一种粒子群优化贝叶斯网络的财务预警方法.pdf

Computer Science and Application 计算机科学与应用, 2016, 6(3), 195-201 Published Online March 2016 in Hans. /journal/csa /10.12677/csa.2016.63025 Particle Swarm Optimized Bayesian Network for Financial Early Warning Mingjuan Xu, Shaoshuang Xu School of Information Engineering, West Anhui University, Lu’an Anhui th th th Received: Mar. 8 , 2016; accepted: Mar. 27 , 2016; published: Mar. 30 , 2016 Copyright © 2016 by authors and Hans Publishers Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). /licenses/by/4.0/ Abstract 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 evalu- ation index system of financial capability of corporation, this paper proposes a new method of fi- nancial 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 com- parisons have shown that the proposed algorithm has better effectiveness and the average correct rate in the financial crisis early warning. Keywords Financial Early Warning, Particle Swarm Optimization (PSO), Bayesian Network, Cash Flow Capacity, Data Mining 一种粒子群优化贝叶斯网络的财务预警方法 徐明鹃，徐绍双 皖西学院信息工程学院，安徽 六安 收稿日期：2016年3月8 日；录用日期：2016年3月27 日；发布日期：2016年3月30 日 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 文章引用: 徐明鹃, 徐绍双. 一种粒子群优化贝叶斯网络的财务预警方法[J]. 计算机科学与应用, 2016, 6(3): 195-201. /10.12677/csa.2016.63025 徐明鹃，徐绍双 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。