VIKALPA • VOLUME 39 • NO 3 • JULY - SEPTEMBER 2014 35

R E S E A R C H

includes research articles thatfocus on the analysis and

resolution of managerial andacademic issues based on

analytical and empirical or caseresearch

ExecutiveSummary

Indication of Overreaction with orwithout Stock Specific PublicAnnouncements in Indian Stock market

Sitangshu Khatua and H K Pradhan

KEY WORDS

Overreaction

Abnormal Returns

Price Reversal

Specified Events

Unspecified Events

Stock market overreacts to both anticipated and unanticipated stock-specific news.

But even in the absence of any firm-specific news, evidences of extreme price changes

have been observed in the stock market. This particular phenomenon creates the need

of further study to examine the existence of overreaction even if there is no specific

public news in the market.

The present study tries to find out how stocks overreact in the case of unspecified

events in comparison to specified news in the Indian stock market. Specified events

can be monitored up to a certain extent because of their known and repetitive nature.

The magnitude of uniqueness of the unspecified events increases uncertainty. Infor-

mation diffusion is more asymmetric, which leads to more stock market overreaction.

The study also examines whether there is a relationship between the magnitude of

price reversals and the magnitude of gain or loss in the stock market return.

Significant cumulative abnormal returns are found, indicating the existence of an

overreaction effect. It is also found that the magnitude of price reversal is inversely

proportional to the stock return during the event period. The overreaction effect con-

tinues up to about two days after the event date, for the present sample.

Thus, the study provides an understanding of overreaction effects, which would en-

able investors to prepare trading strategies for higher returns. It can be said that the

Indian stocks show strong overreaction and reversal effect. It shows that a trading

strategy can be used to make contrarian profit from the overreaction and reversal

exhibited by the Indian stocks. An investor could buy the largest percentage losers

stocks or sell largest percentage gainers stocks, then sell the former one and buy the

latter one after two trading days. In this way, the optimum utilization of overreaction

effects may increase investors’ return. Overreaction is more prominent in the case of

unspecified events rather than specified events. Stock prices overreact to private news

but underreact to subsequent public announcements. Overreaction increases due to

information asymmetry and leakage. In the case of any macro/global issues, overreac-

tion is also more because of market integration and globalization.

36

Stock market overreacts both on anticipated and un-

anticipated stock-specific news. But even in the ab-

sence of any firm-specific news, evidences of ex-

treme price changes have been observed in the stock mar-

ket. This particular phenomenon creates the need for

further study to examine the existence of overreaction even

if there is no specific public news in the market. The

present study tries to find out how stocks overreact in the

case of unspecified events in comparison to specified

news. Specified events can be monitored up to a certain

extent because of their known and repetitive nature. The

magnitude of uniqueness of the unspecified events in-

creases uncertainty. Information diffusion is more asym-

metric, which leads to more stock market overreaction.

The study also examines whether there is a relationship

between the magnitude of price reversals and the magni-

tude of gain or loss in stock market return.

REVIEW OF LITERATURE

Market overreaction is a very familiar trend and an age-

old craze amongst traders. Pigou (1929) defined it as a

‘conducting rod along which an error of optimism or pes-

simism, once generated, propagates itself about the busi-

ness world.’ People have a tendency to overreact with or

without any definite reason. Cognitive psychology re-

search has shown the irrationality of individual decision-

making process (Kahneman et al., 1982). Ackley (1983)

also found that abnormality in price-movement may cre-

ate an accumulation of momentum in one direction which

can overshoot the long-run equilibrium price.

Various studies have examined whether stocks under-

react or over-react. The pioneer study of overreaction by

DeBondt and Thaler (1985) prepared two portfolios of

stocks – winners and losers. They observed that initial

abnormality in stock price was followed by a significant

price change in the opposite direction. In another study

(1987), they found that the reversal effect was more in the

month of January. Zarowin (1989) checked the overreac-

tion theory by including size and seasonal effects. He

found that losers did better than the winners only in Janu-

ary. In another study, Zarowin (1990) controlled size dif-

ferences in examining overreaction and found that losers

performed better than winners not due to overreaction,

but due to size difference between the winners and los-

ers.

Atkins and Dyl (1990) found significant abnormal returns

for the winner portfolio of NYSE stocks, one day after the

day of extreme price change. For losers, the two-day cu-

mulative average abnormal returns were positive and sta-

tistically significant. They found that market overreacted

to both favourable and unfavourable events. Peterson

(1995) differentiated between option and non-option firms

in the sample of NSM firms depending on the availability

of option-trading. He found that for option-firms, the three-

day Cumulative Abnormal Returns (CAR) following the

event day was significantly lower than that for non-op-

tion firms.

In their seminal study, Larson and Madura (2003) inves-

tigated the adjusted daily closing return of stocks of New

York Stock Exchange (NYSE) during 1988-1998. For their

sample of winners, they experienced overreaction with

respect to uninformed events. On the other hand, under-

reaction was found for the sample of losers with respect

to both informed and uninformed events. The work by

Larson (2004) also suggested that the stock price revers-

als were associated with extreme stock price decliners

(5% or less) for real estate investment trusts stocks.

Ma, Tang, and Hasan (2005) conducted their study with

stocks taken from the Wall Street Journal (WSJ) list. Con-

sidering the size of stocks as a controlled variable, they

showed that stock returns changed in opposite direction

within two-days after the event date. Giannetti et al. (2006),

by using daily and intra-day data, showed that extreme

positive or negative stock-price movements during night

sessions were followed by reversal. Guitierrez and Kelley

(2008) extended their study to momentum in weekly re-

turns – in a comparatively short horizon. They divided

events into implicit (informed) and explicit (uninformed)

events and found short-run reversal and long-run mo-

mentum.

This study tries to fill this gap by exploring the underly-

ing features of differences in the characteristics of overre-

action with or without any public news considering daily

returns. In the multiple regression analysis, an event speci-

fication (dummy) variable is added to capture the above

feature. This study differs from the previous research not

only in terms of investigating period, examining pre- and

post-global financial tsunami (2007) in the context of an

emerging market like India but also in the selection crite-

rion of stocks and sampling techniques.

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VIKALPA • VOLUME 39 • NO 3 • JULY - SEPTEMBER 2014 37

HYPOTHESES

To capture the several dimensions of overreactions in In-

dian stock market, the following hypotheses were tested.

