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Online Open Access publishing platform for Management Research Copyright by the authors - Licensee IPA- Under Creative Commons license 3.0 Research Article ISSN 2229 3795 Shareholder s wealth creation in mergers and acquisitions in Indian IT industry: An event study Anirban Ghatak 1, Aamukta 2 1- Associate Professor, Inst. Of Management, Christ University, Bangalore 2- Research Scholar, Christ University, Bangalore aamukta@res.christuniversity.in ABSTRACT The aim of this paper is to analyze if acquiring firm shareholders ascertain positive or negative abnormal returns around Information Technology merger announcement. The study has employed event study methodology to estimate abnormal returns. The study includes sample size of 52 mergers in Information Technology from 2006-2015. As daily stock data is used to ascertain abnormal returns, there is an issue of non-synchronous trading. The models like Scholes and William, Dimson aggregated coefficient and Fowler and Rorke are employed to ascertain unbiased beta and alpha value (model parameters). The other model used are mean adjusted model and market adjusted model are employed for robustness check. Market model is the core model to ascertain the effect of Information Technology merger announcement on the shareholder s abnormal return. The research has employed a short event window of 61 days and focusses on the narrow 3- day window to capture significant effect of Information technology merger announcement on shareholders wealth. The findings suggest that acquiring firm shareholders have gained a positive and insignificant abnormal returns from the Information Technology merger announcement. Keywords: Mergers and acquisitions, Event study methodology, Acquiring firm, Information technology, Shareholder wealth creation. 1. Introduction The fundamental question asked by many researchers is, how value can be created through mergers and acquisitions? The reason being for any firm, shareholder s wealth maximization is the goal. Koller, Goedhart and Wessels (2005, pg.19) argue that firms devoted towards value creation can create more opportunities, stronger economies and better living standards for individuals. Moreover, Hillman and Keim (2001) argue that developing strong associations with shareholders can enhance firm s ability to develop intangible assets that create competitive advantage. There are many ways in which merger can create wealth for bidder firm shareholders. According to traditional view of wealth creation through mergers is that, the overall benefit to the shareholder is achieved when the unification of new firm is more valuable than the addition of the independent firm ( Pilloff and Santomero (1996)). The economic rationale of merger is that the benefit of a merger can be obtained through expense reduction, through increase in monopoly power and through achieving economies of scale and scope (Pilloff and Santomero (1996); Kiymaz (2004)). Berger and Hannan (1998) and DeYoung et al. (2007) argue that large sized firms determine higher prices their by generating higher profits. 200

Over the years, increase in number of merger and acquisitions has caught the attention of researchers who are curious to know the influence of merger announcement on shareholder s wealth creation. The purpose of this research is to analyze whether acquiring firm shareholders ascertain positive or negative abnormal returns around Information Technology merger announcement. The analysis is extended to 52 mergers during the period 2006 to 2015. This paper differs from previous studies on two aspects. First, this paper introduces three new merger event study models. The previous studies have employed standard market model, market adjusted model and mean adjusted model to ascertain abnormal returns. Chuang (2010), Scholes, Fowler and Dimson exhibit the importance of using -Scholes and William model, Fowler and Rorke model and Dimson aggregate coefficients model to overcome the problem of nonsynchronous trading. These three models will help I overcoming the bias in estimating abnormal returns. Therefore, this paper will employ these three models along with market model, market adjusted model and mean adjusted model. Second, this paper will introduce a new variable- merger advisor which will help explaining variation in abnormal returns. Previous empirical studies have explored the possibility of investment banker, investment bank reputation, and top 10 investment banks to explain variation in abnormal returns. Further sections in this paper include-literature review, research methodology, analysis of the data and finally research implication and future research work. 2. Literature review According to the prior literature there are three major motives of mergers. They are synergy, hubris and agency motive (Berkovitch and Narayanan (1993); Zhang (1998); Chuang (2010)). The synergy motive suggests that mergers occur when the consolidated firms result in economic gains. Managers aim to maximise shareholder wealth by engaging in transactions which result in gains for the firm (Berkovitch and Narayanan (1993); Zhang (1998); Becher (2000); Lensink and Maslennikova (2008); Carline et al. (2009)).). This implies that acquirers gain during the takeover activity. The hubris motive suggests that managers may overpay to targets due to errors in valuation (Roll (1986); Berkovitch and Narayanan (1993); Zhang (1998); Becher (2000); Lensink and Maslennikova (2008)). Lensink and Maslennikova (2008) argue that The acquirer mistakenly (because of hubris or selfconfidence) believes that the value of the target is higher than its actual market value. As a result, the bidder overpays and realises negative gains while shareholders of the target realise a profit. (p. 186). Agency motive suggests that managers engage in mergers to accomplish their motivate at shareholders expense (Berkovitch and Narayanan (1993)). Carline et al. (2009) also argue that managers aim to increase firm size to pursue their own interests. The merger may not necessarily create value for acquirers, if the merger is to accomplish manager s motive. Chuang (2010) employed a 61day event window and estimation period of 256 days. The sample for the study is 1424 bidder firms. The author had employed all three models given by Scholes and William, Dimson and Fowler and Rorke to examine if there exists the issue of non-synchronous trading. The results state that bidder firm experienced a CAR of -0.63% over a 3-day (-1,+1) window. U.S. bidder firm obtain Cumulative Abnormal Return of - 0.91% over a 3-day (-1,+1) window, compared to Cumulative Abnormal Return of 0.97% for bidder firm from the markets outside the U.S. and EU market and Cumulative Abnormal Return of -2.55% for EU bidder firm. Bidder firm obtain higher Cumulative Abnormal Return of 0.05% for deals that are diversified compared to CAR of -0.89% for deals that are 201

focused over a 3-day window. Bidder gains more when the mode of payment is cash. Sarwar (2015) studies the impact of mergers in US pharmaceutical on acquirer shareholders wealth. The final sample size is 17 acquisitions out of which 85% target firms are from US and remaining 15% are foreign companies. The event window for this study is 61 days and estimation period is 150 trading days. Model used to calculate expected return is market model. The result states that acquiring company shareholders did not gain returns from merger announcement. Abnormal return on the event day was 0.07% and abnormal return the day after event day was 0.29%, both are not statistically significant. Cumulative abnormal return was zero one day after the event day. The acquirer company experienced a positive Cumulative Abnormal Return of 2.78% on (t+19) event window. Overall the acquiring company shareholders gained positive and insignificant returns from merger announcements. Cybo-Ottone and Murgia (2000) analysed a sample of 54 bank mergers from 14 European banking markets for the year 1988 to 1997. The authors report that Cumulative Abnormal Return of bidding firms are 0.99% over a 3- day (-1,+1) window, significant at 0.01. Cybo-Ottone and Murgia state that the results for European bidder banks are significantly different from US bidder banks as U.S. bidders in general experience negative announcement returns. Cumulative abnormal returns of acquiring firms in cross-borders deals are higher than those in domestic deals, at 2.00% and 0.19%, respectively, over an 11-day (-10,0) window, both are statistically insignificant. With a larger sample size of 187 mergers, Goergen and Renneboog (2004) studied the effect of European domestic and cross border takeover bid on shareholders wealth creation. The results of the study suggest that the acquiring firm shareholders had gained positive abnormal return of 0.7% on the announcement day. Over a 5-day window, bidder firm shareholders earn cumulative abnormal return of 1.5% when bidder firm acquires firm in UK compared to abnormal return of 0.9% earned by bidder firm shareholders when bidder firm acquires firm located in Continental Europe. The bidding firm shareholders earn abnormal return of 1% when the method of payment is equity than abnormal return of 0.4% when the mode of payment is cash. Bowers and Miller (1990) state that, choice of investment banker can influence shareholders wealth in two ways: (a) help in searching for acquisition candidate and (b) negotiate the acquisition on favorable terms for the firm they are representing. Investment bankers are divided into two groups: first-tier bankers and second-tier bankers. The results show that the acquiring firm shareholders had gained positive and higher cumulative abnormal return of 3% when they choose first tier investment banker than the second tier investment banker. Authors also state that, first tier investment bankers have expertise in choosing acquisition partners that result in creating wealth shareholders. 3. Hypothesis Hypothesis for acquiring firm shareholders abnormal return H0: Acquiring firm shareholders ascertain positive and significant abnormal return around Information Technology merger announcement. H1: Acquiring firm shareholders ascertain positive and insignificant abnormal return around Information Technology merger announcement. Hypothesis for cross sectional regression analysis 202

