AIR-Quant Presentation: Guillaume Coqueret on Machine Learning for Factor Investing: an Introduction.

Statistical learning is increasingly used in financial economics, by academics and practitioners alike. This talk provides an overview of how experts exploit firm-level characteristics to understand and predict the performance of stocks. We will cover why and how they do it, and also discuss some limitations.

NOTE: the content will not be too technical, but we will point to more sophisticated resources for those interested in implementation details.

Guillaume is an associate professor of finance and data science at EMLYON Business School. His research interests revolve around applications of numerical methods and machine learning in financial economics. Guillaume held different positions as assistant professor and adjunct lecturer at Imperial College and Montpellier Business School. Prior to that Guillaume was a senior quantitative researcher at EDHEC-Risk Institute, focussing on active allocation to equity factor indices, equity portfolios with liabilities hedging properties and factor risk parity. Guillaume is a regular consultant and lecturer on topics such as machine learning applications for financial markets.

He holds a Master degrees in Finance from University of Paris I, Master of Science in Management from ESSEC Business School and a Master in Probability and Finance from Pierre and Marie Curie University. He completed his education with a Ph.D at ESSEC Business School in Finance and Applied Mathematics.

Guillaume research has been published by many journals (Journal of Banking and Finance, Annals of Operations Research, Journal of Portfolio Management, Quantitative Finance, Journal of Mathematical Economics, European Journal of Operational Research). In 2020, he co-wrote “Machine Learning for Factor Investing” (CRC/Chapman Hall).

He is an advisory board member for the Journal of Machine Learning in Finance.

For any further queries, please contact peter.vodicka@cass.city.ac.uk

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