Price Impact of Block Trades and Price Behavior surrounding the Bombay stock exchange
Security Exchange Board of India, (SEBI), the regulatory authority for capital market operations in India has classified large trade sizes into two categories - 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 50 Millions executed through a single transaction on block deal window. The block deal window operates for only the first 35 minutes of a trading day and the price of trade is subject to an upside and downside cap of ± 1 % of the ruling market price/previous day closing price. In a block trade, investors are not given a prospectus: the shares are marketed to institu tional investors through telephone calls. SEBI has also made it mandatory for the stock brokers to disclose on a daily basis the block deals made through Data Upload Software (DUS).
A block trade can be structured in a number of ways:
• Bought deal. In a bought deal, the investment bank acting as manager of the block trade buys the shares from the seller befor e it starts its marketing efforts. The manager will generally resell the shares as soon as possible after they are acquired from the seller. To the extent that it is able to resell the shares at a higher price, the manager will keep the difference.
• Non-risk deal. This is often known as an accelerated equity offering (AEO). Here, the
manager will build a book of demand for the seller before agreeing on a price (based on that demand). Frequently, the manager will receive a commission from the seller. Sometimes the manager will earn an agreed spread.
• Back-stopped deal. This falls somewhere between a bought deal and an AEO. In a back-
stopped deal, the manager does not take the shares onto its own books before marketing
(as with a bought deal), but it do es guarantee the selling shareholder a minimum price.
Bulk tr ades refer to situations where the total quantity traded in a day (in normal trade window) by a particular client is greater than 0.5% of number of equity shares of company listed on the exchange. Bulk deals happen all through the trading day. Bulk deals carried out for the day should be revealed by a broker on the same day to the stock exchange using the DUS.
Trading Mec hanism for Bloc k Trades
Like other markets, there are two economically dis tinct trading mechanisms for large-block transactions in India.
1) Firstly, some large pre-negotiated trades (or in cases where brokers facilitate the trade by
locating counter-parties to the trade) are transacted in a separate „ block deal window’
(like upstairs market in NYS E), which opens for only 35 minutes at the market opening.
2) Secondly, a large quantity order can be sent directly to the normal trade window through limit or market orders (like downstairs market in NYS E), which constitutes the continuous intraday markets and batch closing period. The focus is only on the large size trades executed in the normal trade window.
There are various reasons for excluding the trades executed through the block deal window.
1) Firstly, these are pre-negotiated trades and the objective of the trades may be different from the information-motivated trades , like taxation benefits, family arrangements, within group transfers, etc. In India, the benefits of a reduced tax rate in capital gains is available only for sha res transacted through a recognized stock exchange and hence parties execute such large pre- negotiated trades through the stock exchange.
2) Secondly, it is difficult to determine the nature (buy/sale) of the block orders since there
are two parties taking opposite stand in the upstairs transaction, resulting in simultaneous purchase and sale of stocks. Unavailability of exact time stamps for these trades makes the comparison with most recent market prices impossible.
3) Lastly, the price of block transaction in upstairs market is subject to floor and cap price of
± 1 % of the ruling market price/ previous day closing price, limiting the benefits of private information.
Although large orders through the normal trade window are subject to front-running by the brokers and come with a risk that the buyer may not get the entire bid quantity, but still some investors may prefer to trade large quantity in the normal market, rather than the block deal window in order to keep their identity secret.
This behavior may also be because trading at the normal window is free from the pricing (± 1%)
and timing (first 35 minutes of trading) restriction that is applicable to the block deal window.
Guidelines for Execution of Bulk and Block De als on the Stock Exc hang es
In India, reporting of bulk and block trades became mandatory since 14th January, 2004 and 2nd
September, 2005 respectively.
1) SEBI had issued a circular (reference no. SEBI/MRD/S E/C ir-7/2004) on January 14,
2004 on disclosures of details of “bulk ” deals with a view to impart greater transparency to the market on such transactions executed on the stock exchanges. In terms of paragraph 1.1 of that circular, a “bulk” deal constituted of “all transactions in a scrip (on an exchange) where total quantity of shares bought/sold is more than 0.5% of the number of equity shares of the company listed on the exchange”. Thus the quantitative limit of 0.5% could be reached through one or more transactions executed during the day in the normal market segment.
2) There is however a felt need of the market to execute large trades through a single transaction easily without putting either the buyer or the seller in a disadvantageous position. In order to facilitate execution of such large trades, the stock exchanges a re being permitted to provide a separate trading window.
