财务困境预警SVM模型的构建及实证研究被引量：11

Financial Distress Prediction Based on Parametric and Non-Parametric Methods

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摘要精确的财务困境预测对企业管理层、投资者、债权人、监管层等有着十分重要的意义。本文以支持向量机(SVM)为基础,选取高斯核函数为内积函数,分析惩罚参数和核参数对模型的影响,并通过5-fold cross-vali-dation方法与网格搜索算法选取最优参数,构建财务困境预警的SVM模型。相关的实证结果表明,SVM模型在提前3年的情况下仍能取得较好的预测效果,在预测效率上优于传统的多元判别分析方法。 Accurate financial distress prediction is important for managers,investors,creditors and supervisors.This paper deals with the application of SVM with Gaussian kennel function as the inner function to financial distress prediction.The effects of penalty parameter and kennel parameter on the model are analyzed and the method of 5-fold cross-validation and the Grid Algorithm are used to optimize the SVM classifier parameter.Analysis of the experimental results proves that it is advantageous to the traditional method of MDA.

作者谢赤罗长青王蓓

机构地区湖南大学工商管理学院西北大学经济管理学院

出处《当代经济科学》 CSSCI 北大核心 2007年第6期96-100,共5页 Modern Economic Science

基金国家社会科学基金重点资助项目(07AJL005) 全国高校青年教师奖励基金资助项目(教人司2002[123]) 教育部博士点专项科研基金资助项目(20070532002)阶段性研究成果

关键词财务困境预警支持向量机 Financial distress Prediction Support vector machine

分类号 F275 [经济管理—企业管理] F224 [经济管理—国民经济]

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