Stock Exchange Governance and Market Quality
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Stock Exchange Governance and Market Quality Stock Exchange Governance and Market Quality Document Transcript

  • Stock Exchange Governance and Market Quality Chandrasekhar Krishnamurti Division of Banking and Finance Nanyang Business School Nanyang Technological University S3-B1-B-76 Nanyang Avenue Singapore 639798 Tel: (65) 6790-5702 Fax: (65) 6791-3697 E-mail: ackrishna@ntu.edu.sg John M. Sequeira Department of Finance and Accounting NUS Business School National University of Singapore 1 Business Link, BIZ1 Building, Singapore 117592 Tel: (65) 6874 3033 Fax: (65) 6779 2083 E-mail: bizjms@nus.edu.sg and Fu Fangjian William E. Simon School of Business University of Rochester Rochester, NY 14627 Tel: (716) 2754318 Email: fufa@simon.rochester.edu Revised: July 2002 Corresponding Author: Chandrasekhar Krishnamurti Division of Banking and Finance Nanyang Business School Nanyang Technological University S3-B1-B-76 Nanyang Avenue Singapore 639798 Tel: (65) 6790-5702 Fax: (65) 6791-3697 E-mail: ackrishna@ntu.edu.sg
  • Stock Exchange Governance and Market Quality Abstract Does the organization structure of a stock exchange matter? We address this interesting issue utilizing the unique setting prevailing in India. India has two major stock markets, namely, the Bombay Stock Exchange (BSE) and the National Stock Exchange (NSE). These two exchanges adopt similar trading systems, trade essentially identical stocks, and follow the same trading hours. However, both exchanges have different organizational structures: BSE is a mutualized form of organization whereas NSE is structured as a demutualized exchange. Domowitz and Steil (1999) propose that demutualized exchanges have better incentives to provide innovative services that increase the value of the exchange. Their proposition implies that demutualized exchanges are superior to mutualized exchanges in governance. Another inference that can be drawn from Domowitz and Steil’s work is that a demutualized exchange, due to its superior governance structure, should provide a better quality market. Using the Hasbrouck (1993) measure of market quality, we demonstrate empirically that NSE provides a better quality market compared to BSE. We attribute this divergence in market quality to the differences in the governance of the respective stock exchanges. Furthermore, we show that NSE provides a better quality market even after controlling for firm specific factors. Our study has important policy implications for other emerging countries. We show that entrenched monopolies may be successfully persuaded to modify their behavior by engendering competition in the market place, utilizing technology as a catalyst. Another contribution of our study is that we show that latest technological advances in networking, communications, and information processing, may be utilized to break down barriers to entry in the trading services industry. 2
  • 1. Introduction In sharp contrast to the plethora of studies that have examined the impact of corporate governance provisions adopted by manufacturing and service firms,1 there has been very little research attention devoted to the governance of stock exchanges. A notable exception is the recent work of Domowitz and Steil (1999), who examine the interrelationships between stock exchange automation, governance, and the quality of markets. Traditionally, stock exchanges have been organized as non-profit, mutual/ membership associations. A recent trend has been the conversion of mutualized exchanges into publicly owned corporations, which are themselves listed and traded on a stock exchange. Domowitz and Steil (1999) list several benefits of the demutualized stock exchanges as compared to mutualized stock exchanges. The primary driver for such benefits is the favorable governance structure associated with demutualized exchanges. In their paper, Domowitz and Steil (1999) argue that members of mutualized stock exchanges have incentives to oppose innovations even if these increase the value of the exchange. Since traditional stock exchanges are mostly regional monopolies, they could, in the extreme case, even oppose enhancements to quality of services that they provide if such improvements are thought to diminish the welfare of the respective exchange members. One important implication of the Domowitz and Steil (1999) argument is that demutualized stock exchanges should provide a better quality market than mutualized ones. For expositional convenience, we will refer to this as the “Domowitz and Steil Proposition.” In fact, the primary focus of our paper is to examine the Domowitz and Steil Proposition empirically using data from two competing stock exchanges in India 1 Gompers, Ishii, and Metrick (2001), in a recent study, find a striking relationship between corporate governance and stock returns. In their paper, they also conclude that firm value is highly correlated with a “Governance Index” that they develop. 3
  • which differ in their governance structure – namely, the Bombay Stock Exchange and the National Stock Exchange. Our efforts to make direct comparisons of market quality in the two stock exchanges are greatly facilitated by the simultaneous trading of at least forty major stocks on both the exchanges, with both utilizing similar trading systems. While prior studies have compared the quality of stock markets with different trading systems, reliable comparisons are, in fact, difficult to achieve.2 Our study, therefore, makes unique contributions to the literature in several ways. First, we compare the quality of two markets, which are similar in most respects except their governance structure. Previous studies to date have not, as yet, examined the impact of governance structure on the quality of stock markets. Second, since our study is conducted in an emerging market setting, it has policy implications for other emerging markets3. Third, our paper describes how advances in trading technology have implications for competition in the stock market trading industry. In particular, we show the importance of information technology in breaking down barriers to entry. Finally, we draw some policy implications for regulators who supervise “natural monopolies” such as stock exchanges. The rest of the paper is organized as follows. Section 2 provides the background information on Indian stock markets outlining the principal differences between the two stock markets analysed. Section 3 discusses the methodology used in this paper. Section 4 describes the data and empirical results are presented in Section 5. The final section contains our conclusions. 2 Prior studies have compared quality of different markets by examining measures of liquidity and execution costs of comparable stocks. In such studies, one cannot be certain that firm-specific characteristics are not, in fact, driving the observed results. See, for instance, Affleck-Graves et.al (1994), Huang and Stoll (1996b), and Bessembinder and Kaufman (1997). 3 Parisi, Nail and Soto (1991) compare the Santiago Stock Exchange with the Electronic Stock Exchange to examine which exchange plays a leadership role. They are, however, unable to examine the effect of governance on market quality since observed differences may be attributable either to the variations in trading systems or differences in governance. 4
  • 2. Bombay Stock Exchange and the National Stock Exchange – A Tale of Two Stock Exchanges 2.1 Genesis of the National Stock Exchange Until the early 1990’s, the Bombay Stock Exchange (BSE) was the premier stock exchange in India. At that time, BSE was plagued with a variety of problems, principal among them, the outdated trading and settlement procedures, and poor regard for investor protection. The recalcitrant attitude of the brokers and the administration of BSE frustrated the efforts of the regulator, namely, the Securities Exchange Board of India (SEBI), in pushing through critical reforms to bring the premier exchange of India on par with the best run exchanges in the world. The complacency of the administration of BSE may be attributed to their overestimating the significance of barriers to entry in the provision of stock broking and trading services. BSE also underestimated the determination of the Government of India to reform the stock markets, who took the unprecedented step in creating a new stock exchange with the help of government owned and controlled financial institutions. The recent technological advances in communication and computing technologies aided the efforts of these institutions. A new stock exchange – the National Stock Exchange (NSE), emerged as a consequence of the government’s efforts and initiatives. The ensuing competitive market structure in Indian stock markets, as an outcome of the reform process, is unique and without parallels. NSE dramatically altered the stock market landscape of the country within a short period of its inception, using current best practices in computing and network technology to provide state of the art trading services to Indian investors. In short, NSE dramatically improved the quality of trading services and thereby challenged the dominant position held by BSE until that time. 5
