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Dynamic Financial Distress Prediction Based on Rough Set Theory and EWMA Model

Xinzhong Bao, Qiuyan Tao

Abstract


Most of the previous studies about financial distress prediction are based on static cross-sectional data as a sample, which results in neglecting the time series characteristics of a company's financial indicators. A static model does not consider the importance of a company’s history and can therefore inaccurately place a company into the financial crisis category, which reduces the accuracy of the model. Secondly, the previous studies are studying on annual data, which is in a lack of timeliness. By using quarterly data of companies along with considering the cumulative historical impact of their financial indicators, a dynamic prediction model of financial crises can be created. This study combines rough set theory and exponential weighted moving average (EWMA) control charts to set and test a prediction model. Empirical studies show that the accuracy of this model is up to 83.33%, which shows that the predicting effect is ideal for financial crisis.

Keywords


Financial Distress, Dynamic Prediction, Rough Set, Exponential Weighted Moving Average (EWMA).

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