# Stock Price Reaction To Merger And Acquisition Announcements Finance Essay

Mergers and Acquisitions are seen as an important exercise in corporate restructuring. India is emerging as one of the top countries in M&A deals. In an efficient market M&A announcements will have an immediate effect on the stock price of that firm. Investors can earn significant returns during an M&A deal. Event Study Methodology is used to find the effect of an event on a specific dependent variable. Here the merger or acquisition announcement is an event and the underlying stock price is the dependent variable. The event study will study the change in stock price beyond expectation which we call the abnormal return over a period of time called an event window.

This paper uses an event study methodology to measure the abnormal returns (actual – predicted returns) during an event window, on stock prices of selected firms which have undergone an M&A deal. An econometric model is also developed using ARIMA Box Jenkins Methodology for calculating predicted returns during the event window 11 days. 5 days before and 5 days after the event. An estimation window of 120 days prior to the event window is used for forecasting the returns during the event window.

The findings of this study show that the investors can earn significant returns during an M&A deal. Selected firms on an average show negative impact during the event day. But in most of the cases, abnormal returns of firms show that this trend reverses. We find that the abnormal returns fall during the event date and a couple of days surrounding event date. But the Abnormal Returns during the event day are not so significant in all cases. We can attribute this to information leakage, Insider trading activities or anticipation of information by investors.

## INTRODUCTION:

Mergers and Acquisitions and Corporate Restructuring are among the key areas of Corporate Finance. Every day investment bankers work on M&A transactions to bring separate companies together and form a larger one. M&A deals often make news. Deals are worth hundreds of thousands or millions or even billions of dollars. They have become popular due to enhanced competition, breaking of trade barriers, free flow of capital across countries and globalization of businesses.

Mergers and acquisitions (M&As) in India surged by 270% in terms of deal value from Jan 2011 to march 2011. The deal value came to around $18.31 billion. According to Hong Kong based research agency mergermarket, this is nearly four times of the previous year’s first quarter of $4.94 billion1. Experts believe that this trend is set to continue as healthy firms in India are seeking for more of M&A deals.

“Inbound M&A drove deals in Q1 2011 with India proving itself an attractive investment destination as it lured buyers in the energy, insurance and IT space. Despite the ongoing wave of corporate scandal and political corruption, India will continue to entice suitors on the back of strong fundamentals such as its growing population. Buyers from typical markets such as the US, Europe and Japan could be joined by those from Korea and Russia and deals across borders – consumer, financial services, energy, industrial, engineering and chemicals – will continue. Overseas activity in energy, consumer and IT is also expected to grow.”

- Spokesperson, Mergermarket Asia Pacific

So how does an investor benefit from this? A popular belief is that mergers and acquisitions strengthen businesses by making their operations more synergetic. Announcements of mergers and acquisitions immediately impact a target company’s stock price, as induced reaction in the stock market cause investors to revise expectations about the company’s future profitability2.

1 http://www.indiaincorporated.com/index/item/158-india-on-big-ticket-ma-spree-in-2011.html

2.Panayides and Gong, 2002, The Stock Market Reaction to Merger and Acquisition Announcements in Liner

Shipping

M&As in India has grown rapidly after the economic reforms of 1991.In the past few years we have seen major M&A deals like the Tata steel-Corus, Vodafone-Hutch Essar, Hindalco-Novellis, Ranbaxy-Daiichi Sankyo, Tata motors-Jaguar Land Rover3and the very latest which is still running in news is the Cairn-Vedanta deal 4.

An event study is a statistical method used to study the impact of a corporate event. In a corporate context, the usefulness of event studies arises from the fact that the magnitude of abnormal performance at the time of an event provides a measure of the unanticipated impact of this type of event on the wealth of the firms’ claimholders 5.

Here the corporate event of interest is mergers and acquisitions and its impact on the stock price of the underlying firm is studied. Event studies are frequently used to test market efficiency 6. Measurement of Abnormal Returns in an event window is the central focus in an event study. Abnormal Returns is the difference between the Actual returns and Expected returns.

Expected returns during the event window are calculated using the Box Jenkins Methodology, popularly known as the ARIMA Model. Box-Jenkins forecasting models are based on statistical concepts and principles and are able to model a wide spectrum of time series behavior. It has a large class of models to choose from and a systematic approach for identifying the correct model form. There are both statistical tests for verifying model validity and statistical measures of forecast uncertainty.

3. http://business.mapsofindia.com/finance/mergers-acquisitions/mergers-and-acquisitions.html

4. http://www.indiaincorporated.com/maas/item/161-vedanta-grabs-%C2%A3920mn-stake-in-cairn-india.html

5. S.P. Kothari and Jerold B. Warner, 2006, Econometrics of Event Studies.

6. Brown and Warner, 1980, Using daily stock returns - The case of event studies

## Objective of the Project:

To Measure the Abnormal Returns on Stock Prices around the Event.

To Develop an Econometric Model in Predicting Abnormal Returns.

To test the hypothesis that investors do not earn any abnormal returns during an event

## Limitations of the Study:

Larger samples are difficult to use because abnormal returns has to be calculated individually. Using all the samples would have made the research more robust.

Various forecasting models are available. We will be sticking to ARIMA model.

Valuation of the firm after a merger or acquisition and its financial performance is out of scope.