The variations of overreactions may be driven by (1) the

relationship between the magnitude of extreme price

change and post-event price reversal; (2) pre-announce-

ment information seepage as determined by abnormality

in stock return during the pre-event days; (3) sector-based

types of stocks (only technology vs. non-technology stocks

were considered); and (4) types of events - specified and

unspecified, i.e. with or without public news.

Magnitude of Price Change and Price Reversal

Previous studies reveal that the degree of overreaction

depends on initial price change. Extreme upward or down-

ward movement of stock prices is followed by subsequent

price correction in the opposite direction (Akhigbe, Gosnell,

& Harikumar, 1998; Lam & Liu, 2008). To investigate the

relationship between the post-event price reversal and

the accumulated abnormal return during the event pe-

riod (Nam, Kim, & Arize, 2006; Mazouz, Joseph, & Joulmer,

2009), the following hypothesis was tested:

H1: There is an inverse relationship between the

event day CARs and post-event-day CARs due to

overreaction.

Pre-announcement Information Seepage

The behavioural analysts suggest that the analysts rec-

ommend buying or selling depending on the private in-

formation because of over-confidence or self-attribution

bias (Daniel, Hirshliefer, & Subramanyam, 1998). It oc-

curs when people attribute a winning result to their own

skills but blame an unsuccessful result to their bad luck.

A typical investor in this context overestimates the ex-

actitude of his private information rather than the public

information and changes his self-reliance asymmetrically

to confirming versus disconfirming a piece of news. Thus,

the introduction of confirming news tends to increase the

overconfidence of the investor, which may activate fur-

ther overreaction (Chui, Titman, & Wei, 2010). Such trig-

gering overreaction causes momentum in the security

prices and continues until the arrival of negative news.

At this time, the investors’ over-optimism is gone, and the

price falls drastically or correction starts giving rise to an

overreaction pattern. Thus, in the long run, with the ar-

rival of further public information, the momentum is even-

tually reversed towards fundamental values. This find-

ing may account for both short-run momentum and

long-run reversal. If information is leaked, prices may in-

corporate significant information prior to public an-

nouncement. The three-day pre-event period cumulative

abnormal return preceding the event date is used to cap-

ture this particular effect (Bhattacharya, Daouk, Jorgenson,

& Kehr, 2000; Giannetti et al., 2006; Wang & Xi, 2010).

H2: There is a significant relationship between

overreaction and information seepage.

Technology vs. Non-technology Stocks

Akhigbe, Larson, and Madura (2002) assessed that tech-

nology stocks are more liquid and highly monitored by

the market in comparison to the non-technology stock.

They found a greater degree of overreaction in extreme

positive changes in technology (winners) stock price than

in non-technology stock price of New York Stock Exchange

(NYSE). Also, more underreaction was experienced in

extreme negative changes in technology (losers) stock

prices than in non-technology stock prices. In the context

of Indian market, P/E ratio is more than 20 for the overall

market whereas P/E ratio is 30 for the technology sector

(Purfield, 2007). Thus, the investors have higher confi-

dence in technology stocks.

While examining overreaction in the present sample

across the various sectors, Indian technology stocks are

found to overreact more both in the case of gainers or

losers in comparison to non-technology stocks. The analy-

sis of technology stocks is different because their pricing

behaviour is not similar to other stocks. As the produc-

tion process of technology firms is still evolving and sub-

ject to modifications and possible improvement, the

investors treat technology stocks differently from other

stocks. Consequently, the future market share of technol-

ogy stocks is subject to more uncertainty than other firms.

Both pre- and post-event beta of technology gainers and

loser stocks are always more than non-technology stocks

(See Table 2, panels C and D). To investigate the differ-

ence between technology stocks and other stocks in the

context of overreaction, the following hypothesis is tested.

H3: There is a significant difference in CAR for

technology and non-technology stocks for similar

extreme stock price changes.

38

Type of Announcements - Specified andUnspecified News

Events can be classified as specified or unspecified. In

the case of specified events, investors are aware of stock-

specific public news or announcements which are pub-

lished in any public domain. But many times overreaction

occurs even without any stock-specific news. It may be

because of any macro/global events, unknown incident,

insider trading, information asymmetry or leakage. These

events are coined by the researchers as unspecified or

uninformed news (Zivney, Bertin, & Torabzadeh, 1996;

Danieletal,1998; Lo & Coggins, 2006). The degree of over-

reaction would depend on the underlying causes of ab-

normal price movement, which cannot be explained by

an announcement. Thus, it can be hypothesized that the

degree of overreaction is more when the reasons behind

the news are not specified publicly. In earlier chapters,

overreaction has been examined only in the case of any

public announcements like quarterly disclosure or stock

split announcements. On the other hand, abnormality in

price movement may happen even without any public

event.

H4: There is a significant difference in CAR for

specified and unspecified events or news.

Control Variables

While examining overreaction, earlier research has ob-

served the impact of size, volatility, etc. Overreaction can

be explored further in the new backgrounds after control-

ling the following variables:

Size Effect: Market participants monitor larger firms more

closely than smaller firms. Therefore, there are higher

chances of error in evaluation of smaller stocks in response

to new information in the market. For a sample of stocks

that experiences overreaction or underreaction, it is found

that smaller firms evidence larger reversals (Cox &

Peterson, 1994).

Volatility Effect: The pre-event volatility of the stock re-

turn is associated with the degree of post-event correc-

tion. Peterson (1995) included this control variable in his

cross-sectional analysis and found that firms with larger

degrees of return volatility experienced more pronounced

corrections, as they had a higher uncertainty.

Seasonality Effect: Overreaction is generally more promi-

nent in certain periods of the year. The seasonality effect

is considered to prevent the results from being confounded

by the time of the year in which the event occurred more

frequently (Zarowin, 1989; 1990). In the present sample,

for the month of April, CAR is found to be almost 23 and

27 percent in the case of gainer and loser stocks respec-

tively.

Estimation of AR and CAR

Under general conditions, OLS is a consistent estimation

procedure for the market model parameters (Mackinlay,

1997). For the ith firm in event time, the OLS estimators of

the market model parameters for an estimation window

of observations are:

... (1)

... (2)

... (3)

... (4)

... (5)

Rit and R

mt are the return in event period t for security i

and the market m respectively. L1 is the estimation win-

dow (T0+1

to T1 days) for firm i. From the market model,

one can measure and analyse the abnormal returns (ARit).

Let ARit, t = T

0, ……,T

2, be the sample of L

2 abnormal re-

turns for firm i in the event window (T0 to T

2 days).

