Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (2): 125-133.doi: 10.11896/jsjkx.241000110

• Database & Big Data & Data Science • Previous Articles     Next Articles

Study on Integrated Model of Securities Illegal Margin Trading Accounts Identification Based on Trading Behavior Characteristics

ZUO Xuhong1, WANG Yongquan1,2, QIU Geping1   

  1. 1 School of Law and Criminal Justice,East China University of Political Science and Law,Shanghai 201620,China
    2 Department of Intelligent Science and Information Law,East China University of Political Science and Law,Shanghai 201620,China
  • Received:2024-10-21 Revised:2024-12-09 Online:2025-02-15 Published:2025-02-17
  • About author:ZUO Xuhong,born in 1990,Ph.D candidate,national special economic investigation researcher.His main research interests include data mining and machine learning.
    WANG Yongquan,born in 1964,Ph.D,professor,Ph.D supervisor.His main research interests include big data and artificial intelligence,cyberspace security and cybercrime,digital forensics.
  • Supported by:
    National Key Research and Development Program of China(2023YFC3306100,2023YFC3306105,2023YFC3306103) and Major Projects of National Social Science Foundation(20&ZD199).

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Abstract: In response to issues with securities illegal margin trading accounts,such as the scarcity of public data,the unscientific feature selection,the limited research on identification methods that are lack of precision,an integrated model for the identification of securities illegal margin trading accounts based on trading behavior characteristics(CFS-RF-BP) is proposed.This integrated model consists of four main steps:dataset construction,feature extraction,feature selection,and account identification.According to the characteristics of securities account trading data,a simulated dataset is automatically generated,and the relevant features of securities illegal margin trading accounts are detailed labeled and calculated.Using a heterogeneous feature selection model,combined with methods such as logarithm and normalization,the importance of seventeen essential features primarily associated with securities illegal margin trading accounts is evaluated,and five of these features are selected to form a key feature set.Based on this,the BP neural network is employed to construct the classifier for the integrated model of securities margin trading account identification,optimizing key parameters such as weights and biases,thereby achieving automatic classification of securities illegal margin trading accounts.Simulation experiments indicate that the proposed CFS-RF-BP model has achieved excellent results in terms of feature selection,precision,recall rate,and processing efficiency of identification.

Key words: Securities illegal margin trading, Securities trading data, Feature extraction, Account identification, machine learning

CLC Number: 

  • TP391
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