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An Intelligent System for Insider Trading Identification in Chinese Security Market

Author

Listed:
  • Shangkun Deng

    (China Three Gorges University
    China Three Gorges University)

  • Chenguang Wang

    (China Three Gorges University)

  • Zhe Fu

    (Beijing Normal University)

  • Mingyue Wang

    (China Three Gorges University)

Abstract

Insider trading is one kind of criminal behaviors in security markets. It has existed since the birth of the security market. Until 2018, the history of the Chinese security market is less than 30 years, nonetheless, insider trading behavior frequently occurred. In this study, we mainly explore the features of insider trading behavior by studying relevant indicators during the sensitive period (time window length before the release of insider information). For this purpose, an intelligent system with an integration method of Principal Component Analysis (PCA) and Random Forest (RF) is proposed to identify insider tradings in Chinese security market. In the proposed method, we first collect twenty-six relevant indicators for insider trading samples that occurred from 2007 to 2017 and corresponding non-insider trading samples in Chinese security market. Next, by using the PCA, indicator dimension is reduced and principal components are extracted. Then, relations between insider trading samples and principal components are learnt by the RF algorithm. In the identification phase, the trained PCA-RF model is applied to classify the insider trading and non-insider trading samples, as well as analyzing the relative importance of indicators for insider trading identification. Experimental results showed us that under the 30-, 60-, and 90-days time window lengths, recall results of the proposed method for the out-of-samples identification were 73.53%, 83.87%, and 79.41%, respectively. We further investigate the voting threshold of RF for the proposed method, and we found when the voting threshold of RF was increased to more than 70%, the proposed method produced identification accuracy up to more than 90%. In addition, the relative importance result of RF indicated that three indicators were crucial for insider trading identification. Moreover, identification accuracy and efficiency of the proposed method were substantially superior to benchmark methods. In summary, experimental results indicated that the proposed method could be efficiently applied to Chinese security market. Thus, the proposed method can provide useful suggestions to market regulators for insider trading investigations.

Suggested Citation

  • Shangkun Deng & Chenguang Wang & Zhe Fu & Mingyue Wang, 2021. "An Intelligent System for Insider Trading Identification in Chinese Security Market," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 593-616, February.
    • Handle: RePEc:kap:compec:v:57:y:2021:i:2:d:10.1007_s10614-020-09970-8
    DOI: 10.1007/s10614-020-09970-8
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    References listed on IDEAS

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    Cited by:

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    2. Shangkun Deng & Yingke Zhu & Xiaoru Huang & Shuangyang Duan & Zhe Fu, 2022. "High-Frequency Direction Forecasting of the Futures Market Using a Machine-Learning-Based Method," Future Internet, MDPI, vol. 14(6), pages 1-21, June.
    3. Prashant Priyadarshi & Prabhat Kumar, 2024. "A comprehensive review on insider trading detection using artificial intelligence," Journal of Computational Social Science, Springer, vol. 7(2), pages 1645-1664, October.

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