of information shares. For a bivariate case, Baillie et al. (2002) and

Tse (1999) show that the average of the information shares given by two permutations is a reasonable

estimate of a market's role in price discovery. We use the average of information shares to interpret the

results.

3.5. Information linkage between the SSF and stock markets

Following Chan, Chung, and Fong, (2002), we use a multivariate VAR model to analyze the informational

role and interdependency between SSFs and their stocks.7 The model also investigates the dynamic rela-

tionship among trades and quote revisions for the SSFs and their stocks. The following VAR model is usedin the regression.

rt = a1rt1 + + aprtp + bozt + b1zt1 + + bpztp + 1;t 3

zt = c1rt1 + + cprtp + d1zt1 + + dpztp + z;t 4

where is rt the trade return for stocks and SSFs at transaction time t, and zt is the quote return for stocks

and SSFs at transaction time t, defined as the change in midquote from the quote following transaction t

1 to the quote following transaction time t.

We use minute-to-minute trade and quote prices to calculate the returns. We calculate the mean and

standard deviation for these return variables for each security on a daily basis. Following Easley, O'Hara, andSrinivas (1998), the return variables for each security are standardized by subtracting the mean and then

dividing by the standard deviation. The standardized returns help to control for cross-sectional variations

across different SSFs and stocks. We choose six lags for each explanatory variable in the regression. In the

trade returns regressions, we use separate lagged stock and SSF returns as explanatory variables separately

to eliminate the possibility of multicollinearity.

Theleadlag relationship between trade and quote returns should demonstrate the informational effects

among them. When the leadlag relationships of returns are used in more than one market setup, they

explain the informational role and their linkages between the individual markets. If informed investors

submit limit orders in the stock market only, SSF quotes will not show predictive ability for stock returns. If

informed investors submit limit orders to the SSF market to disguise their private information and exploit

it by transacting in the stock market, the SSF quotes will have predictive ability for both stock and SSFreturns. If the markets are dependent on each other, the returns on SSFs and stocks will have predictive

ability for each other.

4. Empirical results

4.1. Relationship between the stock and SSF markets

Fig. 1 illustrates monthly trading turnover of all sample stocks and SSFs. We find that the turnover of

SSFs is higher than the turnover of stocks, except in the months of May and June, 2004. Overall, wefind that

SSFs have substantial trading, almost 1.6 times the number of trades of stocks. Trading volume is highest

for both stocks and SSFs in January, 2004. Trading volume gradually slows down in subsequent months,and reaches its lowest level in June, 2004. Later, the trading volume gains in both segments.

7 Chan et al. (2002) analyze the informational linkage between the option and stock markets by integrating quote returns and net

trade volume to see the predictive power for both option and stock returns.



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4.2. Trade size in the SSF market

First, we calculate the average number of monthly trades, number of contracts per trade, percentage

of trades, and percentage of volume in the SSF market. We try to understand the kind of investors

dominating the SSF market. Table 1 exhibits our findings. We expect institutional investors to make use of

the advantages offered by SSF trading. We examine this assumption using size of trades transacted in the

SSF market.We find that single contract trades overwhelmingly dominate the SSF market. On average, single

contract trades account for more than 93% of all contracts traded. Two contract trades constitute only 4.35%

of total trades, while trades in three or more contracts comprise only 2.47% of total trades. The notional

value of a single contract size is comparatively small (approximately USD 4500). Trade size clearly suggests

that institutional trades do not rule the SSF market. In this market, we assume that institutional investors

would tend to deal in more than a single contract trades, considering the transaction and other attendant

Table 1

Trade size in SSFs.

