Dimensionality reduction techniques to support insider trading detection

ArXiv ID: 2403.00707 “View on arXiv”

Authors: Unknown

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: Market Abuse Detection, Insider Trading, Unsupervised Machine Learning, Autoencoders, Anomaly Detection, Equities

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper employs standard dimensionality reduction techniques (PCA and autoencoders) with relatively light mathematical derivations, but demonstrates high empirical rigor by applying the method to a unique, large-scale, real-world dataset of investor trading records from Consob around specific price-sensitive events, yielding actionable, data-heavy insights for market surveillance.

  flowchart TD
    A["Research Goal: <br>Support Market Surveillance<br>for Insider Trading Detection"] --> B["Input: <br>Investor Trading Positions<br>around Price Sensitive Events PSEs"]
    B --> C["Dimensionality Reduction<br>Techniques"]
    C --> D["Principal Component Analysis PCA"]
    C --> E["Autoencoders AE"]
    D --> F["Compute Reconstruction Errors"]
    E --> F
    F --> G{"Apply Suspiciousness Conditions"}
    G --> H["Outcomes: <br>Identification of Contextual Anomalies<br>Support for Market Abuse Detection"]