Mining Illegal Insider Trading of Stocks: A Proactive Approach

Sheikh Rabiul Islam, Sheikh Khaled Ghafoor and William Eberle

Papers from arXiv.org

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.

Date: 2018-07, Revised 2018-11
New Economics Papers: this item is included in nep-big, nep-cmp, nep-isf, nep-knm and nep-mst
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Citations: View citations in EconPapers (5)

Published in 2018 IEEE International Conference on Big Data (Big Data)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1807.00939

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