Month One-contract('000)

Two-contract('000)

Three-or- morecontract ('000)

Total contract('000)

% of onecontract

% of twocontract

% of three ormore contract

Jan-04 2330 113 60 2503 95.17 3.23 1.60

Feb-04 1712 68 37 1818 96.22 2.48 1.30

Mar-04 1680 319 169 2168 83.73 11.89 4.38

Apr-04 1980 132 81 2193 93.44 4.21 2.35

May-04 1641 113 72 1827 92.07 3.94 3.98

Jun-04 1691 100 63 1855 94.19 3.33 2.48

Jul-04 1785 110 69 1965 94.67 3.38 1.95

Aug-04 1807 115 74 1997 94.35 3.55 2.10

Sep-04 1845 118 82 2046 93.91 3.85 2.24

Oct-04 1858 117 80 2055 93.91 3.87 2.21

Nov-04 1768 113 80 1962 93.48 4.05 2.47

Dec-04 2552 168 114 2835 93.05 4.43 2.52Average 1887 132 82 2102 93.18 4.35 2.47

We calculate trade sizes for all SSF transactions. We derive monthly contracts in the SSF market from daily trade data. The trade size is

segmented into three parts i.e. one-contract trades, two-contract trades, and three-or-more contract trades. From monthly data, we

compute the monthly average trade size in the SSFs.

Fig. 1. Monthly trading turnover in stock vis--vis SSF. We calculate monthly stock and SSF turnover. The straight line indicates the

monthly stock turnover, while the dotted line signifies the SSF turnover. The turnover is denoted in Indian currency in billion rupees.



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costs associated with doing single-contract transaction. The trading pattern above (overwhelmingly single

contract trades) is consistent in almost all months. Therefore, we believe that there is a strong retail

participation in the SSF market, as claimed by the exchange and other market intermediaries.8

4.3. Bid-ask spreads

Table 2 presents the percentage spread for stock and SSF quotes. Percentage spread is measured as100%x (Ask PriceBid Price) / Midquote, where midquote is the average of bid and ask prices. Wefind that

the mean percentage spread of stock quotes ranges between 1.19% and 2.60%. SSF quotes show mean

percentage spreads between 1.49% and 3.51%. The differences between stock and SSF quotes vary between

0.11% and 1.08%. Overall, the mean percentage spread for stock quotes is 34% lower than SSF quote spreads.

Similarly, volatility in stock quotes is 21% lower than the volatility in SSF quotes. The difference between

the average mean spread of stock and SSFs quotes is 0.56%.

Stock quotes show lower spreads than SSF quotes. It is important to note that there is a difference

between quote-setting behavior in stocks and SSFs. We assume that SSFs should have lower spreads, but

when we analyze trade size, wefind that SSF trading is mostly driven and influenced by retail participation.

Hence, the higher spread observed in SSF quotes is not surprising. It is interesting to note that the spreads

8 NSE monthly derivatives update (December 2004) mentions that non-institutional investors contribute more than 95% of total

derivatives trading. FOW (Issue 403 dated December 01, 2004) reports that in the SSFs, the main participants are retail traders and

proprietary trading by member firms, followed distantly by foreign institutional business, and domestic mutual funds. Bloomberg

News (April 05, 2006) reports that retail investors account for 63% of the total trading in stock futures in the Indian market.

Table 2

Percentage spread in stock and SSF quotes.

