3 papers in the archive (2026), average empirical rigor 7/10, average math complexity 7/10, 2 with linked code. Quadrant mix: Holy Grail ×3.
Works mostly in Reinforcement Learning for Trading, Portfolio Optimization, Risk Management & Tail Risk, Market Microstructure.
Frequent co-authors: Seyedreza Tavakoli (2), Amir Amini (2), Mohammad Hosein Rohban (2), Ali Habibnia (2), Matin Tavakoli (1), Faezeh Salmani (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%).
Score-based diffusion methods usually perturb data with Brownian Gaussian noise, corresponding to a light-tailed forward stochastic differential equation (SDE). For market time series this SDE can be a poor abstraction: limit order book (LOB) order flow is heavy-tailed, bursty, and occasionally jump
Financial reinforcement learning often translates an investment objective into an additive risk-adjusted reward. An agent can optimise that reward accurately yet implement a different financial preference. We develop a decision audit for terminal mean-variance equilibrium that separates reward missp
Robust financial-risk inference requires more than mathematical correctness. Models must accurately apply conditional rules, maintain evolving financial states, and make decisions that respect operational constraints. This work introduces RiskReason, a benchmark for verifiable financial-risk reasoni