Academy of Accounting and Financial Studies Journal (Print ISSN: 1096-3685; Online ISSN: 1528-2635)


Predicting Financial Distress of Chinese Public Listed Companies

Author(s): Huang Zhixiang

In this research, a prediction model based on Principal Component Analysis (PCA) and a Binary Logistics Regression (BLR) model is constructed based on the data of Chinese public listed companies from the year 2013 to 2018, and compared with two of the most famous financial distress prediction models including Altman’s Z-score model and Ohlson’ O-score model in terms of their ability in predicting financial distress of Chinese public firms. The predictive results from each model are evaluated using Mann-Whitney U test, descriptive analysis, ROC curve and AUC. The findings consistently revealed that, except profitability, solvency and cash flow, a company’s operating capability and growth capacity are also significantly differentiated from financially distressed and financially stable companies. Further, all of the four models are able to predict financial distress of Chinese public companies. However, among the four models in this research, the Altman’s Z-score model is able to provide remarkable accuracy one year before their occurrence in Chinese public listed firms.

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