  • Indian investors were attracted to the high quality of trading services and systems provided by NSE. In particular, NSE’s systems and procedures explicitly incorporated investor protection. As a result, BSE rapidly lost its market share. In order to stem the erosion of its market share further, BSE was forced to take steps to improve its services. NSE, however, retained its first-mover advantages on account of its early lead in adopting leading edge technologies and procedures. Another significant difference between the two exchanges pertains to governance. BSE follows the mutual form of organization whereby stockbrokers own and operate the exchange. The president of the exchange is elected by the broker members and, therefore, represents the interests of the brokers; the interests of investors are consequently relegated to a secondary position. On the other hand, NSE follows the corporate or demutualized form of organization, defined as the separation of ownership of the exchange from membership. According to Domowitz and Steil (1999), the incentives of a mutualized exchange differ significantly from a demutualized one. Members of mutualized exchanges have incentives to oppose innovations, even if such innovations enhance the value of the exchange. This possibility arises if innovations that reduce the demand for their intermediation services result in a decline in their brokerage profits that is not balanced by a corresponding increase in the value of the exchange. This line of reasoning implies that NSE has incentives to adopt actions that increase the quality of its market, while BSE is more likely to resist changes that increase quality, but have a deleterious effect on their brokerage profits. Thus, we have reasons to believe that the quality of NSE’s market will be higher than that of BSE, ceteris paribus. Hence, market quality comparison between NSE and BSE forms the core motivation for our paper. 6
  • 2.2 Differences between NSE and BSE In this section, we summarize the principal differences between NSE and BSE. Since the overall objective of our paper is to make quality of market comparisons between the two exchanges, we first start by outlining the principal differences between them. This would allow us to appreciate the theoretical underpinnings and enable us to formulate appropriate expectations regarding quality differences. We use the following dimensions with which to make these comparisons: Ownership, Governance, Trading Systems, Connectivity, Leadership, and Technology use. 2.2.1 Ownership NSE has been incorporated as a tax paying company and is owned by a group of large developmental financial institutions. On the other hand, BSE is organized as a brokers’ association. The broker members who “own” seats on the exchange own and operate BSE. 2.2.2 Governance NSE is professionally managed by a fulltime Managing Director who reports to a Board of Directors, and is, as such, managed by professionals who do not directly or indirectly trade on the exchange; ownership and management of the exchange are, therefore, completely separated. Until NSE emerged, most of the stock exchanges in India were owned, controlled and managed by brokers who held seats on the various exchanges. This mutualized form of organization was prevalent in Indian stock exchanges, and BSE was no exception. In the case of BSE, one of the broker members is elected to administer the exchange and is designated as the president of BSE, who also owns a seat on the exchange, like the other broker members. Unlike BSE, NSE is the first demutualized exchange in India. 7
  • 2.2.3 Trading System NSE uses the latest innovations in computing and network technologies to bring the best available trading system to its customers, and endeavours to constantly improve the quality of its trading system in its bid to attract and retain its customers. A fully automated screen based trading system, referred to as NEAT and developed by NSE, enables members from all over the country to trade with one another on a real-time basis with ease and efficiency. Three of the more prominent features of NEAT are discussed below. First, NEAT is a completely automated system for order matching that is completely order driven and provides complete anonymity to its trading members. Second, NEAT operates on a strict price time priority such that all buy orders received on the system are sorted with the best-priced order getting the first priority for matching with an incoming market sell order. For instance, within orders which have the same price, time priority is enforced, that is, orders placed first are executed first. This matching process is computerised, keeping the system transparent, objective and fair. Each order remains in the system until it is matched with an incoming market order or until it is cancelled, whichever occurs earlier. Third, is the tremendous flexibility afforded by NEAT that provides users with several time related orders such as Good-Till-Cancelled, Good-Till-Day, Immediate-or-Cancel. Moreover, traders are able to take advantage of price-related and volume-related orders that may be built into an order. Fully accessible information on total order depth in a security, last traded price, quantity traded during the day, amongst others, allows traders to make informed decisions regarding price and order quantity. BSE has, however, been a laggard in the use of technology. Floor trading was still the prevalent mode of transacting when NSE commenced operations in November 1994. Since then, a rapid erosion of market share forced BSE to establish its own computerized 8
  • trading system. This move was a strategic response to a comparatively superior trading system established by NSE. By June 1995, BSE inaugurated its own electronic trading system known as BOLT. BOLT adopted many of the features that were built into NEAT. BSE is a laggard compared to NSE; NSE stands out clearly as the originator of various new initiatives. In fact, whenever NSE unveils a new initiative, BSE usually responds, rather reluctantly after a time lag, with a view to protect its market share rather than a genuine effort to increase customer satisfaction. 2.2.4 Connectivity NSE established a national network of terminals in over 300 towns and cities which included smaller towns and cities not currently served by any stock exchange. A reliable network technology is employed which uses satellite communication to link all member terminals to its main computer located in Bombay. This equal access to traders all over the country sharply contrasts BSE, which serves only the city of Bombay. BSE was initially operated as a regional monopoly with a charter allowing its trading members exclusive rights and access to its trading floor in Bombay. Shortly after NSE commenced its trading operations, its runaway success seemed to threaten the very livelihood of BSE brokers. The cost of a seat on BSE fell from about Rs. 40 million to about Rs. 10 million within a year of the opening of NSE. To counter this, BSE quickly set up BOLT, which started operating from 1995. The connectivity of BOLT was still, however, restricted to the city of Bombay, while NEAT’s influence continued to over the entire country. As the first automated exchange in India, NSE had a freer hand in determining its reach. With no physical trading floor limitation, it could logically argue that its reach is able to cover the entire country. BSE and all the other regional exchanges in India are regional monopolies, and according to their charter, are explicitly prohibited from setting up offices or providing their services in locations outside their respective jurisdictions. This 9
  • explicit prohibition served to limit the connectivity and reach of BSE; for instance, a BSE member could not set up an office and provide trading services in the city of Delhi, which has its own stock exchange. NSE is unaffected by these limitations, has enjoyed much network externalities, at a cost to its most powerful rival, BSE. 2.2.5 Leadership From the outset, NSE behaved like a leader – it set the trend for other regional exchanges including BSE to follow. This leadership status bestowed NSE with the first- mover advantages that BSE sadly lacked. After ceding its position as the premier exchange in the country, BSE has had little choice but to trail behind NSE. NSE has managed to maintain its position by being the innovator since its inception; its moves are carefully thought out and flawlessly executed. Moreover, NSE is motivated by its ambition to meet or exceed current international standards pertaining to the provision of trading services whereas BSE has had to consider appropriate responses to major NSE initiatives. Frequently, BSE’s ability to react has been stalled by the actions of its own members whose private incentives conflict with those of the exchange as a whole. 2.2.6 Technology NSE owes its existence to advances in information technology, which it has effectively employed to break down barriers to entry in the stock market trading business. In continuing to apply the latest technology, NSE views itself as the front-runner exchange. Having pioneered the use of automated order matching and increasing its reach to cover the entire country via satellites, NSE has, in effect, cleverly used technology to break the monopoly of BSE. For instance, on NSE, the time lag between placing an order and getting confirmation from anywhere around the country, takes less than two seconds. NSE’s superior technology enabled it to consolidate a previously highly fragmented order book, and this consolidation has resulted in substantial increases 10
  • in liquidity. BSE began the decade of the 1990’s with a deep suspicion of technology; its members seeing technology as a major hindrance to maintaining its rent seeking activities. In fact, BSE’s members were not in favour of a transparent system. When BSE chose rather reluctantly to embrace automation, its primary motive was to recapture some order flow that it had lost to NSE. In choosing to implement this technology, BSE was torn between the goals of value maximization of the exchange and the rent seeking activities of its broker members. As a consequence, BSE has perpetually lagged behind NSE in adopting new and innovative technology. 2.3 Automation, Governance and Quality of Markets Although we identify six major differences between NSE and BSE, the ownership structure is considered to be the main driving force distinguishing both markets. In fact, we argue that it is the difference in ownership that determines the characteristics of each exchange. Everything that NSE has achieved since inception, BSE potentially could have accomplished in a superior manner. As the premier exchange in India, BSE had first- mover advantage and the resources to pursue excellence and to maintain its dominant status. That BSE chose not to pursue NSE’s strategy of creating a high quality automated nationwide market for trading securities, needs to be emphasized. Had BSE adopted NSE’s approach, it would have pre-empted the creation of NSE. BSE’s adherence to its status quo in trading technology becomes apparent once its ownership structure is detailed. Essentially, BSE is organized as a mutualized exchange or a broker’s association, where ownership is not separated from exchange membership. As exchange members receive their profits from intermediating non-member transactions, these members have vested financial interests in resisting innovations that may actually increase the value of the exchange, especially if these innovations reduce their potential profits. The brokers of BSE resisted automation and the attendant improvements in 11
