About
Portfolio Manager for the hedge fund Eisler Capital.
I teach the course "Data Analysis in Finance", alongside with Augustin Landier, in the Double Degree Data & Finance of École Polytechnique and HEC Paris (as well as in the Master of International Finance of HEC).
With Raphael Douady, I co-authored the book "Artificial Intelligence for Financial Markets: The Polymodel Approach", published by Springer.
I have been Head of Intraday Research for the hedge fund AXA Investment Managers Chorus, a role during which I developed a team of 4 quantitative researchers working on an Equity Market Neutral portfolio. Prior to this, I worked at Societe Generale as banker and financial advisor to small businesses, and as financial manager in an aerospace company. I hold a PhD in Applied Mathematics from Paris 1 Pantheon-Sorbonne University.
Activity
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I'm happy to share that I'm starting a new position as a visiting researcher student at the Massachusetts Institute of Technology. 🥳
I'm happy to share that I'm starting a new position as a visiting researcher student at the Massachusetts Institute of Technology. 🥳
Liked by Thomas Barrau
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The AXA Center of Expertise on Generative AI is now live, bringing together our experts from AXA entities around the world. It is such an honour to…
The AXA Center of Expertise on Generative AI is now live, bringing together our experts from AXA entities around the world. It is such an honour to…
Liked by Thomas Barrau
Experience
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Education
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Graduated with Highest Honors
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Maximum grade obtained for the Master Thesis (10/10)
Degree Pass Mark 99/110 -
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Graduated with Honors
Publications
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Artificial Intelligence for Financial Markets: The Polymodel Approach
Springer
This book introduces the novel artificial intelligence technique of polymodels and applies it to the prediction of stock returns. The idea of polymodels is to describe a system by its sensitivities to an environment, and to monitor it, imitating what a natural brain does spontaneously. In practice this involves running a collection of non-linear univariate models. This very powerful standalone technique has several advantages over traditional multivariate regressions. With its easy to interpret…
This book introduces the novel artificial intelligence technique of polymodels and applies it to the prediction of stock returns. The idea of polymodels is to describe a system by its sensitivities to an environment, and to monitor it, imitating what a natural brain does spontaneously. In practice this involves running a collection of non-linear univariate models. This very powerful standalone technique has several advantages over traditional multivariate regressions. With its easy to interpret results, this method provides an ideal preliminary step towards the traditional neural network approach.
The first two chapters compare the technique with other regression alternatives and introduces an estimation method which regularizes a polynomial regression using cross-validation. The rest of the book applies these ideas to financial markets. Certain equity return components are predicted using polymodels in very different ways, and a genetic algorithm is described which combines these different predictions into a single portfolio, aiming to optimize the portfolio returns net of transaction costs. Addressed to investors at all levels of experience this book will also be of interest to both seasoned and non-seasoned statisticians.Other authors -
Languages
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Français
Native or bilingual proficiency
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Anglais
Full professional proficiency
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Espagnol
Professional working proficiency
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