I am a PhD Candidate in Financial Machine Learning at the University of Twente. My research focuses on two main areas: narrative modeling in macroeconomics and volatility modeling in financial markets. I design NLP pipelines to extract narrative signals from heterogeneous sources, and link these signals to financial market dynamics using statistical and machine learning methods. On the quantitative side, I work on both implied and realised volatility, with a particular interest in high-frequency data, market microstructure, and derivatives-based strategies. I aim to connect textual and quantitative information to better understand structural breaks, volatility regimes, and macro-financial relationships.
I hold an engineering degree in Mathematics and Data Science from Polytech Clermont-Ferrand (Université Clermont Auvergne), and a dual MSc in Management from Clermont School of Business.
As part of my PhD, I’ve had the opportunity to work with Deutsche Börse on the classification and market impact of high-frequency traders in European markets, and with Quoniam Asset Management, where I developed narrative-driven macro signals applied to factor investing strategies.
Before my PhD, I gained experience as a working student at Atos (on-site at the client Michelin) during my final year of engineering school, and at BNP Paribas during my MSc. I also co-founded 202 Asset Management, a former digital asset hedge fund focused on systematic strategies. I also work as a PhD researcher on the SNSF-funded Narrative Digital Finance project at Berner Fachhochschule (BFH).
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