不平衡样本下的金融市场极端风险预警研究  

Research on Extreme Risk Warning in Financial Market from Imbalance Distribution of Samples

作　　者：温廷新[1] 孔祥博[1] WEN Tingxin;KONG Xiangbo(Research Institute of System Engineering,Liaoning Technical University,Huludao,Liaoning 125105,China)

机构地区：[1]辽宁工程技术大学系统工程研究所,辽宁葫芦岛125105

出　　处：《计算机工程与应用》2020年第8期256-260,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.713711091);辽宁省社科基金(No.L14BTJ004)。

摘　　要：为了提高金融市场极端风险识别及预警能力,采用沪深300指数作为研究数据,通过少数类样本过采样算法(SMOTE)解决样本不均衡问题,利用因子分析提取特征,通过粒子群(PSO)优化的最小二乘支持向量机(LSSVM)算法构建(SMOTE-PSO-LSSVM)预测模型。使用SMOTE-PSO-LSSVM模型对2007—2010年沪深300指标样本进行预测,样本含极端风险样本193条,模型成功识别风险样本154条,识别准确率达到了83.1%。研究结果表明SMOTE-PSO-LSSVM模型对金融风险数据识别能力较强,能够较为精准地识别风险样本,且求解速度快运行效率高,比传统BP网络和支持向量机等方法性能更优秀。该研究结论对金融市场的风险识别、市场趋势把控、股市交易管制以及投资者决策具有一定意义。In order to improve the ability of extreme risk identification and early warning in the financial market,the CSI 300 index is adopted as the research data,and the over-sampling algorithm(SMOTE)of a small number of samples is adopted to solve the problem of sample imbalance.The feature is extracted by factor analysis,and the prediction model(SMOTEPSO-LSSVM)is constructed by the LSSVM algorithm optimized by PSO.The SMOTE-PSO-LSSVM model is used to predict the CSI 300 index samples from 2007 to 2010.The samples include 193 extreme risk samples,and 154 risk samples are successfully identified by the model,with the recognition accuracy reaching 83.1%.The results show that SMOTE-PSO-LSSVM model has a strong ability to identify financial risk data,can identify risk samples more accurately,has a fast solution speed and high efficiency,and has better performance than traditional BP network and support vector machine.The conclusion of this paper has certain significance for risk identification,market trend control,stock market transaction control and investor decision.

关 键 词：少数类过采样 金融市场极端风险 粒子群 最小二乘支持向量机 

分 类 号：F832[经济管理—金融学] TP391[自动化与计算机技术—计算机应用技术]