Panel A

Monthly percentage spread of stock and SSF quotes

Month N Mean Standard deviation t-statistic

Stock SSF Difference Stock SSF Difference Stock SSF Difference

Jan-04 21 2.21 2.68 0.48 0.54 0.53 0.23 18.88 23.44 9.69

Feb-04 19 1.76 2.49 0.73 0.34 0.43 0.23 22.84 24.99 13.84

Mar-04 22 1.69 2.31 0.62 0.18 0.18 0.16 44.28 60.45 18.13

Apr-04 20 1.60 2.12 0.52 0.19 0.13 0.16 38.65 72.25 14.93

May-04 21 2.60 3.51 0.91 1.66 1.53 0.29 7.18 10.50 14.60

Jun-04 22 1.61 2.69 1.08 0.18 0.27 0.17 41.41 47.16 29.27

Jul-04 22 1.54 2.50 0.96 0.30 0.36 0.20 24.46 32.44 23.07

Aug-04 21 1.29 1.85 0.56 0.09 0.15 0.14 66.82 55.33 17.77

Sep-04 22 1.19 1.49 0.30 0.08 0.12 0.11 66.92 58.77 12.84

Oct-04 20 1.35 1.64 0.29 0.13 0.15 0.13 48.11 47.52 9.80

Nov-04 19 1.34 1.51 0.16 0.13 0.15 0.12 45.72 43.24 5.87Dec-04 23 1.40 1.51 0.11 0.10 0.16 0.16 68.71 46.55 3.36

Panel B

Mean percentage spread of stock and SSF quotes

Variable N Mean Standard deviation Standard error t-statistic p-value

Stock 252 1.63 0.65 0.04 39.80


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are wider in stock and SSF quotes during June and July 2004, when the trading volume of the SSF market

shrinks and becomes almost equal to that of the stock market. When relative trading volume in the SSF

market increases, the spread differences narrow.

4.4. Price discovery and information share between the SSF and stock markets

4.4.1. Results from trade transactions

Table 3 reports the price discovery results for trade prices of SSFs and their stocks at one-minute

intervals. Both models show that the stock market produces higher price discovery in all months except

July and August, 2004. In these two months, each market contributes almost equally in price discovery and

transmission.

We notice that the average information shares for stocks and SSFs are 0.72 and 0.28, respectively. The

findings suggest that information production and price discovery occur in the stock market. Despite higher

turnover volume in the SSF market, as shown in Fig. 1, the contribution of SSFs to price discovery is modest.

The Gonzalo and Granger model provides similar results; 0.74 (stocks) and 0.26 (SSFs). Thus, our result is

in contrast with other findings showing futures market to be more efficient in price discovery. However,

we find that both markets have almost equal roles in price discovery during July and August.We consider two significant events in this period. First, there was a change in regulatory limitations for

Foreign Institutional Investors (FIIs) trading in stock index futures. Second, in the case of SSFs, there was a

relaxation in market-wide position limits. These two factors may have influenced price-discovery and

information-share contribution. It is worth noting that FIIs are sophisticated investors and are primary

players in institutional trades for derivative products, including SSFs, while domestic institutional investors

are relatively inactive in derivative products, particularly in SSFs. The relaxation of regulation in stock index

futures also affects the 30 sample stocks, since they are constituents of the indexes. The FIIs benefit from

increases in market-wide position limits. During our sample period, they average 60% of the total open

interest in the SSFs.9 Open interest is a measure of how much interest a particular product garners from

investors. FIIs have a higher level of open position in the SSFs which demonstrates their level of interest.

Chan and Lakonishok (1995) report that the estimates of the price impact of institutional trades aresubstantially higher when trades are evaluated not individually but in the broader context of a package.

Table 3

Information share from trades.

Month Hasbrouck information share GonzaloGranger factor weights

Stock SSF Stock SSF

Jan-04 0.79 0.21 0.84 0.16

Feb-04 0.77 0.23 0.84 0.16

Mar-04 0.78 0.22 0.80 0.20

Apr-04 0.72 0.29 0.77 0.23

May-04 0.66 0.34 0.70 0.30

Jun-04 0.80 0.20 0.83 0.16

Jul-04 0.50 0.50 0.51 0.49

Aug-04 0.49 0.51 0.39 0.61

Sep-04 0.81 0.19 0.84 0.16

Oct-04 0.76 0.24 0.80 0.20

Nov-04 0.78 0.22 0.80 0.20

Dec-04 0.76 0.24 0.79 0.21

Average 0.72 0.28 0.74 0.26

The table reports price discovery results based on the Hasbrouck (1995) model and the Gonzalo and Granger (1995) model for stockand SSF trades. The prices are calculated at one-minute intervals. The information share is the proportion of variance in the implicit

efficient price of the stock that is attributable to innovations in that market. The panel represents the information share on a monthly

basis, and we then compute the average over all months to get the overall information share for entire period.

