Essay I: Alternative Approaches to Business Failure Prediction Models. The main purpose of this essay is to compare the prediction accuracy of the widely used bankruptcy forecasting models: Altman’s Multivariate Discriminant Analysis (MDA) (1968), Ohlson’s Logit model (1980), Zmijewski’s Probit model (1984), and Shumway’s Hazard model (2001). Since Hazard model is able to solve theoretically and empirically the inconsistency sample selection problem and to capture the time-varying covariates in the bankruptcy data, our empirical results show with cautiously chosen cutoff at 0.021 implied bankruptcy probability level, the out-of-sample hazard model with stepwise methodology results in classifying 82.7% of default firms and 82.8% of non-default firms.

Essay II: The Relationship between Financial Distress and Earnings Management: An Empirical Evidence. Prior research on the explicit incentives for earnings management has been inconclusive. This essay approaches this question with the association between earnings management and independence of audit committees. To this end, we test the monitoring effectiveness of earnings management by fully and/or partially independent audit committees especially for financially distressed firms, for which managers have a strong motivation to manipulate reported earnings to camouflage the firm’s weak performance. Our results show that independent audit committees monitor earnings management, especially upward adjustment of reported earnings, of financially distressed firms more strictly than that of financially non-distressed firms. The results also show that fully independent audit committees are more effective in constraining earnings management than partially independent audit committees, supporting the requirement of 2002 Sarbanes-Oxley Act for fully independent audit committees.

Essay III: Re-Examining the Phenomenon of Post-Earnings Announcement Drift: Quadratic and Quantile Regression Approach. Previous studies show that there is model misspecification problem with the market model, which is failing to capture the revision of systematic risk on earnings announcement. Nevertheless, the misspecification of the market model employed to estimate abnormal returns has been identified in many studies as a possible source that causes the drift. The empirical results show that the post-earnings announcement drift is no longer exist after we incorporate the estimated abnormal returns with the 50th quantile coefficients median coefficients (instead of the mean coefficients from OLS) into a quadratic market model to monitor how the market revises its assessment of systematic risk on the quarterly earnings announcement.