Abstract
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.
Highlights
Financial distress prediction is an emerging research topic
The above nine classification methods are used in Experiment 1 to make financial prediction at the three-time nodes of t-3, t-4, and t-5, and 10-fold cross-validation, leave one out cross-validation (LOOCV), and bootstrapping are employed as three comparative verification methods
We proposed a data segmentation approach based on the number of times being labeled special treatment (ST) and make financial distress prediction for positive, negative, and all companies
Summary
Financial distress prediction is an emerging research topic. A troubled company will have a huge impact on the entire financial system of business owners, investors, and credit institutions. How to distinguish a troubled company from normal companies is undoubtedly. Org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Zhu et al Financial distress prediction: a novel data segmentation research on Chinese. As China becomes one of the main markets for international investors, the financial distress prediction of Chinese companies has attracted more and more attention
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