H0: There is no positive association between the acquiring firms cumulative abnormal return and the merger advisor. H1: There is a positive association between the acquiring firms cumulative abnormal return and the merger advisor. H0: There is no positive association between the acquiring firms cumulative abnormal return and cross-border deals vs domestic deals. H1: There is a positive association between the acquiring firms cumulative abnormal return and cross border deals vs domestic deals. H0: There is no positive association between the acquiring firms cumulative abnormal return and merger financing. H1: There is a positive association between the acquiring firms cumulative abnormal return and merger financing. 4. Database and sample selection For computing the influence of Information Technology merger announcement on acquiring firm shareholders wealth creation, the mergers announced during the period 2006 to 2015 were considered for the research. For the purpose of research, only publicly listed Indian Information Technology companies were considered. The information on the announcement date, method of payment, merger advisor and cross border vs domestic deal was obtained from the press release, the economic times, the financial express and wall street journal for the period. Historical Daily stock price data and Historical Index data were taken from Bombay stock exchange website. The index taken for the study is S&P BSE IT as all the publicly listed Information Technology companies trade under this index. The study considered only those acquisitions where the method of payment and cross border vs domestic deal were clear. The final sample size is 52 acquisitions. 5. Methodology Fama et al. (1969) developed event study methodology to examine the efficiency with which the market adjusts to new information. Mackinlay (1997) argues that there are two common choices for modelling the expected return, that is market model and the mean adjusted returns model. However, Brown and Warner (1985) argue that there are 3 models to compute expected return- the market model, the market adjusted returns model and the mean adjusted returns model. Dyckman et al. (1984) state that these three models have similar abilities in computing abnormal returns. As Strong (1992) and Bessler and Murtagh (2002) report that the most popular model is the market model to calculate expected return. The market model is the core model in ascertaining the influence of Information Technology merger announcement on shareholder wealth creation. Hart and Apilado (2002) point out that the standard market model assumes linearity, homoscedasticity, and independence in stock returns. (p. 314). In addition, Eckbo (1983) similarly argues that the regression coefficients of the market model reflect systematic co-movements of the share return with the return on the market portfolio, while the serially uncorrelated, zero mean error term picks up the impact of non-market factors (such as firm- or industry-specific) information events and random price fluctuations. (p. 251). Linn and McConnell (1983) also argue that according to this model each security s period t return is expressed as a linear function of the contemporaneous return on the market portfolio plus a stochastic error term which reflects security specific effects. (p.375). Estimation period is required to compute the model parameters (Alpha and 203

Beta). According to Bartholdy et al. (1994) the standard estimation period is between 200 and 250 observations. (p. 228). The estimation period taken for this study is 256 days. The expected return is calculated as: Rit = α i + βi R m,t, where αi and βi are model parameters. Rmt is the return on market index on day t. Rit is the return on stock i on day t. The abnormal return (AR) for a security i on day t is calculated as: AR i,t = R i,t- (α i + βi R m,t), where Rit is the actual return of a particular company s security i on day t and (α i + βi R m,t) is the expected return on day t. Standard length of event window should range from 21 days to 121 days (Peterson, 1989). This paper follows the study of Chuang (2010), Peterson and Peterson (1991) and Kiymaz and Mukherjee (2001) who used 61day event window (-30, +30), where 0 is the announcement day. The rationale behind using a shorter event window is that, according to Goergen and Renneboog (2004), the measurement error may be substantial when using narrow event windows especially if there was a leakage of information before the first mention in the financial press. (p. 16). Additionally, Caves (1989) argue that it is complicated to capture longer run returns following the transaction. Moreover, this paper focusses on the narrow 3- day event window to capture significant impact of Information Technology merger announcement on shareholders wealth. (-1, 1) (Ekholm, Chuang 2010). Daily stock price data is applied to investigate the shareholder wealth (Asquith et al. (1983); Eckbo (1983); Brown and Warner (1985); Zhang (1998); Cybo-Ottone and Murgia (2000); Akhigbe at al. (2004); Gleason et al. (2006)). However, prior studies argue that when using daily stock price data, there is a problem in calculating the market model parameters due to the issue of infrequent trading (Scholes and William (1977); Dimson (1979); Fowler and Rorke (1983); Cohen et al (1983); Chuang (2010)). To overcome the bias, the three models used are - Scholes and William model, Dimson aggregated coefficients model and Fowler and Rorke model. This paper will use all the three models to overcome the bias of infrequent trading. Scholes and Williams (1977) method simply assumes that the share does not trade for any day and for the following day. Therefore, the estimators of alpha and beta can be calculated as: βi = βi - + βi + βi + αi = ----1------- it - β ----1------- m,t 1+2 ρm