3 papers in the archive (2023–2026), average empirical rigor 4.2/10, average math complexity 8.7/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Portfolio Optimization, Risk Management & Tail Risk, Stochastic Control & Optimal Stopping.
Frequent co-authors: Xiangyu Cui (1), Lingjie Kong (1), Weiping Wu (1), Yu Lin (1), Ke Zhou (1), Jonathan Li (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%).
This paper addresses the importance of incorporating various risk measures in portfolio management and proposes a dynamic hybrid portfolio optimization model that combines the spectral risk measure and the Value-at-Risk in the mean-variance formulation. By utilizing the quantile optimization techniq
Worst-case risk evaluation is central to finance and operations research, yet existing analytical results remain fragmented and largely confined to ambiguity sets with specific structures. Using spectral risk measures as the underlying class, we develop a convex variational theory that unifies these
This study addresses limited attention allocation in a stochastic linear quadratic system with multiplicative noise. Our approach enables strategic resource allocation to enhance noise estimation and improve control decisions. We provide analytical optimal control and propose a numerical method for