Abstract: This paper proposes a financial crisis warning model, based on Least Squares Support Vector Machines(LS-SVM) of which related parameters is optimized through Particle Swarm Optimization(PSO). A case study based on financial data acquired from listed companies is carried out using the method proposed to detect the finance problem. It is shown that LS-SVM with parameters optimized by PSO, is simple and effective.

Key words: Least Squares Support Vector Machines(LS-SVM), Particle Swarm Optimization(PSO), pattern classification, financial warning

摘要： 提出一种基于粒子群优化算法优化有关参数的最小二乘支持向量机的财务预警模型。通过提出适当的验证性能指标，用粒子群优化算法优化最小二乘支持向量机的有关参数，利用上市公司的财务数据对该方法进行实证财务预警分析。仿真结果表明，该模型的精确度令人满意，该方法是可行且有效的。

关键词: 最小二乘支持向量机, 粒子群优化算法, 模式分类, 财务预警

CLC Number: 

• TP181