3 papers in the archive (2025–2026), average empirical rigor 6.7/10, average math complexity 8.5/10. Quadrant mix: Holy Grail ×3.
Works mostly in Reinforcement Learning for Trading, Portfolio Optimization, Stochastic Control & Optimal Stopping, Risk Management & Tail Risk.
Frequent co-authors: Ahmad Aghapour (1), Erhan Bayraktar (1), Xun Li (1), Xiang Yu (1), Yijie Zeng (1), Gabriël M. J. Gelissen (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 develop a data-driven approach to dynamic portfolio selection that combines vine-copula simulation with reinforcement learning. The investor has constant relative risk aversion and maximises expected terminal utility with a running penalty on the conditional value-at-risk (CVaR) of one-period los
We develop a reinforcement-learning framework for the continuous-time optimal portfolio-consumption via utility maximization under no-shorting portfolio constraints. We adopt the exploratory formulation under entropy regularization and impose no-shorting constraint through a componentwise nonnegativ
In this paper, we tackle the dynamic mean-variance portfolio selection problem in a {"\it model-free"} manner, based on (generative) diffusion models. We propose using data sampled from the real model $\mathbb P$ (which is unknown) with limited size to train a generative model $\mathbb Q$ (from whic