The windows are represented in the following time line:

... (6)

The cumulative abnormal return (CAR) is defined as the

sum of abnormal returns for each day in the event win-

dow for a stock.

Estimation Event Post-EventWindow (L1) Window (L2) Window

T1 T0t T2 T3

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VIKALPA • VOLUME 39 • NO 3 • JULY - SEPTEMBER 2014 39

... (7)

Or, ... (8)

where, CAAR = Cumulative Average Abnormal Return

After finding out ARit for each day in an event, all Abnor-

mal Returns (ARit) are aggregated across time and Cu-

mulative Abnormal Returns (CAR) computed for each

event. A test statistic t is used to draw inferences about

the CAR,

... (9)

where σi is the standard error of the distribution and

n = no. of days in the event window.

DATA

Preliminaries: Sample and Returns

The daily adjusted closing prices and returns for NSE-

100 (National Stock Exchange) stocks and Nifty-100 dur-

ing October 2005 to November 2010 were taken from

Prowess CMIE (Centre for Monitoring Indian Economy)

database. These were then arranged in order of higher

return to lower return. The top deciles (> 90 percentile)

were taken as the gainers and bottom deciles (<10 per-

centile) as the losers. To reduce cross-sample correlation

one event per trading day was taken in both the samples

of gainers and losers (Larson & Madura, 2003). If more

than one event per trading day was available, the stock

showing extreme positive or negative returns was con-

sidered for examination. That means in any event date,

maximum two stocks were taken, one as a gainer and the

other as a loser.

Specified and Unspecified News

For each event, it was checked whether any correspond-

ing corporate announcement existed or not in the same

date for the corresponding stocks. If any event date coin-

cided with the date of any stock-specific announcement

like quarterly disclosure, dividend announcement, stock

split, etc., that event was considered as specified event.

Other event dates were termed as unspecified events.

Specified events were further classified as quarterly an-

nouncements, dividend announcements, other events, etc.

Then, from the entire data base, 5,103 and 3,624 event

dates were considered as sample for gainer and loser

stocks respectively (top and bottom deciles). Within the

initial sample, filtering method was used to build the fi-

nal sample for testing. A firm featuring as gainer or loser

more than once during a month was removed from the

sample, for preventing the contamination of estimation.

Firms having daily returns of less than 100 days estima-

tion period and 50 days post-event periods were also

eliminated from the sample. Thus the final sample size

was 1,284 for gainers and 1,161 for losers. Table 1 shows

the final sample with event-wise distribution and other

details.

Table 1: Event-wise Firm Distribution

Specified & Unspecified News Gainers % of Losers % ofTotal Total

(approx) (approx)

Specified Events: 390 30.4 334 28.73

Quarterly announcement 190 14.8 144 12.4

Dividend announcement 32 2.5 23 2

Other announcements 168 13.1 167 14.33

Unspecified Events: 894 69.6 827 71.27

Total 1,284 100 1,161 100

Average value of the firm 42,394.06 39,472.9(in `crore)

Summary Statistics

Based on the above-mentioned selection criteria, 724

(2,445) specified (unspecified) events qualified for the

sample, with 390 (894) gainers and 334 (827) losers. Tim-

ing, frequency, and summary statistics of the sample are

represented in Table 2. The average market cap of the

firms for the specified (unspecified) gainers and losers

are `42,605.44 (`42,301.72) crore and `41,814.9

(`38,528.79) crore, respectively. The average pre-event

betas for specified (unspecified) gainers and losers are

0.947211 (0.925199) and 0.905499 (0.975108) respectively.

As a comparison, unspecified gainers and losers are of

higher betas and volatility. If the pre-event beta and post-

event beta are compared, the event does not increase it

largely for the gainers stocks. On the other hand, the event

appears to increase the betas of almost all types of loser

stocks. However, none of these changes are statistically

significant.

40

Both Figure 1 and Table 3 show that CAR of losers changes

sign within 55 days, whereas CAR of gainers continues

the momentum. In the short run, reversal effects appear

both for gainers and losers (Figure 2). Plots of CAR given

in Figure 3 also reveal that reversal takes place mostly

during the event windows (1,2) just after the extreme price

change. Though overreaction persists for longer time,

maximum reversal happens during the 1st and 2nd day

after the event date. That is why, (1,2) event windows are

taken for calculating post-event CAR. Results are similar

for specified and unspecified events for both gainers and

losers (Figures 2 and 3). But overreaction is more promi-

nent in the case of unspecified events rather than speci-

fied events and more prominent in the case of losers. People

would be more apprehensive and mentally prepared, and

that is why, more indifferent in the case of specified events

than unanticipated, sudden unknown events. Unspeci-

fied events are more unpredictable because of their unique-

ness and non-repetitive nature.

Figure 1: Cumulative Abnormal Returns (CAR) Plot for allGainers and Losers Stocks

Table 2: Descriptive Statistics of the Sample

N Size Pre-Event βββββ Post-Event βββββ VAR

Panel A: Gainers

Specified Events: 390 42605.44 0.947211 0.943913 2.931985

Quarterly Announcement 190 39349.91 0.92875 0.96155 2.937688

Dividend Announcement 32 58561.65 1.015662 0.943703 2.979773

Other Announcements 168 43230.72 0.954994 0.923942 2.916325

Unspecified Events: 894 42301.72 0.925199 0.94447 2.864163

Total 1,284 42394.05 0.931891 0.944301 2.884782

Panel B: Losers

Specified Events: 334 41814.9 0.905499 0.915946 2.925462

Quarterly Announcement 144 40881.95 0.895982 0.873175 2.896167

Dividend Announcement 23 47189.57 0.850516 0.833201 2.444802

Other Announcements 167 41847.12 0.921689 0.964965 3.02028

Unspecified Events: 827 38528.79 0.975108 0.976044 3.137916

Total 1,161 39472.9 0.955109 0.958778 3.076877

Panel C: Gainers – Technology vs. Non-technology Stocks

Technology stocks: 386 48789.98 0.992345 0.994579 3.932456

Non-technology Stocks: 898 45928.89 0.905908 0.922689 2.434454

Total 1,284 42394.05 0.931891 0.944301 2.884782

Panel D: Losers – Technology vs. Non-technology Stocks

Technology stocks: 351 43576.78 0.999871 0.999989 3.998997

Non-technology Stocks: 810 37694.59 0.935712 0.940919 2.677291

Total 1,161 39472.9 0.955109 0.958778 3.076877

Note: Panel A and B consists of 390 specified and 894 unspecified events that are considered as Gainers, and 334 specified and 827 unspecified as Losers during the2005-2010 for the NSE-100 stocks respectively. Panel C and D shows the statistics of technology vs. non-technology stocks for Gainers and Losers respectively.N = no. of the events or stocks; Size = Average value of the market capitalization of the stocks on the event date in `crore; Pre-event Beta = Average betasof the stocks considering OLS during 100 days before the event; Post-event Beta = Average betas of the stocks considering OLS during 100 days after the event;VAR(Variance or volatility of stock return) = Pre-event (-3,0) period volatility.

