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A machine learning approach to support decision in insider trading detection

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  • Piero Mazzarisi
  • Adele Ravagnani
  • Paola Deriu
  • Fabrizio Lillo
  • Francesca Medda
  • Antonio Russo

Abstract

Identifying market abuse activity from data on investors' trading activity is very challenging both for the data volume and for the low signal to noise ratio. Here we propose two complementary unsupervised machine learning methods to support market surveillance aimed at identifying potential insider trading activities. The first one uses clustering to identify, in the vicinity of a price sensitive event such as a takeover bid, discontinuities in the trading activity of an investor with respect to his/her own past trading history and on the present trading activity of his/her peers. The second unsupervised approach aims at identifying (small) groups of investors that act coherently around price sensitive events, pointing to potential insider rings, i.e. a group of synchronised traders displaying strong directional trading in rewarding position in a period before the price sensitive event. As a case study, we apply our methods to investor resolved data of Italian stocks around takeover bids.

Suggested Citation

  • Piero Mazzarisi & Adele Ravagnani & Paola Deriu & Fabrizio Lillo & Francesca Medda & Antonio Russo, 2022. "A machine learning approach to support decision in insider trading detection," Papers 2212.05912, arXiv.org.
    • Handle: RePEc:arx:papers:2212.05912
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    References listed on IDEAS

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    4. Margarita Baltakien.e & Kk{e}stutis Baltakys & Juho Kanniainen & Dino Pedreschi & Fabrizio Lillo, 2019. "Clusters of investors around Initial Public Offering," Papers 1905.13508, arXiv.org, revised Nov 2019.
    5. Michele Tumminello & Salvatore Miccichè & Fabrizio Lillo & Jyrki Piilo & Rosario N Mantegna, 2011. "Statistically Validated Networks in Bipartite Complex Systems," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    6. Fabio Saracco & Mika J. Straka & Riccardo Di Clemente & Andrea Gabrielli & Guido Caldarelli & Tiziano Squartini, 2016. "Inferring monopartite projections of bipartite networks: an entropy-based approach," Papers 1607.02481, arXiv.org, revised May 2017.
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    8. Margarita Baltakienė & Kęstutis Baltakys & Juho Kanniainen & Dino Pedreschi & Fabrizio Lillo, 2019. "Clusters of investors around initial public offering," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-14, December.
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    Cited by:

    1. 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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