I have always been intrigued by the intersection of finance and law, which motivated me to assist Professor Daniel Taylor‘s research on insider trading at the Wharton Forensic Analytics Lab. This opportunity allowed me to apply data-driven analysis to real-world misconduct in securities trading, while gaining insights into the regulatory and legal frameworks that govern corporate disclosures and trading practices.
As the director of the Wharton Forensic Analytics Lab, Professor Taylor’s research on the trading of corporate insiders and associated disclosures drove the SEC’s decision to mandate electronic reporting of Form 144 filings and the SEC’s decision to amend Rule 10B5-1.
My project focused on identifying strategic Form 4 late filings using statistical computing tools like R and SAS and a large dataset from the WRDS Sec Analytics Suite. I assisted in writing a program that enabled us to filter through all SEC filings for the last five years and identify trades that earned positive risk-adjusted returns before publicly listed companies disclosed MNPI. I was then tasked with manually filtering through the results and researching each trader and issuing company. I then visualized the trade and disclosure timelines, and, for all cases, Professor Taylor deemed a good case for the SEC to potentially file insider trading charges, I did write-ups. We then shared our results with the SEC, which may decide to pursue further action.
I was also challenged by investigating industries I previously had little exposure to. For example, I was tasked with investigating trades by a hedge fund in mostly distressed small-cap biotech companies. For this project, I used 8-K filings from the SEC Analytics Suite and programmed an algorithm that helped me discern the date difference and price reaction between filings. In addition, I was surprised to see that some directors had previous relationships with the hedge fund. This led me to use the respective issuer companies’ filings to identify board changes and relationships to the hedge fund that pose a conflict of interest and may be undisclosed.
Another project that I found very interesting focused on identifying stock price reactions by using millisecond tick-by-tick data from NYSE and Nasdaq to release short-seller reports of popular firms. As HFT firms track these reports, price reactions may initially overreact due to selling pressure from algorithms. We investigated if it’s possible to earn positive returns by “fading” or taking an opposite position to the initial price reaction.
Throughout my research, I gained insights into various data processing and analysis techniques and a greater understanding of the legal work behind the SEC and how the agency pursues charges in insider trading cases. These skills will help me further down my career with handling large financial datasets. Most importantly, though, I genuinely enjoyed the research experience and had a great time assisting Professor Taylor.
In sum, SPUR offers fantastic opportunities for students passionate about research. If you have a research question you’d like to explore, SPUR equips you with the tools and mentorship needed to create your own research project. The program also serves as a gateway to deeper involvement in Penn’s research community, connecting you with fellow students and faculty who share your academic interests.