## Report Organization:

Section 2 presents the Literature Review on M&A effect. Section 3 presents the methodology explaining what an event study analysis is and the steps involved in conducting such a study. A Null and Alternate Hypothesis which needs to be tested is defined. It briefly outlines the Box-Jenkins ARIMA methodology of forecasting the stock returns. Section 4 provides the sample data collected for the analysis. Section 5 presents the Findings and Analysis. Section 6 presents the conclusion. Section 7 provides the scope for further studies.

## LITERATURE REVIEW:

Isfandiyar Shaheen (2006) in his paper Stock Market Reaction to Acquisition Announcements using an Event Study Approach has determined that target firms experience significant positive abnormal returns during an acquisition announcement. The abnormal returns are maximum the day after an event. He has developed a linear regression model in predicting abnormal returns. The event window chosen is 11 days i.e. [-5, +5] and 120 days prior to event is used for estimating the normal returns. The Normal Returns is computed using the market model.

Nikolas Papasyriopoulos’s team has used the event study methodology introduced by Brown and Warner (1985) for six Greek industrial and construction firms. They have measured the abnormal returns on stock prices on the day of the acquisition announcement. Estimation period and event period in their market model is -211 -11 -10, +10 respectively. In order to allow for asymmetric effect of news on the abnormal returns they use an E-GARCH model for period -211,-1. Results show that on event day, AAR goes slightly positive, while CAAR remain positive.

Roland and Henryk in their paper ARIMA modeling of Event Induced Stock Price Reactions in Austria have used an event study to study impact of corporate announcements on the stock prices. They have used ARIMA time series models in forecasting the normal returns. But they have taken dividend announcement as an event.

Carl B. McGowan, Jr and Zunaidah Sulong in “A Note On The Effect Of M&A Announcements On Stock Price Behavior And Financial Performance Changes: The Case Of Arab Malaysian Bank Berhad And Hong Leong Bank Berhad” examined the effect of M&A announcements on the stock price behavior for two major banks of Malaysia. Their analysis uses the event study technique, the Naïve Model, a model that is based on Market Model with constrained α = 0 and β = 1 to compute the abnormal returns surrounding the M&A announcement date .The results from event study showed that the M&A completion announcements are treated as positive information by the market.

Panayides and Gong (2002) in The Stock Market Reaction to Merger and Acquisition

Announcements in Liner Shipping studied the stock reaction to M&A announcements in liner shipping. Their event study analysis led to the conclusion that all firms saw their stock price increase rapidly on the announcement of the proposed events.

S.P. Kothari and Jerold B. Warner, 2006 in Econometrics of Event Studies provide an overview of event study methods. They present new evidence illustrating that, event study methods can vary by calendar time period and can depend on event sample firm characteristics such as volatility. This reinforces the importance of using stratified samples to examine event study statistical properties.

PengCheng Zhu and Shavin Malhotra , 2008 in Announcement Effect and Price Pressure: An Empirical Study of Cross Border acquisitions by Indian firms examines the short term stock performance of a sample on Indian Firms acquiring US firms in the period 1999-2005. Their Study shows that Indian Stock Market react positively to the acquisition announcement. They find that the positive abnormal returns last only for three days after which the returns become negative.

Arun Kumar Gopalaswamy, Debashis Acharya, Jaideep Malik, 2008 in Stock price reaction to merger announcements: an empirical note on Indian markets investigates the differences in stock price reaction of target and acquiring companies due to merger announcements. The role of insider information before merger announcements is also tested. The investigation has been carried out using traditional event study methodology. Various event windows have been considered and compared. They see an upward trend in cumulative abnormal returns for companies in the pre-announcement period which is an indicative of insider information or anticipation. The evidence also suggests that around the announcement period the returns for the acquiring companies are higher than those for the target companies. In the post amalgamation period there is a downward trend in the cumulative returns implying a negative result of the merger.

## METHODOLOGY:

## Event Study Methodology:

An event study measures the impact of a particular event from a firm’s stock price. Events can be anything like Dividend announcement, Earnings announcement, New Product Launch or Product Recall, Merger or Acquisition announcement, Change in Management. Etc.,

The event study determines whether there is an abnormal stock price effect associated with an event. From this, we can infer the significance of the event. The key assumption of the event study is that the market must be efficient. So that the effects of the event will be reflected immediately in the stock prices of the company.

Basic Idea: To calculate the Abnormal Return.

This gives the impact of an event on Stock Price.

Abnormal Return = Actual return when the event took place –

Normal Return If the Event had not taken place.

## TimeLine:

T0

T1

Event

Event Window

Estimation Window: To estimate the normal return

## Figure 1: Event Study TimeLine

## Event Study Procedure:

Event Identified: Mergers and Acquisitions.

Event Window: This is the period over which the stock price Abnormal Returns is calculated. The event window is 11 days so as to incorporate the markets ongoing adjustments to the news. Thus the event window is [-5, +5].7

Estimation Window: This is the period over which the stock price Normal Returns is calculated. A period of 120 days is adequate since daily returns daily returns data for the 120 days prior to the event date is sufficient in formulating a benchmark for normal returns. Thus the estimation window is [-120, -5].8

Model to Estimate Normal Return: Auto Regressive Integrated Moving Average (ARIMA) model to be used in estimating the normal return during the event window.9

Abnormal return (AR): Calculate abnormal return using data in the event window.

