Dimensionality reduction techniques to support insider trading detection

Consob - Scuola Normale Superiore di Pisa

FinTech no. 12 - February 2024 [PDF]
 

Abstract
Identification of market abuse is an extremely complicated activity that requires the analysis of large and complex datasets. We propose an unsupervised machine learning method for contextual anomaly detection, which allows to support market surveillance aimed at identifying potential insider trading activities. This method lies in the reconstruction-based paradigm and employs principal component analysis and autoencoders as dimensionality reduction techniques. The only input of this method is the trading position of each investor active on the asset for which we have a price sensitive event (PSE). After determining reconstruction errors related to the trading profiles, several conditions are imposed in order to identify investors whose behavior could be suspicious of insider trading related to the PSE. As a case study, we apply our method to investor resolved data of Italian stocks around takeover bids.

Authors
This paper, which is the result of a collaboration between Consob and the Scuola Normale Superiore di Pisa, was edited by:
- Adele Ravagnani - SNS Pisa (adele.ravagnani@sns.it);
- Fabrizio Lillo - SNS Pisa, Dipartimento di Matematica, Università di Bologna (fabrizio.lillo@sns.it);
- Paola Deriu - Consob, Markets Division (p.deriu@consob.it);
- Piero Mazzarisi - SNS Pisa, Dipartimento di Economia Politica e Statistica, Università di Siena (piero.mazzarisi@sns.it);
- Francesca Medda - Consob, General Direction (f.medda@consob.it);
- Antonio Russo - Consob, Markets Division (a.russo@consob.it).

The authors are the only responsible for errors and imprecisions. The opinions expressed in the paper are the authors' personal views and are in no way binding on CONSOB.

Keywords: Dimensionality reduction, Principal component analysis, Autoencoder, Insider trading, Market abuse, Unsupervised learning