Enterprise Financial Risk Early Warning Method Based on Hybrid PSO-SVM Model

Gang Qiao; Lihui Du

doi:10.6180/jase.201903_22(1).0017

Enterprise Financial Risk Early Warning Method Based on Hybrid PSO-SVM Model

Gang Qiao¹ and Lihui Du This email address is being protected from spambots. You need JavaScript enabled to view it.²

¹Department of Economics and Management, Anhui Vocational College of Electronics and Information Technology, Bengbu 233060, P.R. China
²School of Economics and Trade, Hunan University, Changsha, P.R. China

Received: March 28, 2017
Accepted: October 16, 2018
Publication Date: March 1, 2019

Download Citation: ||https://doi.org/10.6180/jase.201903_22(1).0017

ABSTRACT

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’s 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-ST companies. 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.

Keywords: Enterprise Financial Risk, Risk Early Warning, Particle Swarm Optimization, Support Vector Machine

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