3 papers in the archive (2023–2026), average empirical rigor 7.5/10, average math complexity 6.7/10. Quadrant mix: Holy Grail ×3.
Works mostly in Machine Learning for Market Forecasting, Reinforcement Learning for Trading.
Frequent co-authors: Molei Qin (2), Bo An (2), Haochong Xia (1), Yao Long Teng (1), Regan Tan (1), Junzhe Jiang (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 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.
Stock market indices serve as fundamental market measurement that quantify systematic market dynamics. However, accurate index price prediction remains challenging, primarily because existing approaches treat indices as isolated time series and frame the prediction as a simple regression task. These