## ARi, t = Ri, t – E (Ri, t) 10

Ri, t = Actual Returns

E (Ri, t) = Expected return on security ‘i’ during time period‘t’.

7. According to Panayides and Gong (2002), an 11 day event window captures the effects of an event of interest.

8. According to Brown and Warner (1985) and Dyckman, Philbrick and Stephan (1984), an estimation period of 120

days is adequate since daily returns data for the 120 days prior to the event date are sufficient in formulating a

benchmark for normal returns.

9. Roland Mestel And Henryk Gurgul, ARIMA Modeling Of Event Induced Stock Price Reactions in Austria,

CEJOR (2003) 11: 317-333.

10. Brown and Warner, Event studies with daily returns, Journal of Financial Economics (1985) 3-31.

Cumulative Abnormal returns (CAR): Calculate cumulative abnormal return using the abnormal returns. This measures the impact over a number of days. The cumulative abnormal return for security i is the sum of abnormal returns in an event window time period.11

Average Abnormal returns (AAR): The sample average abnormal return at time t,

ARt is the arithmetic mean of n stocks.12

Cumulative Average Abnormal returns (CAAR): The cumulative average abnormal average return of CAR from event time t0 to t1 is the sum of ARt from t0 to t1.13

11,12, 13. Carl B. McGowan and Zunaidah Sulong (2008), A Note On The Effect Of M&A Announcements On Stock Price Behavior And Financial Performance Changes: The Case Of Arab Malaysian Bank Berhad And Hong Leong Bank Berhad, International Business & Economics Research Journal, Volume 7, Number 9

## Hypothesis Testing:

Since this study empirically examines stock market reaction to mergers and acquisitions announcement the hypotheses being tested are:

H01: The investors cannot earn abnormal returns by trading in the stocks after the M&A Announcements.14

H02: The Average Abnormal Returns and the Cumulative Average Abnormal Returns are close to zero.15

The test statistic is simply the ratio of day t average abnormal returns to its estimated standard deviation 16, where

## )2

N-1

Where AR (bar) is the average of the ARs over the period

(N = number of days from t = m until t = n).

14, 15. Neetu Mehndiratta & Shuchi Gupta (2010), Impact Of Dividend Announcement On Stock Prices, International Journal of Information Technology and Knowledge Management, Volume 2, No. 2, pp. 405-410

16. Carl B. McGowan and Zunaidah Sulong (2008), A Note On The Effect Of M&A Announcements On Stock Price Behavior And Financial Performance Changes: The Case Of Arab Malaysian Bank Berhad And Hong Leong Bank Berhad, International Business & Economics Research Journal, Volume 7, Number 9

where N is the number of days in the CAR statistics.

The null hypotheses that the M&A completion announcement does not have any significant influence of the stocks return (i.e., H0: AR = 0 and CAR = 0) are to be tested at 5 percent significant level. If the AR or CAR is statistically significant, then, the M&A announcement are seen to have an effect on the stock returns17.

17. Carl B. McGowan, Jr and Zunaidah Sulong in “A Note On The Effect Of M&A Announcements On Stock Price Behavior And Financial Performance Changes: The Case Of Arab Malaysian Bank Berhad And Hong Leong Bank Berhad

## The Box–Jenkins (BJ) ARIMA Methodology:

If Yt is modeled as (Yt − δ) = α (Yt −1 − δ) + ut Where δ is the mean of Y and where ut is an uncorrelated random error term with zero mean and constant variance σ2 (i.e. white noise), then Yt follows a first-order autoregressive or AR (1) stochastic process. This model says that the forecast value of Y at time t is some proportion α of its value at time (t − 1) plus a random value. In general pth-order autoregressive or AR (p) process is given by:

## (Yt − δ) = α1 (Yt −1 − δ) + α2(Yt −2 − δ) + ·· ·+αp(Yt −p − δ) + ut

If Yt is modeled as Yt = μ + β0ut + β1ut−1 ; Where: μ is a constant white noise stochastic error term. Here Y at time t is equal to a constant plus a moving average of the current and past error terms. We say that Y follows a first-order moving average, or an MA (1), process.

In general an MA (q) process is given by:

## Yt = μ + β0ut + β1ut −1 + β2ut−2 + ·· ·+βqut−q

A moving average process is a linear combination of white noise error terms.

If Y has properties of both AR and MA then it is called an ARMA process.

If Yt is written as Yt = θ + α1 Yt −1 + β0ut + β1ut−1 then it is called an ARMA(1, 1) process since it has one AR and one MA term. θ is a constant. Thus an ARMA ( p, q) process will have p AR and q MA terms.

If the time series are non-stationary then we can say that it is an integrated series. An integrated time series of order 1 denoted as I(1), then its first differences I(0) is stationary. Thus if a time series is I(d), then after differencing it for d times we get an I(0) series. To apply ARMA (p, q) model we have to first difference the series d times to make it stationary. The time series ARIMA (p, d, q) is an Auto Regressive Integrated Moving Average time series, where p denotes the number of autoregressive terms, d the number of times the series has to be differenced before it becomes stationary, and q the number of moving average terms.

The BJ methodology helps in identifying the type of process the time series follows i.e. whether it is an AR or MA or ARMA or ARIMA process. The objective of BJ methodology is to identify and estimate a statistical model which can be used for forecasting. It consists of the following steps:

## Identification of the model:

There are two phases to the identification of an appropriate Box-Jenkins model.