  • market quality as they perceived that automation would lead to market transparency. In a transparent system, these brokers would be precluded from certain rent-seeking activities which are deemed illegal. Unlike the automated system, a manual trading system would not easily detect the illegal activities of these brokers. It is with these considerations that the floor trading system continued in spite of alleged inefficiencies. This culture of pleasing the brokers at the cost of investors is very strong in BSE. According to Domowitz and Steil, “trading market automation permits demutualisation”, meaning that the corporate structure of organization of a stock exchange is feasible when computerized trading replaces floor trading. We illustrate in this paper a situation where the presumed causality runs in the reverse direction. NSE’s corporate organizational structure allowed it to pursue trading automation unhindered by potential conflicts of interest with its broker-members. Based on a governance structure comprising professional managers who report to the Board of Directors, NSE’s primary objective is consequently, value maximization. Had NSE adopted the mutual form of organization, it would have failed to incorporate trading automation since the interests of the broker members would have taken precedence to goals of value maximization for the exchange. Several prominent financial writers around this period, in fact, questioned the wisdom of the government of India in “starting a second stock exchange in Bombay”. That NSE did not become the “second exchange” in Bombay is mainly attributable to its governance as compared to BSE. NSE’s professional managers had absolute freedom to decide on a trading technology that was most appropriate. In order to survive, NSE needed to break down the barriers to entry created and maintained by existing stock exchanges. NSE was quick to dismiss floor trading as a preferred option, seeing this as merely mimicking the other exchanges who subjected themselves to the inherent limitations associated with floor trading. In its drive to create a better securities market, 12
  • NSE had two basic objectives: to improve the quality of the market and reduce the cost of transacting. NSE viewed these objectives as necessary to enable them to capture market share and become the dominant player in the business of trading securities. NSE’s choice of trade automation and nationwide connectivity through satellites served to break barriers of entry in the stock broking business. In addition, these initiatives created positive externalities. Having established the superiority of NSE’s governance as compared to BSE, we next turn to market quality comparisons in order to substantiate the Domowitz and Steil (1999) proposition. 3. Empirical Methods 3.1 Prior Literature A unidimensional quantification of market quality is absent in extant literature. Prior research has focused on liquidity, informational efficiency, and volatility characteristics of markets, as the criteria for market quality comparisons. Another view of market quality is based on transaction costs. In stock markets, transaction costs may be classified either as explicit or implicit costs (execution costs). The execution cost is the difference between the actual transaction price and the benchmark price that is considered to be in some sense, efficient. Unlike commission costs which are explicit, execution costs are not directly observed and are not easily measured. Hence, most prior studies have defined different measures of execution costs4 and compared these estimates across markets (e.g., Roll 1984, Berkowitz, Logue and Noser 1988, Stoll 1989, Huang and Stoll 1994, Chan and Lakonishok 1993, Hasbrouck 1993). 4 Measurement of this difference between the actual transaction price and the benchmark price is generally aimed at estimating this figure for both the buyer and seller engaged in a particular transaction. This difference is often calculated as an average transaction cost measure in comparative market analysis, and is used to determine and evaluate market microstructure studies when transaction costs are minimized [Huang and Stoll (1996b) and Bessembinder and Kaufman (1997)]. Such a procedure is applied in this paper. 13
  • Several measures of transaction (or execution) cost have been used, principal among them, are quoted bid-ask spreads and effective bid-ask spreads. In a pure dealership market, transactions take place only at the quoted bid or the ask price. Appropriate measures of transaction cost should, therefore, be based on such quoted/posted bid-ask spreads (see for example, Demsetz 1968, Branch and Freed 1977, Benston and Hagerman 1974, Huang and Stoll 1996, Barclay et al. 1999). In many security markets, however, trades frequently take place inside the spread.5 When this occurs, the quoted spread will tend to overstate the investors’ expected trading costs, making the effective spread, which is simply the average difference between the price at which a dealer sells at one point in time and buys at an earlier point in time, a better measure for trading costs (e.g., Roll 1984, Stoll 1985). Using two samples of market orders, one based on orders submitted by retail brokers and another submitted electronically to the NYSE, Petersen and Fialkowski (1994) estimate the spread generated using both orders and report a significant difference between the posted spread and the effective spread paid by investors. Applications of this effective spread method to measure trading costs have been used in numerous studies (e.g., Barclay 1997, 1999, Bessembinder 1997, Christie and Huang 1994, Christie et al. 1994, Huang and Stoll 1996b). An alternative approach to measure transaction costs is to calculate the difference between the actual transaction price and the efficient price. Broadly defined, the efficient price, which is considered as the true value of a stock, is an unbiased price estimate that can be achieved in any relevant trading period by any randomly selected trader. Beebower (1989), for example, uses daily high-low midpoint prices and closing prices as 5 Blume and Goldstein(1992), for example, find that between 12 to 31 percent of the trades occur inside the spread. Once transactions occur inside the quoted spread, it is often the case that the quoted spread will tend to overstate the investors’ expected trading costs and, as such, is not an appropriate measure of transaction costs. 14
  • efficient prices, and the effective spread to compute transaction costs. This measure may, however, place an excessive weight on trades that are not representative of most trades over a particular trading period and is, therefore, likely to be a biased representation of actual transaction prices. To address this, Berkowitz et al. (1988) propose a measure of transaction costs based on the volume-weighted average price over the trading day. Although their measure yields results that appear less biased as compared to other measures that only use single prices, two main problems may arise: (1) the measure may weight an aberrant trade very heavily, especially when a very thinly traded stock with small market value experiences a relatively large transaction; (2) when gaming takes places, that is, traders adjusting their trading behavior to affect the “efficient price” when they become aware that they are being evaluated on this basis. Another approach introduced in Hasbrouck and Schwartz (1988) is an overall measure of average execution costs for all trades of a certain stock, in a particular market, over a certain period of time. This measure relies on the notion that execution costs increase the volatility of short-term price movements relative to the volatility of long-term price movements. Although the measure is an average value for all trades in a certain market, it is strongly influenced by the preponderance of small trades since it was not designed to measure how execution costs are affected by trading size. A newer approach developed by Hasbrouck (1993) measures the transaction costs in stock markets based on the methodology that a non- stationary time series can be divided into a random-walk component and a residual stationary component. When applied to stock transaction prices, the random-walk component is identified as the efficient price with the stationary component (termed the pricing error) representing the difference between the efficient price and the actual transaction price. 15
  • The dispersion of the pricing error that results from this division, measures how closely actual transaction prices follow a random walk and, therefore, constitutes an appropriate measure for transaction costs. In our paper, we apply the Hasbrouck (1993) method to compute the variance of pricing errors, which is used as the principal metric for market quality comparison. Having restricted access to the order book, we were not able to obtain data on quoted bid and ask prices, and hence, unable to compute quoted or effective spreads. We partially overcome this handicap by using the methodology of Roll(1984). Roll’s technique involves extracting spreads from transaction prices. Comparisons using these extracted spreads are used to infer about the relative quality of NSE and BSE. We apply the multivariate regression approach of Hasbrouck and Schwartz (1988) to identify the source of the observed differences in market quality. This approach uses the intuition that firm-specific trading characteristics may have an effect on overall market quality. In our paper, we use two different methods of transactions cost measurement, namely, the Hasbrouck (1993) approach to estimate the dispersion of the pricing error, and the Roll (1984) approach, to compute the implicit effective bid-ask spread. An extension of the Hasbrouck (1993) method that includes other factors that may affect transaction costs is also discussed6. 3.2 Hasbrouck’s (1993) approach Hasbrouck’s (1993) approach to measure transaction costs is based on the fact that any time series that exhibits homogeneous non-stationarity may be decomposed into two separate components, namely, a stationary series and a pure random walk. The stationary component is defined as the predictable momentum in the series at each point of time while the random-walk component is simply taken as the mid-point of the 6 This method relies on the methodology introduced in Hasbrouck and Schwartz (1988). 16
