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Financial distress prediction: a novel data segmentation research on Chinese listed companies

    Fang-Jun Zhu   Affiliation
    ; Lu-Juan Zhou   Affiliation
    ; Mi Zhou   Affiliation
    ; Feng Pei   Affiliation

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.


First published online 04 November 2021

Keyword : financial distress prediction, Chinese listed companies, ensemble learning, data mining, data segmentation, special treatment

How to Cite
Zhu, F.-J., Zhou, L.-J., Zhou, M., & Pei, F. (2021). Financial distress prediction: a novel data segmentation research on Chinese listed companies. Technological and Economic Development of Economy, 27(6), 1413-1446. https://doi.org/10.3846/tede.2021.15337
Published in Issue
Nov 18, 2021
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This work is licensed under a Creative Commons Attribution 4.0 International License.

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