Changing the data, if necessary, into a stationary time series:

Dickey-Fuller test and Augmented Dickey-Fuller test is used in conducting the test for stationarity or popularly called unit root test. The Dickey-Fuller test assumes that the error term is uncorrelated. But if are correlated, Dickey and Fuller have developed a test, known as the augmented Dickey–Fuller (ADF) test. This test is conducted by “augmenting” the preceding equation by adding the lagged values of the dependent variable. The ADF test consists of estimating the following regression:

Where is a pure white noise error term and where and so on. The number of lagged difference terms to include is often determined empirically, In ADF we test whether δ = 0.

Determining the tentative model by analyzing the autocorrelation and partial autocorrelation functions:

The autocorrelation coefficient measures the relationship, or correlation, between a set of observations and a lagged set of observations in a time series. Given the time series (Z1, Z2, Z3, …., Zn), the autocorrelation between Zt, and Zt+k (denoted by k) measures the correlation between the pairs (Z1, Z1+k), (Z2, Z2+k), (Z3, Z3+k), .… ,(Zn, Zn+k). The sample autocorrelation coefficient (rk) is an estimate of k. When the sample autocorrelation coefficients are computed for lag 1, lag 2, lag 3, and so on and are graphed (rk versus k), the result is usually called the sample autocorrelation function (ACF) or a correlogram. This graph is useful both in determining whether or not a series is stationary and in identifying a tentative ARIMA model.

A partial correlation coefficient is the measure of the relationship between two variables when the effect of other variables has been removed or held constant. The partial autocorrelation coefficient (kk) is the measure of the relationship between the stationary time-series variables Zt and Zt+k when the effect of the intervening variables Zt+1, Zt+2,…., Zt+k has been removed. This adjustment is made to see if the correlation between Zt and Zt+k is due to the intervening variables or if there is something else causing the relationship.

## Type of Model

## Typical pattern of ACF

## Typical pattern of PACF

## AR(p)

Decays exponentially or with damped sine wave pattern or both

Significant spikes through lags p

## MA(q)

Significant spikes through lags q

Declines exponentially

## ARMA(p, q)

Exponential decay

Exponential decay

## Table 1: Model selection from patterns of ACF and PACF

Since we are considering the stock returns which are already stationary we need not take the first difference. So the models developed in our case can be ARMA and not ARIMA.

## Parameter estimation of the chosen model:

In the estimation step, we first compute initial estimates for the parameters of the tentative model and then allow the computer program to generate the final estimates by an iterative process.

## Diagnostic Checking:

Here we check if the estimated residuals are white noise. After the equation of the tentative model has been derived, diagnostic checks are performed to test the adequacy and closeness of fit of the model to the data. We do this by running tests on the residuals ( and by testing the significance and relationships of the parameters.

If the residuals are truly random, the autocorrelations and partial autocorrelations calculated using the residuals should be statistically equal to zero. If they are not, this is an indication that we have not fitted the correct model to the data. When this is the case, the residual ACF and PACF will contain information about which alternate models to consider.

## Forecasting:

Once the appropriate model has been found, it can be integrated (trend introduced into the model) and future forecasts can be found.

## The above tests are carried out using the statistical package E-Views.

## Model Selection Process in Box Jenkins Methodology:

## ARMA (1, 0)..??

## ARMA (1, 1)..??

## ARMA (2, 0)..??

## ARMA (2, 1)..??

## ARMA (0, 1)..??

## ARMA (3, 0)

## ARMA (2, 2)

## ARMA (3, 2)

## ARMA (1, 2)

## ARMA (0, 2)

## Figure 2: Step Wise Model Selection by Box Jenkins Methodology.

## In General:

## ARMA (n, n - 1)

## ARMA (n-1, n-1)

## ARMA (n, 0)

## ARMA (n-2, n-1)

## ARMA (0, n-1)

## Figure 3: Generalized Model Selection by Box Jenkins Methodology.

## DATA:

Two samples of data were collected.

## Target Firms (Sample 1):

The first sample consists of those firms in S&P CNX Nifty which were merged or acquired by some other firm and are therefore a part of target/Seller Co. The recent deal of these firms is taken for study.

## Table 2: Sample selected of target firms (sample 1).

## (Source: www.nseindia.com and www.business-beacon.com)

## Acquirer Firms (Sample 2):

The second sample consists of those firms which have merged or acquired some other firm and are hence a part of merged/acquirer Co. M&A’s from 2009 onwards is taken.

## Table 3: Sample selected of acquirer firms (sample 2).

## (Source: www.nseindia.com and www.business-beacon.com)

## FINDINGS & ANALYSIS:

ARMA model is fitted for all the 38 selected firms (22 target firms + 16 acquirer firms).

A brief analysis of one firm (ACC limited) from the target firms is shown below.

## Graph of Returns Series: (Source: EViews)

1. returns graph.bmp

## Histogram and Descriptive statistics of a Return: (Source: EViews)

returns histogram.bmp

## Result of Unit Root Test:

2. ADF.bmp

## (Source: EViews and Researcher’s own calculation)

The NULL Hypothesis states that Returns has a unit root, meaning the data is non stationary. From the ADF test statistic, since the t-value is statistically significant at 1%, 5% and 10% levels we reject the null hypothesis and say the data is stationary. If the ADF test statistic was not statistically significant then we would take the first difference of the series and tested for stationarity.