  • predictive distribution for the future path of the original series. When applied to stock transaction prices, the random-walk component is taken to be the efficient price. The residual stationary component resulting from the decomposition (also known as the pricing error) represents the deviation of transaction prices from the efficient price. Since the dispersion of the pricing error tracks how closely actual transaction prices follow the efficient price, it is, therefore, viewed as an appropriate measure of transaction costs. Hasbrouck proposes the standard deviation of the pricing error, σ s , as a summary measure of market quality that measures how closely the transaction price tracks the efficient price. This measure of market quality can then be applied to different markets to make appropriate inferences about market quality. The role of σ s as a proxy for market quality depends on the assumption that as transaction costs and other trading barriers are reduced, actual transaction prices should track the efficient prices more closely. The details regarding the estimation procedure are provided in Appendix. 3.3 Roll’s (1984) approach In our paper, we also use Roll’s (1984) approach to measure the effective bid-ask spread which is generally considered as the transaction cost of that stock. The Roll (1984) approach is a simple but efficient method that allows the effective bid-ask spread to be inferred directly from a time series of transaction prices only, based on the following assumptions: (1) the stock is traded in an informationally efficient market; (2) the probability distribution of observed price changes is stationary. To compute the effective bid-ask spread, Roll (1984) shows that the existence of trading costs in a perfect market leads to negative serial dependence in successive observed transactions price changes in the otherwise random walk behavior of the 17
  • efficient price of a stock. On this basis, Roll (1984) derives the covariance between successive price changes as follows: Cov (∆Pt , ∆Pt +1 ) = − s 2 / 4 , (12) where ∆Pt = Pt − Pt −1 and ∆Pt +1 = Pt +1 − Pt ; the Cov(∆Pt , ∆Pt +1 ) term represents the first- order serial covariance of transaction price changes with a value equal to the negative square of one-half of the bid-ask spread; and ∆P is the underlying stock return with a variance of s 2 / 2 and an autocorrelation coefficient of –1/2. In this model, s is not the quoted bid-ask spread. Since successive price changes are recorded from actual transactions, s is, in fact, the effective bid-ask spread, a dollar- weighted average spread faced by investors who trade at the observed prices. This, as such, makes s an appropriate measure of trading cost. Rewriting equation (12) allows us to define the effective bid-ask spread in relation to the negative covariance in price changes as follows: ∧ Spread = 2 − Cov , (13) ∧ where Spread now represents the estimated effective bid-ask spread. We use the percentage returns of prices, namely, Pt − Pt −1 / Pt −1 , to compute Cov( ∆Pt , ∆Pt +1 ) . The average stock transaction price varies across firms, making this measure more appealing as a statistic for comparing effective spreads of different stocks. The resultant measure is ∧ S j , the estimate of the percentage bid-ask spread for stock j which is given as follows: ∧ ∧ S j = 200 − Cov j . (14) 18
  • ∧ Cov j is the estimated serial covariance of returns7 of stock j which is scaled by 200 to convert all measurements into percentage form. Both daily and weekly returns are computed to obtain two different estimates of serial covariances for each stock. 4. Data The dataset used in this study comprises 40 pairs of common stocks that are simultaneously listed and traded on both BSE and NSE. All the selected stocks are Indian index stocks that are observed to have large numbers of observations for each trading day and hence higher liquidity as compared to other similar stocks. We specifically selected these stocks to minimize stock-specific effects; for example, stocks with larger market capitalization generally have lower trading costs (see Hasbrouck 1993). Previous studies, which include Huang and Stoll (1996) and Barclay (1997) that attempt to compare the transaction costs between the NYSE and NASDAQ, employ stocks that are only matched in a limited manner, resulting in findings that tend to be strongly affected by stock-specific factors. For our paper, the forty pairs of stocks that are traded on both BSE and NSE are identical (see Table 1 for a complete listing of the 40 stocks). High frequency intraday transaction data are used in the first part of the study based on Hasbrouck’s (1993) approach to compute transaction costs. All transaction records for the 40 sample stocks cover the period 1 January 1997 to 31 July 1997 are obtained from NSE and BSE. This dataset comprises a total of 144 trading days with the total number of transaction exceeding 15 million. In the second part of the study that applies Roll’s (1984) approach, we use daily and weekly prices data on both markets over the 7 Roll (1984) estimated the implicit bid-ask spread and examined its relation to firm size on the NYSE and AMEX over a period of 20 years. He found that the implicit bid-ask spread was negatively related to the market capitalization of stocks, a result that provides validates his method in estimating the implicit bid-ask spread as a measure of trading costs. 19
  • period 1 January 1997 to 31 March 2000 is used. For our study, we obtain information on market value, average weekly /daily price, average trading size, and average trading number per day, for each pair of stocks traded on both exchanges to determine factors relevant in transaction cost measurement. In constructing the time series of returns and trades, we ignore natural time conventions. Instead, we view the data is viewed as an untimed sequence of observations, with the time subscript t incremented each time a new transaction occurs. Overnight returns are not used to avoid the overnight effects on stock prices. Lee and Ready’s (1991) approach is used to infer the direction/sign of a trade by comparing its price to the price of the preceding trade(s), and is further based on classifying each trade into four categories: an uptick, a downtick, a zero-uptick, and a zero-downtick. A trade is considered an uptick (downtick) if the price is higher (lower) than the price of the previous trade. When the price is at the same level as the previous trade (that is a zero tick), and when the last price change was an uptick, then the trade is considered a zero- uptick. On the other hand, if the last price change was a downtick, then the trade is considered a zero-downtick. Essentially, if two successive prices are the same, the latter one is assumed to follow the sign of the preceding one. A trade is also classified as a buy (sell) transaction if it occurs on an uptick (downtick) or a zero-uptick (zero-downtick). Such an approach allows us to classify trade directions since quote data are not available and is widely used by many studies8. 5. Empirical Results In this section, we present the empirical results of transaction costs comparisons for “multi-trading” stocks that are listed simultaneously on NSE and BSE. We compare both markets using two different methods to compute the transaction costs: the standard 8 On such example is found in Holthausen, Leftwich and Mayers (1987). 20
  • deviation of the pricing error using Hasbrouck’s (1993) approach, and the daily price covariance using Roll’s (1984) approach. 5.1 Standard deviation of the pricing error using Hasbrouck’s (1993) approach Hasbrouck’s (1993) approach involves the estimation of the standard deviation of the pricing errors which is obtained sequentially. In the first stage, equation (3) is estimated using a VAR length of three lags, consistent with current best practice9. The trade innovations v1,t , v 2,t from the estimated VAR model are transformed into their vector moving average representations (VMA) using equation (4). Estimates from the first equation in the VMA are used to compute the α ' s and β ' s. The estimated pricing error given by equation (8) and equation (9) (with all η t = 0 ) are used to obtain the Beveridge and Nelson (1981) adjusted estimate of S t , from which, the variable of interest, namely, the standard deviation of the pricing error, σ s , is obtained using equation (11). Standard deviations of the pricing errors for the paired sample stocks from both markets are computed and are compared to determine which exchange is more cost-efficient. Table 2 lists the computed values of Hasbrouck’s (1993) measure for all forty multi- trading stocks on both markets and the ratio of this measure in NSE relative to its value for BSE. The smallest standard deviation of the pricing error is 0.0152 and 0.0934 for NSE and BSE, respectively; the largest values are 0.8639 and 1.4901 for NSE and BSE, respectively. Based on Hasbrouck’s (1993) approach, the standard deviation of pricing error represents the percent value of transaction costs for a particular stock. Clearly, both the minimum and maximum values of Hasbrouck’s measure are greater on BSE. Moreover, the mean standard deviation of the pricing error is 0.27 and 0.64 on NSE and BSE, 9 Serial dependencies beyond three lags tends to be negligible and have little impact on the estimates in the model. 21
