支持向量机
机器学习
人工智能
计算机科学
水准点(测量)
佣金
构造(python库)
过采样
股票市场
库存(枪支)
财务
业务
工程类
古生物学
计算机网络
机械工程
程序设计语言
地理
大地测量学
带宽(计算)
马
生物
作者
Qingbai Liu,Chuanjie Wang,Zhang Ping,Kaixin Zheng
标识
DOI:10.1016/j.irfa.2021.101887
摘要
In this paper, we apply machine-learning techniques to construct detecting models of stock market manipulation. By combining manually collected China Securities Regulatory Commission punishment cases from 2014 to 2016 with financial information of listed companies, we construct a training set and a test set to compare the detecting ability of support vector machine (SVM) and logistic model. Considering imbalanced data, we further incorporate Borderline Synthetic Minority Oversampling Technique (Borderline SMOTE) to oversample minority class and then find that Borderline SMOTE–SVM performs better than SVM and benchmark model in detecting manipulation. To enhance detecting performance of the models, we innovatively introduce market sentiment indicators which are extracted from analyst rating reports, financial news, and Guba comments into our indicators set. The results indicate that the new indicators generate significant marginal increment to the model accuracy.
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