## Correlogram of Returns:

3. corrof ret.bmp

## (Source: EViews and Researcher’s own calculation)

The coefficient values in the correlogram are zero or near zero within 95% confidence limits. This shows that the return series is stationary.

## Estimated Equation of Returns:

4. arma22eqn.bmp

## (Source: EViews and Researcher’s own calculation)

At 5% level the Prob value shows that AR (2) and MA (2) are significant.

The criterions to judge for the best model are as follows:

Relatively small of Schwarz criterion

Relatively small of Standard Error of Regression

Relatively high adjust R2

Q- Statistics and correlogram show that there is no significant pattern left in the ACFs and PACFs of the residuals, it means the residuals of the selected model are white noise.

## Correlogram of Residuals:

5. corr of resid.bmp

## (Source: EViews and Researcher’s own calculation)

The correlogram of residuals show that there is no significant pattern in the ACFs and PACFs of the residuals, it means the residuals of the selected model are white noise. We can stop at here and don't need to further consider another AR (p) and MA (q).

## Graph of forecasted returns:

7. fg.bmp

## (Source: EViews and Researcher’s own calculation)

The Theil Inequality Coefficient is an indicator of the quality of forecasts.

The Bias Proportion indicates to what extent the forecasts deviate systematically from the level of observation. A low value shows that this unsatisfactory feature of the model is not very important.

The Variance Proportion indicates whether the variability of forecasts deviates from the variability of the data. In the case of dynamic forecasts this proportion is usually high, because forecasts tend to be constant.

The Covariance Proportion is a measure of unsystematic errors and should ideally be equal to 1.0. A large proportion indicates satisfactory forecasting quality

## Graph of actual v/s forecasted returns: (Source: EViews and Researcher’s own calculation)

9. a vs f.bmp

## ARMA Model fit for all selected firms: (Source: EViews and Researcher’s own calculation)

## TARGET FIRMS

## ACQUIRER FIRMS

## Firm

## Model

## Firm

ACC

ARMA(2,2)

ACC

AIRTEL

AR(6)

AIRTEL

AXIS BANK

MA(4)

CIPLA

CAIRN-SESAGOA

ARMA(23,23)

HCL TECH

CAIRN-VEDANTA

ARMA(16,16)

ICICI

CIPLA

AR(1)

IDFC

GRASIM

ARMA(1,1)

M&M

HCLTECH

AR(1)

ONGC

HDFC

ARMA(1,1)

PNB

HINDALCO

MA(10)

RCOM

ICICI

ARMA(4,4)

RIL

JINDAL STEEL

ARMA(1,1)

RPOWER

KOTAK BANK

AR(4)

SBI

L&T

ARMA(2,2)

SESAGOA

ONGC

ARMA(1,1)

TATA MOTORS

PNB

MA(5)

TATA STEEL

RANBAXY

MA(8)

## Table 4: ARMA Model fit for all selected firms.

SBI

MA(14)

SIEMENS

ARMA(6,6)

TATA MOTORS

MA(21)

TATA POWER

ARMA(3,13)

TATA STEEL

ARMA(1,1)

The Abnormal Returns calculated for SAMPLE 1 firms is tabulated below.

