Research Area:  Machine Learning
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.
Keywords:  
Mining Illegal Insider Trading Of Stocks
Machine Learning
Deep Learning
Author(s) Name:  Sheikh Rabiul Islam; Sheikh Khaled Ghafoor
Journal name:  
Conferrence name:  IEEE International Conference on Big Data
Publisher name:  IEEE
DOI:  10.1109/BigData.2018.8622303
Volume Information:  
Paper Link:   https://ieeexplore.ieee.org/document/8622303