Exploring Insider Trading Within Hypernetworks

Abstract

Insider trading can have crippling effects on the economy and its prevention is critical to the security and stability of global markets. It is hypothesized that insiders who trade at similar times share information. We analyze 400 companies and 2,000 insiders, identifying interesting trading patterns in these networks that are suggestive of illegal activity. Insiders are classified as either routine or opportunistic traders, allowing us to concentrate on well timed and highly profitable trades of the latter. Using trade classification and analyzing each trader’s role in a hypernetwork, reveals cliques of opportunistic and routine traders. This idea forms the basis of a graph based detection algorithm that seeks to identify traders belonging to opportunistic cliques. The ideas of trade classification and trading cliques present interesting opportunities to develop more robust policing systems which can automatically flag illegal activity in markets, and predict the likelihood that such activity will occur in the future.