A machine learning approach to support decision in insider trading detection
This work addresses the problem of market surveillance for regulators and financial institutions by providing tools to detect insider trading, though it appears incremental as it builds on existing unsupervised techniques.
The paper tackles the challenge of detecting insider trading by proposing two unsupervised machine learning methods: one identifies discontinuities in individual trading activity around price-sensitive events, and the other detects coherent groups of investors acting as potential insider rings. As a case study, these methods were applied to Italian stock data around takeover bids.
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.