EVA对企业绩效评价模式的改进——神经网络在绩效评价中的应用被引量：13

Improvement of Traditional Performance Measurement with EVA:An Application of Neural Network

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摘要利用经济附加值(EVA)来改进中国传统绩效评价模式,并建立了整合EVA的绩效评价(IEPM)模型。以沪深两市A股上市公司为研究样本,采用BP神经网络方法检验了整合EVA的绩效评价模型相对于传统评价模式的有效性。结果显示,整合EVA的绩效评价模型要显著优于传统评价模式,而且其预测能力也显著强于传统模式,说明用整合EVA的绩效评价模型来评价和预测企业的经营业绩是更为有效的。 This paper made a study on how to improve traditional performance measurements with Economic Value Added （EVA）. It presented the integrated EVA performance measurement （IEPM） model and tested the validity. The difference between IEPM model and traditional performance measurements was empirically analyzed using BP neural network with the data from China＇s listed companies. The results showed that IEPM model performed better than traditional performance measurements and the predictive ability of IEPM model was also proved to be superior to that of traditional performance measurements. So it is effective and reasonable to use IEPM model to evaluate and predict the firm＇s performance.

作者陈琳王平心汪方军

机构地区西北工业大学管理学院西安交通大学管理学院

出处《系统工程》 CSCD 北大核心 2006年第3期88-94,共7页 Systems Engineering

基金国家自然科学基金资助项目(70172010)

关键词绩效评价经济附加值神经网络 Performance Measurement Economic Value Added Neural Network

分类号 F272 [经济管理—企业管理]

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参考文献13

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