EURO-Online login
- New to EURO? Create an account
- I forgot my username and/or my password.
- Help with cookies
(important for IE8 users)
2706. Dimensionality reduction techniques to support insider trading detection
Invited abstract in session WD-40: Experimental economics and game theory 3, stream Experimental economics and game theory.
Wednesday, 14:30-16:00Room: 96 (building: 306)
Authors (first author is the speaker)
| 1. | Adele Ravagnani
|
| Scuola Normale Superiore | |
| 2. | Fabrizio Lillo
|
| Scuola Normale Superiore and Università di Bologna | |
| 3. | Paola Deriu
|
| Commissione Nazionale per le Società e la Borsa | |
| 4. | Piero Mazzarisi
|
| Università di Siena | |
| 5. | Francesca Medda
|
| UniversUniversity College London and Commissione Nazionale per le Società e la Borsa | |
| 6. | Antonio Russo
|
| Commissione Nazionale per le Società e la Borsa |
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.
Keywords
- Machine Learning
- Finance and Banking
Status: accepted
Back to the list of papers