We propose a framework for an ensemble bankruptcy classifier that uses if-then rules to combine the outputs from a heterogeneous set classifiers. A genetic algorithm (GA) induces the rules using an asymmetric, cost-sensitive fitness function that includes accuracy and misclassification costs....

We examine the relationship between corporate governance and default risk for a sample of firms cited in the Securities and Exchange Commission's (SEC's) Accounting and Auditing Enforcement Releases (AAERs). Using hazard analysis of actual default incidence and OLS regressions of a continuous...

This paper develops an adaptive ensemble model for bankruptcy classification of firms cited in the SEC's Accounting and Auditing Enforcement Releases (AAER). We develop a Genetic Algorithm (GA) model for bankruptcy classification of AAER firms. Our research contributes to the bankruptcy...

We examine the relationship between corporate governance and default risk for a sample of firms cited in the Securities and Exchange Commission’s Accounting and Auditing Enforcement Releases (AAERs). Using hazard analysis of actual default incidence and OLS regressions of a continuous variable...

SUMMARY This paper proposes a framework for an ensemble bankruptcy classifier that uses if–then rules to combine the outputs from a heterogeneous set of classifiers. A genetic algorithm (GA) induces the rules using an asymmetric, cost‐sensitive fitness function that includes accuracy and...