Abstract
Financial distress prediction models are much challenged in identifying a distressed company two or more years prior to the occurrence of its actual distress, on the grounds that the distress signal is too weak to be captured at an early stage. The paper innovatively proposes to predict the distressed companies by a factorial discriminant model based on interval data. The main idea is that we use a new data representation, i.e., interval data, to summarize four-quarter financial data, and then build a interval-data-based discriminant model, namely i-score model. Interval data makes both average and volatility information comprehensively included in the proposed prediction model, which is expected to improve prediction performance on the distressed companies. A comparison based on a real data case from China’s stock market is conducted. The i-score model is compared with five commonly used models that are based on numerical data. The empirical study shows that i-score model is more accurate and more reliable in identification of companies in high risk of financial distress in advance of 2 years.
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Acknowledgements
The research of Rong Guan is supported by National Natural Science Foundation of China (Grant No. 71401192), the Fundamental Research Funds for the Central Universities (QL18009), and the Program for Innovation Research in Central University of Finance and Economics. The research of Huiwen Wang is supported by National Natural Science Foundation of China (Grant No. 71420107025). The research of Haitao Zheng is partially supported by National Natural Science Foundation of China (Grant Nos. 71371021, 71873012), and Humanities and Social Sciences Planning Fund of Ministry of Education (Grant No. 17YJA790097).
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Appendices
Appendix
Data and Figures
For reader’s convenience, we provide the stock ID information of the 147 selected company pairs in Table 7. Besides, descriptive figures of the samples of the interval-valued data and numerical data are respectively shown in Figs. 4 and 5. Each of the 21 subfigures corresponds to two of the seven financial ratios. In Fig. 5, distressed companies are shown as light brown crosses, whereas healthy companies are drawn as black circles. In Fig. 4, rectangles in light brown and black correspond to distressed and healthy companies, respectively.
Pair-wise visualizations of interval data of the selected 7 financial ratios
Pair-wise visualizations of numerical data of the selected 7 financial ratios
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Guan, R., Wang, H. & Zheng, H. Improving accuracy of financial distress prediction by considering volatility: an interval-data-based discriminant model. Comput Stat 35, 491–514 (2020). https://doi.org/10.1007/s00180-019-00916-9
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DOI: https://doi.org/10.1007/s00180-019-00916-9