T-2 T-2 where, βi and αi are the model parameters that are adjusted to the problem of infrequent trading, βi -, βi and βi + are the coefficients of simple regression of security return against lagged, lead and synchronous market return, ρ m is the coefficient of first order serial correlation for market return, R i,t is the return of security i on day t, R m,t is the return of the market on day t and T is number of days in estimation period. The alpha value is estimated for 254 days ( -285 to -32 days). According to Chuang (2010) and Dimson (1979), Scholes and William method will not be able to obtain accurate unbiased beta value if the shares are traded in every fourth period. To overcome the shortcoming the Dimson had suggested a model. β = i,k Where, β is the unbiased beta estimate when using Dimson aggregated coefficients method.βk is the coefficient of multiple regression of stock return and market return from day t-n to day t+n. n denotes share trade in every n period. Alpha coefficient is estimated in estimation period of 250 days ( -283 days to -34 days). Fowler and Rorke method was developed to obtain unbiased beta estimate of model parameters. According to Fowler and Rorke (1983); Chuang (2010) Dimson method cannot generate unbiased beta estimate in accordance with Scholes 204

and William. Therefore Fowler and Rorke method is developed in accordance with Scholes and William method. This model is taken from study of Chuang (2010). The formula is βi = (1+ ρ1+ ρ2 ) β -2 + (1+2 ρ1+ ρ2 ) β -1 +β 0 + (1+2 ρ1+ ρ2 ) β +1 + ( 1+ 2 ρ1+ 2 ρ2 ) ( 1+ 2 ρ1+ 2 ρ2 ) ( 1+ 2 ρ1+ 2 ρ2 ) (1+ ρ1+ ρ2 ) β +2 ( 1+ 2 ρ1+ 2 ρ2 ) Where, βi is the unbiased beta estimate, βi -2, βi -1, βi 0, βi +1, βi -2 - the beta estimates obtained from simple regression of stock return against lag 1, lag 2, synchronous, lead 1 and lead 2 of market return respectively. ρ1, ρ2 are the first and second order serial correlation of market return respectively. Fowler and Rorke method assumes that the first and second order serial correlation coefficients are non-zero but other serial order correlation coefficients are zero. Therefore, alpha value is estimated from period -284 to -33 days. The formula is: αi = ----1------- it - β ----1------- m,t AR i,t = R i,t- (α i + βi R m,t) 252 252 Market adjusted returns model- Brown and Warner (1980) argue that the market adjusted returns model takes into account market wide movements which occurred at the same time that the sample firms experienced events. (p. 213).E(Ri,t)= E(Rm,t) = Kt, for all stock I, where, E(Ri,t) is the expected return of security i on day t and E(Rm,t) is the expected return for market on day t. Kt is a constant on day t. Return of security i on day t will be- Ri,t = Rm,t+ Ei,t Abnormal return formula is: AR i,t = R i,t R m,t.the market adjusted returns model can be regarded as restricting the market model alpha to be 0 and beta to be 1 (Maynes and Rumsey (1993)). Thus, E (R i,t) = Ki, for all stock i, E(R i,t) is the expected return for stock i on day t and Ki denotes constant for stock i. Thus, AR i,t = R i,t - Ki. The expected return (Ki) is taking the average of stock return during the estimation period. Here the estimation period is 256 days (-286, -31). The formula is Ki = 1 256 it Cross sectional t-statistics, Wilcoxon sign test and Sign test are used to examine the significance level for mean abnormal return. Strong (1992) argues that the most naive test procedure (cross sectional t- statistics) would be to calculate the average abnormal return and its standard error across event securities to give a t-statistic as follows: (p. 544, 545). Dodd (1980) employed the cross-sectional t-statistic to test whether mean abnormal return is 0. t = ut ut = 1 S(ut)/ n n where, ut = is the mean abnormal return across securities on day t, s(ut) = is the standard deviation of the mean abnormal return across securities on day t, a = is the abnormal return for stock i on day t and n = number of securities.corro and Zivney (1992) argue that the sign test statistic does not require normal distribution of the mean abnormal return. Zivney and Thompson (1989) suggest that a properly specified sign test may provide a more powerful test for ARs than the t-test in event studies. Brown and Warner (1980) argue that In the sign test for a given sample, the null hypothesis is that the proportion of sample securities having 205

positive measures of abnormal performance is equal to 0.5; the alternative hypothesis is that the proportion of sample securities having positive performance measures is greater than 0.5. (p.218). Z = P- 0.5 - ½ N 0.5* 0.5/N Where, P is the proportion of AR on day t with positive sign and N is number of securities. The Wilcoxon signed rank test has been performed in prior empirical studies (Bradley (1988); Becher (2000); Becher and Campbell (2005); Ismail and Davidson (2005)). Becher (2000) argues that the Wilcoxon signed rank test relaxes the assumption of normality, but assumes a symmetric distribution. Z= T u σt σ T = n(n+1)(2n+1) 6 T is the sum of signed rank values, σ T is the standard deviation and n is the number of positive and negative mean abnormal return. 