Majority voting combination of multiple case-based reasoning for financial distress prediction

Abstract

Financial distress prediction of companies is such a hot topic that has called interest of managers, investors, auditors, and employees. Case-based reasoning (CBR) is a methodology for problem solving. It is an imitation of human beings’ actions in real life. When employing CBR in financial distress prediction, it can not only provide explanations for its prediction, but also advise how the company can get out of distress based on solutions of similar cases in the past. This research puts forward a multiple case-based reasoning system by majority voting (Multi-CBR–MV) for financial distress prediction. Four independent CBR models, deriving from Euclidean metric, Manhattan metric, grey coefficient metric, and outranking relation metric, are employed to generate the system of Multi-CBR. Pre-classifications of the former four independent CBRs are combined to generate the final prediction by majority voting. We employ two kinds of majority voting, i.e., pure majority voting (PMV) and weighted majority voting (WMV). Correspondingly, there are two deriving Multi-CBR systems, i.e., Multi-CBR–PMV and Multi-CBR–WMV. In the experiment, min–max normalization was used to scale all data into the specific range of [0, 1], the technique of grid-search was utilized to get optimal parameters under the assessment of leave-one-out cross-validation (LOO-CV), and 30 hold-out data sets were used to assess predictive performance of models. With data collected from Shanghai and Shenzhen Stock Exchanges, experiment was carried out to compare performance of the two Multi-CBR–MV systems with their composing CBRs and statistical models. Empirical results got satisfying results, which has testified the feasibility and validity of the proposed Multi-CBR–MV for listed companies’ financial distress prediction in China.