single stock future

8/2/2019 Single Stock Future

1/32

Single-Stock Futures: Evidence from the Indian Securities Market

Umesh Kumar

Yiuman Tse

January 2007

JEL classification codes: G11, G14Keywords: Single-Stock Futures, Price Discovery, Information Share

0000000Umesh Kumar is a PhD student at the University of Texas at San Antonio, and Yiuman Tse is a Professor

of Finance at the University of Texas at San Antonio. We thank Paramita Bandyopadhyay and Yulin Shifor computational assistance. Please address all correspondence to Umesh Kumar, One UTSA Circle, Department of Finance,University of Texas at San Antonio, San Antonio, TX- 78249-1644. Phone: (210) 458-7392. Fax: (210)

458-6320. Email:[email protected].


2/32

1


Abstract

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 SSF market are more price

innovative than quotes in the stock market (39% and 61%, respectively). Thus, stocks and

their SSFs are mutually dependent in terms of price innovation and formation, and no

market simply free-rides on another market. Even without a vigorous stock lending

mechanism, retail participation has catapulted SSFs into a position to capture the

dynamism and vibrancy of the market.


3/32

2


1. Introduction

Single-stock futures (SSFs) represent a significant development in stock-related

derivatives. It is an academic as well as practical conundrum 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 the

role of SSFs and their effectiveness in terms of price information and transmission.

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

substantially since their inception in 2001. Why have other markets struggled to generate

interest among investors for SSFs? A stock futures contract provides a way to take

advantage of arbitrage, speculative, and hedging opportunities, 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.

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 some 20 years after its inception. On

November 8, 2002, two exchanges, OneChicago and the Nasdaq Liffe Market (NQLX),

started SSF trading. Single-stock futures offer a cheap and flexible way to gain equity

market exposure for a wide range of purposes, such as hedging, speculation, and financial

engineering. Yet the development of SSFs has been unimpressive in the US, the largest

and the most sophisticated securities market in the world. Nor has SSF trading fared well

in exchanges of other countries that have launched SSFs.


4/32

3

Research so far has concentrated on developed and matured markets for an

understanding of the reception of SSFs. We look at the Indian market, where we see

remarkable progress in SSF trading. Since their launch in November 2001, SSFs have

showed incredible progress, making the NSE, the most vibrant SSF market in the world.

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. Thus, it is the largest global exchange for single-stock futures. In 2004, the

NSE traded more than 25 million SSF contracts. Euronext.liffe, the second-largest

exchange for this product, was far behind, at 7.5 million contracts.

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 12 months (252 trading days). We examine SSF evolution

and benefits, their failures and successes. We also examine the roles of regulatory forces

and institutional trades in price discovery of SSFs and their underlying stocks.

Our research makes several contributions to the literature on single-stock futures by

relating their benefit and success through retail participation. We evaluate the

contribution of timely regulatory initiatives in broadening the SSF market. We attempt to

corroborate how the success of SSFs may alter the dynamics of price leadership and

information share.

There is some evidence 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)


5/32


6/32

5

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. Because institutional investors are more sophisticated and have greater

resources, they are in a better position to influence price discovery for a security. A price

pressure hypothesis implies that institutional trades influence price formation in a market

more than trades by individuals. However, in the SSF market in India, there are fewer

institutional investor trades than the retail investor trades.

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 of the three markets the equity market, the stock lending market, and the SSF

market, whichever two first appear would act as a hidden market for the third. In the case

of India, the equity and SSF markets surfaced first, and so the SSF market may be seen as

a supplement for 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 for the SSF market.

This hypothesis further theorizes that even if the third market is introduced later on, it

will not necessarily expand or develop, as the other two markets would continue to offer

a hidden market. That may be the reason for lackluster response to SSFs in the US or

other developed markets that have vibrant stock lending markets.

Our general results are inconsistent with the view that derivatives markets (in our

case, SSFs) accounts for more of an information share and are responsible for more price

discovery in multi-market trading in the same underlying security. Overall, we believe

that direct retail participation is a necessary ingredient for the development of a healthy

SSF market. Our results indicate that institutional trading (or the lack of trading) has an


7/32


8/32

7

shown sluggish growth in most exchanges. Consequently, the instrument has not altered

the dynamics of equity investing.