9 Data obtained from SEBI's Annual Report 200405.



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Frino, Walter, and West (2000) document that investors with better market-wide information are more

likely to trade in stock index futures, strengthening the price discovery role of futures market significantly

around macroeconomic news releases. Bozcuk and Lasfer (2005) find that the type of investors behind the

trades, the combination of the size of the trades, and the investors' resulting level of ownership are major

determinants of the price impact.

In our case, FIIs are major shareholders in the sample stocks. These findings corroborate that, after the

relaxation in position limits in July 2004, the increase in FIIs trading in the SSFs alters the informationcontent in prices.10 The bulk of the trades in the NSE do not come from any innate complementary hedging

function that SSFs offer, or even from any competitive advantage they enjoy over the stock markets.

4.4.2. Results from competitive quotes

A market that is a price leader or information producer does not necessarily also provides the best

quotes. It simply indicates that the market impounds information faster than the others. Moreover, SSFs

may not trade at exactly the same price as their stocks, but they will trade at a price that is very close

because of the well-known cost-of-carry relationship.

Table 4 reports price discovery and information share results for stock and SSF quotes. To understand the

quality of quotes, it is important to understand the characteristics of the markets from which these quotes

originate. Price discovery inferred from quotes does not necessarily reflect the market in which informedtraders trade. When similar securities are traded in multiple markets, informed investors sometimes

conceal their private information by submitting limit orders in the market where they do not intend to

execute trades. They may submit limit orders in a market where the cost is lower to exploit their private

information and subsequently cancelling these limit orders. In such a situation, the private information is

first reflected in the quote revisions and not in trades. There is a separation between price leadership from

quotes and price leadership from trades.

These results are different from those reported in Table 3 using trades. We find that stock and SSF

quotes yield an average information share of 0.39 and 0.61, respectively, while common factor coefficients

are 0.37 and 0.63, respectively. This indicates that SSF quotes lead stock quotes in price discovery.

10 The Holden and Subrahmanyam (1992) model illustrates that greater competition among strategic informed traders, such as

institutional investors, results in faster incorporation of private information. Boehmer and Kelley (2005) show that increases in

institutional trading volume are associated with greater informational efficiency. Moreover, Dey and Radhakrishna (2001) and Fehle

(2004) find that institutional trading and institutional ownership are negatively related to bidask spread.

Table 4

Information share from quotes.

Month Hasbrouck information share GonzaloGranger factor weights

Stock SSF Stock SSF

Jan-04 0.44 0.56 0.44 0.56

Feb-04 0.39 0.62 0.38 0.62

Mar-04 0.41 0.59 0.41 0.59

Apr-04 0.41 0.60 0.40 0.60

May-04 0.56 0.44 0.57 0.43

Jun-04 0.48 0.52 0.48 0.52

Jul-04 0.46 0.54 0.42 0.58

Aug-04 0.39 0.62 0.31 0.69

Sep-04 0.26 0.74 0.24 0.76

Oct-04 0.29 0.71 0.28 0.72

Nov-04 0.29 0.71 0.28 0.69

Dec-04 0.29 0.71 0.27 0.74

Average 0.39 0.61 0.37 0.63

The table reports price discovery result based on the Hasbrouck (1995) model and the Gonzalo and Granger (1995) model for stockand SSF quotes. Thequotes are computed at one-minute intervals. Thepanelrepresents the information share on a monthly basis, and

then we compute average to get overall information share for entire period.



12/15

The above results suggest that trades on the stock market have higher information share while quotes

on the SSF market contribute more in price discovery. The Hasbrouck (1995) model does not determine

which market has the best prices. The information share in the model measures who moves first in

the process of price adjustment. Hasbrouck (1995) finds that information share of non-NYSE (regional

exchanges and Nasdaq) quotes is negligible, about 5%. Harris et al (1995) demonstrate that non-NYSE

trades provide substantial price discovery, about 30%. This suggests that non-NYSE quotes have negligible

information share (5%), while the non-NYSE trades have substantially higher information share (30%). Tse

(2000) further shows that non-NYSE trades provide price discovery, but non-NYSE quotes do not. This is

similar to our finding that quotes on the stock market have low information share (39%), while trades on

this market have high information share (72%).

