Artificial intelligence techniques for financial distress prediction

Junhao Zhong; Zhenzhen Wang; Junhao Zhong; Zhenzhen Wang

doi:10.3934/math.20221145

AIMS Mathematics

2022, Volume 7, Issue 12: 20891-20908. doi: 10.3934/math.20221145

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Artificial intelligence techniques for financial distress prediction

Junhao Zhong ¹,
Zhenzhen Wang ^{2
,
,}

1.
School of Economics and Finance, South China University of Technology, Guangzhou 510006, China
2.
School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou 510006, China

Received: 05 September 2022 Revised: 31 October 2022 Accepted: 08 November 2022 Published: 21 November 2022
MSC : 62P20, 91G70

Artificial intelligence (AI) models can effectively identify the financial risks existing in Chinese manufacturing enterprises. We use the financial ratios of 1668 Chinese A-share listed manufacturing enterprises from 2016 to 2021 for our empirical analysis. An AI model is used to obtain the financial distress prediction value for the listed manufacturing enterprises. Our results show that the random forest model has high accuracy in terms of the empirical prediction of the financial distress of Chinese manufacturing enterprises, which reflects the effectiveness of the AI model in predicting the financial distress of the listed manufacturing enterprises. Profitability has the highest degree of importance for predicting financial distress in manufacturing firms, especially the return on equity. The results in this paper have good policy implications for how to use the AI model to improve the early warning and monitoring system of financial risks and enhance the ability of financial risk prevention and control.
- artificial intelligence,
- financial distress,
- manufacturing,
- risk prevention
Citation: Junhao Zhong, Zhenzhen Wang. Artificial intelligence techniques for financial distress prediction[J]. AIMS Mathematics, 2022, 7(12): 20891-20908. doi: 10.3934/math.20221145

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Abstract

Artificial intelligence (AI) models can effectively identify the financial risks existing in Chinese manufacturing enterprises. We use the financial ratios of 1668 Chinese A-share listed manufacturing enterprises from 2016 to 2021 for our empirical analysis. An AI model is used to obtain the financial distress prediction value for the listed manufacturing enterprises. Our results show that the random forest model has high accuracy in terms of the empirical prediction of the financial distress of Chinese manufacturing enterprises, which reflects the effectiveness of the AI model in predicting the financial distress of the listed manufacturing enterprises. Profitability has the highest degree of importance for predicting financial distress in manufacturing firms, especially the return on equity. The results in this paper have good policy implications for how to use the AI model to improve the early warning and monitoring system of financial risks and enhance the ability of financial risk prevention and control.

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