6. Analysis The market model parameters Table 1: Model parameters Mean Maximum Minimum SD Negative Positive Sample Alpha 0.031 0.361-0.246 0.131 0.423 0.577 52 Beta 0.733 1.301 0.245 0.269 0 1 52 The model parameters are said to have an effect on the abnormal return of the acquiring firm. As the model parameters are an important component in ascertaining the expected return. Abnormal return is calculated as difference between actual return and expected return. Hence this section will discuss the model parameters for the acquiring firm. The table1 presents the description of the market model parameters for the acquiring firm. The table shows that the mean beta value is 0.733. It is known that market adjusted model assumes that alpha value is 0 and beta value is 1, the mean beta value for market model is less than 1 which indicates that there could be problem of infrequent trading. Hence, in the further sections an analysis on the problem of infrequent trading is provided. The maximum value of beta is 1.301 and minimum value of beta is 0.245. Moreover, it is interesting to find that the percentage of positive beta values is 100% and negative beta values is 0. This indicates that the acquiring firms performance was consistent with the overall market performance. The mean value of alpha is 0.03, which is lower than the beta value. This indicates that the acquiring firms abnormal returns are mainly affected by the beta values. The percentage of positive alpha values is 42.3% whereas the negative alpha value is 57.7%. The problem of infrequent trading Table 2: The market model parameters adjusted to problem of non-synchronous trading 206

Alpha Beta MM SW DM FR MM SW DM FR Mean 0.031 0.039 0.039 0.046 0.733 0.664 0.574 0.593 Maximum 0.361-0.244 0.347 0.49 1.301-0.163 2.122 1.967 Minimum -0.246 0.397-0.499-0.449 0.245 2.084-0.483-0.872 SD 0.131 0.145 0.149 0.17 0.269 0.442 0.544 0.539 Negative 0.423 0.423 0.404 0.442 0 0.019 0.115 0.096 Positive 0.577 0.577 0.596 0.558 1 0.981 0.885 0.904 Sample 52 52 52 52 52 52 52 52 As discussed above, there is a possibility of model parameters getting affected due to problem of infrequent trading. If the model parameters are affected by the problem of infrequent trading, the abnormal returns thus ascertained on the announcement date may be biased. Therefore, this section discusses the model parameters calculated from the models- Scholes and William (SW), Dimson (DM) and Fowler and Rorke (FR), which adjusts for the issue of non-synchronous trading. As per the table 2, the average beta value is 0.664, 0.574 and 0.593 for SW, DM and FR model respectively. The average beta value is 0.733 for MM model which is comparatively higher than the other models. This indicates that the beta values are insensitive to non-synchronous trading adjustment approaches. The mean beta value varies depending upon the model used to adjust for the problem of non- synchronous trading. Additionally, it is the lead and lag market returns that can give different beta values. When considering the mean beta values ascertained from Scholes and William (SW), Dimson Method (DM) and Fowler Method (FR) models respectively, the SW model gives the maximum mean beta value of 0.664, whereas the DM model gives the minimum mean beta value of 0.574. The range in the mean beta value (maximum- minimum) suggests that the abnormal return of the acquiring firm can vary significantly. From the table 2, it can be seen that the percentage of positive beta values are more than the percentage of negative beta values. Higher percentage of positive beta value suggests that acquiring firm s performance is consistent with the market performance. When considering the alpha values, the results suggest that the alpha values ascertained from models SW, DM and FR are 0.039, 0.039 and 0.046 respectively. These values are lower than the beta values. This indicates that cumulative abnormal return (adjusted to issue of nonsynchronous trading) are mainly driven by beta values. Table 3: Mean Mean P -Value Sign Test Wilcoxon Market model 0.095 0.348 0.971 0.342 Scholes and William 0.151 0.589 0.139 0.423 Dimson Method 0.176 0.63 0.139 0.964 Fowler Method 0.186 0.662 1.248 0.464 Day 0: Abnormal Return p- value: cross sectional t-test Wilcoxon test: Wilcoxon signed rank test In order to identify whether the problem of non-synchronous trading is to be taken into consideration when analyzing the acquiring firm announcement returns, this paper presents the results of abnormal returns gained by acquiring firm shareholders on day 0 in the table 3. As the table 3 shows, acquiring firm shareholders mean abnormal returns are 0.151, 0.176 207

and 0.186 on day 0 for SW, DM and FR models respectively, all are statistically insignificant at the level 0.01 (referred to P- value to check the significance level). This indicates that the use of different models to adjust for problem of non- synchronous trading does create a significant difference on the abnormal return for the shareholders of the acquiring firm. When considering the mean abnormal return for all the four methods, the result obtained from SW, DM and FR models shows a significant difference from the market model. Therefore, it can be said that non synchronous trading issue is to be considered when analyzing abnormal returns for the acquiring firm. The problem of non-synchronous trading issues were solved by three different models. Scholes and William method and Dimson method have limitations. Fowler and Rorke method is the method which has overcome the limitations of both the methods. Hence, the analysis of the abnormal return is on the basis of Fowler and