## -5

## -4

## -3

## -2

## -1

## 0

## 1

## 2

## 3

## 4

## 5

## ACC

-0.0080

-0.0067

-0.0150

-0.0505

-0.0557

0.0168

0.0031

-0.0690

-0.0391

-0.0431

-0.0664

## AIRTEL

0.0127

0.0017

-0.0038

-0.0019

-0.0116

0.0151

0.0113

0.0299

0.0164

-0.0138

0.0063

## AXIS BANK

0.0167

0.0560

-0.0076

0.0049

-0.0404

-0.0587

-0.0095

-0.0093

-0.0057

0.0271

0.0461

## CAIRN-SESAGOA

0.0122

0.0025

0.0329

-0.0154

-0.0413

-0.0247

0.0093

0.0044

-0.0133

-0.0165

-0.0134

## CAIRN-VEDANTA

-0.0180

-0.0062

-0.0072

0.0103

0.0451

-0.0671

0.0235

0.0164

0.0085

0.0012

-0.0001

## CIPLA

-0.0168

-0.0060

0.0168

-0.0157

-0.0113

0.0282

0.0493

0.0079

-0.0297

-0.0012

0.0058

## GRASIM

-0.0183

-0.0225

-0.0220

-0.0099

0.0015

-0.0069

0.0339

-0.0478

0.0010

0.0151

0.0015

## HCLTECH

0.0057

-0.0007

0.0172

0.0102

0.0153

0.0158

-0.0028

-0.0145

-0.0236

-0.0127

-0.0296

## HDFC

-0.0125

-0.0061

0.0565

-0.0352

0.0351

-0.0286

0.0415

-0.0228

0.0219

-0.0158

0.0638

## HINDALCO

-0.0057

0.0315

0.0015

-0.0089

-0.0264

0.0097

-0.0108

-0.0693

-0.0090

-0.0103

-0.0255

## ICICI

0.0344

-0.0297

-0.0083

0.0077

0.0111

-0.0003

0.0036

0.0096

0.0196

0.0010

0.0068

## JINDAL STEEL

-0.0318

0.0035

-0.0174

-0.0152

-0.0027

-0.0082

-0.0038

-0.0064

-0.0051

0.0192

0.0514

## KOTAK BANK

-0.0085

-0.0352

-0.0007

0.0173

-0.0257

0.0286

-0.0014

-0.0096

0.0009

0.0114

-0.0176

## L&T

-0.0193

-0.0128

0.0089

-0.0091

-0.0215

-0.0180

0.0071

0.0116

-0.0045

-0.0049

-0.0092

## ONGC

0.0263

-0.0229

0.0169

-0.0369

0.0598

-0.0872

0.0209

-0.0214

0.0364

-0.0243

0.0208

## PNB

0.0231

-0.0104

0.0158

0.0010

-0.0005

-0.0102

-0.0108

-0.0198

-0.0454

0.0409

-0.0619

## RANBAXY

-0.0320

0.0156

-0.0441

0.0233

0.0549

-0.0362

-0.0395

0.0486

0.0228

0.0079

0.0552

## SBI

-0.0082

-0.0275

-0.0140

0.0035

0.0083

-0.0901

-0.0620

0.0308

-0.0096

0.0222

-0.0018

## SIEMENS

0.0012

-0.0098

0.0233

-0.0067

-0.0154

0.1572

-0.0133

0.0015

-0.0066

0.0038

-0.0080

## TATA MOTORS

0.0317

0.0041

0.0280

-0.0349

-0.0609

-0.0155

-0.0332

-0.0030

0.0062

0.0355

-0.0256

## TATA POWER

0.0083

-0.0600

0.0527

0.0205

0.0163

-0.0191

-0.0023

-0.0060

0.0125

-0.0206

0.0311

## TATA STEEL

-0.0427

-0.0344

0.0487

-0.0113

-0.0122

0.0310

-0.0048

0.0015

-0.0066

-0.0040

-0.0058

## Table 5: Abnormal Returns of target firms (sample 1).

## t- Statistic for the above is:

## -5

## -4

## -3

## -2

## -1

## 0

## 1

## 2

## 3

## 4

## 5

## ACC

2.5907

-3.0102

0.4528

-0.5106

0.6897

-0.4426

0.0417

-0.1436

-1.0550

-1.2362

0.8200

## AIRTEL

1.8240

0.2914

1.4770

-3.2554

-1.4662

0.8136

0.0225

0.1122

0.0086

0.5519

-0.6205

## AXIS BANK

2.6087

2.8729

2.4721

1.4941

-1.3367

-1.9656

-2.0245

-1.3376

0.6345

0.3335

0.9587

## CAIRN-SESAGOA

2.2401

-2.5973

1.1720

0.9563

2.1264

2.3801

0.4326

-1.1886

0.9843

-0.0244

-0.2391

## CAIRN-VEDANTA

-74.8188

0.9067

-3.4168

0.0515

-2.0796

-3.5604

0.2090

-0.4051

0.1299

-1.3669

0.3703

## CIPLA

2.8154

1.5183

-0.4131

-1.9530

-0.3129

-0.9523

-1.6103

-1.0174

-0.4739

-2.3384

2.1848

## GRASIM

-6.2802

-0.2038

-3.4984

0.6437

-2.5442

1.5059

-0.0888

0.7676

0.7638

-0.3206

-0.7783

## HCLTECH

2.1277

2.0517

-2.6135

-2.3764

1.8458

0.1776

1.4487

-1.8242

-0.0830

-0.1461

-1.0588

## HDFC

3.4808

3.1313

-2.5352

-0.7243

0.4723

-0.6670

0.4400

-0.6451

0.5163

0.4198

0.7730

## HINDALCO

3.7526

2.4396

2.2634

-0.8270

-0.7674

-1.7090

-0.7619

-0.8453

0.4003

1.2458

0.8479

## ICICI

4.0897

3.1089

1.1856

-2.0946

0.1873

-0.1225

1.5483

-0.6566

0.3233

-0.2870

0.4924

## JINDAL STEEL

3.7963

-2.2813

-0.8145

-0.3696

1.8361

1.7245

-1.0057

-1.0180

-0.2087

1.5972

0.5943

## KOTAK BANK

-3.7395

0.0471

2.4831

-1.9506

-0.0115

-0.0076

-0.8028

0.8834

-0.7599

-0.9270

0.4885

## L&T

5.9216

4.0473

-1.5411

3.2397

1.5839

-1.2816

-0.3699

1.0796

0.4511

-0.7771

-1.1104

## ONGC

8.5309

5.9215

4.1450

-0.3131

0.7011

2.1755

-1.9845

0.4797

-0.1978

-1.1302

2.4139

## PNB

-3.0724

2.5651

2.4633

0.5086

-0.0841

-1.6254

-0.2958

0.4960

0.1180

-0.2817

-0.8467

## RANBAXY

2.5907

-3.0102

0.4528

-0.5106

0.6897

-0.4426

0.0417

-0.1436

-1.0550

-1.2362

0.8200

## SBI

1.8240

0.2914

1.4770

-3.2554

-1.4662

0.8136

0.0225

0.1122

0.0086

0.5519

-0.6205

## SIEMENS

2.6087

2.8729

2.4721

1.4941

-1.3367

-1.9656

-2.0245

-1.3376

0.6345

0.3335

0.9587

## TATA MOTORS

2.2401

-2.5973

1.1720

0.9563

2.1264

2.3801

0.4326

-1.1886

0.9843

-0.0244

-0.2391

## TATA POWER

-74.8188

0.9067

-3.4168

0.0515

-2.0796

-3.5604

0.2090

-0.4051

0.1299

-1.3669

0.3703

## TATA STEEL

2.8154

1.5183

-0.4131

-1.9530

-0.3129

-0.9523

-1.6103

-1.0174

-0.4739

-2.3384

2.1848

## Table 6: t- statistic of Abnormal Returns of target firms (sample 1).

## 10% = 1.383

## 5% = 1.833

## 1% = 2.821

Significant at:

From the above table we can say that significant abnormal returns are observed in the periods of pre-event date rather than during the post event date.

