{"id":6525,"date":"2026-06-20T17:18:33","date_gmt":"2026-06-20T09:18:33","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=6525"},"modified":"2026-08-21T15:09:12","modified_gmt":"2026-08-21T07:09:12","slug":"enterprise-financial-risk-early-warning-method-based-on-hybrid-pso-svm-model","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=enterprise-financial-risk-early-warning-method-based-on-hybrid-pso-svm-model","title":{"rendered":"Enterprise Financial Risk Early Warning Method Based on Hybrid PSO-SVM Model"},"content":{"rendered":"\n
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<\/i> 2019<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 22, Issue 1<\/a><\/p>\n<\/div>\n\n\n\n

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Gang Qiao1<\/sup> and Lihui Du2<\/sup><\/i><\/a><\/p>\n\n\n\n

1<\/sup>Department of Economics and Management, Anhui Vocational College of Electronics and Information Technology, Bengbu 233060, P.R. China <\/p>\n\n\n\n

2<\/sup>School of Economics and Trade, Hunan University, Changsha, P.R. China<\/p>\n<\/div>\n\n\n\n

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Received:\u00a0March 28, 2017
Accepted:\u00a0October 16, 2018
Publication Date:\u00a0June 20, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

Index system for the enterprise financial risk early warning.<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.201903_22(1).0017<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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In order to ensure the healthy and orderly development of enterprises, it is of great importance to effectively predict enterprise financial risk in advance, and then provide early warning information to enterprise\u2019s managers. Firstly, we design an index system for enterprise financial risk early warning,
which contains five aspects: 1) Profitability, 2) Debt paying ability, 3) Operation ability, 4) Growth ability, and 5) Non-financial indicators. Secondly, we propose a novel hybrid PSO-SVM model based enterprise financial risk early warning algorithm by converting the proposed problem to a classification
problem. As the performance of SVM classifier highly relies on parameter selection, we introduce the PSO algorithm to estimate optimal parameters for SVM. Thirdly, we choose several ST companies of the listed companies in China as financial crisis enterprises, and compare their running states with
Non-STcompanies. Main contributions of this paper lie in that we propose a hybrid PSO-SVM model based enterprise financial risk early warning algorithm by solving a classification problem. Experimental results show that the proposed algorithm is able to effectively provide early warning information for enterprise financial risk, and performs better than BP neural network and SVM without parameter optimization.<\/p>\n\n\n\n

Keywords:\u00a0<\/em>Enterprise Financial Risk, Risk Early Warning, Particle Swarm Optimization, Support Vector Machine<\/p>\n\n\n\n

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