This week Ryan and I discuss two editorials on machine learning and its impact and use within Research. The first paper is an Editorial by Rob Arnott, Cam Harvey, and Harry Markowitz discussing, in their opinion, proper protocols for research (back-testing) in the era of machine learning. The Second paper, summarized by Elisabetta, is by Joseph Simonian, Marcos Lopez de Prado, and Frank Fabozzi. In their paper, they discuss, at a high-level, how data science and machine learning can help research.
Jack Vogel, Ph.D., conducts research in empirical asset pricing and behavioral finance, and is a co-author of DIY FINANCIAL ADVISOR: A Simple Solution to Build and Protect Your Wealth. His dissertation investigates how behavioral biases affect the value anomaly. His academic background includes experience as an instructor and research assistant at Drexel University in both the Finance and Mathematics departments, as well as a Finance instructor at Villanova University. Dr. Vogel is currently a Managing Member of Alpha Architect, LLC, an SEC-Registered Investment Advisor, where he heads the research department and serves as the Chief Financial Officer. He has a PhD in Finance and a MS in Mathematics from Drexel University, and graduated summa cum laude with a BS in Mathematics and Education from The University of Scranton.
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