PREDICTING FINANCIAL DISTRESS OF CHINESE PUBLIC LISTED COMPANIES

Abstract

In this research, a principal components prediction model based on principal components analysis (PCA) and a binary logistics regression (BLR) model are founded 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_(China)-score model (adjusted by Chen and Holdings, 2007) and Ohlson’ O-score model in terms of their abilities of predicting financial distress of Chinese public firms. The predictive results from each models are evaluated using methods of 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, amongst the four models in this research, the Altman’s Z_(China)-score model is able to provide remarkable accuracy in the prediction of financial distress one year before their occurrence in Chinese public listed firms. On the other hand, the PCA model and BLR model developed in this research and the Ohlson’s O-score model does not provide considerable accuracy for doing this job. Therefore, the Altman’s Z_(China)-score model is considered to be the recommended tool for investors or managers to evaluate a company’s potential risk of going through financial distress, to facilitate decision making regarding investment and management.