  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.
