Mining Illegal Insider Trading of Stocks: A Proactive Approach

TL;DR

This paper introduces a deep learning and signal processing approach to proactively detect illegal insider trading by analyzing large heterogeneous data sources, aiding analysts with visualization tools.

Contribution

It presents a novel combination of deep learning, signal processing, and visualization techniques for early detection of illegal insider trading.

Findings

High success rate in detecting insider trading patterns

Effective analysis of structured and unstructured data

Enhanced understanding through visualization tools

Abstract

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.