3 papers in the archive (2023–2026), average empirical rigor 8/10, average math complexity 7/10. Quadrant mix: Holy Grail ×3.
Works mostly in Market Simulation & Agent-Based Models, Machine Learning for Market Forecasting, High-Frequency Trading & Optimal Execution, Reinforcement Learning for Trading.
Frequent co-authors: Bo An (3), Haochong Xia (2), Xinrun Wang (2), Yao Long Teng (1), Regan Tan (1), Simin 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%).
Algorithmic trading relies on machine learning models to make trading decisions. Despite strong in-sample performance, these models often degrade when confronted with evolving real-world market regimes, which can shift dramatically due to macroeconomic changes-e.g., monetary policy updates or unanti
In quantitative finance, the gap between training and real-world performance-driven by concept drift and distributional non-stationarity-remains a critical obstacle for building reliable data-driven systems. Models trained on static historical data often overfit, resulting in poor generalization in
The financial markets, which involve more than $90 trillion market capitals, attract the attention of innumerable investors around the world. Recently, reinforcement learning in financial markets (FinRL) has emerged as a promising direction to train agents for making profitable investment decisions.