Significant values at 10%: 39.25%

Significant values at 5%: 19.42%

Significant

Values at:

## -5

## -4

## -3

## -2

## -1

## 0

## 1

## 2

## 3

## 4

## 5

10%

## 100%

## 72.72%

## 63.63%

## 45.45%

## 40.9%

## 50%

## 27.27%

## 4.54%

## 0%

## 1.36%

## 1.36%

5%

## 40.90%

## 27.27%

## 31.81%

## 22.72%

## 31.81%

## 22.72%

## 1.36%

## 0%

## 0%

## 9.09%

## 1.36%

## Table 7: Percentage of significant Abnormal Returns for target firms during the event window.

From the above observation we can say that abnormal returns are concentrated during the periods of pre-event data and investors can earn significant returns during these periods.

Abnormal Returns during the event day is not so significant in all cases. This may be due to information leakage, Insider Trading activities etc

## Figure 4: Graph of Average Abnormal returns of target firms (sample 1).

## (Source: EViews and Researcher’s own calculation)

Average Abnormal returns show significant negative returns during the event date.

## Figure 5: Graph of Cumulative Average Abnormal returns of target firms (sample 1).

## (Source: EViews and Researcher’s own calculation)

The cumulative average abnormal returns show negative abnormal returns during the event window. This suggests that on an average for the sample selected the stock prices declined during the event window period.

The Abnormal Returns calculated for SAMPLE 2 firms is tabulated below.

