3 papers in the archive (2025), average empirical rigor 4.8/10, average math complexity 8/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Stochastic Control & Optimal Stopping, Portfolio Optimization, Insurance & Actuarial Risk, Machine Learning for Market Forecasting.
Frequent co-authors: Junyi Guo (1), Xia Han (1), Jingshu Peng (1), Yanyan Shen (1), Xujia Li (1), Lei Chen (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%).
Stock recommendation is critical in Fintech applications, which use price series and alternative information to estimate future stock performance. Although deep learning models are prevalent in stock recommendation systems, traditional time-series forecasting training often fails to capture stock tr
We study the optimal liquidation problem in both lit and dark pools for investors facing execution uncertainty in a continuous-time setting with market impact. First, we design an optimal make–take fee policy for a large investor liquidating her position across both pools, interacting with small in
This paper investigates the dynamic reinsurance design problem under the mean-variance criterion, incorporating heterogeneous beliefs between the insurer and the reinsurer, and introducing an incentive compatibility constraint to address moral hazard. The insurer’s surplus process is modeled using t