FINANCIAL DISTRESS PREDICTION: A NOVEL DATA SEGMENTATION RESEARCH ON CHINESE LISTED COMPANIES
针对中国股市ST预警机制，提出按ST标记次数将公司分为负向和正向两类，通过数据分割发现负向样本严重干扰预测效果，而正向样本能提升预测精度，并构建了优化模型。
In the Chinese stock market, the unique special treatment (ST) warning mechanism can signal financial distress for listed companies. In existing studies, classification model has been developed to differentiate the two general listing states. However, this classification model cannot explain the internal changes of each listing state. Considering that the requirement of the withdrawal of ST in the mechanism is relatively loose, we propose a new segmentation approach for Chinese listed companies, which are divided into negative companies and positive companies according to the number of times being labeled ST. Under the framework of data mining, we use financial indicators, non-financial indicators, and time series to build a financial distress prediction model of distinguishing the long-term development of different Chinese listed companies. Through data segmentation, we find that the negative samples have a huge destructive interference on the prediction effect of the total sample. On the contrary, positive companies improve the prediction accuracy in all aspects and the optimal feature set is also different from all companies. The main contribution of the paper is to analyze the internal impact of the deterioration of financial distress prediction in time series and construct an optimization model for positive companies.