AI-Powered Systems for Detecting Insider Trading
Abstract
Recent years have witnessed the increasing significance of artificial intelligence (AI) and machine learning (ML) across the field of finance. In particular, they have been employed to detect insider trading, which poses a direct threat to the integrity of the markets and diminishes the trust of ordinary investors. There is mounting evidence revealing that traditional insider trading detection triggers are somewhat ineffective, a fact that necessitates the use of sophisticated technology that would collect numerous alternative data sets to come up with robust signals in a timely manner, and hence would promptly inform the investors about abusive activities. Signals from unstructured texts as well as unusual price and volume movements, high-frequency trading, illiquidity, derivatives, and options trading may provide an edge in the rapid detection of insider trading. With few exceptions, there has been little research done involving the latest advances in AI. Indeed, ML and AI-powered systems might dip into social media or opinion data about directors' sale or purchase transactions.
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