A Predictive Analytics Framework for Insider Trading Events

摘要

Financial markets are driven by complex dynamics and interplay, often stemming from convoluted investor interactions, asset and inter-market complexities. Such intricacies make it difficult to identify illegal trading activities like insider trading. Despite several advancements, the detection of such financial markets events remains elusive due to complex interactions among the market constituents. In this paper, we propose a novel solution to detect illegal trading activities driven by material nonpublic information. We accomplish this task by deploying a multistage methodology that includes a predictive modeling approach without the added constraint of having training data with the events of interest, an event prediction and detection methodology based on unstructured and structured data, a classification, and an evaluation approach to identify illegal insider trading events with good confidence. O ur r esults o n t he r eal t est d ata confirm the efficacy o f t he p roposed s olution t o d etect i nsider trading activities in the U.S. equity markets.