摘要
企业财务困境预测是财务实务界和理论界关注的热点问题之一。为了有效识别出在未来两年内有可能陷入财务困境的企业,该文提出了一种遗传算法优化灰色案例推理的新方法进行企业财务困境预测,并采用实证研究予以验证。理论研究中主要构建了财务困境预测的企业案例描述、基于灰色相似度的k近邻案例检索、基于相似度加权投票组合的评价结果集成和基于遗传算法的案例特征权重向量优化等四个关键内容。实证研究中,收集了270个上市公司ST前一年和前两年的数据为初始样本,通过格点搜索技术进行参数优化,采用余一交叉验证准确率作为评价标准,通过与多元判别分析、Logistic回归、BP神经网络、支持向量机的预测结果比较发现:该方法在企业财务困境预测中的准确率有较大提高。
Financial distress prediction is a hot topic in both theoretical and plactical area of finance. In order to identify those companies that are possible to fall into financial distress in less than two years, a new method for financial distress prediction is proposed based on grey case - based reasoning whose feature weight vector is optimized by the genetic algorithm. Meanwhile, empirical research is used to provide some evidence. There are four key techniques in the new method, i.e. enterprise case representation for financial distress prediction, k - nearest neighbor case retrieval based on the grey similarity, combination of target case class based on the similarity weighted voting, and feature weight vector optimization based on the genetic algorithm, they have been build up. In the empirical experiment with 270 Chinese listed companies＇ one - year and two year data before they become Special Treatment（ST） companies, grid - search technique is utilized to determine parameter values ; Leave - One - Out Cross -Validation （LOO -CV） accuracy is employed as an assessment. Experiment results indicate that this new method significantly outperforms multi discriminant analysis, Logistic regression, BP neural networks, and support vector machine.
出处
《科研管理》
CSSCI
北大核心
2009年第2期119-125,共7页
Science Research Management
基金
国家自然科学基金项目(70801054)
浙江省自然科学基金项目(Y607011)
关键词
财务困境预测
灰色案例推理
遗传算法
实证研究
financial distress prediction
grey case -based reasoning
genetic algorithm
empirical study
