3 papers in the archive (2024–2025), average empirical rigor 4.8/10, average math complexity 7.7/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Stochastic Control & Optimal Stopping, Risk Management & Tail Risk, Reinforcement Learning for Trading.
Frequent co-authors: Liyuan Lin (1), Ruodu Wang (1), Chaoyi Zhao (1), Yang Li (1), Zhi Chen (1), Steve Y. Yang (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%).
Traditional stochastic control methods in finance rely on simplifying assumptions that often fail in real world markets. While these methods work well in specific, well defined scenarios, they underperform when market conditions change. We introduce FinFlowRL, a novel framework for financial stochas
We study the problem of choosing the copula when the marginal distributions of a random vector are not all continuous. Inspired by four motivating examples including simulation from copulas, stress scenarios, co-risk measures, and dependence measures, we propose to use the checkerboard copula, that
Motivated by recent empirical findings on the periodic phenomenon of aggregated market volumes in equity markets, we aim to understand the causes and consequences of periodic trading activities through a game-theoretic perspective, examining market interactions among different types of participants.