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. Institutional investors and other sophisticated

traders are showing enthusiasm in the SSF market recently, but the retails investors are

wary and circumspect in dealing with single-stock futures. Commenting on this poor

retail investors response, Jones and Brooks (2005) state that single-stock futures prices in

the US often have little relation to the prices of underlying stocks. Their findings 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 the SSF market has anything to do with the

bias shown by investors due to unfamiliarity of the products, or the long side of the stock

market, or some other considerations. Stock options have limited-liability features

making them preferable for hedging or providing potential investment profits. 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.


9/32

8

2.2. Market Design and Structure in India

India has a modern securities market. 5,600 firms are listed on two major stock

exchanges. The exchanges 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 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 of the value of equity trading.

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 of the derivative segment are available in more than 300 cities

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

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 of in India, because even the U.S. did

not have SSF trading.


10/32

9

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 %, a

sharp contrast to most mature markets where the top ten member firms might have more

than 60 % of the total SSF trading2. 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.

Despite new derivative trading in India, NSE conducted 35 times more trading in

SSF contracts than did OneChicago in the first two months of 2005. This paper looks at

plausible reasons for the success of SSFs in India. The practice ofbadla and accounting-

period trading has been credited to an extent for this success. This futures-like practice

was eliminated just before the SSFs were introduced. Hence, familiarity with badla and

accounting-period trading was transformed into a demand for SSFs, ensuring an

enormously successful new product. Indian traders were not at all accustomed to the idea

of trading a broad market index. Therefore, broad market (or stock) index futures got off

to a much slower start, although lately it is beginning to catch up.

3. Data Construction and Methodology

3.1. Data Source

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

National Stock Exchange of India (NSE) for 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

99999992 NSE monthly derivates update (December 2004) reports that the top five members make 12 % valuewise

contribution while the next five have 9 %.


11/32

10

contributes almost 2/3rd 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 details of 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 coming to the NSE trading system (right to trade

against them, without any obligation) and they are free options which anyone can exploit.

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

in time. The order book snapshots times for stocks are 11 A.M., 12 noon, 1 P.M., and 2

P.M. The order book snapshots times for SSFs are 11 A.M., 12 noon, 1 P.M., 2 P.M., and

3 P.M. The normal market 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 exchange selects SSFs in a scientific manner from among the top 500 stocks in

terms of average daily market capitalization and daily traded value from the previous six

months. We restrict our attention to only those SSFs that have daily trading volume

above 1,000 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. While

analyzing, we find that some firms merged, changed their name or split up, and

sometimes their volume became too low. Therefore, we eliminated such SSFs from our

sample. In some cases, we could not obtain the desired data series from the raw data, so

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 almost

80-85% of total trading volume. Similarly, their stocks represent 85-90% of total stock


12/32

11

trading volume. These stocks are constituents of the primary index of the exchange (the

S&P Nifty Index).

Futures on the S&P Nifty Index are quite popular among portfolio investors as they

provide a better hedging mechanism than others that are available. Our dataset is more

comprehensive and larger than the datasets used in many previous studies. It covers

almost all of the trading volume of both market segments. The integrity of the data is

strong, since the data are obtained directly from the exchange.

3.2. Data Preparation and Analysis

We create two series of data. The first series contains trade price data for stocks and

SSFs. Similarly, the second series contains quote price data for stocks and SSFs. First, we

deduce minute-by-minute trade price each trading day for the stocks and SSFs. It is

calculated by filtering opening and closing price of each minute, and then calculating the

average price for every minute from the opening and closing price. Second, to obtain

quote price data series for the stocks and SSFs, we create minute-by-minute price from

the picture of the limit order book by merging all snapshots on a daily basis. This data file

conveys the picture of quotes available in the limit order book. Third, we filter the

highest bid quote and lowest ask quote for every minute from snapshots data file. Fourth,

we calculate the mid-quote every minute from the bid and ask quote. Thus, we obtain a

minute-by-minute quote price data series on a daily basis for our sample stocks and SSFs.

Fifth, we omit the outliers, if any, from trade and quotes, to avoid contamination of the

data series. We thus obtain 2,429,656 and 1,648,650 minute-by-minute observations from

the trade and quote files, respectively.


13/32


14/32

13

downstairs stock markets. A number of other studies have also used the information-

share and/or permanent-transitory models (see Tse & Erenburg, 2003, and the references

therein).

