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Research Article | Open Access

Artificial intelligence techniques for financial distress prediction

Junhao Zhong1Zhenzhen Wang2( )
School of Economics and Finance, South China University of Technology, Guangzhou 510006, China
School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou 510006, China
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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.

CLC number: 62P20, 91G70

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AIMS Mathematics
Pages 20891-20908

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Cite this article:
Zhong J, Wang Z. Artificial intelligence techniques for financial distress prediction. AIMS Mathematics, 2022, 7(12): 20891-20908. https://doi.org/10.3934/math.20221145

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Received: 05 September 2022
Revised: 31 October 2022
Accepted: 08 November 2022
Published: 15 December 2022
©2022 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)