RESEARCH PROPOSAL PRICE BEHAVIOR AROUND BLOCK TRADES ON THE NATIONAL STOCK EXCHANGE, INDIA BACKGROUND Although it has been empirically observed that information about block trades has mixed signaling effect in terms of permanent and temporary price impacts, yet it has been used extensively by professional traders to make informed investment decisions. Block trades can be classified as block purchases and block sales. A number of studies have documented a price continuation (further increase) following block purchases, and a price reversal (increase in price) following block sales, creating an asymmetry in reactions [Holthausen, Leftwich and Mayers (1987, 1990), Chan and Lakonishok (1993, 1995)]. Further, studies have found that the magnitude of the permanent price impact of block purchases is greater than the price impact of block sales, [Gemmill (1996), Aitken and Frino (1996), Keim and Madhavan (1995, 1996, 1997)] In India, reporting of bulk and block trades became mandatory w.e.f. 14 January 2004 and 2 September 2005. The literature studying stock price behavior surrounding block trades in India is very scanty. In fact, no study has been undertaken so far in Indian context. The proposed study aims to analyze the price behavior surrounding block transactions at the National Stock Exchange of India. The proposed study aims at capturing the permanent and temporary price impact of block trades KEY DEFINITIONS Block trades are large volume trades and various studies have been done to study the effect of block trades on subsequent prices. However, no standard definition has been used for block trades in past i
studies. For example, Frino, et al., 2005 considered the largest 1% of on-market transactions for each stock in each calendar year as block trade. Gemmill, 1996 considered the 20 largest customer purchases and 20 largest customer sales in each month as block trade. Kraus and Stoll, 1972 used block trade as defined by the NYSE for the purpose of reporting (10000 shares and over) but with minimum cut-off value. For India, SEBI has classified large trade size into two categories viz. block trade and bulk trade. Block trades are defined as a single trade with a minimum quantity of 500,000 shares or of a value of 5 crores executed through a single transaction on block deal window. And bulk trades refers to situations where the total quantity traded in a day by a particular client is greater than 0.5% of number of equity shares of company listed on the exchange. As per the SEBI guidelines, wherever the cumulative quantity traded under a single client code exceeds the above limits for bulk trades, disclosure is to be made within one hour from the closure of the trading hours (i.e. 5.00 pm). For block deals, member broker has to make a disclosure on daily basis through DUS (Data Upload Software), with respect to all deals that have been executed by them on behalf of their 'client; or 'own' account in block deal window (SEBI circular No. MRD/Dop/SE/Cir-19/05 dated September 2, 2005). Because of this, the information about the block trades generally gets disseminated during the market hours. Further, various brokers firm in India have designed software, which are able to capture information about such trade on a real time basis. Total price impact is defined as the difference between the equilibrium price before the block trade and the block trade price and temporary price impact is defined as the difference between block trade price and equilibrium price post the block trade. The difference between the total price impact and the temporary price impact (i.e. ii
difference between equilibrium price before block trade and equilibrium price after block trade) is called as permanent price impact. RESEARCH OBJECTIVE We are concerned with studying the specific price behavior surrounding block transactions, and in particular: Estimating the permanent and temporary effect on stock prices due to block trades Estimating the change in volatility subsequent to the block trade The findings of these studies can be incorporated in trading strategies of market practitioners to gain from the fluctuation in the asset prices post block trade. Further, a significant impact of block trade will mean that it may affect the stable intraday patterns (that is modeled in part I of this proposal). We can also model this factor (arrival of block trade information) as an indicator variable in the regression equation mentioned in Part I above to verify if the impact is significant. LITERATURE SURVEY Previous researchers have observed that: (i) the magnitude of the permanent price impact of block purchases is greater than the price impact of block sales [Gemmill (1996), Aitken and Frino (1996), Keim and Madhavan (1995, 1996, 1997)], and (ii) there appears to be a price continuation following block purchases, and a price reversal following block sales, creating an asymmetry in reactions Holthausen, Leftwich and Mayers (1987, 1990), Chan and Lakonishok (1993, 1995). The asymmetry in the magnitude of the permanent impact of block trades suggests that purchases are more informative than sales, while the directional asymmetry in price behavior following block trades suggests that block sellers pay a liquidity premium, while block buyers do not. The literature goes on to iii
