3 papers in the archive (2024–2026), average empirical rigor 4.7/10, average math complexity 8.3/10. Quadrant mix: Holy Grail ×1, Lab Rats ×2.
Works mostly in Stochastic Control & Optimal Stopping, Market Microstructure, Machine Learning for Market Forecasting, Volatility Modeling & Forecasting.
Frequent co-authors: Alberto Gennaro (1), Julius Graf (1), Hao Wang (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%).
In this work, we investigate the market-making problem on a trading session in which a continuous phase on a limit order book is followed by a closing auction. Whereas standard optimal market-making models typically rely on terminal inventory penalties to manage end-of-day risk, ignoring the signifi
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
We are considering the problem of optimal portfolio delegation between an investor and a portfolio manager under a random default time. We focus on a novel variation of the Principal-Agent problem adapted to this framework. We address the challenge of an uncertain investment horizon caused by an exo