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Detecting Pump &Dump stock market manipulation from online forums

Author

Listed:
  • D. Nam

    (Queen’s University)

  • D. B. Skillicorn

    (Queen’s University)

Abstract

The intersection of social media, low-cost trading platforms, and naive investors has created an ideal situation for information-based market manipulations, especially pump &dumps. Manipulators accumulate small-cap stocks, disseminate false information on social media to inflate their price, and sell at the peak. We collect a dataset of stocks whose price and volume profiles have the characteristic shape of a pump &dump, and social media posts for those same stocks that match the timing of the initial price rises. From these we build predictive models for pump &dump events based on the language used in the social media posts. There are multiple difficulties: not every post will cause the intended market reaction, some pump &dump events may be triggered by posts in other forums, and there may be accidental confluences of post timing and market movements. Nevertheless, our best model achieves a prediction accuracy of 85% and an F1-score of 62%. Such a tool can provide early warning to investors and regulators that a pump &dump may be underway.

Suggested Citation

  • D. Nam & D. B. Skillicorn, 2025. "Detecting Pump &Dump stock market manipulation from online forums," Digital Finance, Springer, vol. 7(1), pages 1-20, March.
  • Handle: RePEc:spr:digfin:v:7:y:2025:i:1:d:10.1007_s42521-024-00121-4
    DOI: 10.1007/s42521-024-00121-4
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    References listed on IDEAS

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    1. Rajesh K. Aggarwal & Guojun Wu, 2006. "Stock Market Manipulations," The Journal of Business, University of Chicago Press, vol. 79(4), pages 1915-1954, July.
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    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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