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 variance of the innovations to the common factor, and measures

each markets relative contribution to this variance. This contribution is called the

markets information share. The Gonzalo and Granger (1995) model, however, focus on

the error correction process and the components of the common factor. This process

involves only permanent (as opposed to transitory) shocks that result in a disequilibrium.

The Gonzalo and Granger model measures each markets contribution to the common

factor, where contribution is defined as a function of the markets error correction

coefficients.

The feature that distinguishes the models from each other is that the Hasbrouck

(1995) model decomposes 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.

In contrast, the Gonzalo and 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 on common factor.


15/32

14

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

error correction model (VECM) in the following form:

Xt= Xt-1 + i X=

k

i 1t-i+ t (1)

where Xt = {Xit} is an n x 1 vector of cointegrated prices. and s are n x n matrices of

parameters, and tis an n x 1 vector of serially uncorrelated residuals with a covariance-

covariance matrix = {ij}. The long run relation matrix has a reduced rank ofr< n

and can be decomposed as = , where and are n x r matrices. The matrix

consists of the cointegrating vectors and is the error correction (or equilibrium

adjustment) matrix. Ifr= n-1 and is spanned by the differentials of each pair of price

series, then allxitare 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=J(=

k

1

) + *(L)t (2)

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

matrix of polynomials in the lag operator,L.

The Hasbrouck(1995) model defines a markets contribution to price discovery as

its information sharethe markets 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.


16/32

15

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) provide various analytical

examples to show that the average of the information shares given by two permutations is

a reasonable estimate of a markets role in price discovery. We use average information

shares to interpret the results.

4. Empirical Results

4.1. Relationship between the Stock and SSF Markets

Figure 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. This may be attributed to political instability caused by the general

election in the country at that time. The general election and the delay in formation of the

Federal Government contributed to lowering of trading volume. Overall, we find 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


17/32

16

[Insert Figure 1 here]

slows down in subsequent months, and reaches its lowest in June, 2004. Later, trading

volume gains in both segments.

4.2. Trade Size in the SSF Market

First, we calculate 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. It is important to note that we use executed contracts, not the underlying trade

order or quote. We assume that SSF trading should be dominated by institutional

investors (informed traders), since they are in better position to exploit the advantages

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

the SSF market.

[Insert Table 1 here]

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 all trades. The notional value of a single contract size is

comparatively small (4,500, in terms of USD). Trade size clearly indicates that

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

institutional investors would tend to deal in larger trade sizes, considering the transaction


18/32

17

and other attendant costs associated with doing single-contract transaction. The trading

pattern above (i.e. overwhelmingly single contract trades) is consistent in almost all

months. Therefore, we believe that there is strong retail participation in the SSF market,

as claimed by the exchange and other market intermediaries3. The fact that retail

investors (uninformed traders) are dominant traders in a healthy SSF market is unique.

4.3. Bid-Ask Spreads

Table 2 presents the percentage spread for stock and SSF quotes. Percentage spread

is measured as 100% x (Ask Price - Bid Price)/Midquote, where midquote is the average

of bid and ask prices. We find 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 difference between stock and SSF quotes varies between 0.11 % and

1.08 %. Overall, the mean percentage spread for stock quotes is 34% higher than SSF

quote spreads, while 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 the trade size, we find that the SSF

171717171717173 NSE monthly derivatives update (December 2004) mentions that non-institutional investors contributemore 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.


19/32

18

trading is mostly driven and influenced by retail participation. Hence, the higher spread

observed in SSF quotes is not surprising. The presence of institutional investors lowers

the spread, since their trades have more informational contents. The result augments the

evidence of higher retail participations in the SSF market. It is interesting to note that the

spreads are wider in stock and SSF quotes during June and July 2004, when there is

political turmoil in the country. The trading volume of the SSF market shrinks and

becomes almost equal to that of the stock market. When SSF trading volume increases,

the spread differences narrow.

4.4. Information and Price Discovery in the stock and SSF markets

4.4.1 Results from Trade Transactions

Table 3 reports the price discovery result for trade prices of SSFs and their stocks at

one-minute interval. 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

Figure 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 more efficient in price discovery.


20/32

19

However, we find that July and August show that both markets have almost equal role in

price leadership.

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 influence 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 dormant in derivative products, particularly in the

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 SSFs4. 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 are substantially higher when trades

are evaluated not individually but in the broader context of a package. Frino, Walter, and

West (2000) document that investors with better market-wide information are more likely

to trade in stock index futures, and the lead of futures market strengthens significantly

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

investors behind the trades and the combination of the size of the trades and the investors

resulting level of ownership are major determinants of the price impact.

