It has been extensively discussed on M&A performance, but most of these papers focused on the foreign M&A cases and specific industry. Besides, all M&A databases in Taiwan are imperfect, and M&A cases in SDC was started from 1991, TEJ. Most of domestic papers used SDC M&A database to filter proper samples to research M&A cases in Taiwan, but the sample sizes were usually less than 100. The research method was limited to multiple regressions, factor analysis, matching method and so on.
Owed by the launch of TEJ M&A database in 2014, I increase the sample sizes and classify these data into six groups, trying to find the relationship between all of them.
This new database solves the small sample size problem. I filter 872 M&A cases and use one-way ANOVA and regression to test the two hypotheses which are mentioned in Chapter 2.
According to the table 4-1 to 4-12, the results suggest that the lower industrial relationship can create better M&A performance than the higher industrial relationship.
Also, the regression model shows a striking effect of the seven independent variables on performance in the stock annual rate of return and the revenue growth rate, especially in the long term period.
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In the regression model, contrast to the vertical and horizontal M&A, the cross industry M&A can create better short term stock rate of return. But the long term stock rate of return of cross industry M&A is not significantly better than the other two M&A types. Besides, the bigger the acquirer size, the worse the revenue growth rate and vice versa.
The one-way ANOVA results indicate that there exists significant difference in the six groups (H1) and the four groups (H2), especially in t=1, 2, 3, 4, performance of cross industry indeed better than the other two M&A types.
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