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Table 3: Cumulative Abnormal Returns (CAR) for all Gainersand Losers Stocks

Average CAR Gainers CAR Losers CAR

CAR 1 day 3.64 -3.31

CAR 10 days 3.87 -2.49

CAR 20 days 4.64 -1.68

CAR 30 days 5.48 -1.18

CAR 40 days 6.32 -0.80

CAR 45 days 6.75 -0.55

CAR 50 days 6.97 -0.17

CAR 55 days 7.35 0.34

CAR 60 days 7.76 0.63

CAR 65 days 8.18 0.84

CAR 70 days 8.45 1.16

CAR 75 days 8.86 1.39

CAR 80 days 9.32 1.61

CAR 90 days 10.09 2.32

CAR 100 days 10.56 2.82

CAR 150 days 12.74 5.60

CAR 180 days 14.09 7.40

CAR 250 days 14.91 8.45

Note: CAR for the Gainers and Losers for various event windows is pre-sented. The estimation period is 100 days prior to the event day.

Figure 2: CAR Plot for both Gainers and Losers (specified andunspecified combined) in Various Time Intervals

Figures 3a & 3b: CAR Plot for Specified and Unspecified Events in Various Time Intervals

3a: For Gainers 3b: For Losers

42

Event windows are fixed after making a detail sensitivity

analysis for this study. Loser stocks take 50 days’ time to

achieve positive returns. Here, CAR (50 days) gives a typi-

cal pattern when CAR plot is drawn for 50 days for both

gainers and losers. Finally, this evidences over-reaction

in the Indian stock market while rejecting the null hy-

pothesis that the event does not have informational con-

tent. It is also checked whether the overreaction hypothesis

is applicable and contrarian profit is present or not in the

Indian stock market.

Abnormal Returns (AR) were calculated according to the

market model by using OLS method for the estimation

period, event windows, and post-event days for all the

good and bad news. For each good and bad news, market

model parameters (intercepts and slopes, i.e. α and β re-

spectively) over the pre-event estimation period, the esti-

mation window (-104 days to -4 days), event window (-3

to +3 days), and post-event days (+4 to 225 days) were

taken according to Larson and Madura (2003) and Ma et

al. (2005). CAR was aggregated for each event across the

time-length and then Average of CAR for both gainers

and losers were calculated. The standard t-test was used

to examine the significance of AR and CAR. In non-para-

metric tests, outliers are identified and non-normality is

checked using binomial Z-statistics. Statistical signifi-

cance of the abnormal returns on the event day and pre-

event and post-event days was computed to investigate

the overreaction effect. AR and CAR for stocks are shown

in Panel, A and B respectively of Table 4.

For selecting the event window, a detailed sensitivity

analysis was done by choosing the event windows. Out

of these, event windows (-1,0), (1,2), (1,3), (2,3), (4,10) and

(11,20) were taken for the final sample which gave a bet-

ter pattern of CAR plot and comparatively more signifi-

cant CAR in the case of gainers and losers (Mackinlay,

1997). Event window (a,b) signifies that abnormal returns

are to be aggregated from the ‘a’ day to ‘b’ day from the

day of event (0 day). Panel A of Table 4 reports the daily

abnormal returns for both gainers and losers for the days

from -3 to +3. Panel B presents the cumulative abnormal

returns for the same sample for various event windows, (-

1,0), (1,2), (1,3), (2,3), (4,10) and (11,20). The result shows

that gainers and losers sample records average abnormal

returns of + 3.96 percent and -3.95 percent on the event

date respectively and these are highly statistically sig-

nificant. Now, if overreaction effect exists, significant price

reversals or contrarian profit can be expected because of

highly significant and large abnormal returns.

In Tables 5 and 6, AR and CAR for the same event win-

dows for gainers and losers respectively are tabulated in

a subgroup differentiated by news categories. All stock-

Table 4: Abnormal Returns for the Gainers and Losers

Panel A: Gainers t-value Losers t-valueDay AR AR

-3 -0.047351934 -2.98394566 0.193078379 1.95452928

-2 -0.049477611 -3.96451233 0.443100575 3.588099082

-1 -0.052413212 -3.45632123 0.317023226 2.534095788

0 3.961231901 23.34567233 -3.95642227 -43.78007644

+1 -0.037265143 -2.34567891 -0.296376893 -1.96902747

+2 -0.21708935 -0.00987651 0.043232127 2.024481596

+3 -0.025519787 -008956785 0.132297479 1.331953824

Panel B: Gainers t-value LosersEvent Period CAR (%) CAR (%) t-value

(-1 , 0) 3.908818689 15.45678923 -3.639399043 -41.31049541

(1,2) -0.254354493 -5.34567822 -0.253144766 -6.89537653

(1,3) -0.27987428 -3.45672989 -0.120847287 -0.75067433

(2,3) -0.242609138 -1.69789912 0.175529606 2.392860561

(4,10) -0.640298395 -0.09876545 0.578541213 3.798006279

(11,20) -0.400094279 -0.21389765 -0.036492669 1.006555651

Note: The daily average abnormal returns and their t-statistics for the samples of gainers and losers for -3 to + 3 days are presented in Panel A. CAR andtheir respective t-statistics for the same sample for various event windows is presented in Panel B. The estimation period is 100 days prior to the eventday.

