The stock market is a nonlinear, nonstationary, dynamic, and complex system. There are several factors that affect the stock market conditions, such as news, social media, expert opinion, political transitions, and natural disasters. In addition, the market must also be able to handle the situation of illegal insider trading, which impacts the integrity and value of stocks. Illegal insider trading occurs when trading is performed based on non-public (private, leaked, tipped) information (e.g., new product launch, quarterly financial report, acquisition or merger plan) before the information is made public. Preventing illegal insider trading is a priority of the regulatory authorities (e.g., SEC) as it involves billions of dollars, and is very difficult to detect. In this work, we present different types of insider trading approaches, techniques and our proposed approach for detecting and predicting insider trader using a deep-learning based approach combined with discrete signal processing on time series data.

本文介绍了一种利用深度学习结合离散信号处理的方法，从大量异构结构化和非结构化数据中主动检测和预测非法内部交易，并利用树形图可视化方法来帮助分析师理解大量非结构化数据。应用结果表明，该方法能够成功检测非法内幕交易模式。

股票非法内幕交易挖掘：一种主动方法