Financial Distress Warning: An Evaluation System including Ecological Efficiency

Shuang Wu, Hui Zhang, Yuan Tian, Liyuan Shi and Daqing Gong

Discrete Dynamics in Nature and Society, 2021, vol. 2021, 1-9

Abstract: This article established an evaluation system including ecological efficiency that can provide a more accurate financial distress warning for companies. Based on the data of listed companies, Data Envelopment Analysis (DEA) is applied to evaluating the business efficiency, financial efficiency, financing efficiency, human capital efficiency, and ecological efficiency, and the accuracy of the evaluation system that includes ecological efficiency is measured by artificial neural networks (ANNs). Besides, the logit model is applied to test the results. Our experiments indicate that participating in ecological efficiency improves the evaluation system of financial distress warnings, and its accuracy is much better than the traditional evaluation system in the long run. The logit model confirms the essential of ecological indicators in financial distress warning, and the behavior of observing environmental regulation will prevent enterprises from getting into financial distress. Finally, suggestions on improving green finance and promoting technological innovation are propounded, in which technological innovation (TI) is the core of an enterprise’s competitiveness, and green finance can accelerate that process.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:5605892

DOI: 10.1155/2021/5605892

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