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Table 5: AR for Gainers’ Stocks With or Without Specific News Announcements

Panel A: AR (Gainers) Specified Events Unspecified Events

Day AR (%) t-value AR (%) t-value

-3 0.147540325 0.23451980 -0.132485375 -1.58934098

-2 0.049705048 1.23896745 -0.092802888 -0.80942909

-1 -0.158985043 -1.89675012 -0.005860174 -0.85925814

0 4.037195257 3.45789012 3.928049352 2.72332766

+1 -0.194687537 -0.23638278 0.031500596 1.19610341

+2 -0.229965951 -0.23865784 -0.211464553 -0.09825398

+3 0.067561593 0.98075327 -0.066179885 -0.78441397

Panel B: CAR(Gainers) Specified Events Unspecified Events

Event Period CAR (%) t-value CAR (%) t-value

(-1 , 0) 3.878210215 2.89127965 3.922189178 3.183334271

(1,2) -0.424653488 -2.32785643 -0.179963957 -1.942363105

(1,3) -0.357091895 -1.07543290 -0.246143842 -1.37393663

(2,3) -0.162404358 -0.92378636 -0.277644438 -0.453412236

(4,10) -1.049118918 -0.00978943 -0.461716145 -0.369817336

(11,20) -0.550416396 -0.00023965 -0.334430106 -0.461736458

Note: The daily average abnormal returns and their t-statistics for the samples of specified and unspecified gainers for -3 to + 3 days are presented inPanel A. CAR and their respective t-statistics for the same sample for various event windows is presented in Panel B. The estimation period is 100days prior to the event day.

Table 6: AR for Losers’ Stocks With or Without Specific News Announcements

Panel A: AR (Losers) Specified Events Unspecified Events

Day AR (%) t-value AR (%) t-value

-3 0.336929148 0.0985478 0.135089538 0.977855912

-2 0.816933738 1.23703012 0.292401684 2.318854771

-1 0.606156645 1.79564701 0.200468329 1.562047568

0 -4.267667468 -3.45670211 -3.830953715 -2.039538772

+1 -0.379574676 -1.90745012 -0.262838366 -0.548060107

+2 -0.062017098 -0.90693330 0.085659989 0.651276768

+3 0.240970642 0.00879010 0.088489367 0.739695784

Panel B: CAR(Losers) Specified Events Unspecified Events

Event Period CAR (%) t-value CAR (%) t-value

(-1 , 0) -3.661510823 -2.34905111 -3.630485386 -3.092629792

(1,2) -0.441591773 -1.92340781 -0.177178378 -1.860752928

(1,3) -0.200621132 -1.09807651 -0.088689011 -1.303650163

(2,3) 0.178953544 0.23451200 0.174149355 0.01507213

(4,10) 0.649889364 0.90087901 0.549779487 0.172226777

(11,20) -0.74990146 -0.00902345 0.251095284 0.552620295

Note: The daily average abnormal returns and their t-statistics for the samples of specified and unspecified losers for -3 to + 3 days are presented inPanel A. CAR and their respective t-statistics for the same sample for various event windows is presented in Panel B. The estimation period is 100days prior to the event day.

specific publicly announced events like quarterly an-

nouncement, dividend announcement, mergers, etc. are

known as specified events. Events without any public

notification specific to the company are unspecified

events. They might be global events, macro level changes

which might affect a particular company or industry.

These events can be explicit or implicit to a firm, but it

should not be publicly mentioned either in the company

44

website or in the NSE website1.

RESULTS

The overreaction effect is examined by analysing the sta-

tistical significance of cumulative abnormal returns on

and after the event day when the stocks show the maxi-

mum price gain or loss. The other effects are also tested to

study overreaction in the Indian stock market.

Regression Estimations

The following regression analysis was used to examine

whether there was an inverse relationship between the

price reversals and magnitudes of price gains or losses

after controlling the size variable.

CAR(P)i = a1 + b1Sizei+ b2 CAR(E)i + ei

where,

CAR(P) = Cumulative Abnormal Returns of the Post

event days (1,2) i.e. +1 to +2 post-event days

for the stock‘i’;

Sizei = Log of average value of the market capitaliza-

tion of the stock (i) in ̀ crore on the event date;

CAR(E)i = Cumulative Abnormal Return of the event pe-

riod (-1,0) i.e -1 to event day(0 day)for stock

(i).

The records evidence significant market overreaction for

NSE stocks, both for gainers and losers. It is found from

Tables 5 and 6 that CARs are more statistically signifi-

cant for event window of (-1,0) and (1,2) for both gainer

and loser stocks. That is why, the relationship between

the stock return during announcement (CAR(P)i for the

period -1 to 0 days) and the subsequent stock price re-

versal (CAR(E)i for the period 1 to 2 days) are explored.

Table 7 shows the results of reversal effects and multiple

regression analysis for both gainers and losers. For

gainers, the coefficient CAR(E)i and Size

i is significantly

negative at five percent level, which indicates the proof of

contrarian strategy. Though significance level is low, the

coefficients are negative for losers. It can be said that there

is an inverse relation between the two CARs – the stock

return during announcement and reversal return after

the event. The reversal overreaction effects last mainly for

two days after the event date.

Tables 8 and 9 represent cumulative abnormal returns

for gainers and losers respectively. The mean abnormal

returns of gainers or losers are given in Panel A for sev-

eral event windows: -3, -2, -1, 0, 1, 1-2, 1-3, 2-3, 4-10, and

11-20. The mean abnormal returns during -3 to -1 days

are used to find the seepage of information before the event

date. Various event windows are considered to find the

exact timing of overreactions. Panels B and C report the

results about the subsets of specified and unspecified

events as defined earlier. Out of 1,284 gainers, 390 are

specified and 894 are unspecified events. CAR is nega-

tive for days 1, 1-2, 1-3, 2-3, 4-10 and 11-20 for all the

events. Thus, the sample of gainers experiences overreac-

tion and it persists for a longer time.

In Table 9, out of 1,161 losers, 334 are specified and 827

are unspecified events. The cumulative abnormal return

is negative and statistically significant for days 0 and 1-2

for all the events. Thus, the sample of losers experiences

under-reaction during two days after the event date. Sub-

1 www.nseindia.com

Table 7: Reversal Effects and Multiple Regression Analysis

Variable Gainers t-stat Losers t-stat

Constant 4.007019** 2.713497 0.065673 0.038903

Size -0.0377334** -2.683202 -0.022919 -0.138860

CAR(E) variable -0.099098** -2.536404 -0.047180 1.184950

F Value 5.975368 Prob(F)=0.002657 0.732092 Prob(F)=0.481313

R2 0.014865 0.002330

Sample Size 1284 1161

Note: CAR(P)i = CAR of the post-event days (1-2) i.e. +1 to +2 post-event days form the event date of the stock i; Sizei = Log of average value of the marketcapitalization of the stocks on the event date in `crore of stock i and CAR(E)i = Cumulative Abnormal Returns of the event period (-1-0) i.e., -1 toevent day (0 day) for the stock i.** Statistically significant at 5 percent level

INDICATION OF OVERREACTION WITH OR WITHOUT STOCK SPECIFIC PUBLIC ...