  • respectively, suggesting average transaction costs for the paired sample stocks are 0.27 percent of stock prices on NSE, as compared to 0.64 percent of the stock prices on BSE. The mean ratio (NSE/BSE) of 0.4034 confirms that the average transaction cost of these multi-trading stocks on NSE is lower than BSE. Hasbrouck’s (1993) results of 0.33 percent of stock prices on the NYSE are closer to NSE’s figure of 0.27% while BSE reports values of 0.64%. A Wilcoxon signed ranks test is also used to compare the mean standard deviation of the pricing error for these “multi-trading” stocks to determine whether there is significant difference in Hasbrouck’s (1993) measure between the two markets. The test statistic is highly significant at the 1% level with a value of –5.417, strongly supporting our hypothesis that the average standard deviation for the pricing error for “multi-trading” stocks on NSE is significantly lower for identical set of stocks traded on BSE. Since the standard deviation of the pricing errors is used as a measure for transaction costs, all our results based on Hasbrouck’s (1993) approach suggest that the average trading costs on NSE are significantly lower than BSE. From the list of forty, only two stocks (or 5% of the total) register higher values for Hasbrouck’s (1993) measure on NSE with the remaining 38 stocks (95%) exhibiting lower values on NSE; no stocks are found with equal values for Hasbrouck’s measure. The standard deviation of the pricing errors are indeed smaller on NSE for most stocks, providing further confirmation that the trading cost on NSE is lower than BSE even at the individual stock level. 5.2 Daily price covariance using Roll’s (1984) approach We conduct robustness check using Roll’s (1984) approach. We compute two types of effective bid-ask spreads using the daily price data: a rupee-spread and a percentage-spread. The rupee effective bid-ask spread enables us to compare the bid-ask 22
  • spreads of paired sample stocks on both markets. As the rupee-spread is positively related to the specific stock’s price, it is therefore, unsuitable for comparison across different stocks. This measure is primarily used to compare identical stocks traded on two different markets. Percentage bid-ask spreads, on the other hand, which use the stock price level as the denominator, allows spreads of stocks, which vary in the level of stock price level, to be compared. Table 3 presents the rupee-based and percentage-based covariances of the daily prices for 39 out of the 40 multi-trading stocks10. Roll (1984) states that the covariance of stock prices in an efficient market are expected to be negative since market makers adjust their quotes around the efficient price quickly. We find, however, that most of the covariances computed for the 39 stocks are, in fact, positive; for rupee-based covariances, a total of 10 (or 12.8%) are negative; only 7 (or 8.9%) are negative for percentage-based covariances. Our empirical result is, however, not surprising and is consistent with Roll (1984) and Harris (1990), who also obtain positive covariances using daily prices. Roll (1984) and Harris (1990) argue that since the stock market is far from being fully efficient, the speed of price adjustment tends to be slower, and usually takes between several hours to weeks to adjust to the right level. This consequently results in positive correlation in daily price rather than the theoretical proposition of negative serial correlation. Amihud and Mendelson (1987) and Hasbrouck and Ho (1987) also obtain similar results. Moreover, Harris (1990) finds that when longer time intervals are used (for example, weekly prices), the number of stocks having positive covariances tend to fall as more negative correlations are expected, given that the price adjustment will take place. Based on these 10 Data pertaining to Ponds India Limited was not readily available. 23
  • factors, we decided to abandon spread estimation using Roll’s methodology with daily. We estimate spreads using weekly data based on the findings of Harris (1990). 5.3 Estimation of effective spreads for weekly prices using Roll’s (1984) approach Effective bid-ask spreads computed using the rupee-based covariance of weekly transaction prices are given in Table 4. Unlike the results obtained using daily data, 32 stocks on NSE and 30 stocks on BSE exhibit negative covariances when weekly prices are used. Over weekly intervals, the Indian stock price prices tend to move closer to their efficient price levels. Based on the assumption of an informationally efficient market, effective spreads that are inferred from any observation interval should be equal. Our results using daily and weekly prices differ. We find that covariances estimated from the daily prices exhibit more positive values than those estimated from weekly prices. Roll (1984) attributes this deviation from the theory to informational inefficiency of the respective market. For our study, this clearly indicates that both NSE and BSE are not perfectly information- efficient markets. Table 4 also presents the rupee-based bid-ask spread, which we use as a measure of transaction costs for the multi-trading stocks on NSE and BSE. Out of 39 stocks, we have a total of 29 usable pairs of estimations (accounting for 74% of the total). These are indicated in bold. Of these 29 pairs, 20 stocks have lower bid-ask spreads on NSE than BSE. As the bid-ask spread is regarded as a proxy for transaction costs, this suggests that transaction costs on NSE are lower than BSE. Use of the rupee-based bid-ask spreads is inappropriate in a comparison of trading costs across different stocks as these spreads are directly price related. A better measure of trading costs in this case is the percentage-based bid-ask spread, results of which are presented in Table 5. A total of 29 stocks (or 74.4%) on NSE and 30 stocks (or 76.9%) on 24
  • BSE exhibit negative covariances. The ratio of the bid-ask spread for stocks on NSE to the bid-ask spread for the identical stocks on BSE are shown in the last column. Of the 29 usable pairs, 20 stocks have ratios less than unity indicating that spreads are lower in NSE than BSE. The mean effective bid-ask spread is 3.469 and 3.916 percent of the stock price on NSE and BSE, respectively, while the mean bid-ask spread ratio is 0.8925, suggesting the bid-ask spread on NSE is about 11% lower as compared to BSE. This difference in the effective spreads is statistically significant11, implying that transaction costs on BSE are, on average, significantly higher than NSE. Also, 69% of the stocks are found to have a higher bid-ask spread on BSE. We have thus shown empirically that NSE is a better quality market than BSE, a finding that is consistent with the Domowitz and Steil (1999) proposition. Can we attribute the better quality governance in NSE as compared to BSE for the observed differences in the quality of markets? Outlined below are possible structural explanations that explain our findings. 5.4 Why is the quality of the market better on NSE? We find substantial empirical evidence in our paper that the transaction costs on NSE is lower than BSE. Important differences in market structures between the two exchanges may possibly account for the lower cost structure on NSE, which is discussed below. BSE commenced operations in 1875 and is the oldest stock market in India, which for many years, has been the major Indian stock market. Unlike NSE, BSE was beset with problems related to an outdated trading and settlement mechanism, lack of transparency in transactions, delays in physical delivery and registration of transfer, poor disclosure, price manipulation, insider trading, poor liquidity and excessive speculation. NSE, was 11 The Wilcoxon test statistics of -2.693 and standard t-test statistic of -2.88 in both markets are statistically significant. 25
  • established in 1994 by policy makers with the intent of improving the trading systems, practices and procedures in Indian stock markets. Market reforms were carried out using a combination of new technology, new processes and enforcement of new regulations, with NSE pioneering the introduction of a national network of computerized trading, a clearinghouse, a completely automated screen based trading system, and special facilities for institutional trading in India. NSE has transformed and improved the Indian stock markets by increasing market transparency, providing more efficient settlement and delivery, and implementing collateralization based on risk of positions undertaken. In Table 6, we summarize the main structural differences between the two exchanges that enable NSE to operate under an umbrella of lower transaction costs, and which has allowed NSE to dominate BSE. The factors shown in italics are related to ownership/governance. Following Domowitz and Steil (1999), we argue that a demutualized ownership structure has enabled NSE to score higher on these governance variables: use of technology, internal control systems, transparency, regulation and investor protection. Small and medium investors would be attracted to NSE due to the perceived better quality of governance and protection. In addition, the geographical reach, which is not a governance factor, is likely to augment trading activity on NSE, and therefore have a beneficial effect on execution costs. While some of these factors such as technology use and investor protection serve to enhance the market quality by attracting liquidity traders, it is not clear whether having a more transparent market with better internal control systems, transparency and regulation enhance the quality of market as measured by execution costs (transaction costs). A market with less regulations and weak internal control systems would be very attractive to traders who seek to exploit inside information. Such a market would also be attractive to institutional traders if the exchange is negligent in enforcing regulations. In fact BSE 26