## -5

## -4

## -3

## -2

## -1

## 0

## 1

## 2

## 3

## 4

## 5

## ACC

0.0231

-0.0525

0.0081

-0.0092

0.0130

-0.0084

0.0008

-0.0027

-0.0207

-0.0256

0.0180

## AIRTEL

0.0119

0.0021

0.0158

-0.1067

-0.0521

0.0306

0.0008

0.0043

0.0003

0.0217

-0.0246

## CIPLA

0.0026

0.0101

0.0148

0.0103

-0.0101

-0.0186

-0.0246

-0.0178

0.0086

0.0046

0.0139

## HCL TECH

0.0022

-0.0257

0.0118

0.0097

0.0255

0.0373

0.0068

-0.0211

0.0179

-0.0004

-0.0044

## ICICI

-0.0167

0.0064

-0.0270

0.0005

-0.0199

-0.0903

0.0055

-0.0107

0.0035

-0.0382

0.0108

## IDFC

0.0262

0.0172

-0.0047

-0.0264

-0.0042

-0.0132

-0.0249

-0.0163

-0.0076

-0.0522

0.0705

## M&M

-0.0104

-0.0005

-0.0478

0.0093

-0.0533

0.0375

-0.0022

0.0201

0.0210

-0.0088

-0.0218

## ONGC

0.0029

0.0050

-0.0089

-0.0106

0.0110

0.0011

0.0102

-0.0150

-0.0007

-0.0012

-0.0091

## PNB

0.0145

0.0484

-0.0679

-0.0200

0.0132

-0.0191

0.0127

-0.0191

0.0154

0.0126

0.0239

## RCOM

0.0546

0.0455

0.0527

-0.0207

-0.0205

-0.0582

-0.0273

-0.0322

0.0153

0.0504

0.0350

## RIL

0.0227

0.0295

0.0115

-0.0318

0.0028

-0.0019

0.0259

-0.0115

0.0057

-0.0051

0.0088

## RPOWER

0.0177

-0.0192

-0.0074

-0.0034

0.0195

0.0208

-0.0131

-0.0144

-0.0030

0.0255

0.0096

## SBI

-0.0396

0.0005

0.0549

-0.0514

-0.0003

-0.0002

-0.0216

0.0254

-0.0221

-0.0278

0.0150

## SESAGOA

0.0139

0.0219

-0.0124

0.0725

0.0390

-0.0360

-0.0106

0.0321

0.0134

-0.0243

-0.0382

## TATA MOTORS

0.0097

0.0069

0.0242

-0.0020

0.0045

0.0153

-0.0230

0.0055

-0.0023

-0.0155

0.0439

## TATA STEEL

-0.0374

0.0514

0.0770

0.0161

-0.0027

-0.0604

-0.0110

0.0187

0.0045

-0.0107

-0.0334

## Table 8: Abnormal Returns of acquirer firms (sample 2).

## t- Statistic for the above is:

## -5

## -4

## -3

## -2

## -1

## 0

## 1

## 2

## 3

## 4

## 5

## ACC

-1.1342

-0.6510

-1.3206

-3.8733

-3.6411

0.7846

0.1295

-2.5768

-1.4526

-1.5830

-2.2507

## AIRTEL

5.6969

0.6485

-0.9581

-0.4164

-1.6223

1.9487

1.4227

2.7105

1.4211

-1.0558

0.4808

## CIPLA

3.5424

3.1501

-0.4191

0.2717

-1.7998

-1.9891

-0.3188

-0.3109

-0.1910

0.8722

1.3534

## HCL TECH

2.1473

0.3945

2.3960

-1.0971

-2.2950

-1.3025

0.4749

0.2231

-0.6638

-0.8150

-0.6581

## ICICI

-3.0669

-0.9995

-1.0753

1.3937

2.8354

-2.5176

0.8493

0.5824

0.3000

0.0418

-0.0033

## IDFC

-2.7566

-0.9016

2.0869

-1.5890

-1.0479

2.0822

2.4575

0.3916

-1.3173

-0.0533

0.2571

## M&M

-5.0114

-3.6431

-2.8092

-1.2558

0.1781

-0.8330

2.2137

-2.3815

0.0497

0.7080

0.0715

## ONGC

2.3997

-0.2726

2.6601

1.3591

1.6557

1.4642

-0.2608

-1.2602

-1.7599

-0.9183

-1.8032

## PNB

-1.8507

-0.7390

3.2886

-1.5900

1.4882

-1.0810

1.4630

-0.7565

0.7207

-0.5039

1.7796

## RCOM

-3.3374

2.3142

0.1028

-0.6266

-1.7580

0.5944

-0.6587

-2.8139

-0.3657

-0.4170

-1.0199

## RIL

3.7074

-2.0646

-0.5524

0.5136

0.7346

-0.0192

0.2374

0.6288

1.2266

0.0646

0.4242

## RPOWER

-3.3202

0.3562

-1.5945

-1.2902

-0.2315

-0.6867

-0.3164

-0.5279

-0.4227

1.3929

2.3700

## SBI

-5.5800

-3.4903

-0.0737

1.4301

-1.8395

1.6527

-0.0792

-0.5498

0.0543

0.6317

-0.9451

## SESAGOA

-4.7828

-2.8548

1.3399

-1.3675

-2.6256

-2.0076

0.7096

1.0106

-0.3909

-0.4263

-0.8009

## TATA MOTORS

3.0417

-2.0645

1.3604

-2.1919

2.3388

-2.3342

0.5512

-0.5559

0.9025

-0.5935

0.5012

## TATA STEEL

2.4201

-1.0797

1.3147

0.0819

-0.0393

-0.8147

-0.8560

-1.4992

-2.5326

1.7406

-2.1208

## Table 9: t- statistic of Abnormal Returns of acquirer firms (sample 2).

## 5% = 1.833

## 1% = 2.821

## 10% = 1.383

Significant at:

Significant values at 10%: 40.34%

Significant values at 5%: 18.75%

Significant

Values at:

## -5

## -4

## -3

## -2

## -1

## 0

## 1

## 2

## 3

## 4

## 5

10%

## 93.75%

## 43.75%

## 37.5%

## 18.75%

## 68.75%

## 50%

## 25%

## 31.25%

## 25%

## 18.75%

## 31.25%

5%

## 31.25%

## 18.75%

## 25%

## 6.25%

## 25%

## 37.5%

## 12.5%

## 25%

## 6.25%

## 0%

## 18.75%

## Table 10: Percentage of significant Abnormal Returns for target firms during the event window.

From the above observations we can say that abnormal returns are mostly concentrated during the periods of pre-event data and investors can earn significant returns during these periods. At 5% level of significance we observe that for six out of sixteen firms’ i.e. 37.5% of them showed significant Abnormal Returns during the event day. But in most cases abnormal returns are concentrated during the periods of pre-event data and investors can earn significant returns during these periods.

## Figure 6: Graph of Average Abnormal returns of acquirer firms (sample 2).

## (Source: EViews and Researcher’s own calculation)

## Figure 7: Graph of Cumulative Average Abnormal returns of acquirer firms (sample 2).

## (Source: EViews and Researcher’s own calculation)

The cumulative average abnormal returns trend shows significant fall in abnormal returns during the event window. This suggests that on an average for the sample selected the stock prices declined during the event window period.

## The working for all the firms can be found in the below files:

## CONCLUSION:

The findings of this study shows that M&A deals is an important source of information for the investors. The investors can earn significant returns during an M&A deal.

We find that M&A deals for S&P CNX Nifty sample companies selected show on an average negative impact during the event day. But in most of the cases, abnormal returns of firm show that this trend reverses. We find that the abnormal returns fall during the event date and a couple of days surrounding event date.

But the Abnormal Returns during the event day is not so significant in all cases. We can attribute this to information leakage, Insider Trading activities or anticipation of information by investors.

## SCOPE FOR FURTHER STUDIES:

The study can be conducted using the Market Model by Brown and Warner for calculating the expected returns and can be compared with the Box-Jenkins Methodology.

The Study can be conducted for varying event windows say [-10,+10], [-10,+5], [-15,+10], [-25, +15], [-30, +30] etc

The estimation window can be further increased to one year/two year for robust estimation.

The sample size can be increased for more accurate results.

Study can be conducted using samples where the firms which have undergone a deal are both listed in the same stock exchange. The abnormal returns of the target company and the acquirer company can thus be compared.

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