Rorke method. Acquiring daily abnormal return and cumulative abnormal return: Day Mean Abnormal Return Table 4: Abnormal return for the acquiring firm shareholders Fowler and Rorke Model P- Value Sign test Wilcoxon Mean Abnormal Return Market Adjusted Model P- Value Sign Test Wilcoxon -30 0.079 0.28-0.139 0.884-0.101-0.369 0.139 0.56-29 -0.095-0.347 0.416 0.722 0.12 0.544-0.139 0.649-28 0.214 0.653 1.248 0.263 0.126 0.384 0.139 0.956-27 0.035 0.131-0.971 0.642-0.06-0.211-0.139 0.695-26 0.37 1.541 1.525 0.175 0.352 1.559 1.248 0.092-25 0.321 1.066 0.971 0.434-0.028-0.102 0.416 0.927-24 0.067 0.259 0.139 0.978-0.051-0.203 0.693 0.884-23 0.022 0.102-0.139 0.899-0.008-0.035-0.693 0.689-22 0.188 0.57 0.416 0.913-0.103-0.424-1.525 0.295-21 -0.244-0.791 0.139 0.33 0.027 0.116 0.693 0.949-20 0.124 0.486 0.416 0.709-0.134-0.611-0.416 0.407-19 -0.138-0.531-0.971 0.209-0.331-1.418-2.635 0.015-18 -0.149-0.436-1.248 0.512-0.285-0.987-1.803 0.133-17 0.204 0.66-1.525 0.729 0.051 0.205-1.525 0.764-16 0.066 0.33 0.693 0.841-0.217-0.851-0.139 0.597-15 0.339 1.441 1.248 0.251 0.319 1.148 0.971 0.222-14 -0.248-1.037-1.525 0.048-0.116-0.646 0.139 0.418-13 0.249 0.821 0.139 0.806 0.231 0.843 0.693 0.402-12 -0.156-0.623 0.693 0.834 0.106 0.437 0.416 0.387-11 0.387 1.077 1.248 0.135 0.455 1.64 2.08 0.04-10 -0.116-0.363-0.971 0.585-0.3-0.961-1.248 0.209-9 0.262 0.496-0.416 0.455-0.185-0.463-3.19 0.011-8 -0.273-0.963-0.416 0.675-0.56-1.961-2.08 0.028-7 0.578 1.74 1.248 0.117 0.23 0.751 1.248 0.585-6 -0.233-0.965-1.803 0.244-0.293-1.182-1.248 0.196-5 0.441 1.351 0.139 0.579 0.266 0.895-0.971 0.949-4 -0.251-0.719-1.525 0.397 0.032 0.106-0.693 0.778 208

-3 0.089 0.381-1.248 0.855 0.176 0.761-0.139 0.736-2 0.678 2.241 1.248 0.056 0.464 1.78 1.248 0.122-1 0.027 0.101 0.139 0.942-0.053-0.195 0.139 0.899 0 0.186 0.668-0.139 0.642 0.175 0.589-0.139 0.554 1 0.641 1.556 0.971 0.316 0.63 1.469 0.971 0.24 2-0.03-0.113 0.139 0.956 0.018 0.068 0.693 0.906 3-0.076-0.174-1.803 0.153 0.331 0.737-0.971 0.56 4-0.292-0.953-2.08 0.084-0.076-0.276-0.693 0.472 5-0.569-1.341-0.139 0.536-0.444-1.149 0.971 0.792 6-0.239-0.639 0.139 0.985-0.245-0.695 0.139 0.863 7-0.358-1.379-0.971 0.119-0.399-1.589-1.525 0.119 8 0.364 1.131-0.693 0.913 0.493 1.631-1.525 0.806 9 0.693 1.571 0.416 0.247 0.491 1.553 2.357 0.126 10 0.36 0.84-0.416 0.978 0.262 0.623-0.416 0.985 11-0.585-2.514-2.08 0.02-0.78-2.907-3.19 0.009 12 0.011 0.032 0.971 0.927 0.247 0.877 0.139 0.397 13-0.006-0.019-0.416 0.489 0.219 0.771 0.139 0.993 14 1.015 2.773 0.971 0.037 0.984 2.74-0.416 0.028 15-0.393-1.038-2.08 0.09-0.554-1.41-2.357 0.042 16 0.019 0.073 0.693 0.669-0.026-0.1 1.525 0.675 17-0.271-0.867 1.248 0.771-0.183-0.593 1.248 0.764 18 0.278 0.845-0.416 0.702 0.121 0.384 0.693 0.899 19 0.252 0.898 0.693 0.316 0.187 0.847 0.139 0.397 20 0.415 1.63 1.248 0.175 0.378 1.348 1.248 0.274 21-0.231-0.853 0.693 0.695 0.051 0.202 1.803 0.412 22 0.084 0.247-1.248 0.623 0.165 0.536 0.139 0.884 23 0.064 0.223-0.416 0.689-0.175-0.796-1.803 0.172 24-0.108-0.509-0.693 0.434 0.123 0.583 0.693 0.729 25-0.205-0.832-0.693 0.193-0.215-0.906 0.139 0.377 26 0.452 1.106 0.139 0.629 0.507 1.227 0.139 0.729 27-0.177-0.63 0.139 0.348-0.151-0.624-0.416 0.477 28 0.329 1.483 0.693 0.303-0.081-0.296 0.693 0.92 29-0.17-0.648-0.971 0.202-0.285-1.344-1.525 0.113 30 0.037 0.148-1.525 0.629-0.043-0.181-0.693 0.757 The empirical results from the previous research on acquiring firms performance around merger announcement is ambiguous as there is no clear consensus on whether the merger announcement create value for the shareholders or not. Some of the previous empirical studies have found that the acquiring firm shareholders gained positive and significant abnormal returns around the mergers announcement and some of the previous empirical studies have found that the acquiring firm shareholders have experienced negative abnormal returns. This section will present results as to ascertain whether the acquiring firm will gain positive or negative abnormal returns. As can be seen in the table 4, the acquiring abnormal returns vary over the event period. For example on the event day i.e. day -2 the acquiring firm shareholders experienced abnormal returns of 0.678 and 0.464 from Fowler and Rorke model and market adjusted model respectively, both of which are statistically insignificant at 0.01 level. Moreover the results show that the bidder firm has experienced 0.186 and 0.175 209

abnormal return on Day 0 from Fowler and Rorke model and Market adjusted model respectively, both of which are insignificant at 0.01 level (the discussion of significance level is based on p- value). It can be noted that majority of the abnormal returns are positive during the event period. Acquiring abnormal returns are also tested using the non-parametric test like sign test and Wilcoxon. The significance level varies across the event period and it is insignificant even on day 0. The abnormal returns have been insignificant on day 0 irrespective of the statistical significance tests used. This indicates that the Information Technology mergers and acquisitions announcement has a positive and insignificant impact on the acquiring firms shareholders return. In addition to acquiring daily abnormal return, the figure captures the movement of cumulative abnormal return during the event period. Figure 1: Cumulative abnormal The figure 1 this shows that the cumulative abnormal returns