Tests used in the study

The long run performance of the OFDI-related Indian corporates involved in acquisition activity is assessed using two methods. They are BHAR and CAR.

The present study uses parametric tests and non-parametric tests to decide whether or not to reject null hypotheses. This is in line with prior studies (S P Kothari & Warner, 1997b) (Ikenberry, et al., 1995) which recommended

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consideration of nonparametric procedures as they have been used in few studies and seem likely to reduce misspecifications. Zhu and Malhotra (2008) used parametric tests and non-parametric tests (such as Wilcoxon ranked sign test and sign test) to check the robustness of the findings of the abnormal returns on Indian international acquisition of US firms. Under a parametric approach the study uses the test statistic of t-test, and Anova F-test. The t-value, p-value and f-value are used to decide whether or not the null hypotheses should be rejected in the hypotheses test.

The t-test assesses whether the means of two groups are statistically different from each other. This analysis is appropriate whenever the means of two groups are compared. Under the BHAR approach, the t-test is used to assess the significance in the relationship between the firms‘ returns of the OFDI-related Indian corporates involved in acquisition activity with the matching firm. In the CAR approach, the risk adjusted firm returns are compared with the benchmark returns (BSE Index). In the case of Tobin‘s Q, the three years‘ mean of before acquisition and the three years‘ mean of Tobin‘s Q after acquisition event are compared and tested.

The critical value(s) for a hypothesis test is a threshold where the values of the test statistic are compared to decide whether or not the null hypotheses should be accepted or rejected. In the present study the mean cumulative abnormal market returns of the OFDI-related Indian corporates involved in acquisition activity are

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compared with the critical values to determine whether or not the null hypothesis is rejected. The level of significance at which the test is carried out is at 1%, 5%

and 10%.

The F-test commonly used in one-way ANOVA is based on the assumption that all of the groups share a common, but unknown, standard deviation (σ). In practice, this assumption rarely holds true, which leads to problems controlling the Type I error rate. Type I error is the probability of incorrectly rejecting the null hypothesis (concluding the samples are significantly different when they are not). To have robustness in testing the null hypotheses the present study considers the Anova F-test.

The p-value is compared with the actual significance level of test results and, if it is smaller, the result is significant. That is, if the null hypothesis were to be rejected at the 5% significance level, this will be reported as "p < 0.05".

Small p-values suggest that the null hypothesis is unlikely to be true. The smaller it is, the more convincing the rejection of the null hypothesis. It indicates the strength of evidence for say, rejecting the null hypothesis H0, rather than simply concluding "Reject H0' or "Do not reject H0".

The Wilcoxon Mann-Whitney test is one of the most powerful nonparametric tests for comparing two populations. It is used to test whether two independent samples of observations are drawn from the same or identical distributions. An advantage

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with this test is that the two samples under consideration may not necessarily have the same number of observations.

5.3.7 Tests for assessing the performance of Indian corporates involved in the OFDI related M&As in the pre-acquisition and post-acquisition period

For the purpose of academic analysis the study examines the relationship between OFDI‘s by Indian corporates with the size and performance drivers during the period 2000 – 2008 (they include: the total assets (size), sales, PAT, PBDIT and Dividends). For this purpose a correlation matrix is used. In statistics correlation, (often measured as a correlation co-efficient), indicates the strength and direction of a linear relationship between two random variables. In general statistical usage, correlation refers to the departure of two variables from independence. The correlation is 1 in the case of an increasing linear relationship, −1 in the case of a decreasing linear relationship, and some value in between in all other cases, indicating the degree of linear dependence between the variables. The closer the coefficient is to either −1 or 1, the stronger the correlation between the variables.

If the variables are independent then the correlation is 0, but the converse is not true because the correlation coefficient detects only linear dependencies between two variables.

The study also looks into the annual percentage growth rate of change in sales, dividends and profit after taxes in the pre- and post-acquisition periods and

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compares the change in annual growth rate between the pre- and post-acquisition periods. For this purpose it uses the following equation: percentage change = [(latest-past)/past *100]/N, where N represents the number of years between the two values i.e. latest and past periods.

While calculating the change in growth rate in the pre-acquisition period the period considered is four years prior to the acquisition event year. The earlier period in acquisition period is denoted as past; the later period of the pre-acquisition period is denoted as latest. Like-wise the period considered while calculating the change in growth rate in the post-acquisition period is also four years following the acquisition event year. The earlier period of post-acquisition period is denoted as past; the period closer to of the post-acquisition period is denoted as latest. The change in growth rate is examined to look into the changes in performance drivers after the OFDI related M&A‘s by the Indian corporates.

5.4 Part C: Research methods for explaining the empirical