文献类型：期刊文献

英文题名：Dynamic financial distress prediction based on rough set theory and EWMA model

作者：Bao, Xinzhong[1]; Tao, Qiuyan[1]

第一作者：鲍新中

机构：[1] Management School, Beijing Union University, Beijing, 100101, China

第一机构：北京联合大学管理学院

年份：2013

卷号：48

期号：18

起止页码：339-346

外文期刊名：International Journal of Applied Mathematics and Statistics

收录：EI(收录号：20140417238671);Scopus(收录号：2-s2.0-84892659607)

语种：英文

外文关键词：Forecasting - Industry - Mathematical models - Rough set theory

摘要：Most of the previous studies about financial distress prediction are based on static crosssectional 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. ? 2013 by CESER Publications.