3 papers in the archive (2025–2026), average empirical rigor 5/10, average math complexity 8.8/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Stochastic Control & Optimal Stopping, Market Microstructure, High-Frequency Trading & Optimal Execution, Reinforcement Learning for Trading.
Frequent co-authors: Philippe Bergault (1), Leandro Sánchez-Betancourt (1), Etienne Chevalier (1), Vathana Ly Vath (1), Sergio Pulido (1), Tomas Espana (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
We investigate the use of Reinforcement Learning for the optimal execution of meta-orders, where the objective is to execute incrementally large orders while minimizing implementation shortfall and market impact over an extended period of time. Departing from traditional parametric approaches to pri
We study an optimal execution strategy for purchasing a large block of shares over a fixed time horizon. The execution problem is subject to a general price impact that gradually dissipates due to market resilience. This resilience is modeled through a potentially arbitrary limit-order book shape. T
We develop a mixed control framework that combines absolutely continuous controls with impulse interventions subject to stochastic execution delays. The model extends current impulse control formulations by allowing (i) the controller to choose the mean of the stochastic delay of their impulses, and