Entropy-based Time-series Financial Distress Model Based on Attribute Selection and MetaCost Methods for Imbalance Class

Financial distress prediction is an important and challenging issue in the financial field. Now, many methods have been proposed to forecast company bankruptcy and financial crisis, and many studies show that artificial intelligence is better than traditional statistical methods in prediction capacity. To overcome the imbalance class, this study employs the MetaCost algorithm to add cost-sensitive classification in the training of base classifiers, then establishes a financial crisis prediction model. In a time series and non-stationary problems, this study proposes a novel time-series financial distress model based on artificial intelligence (including attribute selection and classifiers) to predict the financial distress of a company. All in all, the proposed model has several advantages: (1) utilize the MetaCost algorithm to handle the imbalance class; (2) the proposed model is a seasonal time-series model; (3) employ attribute selection to find the core attributes and reduce data dimension; (4) the research results can be provided to investors and decision makers as reference. At last, the results show that the proposed method is better than the listed classifiers and the MetaCost algorithm is superior to the general classifier method, and the MetaCost method raises a little sensitivity, it lifts to identify the companies’ financial health when the companies are actually healthy; and type II errors are reduced by 21.6%, it denotes that the proposed method can raise the correct classification of financial distress.