  • would be more attractive to insiders and institutional traders on account of the perceived laxity of that exchange. Such a market can theoretically provide lower execution costs if some traders place limit orders of large sizes. Based on the comparison of the two exchanges, we expect NSE to have a higher degree of trading activity (number of trades) and BSE to have a larger trade size. Both these factors are expected to result to result in an improvement in market quality via a reduction in execution costs. Some of the governance factors for which we have no proxies: technology use and investor protection, may have an influence on market quality. To disaggregate the source of market quality differences we conduct a multivariate study as explained below. 5.4.1 Multivariate analysis using Hasbrouck and Schwartz’s (1988) method Hasbrouck and Schwartz (1988) compare execution costs across different markets by using a multivariate approach. We use a variation of their model to examine the effect of governance and other factors on trading costs. We use a multivariate regression model to control for firm-specific factors that have a bearing on execution costs. Our explanatory variables include the average price (AP), the average size per trade (AST), the average number of trades per day (ATD), and the market value (MV) for a particular stock over the sample period. The AST variable is used to account for the occurrence of block trades of a particular stock while the ATD is used as a proxy for the liquidity of the stock on a particular exchange. We argued in the previous section that ATD would be higher on NSE as compared to BSE while the AST of BSE is expected to be larger. Price and market capitalization are used as control variables following the work of Hasbrouck and Schwartz (1988). We regress transaction costs against a list of variables, which include the stocks’ price, liquidity, market value, according to the following equation: 27
  • C i = a 0 + a1 APi + a 2 ASTi + a3 ATDi + a 4 MVi + a5 Di + ei , (15) where C i represents the transaction costs for stock i , defined as the standard deviation of the pricing errors; APi , is the average price for stock i ; ASTi , is the average size per trade for stock i ; ATDi , is the average number of trades per day used as the proxy for liquidity; MVi , is the market capitalization for stock i ; and Di , is a dummy variable to distinguish between the two markets, set equal to one for NSE stocks and zero for BSE stocks. The dummy variable is included to capture potential governance factors that distinguish the two markets. Table 7 provides descriptive statistics pertaining to AP, AST and ATD on both exchanges. The average stock price of 546.46 rupees is higher on BSE as compared to 467.11 rupees on NSE. Stocks traded on NSE have a smaller mean AST of 259 shares as compared to 363 shares on BSE, suggesting larger trades on BSE as compared to NSE. This is mainly due to the characteristics of BSE associated with frequent insider trading and institutional block trading. Wilcoxon and t-test statistics for the difference in AST between these two markets are significant at the 1% level. The mean value of 2299 daily trades on NSE is larger than the 1196 recorded for BSE and the difference is statistically significant using both the Wilcoxon and the standard t-test. This indicates that the trading activity on NSE is higher compared to BSE. As the average trade per day is often used as a proxy for the liquidity of stocks, this is indicative that NSE is relatively more liquid than BSE. The increased trading activity is perhaps a natural consequence of the nation-wide network established by NSE as compared to a city-wide network employed by BSE. 28
  • A total of fifteen regressions are estimated based on all possible combinations of the independent variables in our study12. Selected results of these regressions are presented in the Table 8. Values of the adjusted R-square range from 0.278 to 0.403. The dummy variable, Di , is negative and statistically significant in all regressions, suggesting that transaction costs on NSE are lower than BSE even after controlling for trading activity, trade size and the control variables. Estimates of the average price (AP) are positively related to transaction costs in all regressions and significant in regressions 4 and 5. The statistical significance wanes as other trading activity and market value variables are included.13 A negative relation is observed between the average size per trade (AST) and transaction costs, implying that larger trade size lowers transaction costs. This negative relation is significant in regressions 3 and 6. The number of trades per day (ATD) is significant and negatively related to transaction costs in regressions 4 and 7. The significance drops when the AST variable is included (correlation of 0.23). As expected, stocks which trade actively tend to be associated with lower transaction costs. Also, since the ATD is a proxy for liquidity, it suggests that higher liquidity does, in fact, result in lower transaction costs. The market capitalization (MV) is negatively related to transaction costs indicating that stocks with larger market value generally exhibit lower transaction costs, a result consistent with that obtained in Roll (1984) and Hasbrouck (1993). In all cases, the dummy variable is negative and statistically significant. We cautiously interpret this finding to indicate that the residual impact of superior governance in the NSE results in a lower transaction cost as compared to BSE. Interestingly, BSE benefits from its ability to 12 This procedure is valid when the number of regressors is small since the total number of regressions is a function of [2exp(k) – 1], where k represents the number of regressors (see Maddala, 1977). 13 The correlation between AP and MV is 0.39, while AP has a negative correlation of –0.28 with the AST variable. 29
  • attract insiders/institutional traders as evidenced by the negative impact of the AST variable. Our descriptive statistics showed that BSE has significantly higher trade size as compared to NSE. Our results are consistent with Krishnamurti and Lim (1999), who similarly find that NSE, with lower execution costs, lower price volatility, and higher liquidity, is a better- quality market than BSE. It is, therefore, not surprising to observe that NSE has taken over much of the transaction volume from BSE since its inception. This is clearly demonstrated in Figure 2 which tracks the trading volume of both markets over the period April 1996 until January 1998; and which shows NSE having a consistently higher trading volume as compared to BSE since its inception. 6. Conclusion The primary role of a stock exchange is to provide trading services to its ultimate clients. Prior studies have concentrated on comparing the quality of markets with different trading systems. Little attention has been paid to the critical issue of the quality of markets and stock exchange governance. The reason for this neglect is due to the fact that until recently, most major exchanges have been organized as mutual associations. A recent trend has been in the demutualization of stock exchanges, ostensibly, to better deal with emerging competition from electronic trading networks. In a recent paper, Domowitz and Steil (1999) provide convincing arguments in favor of demutualized exchanges. They reason that members of a mutualized exchange have incentives to oppose innovation that increase the quality of its services, and hence the value of the exchange, while stakeholders of a demutualized exchange, are likely to favor any measure that enhances the value of the firm. 30
  • In our paper, we examine the Domowitz and Steil proposition empirically using data from a big emerging market, namely, India, which has two major exchanges with distinguishably different organizational structures. The National Stock Exchange adopts a demutualized form of organization unlike the Bombay Stock Exchange which continues to pursue the mutual structure. Consistent with the Domowitz and Steil proposition, we find that National Stock Exchange has a better quality market as compared to Bombay Stock Exchange based on the market quality measure derived by Hasbrouck (1993). Our basic conclusions are robust to an alternate measure of market quality. Multivariate regression results indicate that the superior governance of NSE is at least partially responsible for its better market quality. Our study has implications for emerging markets where regulators do not have sufficient experience or clout in dealing with recalcitrant monopolists. We have shown that competition has been more effective than regulatory dictates in transforming the errant behaviour of the members on the Bombay Stock Exchange; findings that surely have important policy implications for other emerging nations. There exists evidence that economic incentives are likely to be more potent in transforming the behavior of rent seekers rather than directives from policy regulators. The role played by technology in transforming Indian stock markets is particularly evident in our findings. Applying the latest technology, the National Stock Exchange was able to penetrate the barriers to entry in the stock-broking industry, which had the consequent benefit of reducing the cost of providing these services. Being able to pass on the lower trading costs to the investors, the National Stock Exchange has posed a serious threat to the very existence of the oldest exchange in India, namely the Bombay Stock Exchange. That technology is responsible for breaking barriers to entry and for dramatically improving in the quality of markets in Indian stock exchanges, is 31
  • unequivocal; the Bombay Stock Exchange must re-examine its governance structure if it wishes to remain relevant in a global environment where only the best will survive. 32