191919191919194 Data obtained from SEBIs Annual Report 2004-05.


21/32

20

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 information content in prices. The bulk of the trades in the

NSE do not come from any innate complementary hedging function that the SSFs offer,

or even from any competitive advantage they enjoy over the stock markets.

4.4.2 Results from Competitive Quotes

Being a price leader or information producer does not necessarily mean that a

market 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. It is an accepted notion

that the price discovery inferred from quotes does not necessarily reflect the market

where the informed trader trades. The NSE has a trading mechanism for stocks and SSFs

that is based on anonymous order-driven markets operating strictly on a price-time

priority. Still, price discovery inferred from the quotes does not necessarily reflect the

concentration of informed traders. It is possible that a market receives the best quotes,

with more informational content, but the quote may not result into a trade. Hence, there is

separation between price leadership from quotes and that from trades.



22/32

21

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 result indicates

that SSF quotes lead stock quotes in price discovery. While the SSF market contributes

more to price discovery for quoted prices, we find that this dominance does not extend to

the prices for executed trades.

This result is contrary to the belief that a market leading in price production in

executed trades would tend to lead in the quotes also. A plausible explanation behind

such result may be the transaction costs. We know that transaction cost is an important

factor in placing the quotes when there is no market maker. In the NSE, there is no

designated market maker in the stock market. The transaction costs in the derivates

market are significantly lower than those in the stock market. A lower transaction cost

enables traders to post quotes even for unprofitable informed trades. However, such

quotes may not necessarily result into a trade. Therefore, it is possible that the derivatives

market may not lead in price production for the executed trades.

While making careful observation of our results in Table 4, we note that the

information share dramatically increases to 0.74 in September 2004 and continues to

remain above 0.70 afterwards. The transaction cost increased from September 2004 in the

stock and SSF markets are due to first-time imposition of Securities Transaction Tax. A

higher level tax was placed on the trades in stock market. This increased the transaction

costs for the stock market. Overall, we conclude from these results that the SSF market


23/32

22

attracts investors at all levels of sophistication, including individuals as well as

institutional investors.

While the SSF market may reveal a stocks intrinsic value through quote data, that

information is disseminated to all market participants through the trade prices in the stock

market. In this way, the two markets are mutually dependent upon each other for price

formation. Neither market is a free rider on the other.

5. Conclusion

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

purpose products, but have not gained market share in developed countries. By contrast,

SSFs have done well in the Indian securities market. Hence, we study the Indian SSF

market to understand its characteristics and the price discovery process for SSFs and their

underlying stocks.

This 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 transmission leadership. However,

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

that SSF quotes are better and more informative than the stock quotes. The SSF market

attracts sophisticated investors, both individuals and institutions. Quotes posted in the

SSF market reveal a stocks intrinsic value, but this intrinsic value is disseminated to all

market participants through trades in the stock market. This indicates mutual dependency

in price formation between these markets.


24/32

23

The absence of an efficient or active stock lending mechanism in the Indian stock

market seems to have influenced the SSF market. We argue that of the three markets

the equity market, the stock lending market, and the SSF market- whichever two appear

first would act as a covert market for the third market. In India, equity and SSF markets

appeared first, therefore the SSF market has taken a stride for stock lending market. In

developed countries, including the U.S., a vibrant stock lending market overshadows the

perceived benefits of SSFs. Therefore, the SSF market has not received the kind of

momentum it has received in India.

Further, strong retail participation is imperative for the success of the SSF market.

Therefore, it is essential that the SSF contract should be affordable to retail investors as

evident in the Indian market. Retail investors participation attracts more informed

traders, improving liquidity and subsequently generating more volume. Regulatory

intervention is also a vital consideration in the expansion of the SSF market.

Overall, our findings demonstrate that for a vibrant SSF market to exist, it needs to

be inter-dependent with stock markets in terms of price discovery, innovation, and

leadership. The interdependency between stock and SSF markets compliment each other,

benefiting all market participants by providing liquidity, price integration, and price

efficiency.


25/32

24

Appendix

Contract specifications for single-stock futures in the NSE.

Contract Size As specified by the exchange subject to minimum value of Rs. 0.2

million.

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 Up-front initial margin on 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 underlying security on the last trading day of the

futures contract.

