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Mining Illegal Insider Trading of Stocks: A Proactive Approach

Author

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  • Sheikh Rabiul Islam
  • Sheikh Khaled Ghafoor
  • William Eberle

Abstract

Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock market. In this work, we present an approach that detects and predicts illegal insider trading proactively from large heterogeneous sources of structured and unstructured data using a deep-learning based approach combined with discrete signal processing on the time series data. In addition, we use a tree-based approach that visualizes events and actions to aid analysts in their understanding of large amounts of unstructured data. Using existing data, we have discovered that our approach has a good success rate in detecting illegal insider trading patterns.

Suggested Citation

  • Sheikh Rabiul Islam & Sheikh Khaled Ghafoor & William Eberle, 2018. "Mining Illegal Insider Trading of Stocks: A Proactive Approach," Papers 1807.00939, arXiv.org, revised Nov 2018.
    • Handle: RePEc:arx:papers:1807.00939
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    References listed on IDEAS

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    1. Gishan Dissanaike & Kim-Hwa Lim, 2015. "Detecting and Quantifying Insider Trading and Stock Manipulation in Asian Markets," Asian Economic Papers, MIT Press, vol. 14(3), pages 1-20, Fall.
    2. Cumming, Douglas & Johan, Sofia & Li, Dan, 2011. "Exchange trading rules and stock market liquidity," Journal of Financial Economics, Elsevier, vol. 99(3), pages 651-671, March.
    3. Ahern, Kenneth R., 2017. "Information networks: Evidence from illegal insider trading tips," Journal of Financial Economics, Elsevier, vol. 125(1), pages 26-47.
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    Cited by:

    1. Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
    2. Zhang, Junting & Liu, Haifei & Bai, Wei & Li, Xiaojing, 2024. "A hybrid approach of wavelet transform, ARIMA and LSTM model for the share price index futures forecasting," The North American Journal of Economics and Finance, Elsevier, vol. 69(PB).
    3. James, Robert & Leung, Henry & Prokhorov, Artem, 2023. "A machine learning attack on illegal trading," Journal of Banking & Finance, Elsevier, vol. 148(C).
    4. Sheikh Rabiul Islam & William Eberle & Sheikh K. Ghafoor & Sid C. Bundy & Douglas A. Talbert & Ambareen Siraj, 2019. "Investigating bankruptcy prediction models in the presence of extreme class imbalance and multiple stages of economy," Papers 1911.09858, arXiv.org.
    5. Wingyan Chung & Yinqiang Zhang & Jia Pan, 2023. "A Theory-based Deep-Learning Approach to Detecting Disinformation in Financial Social Media," Information Systems Frontiers, Springer, vol. 25(2), pages 473-492, April.

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