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随着我国利率市场化改革深化、互联网金融的迅速发展,商业银行所处环境更为复杂,行业竞争加剧,如何加强商业银行绩效评价、实现长远快速发展尤为重要。本文引入EVA值,从银行绩效各影响因素方面选取指标,通过筛选,最终选定17个指标构建上市商业银行绩效评价体系;利用灰色关联度法进行指标权重计算并确定各商业银行的绩效值;基于BP神经网络技术构建上市商业银行绩效评价模型,对样本银行的绩效进行模型训练和仿真验证,结果证实所构建的评价模型具有很好的泛化能力,能够有效的对商业银行绩效进行评价。
With the deepening of the marketization of interest rates in our country and the rapid development of Internet finance, the environment in which commercial banks are located is more complex and the industry competition is aggravated. It is particularly important to enhance the performance evaluation of commercial banks and achieve long-term rapid development. This article introduces the EVA value, selects the index from the aspects of the bank performance, selects 17 indexs to construct the performance appraisal system of listed commercial banks by screening and finally selects the index weights by gray relational method and determines the performance value of each commercial bank; Based on the BP neural network technology, this paper constructs a performance evaluation model of listed commercial banks, and conducts model training and simulation verification on the performance of the sample banks. The results show that the evaluation model constructed has good generalization ability and can effectively evaluate the performance of commercial banks.