集成机器学习模型在不平衡样本财务预警中的应用  

The application of the integrated machine learning model in the financial crisis of imbalanced sample

作　　者：张露 刘家鹏 江敏祺 Zhang Lu;Liu Jiapeng;Jiang Minqi(School of Economics and Management,China Jiliang University,Hangzhou 310018,China;School of Information Management and Engineering,Shanghai University of Finance and Economics,Shanghai 200000,China)

机构地区：[1]中国计量大学经济与管理学院,浙江杭州310018 [2]上海财经大学信息管理与工程学院,上海200000

出　　处：《电子技术应用》2021年第8期34-38,共5页Application of Electronic Technique

基　　金：国家自然科学基金(18BGL224)。

摘　　要：基于上交所主板市场A股企业的财务指标数据来预测企业的财务风险,样本数据包括1227家正常上市企业和42家被财务预警的企业,数据严重不平衡,通过重采样技术解决了分类器在不平衡样本中失效的问题,运用Bagging思想的集成机器学习对预测模型进行提升与优化。正确挑选出有财务危机企业的概率最高达到92.86%,在此基础上,样本的整体准确率在经过模型的集成之后提高了5.4%。集成模型提高了对上市企业的财务预警能力,能为企业的正常经营和投资者的安全投资提供一定的借鉴。This paper forecast the financial risk of enterprises based on the financial index data of A-share enterprises in the main board market of Shanghai Stock Exchange.The samples included 1227 normal listed enterprises and 42 enterprises which have been financial warning.The data was seriously unbalanced.The problem of classifier failure in unbalanced samples was solved by resampling technology in some certain.The integrated machine learning based on Bagging was used to improve and optimize the prediction model.The highest probability of correctly selecting enterprises with financial warning was 92.86%.On this basis,the overall accuracy of the sample was improved by 5.4%after the integration of the model.The integrated model improved the financial early warning ability of listed enterprises which could provide some reference for the normal operation of enterprises and the safety investment of investors.

关 键 词：财务预警预测 集成机器学习 不平衡采样技术 

分 类 号：TN99[电子电信—信号与信息处理] TP391[电子电信—信息与通信工程]