VIKALPA • VOLUME 39 • NO 3 • JULY - SEPTEMBER 2014 45

sequently, the return becomes positive. The gainers’ stocks

lead to increase in stock price. The extreme increases of

stock price are followed by a correction or reversal in gen-

eral. On the other hand, any loser event causes extreme

price reductions. It would be followed by further reduc-

tion up to two days from the event date. Thus, the reac-

tion of stock price to the extreme abnormal price movement

varies with the level of initial price changes. Table 10

reports the difference between cumulative abnormal re-

turn of specified and unspecified events, for both gainers

and losers. Panel A shows that the mean difference of

CAR for gainers is negative and statistically significant

for days 1,2 and 1,3. Panel B reports the results of losers.

In this case, the mean difference of CAR is positive and

statistically significant for days 2,3. These suggest that

the specific release of news decreases the chances of ab-

normal stock price changes. Hence, by segregating speci-

fied and unspecified events, it can be inferred that

existence of public news causes market overreaction.

Table 8: Estimated Abnormal Returns Associated with Gainers using the Market Model

Good News Day -3 Day -2 Day -1 Day 0 Day 1 Day 1-2 Day 1-3 Day 2-3 Day 4-10 Day 11-20

Panel A: All Gainers (N = 1284)

Mean CAR -0.04735193 -0.0494776 -0.0524132 3.9612319 -0.03726514 -0.25435449 -0.279874 -0.242609 -0.6402984 -0.40009428

Panel B: All Specified Gainers (N = 390)

Mean CAR 0.1475403 0.0497050 -0.15899 4.037195 -0.194687 -0.424653 -0.35709 -0.16240 -1.04912 -0.550416

Z-statistic 1.589341 0.809429 -0.8593 0.723383 -1.1961 -0.94236 -0.3739 0.453412 -1.3698 -0.46174

%greater than zero 50.8264 50.00 41.32 99.17 43.80 44.62 45.04 44.21 41.32 45.45

Z-statistic*** -15.895 -17.0965 -15.516 -10.7666 -18.2801 -16.0955 -17.006 -16.2307 -18.562 -17.5689

Panel C: All Unspecified Gainers (N = 894)

Mean CAR -0.132485 -0.09280 -0.00586 3.928049 0.0315006 -0.17996 -0.24614 -0.27764 -0.46172 -0.334430

Z-statistic 1.589341 0.809429 -0.8593 0.723383 -1.1961 -0.94236 -0.3739 0.453412 -1.3698 -0.46174

%greater than zero 44.76534 45.30686 47.83394 98.916968 47.11191 45.48736 46.38989 46.38989 44.94585 41.51625

Z-statistic*** -25.519 -24.0092 -24.539 -14.65 -31.8667 -26.5352 -17.0888 -26.5335 -27.128 -22.7055

Note: Panel A represents the cumulative abnormal returns and the z-statistics of all gainers for various windows like -3, -2, -1, 0,1, 1-2, 1-3, 2-3, 4-10, 11-20.Panel B and C represent the cumulative abnormal returns and the z-statistics for Specified and Unspecified events respectively; *** Significant at1% level, **Significant at 5% level, *Significant at 10% level.

Table 9: Estimated Abnormal Returns Associated with Losers using the Market Model

Bad News Day -3 Day -2 Day -1 Day 0 Day 1 Day 1-2 Day 1-3 Day 2-3 Day 4-10 Day 11-20

Panel A: All Losers (N = 1161)

Mean CAR 0.193078 0.443101 0.317023 -3.95642 -0.29638 -0.25314 -0.12085 0.17553 0.578541 -0.03649

Panel B: All Specified Losers (N = 334)

Mean CAR 0.336929 0.816934 0.606157 -4.26767 -0.37957 -0.44159 -0.20062 0.178954 0.649889 -0.7499

Z-statistic 0.977856 2.318855** 1.562048 -2.0395** -0.54806 -2.86075** -0.30365 0.015072 0.172227 -1.55262

% greater than zero 51.38122 60.22099 51.9337 1.104972 39.77901 43.09392 46.96133 50.82873 52.48619 48.61878

Z-statistic*** -16.0134 -16.7827 -14.0749 -4.60608 -15.0792 -14.5829 -13.239 -10.5085 -15.2523 -14.5017

Panel C: All Unspecified Losers (N = 827)

Mean CAR 0.13509 0.292402 0.200468 -3.83095 -0.26284 -0.17718 -0.08869 0.174149 0.549779 0.251095

Z-statistic 0.977856 2.318855** 1.562048 -2.0395** -0.54806 -0.86075 -0.30365 0.015072 0.172227 -1.55262

% greater than zero 48.55234 51.44766 48.77506 0.668151 0.615381 45.21158 46.7706 50.11136 52.33853 49.88864

Z-statistic*** -22.0084 -20.8899 -19.6276 -31.8716 -29.8795 -22.1425 -22.0234 -25.1937 -23.3917 -24.535

Note: Panel A represents the CAR and the z-statistics of all losers for various windows like -3, -2, -1, 0,1, 1-2, 1-3, 2-3, 4-10, 11-20. Panel B and C representsthe cumulative abnormal returns and the z-statistics for specified and unspecified eventsrespectively; *** Significant at 1% level, **Significant at 5%level, *Significant at 10% level,*** Significant at 1% level, **Significant at 5% level, *Significant at 10% level.

46

Least Square Results

To test the relationship between the unspecified and speci-

fied events in the context of market overreaction, the fol-

lowing multiple regression model was tested. The effects

of magnitude of initial price change, pre-event informa-

tion seepage were also investigated. Control variables are

size, volatility, and April effect. The following test was

done for both the gainer and loser samples as well as for

technology and other stocks in the case of gainers and

losers:

CAR(G) i (j, k) = α0 + α1MAGi + α2ESPi + α3ISPi + α4Sizei +

α5 APRi + α6VARi + α7Di + ei

where,

CAR(G) i (j, k) = up to k’th day CAR starting from day j for

the stock i and CARs have been considered for day 1, 1-2,

1-3, 2-3, 4-10, 11-20;

MAGi (Magnitude) = magnitude of the stock price return

on the event day for stock i;

ESPi(Event Specification) = dummy variable, equal to 1

if the event is not specified by corporate announcement

published in www.nseindia.com or otherwise 0;

ISPi(Information Seepage) = 3 day pre-event period cu-

mulative abnormal return preceding the event dates;

SIZEi = Average market capitalization value of the stocks

on the day prior to the pre-event period (day -4);

APRi = 1 if the event date is in April, and zero otherwise;

VARi = Pre-event volatility = standard deviation of 3

days pre-event period of daily stock return preceding the

event date; and

Di = 1 if the corresponding stock is technology stock, and

zero otherwise.

