基于熵权-BP神经网络的涉农类上市企业融资风险预警研究
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摘要
针对涉农类上市企业的融资风险预警问题,首先从企业的资金流融入以及融出两个层面构建了含有15个指标的融资风险预警指标体系;然后收集了24家涉农类上市企业2017年的相关指标数据,并利用熵权法确定各指标层的权重值,结合指标数据计算得到24家上市企业的融资风险预警指数RWI值,即为BP神经网络的输出层;最后通过BP神经网络工具箱建立涉农类上市企业的融资风险预警模型,并进行了神经网络训练以及仿真预测。结果发现,所建立的BP神经网络融资风险预警模型对涉农类上市企业的融资风险预警具有较好的适用性,对企业的融资风险预测与控制具有实践意义。
For the early warning of financing risks for listed agricultural enterprises, firstly, from the integration of the company's capital flow and the integration of two levels, the financing risk early warning indicator system with 15 indicators is constructed; Then collected data on the relevant indicators of 24 agricultural-related listed companies in2017,and use the entropy weight method to determine the weight value of each indicator layer, combined with the indicator data, the RWI value of the financing risk early warning index of 24 listed companies is calculated, which is the output layer of BP neural network; Finally, the BP neural network toolbox is used to establish the financing risk early warning model for agricultural listed companies, and conducted neural network training and simulation prediction, it is found that the established BP neural network financing risk early warning model has good applicability to the financing risk warning of agricultural listed companies, it is of practical significance to predict and control the financing risk of enterprises.
For the early warning of financing risks for listed agricultural enterprises, firstly, from the integration of the company's capital flow and the integration of two levels, the financing risk early warning indicator system with 15 indicators is constructed; Then collected data on the relevant indicators of 24 agricultural-related listed companies in2017,and use the entropy weight method to determine the weight value of each indicator layer, combined with the indicator data, the RWI value of the financing risk early warning index of 24 listed companies is calculated, which is the output layer of BP neural network; Finally, the BP neural network toolbox is used to establish the financing risk early warning model for agricultural listed companies, and conducted neural network training and simulation prediction, it is found that the established BP neural network financing risk early warning model has good applicability to the financing risk warning of agricultural listed companies, it is of practical significance to predict and control the financing risk of enterprises.
引文
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[3] Tam and Kiang, Predicting Bank Failures.A Neural Network Approach[J].Management Science,2012(08):926-947
[4]杨保安,季海,徐晶,等.BP神经网络在企业财务危机预警之应用[J].预测,2001(02):49-54,68
[5]姜雨杉.基于BP神经网络的节能环保行业上市公司财务风险预警研究[D].南京:南京航空航天大学,2018
[6]卢伟.YTST农业上市公司债务融资风险评估与管控研究[D].济南:山东大学,2018
[7]杨红娟,李杰云.基于熵权-BP神经网络模型的生态系统安全评价[J].昆明理工大学学报:自然科学版,2017,42(05):112-119
[8]谢阿红,薛倩玉,朱家明.基于熵权-TOPSIS法对医疗器械类企业财务风险的实证研究[J/OL].高师理科学刊:1-6[2019-01-20].
[9]孙妍姑.基于BP神经网络的图像识别技术研究[J].淮南师范学院学报,2010,12(05):22-23
[10]张立钢.企业融资风险的防范与控制——基于融资风险预警模型的实证研究[J].经济师,2018(05):282-283+285
[11]谢丽强.基于BP神经网络下的煤炭上市公司融资风险预警研究[D].太原:山西财经大学,2018
[12]朱庆锋,徐中平,王力.基于模糊综合评价法和BP神经网络法的企业控制活动评价及比较分析[J].管理评论,2013,25(08):113-123
[13]杨桂元.数学建模[M].上海:上海财经大学出版社,2014
[14]魏如约,朱家明.基于DEA模型的我国新能源上市企业融资效率分析[J].北京印刷学院学报,2018,26(07):76-80
[15]刘媛,朱家明,李均红,等.基于OLS多项式曲线拟合对众包任务定价规律的研究[J].齐齐哈尔大学学报:自然科学版,2018,34(05):91-94
[16]朱家明,邢康.基于因子分析法的我国农业类上市公司综合能力评价[J].山东农业工程学院学报,2018,35(07):1-4
[17]李琳洁,朱家明,陈富媛.基于模糊评价和理想解法对城市宜居性的定量评估[J].齐齐哈尔大学学报:自然科学版,2018,34(02):64-70
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