Mining Illegal Insider Trading of Stocks: A Proactive Approach
This work addresses the challenge of detecting illegal insider trading for financial regulators and analysts, though it appears incremental as it builds on existing methods with new data integration.
The paper tackles the problem of detecting illegal insider trading in stocks by developing a proactive approach that combines deep learning with discrete signal processing on time series data and a tree-based method for visualization. The result is a system with a good success rate in identifying illegal patterns, as validated on existing data.
Illegal insider trading of stocks is based on releasing non-public information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Detecting illegal insider trading is difficult due to the complex, nonlinear, and non-stationary nature of the stock market. In this work, we present an approach that detects and predicts illegal insider trading proactively from large heterogeneous sources of structured and unstructured data using a deep-learning based approach combined with discrete signal processing on the time series data. In addition, we use a tree-based approach that visualizes events and actions to aid analysts in their understanding of large amounts of unstructured data. Using existing data, we have discovered that our approach has a good success rate in detecting illegal insider trading patterns.