Scientific Beta

Mikheil Esakia, Senior Quantitative Research Analyst at Scientific Beta, will be presenting our research on "What Drives the Performance of Machine Learning Factor Strategies?" at the Financial Management Association European Conference, which will be held in Braga, Portugal from 10 to 12 June 2026.

Mikheil's session will take place on the second day of the event (Thursday, 11 June) and will be chaired by Alexandre Rubesam, PhD, Associate Professor of Finance at IESEG School of Management.

Mikheil's presentation will focus on the findings of our research paper, which shows that machine learning methods can generate substantial outperformance in real-world equity investments, but only when strategies are built from the ground up with implementation discipline and information breadth as core design principles.

Mikheil Esakia is a Senior Quantitative Research Analyst at Scientific Beta and a PhD candidate at EDHEC Business School. His research focuses on empirical asset pricing, and his work has been published in the Financial Analysts Journal, Journal of Portfolio Management, and Journal of Investing. Before joining Scientific Beta, he worked as an Operational Risk Analyst at Liberty Bank, Georgia. He holds a Master's degree in Finance from EDHEC Business School and a Bachelor's degree from Free University of Tbilisi. In 2023, Mikheil and Felix Goltz have won the 2023 Graham and Dodd Awards of Excellence for their recent research on Macroeconomic Exposures in Equity Portfolios.