Detecting Insider Trading in the Era of Big Data and Machine Learning

43 Pages Posted: 17 Oct 2022

See all articles by Christian T. Lundblad

Christian T. Lundblad

University of North Carolina Kenan-Flagler Business School; Frank Hawkins Kenan Institute of Private Enterprise

Zhishu Yang

Tsinghua University - School of Economics & Management

Qi Zhang

Shanghai Jiao Tong University (SJTU)

Date Written: September 23, 2022

Abstract

Reliably detecting insider trading is a major impediment to both research and regulatory practice. Using account-level transaction data, we propose a novel approach. Specifically, after extracting several key empirical features of typical insider trading cases from existing regulatory actions, we then employ a machine learning methodology to identify suspicious insiders across our full sample. Our identified outliers earn, on average, a significantly higher return relative to a random sample. Further, we find that the trading patterns of selected suspicious insiders exhibit similarities with the changes in a firm’s central decision-makers. We also find that insiders are more likely to use multiple accounts to trade around a major information event; we observe this via the IP address attached to each transaction. Taken together, our approach significantly augments an otherwise elusive ability to detect insider trading.

Keywords: Detect insider trading, Machine learning, Big data

JEL Classification: G14, G15

Suggested Citation

Lundblad, Christian T. and Yang, Zhishu and Zhang, Qi, Detecting Insider Trading in the Era of Big Data and Machine Learning (September 23, 2022). Available at SSRN: https://ssrn.com/abstract=4240205 or http://dx.doi.org/10.2139/ssrn.4240205

Christian T. Lundblad

University of North Carolina Kenan-Flagler Business School ( email )

Kenan-Flagler Business School
Chapel Hill, NC 27599-3490
United States
919-962-8441 (Phone)

Frank Hawkins Kenan Institute of Private Enterprise ( email )

Campus Box 3440, The Kenan Center
Chapel Hill, NC 27599-344
United States

Zhishu Yang (Contact Author)

Tsinghua University - School of Economics & Management ( email )

Beijing, 100084
China
+86-10-62771769 (Phone)
+86-10-62785562 (Fax)

Qi Zhang

Shanghai Jiao Tong University (SJTU) ( email )

China

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