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  • Christie, W. G., and Schultz, P., 1994, Why do NASDAQ market makers avoid odd- eighth quotes?, Journal of Finance 49, 1813-1840. Demsetz, H., 1968, The cost of transacting, Quarterly Journal of Economics 82, 32-53. Domowitz, I., and Steil, B., 1999, Automation, Trading Costs, and the Structure of the Trading Services Industry, Brookings-Wharton Papers on Financial Services. Glosten, L., 1987, Components of bid-ask spread and the statistical properties of transaction prices, Journal of Finance 42, 1293-1307. Gompers, P.A., Ishii, J.L., and Metrick, A., 2001, Corporate Governance and Equity Prices, Working Paper, Wharton Business School, University of Pennsylvania. Harris, L., 1990, Statistical properties of the Roll serial covariance bid/ask spread estimator, Journal of Finance 45, 579-590. Hasbrouck, J., 1991b, The summary informativeness of stock trades: An econometric analysis, Review of Financial Studies 4, 571-595. Hasbrouck, J., 1993, Assessing the quality of a security market: A new approach to transaction-cost measurement, Review of Financial Studies 6, 191-212. Hasbrouck, J., and Ho, T, 1987, Order arrival, quote behavior and the return generating process, Journal of Finance 42, 1035-1048. Hasbrouck, J., and Schwartz, R. A., 1988, Liquidity and execution costs in equity markets, Journal of Portfolio Management 14, 10-16. Holthausen, R. W., Leftwich, R. W., and Mayers, D., 1987, The effect of large block transactions on security prices: A cross-sectional analysis, Journal of Financial Economics 19, 237-267. Huang, R. D., and Stoll, H. R., 1996a, Competitive trading of NYSE listed stocks: Measurement and interpretation of trading costs, Financial Markets, Institutions and Instruments 5, No. 2, 1-55. Huang, R. D., and Stoll, H. R., 1996b, Dealer versus auction markets: A paired comparison of execution costs on NASDAQ and the NYSE, Journal of Financial Economics 41, 313-357. Krishnamurti, C., and Lim, E., 1999 Competition, liquidity and volatility: A comparative study of Bombay Stock Exchange and National Stock Exchange, working paper, Nanyang Technological University, Singapore. Lee, C. M., and Ready, M. J., 1991, Inferring trade direction from intraday data, Journal of Finance 46, 733-746. 34
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  • Appendix: Hasbrouck’s (1993) approach According to Hasbrouck (1993), the observed transaction price at time t, Pt , may be decomposed into two components as follows: Pt = M t + S t , (1) where M t is the efficient price, representing the value of a certain stock that includes all public information available at time t and all the private information that may be inferred from previous stock transactions and S t , the pricing error, representing the deviation of the actual transaction price from this efficient price.14 Two important assumptions that Hasbrouck (1993) makes are: (1) The efficient price, M t , follows a random walk: M t = M t −1 + Wt , (2) where Wt are uncorrelated increments, E (Wt ) = 0 , E (Wt 2 ) = σ w , and E (WtWτ ) = 0 for 2 t ≠τ ; (2) The pricing error, S t , is a zero-mean covariance-stationary stochastic process. S t , the pricing error, is central to Hasbrouck’s (1993) approach capturing various microstructure effects such as price discreteness, inventory control, the non-information based component of the bid-ask spread (order processing costs) and the transient component of the price response to a block trade that cannot be explicitly modeled. From a transaction cost perspective, the pricing error, S t , is defined as the cost to the buyer while – S t , is the respective cost to the seller, maintaining the zero-sum principle as discussed earlier. 14 Here t is assumed to index stock transactions. 36
  • To assess the market design and regulation, the standard deviation of the pricing error, σ s , is proposed as a summary measure of market quality that measures how closely the transaction price tracks the efficient price. The role of σ s as a proxy for market quality depends on the assumption that as transaction costs and other trading barriers are reduced, actual transaction prices should track the efficient prices more closely. Derivation of σ s requires the estimate of parameters from a vector autoregressive (VAR) model that explicitly captures the dynamic structure of stock transactions15. In the bivariate VAR model that we have adopted below, current trades and transaction prices are dependent on information derived from the previous transactions: Rt = a1 Rt −1 + a 2 Rt − 2 + L + b1 X t −1 + b2 X t − 2 + L + v1,t (3) X t = c1 Rt −1 + c 2 Rt − 2 + L + d 1 X t −1 + d 2 X t − 2 + L + v 2,t , where X t represents the trade variable, in this case, the trade volume at time t, which is signed to be positive if the agent who initiates the trade is buying, and negative if the agent is selling; v1,t , v 2,t represent trade innovations, which are zero-mean, serially uncorrelated disturbances; and Rt is the standard returns variable computed by taking the first difference of the natural logarithm of observed transaction prices, that is, Rt = Pt − Pt −1 . Hasbrouck then transforms the VAR model in equation (3) to a vector moving average (VMA) model to express the exogenous variables in terms of current and lagged disturbances as follows: Rt = a 0 v1,t + a1 v1,t −1 + a 2 v1,t − 2 + L + b0 v 2,t + b1* v 2,t −1 + b2 v 2,t − 2 + L * * * * * . (4) X t = c0 v1,t + c1 v1,t −1 + c 2 v 2,t − 2 + L + d 0 v 2,t + d1* v 2,t −1 + d 2 v 2,t − 2 + L * * * * * 15 One major advantage of a VAR model is that it is a powerful framework that is general enough to capture unspecified lagged dependencies. From a market microstructure perspective, this is essential, as market imperfections result in lagged effects that include inventory control mechanisms, lagged price adjustment, and price discreteness. 37
  • From equations (1) and (2), the pricing error is given by S t = αWt + η t , where η t is a disturbance term that is uncorrelated with Wt . Accordingly, it follows that: Rt = Pt − Pt −1 = Wt + S t − S t −1 . (5) Since Wt and S t are serially uncorrelated, Rt possesses nonzero autocovariances at the first lag only. This implies that Rt may be represented as a first-order moving average process, namely: Rt = ε t − aε t −1 . (6) Two parameters, namely, { a, σ ε2 }, fully characterize the mean and autocovariances of the return process. Applying the identification restriction in Beveridge and Nelson (1981) refereed to as the BN restriction that η t =0, and using equations (5) and (6), we obtain: ε t − ε t −1 = Wt + αWt − αWt −1 . (7) Equation (7) implies that α = a 1 − a , Wt = (1 − a)ε t , S t = αWt , σ w = (1 − a) 2 σ ε2 , and 2 σ s2 = a 2σ ε2 . When transaction prices are available, the pricing error for a particular transaction may be estimated using equation (8) as follows: ∧ S t ( Rt , Rt −1 ,L) = − aε t = − a( Rt + aRt −1 + a 2 Rt −2 + L) (8) ∧ where S t represents the estimated pricing error. As Rt in equation (4) is used to determine the value of the pricing error, it follows that: S t = α 0 v1,t + α 1v1,t −1 + L + β 0 v 2,t + β 1v 2,t −1 + L + η t + γ 1η t −1 + L , (9) where, η t is a disturbance term that is orthogonal to all components of vt . Thus, using the BN restriction, the value of the pricing error may be computed directly if estimates of the α ' s and β ' s in equation (9) are available. Use of the BN restriction implies that the pricing error is information correlated [see Glosten (1987)]. 38
  • Using Hasbrouck’s (1991b) approach and the BN restriction, values of α ' s and β ' s are computed as follows: ∞ ∞ α j = − ∑ ak , * β j = − ∑ bk* , (10) k = j +1 k = j +1 * * where the a k ’s and bk ’s are estimates obtained from equation (4). Accordingly, the variance of the pricing error, σ s , is given by: 2 α j  σ s2 = ∑ [α j β j ]Cov(v)   , ∞ (11) j =0 β j  which is an identification-invariant best linear estimate of S t , conditional on current and lagged vt . The standard deviation of the pricing error is obtained by taking the square root of the variance. This measure tracks how closely the transaction price is to the efficient price and allows a meaningful measure of transaction costs of stocks to be obtained. 39
  • Table 1 List of the 40 paired sample stocks traded on both NSE and BSE Company Name Asea Brown Boveri Limited Associated Cement Andhra Valley Power Supply Co. limited Arvind Mills Limited Ashok Leyland Limited Asian Paint India Limited Bharat Heavy Electricals Limited BSES Castrol India Limited Cochin Refineries Limited Colgate Palmolive India Limited East India Hotels Limited Grasim Industries Limited Gujarat Ambuja CMT Housing Development Finance Limited Hindustan Lever Limited Hindustan Petroleum Industrial Credit and Investment Corporation of India Limited Industrial Finance Corporation of India Limited Indian Hotel Company Limited Indo Gulf Fertilizers and Chemicals Corp. Limited Indian Rayon and Industries Limited ITC Larsen & Toubro Mahindra & Mahindra Limited Mangalore Refinery and Petrochemicals Limited Mahanagar Tel Nigam Limited Nestle India Limited Oriental Bank of Commerce Ponds India limited Ranbaxy Laboratories Limited Reliance Industries Limited State Bank of India Tata Chemicals Limited Tata Power Co. Tata Tea Tata Engr. & Loco. Thermax Limited Tata iron & Steel Co. TVS-Suzuki 40
  • Table 2 Hasbrouck’s (1993) measure for Paired Sample Stocks in NSE and BSE Stocks BSE(%) NSE(%) Ratio (BSE/NSE) Andhra Valley Pwr. 0.4370 0.1716 0.3927 Arvind Mills 0.5458 0.1248 0.2287 Asea Brown Boveri 1.3727 0.5550 0.4043 Ashok Leyland 0.9676 0.1819 0.1880 Asian Paints 0.9762 0.3578 0.3665 Associated Cement 0.8645 0.4310 0.4986 Bharat Heavy Els. 0.3483 0.2159 0.6199 Bses 0.3980 0.0972 0.2442 Castrol India 0.6287 0.2379 0.3784 Cochin Refineries 0.2894 0.1852 0.6399 Colgate-Palmolive 0.5111 0.1529 0.2992 Eih 1.4901 0.6690 0.4490 Grasim Inds. 1.3783 0.3778 0.2741 Gujarat Ambuja Cmt 0.4223 0.1429 0.3384 Hdfc Bank 0.8448 0.8639 1.0226 Hindustan Lever 0.9546 0.4183 0.4382 Hindustan Ptl Corp. 0.4831 0.3289 0.6808 Icici 0.3896 0.0750 0.1925 I F C I Ltd. 0.0934 0.0152 0.1627 Indian Hotels Co. 1.1899 0.7472 0.6280 Indian Rayon&Inds. 1.0697 0.3229 0.3019 Indogulf Fert. 0.1174 0.0771 0.6567 Itc 0.4136 0.1086 0.2628 Larsen & Toubro 0.3786 0.0904 0.2388 Mahanagar Tel Nigam 0.2705 0.1101 0.4070 Mahindra & Mahindra 1.0382 0.3837 0.3699 Mrpl 0.0955 0.0350 0.3665 Nestle India 0.8387 0.2679 0.3194 Oriental Bk.Of Com. 0.1226 0.0513 0.4184 Ponds 0.7593 0.8611 1.1341 Ranbaxy Labs. 1.1797 0.4189 0.3551 Tata Chemicals 0.6699 0.1664 0.2484 Tata Engr.& Loco. 0.4220 0.1184 0.2806 Tata Iron & Steel 0.4020 0.0625 0.1555 Tata Power Co. 0.2988 0.0792 0.2651 Tata Tea Co. 0.5675 0.1666 0.2936 Thermax 0.5792 0.3842 0.6633 Tvs-Suzuki 0.9522 0.6084 0.6389 Reliance Inds. 0.4919 0.0751 0.1527 State Bank Of Ida. 0.3804 0.0617 0.1622 Mean 0.6400 0.2700 0.4034 41
