Reseach on effects of merger and acquisition on financial performance of commercial banks in Vietnam during period 2011-2015

ve impact on financial performance of banks surveyed. For control variables, total_asset also has negative influence on ROE of banks. The results for total_asset is that estimated coefficients on the total_asset is negative (β2= - - 1.208E-09***) and statistically significant at the 1% level. Thus, according to this result, the 846 increase of banks’s size doesn’t really increase the ability to create profit of bank, but make it weaker. However, the decrease of ROE ratios derived from too large scale of total_asset is really small, but still meaningful. In the context of participating on M&A, lvr shows the changes in the same way with ROE. The estimated coefficients of lvr is positive (0.019304) and statistically significant at the 5% level. Thus, as ROE increase, banks have to face with problem of debt to equity ratio increases, which means that the solvency ability of banks decreases. For GROWTH, the estimated coefficients on the GROWTH is positive (β3= -0.006723) and not statistically significant. This means that with the sample of banks being surveyed, GROWTH has positive impact on ROE of banks. As GROWTH increase, along with the improvement in net sale (because GROWTH is calculated by (SALEn – SALEn-1)/SALEn-1), profitability of bank also increases. However, this conclusion does not have statistical meaning, so if the sample has larger amount of observation, this result could be changed. The estimated intercepts are positive, indicating that there is 0.008441 in value of ROE is unexplained by the dependent variables. However, the probability (p-value) of intercept has no statistical meaning (larger than 10%). Therefore, this value could be changed if the sample is in larger scale. With regard to the coefficient of determination R squared of the model is pretty large 56.9%, which means that the dependent variables ROE can be explained 56.9% by this model. Thus, this model has high meaning in explaining the value of ROE. Thus, by using OLS regression, we have the estimated model as following: ROE = 0.008441 + -0.033895* MGR + 0.019304*LVR +-1.208E-09*total_asset +- 0.006723*GROWTH + Ɛ (4.1) Diagnostic test to evaluate the fitness of the regression model In order to examine the fitness of the model (4.1) F-test would be used in this part of the research. We use Wald test to undertake F-test, the result of the model 4.1 is presented in appendix 3, with the probability <1%, so the null hypothesis is rejectes, which means that at least a coefficient is different to zero. So, the model (4.1) can explain ROE well. The similar result outcome in case of multivariate regression model. (Appendix 6) Thus, both model explain well for the population. A VIF of 1 means that there is no correlation among the independent variables and the remaining independent variables, and then the variance of coefficients is not inflated at all. The general rule of thumb is that VIFs exceeding 4 implies further investigation, while VIFs exceeding 10 means that there exists serious multicollinearity in the estimation, and the model needs to be corrected. 847 The result of VIF test for the model 4.1 as following Table 4.7: Results of VIF test Coefficient Uncentered Centered Variable Variance VIF VIF C 0.000729 23.02029 NA MGR 0.000244 3.854226 1.927113 TOTAL_ASSET 9.27E-20 44.81087 6.937204 GROWTH 6.32E-05 1.958034 1.097571 LVR 2.17E-05 86.81447 6.349570 Looking at Centered VIF, we can see that all the value of Centered VIF < 10. Thus, the multicollinearity in the model is not serious, and acceptable. Thus, we don’t have to justify the model for resolving the problem of multicolinearity. Checking the autocorrelation of residuals To check the autocorrelation, the LM test is used, with the amount of lag is 2. The resulst as following: Table 4.8. The result of LM test Breusch-Godfrey Serial Correlation LM Test: F-statistic 1.078608 Prob. F(2,17) 0.3622 Obs*R-squared 2.702543 Prob. Chi-Square(2) 0.2589 As stated in the Chapter 3, The value of Prob-Chi-square (2) equal is the p-value of Obs*R-squared is the result of this test, which equals to 0.2589 > 5%. Therefore, we accept the null-hypothesis, that there is no auto-correlation of residuals in the model. 6. Recommendations and suggestions. This study attempts to look into and make a comparative analysis of the effects of M&As in the Vietnamese banking system. It focuses on commercial banks taking part in M&A from 2010-2015. Though the period is short, but the result can be noticeable. The univariate analysis exposed profitability after M&A for commercial banks with the t-test showing significant difference in profitability before and after merger. The indication from panel methodology shows that that M&A has negative impact on the profitability of banks. This research helps to evaluate the impact of M&A on banks through the first period of restructuring reform of Vietnamese banking system. Overall, it can be inferred from this research, that M&A does not always enhance the profitability of banks. However, it would be mistaken to propose that M&A activities always has negative impacts on banks. 848 Recommendation for further research This research has some limitations of small data scale. Therefore, there are some suggestion for further research as following. This research can be undertaken with the larger amount of cases in the future, and at the longer time span. With the larger scale of time and expense, data should be collected more fully. Besides, the analysed of financial performance should be more comprehensive in terms of profitability, liquidity, solvency, quick ratio, current ratios and some other specialised ratios of banking industry. 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