https://doi.org/10.1140/epjds/s13688-024-00500-2
Research
A machine learning approach to support decision in insider trading detection
1
Dipartimento di Economia Politica e Statistica, Università di Siena, Siena, Italy
2
Scuola Normale Superiore, Pisa, Italy
3
Consob, Rome, Italy
4
Dipartimento di Matematica, Università di Bologna, Bologna, Italy
5
University College London, London, UK
Received:
28
July
2023
Accepted:
11
October
2024
Published online:
28
October
2024
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 her own past trading history and on the present trading activity of 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.
Key words: Machine learning / Insider trading / Market abuse / Unsupervised learning / Statistically validated networks
Piero Mazzarisi and Adele Ravagnani contributed equally to this work.
© The Author(s) 2024
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