Source: www.nseindia.com


26/32

25

References

Ang, J. S., & Cheng, Y. (2005). Financial Innovations and Market Efficiency: The Case for

Single Stock Futures.Journal of Applied Finance, 15(1), 38-51.

Baillie, R.T., Booth, G.G., Tse, Y., & Zabotina, T. (2002). Price Discovery and Common Factor

Models.Journal of Financial Markets, 5, 309-321.

Booth, G.G.,Lin, Ji-Chai., Martikainen, T., & Tse, Y. (2002). Trading and Pricing in Upstairs

and Downstairs Stock Markets. The Review of Financial Studies, 15, 1111-1135.

Bozcuk, A., & Lasfer, M. A. (2005). The Information Content of Institutional Trades on the

London Stock Exchange.Journal of Financial and Quantitative Analysis, 40(3), 621-644.

Chan, L. K. C., & Lakonishok, J. (1995). The Behavior of Stock Prices around Institutional

Trades. The Journal of Finance, 50, 1147-1174.

De Jong, F. (2002). Measures of Contributions to Price Discovery: A Comparison. Journal of

Financial Markets, 5, 323-328.

Frino, A., Walter, T., & West, A. (2000). The Lead-Lag Relationship between Equities and Stock

Index Futures Markets around Information Releases.Journal of Futures Markets, 20, 467

487.

Gonzalo, J., & Granger, C. (1995). Estimation of Common Long-Memory Components in

Cointegrated Systems.Journal of Business & Economic Statistics, 13, 27-36.

Harris, F.B., McInish, T. H., &. Wood, R. A. (2002). Security price adjustment across exchanges:

An investigation of common factor components for Dow stocks. Journal of Financial

Markets, 5, 277-308

Hasbrouck, J. (1995). One Security, Many Markets: Determining the Contributions to Price

Discovery. The Journal of Finance, 50, 1175-1199.
http://proquest.umi.com/pqdweb?index=9&did=520494631&SrchMode=3&sid=2&Fmt=10&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1158350300&clientId=2944&aid=1http://proquest.umi.com/pqdweb?index=9&did=520494631&SrchMode=3&sid=2&Fmt=10&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1158350300&clientId=2944&aid=1http://www3.interscience.wiley.com/cgi-bin/jhome/34434http://www3.interscience.wiley.com/cgi-bin/jissue/71500130http://www3.interscience.wiley.com/cgi-bin/jissue/71500130http://www3.interscience.wiley.com/cgi-bin/jhome/34434http://proquest.umi.com/pqdweb?index=9&did=520494631&SrchMode=3&sid=2&Fmt=10&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1158350300&clientId=2944&aid=1http://proquest.umi.com/pqdweb?index=9&did=520494631&SrchMode=3&sid=2&Fmt=10&VInst=PROD&VType=PQD&RQT=309&VName=PQD&TS=1158350300&clientId=2944&aid=1


27/32

26

Hasbrouck, J. (2002). Stalking the Efficient Price in Market Microstructure Specifications: An

Overview.Journal of Financial Markets, 5, 329-336.

Huang, R.D. (2002). The Quality of ECN and Nasdaq Market Makers Quotes. The Journal of

Finance, 57, 1285-1319.

Jones, T., & Brooks, R. (2005). An Analysis of Single Stock Futures Trading in the U.S.

Financial Services Review 14(2), 85-95.

Lee, C., & Tong, H.C. (1998). Stock Futures: The Effects of their Trading on the Underlying

Stocks in Australia.Journal of Multinational Financial Management, 8(2-3), 285-301.

Lehmann, B. N. (2002). Some Desiderata for the Measurement of Price Discovery across

Markets.Journal of Financial Markets, 5, 259-276.

Martens, M., Kofman, P., & Vorst, T.C.F. (1998). A threshold error-correction model for intraday

futures and index returns.Journal of Applied Econometrics, 13, 245-263.

Tse, Y., & Erenburg, G. (2003). Competition for Order Flow, Market Quality, and Price

Discovery in the Nasdaq 100 Index Tracking Stock. The Journal of Financial Research,

26, 301-318.


28/32

27

Figure 1

Monthly Trading Turnover in Stock vis--vis SSFWe calculate monthly stock and SSF turnover. The straight line indicates the monthly stock turnover in the

exchange. The dotted line signifies the SSF turnover in the exchange. The turnover is denoted in Indiancurrency in billion rupees.

