文献详情
Enhancing intraday stock price manipulation detection by leveraging recurrent neural networks with ensemble learning
文献类型：期刊
通讯作者：Xu, W (reprint author), Renmin Univ China, Sch Informat, Beijing 100872, Peoples R China.
期刊名称：NEUROCOMPUTING影响因子和分区
年：2019
卷：347
页码：46-58
ISSN：0925-2312
关键词：Stock price manipulation; Deep learning; Ensemble learning; Machine learning; Fraud detection
所属部门：信息学院
摘要：With the rapid development of the stock markets in developing countries, determining how to efficiently detect stock price manipulation activities to protect the interests of ordinary investors is really an important problem. Previous studies have introduced machine learning techniques into stock price manipulation detection and achieved better experimental results than traditional multivariate statistical techniques. Some characteristic features show statistically significant differences betwee ...More
With the rapid development of the stock markets in developing countries, determining how to efficiently detect stock price manipulation activities to protect the interests of ordinary investors is really an important problem. Previous studies have introduced machine learning techniques into stock price manipulation detection and achieved better experimental results than traditional multivariate statistical techniques. Some characteristic features show statistically significant differences between manipulated and non-manipulated stocks, but this complementary information has rarely been considered in the manipulation detection model. The main contribution of our research work is the design of a novel RNN-based ensemble learning (RNN-EL) framework that combine trade-based features derived from trading records and characteristic features of the list companies to effectively detect stock price manipulation activities. Based on prosecuted manipulation cases reported by the China Securities Regulatory Commission (CSRC), we built a specific dataset containing labeled samples with trading data and characteristic information to conduct empirical experiments. The experimental results show that our proposed method outperforms state-of-the-art approaches in detecting stock price manipulation by an average of 29.8% in terms of AUC value. The managerial implication of our work is that government regulators can apply the proposed methodology to efficiently identify suspicious trading behaviors among huge amounts of trading activities in time to take action to ensure a fair trading environment. (C) 2019 Elsevier B.V. All rights reserved. ...Hide

DOI：10.1016/j.neucom.2019.03.006
百度学术：Enhancing intraday stock price manipulation detection by leveraging recurrent neural networks with ensemble learning
语言：外文
被引频次：1
人气指数：1
浏览次数：1
基金：National Natural Science Foundation of ChinaNational Natural Science Foundation of China [U1711262, 71771212]; Humanities and Social Sciences Foundation of the Ministry of Education [14YJA630075, 15YJA630068]; Hebei Social Science Fund [HB13GL021]; Fundamental Research Funds for the Central UniversitiesFundamental Research Funds for the Central Universities; Research Funds of Renmin University of China [15XNLQ08]
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