Three essays on empirical corporate finance - Xu, Bo (2025)
This thesis contains three independent essays in empirical corporate finance with distinct perspectives. The first essay (Chapter 2) discusses the association between managerial ability and corporate financing decisions. Existing literature emphasizes the significant role of managers in capital structure management. Baseline results show that firms with high-ability managers strategically decrease the leverage ratio. This finding is robust after addressing endogeneity concerns and across alternative empirical specifications. The cross-sectional results suggest that the negative association between managerial ability and firm leverage is more pronounced when firms are less financially constrained, have lower uncertainties, make no attempt to meet or beat analyst’s earning expectations and with CEOs who have longer tenures. Additional analyses also find that skilled managers tend to lower the leverage adjustment speeds, show less (more) propensity on issuing debt (equity) and increase internal operating cash flows. The second essay (Chapter 3) provides a new insight on how managerial compensation frictions, especially CEO tax burdens could impact corporate environmental performances. Recent studies demonstrate that managers with higher tax burdens are less likely to sell stocks, becoming overexposed to firm-specific risks, thereby significantly reducing their propensity to engage in risky corporate decisions and prompting a greater focus on firm stability over the long run. The findings indicate that firms decrease the generation of corporate toxic waste when CEOs’ tax burdens increase. This conclusion is valid across a range of identification tests as well as different robustness tests. Results from a series of cross-sectional analyses reveal that the negative relationship between CEO tax burdens and the corporate toxic waste generation is more pronounced in firms with stronger external requirements on environmental offences, fewer financial constraints, and for CEOs with limited hedging opportunities. Further analysis also shows that higher CEO tax burden contributes to better CSR engagements. The third essay (Chapter 4) explores the determinants of corporate risk-taking by deploying machine learning. Using a boarder series of linear and nonlinear machine learning models, results find evidence that machine learning can significantly predict and select the efficient determinants of firm’s risk-taking, as well as the heterogeneities of risk appetites across sub-samples. Comparing all ML methods, the results indicate that extreme gradient boosting (XG-Boost) outperforms the other models in predicting corporate risk-takings as well as classifying their magnitudes. To interpret the predictive performance, the tree-based feature importance and SHapley Additive exPlanations (SHAP) plots indicate that firm-specific attribute more in explaining risk-taking behavior. Overall, this study contributes prior studies on firm risk-taking decisions by showing the outstanding predictive power of machine learning tools and providing novel insights for practitioners.