describe this asymmetry as both intriguing (Holthausen et al., 1987, p. 90; Chan and Lakonishok, 1993, p. 175) and a key puzzle (Chan and Lakonishok, 1993, p. 197). Various studies have found that the impact of block trade on return volatility is negligible. Gemmill, 1996 found no significant change in volatility immediately after the block trade and Holthausen, et al. (1990) found an increase in volatility only upto 3 trades post block trade. Gemmill (1996) also observed there is a significant relationship between permanent price impact and block size both in case of block purchase and sale; but the relationship between total price impact and block size is significant only in case of block purchases. To examine the price behavior surrounding block transactions, two approaches are used. The first is a transaction time event study approach similar to Holthausen et al (1990). The second approach for estimating the impact of block trades is by comparing the abnormal returns from the open to the block trade, and from the block trade to the close. This approach is consistent with Chan & Lakonishok (1993) and Aitken & Frino (1996). Frino, Jarnecic, Johnstone, Lepone (2005) confirmed, using the second approach, that there is a directional asymmetry in price behavior following block trades. PROPOSED METHODOLOGY We will try to trace the exact timings for reported bulk deals and consider the same as block trade. Since, the data that is available only for the trades executed (and not trade orders) it is very difficult to trace the timings of the bulk orders directly because the number of trades may be more than one for a particular bulk order. Therefore, although block trades can be identified directly based on the trade size, it will be very difficult to trace bulk orders. In order to separate the effect of block trades that occur at the closing or opening time of the day, we will find the effect separately for block trades occurring during the opening hours and closing hours and block trades occurring at other time during the day (11.00 am to 2.30 pm). iv
DATA AND PRELIMINARY ANALYSIS High frequency data are available from NSE representatives since 1999. However, since the reporting of bulk trades is done only since 2004, we will use high frequency trade data for the period January 2004 to 2007. The study will be done for around 500 companies that are traded since 1999. This will, in a way exclude the price behavior-surrounding block trades in case of newly listed securities. REFERENCES Aitken, M.J., Frino, A., (1996) Asymmetry in stock returns following block trades on the Australian Stock Exchange: A note Abacus 32, 54 61 Frino A., Jarnecic E., Johnstone D. and Lepone A. (2005) Bid-ask bounce and the measurement of price behavior around block trades on the Australian Stock Exchange, Pacific Basin Finance Journal, vol.13:3, pp. 247-262. Gemmill, G., (1996), Transparency and liquidity: A study of block trades in the London Stock Exchange under different publication rules, Journal of Finance 51, 1765 1790 Holthausen, R., Leftwich, R., Mayers, D., (1987). The effect of large block transactions on security prices: A crosssectional analysis, Journal of Financial Economics 19, 237 268. Holthausen, R., Leftwich, R., Mayers, D., (1990) Large block transactions, the speed of response, and temporary and permanent stock-price effects, Journal of Financial Economics 26, 71 95. Keim, D.B., Madhavan, A., (1996) The upstairs market for large-block transactions: Analysis and measurement of price effects, Review of Financial Studies 9, 1 36. Kraus, Alan, and Hans R. Stoll, (1972), Price Impacts of Block Trading on the New York Stock Exchange Journal of Finance 27, 569-588. Chan, L., Lakonishok, J., (1993) Institutional trades and intraday stock price behavior, Journal of Financial Economics 33, 173 199. Chan, L., Lakonishok, J., (1995) The behavior of stock prices around institutional trades, Journal of Finance 50, 1147 1174. SEBI circular No. MRD/Dop/SE/Cir-19/05 dated September 2, 2005. Downloaded from http://www.sebi.gov.in/index.jsp?contentdisp=section&sec_id=1 on 1st November, 2007 v