for all the three models is different from each other and at the same time reported similar for all the three models. The results does not show any downward and upward trend, which implies that there is no information lag and leak. In order to capture the change in acquiring cumulative abnormal return, the cumulative abnormal return is presented for different event windows. Table 5: Cumulative abnormal return Fowler and Rorke Method Market Adjusted Model Event Windows Mean P value Sign test Wilcoxon Event Windows Mean P value Sign test Wilcoxon (-1,1) 0.854 4.64 0.577 0.109 (-1,1) 0.752 3.742 1.153 0.207 0 0.186 0.668 0.139 0.642 0 0.012 0.589 0.139 0.554 (-1,0) 0.213 2.675 0.707 0.1 (-1,0) 0.122 1.065 0.707 0.501 (0,1) 0.827 3.635 0.707 0.1 (0,1) 0.805 3.541 2.121 0.501 The table 5 shows that acquiring firm experiences Cumulative Abnormal Return of 0.854 and 0.752 over a 3-day (-1,+1) event window measured from Fowler and Rorke model and the Market- adjusted model respectively, both statistically insignificant at the 0.01 level. Therefore, it can be concluded that the acquiring firm shareholders experience positive and insignificant abnormal returns around Information Technology merger and acquisition announcement. Positive cumulative abnormal returns suggest that acquiring firm shareholder s gained wealth from Information Technology merger announcements. Overall, the results show that acquirer firm shareholders experience positive returns around Information Technology merger announcement. 210

Regression analysis: This paper provides cross sectional ordinary least square regression analysis to test the effect of merger advisor, cross border vs domestic deal and merger financing on cumulative abnormal return around merger announcement date. The assumption of using ordinary least square regression analysis is that the data should follow normal distribution and explanatory variables or independent variables are not correlated (multicollinearity). Table 6: Variance inflation factor Collinearity Statistics Tolerance Cross border vs domestic deal 0.929 1.077 Merger Advisor 0.929 1.077 Variance Inflation Factor is the test used to see if multicollinearity exists. From table 6, it can be seen that the Variance Inflation Factor is 1.077 which is lower than 3 (Chuang, 2010) which indicates that multicollinearity is not a major issue to be considered in regression analysis. The cumulative abnormal return (CAR) is taken as the dependent variable for regression analysis. The result of the three-day window is used as it captures the major portion of the stock-price effect on the announcement of an acquisition. The following are the explanatory variables: Merger advisor: For the present study legal advisor, investment banker or financial advisor are considered as the merger advisor. To measure the impact of merger advisor on cumulative abnormal return, dummy variable is used. 1= merger advisor advice taken and 0= no merger advisor advice taken. Deal characteristics: include cross border vs domestic acquisitions and Merger financing. Cross border vs domestic acquisitions: cross border acquisitions are those acquisitions when the acquiring company acquires target company that is located across border whereas domestic acquisitions are those acquisitions when the acquiring company acquires target company that is located within the domestic territory. To measure the impact of cross-border vs. domestic deals on the shareholder wealth of the firms, a dummy variable of cross-border deals in the cross-sectional regression analyses. The dummy variable equals to 1 if the deal is classified as cross-border, and 0 in domestic transactions. Merger financing: mergers can be financed either through stock or cash or combination of stock or cash. In the present study all of the acquisitions are financed either through stock or cash. To measure the impact of merger financing on shareholders returns, a dummy variable is used. A dummy variable of 1 is equal to payment is cash and 0 for payment in stock.the multivariate regression model: CAR= α + β1 (merger advisor) + β2 (cross border vs domestic acquisitions) + β3 (merger financing). Table 7: Regression analysis Market Adjusted Model Fowler and Rorke Model Coefficient Value Significance value Coefficient Value Significance value Constant 7.352 0.002 Constant 1.84 0.471 Merger Merger -1.076 0.683 financing financing -1.427 0.624 Cross Cross border vs border vs -5.842 0.036 domestic domestic -0.348 0.908 deal deal VIF 211

Merger Merger -0.247 0.8 1.489 0.17 Advisor Advisor R Square 18.10% R Square 5.90% From Table 7, the results based on market adjusted model, the results show that acquiring cumulative abnormal return has a negative relationship with cross border vs domestic deal, the coefficient value is -5.842, which is significant at 0.05 level. This indicates that acquiring firm will gain higher cumulative abnormal return if it is domestic deal rather than cross border deal. Furthermore, merger financing and merger advisor have negative relationship with cumulative abnormal return with coefficient value of -1.076 and -0.247 respectively, both of which are statistically insignificant. This indicates that the acquiring firm will not gain higher cumulative abnormal return if merger financing is cash and merger advisor is not employed. The R square is 18%, which indicates that 18% of the changes in the dependent