Abstract
Financial distress forecasts are very important for companies, investors, financial institutions and regulatory authorities. In this study, a dynamic financial distress forecast model with time-weighting based on random forest is proposed. In the first part of this study, we briefly review previous scholars’ studies. Then we illustrate the basic concepts of random forest (RF), methods of time-weighting and propose the model with time-weighting based on RF. Finally, the empirical experiment is carried out with sample data of 324 Chinese listed companies’ 41 financial and non-financial indicators. Experimental results demonstrate that RF outperforms SVM in predicting financial distress with a lot of variables and the average accuracy of RF models with time-weighting is higher than that of models without time-weighting.
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