Monthly Trading Volume of 30 Stock and Future

200.00

400.00

600.00

800.00

1000.00

1200.00

1400.00

Jan-04 Feb-04 Mar-04 Apr-04 May-04 Jun-04 Jul-04 Aug-04 Sep-04 Oct-04 Nov-04 Dec-04

Month

Trading

Volume(inBillion

Rupees)

Stock Turnover

Future Turnover


29/32

28

Table 1

Trade Size in SSFWe 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 trade, two-contract trade, andthree-or-more contract trade. From monthly data, we compute the monthly average trade size in the SSFs.

Month One-

Contract

(000)

Two-

Contract

(000)

Three-or-

more

Contract

(000)

Total

Contract

(000)

% of one

Contract

% of two

Contract

% of three

or more

Contract

Jan-04 2,330 113 60 2,503 95.17% 3.23% 1.60%

Feb-04 1,712 68 37 1,818 96.22% 2.48% 1.30%

Mar-04 1,680 319 169 2,168 83.73% 11.89% 4.38%

Apr-04 1,980 132 81 2,193 93.44% 4.21% 2.35%

May-04 1,641 113 72 1,827 92.07% 3.94% 3.98%

Jun-04 1,691 100 63 1,855 94.19% 3.33% 2.48%

Jul-04 1,785 110 69 1,965 94.67% 3.38% 1.95%

Aug-04 1,807 115 74 1,997 94.35% 3.55% 2.10%

Sep-04 1,845 118 82 2,046 93.91% 3.85% 2.24%

Oct-04 1,858 117 80 2,055 93.91% 3.87% 2.21%

Nov-04 1,768 113 80 1,962 93.48% 4.05% 2.47%

Dec-04 2,552 168 114 2,835 93.05% 4.43% 2.52%

Average 1,887 132 82 2,102 93.18% 4.35% 2.47%


30/32

29

Table 2

Percentage Spread in Stock and SSF QuotesThe statistics consist of stock and SSF quote spread for the sample period. The sample data are segmented

into two panels. The percentage spread is measured as 100%x(Ask-Bid)/Miquote, where the midquote isthe average of the bid and ask prices. The stock and SSF quote spread is derived from their mean spread on

a daily basis. We obtain percentage spread difference by subtracting percentage stock spread from the

percentage SSF spread. The significance of percentage spread difference is tested from zero by t-tests.Panel A represents the percentage spread in stock and SSF quotes on a monthly basis, while Panel B

represents the percentage spread in stock and SSF quotes for entire sample period.

Panel AMonthly Percentage Spread of Stock and SSF Quotes

Mean Standard Deviation t valueMonth N

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 9.69Feb-04 19 1.76 2.49 0.73 0.34 0.43 0.23 22.84 24.99 13.84Mar-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 14.93May-04 21 2.60 3.51 0.91 1.66 1.53 0.29 7.18 10.50 14.60Jun-04 22 1.61 2.69 1.08 0.18 0.27 0.17 41.41 47.16 29.27Jul-04 22 1.54 2.50 0.96 0.30 0.36 0.20 24.46 32.44 23.07Aug-04 21 1.29 1.85 0.56 0.09 0.15 0.14 66.82 55.33 17.77Sep-04 22 1.19 1.49 0.30 0.08 0.12 0.11 66.92 58.77 12.84Oct-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 MeanStandard

DeviationStandard Error t value P value

Stock 252 1.63 0.65 0.04 39.80


31/32

30

Table 3

Information Share from TradesThe table reports price discovery results based on the Hasbrouck (1995) model and the Gonzalo and

Granger (1995) model for stock and SSF trades. The prices are calculated at one-minute interval. Theinformation share is the proportion of variance in the implicit efficient price of stock that is attributable to

innovations in that market. The panel represents the information share on a monthly basis, and then we

compute average to get overall information share for entire period.

Hasbrouck Information Share Gonzalo-Granger Factor Weights

Month 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


32/32

31

Table 4

Information Share from QuotesThe table reports price discovery result based on the Hasbrouck (1995) model and the Gonzalo and Granger

(1995) model for stock and SSF quotes. The quotes are computed at one-minute interval. The panelrepresents the information share on a monthly basis, and then we compute average to get overall

information share for entire period.

Hasbrouck Information Share Gonzalo-Granger Factor Weights

Month 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 0.76Oct-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

single stock future

Documents