A Deep Learning Based Illegal Insider-Trading Detection and Prediction Technique in Stock Market
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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.
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