Bankruptcy classification of firms investigated by the US Securities and Exchange Commission: an evolutionary adaptive ensemble model approach

Abstract

This paper develops an adaptive, rule-based model for bankruptcy classification of firms subject to the SEC's Accounting and Auditing Enforcement Release (AAER). In this paper, we use an evolutionary computing method, genetic algorithm (GA), to generate an optimal set of if-then (comprehensible) rules for bankruptcy classification of AAER firms. In particular, we use bagging to improve the model's generalisation accuracy; and to develop a doubly controlled fitness function to guide the operations of the (GA) method. Our research contributes to the bankruptcy literature in several ways. First, it fills a gap in bankruptcy classification by developing a domain specific model for AAER firms. Secondly, the derived set of if-then rules used in an expert system adds to the bankruptcy knowledge base. Thirdly, we use bagging to improve generalisation of bankruptcy classification models. Finally, we demonstrate the key role of the fitness function in successful model performance.