3) A “block” deal will be subject to the following conditions:
Ø The said trading window may be kept open for a limited period of 35 minutes from the beginning of trading hours i.e. the trading window shall remain open from 8.55 am to
Ø The orders may be placed in this window at a price not exceeding +1% from the ruling market price/previous day closing price, as applicable.
Ø An order may be placed for a minimum quantity of 500,000 shares or mini mum value of
Ø Every trade executed in this window must result in delivery and shall not be squared off or reversed.
Ø The stock exchanges shall disseminate the information on block deals such as the name of the scrip, name of the client, quantity of shares bought/sold, traded price, etc to the general public on the same day, after the market hours. Disclosure is to be made within one hour from the close of the trading hours (i.e. 5.00 pm).
Ø There is no change in regard to the disclosure of trade details of ”bulk deals” as specified
in the earlier S EBI circular reference no. SEBI/MRD/SE/C ir -7/2004 dated January 14,
2004, and such disclosures shall be continued to be made by the stock exchanges to the general public on the same day after the market hours.
Ø The stock exchanges shall ensure that all appropriate trading and settlement practices as well as surveillance and risk containment measures, etc., as presently applicable to the normal trading segment are made applicable and implemented in respect of the proposed special window also.
4) The stock exchanges are advised to :
Ø Make necessary amendments to the relevant bye- laws, rules and regulations for the implementation of the above decision immediately.
Ø Bring the provisions of this circular to the notice of the member brokers/clearing members of the Exchange and also to disseminate the same on the website.
Ø Communicate to SEBI, the status of the implementation of the provisions of this circular in the Monthly Development Report for the month of September 2005.
Price Effects of Block Trade
The permanent price effect is explained b y substitution effect (Scholes,1972) and information effect (Chan & Lakonishok, 1993). Due to lack of close substitutes, an excess demand (supply) of a security leads to excess demand (supply) curve that is not perfectly elastic and hence leads to a new equilibrium price. Information effect attributes the permanent price effects to the release of new information, which the informed trader attempts to cash in before it becomes public. Arrival of block trades in the market signals the presence of private information and causes the investors or traders to revise their price expectations depending on the nature of block trades.
The temporary price effect is explained by liquidity costs and price pressure theories. Liquidity cost theories argue that a temporary price impact around a block trade reflects compensation for the liquidity pro vided by the counterparties, described as seller of liquidity (Holthausen, Leftwich & Mayers, 1987). Price pressure hypothesis suggests that the purchase (sale) of a large block is associated with a short –run increase in demand (supply) for the security resulting in premium (discount) (S hleifer, 1986). It is empirically observed that permanent impact is higher for block purchases than in case of block sales. F urther, 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 & Frino, 1996; Keim & Madhavan, 1995, 1996 and 1997).
Hence, the permanent component is the amount by which traders revise their estimates of the
value based on the trade, and the temporary component reflects the transitory discount needed to accommodate the block.
Price effect of block transactions has been estimated using three measures (Madhavan, 2000).
1) The total price effect is usually defined as the difference between the equilibrium price before the block trade and the block trade price. It is calculated from open to the block trade price.
2) The temporary effect is defined as the difference between block trade price and
equilibrium price post the block trade.
3) The difference between the total price impact and the temporary price impact (i.e. difference between equilibrium price before block trade and equilibrium price after block trade) is called permanent price effect.
Figure 1: Total, Temporary and Permanent Effect of Block Trade
REVIEW OF LITERATURE:
Several studies have looked at the impact of block trades on stock prices. A number
of these studies, including Holthausen, Leftwich and Mayers (1987, 1990), Choe, cInish
and Wood (1992) and Chan and Lakonishok (1993) in the USA, and Aitken and Frino (1996) in Australia, have found that an unusual phenomenon is associated with these block trades. Although both block purchases and sales are associated with positive and negative permanent price effects respectively, the returns calculated from the block trade to some post-block period indicate positive returns for both purchases and sales.
In the year 1996 Gemmill (1996) established that an asymmetry exists in the price behavior surrounding buyer - and seller-initia ted trades on the LSE. However, the biases introduced through the existence of the bid -ask spread were ignored in his study. Later in the Year 2006 Mr. Andros Gregoriou in his study The asymmetry of the price impact of block trades and the bid-ask spread Evidence from the London Stock Exchange accounted for the biases in the bid-ask spread using a sample of 1.6 million block purchases and 1.2 million block sales in the LSE over the time period 1998 -2005.
