标题:
一种粒子群优化贝叶斯网络的财务预警方法Particle Swarm Optimized Bayesian Network for Financial Early Warning
作者:
徐明鹃, 徐绍双
关键字:
财务预警, 粒子群优化, 贝叶斯网络, 现金流量能力, 数据挖掘Financial Early Warning, Particle Swarm Optimization (PSO), Bayesian Network, Cash Flow Capacity, Data Mining
期刊名称:
《Computer Science and Application》, Vol.6 No.3, 2016-03-30
摘要:
群智能预测及其在企业财务危机预警中的应用研究具有重要的理论意义和实用价值。文中在在构建上市公司财务能力评价指标体系的基础上，提出一种粒子群优化贝叶斯网络参数学习的财务预警方法，经选取一组上市公司某三年数据分析，实验表明提出的算法在公司财务危机预警的平均正确率可获得较好的预测效果。
It has important theoretical significance and practical value to swarm intelligent forecasting and its applications in financial early warning of enterprises. On basis of the construction of the evaluation index system of financial capability of corporation, this paper proposes a new method of financial early warning by cooperating particle warm optimization into the parameter learning of Bayesian network. The experimental results on the data of a group of listing companies and comparisons have shown that the proposed algorithm has better effectiveness and the average correct rate in the financial crisis early warning.