variable is because of independent variables. From Table 6, the results based on Fowler and Rorke method, the results show that acquiring cumulative abnormal return has a negative relationship with merger financing, the coefficient value is -1.427, which is insignificant at 0.05 level. This result suggests that acquiring firm will not gain higher cumulative abnormal return if merger is financed by cash deal. Therefore, there is no positive relationship between the cumulative abnormal return and merger financing. Furthermore, merger advisor has a positive relationship with cumulative abnormal return with coefficient value of 1.489, which is statistically insignificant. This indicates that the acquiring firm will gain higher cumulative abnormal return if it employs merger advisors. Therefore, there is a positive relationship between the merger advisor and cumulative abnormal return. Moreover, cross border vs domestic deal has a negative relationship with cumulative abnormal return with coefficient value of -0.348, which is statistically insignificant. This indicates that the acquiring firm will gain higher cumulative abnormal return if the acquiring firm engages in domestic deals. Therefore, there is no positive relationship between cumulative abnormal return and cross border vs domestic deal. The R square is 5.9%. Table 8: Regression analysis Mean Adjusted Model Coefficient Value Significance value Constant 7.440 0.027 Merger financing -2.494 0.505 Cross border vs domestic deal -4.283 0.270 Merger Advisor -0.569 0.681 R Square 9% From table 8, the results based on Mean adjusted return method, the results show that acquiring cumulative abnormal return has a negative relationship with merger financing, the coefficient value is -2.494, which is insignificant at 0.05 level. This result suggests that increase in number of cash deals will reduce acquiring firm cumulative abnormal returns. Therefore, there is no positive relationship between merger financing and cumulative abnormal return. Furthermore, merger advisor has a negative relationship with cumulative abnormal return with coefficient value of -0.569, which is statistically insignificant. This indicates that acquiring firm will not gain higher cumulative abnormal return if merger 212

advisor is not employed. Therefore, there is no positive relationship between cumulative abnormal return and merger advisor. Moreover, cross border vs domestic deal has a negative relationship with cumulative abnormal return with coefficient value of -4.238, which is statistically insignificant. This indicates that acquiring firm will not gain higher cumulative abnormal return if it is cross border deal. Therefore, there is no positive relationship between cumulative abnormal return and cross border vs domestic deal. The R square is 9%. It can be concluded that acquirer firm shareholders gained positive returns around Information Technology merger and acquisition announcements, implying that there is no transfer of wealth to target firm shareholders. Several important implications of this study could be that the results with regards to the announcement returns correspond to the prior empirical studies. With regards, to cross sectional regression analysis, it can be noted that only the factor cross border vs domestic acquisition variable makes a significant impact on the abnormal return. The study is new as it includes new variable that is merger advisor as the variable that could have an effect on Cumulative Abnormal Return. The study has also incorporated models that would eliminate the bias created while computing the abnormal return. 5. Conclusion This paper makes contribution for different dimensions in academics and industry. In academics, this paper introduces the impact of new variable, merger advisor on shareholders wealth creation in Information Technology merger announcement. This paper has also introduced three new models to overcome problem of non-synchronous trading or infrequent trading. The empirical evidence in this paper can provide clear picture. With regard to industry, the empirical evidence in this paper will have its relevance for investor and managers. Managers will understand how their decision on appointing merger advisor impact shareholders wealth creation in Information technology merger announcement. This empirical evidence will even guide investors to understand how Information technology merger announcement can have an impact on their wealth creation. The present study is restricted to Indian Information technology firm. This study can be extended to all the Information technology firm globally. The future study can also be include comparison of abnormal returns between two different industries. 8. References 1. Asquith P., Bruner R. F. and Mullins D. W., (1983), The gains to bidding firms from merger. Journal of financial economics, 11, pp 121-139 2. Akhigbe A. and Madura J., (2004), Bank acquisitions of security firms: the early evidence. Applied financial economics, 14, pp 485-496 3. Akhigbe A., Madura J. and Whyte A. M., (2004), Partial anticipation and the gains to bank merger targets. Journal of financial services research, 26, pp 55-71 4. Abagail Mc Williams and Donald Siegel., (1997), Event studies in management research: theoretical and empirical issues, The academy of management journal,40 (3), pp 626-657 213

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