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  .
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 .
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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
Illegal insider trading of stocks is based on releasing
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(private, leaked, tipped)
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Preventing
Detecting
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a priority of the regulatory authorities (e.g., SEC) as it involves billions of dollars, and is very difficult to detect
difficult due to the complex, nonlinear, and non-stationary nature of the stock market
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different types of insider trading approaches, techniques and our proposed approach for detecting and predicting insider trader
an approach that detects and predicts illegal insider trading proactively from large heterogeneous sources of structured and unstructured data
A
the
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. 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