In 2006 a study titled “An event time study of the price reaction to block trades on The Australian stock exchange” By Alex Frino, Elvis Jarnecic & Andrew Lepone analyzes block trades on the Australian Stock Exchange using an event study approach. A major finding in this study is an immediate reversal for the trade subsequent to the block transaction, for both block purchases and block sales.
In April 2010 a recent study by Mr. Sobhesh Kumar Agarwalla & Ajay Pandey Price Impact of Block Trades and Price Behavior Surrounding Block Trades in Indian Capital Market analyzed the permanent (information effect) and temporary (liquidity effect) impact of block trades transacted in the National Stock Exchange of India. They c oncluded that the permanent price impact is more for block purchases than for block sales indicating that block purchases are more informative than block sales, which may be motivated by liquidity need. Unlike in other markets, we observe that the temporar y impact is greater than the permanent impact in case of block purchase.
We in our paper mainly concentrates to study whether the impact of block trades on the price of the stocks is significant or not using a different statistical tool known as paired T-Test.
MAJOR HISTORICAL EVENTS OF BLOCK DEAL:
Sudha Murthy’s Bloc k Deal:
Sudha Murthy, one of the three Trustees’ of Infosys Foundation sold her 2 million shares in Infosys on November5th, 2009. Sudha Murthy sold her shares at Rs. 2151 .80 on the BSE. She transacted at 3.2% discount to the day’s closing price. The Infosys stock responded to the deal. The shares fell 0.74% on the day of the deal struck. On that day, even stock market was in the Red state at the time of closing price. The deal did not i mpact much on the Infosys stock because one of the promoters of Infosys S.Gopala Krishnan bought around
400000 shares in a separate transaction worth of Rs.86.6 crore. The market behaved
indifferently in this case when Sudha Murthy unlocked her value to raise the money to nurture the start-ups. She raised around 173.4 crore for a venture capital firm.
Relianc e Block deal:
Reliance industries Ltd. raised $763 million through a block sale of 33 million shares on January 11th 2010. The deal was a bought deal, UBS (an investment banker) acted as an intermediary to strike the deal. After the block deal is done, the stock of Reliance declined
4.8%. Here in this case the market responded negatively to the block deal.
From the above two historical analysis, it ca n be said that Block deals impact on the stocks of the companies, even though it is very dismal in the case of Infosys.
The data has been collected from BSE india.The data set includes all the block deals happened during the pe riod of Jan 2008 to June 2010. For the purpose of the analysis we have collected the data of the scripts containing open, close, and the clients details that either sold or bought the securities.
A sample data has been shown in the below table:
Code Company Client Name
Type * Quantity
Price Open Clo se
15/01/10 532764 Geecee Vent ARONI COMMERCIALS LIMITED B 921700 96 96.4 96.9
HOL D GOPALA PILLAI VIJAY KUMAR B 2170000 61.75 63 68.1
28/01/10 500139 Fedders Lloyd LLOYD SALES PRIVATE L IMITED B 665087 81.1 79.9 78.8
03/02/10 532631 Fame India INOX L EISURE L IMITED B 15057751 44 44.4 46.1
05/02/10 532631 Fame India INOX L EISURE L IMITED B 1126545 50.75 47.9 50.8
15/02/10 523207 Ca mlin ANAGHA INVESTMENT PVT LTD. B 6000000 23 23.9 25.4
BIMALBHAI DASHRATHBHA I
15/02/10 530117 HK Finechem
PARIKH B 548692 25.1 25 26.5
16/02/10 500117 DCW CRESTA FUND L IMITED B 2550000 20.6 20.55 21.2
16/02/10 533008 OCL Iron
GARIMA BUILDPROP PRIVATE
LIMITED B 48928854 21 22 23.1
POWER ALBULA INVESTMENT FUND L TD B 4307082 108.25 108.55 108.25
17/02/10 504000 Elpro Intl CRESTA FUND L IMITED B 410000 552.25 570 600.9
HOL D SALYA PRIVATE LTD. B 10016550 61.5 58 60.2
PARVATI MINERALS PRIVATE
22/02/10 531681 Amradeep Inds
LTD B 2702750 16.6 16.45 15.65
Exhibit-1 Shows the complete set of data which we have considered for the purpose of analysis.
