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Identifying market abuse activity from data on investors' trading activity isvery challenging both for the data volume and for the low signal to noiseratio. Here we propose two complementary unsupervised machine learning methodsto support market surveillance aimed at identifying potential insider tradingactivities. The first one uses clustering to identify, in the vicinity of aprice sensitive event such as a takeover bid, discontinuities in the tradingactivity of an investor with respect to his/her own past trading history and onthe present trading activity of his/her peers. The second unsupervised approachaims at identifying (small) groups of investors that act coherently aroundprice sensitive events, pointing to potential insider rings, i.e. a group ofsynchronised traders displaying strong directional trading in rewardingposition in a period before the price sensitive event. As a case study, weapply our methods to investor resolved data of Italian stocks around takeoverbids.

A machine learning approach to support decision in insider trading detection