We model an investment bank's choice of resource allocation by the probability of acquirers' mergers and acquisitions frequency in the future to theoretically link the role of investment banks to the acquirer returns. Our model predicts the heterogeneity in the quality of advisory services provided by the same investment bank that leads to the heterogeneity in acquirer returns. Such heterogeneity declines as the likelihood of an industry merger wave rises. Controlling for investment bank fixed effects, acquirer fixed effects and potential self-selection bias, we find empirical evidence supporting our hypotheses.

The structural approach offers an integrated framework to deal with yield spreads and default probability simultaneously. However, structural models perform poorly in predicting corporate bond spreads. It is unclear whether this poor performance is caused by characteristics of individual models, missing factors, or different calibration procedures. This study evaluates the performance of four structural models by incorporating two important factors, personal taxes and the liquidity factor, and calibrating these models to data. To ensure our results are not contingent on the calibration method, we further apply the maximum likelihood estimation method to a large sample of individual bonds. Results consistently show that the ability of structural models to predict spreads improves considerably when personal taxes and liquidity are taken into account. Our findings suggest that the poor performance of standard structural models is more likely due to missing factors than the characteristics of individual models or the calibration procedure.