3 papers in the archive (2025–2026), average empirical rigor 7.7/10, average math complexity 6.8/10. Quadrant mix: Holy Grail ×3.
Works mostly in Market Microstructure, Market Simulation & Agent-Based Models, Reinforcement Learning for Trading, Machine Learning for Market Forecasting.
Frequent co-authors: Carmine Ventre (2), Han Yang (1), Dong Hao (1), Qi Shi (1), Xingtong 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%).
Modeling limit order books (LOBs) dynamics is a fundamental problem in market microstructure research. In particular, generating high-dimensional volume snapshots with strong temporal and liquidity-dependent patterns remains a challenging task, despite recent work exploring the application of Genera
Automatically discovering formulaic alpha factors is a central problem in quantitative finance. Existing methods often ignore syntactic and semantic constraints, relying on exhaustive search over unstructured and unbounded spaces. We present AlphaCFG, a grammar-based framework for defining and disco
Modern generative models for limit order books (LOBs) can reproduce realistic market dynamics, but remain fundamentally passive: they either model what typically happens without accounting for hypothetical future market conditions, or they require interaction with another agent to explore alternativ