To find out the impact of block deals on Indian stock markets, initially we have to see whether the difference in prices before and after the deal are significantly different or not.
This can be found by conducting a paired T -Test on the sample of Data collected. Sample size: A random sample of 100 Block deals
Test: Paired T-Test
Software package: SPSS 13
Sampling Technique: Simple Random sampling
H0: There is No Significant Impact of Block deal on the share price
Ha: There is Significant Impact of Block deal on the share price
A random sample of 100 block deals has been selected and the prices of the scripts before and after the deal are taken and by using SPSS a paired T-test has been conducted. The output of the Test is as follows.
1) Descriptive statistics.
Paired Samples Statistics
This table gives the descriptive statistics of the open & closing prices of scripts on the day when block deal had taken place. In this case, N represents the number of scripts considered for the analysis.i.e , sample size is 100.and the Mean prices of the scripts before and after the deal are 768.709 & 772.616 respectively, with a standard deviation of 751.0097 & 753.58455 respectively. The last column gives the standard error of the mean for each of the two v ariables.
Pair1 Open &Close
Thisagainshowsthatthereare100pairsofobservations(N).Thecorrelation betweenthetwo variablesisgiven in thethird column. In thisexampler=1.Thelastcolumngivethepvaluefor thecorrelation coefficient.Asalways,ifthepvalueislessthan orequaltothealphalevel,then you can rejectthenullhypothesisthatthepopulation correlation coefficient(ρ)isequalto0.In thiscase,p =.000, sowerejectthenullhypothesis.Thatis,wecanconcludethatthepopulation correlation (ρ)isdifferentfrom0.
e Mean i
Pair1 Open- Close
Thecolumnlabeled"Mean"isthedifferenceofthetwomeans(772.616–768.709=-3.9072) Thenextcolumnisthestandarddeviation ofthedifferencebetween thetwovariables(18.45in thisexample.)
withthettest.Inthisexample,thereare99degreesoffreedom.Thecolumnlabeled"Sig.(2- tailed)"givesthetwo-tailedpvalueassociatedwiththetest.Inthiscase,thepvalueis.037.If thishad been aone-tailed test, wewouldneed tolookup thecriticalvalueoftin atable.
Weconsidered significancelevelfortesting hypothesisasα= 5 %.
AsperthestatisticsIfp≤α,thenwewillrejectH0 &AcceptsAlternatehypothesis.Fromour TestwegotP=0.037whichisLessthanα=5%.AndhenceweRejecttheNullHypothesisand cansafely concludethatthereisSignificantImpactofBlockdealontheshareprice.
So, from our study we have statistically proved that there is an Impact of Block Deals on Indian Stock markets, but it’s also very important to find out what kind of Impact this Block Deals have on The Indian Stock Markets.
This exclusive study has been conducted by Dr. Sobhesh Kumar Agarwalla & Ajay Pandey in Their Research Paper “Price Impact of Block Trades and Price Behavior Surrounding Block Trades in Indian Capital Market”
AS per this study Block (large sized) trades are usually associated with presence of private information and are associated with price movements resulting from inventory costs and asymmetric information. Arrival of block trades in the market signals the pr esence of private information and this causes the investors to revise their price expectations depending on the nature of block trades.
It has been empirically observed that information about block trades has mixed signaling effect in terms of permanent and temporary price impact. Still the information on block trades is used extensively by professional traders to take informed investment decisions. While analyzing the price effect of block trades, past studies have not differentiated between days with a single block trade and days with multiple numbers of block trades. Arrival of multiple block trades in a trading day is more likely to increase the confidence on the information arrival, and therefore one would expect that the permanent price impact would be higher on those days, which have multiple block trades than during days with just one block trade.
The permanent price impact (information effect) is more for block purchases than for block sales in the Indian market, implying that block pur chases are more informative than block sales, which may be motivated by liquidity need. Unlike the findings from other markets, we observe that the temporary price impact is greater than the permanent impact in case of block purchases. This may be because of the higher impact costs or more of noise trading in the market
Market does acknowledge the relative information content in different types of block trades and reacts accordingly. This is an important insight especially in case of an order driven market like India. In the case of a quote driven market, the specialist or the dealer takes the opposite position for a trade and can observe block trades. Arrival of multiple block trades increases market confidence regarding the information, and the permanent price impact is found to be higher for days with multiple trades than days with single trades
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