decrease (increase) in fraction of cash employed in transaction an M&A across period 1 and 2. Blockownership depicts a significant negative relationship at a 10% significance level with a coefficient of (-0.038). Moreover, Cashflow_assets depicts a highly significant positive relationship at 1% significance level with a coefficient of (1.863). FinancialLeverage appears to be significantly negatively correlated with fraction of cash at a 5% significance level with a coefficient of (-0.437). Lastly, the variable Runup has a significant positive correlation with fraction of cash at 10% significance level with a coefficient of (0.077). Specification (3) appears to have an R-Square of (0.240), which is relatively higher than specification (2), indicating a slightly more accurate goodness of fit. Results in specification (4) indicate that Credit_ratinglevel is insignificant in explaining the fraction of cash used in transaction M&A. However, given the fact that specification (4) takes into account all control variables across target, bidder and deal specific characteristics, the existence of endogenity is applicable and this will be accounted for in section 6.2.3. FinancialLeverage is significant at a 10% significance level with a coefficient of (-0.375), indicating an inverse relationship with fraction of cash. Blockownership is also negatively correlated with fraction of cash at a 5% significance level with a coefficient of (-0.045). Furthermore, Num_analyst is negatively correlated with fraction of cash at a 1% significance level with a coefficient of (-0.115). On the contrary, Cashflow_assets appeared to have positive relationship with fraction of cash with a coefficient of (1.717). Further scrutinizing the goodness of fit of all four specifications mentioned above, the of specification (1) was observed to be insignificant. Therefore, we do not reject the null hypothesis that all regression coefficients are equal to zero, indicating an overall weak goodness of fit. 6.2.3 Endogenity control for OLS The issue of endogenity stands paramount in many empirical studies and accounting for this goes far and beyond in establishing an unbiased and consistent coefficient estimates.
Table 2: MNP Correlation Matrix Fraction of cash FinancialLeverage Downgrade Cashflow_assets Collateral BookToMKT Blockownership IntraIndustry ln_size Num_analysts Private_Public Rel_dealsize Runup Upgrade Fraction of cash 1.000 FinancialLeverage -0.244 1.000 Downgrade -0.091 0.090 1.000 Cashflow_assets 0.379 0.056 0.000 1.000 Collateral -0.127 0.348 0.167 0.073 1.000 BookToMKT -0.095 -0.246 0.191 -0.414 0.099 1.000 Blockownership -0.215 0.190 0.201 -0.089 0.126 0.199 1.000 IntraIndustry -0.179 0.142 0.084 -0.090 0.072 0.079 0.135 1.000 Ln_size 0.167 -0.345 -0.168 0.097 -0.236 -0.103 -0.423 -0.087 1.000 Num_analysts -0.205 -0.031 -0.008 -0.050 -0.011 0.054 -0.024 0.044 -0.043 1.000 Private_Public -0.021 0.017 0.112 -0.072 -0.046 -0.045 -0.061 0.198 -0.281 -0.169 1.000 Rel_dealsize -0.205 0.351 0.050 -0.040 0.210 -0.089 0.228 -0.004 -0.324 0.224 -0.143 1.000 Runup -0.023 0.189 0.107 -0.097 0.611 0.163 0.101 -0.067 -0.156 -0.066 -0.007 0.233 1.000 Upgrade -0.052 -0.097 -0.317 0.089 -0.140 -0.069 -0.142 0.051 0.191 0.090 -0.082 -0.085 -0.229 1.000
Table 4: Sample descriptive statistics by M&A Payment Method (2003 – 2008) Total Sample (N = 68) Cash (N = 41) Mixed (N = 23) Stock (N = 4) Variable Mean Median Mean Median Mean Median Mean Median Panel A: Bidder Characteristics Credit_ratinglevel 14.529 15.000 14.854 15.000 14.391 15.000 12.000 10.500 Ln_size 8.909 8.240 8.948 8.230 8.930 8.534 8.384 8.182 Collateral 0.204 0.141 0.160 0.129 0.311 0.247 0.038 0.039 FinancialLeverage 0.250 0.214 0.208 0.178 0.334 0.323 0.205 0.239 BookToMKT 0.345 0.307 0.374 0.320 0.298 0.250 0.322 0.327 Blockownership 1.897 2.000 1.829 2.000 2.043 2.000 1.750 1.500 Cashflow_assets 0.124 0.125 0.137 0.135 0.123 0.094 -0.009 -0.012 Runup 0.143 0.152 0.101 0.143 0.271 0.229 -0.172 -0.025 Panel B: Deal Characteristics Rel_dealsize 0.201 0.065 0.134 0.065 0.337 0.126 0.101 0.108 Num_analysts 0.853 1.000 0.805 1.000 0.957 1.000 0.750 1.000 IntraIndustry 0.515 1.000 0.390 0.000 0.739 1.000 0.500 0.500 Panel C: Target Characteristics Private_Public 0.279 0.000 0.293 0.000 0.217 0.000 0.500 0.500
Table 5: Sample descriptive statistics by M&A Payment Method (2008 – 2013) Total Sample (N = 68) Cash (N = 46) Mixed (N = 18) Stock (N = 4) Variable Mean Median Mean Median Mean Median Mean Median Panel A: Bidder Characteristics Credit_ratinglevel 14.353 15.000 14.978 15.000 12.611 13.000 15.000 15.500 Ln_size 8.971 8.934 9.194 9.056 8.219 8.013 9.793 10.050 Collateral 0.186 0.104 0.191 0.117 0.210 0.083 0.010 0.008 FinancialLeverage 0.275 0.231 0.246 0.216 0.336 0.308 0.329 0.316 BookToMKT 0.513 0.439 0.506 0.392 0.493 0.439 0.690 0.692 Blockownership 2.118 2.000 1.913 2.000 2.556 2.000 2.500 2.000 Cashflow_assets 0.113 0.112 0.130 0.122 0.089 0.074 0.019 0.012 Runup 0.066 -0.080 0.157 -0.054 -0.048 -0.326 -0.465 -0.421 Panel B: Deal Characteristics Rel_dealsize 0.207 0.067 0.121 0.049 0.467 0.216 0.025 0.022 Num_analysts 0.691 1.000 0.435 0.000 1.278 1.000 1.000 1.000 IntraIndustry 0.456 0.000 0.435 0.000 0.556 1.000 0.250 0.000 Panel C: Target Characteristics Private_Public 0.294 0.000 0.261 0.000 0.389 0.000 0.250 0.000