基于上市公司舆情网络的股票信息型操纵识别模型
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摘要
股票信息型操纵识别是证券监管部门处罚股票违规交易的主要依据,识别准确率是有效打击操纵行为的核心与关键。基于中国证监会2016年查处的股票信息型操纵案件处罚资料与数据,以上市公司舆情与传播媒体为节点构建了公司舆情网络。网络拓扑结构分析结果表明,公司舆情网络节点与连线数量在信息操纵时期明显较多,网络密度明显偏低。基于逐步逻辑回归构建了股票信息操纵识别模型,实证结果表明:网络密度与点度中心势是识别股票是否被实施信息操纵的主要指标,模型样本内检验准确率为87.14%。依此本文提出三点管理建议:监管资源应向主要监控指标倾向分配;根据证券市场环境动态调整识别模型参数;正确认识股票信息操纵双向效应,适度控制其负向作用。
The recognition of stock information manipulation is the main basis for securities market supervision departments to punish illegal trading of stocks, and the recognition accuracy rate is the core and key to effectively strike the market manipulation. Based on the cases text and data of stock information manipulation published by Chinese securities regulatory commission in 2016,the paper constructs the company public opinion networks which take company public opinions and media as nodes. The analysis results of network topology show that the quantity of nods and connections of the networks are obviously more in the period of information manipulation, and the density of networks is obviously low. Based on the main network parameters selected by stepwise logistic regression, a stock information manipulation recognition model is constructed. The empirical results show that the network density and the degree centralization are the main indicators to recognize information manipulation. Through the in-sample test, the recognition accuracy of the model is 87.14%. Accordingly, this paper puts forward three management suggestions: the supervision resources should be allocated to main monitoring indicators; the model parameters should be adjusted dynamically according to the securities market environment; and the two-way effect of stock information manipulation shold be understood as to control moderately its negative role.
The recognition of stock information manipulation is the main basis for securities market supervision departments to punish illegal trading of stocks, and the recognition accuracy rate is the core and key to effectively strike the market manipulation. Based on the cases text and data of stock information manipulation published by Chinese securities regulatory commission in 2016,the paper constructs the company public opinion networks which take company public opinions and media as nodes. The analysis results of network topology show that the quantity of nods and connections of the networks are obviously more in the period of information manipulation, and the density of networks is obviously low. Based on the main network parameters selected by stepwise logistic regression, a stock information manipulation recognition model is constructed. The empirical results show that the network density and the degree centralization are the main indicators to recognize information manipulation. Through the in-sample test, the recognition accuracy of the model is 87.14%. Accordingly, this paper puts forward three management suggestions: the supervision resources should be allocated to main monitoring indicators; the model parameters should be adjusted dynamically according to the securities market environment; and the two-way effect of stock information manipulation shold be understood as to control moderately its negative role.
引文
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[15]John K.,Narayanan R..Market Manipulation and the Role of Insider Trading Regulations[J].Journal of Business,1997,70(2):217-247.
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[2]Paolo Colla,Antonio Mele.Information Linkages and Correlated Trading[J].The Review of Financial Studies,2010,23(1):203-246.
[3]Robert Charles Giannini.Twitter:An Investigation of the Impact of Network Communication[EB/OL].https://papers.ssrn.com/sol3/results.cfm.2015-10-09.
[4]Judson Caskey,Michael Minnis,Venky Nagar.Disclosure Drifts in Investor Networks[EB/OL].https://www.researchgate.net/publication/228248834,2011-05-20.
[5]Juan Gabriel Brida,David Matesanz,Maria NelaSeijas.Network analysis of returns and volume trading in stock markets:The Euro Stoxx case[J].Physica A:Statistical Mechanics and its Applications,2016,(444):751-764.
[6]Wen Long,Lijing Guan,Jiangjian Shen.A complex network for studying the transmission mechanisms in stock market[J].Physica A:Statistical Mechanics and its Applications,2017,(484):345-357.
[7]F Marcet.Analyst coverage network and stock return comovement in emerging markets[J].Emerging Markets Review,2017,32(9):1-27.
[8]黄玮强,庄新田,姚爽.基于信息传播和羊群行为的股票市场微观模拟研究[J].管理学报,2010,(2).
[9]Xiao-Qian Sun,Xue-Qi Cheng,Hua-Wei Shen.Distinguishing manipulated stocks via trading network analysis[J].Physica A:Statistical Mechanics and its Applications,2011,(390):3427-3434.
[10]Zhi-Qiang Jiang,Wen-JieXie,XiongXiong.Trading networks,abnormal motifs and stock manipulation[J].Quantitative Finance Letters,2013,1(1):1-8.
[11]卞曰瑭,李金生,何建敏.网络近邻择优策略下的股市羊群行为演化模型及仿真[J].中国管理科学,2013,(3).
[12]吴滔,李雪岩.基于复杂网络的股市情绪传播模型的仿真研究[J].五邑大学学报(自然科学版),2013,(3).
[13]谢赤,边慧东,王纲金.牛熊市视角下股票关联网络动态拓扑结构研究---以上证50指数为例[J].复杂系统与复杂性科学,2017,(1).
[14]张建锋,付强,杜金柱.基于Logistic模型的上市公司股票价格操纵预判研究[J].西安理工大学学报,2018,(2).
[15]John K.,Narayanan R..Market Manipulation and the Role of Insider Trading Regulations[J].Journal of Business,1997,70(2):217-247.
[16]Tālis J.Putni n,觢.Mark et Manipulation:A Survey[J].Journal of Economic Surveys,2012,26(5):952-967.