Using Machine Learning to Unveil the Dynamics of Insider Trading in Financial Markets

Abstract

The subject of this study is the insider trading behaviors within the US financial markets, with a focus on transactions by politicians and public officials, and their implications for global economic stability. The purpose is to investigate and analyze these transactions for ethical and legal challenges, and to evaluate their potential impact on market integrity and investor trust. The relevance of this research arises from the substantial influence these figures have on market dynamics, the legal nuances involved in their financial activities, and the broader implications for market transparency and fairness. The scientific novelty is established using econometric modeling and data analytics, particularly the analysis of trading behavior that potentially circumvents the Stop Trading on Congressional Knowledge (STOCK) Act. The methods employed include a Python tool to extract data from financial disclosures and ordinary least squares (OLS) regression to analyze key indicators of insider behavior. The results indicate a significant proportion of trades, approximately 86.67%, were conducted by politicians with noted STOCK Act violations, highlighting a potential gap in the enforcement of current laws and reporting standards. The authors concluded that the findings call for stricter law enforcement, a reevaluation of reporting standards, and comprehensive financial disclosures to maintain market integrity, alongside an urgent need for improved regulatory measures and enhanced transparency mechanisms to mitigate the risks associated with insider trading by individuals in positions of power.