3 papers in the archive (2022–2026), average empirical rigor 2.5/10, average math complexity 8.5/10. Quadrant mix: Lab Rats ×3.
Works mostly in Portfolio Optimization, Stochastic Control & Optimal Stopping.
Frequent co-authors: Yu-Jui Huang (1), Giorgio Ferrari (1), Emanuele Borgonovo (1), An Chen (1), Massimo Marinacci (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 study continuous-time portfolio choice with nonlinear payoffs under smooth ambiguity and Bayesian learning. We develop a general framework for dynamic, non-concave asset allocation that accommodates nonlinear payoffs, broad utility classes, and flexible ambiguity attitudes. Dynamic consistency is
We propose a tractable dynamic framework for the joint determination of optimal consumption, portfolio choice, and healthcare irreversible investment. Our model is based on a Merton’s portfolio and consumption problem, where, in addition, the agent can choose the time at which undertaking a costly l
This paper considers two investors who perform mean-variance portfolio selection with asymmetric information: one knows the true stock dynamics, while the other has to infer the true dynamics from observed stock evolution. Their portfolio selection is interconnected through relative performance conc