The above regression was done to assess the market over-

reaction to the event with or without specific public an-

nouncement about the firm. The analysis investigated

whether market overreaction was affected by the initial

price change and information seepage. Results are pre-

sented in Table 11 – Panel A is for gainers and Panel B for

losers. For gainers, event specification variable is signifi-

cantly negative in 1 and 1,2 days. These results show

more prominent presence of overreaction which is not

complemented by public announcements. The coefficient

of ISP on day 1,2; 1,3, and 2,3 are significantly negative

suggesting the weaker evidence of degree of positive as-

sociation between information seepage and overreaction.

For losers, the results for day 1 and 1,3 depict that overre-

action is more when the initial price change is also more.

Table 10: Abnormal Return Differences between Specified and Unspecified Events

Event Specified Events CAR Unspecified Events CAR Mean Equal P(T<=t) Unequal P(T<=t)Window Mean SD Mean SD Difference Variances two-tail Variances two-tail

t-statistic t-statistic

Panel A: Gainers Specified Events (N = 390) and Unspecified Events (N = 894)

Day 1 -0.19469 2.40897 0.031501 2.55468 -0.226 -1.16888 0.24280 -1.1961 0.232241

Day1-2 -0.42465 3.38225 -0.17996 3.3411 -0.245 -1.59213 0.34397 -1.6712* 0.346508

Day 1-3 -0.35709 3.75745 -0.24614 4.05578 -0.111 -1.61291 0.71677 -1.9739* 0.708612

Day 2-3 -0.1624 3.32953 -0.27764 3.22635 0.115 0.459047 0.64633 0.453412 0.650472

Day 4-10 -1.04912 5.54393 -0.46172 5.61354 -0.587 -1.36313 0.17323 -1.36982 0.171405

Day 11-20 -0.55042 5.75211 -0.33443 6.74357 -0.216 -0.43399 0.66441 -0.46174 0.644459

Panel B:Losers Specified Events (N = 334) and Unspecified Events (N = 827)

Day 1 -0.37957 2.40943 -0.26284 2.44323 -0.117 -0.54482 0.58607 -0.54806 0.584013

Day1-2 -0.44159 3.49018 -0.17718 3.48595 -0.264 -0.8612 0.38946 -0.86075 0.389995

Day 1-3 -0.20062 4.31972 -0.08869 3.83692 -0.112 -0.31932 0.74959 -0.30365 0.761605

Day 2-3 0.178954 3.82353 0.174149 3.05816 0.005 2.016556** 0.98679 2.015072** 0.987985

Day 4-10 0.649889 6.77439 0.549779 6.15314 0.100 1.179414 0.85767 1.172227 0.863373

Day 11-20 -0.7499 7.52775 0.251095 6.78659 -1.001 -1.62252 0.10519 -1.55262 0.121554

Test for Equality of GOOD BADVariance F-Statistic News: News:

1.63*** 3.00***

Note: Panel A represents the cumulative abnormal returns and the t-statistics for specified and unspecified gainers for various windows like 1, 1-2, 1-3,2-3, 4-10, 11-20. Panel B represents the same for losers. *** Significant at the 1% level, **Significant at the 5% level, *Significant at the 10% level.

INDICATION OF OVERREACTION WITH OR WITHOUT STOCK SPECIFIC PUBLIC ...

VIKALPA • VOLUME 39 • NO 3 • JULY - SEPTEMBER 2014 47

It proves the magnitude effect of overreaction, i.e. larger

initial price movement leads to more correction. Like

gainers, even in the case of losers, the results across the

cross-section suggest that unspecified events have over-

reacted more.

At the end it can be said that the Indian stocks show

strong overreaction and reversal effect. A trading strat-

egy can be developed from this to make contrarian prof-

its. An investor can buy the largest percentage of loser

stocks or sell the largest percentage gainer stocks, then

sell the former one and buy the latter one after two trad-

ing days. In this way, the optimum utilization of overre-

action effects may increase investors’ return.

Overreaction is more prominent in the case of unspeci-

fied events than specified events. Stock prices overreact

to private news but underreact to subsequent public an-

nouncements. Overreaction increases due to information

asymmetry and leakage. In the case of any macro/global

issues, overreaction is more also because of market inte-

gration and globalization.

CONCLUDING REMARKS

The primary objective of this study was to understand

the dynamics of overreaction of Indian stock market in

the backdrop of specified and unspecified events. Extreme

high or low stock prices are followed by a reversal lead-

ing to price correction in the market, which indicates the

Table 11: Least Square Estimates

Dependent Variable Size APR VAR D MAG ESP ISP Adj. R2 F-stat

Panel A. Gainers (N = 1284)

CAR 1 -0.05641 0.0891 -2.3412 0.0987 0.027846 -0.3151 -0.0358 -0.0008 0.770221(j =1, k=1) (-0.234) (2.345)** (-1.987)* (0.234) (0.467970) (-2.1055)** (-0.8687)

CAR 1-2 -0.02345 0.0671 -6.2313 0.0987 0.025321 -0.2737 -0.1242 0.00392 2.041562**(j=1, k =2) (-2.110)** (1.989)* (-2.231)** (1.231) (0.319556) (-2.004)** (-2.2630)**

CAR 1-3 -0.02345 0.0981 -3.2345 0.0098 0.017044 -0.0498 -0.1734 0.005436 2.446669**(j =1, k=3) (-0.675) (0.237) (-1.234) (0.0087) (0.182074) (-0.1629) (-2.6744)**

CAR 2-3 -.0.03452 0.0812 -9.2345 0.0078 -0.01080 0.16522 -0.1376 0.004912 2.306411**(j=2, k=2) (-1.345) (1.123) (-2.897)** (0.123) (-0.14044) (0.656960) (-2.5831)**