As discussed in Hasbrouck (1991, Hasbrouck, 1995), while off-NYSE quotes are autoquotes that

simply follow the NYSE quotes, the actual mechanism linking trade and quote responses is confounded

by different market-microstructure aspects, such as liquidity effects, inventory-control behavior, and

price discreteness. Similarly, quotes from the stock market (like the off-NYSE autoquotes) may auto-

matically adjust to the quotes from the SSF, resulting in a higher information share from quotes in the

SSF market.

The proportion of trades by domestic institutional investors in the SSF market is low. This fact mayexplain the lower information share for SSF trades. Additionally, the higher information share for stock

trades may also arise due to the following institutional feature. In the Indian stock market, institutional

investors cannot sell shares unless they possess the shares in their account. Since settlement occurs on

T+2, this requirement imposes a minimum effective holding period of 2 days. Thus, they cannot day

trade, and institutional investors will place a buy order only when they are confident about the stock's

price performance over the next two days. This feature will increase the information content of cash

trades and is consistent with the higher proportion of information share for cash market trades.

The SSFs analyzed in the study are one month contracts. Since the contract period is short, institu-

tional investors generally do not use these SSFs for hedging. The fact that SSF trades are settled only by cash

rather than by stock delivery also encourages speculative trades. Lower transaction cost in the SSFs enables

liquidity providers to revise quotes more quickly, and contributes to the higher information shares of theSSF quotes.

4.5. Results from VAR model

Table 5 presents the results. First, we find that stock returns are positively related to first-lag stock

returns (0.339) and negatively related to second-lag stock returns (0.101). They are positively related to

SSF trade and quote returns. However, there is a weak relationship between stock returns and their quote

returns. The results indicate that lagged stock and SSF returns have predictive ability for the stock returns.

Similarly, the contemporaneous and lagged SSF quote returns can predict the stock returns. The signifi-

cance level of stock and SSF quotes indicates that SSF quote returns can influence the stock returns while

stock quote returns may not. SSF quotes influencing the prices of stocks and SSFs show that they havehigher information content and that stock quotes adjust to SSF quotes.

Second, we find that SSF returns are positively related to the first lag stock returns (0.268) and

negatively related to the second-lag stock returns (0.053). Stock and SSF returns both are positively

related to the first-lag stock returns indicating the higher information flow in trades of the stock market.

Further, wefind that the contemporaneous and lagged SSF quote returns are positively related to stock and

SSF returns indicating the higher information production in the quote revisions. As expected, we find that

quote returns for stock and SSF are negatively related to their own lag returns, respectively.

Overall, the regression results suggest that stock and SSF trade returns have predictive ability for each

other. In case of quotes, only SSF quotes have predictive ability for both stock and SSF returns. This

corroborates the findings in Tables 3 and 4 that trades in the stock market are more informative, while

quotes in the SSF market have more information content. These results are consistent with the informationtransmission between the two markets. This indicates that the markets are mutually dependent and

neither market simply free-rides on the other market. As a robustness check, we use standardized five-

minute returns, raw one-minute returns, and raw five-minute returns in the VAR model. However, the

results remain qualitatively similar.



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Table 5Regression analysis of the relationship between standardized one-minute trade and quote returns of stocks and their SSFs.

