3 papers in the archive (2022–2025), average empirical rigor 5.3/10, average math complexity 7.7/10. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Factor Investing & Asset Pricing, Stochastic Control & Optimal Stopping, Options & Derivatives Pricing, Fixed Income & Interest Rates.
Frequent co-authors: Puneet Pasricha (1), Andrea Della Vecchia (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 introduce an ensemble learning method based on Gaussian Process Regression (GPR) for predicting conditional expected stock returns given stock-level and macro-economic information. Our ensemble learning approach significantly reduces the computational complexity inherent in GPR inference and lend
This paper investigates theoretical and methodological foundations for stochastic optimal control (SOC) in discrete time. We start formulating the control problem in a general dynamic programming framework, introducing the mathematical structure needed for a detailed convergence analysis. The associ
This paper develops a model-free framework for static fixed-income pricing and the replication of liability cash flows. We show that the absence of static arbitrage across a universe of fixed-income instruments is equivalent to the existence of a strictly positive discount curve that reproduces all