CAR 4-10 0.05671 0.0892 -11.234 0.0854 0.01788 -0.61759 0.107194 0.000253 1.066980(j=4, k =7) (0.2395) (1.012) (-0.112) (1.239) (0.134959) (-1.42546) (1.167901)

CAR 11-20 0.09821 0.0123 -9.2345 0.0072 -0.22323 -0.17785 -0.06211 -0.00048 0.872638(j=11, k=10) (0.3457) (0.987) (-0.987) (0.987) (-1.46002) (-0.35571) (-0.58645)

Panel B. Losers (N = 1161)

CAR 1 -0.11892 -0.0987 -11.675 0.2312 0.115281 -0.09293 -0.07906 0.007002 2.478511**(j =1, k=1) (-3.423)** (-2.983)** (-3.123)*** (0.129) (1.762100)* (-0.43512) (-1.96981)*

CAR 1-2 -0.9821 -0.1291 -7.654 0.0754 0.10265 -0.22800 -0.14578 0.008670 2.833669**(j=1, k =2) (-2.345)** (-2.234)** (-2.345)** (1.298) (1.095515) (-0.74533) (-2.53584)**

CAR 1-3 0.0987 -0.1897 -2.345 0.0987 0.228975 -0.08307 -0.06581 0.004376 1.921535*(j =1, k=3) (0.2345) (-1.673)* (-1.098) (0.543) (2.136879)** (-0.23747) (-1.00104)

CAR 2-3 -0.5234 0.02341 -2.098 0.0075 0.113695 0.009860 0.013255 -0.00210 0.559756(j=2, k=2) (-0.9843) (0.987) (-0.987) (1.076) (1.277695) (0.033938) (0.242782)

CAR 4-10 0.0897 0.09871 -1.9082 0.0198 0.017105 0.116520 -0.07553 -0.00389 0.187393(j=4, k =7) (0.2349) (1.231) (-0.972) (1.567) (0.099877) (0.208386) (-0.71888)

CAR 11-20 -0.7632 0.1231 -0.098 0.1876 -0.03826 -0.99008 -0.06697 -0.00001 0.998002(j=11, k=10) (-1.232) (0.234) (-0.009) (1.001) (-0.20203) (-1.60126) (-0.57641)

Note: This table reports the mean coefficient estimates across regressions of CAR on ESP and other variables.Variables are defined as follows:CAR(G)i (j, k) = up to k –th day CAR starting from day j for the stock i and CAR of following days has been considered:- Day 1, 1-2, 1-3, 2-3, 4-10, 11-20; MAGi (Magnitude) = magnitude of the stock price return on the event day for stock i; ESPi (Event Specification) = dummy variable, equal to1 if the event is not specified by corporate announcement published in www.nseindia.com or otherwise 0; ISPi (Information Seepage) = 3 day pre-event period cumulative abnormal return preceding the event dates; SIZEi= Average market capitalization value of the stocks on the day prior tothe pre-event period (day -4); APRi= 1 if the event date is in April, and zero otherwise; VARi= Pre-event volatility = standard deviation of 3 days pre-event period of daily stock return preceding the event date and Di = 1 if the corresponding stock is technology stock, and zero otherwise. t-statisticsare given within parenthesis; *** Significant at the 1% level, **Significant at the 5% level, *Significant at the 10% level.

48

existence of an overreaction effect. The quantum of stock

price reversal after the event is inversely proportional to

the extreme stock price during the event. For the present

study, the overreaction effect continues for about two days

after the event date. The ultimate understanding of over-

reaction effects will guide investors in preparing trading

strategies to perk up returns.

The cross-sectional regression examined the impact of

public news of Indian stocks on market overreaction or

under reaction. Good news stocks show that there is a

statistically significant difference between specified and

unspecified events. Stocks for unspecified events overre-

acted more than that of the specified events. The bad news

stocks overreact due to information leakage and asym-

metric information diffusion for unspecified events and

underreact due to public announcements or specified

events.

The outcome of this study gives an indication about the

future actions to the analyst, broker, investor, trader, and

speculator. If a stock held for long experiences large posi-

tive return, holding of such stock should be reduced by

shorting the stock or buying put option and vice versa.

Findings of this study will help investor analysts, mu-

tual fund managers, and small investors in designing a

trading strategy.

As mentioned in the introduction of this study, this re-

search was initiated to contribute to the existing litera-

ture on Overreaction and Event Study, to offer insights to

the traders and institutional investors and to provide

macro level inputs to the policy makers. Within the limi-

tation, it is expected that the target has been achieved to a

reasonable extent. However, there are other explanations

also which may encourage future research on overreac-

tion.

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Sitangshu Khatua is Professor of Finance at Heritage Busi-ness School, Kolkata. He has served as Faculty Advisor in theAlumni Committee and is also a member of the Board ofGovernors of the School. He has over 19 years of experiencein industry as well as academics. He has also been a visitingfaculty at IIFT, XLRI, and Strategy Academy, Kolkata. He waspreviously associated with JSB, Kolkata, as Associate Dean;XISS, Ranchi as Reader; and NIT, Durgapur as Faculty. He hasbeen conducting corporate training for various public sectorand private sector industries since 2000. He has presentedseveral papers in national and international conferences inIndia and abroad and published articles in rated peer-re-viewed national and international journals. He has authoreda book on Project Management & Appraisal from OxfordUniversity Press. He is a BE from NIT-Durgapur, an M.Techfrom BESU-Shivpur, an MBA, DBF from ICFAI and a Fellowin Management in Finance from XLRI. His major researchareas include asset pricing, project finance, and inclusive fi-nance.

e-mail: sitangshu_k@yahoo.com

H K Pradhan is Professor of Finance and Economics at XLRIJamshedpur, where he has also served as the member of theBoard of Governors and Chairman of the DoctoralProgramme. He has an M Phil & a Ph.D. from the GokhaleInstitute of Politics and Economics, Pune, India. He was aFulbright Post-Doctoral Fellow at the Columbia UniversityBusiness School, New York, during 1999-2000. He is at presenta member of the Reserve Bank of India Technical AdvisoryCommittee (TAC) on Money, Foreign Exchange and Govern-ment Securities Markets, an Independent Director of the SBIMutual Fund, one of the top asset management companies inIndia, where he also serves as a Member of the Director'sCommittees, member of the Board of M-Cril- Micro CreditRating International Ltd, and a Member of Index & OptionCommittee of the National Commodity and Derivative Ex-change (NCDEX), Mumbai.

e-mail: pradhan@xlri.ac.in

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