Explanatory variables

Lagged stock return Lagged SSF returns Lagged stock quote returns

Dependent variable Lag 1 Lag 2 Lag 1 Lag 2 Lag 0 Lag 1 Lag 2

Stock returns 0.339(372.79)

0.101(105.32)

0.002(1.98)

0.002(2.37)

0.004(3.95)

Stock returns 0.261(320.50)

0.051(62.49)

0.002(2.25)

0.001(1.22)

0.004(3.79)

SSF returns 0.268(267.27)

0.053(50.40)

0.001(0.68)

0.001(0.88)

0.001(0.54)

SSF returns 0.052(56.78) 0.000(0.30) 0.001(0.53) 0.000(0.40) 0.001(0.74)

Stock quote returns 0.001(0.61)

0.001(1.17)

0.000(0.44)

0.001(0.63)

0.482(526.11)

0.277(272.79)

SSF quote returns 0.000(0.07)

0.001(1.06)

0.001(1.22)

0.003(2.29)

0.002(2.69)

0.004(3.47)

This table presents the results of the following multivariate VAR model:

rt = a1rt1 + + aprtp + bozt + bozt1 + + bpztp + 1;t

zt = c1rt1 + + cprtp + d1zt1 + + dpztp + 2;t

Where rt is the trade return for stocks and SSFs at transaction time t, and zt is the quote return for stocks and SSFs at transaction time t, which i

transaction t1 to the quotes following transaction time t. The return variables are standardized by subtracting the mean and then dividing b

coefficients for the contemporaneous and first two lags and t-statistics in parenthesis.


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5. Conclusions

Single-stock futures (SSF) are a puzzling derivative product. They are useful multi-purpose products,

but have not gained market share in developed countries. In contrast, SSFs have done well in the Indian

securities market. Hence, we study the Indian SSF market to understand its characteristics and price

discovery process between the SSFs and their underlying stocks.

Our paper provides useful insights into the success of SSFs in the Indian market. We find that the stock

market performs better in terms of price discovery and information share for trades. This result is contrary

to the evidence from other studies, where derivative products enjoy more price discovery and information

share. However, we find that SSF quotes lead stock quotes in price discovery contribution. This means that

SSF quotes are better and more informative than stock quotes. Wefind that stock and SSF trade returns have

predictive ability for each other. The quote analysis indicates that SSF quotes have predictive ability for both

stock and SSF returns. Hence, the markets are mutually dependent and neither market simply free-rides on

the other. Overall, our findings suggest that a vibrant SSF market requires affordable contracts and retail

investors' participation. Regulatory initiative is also a vital component in the expansion of the SSF market.

Acknowledgements

Tse acknowledges the financial support from a summer research grant of U.S. Global Investors, Inc. and

the College of Business at The University of Texas at San Antonio. We would like to thank John Wald and

James Hackard for their useful suggestions.

Appendix A

Contract specifications for single-stock futures in the NSE

Contract size As specified by the exchange subject to a minimum value of Rs. 0.2million

Tick size Rs 0.05

Trading cycle A maximum of three month trading cycle the near month (one), the next month (two),

and the far month (three). New contract is introduced on the next trading day following the

expiry of near month contract

Margins Upfront initial margin on a daily basis

Expiration day Last Thursday of the expiry month or the preceding trading day, if the last Thursday is a holiday

Price band Operating range of 20% of the base price

Settlement day Last trading day

Settlement In cash on T+1 basis

Daily settlement price Closing price of futures contract on the trading day

Final settlement price Closing value of underlying security on the last trading day of the futures contract

Source: www.nseindia.com.

The contract specifications for stock futures and options are similar. The SSF has contracts with 1 month,

2 months, and 3 months to expiry at any point of time. In case of stock options, there are a minimum of 7

strike prices, three in-the-money, one at-the-money and three out-of-the-money for every call and put

option (for American options only). The last Thursday of the respective expiry month is the expiry day and a

new contract is introduced on the next trading day of the near month contract. All derivatives products in

India including SSFs and options are cash settled.

The contract size is a minimum value of Rs. 0.2 million. There is an upfront initial margin for the trades.

The mark to market margin is levied at the daily settlement price. The risk management system for SSFs

and stocks is separately performed and no cross margining is available.

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