# A machine learning approach to support decision in insider trading detection

arXiv: https://arxiv.org/abs/2212.05912 · HTML: https://mlsanity.com/paper/2212.05912/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 41 references that are in the map.
- First posted on arXiv 6 Dec 2022.

## Builds on (most cited first, 4 of 4)

- [A Survey of Human-in-the-loop for Machine Learning](https://mlsanity.com/paper/2108.00941/) (2021)
- [Graph-based Anomaly Detection and Description: A Survey](https://mlsanity.com/paper/1404.4679/) (2014)
- [The Doge of Wall Street: Analysis and Detection of Pump and Dump Cryptocurrency Manipulations](https://mlsanity.com/paper/2105.00733/) (2021)
- [Inferring monopartite projections of bipartite networks: an entropy-based approach](https://mlsanity.com/paper/1607.02481/) (2016)

## Built on by (most cited first, 1 of 1)

- [A Comparative Study of Machine Learning Algorithms for Stock Price Prediction Using Insider Trading Data](https://mlsanity.com/paper/2502.08728/) (2025)

## Abstract (arXiv)

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.