  • Table 3 (1) Covariance Using Daily Price Data Rupee-based cov ( 2 ) Percentage-based cov (3) Stocks NSE BSE NSE (%) BSE(%) Andraval -0.0172* 0.2865 -0.00057* 0.00661 Arvindmi 0.1702 0.2158 0.00294 0.00868 Abb 15.744 14.114 0.00854 0.00674 Ashokley 0.0067 1.5248 0.00375 0.02960 Asianpa 0.9659 2.4130 0.00114 0.00327 Acc 1.0723 1.3080 0.00153 0.00878 Bhel 3.8321 8.3449 0.00385 0.00791 Bses 4.5356 5.2174 0.00546 0.00720 Castrol 0.9956 2.0589 0.00157 0.00265 Cochinre 1.5430 -0.0748* 0.00512 0.00182 Colgate 1.9709 2.6615 0.00143 0.00265 Eihotel 1.9523 2.5452 0.00091 0.00234 Grasim 13.484 14.265 0.01580 0.01860 Gujambce 0.4143 -1.6079* 0.00140 0.00166 Hdfc 0.7911 0.7598 0.00438 0.00611 Hindleve 217.00 226.09 0.00338 0.00481 Hindpetr 2.3589 2.5068 0.00830 0.00836 Icici 0.9455 0.0659 0.00764 0.01270 Ifci 2.7988 -0.0097* -0.00153* -0.00161* Indhotel 14.794 17.377 0.00945 0.00962 Indrayon 6.1814 3.0869 0.02680 0.02300 Indogulf 0.0551 0.2910 0.00402 0.01280 Itc -11.410* -6.5783* 0.00059 0.00162 L&T 1.8837 1.3965 -0.00038* -0.00051* Mtnl 3.4143 3.4463 0.00488 0.00500 Mm 18.382 15.232 0.01490 0.01240 Mrpl 0.0363 0.0110 0.00952 0.00504 Nestle -2.9822* -3.9896* 0.00064 0.00028 Orientba 0.1831 0.6784 0.00631 0.00533 Ranbaxy 44.801 44.691 0.01120 0.00915 Tatachem 0.7023 0.6313 0.00381 0.00435 Telco 0.9063 4.4395 0.00611 0.01100 Tisco 1.0041 0.9195 0.00482 0.00456 Tatapow -0.1244* 0.3158 -0.00091* 0.00225 Tatatea 10.411 8.4523 0.00671 0.00716 Thermax 1.6432 -0.0003* 0.00252 -0.00072* Tvssuzuk 13.048 15.666 0.00343 0.00406 Reliance 0.5314 1.3080 0.00000 0.00009 Sbin 0.7088 2.3413 0.00004 0.00006 Notes: (1) One stock (Ponds India Limited) is not recorded in DataStream, resulting in a sample of 39- paired multi-trading stocks are studied by this method; (2) Covariance of ( pt − pt −1 ) ; covariance of ( pt − pt −1 ) / pt −1 * Negative value of price covariance. ; 42
  • Table 4 Rupee Covariance and Effective Bid-Ask Spreads using weekly data NSE BSE Stock Covariance b-a spread Covariance b-a spread NSE/BSE(1) Andraval -2.7499 3.3166 -3.0020 3.4652 0.9571 Arvindmi -3.2711 3.6172 -4.3000 4.1584 0.8699 Abb 91.281* -19.108 ( 2 ) 50.121* -14.202 1.3455 Ashokley -0.0817 0.5734 -0.1650 0.8124 0.7058 Asianpa -29.744 9.5754 -38.350 12.423 0.7708 Acc -1.0241 2.0308 -4.0930 4.0586 0.5004 Bhel 40.575* -12.778 44.068* -13.317 0.9595 Bses -47.178 13.778 -41.326 12.897 1.0684 Castrol -48.230 13.931 -57.328 15.188 0.9172 Cochinre 0.9826* -1.9890 -3.7942 3.9074 -0.5090 Colgate -30.863 11.144 -36.931 12.191 0.9142 Eihotel -4.5132 4.2615 9.9939* -6.3416 0.6720 Grasim -15.243 7.8317 -19.312 8.8152 0.8884 Gujambce -3.1384 4.0949 -3.4541 3.7283 1.0983 Hdfc 11.252* -6.7288 8.2844* -5.7737 1.1654 Hindleve -2885.7 107.76 -2918.1 108.36 0.9944 Hindpetr -24.052 9.8379 -28.913 10.787 0.9121 Icici -2.1154 2.9175 -3.6010 3.8068 0.7664 Ifci 0.0105* -0.2059 15.5971* -7.9332 0.0260 Indohotel -82.629 18.234 -71.494 16.961 1.0750 Indrayon -35.608 -11.970 -41.362 12.901 -0.9278 Indogulf -0.7107 1.6912 -0.6120 1.5697 1.0774 Itc -165.75 25.826 -167.58 25.968 0.9945 L&T 25.422* -10.114 29.953* -10.984 0.9208 Mtnl -5.0707 4.5171 -3.6333 3.8236 1.1813 Mn -64.203 16.073 -64.816 16.150 0.9953 Mrpl -0.1781 0.8462 -0.2621 1.0276 0.8235 Nestle -15.839 7.9835 -30.258 11.034 0.7235 Orientba -1.9511 2.8021 -1.8365 2.7181 1.0309 Ranbaxy -184.47 27.245 -155.30 24.998 1.0899 Tatachem -13.447 7.3561 -8.1446 5.7247 1.2850 Telco -6.4241 5.0845 -17.058 8.2849 0.6137 Tisco -10.740 6.5736 -15.453 7.8857 0.8336 Tatapow -4.3230 4.1800 -7.1000 5.3453 0.7820 Tatatea -35.607 11.970 -35.591 11.967 1.0002 Thermax 15.067* -7.7864 35.691* -12.632 0.6164 Tvssuzuk -1.3650 2.3435 6.1205* -4.9627 -0.4722 Reliance -6.6987 5.7163 4.5519* -4.2670 -1.3397 Sbin -18.269 8.5485 -24.851 9.9702 0.8574 Notes: (1) the bid-ask spread in NSE/ the bid-ask spread in BSE; (2) The variable used are s j = 2 − Cov j , the estimated bid-ask spread, where Cov j is the serial-covariance of returns on stock j during observation period.; the sign of the covariance was preserved after taking the square root. (Roll, 1984); and * denotes entries with positive covariance of which there are 7 such items in the NSE and on BSE. 43
  • Table 5 Percentage Covariance and Bid-Ask Spreads using weekly data NSE BSE Stock Covariance b-a spread Covariance b-a spread NSE/BSE(1) Andraval -0.00014 2.391 -0.00023 3.033 0.78833 Arvindmi -0.00043 4.138 -0.00089 5.963 0.69388 Abb 0.00041* -2.615 0.00017* -4.045 0.64660 Ashokley 0.00026* -3.237 0.00035* -3.731 0.86760 Asianpa -0.00019 2.757 -0.00022 2.933 0.93999 Acc 0.00000* -0.167 -0.00040 3.975 0.04206 Bhel 0.00060* -4.899 0.00070* -5.307 0.92312 Bses -0.00083 5.755 -0.00079 5.632 1.02184 Castrol -0.00059 4.866 -0.00049 4.432 1.09792 Cochinre -0.00007 1.683 -0.00016 2.553 0.65922 Colgate -0.00033 3.617 -0.00038 3.883 0.93150 Eihotel -0.00000 0.238 -0.00023 3.046 0.07797 Grasim 0.00037* -3.857 0.00045* -4.238 0.91010 Gujambce -0.00028 3.353 -0.00025 3.188 1.05192 Hdfc 0.00011* -2.117 0.00004* -1.318 1.60622 Hindleve -0.00066 5.146 -0.00070 5.299 0.97113 Hindpetr -0.00075 5.488 -0.00091 6.023 0.91117 Icici -0.00006 1.561 -0.00006 1.521 1.02630 Ifci -0.00023 3.003 -0.00029 3.429 0.87577 Indohotel -0.00041 4.035 -0.00032 3.589 1.12427 Indrayon -0.00073 5.430 -0.00108 6.582 0.82498 Indogulf -0.00030 3.458 -0.00064 5.502 0.62850 Itc -0.00066 5.134 -0.00067 5.188 0.98959 L&T 0.00006* -1.589 0.00019* 2.750 0.57782 Mtnl -0.00004 1.223 -0.00004 1.266 0.96604 MM -0.00006 1.521 -0.00004 1.316 1.15578 Mrpl -0.00047 4.336 -0.00095 6.161 0.70378 Nestle -0.00043 4.142 -0.00030 3.476 1.19160 Orientba -0.00032 3.567 -0.00038 3.919 0.91018 Ranbaxy -0.00030 3.481 -0.00032 3.555 0.97918 Tatachem -0.00049 4.436 -0.00037 3.847 1.15311 Telco 0.00021* -2.905 0.00028* 3.365 -0.8633 Tisco -0.00005 1.445 -0.00012 2.154 0.67085 Tatapow -0.00054 4.626 -0.00064 5.044 0.91713 Tatatea -0.00050 4.477 -0.00043 4.152 1.07828 Thermax 0.00022* -2.953 0.00084* 5.786 0.51037 Tvssuzuk 0.00013* -2.263 0.00018* 2.661 0.85043 Reliance -0.00024 3.079 -0.00034 3.693 0.83374 Sbin -0.00012 2.209 -0.00024 3.118 0.70847 Notes: (1) The bid-ask spread in NSE/the bid-ask spread in BSE; (2) the bid-ask spreads are presented as a percentage of the stock prices; (3) The variable used is s j = 2 − Cov j , the estimated bid-asks spread, where Cov j is the serial-covariance of returns on stock j during observation period.; the sign of the covariance was preserved after taking the square root (Roll, 1984); * items with positive covariance for which there are 10 such items in NSE and 9 on BSE 44
  • Table 6 A Comparison of the Bombay Stock Exchange and the National Stock Exchange Factors BSE NSE Geographic Reach Mainly limited to Bombay Across the Whole of India City Ownership Structure Brokers’ Association Organized as a company Use of Technology Laggard and follower Pioneer in computerized trading Internal System Control Insider Trading and market Stickler for market integrity manipulation are rampant Transparency Relatively secretive Follows an open policy Regulation Resists regulatory change Self-regulated; better risk management systems Investor Protection Low standards High standards 45
  • Table 7 Descriptive Statistics of Independent Variables in Multivariate Regressions BSE NSE Number of sample stocks 40 40 Average Price ( AP) (1) Minimum ( 2 ) 20.09 23.46 Maximum (3) 4357.03 3705.17 Mean 546.46 467.11 Average Size per Trade (AST) ( 4 ) Minimum 29 22 Maximum 903 545 Mean 363 259 Wilcoxon test (5 ) -4.396* (0.000) t-test -4.215* (0.000) Average Number of Daily Trades ATD ( 6 ) Minimum 12 46 Maximum 7802 19451 Mean 1196 2299 Wilcoxon test ( 7 ) 4.241*(0.000) t-test 2.143*(0.038) Notes: (1) AP is the average price over the sample period. ( 2) The stock “HDFC Bank” has the highest average price over the sample period on both the NSE and BSE. ( 3) The stock “MRPL” has the lowest average price over the sample period on both NSE and BSE. ( 4) AST is the average size per trade over the sample period. (5) From the Wilcoxon ranks test for AST, we obtain 7 positive ranks, 1 tie and 32 negative ranks; t-statistics are based on positive ranks. (6) ATD is the average number of trades per day computed as the total number of trades/total number of days over the sample period. (7) From the Wilcoxon signed ranks test for ATD, we obtain 36 positive ranks and 4 negative ranks; t-statistics value are based on positive ranks. * significance at the 5% level (p-values in parenthesis) 46
  • Table 8 Regressions with Transaction Cost as Dependent Variable Equation Constant AP AST ATD MV Dummy R2 1 0.800 6.548E-5 -4.573E-4 -1.373E-5 -1.376E-7 -0.404 0.402 (1.650) (-1.947) (-1.149) (-0.218) (-5.493*) 2 0.773 6.995E-5 -4.303E-4 -1.190E-5 -0.397 0.403 (1.810) (-1.917) (-1.220) (-5.592*) 3 0.871 -6.055E-4 -8.965E-6 -0.424 0.377 (-2.946*) (-0.919) (-6.021*) 4 0.608 1.019E-4 -1.934E-5 -0.341 0.373 (2.873*) (-2.125*) (-5.178*) 5 0.585 1.024E-4 -0.363 0.336 (2.823*) (-5.443*) 6 0.886 -6.748E-4 -0.441 0.370 (-3.533*) (-6.501*) 7 0.664 -1.951E-5 -0.349 0.305 (-2.049*) (-5.069*) 8 0.686 -6.742E-7 -0.374 0.278 (-1.085) (-5.188*) Notes: The dummy variable is assigned a value of 1 for all NSE stocks and 0 for BSE stocks; t-statistics are given in parentheses;* denotes significance at the 5 percent level. 47
  • 48
  • Volume (rupee, millions) A 0 5000 10000 15000 20000 25000 pr -9 6 M ay -9 6 Ju n- 96 Ju l-9 6 A ug -9 6 Se p- 96 O ct -9 6 N ov -9 6 De c- 96 Ja n- 97 Fe b- NSE 97 M ar -9 7 A pr -9 7 BSE M ay Figure 1 -9 7 Ju n- 97 Ju l-9 7 A Trading Volume on BSE and NSE ug -9 7 Se p- 97 O ct -9 7 N ov -9 7 D ec -9 7 Ja n- 98 49