3 papers in the archive (2023–2026), average empirical rigor 8.3/10, average math complexity 5.2/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in NLP & LLMs in Finance, Market Simulation & Agent-Based Models, Reinforcement Learning for Trading.
Frequent co-authors: Bo An (2), Wentao Zhang (1), Mingxuan Zhao (1), Jincheng Gao (1), Jieshun You (1), Huaiyu Jia (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%).
Learning effective representations helps characterize the structure and dynamics of equity markets from financial data with a low signal-to-noise ratio. Black-box deep models can capture complex patterns but may overfit sample noise and lack explicit economic structure. Meanwhile, classic linear fin
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.
The rapid advancement of Large Language Models (LLMs) has led to a surge of financial benchmarks, evolving from static knowledge tests to interactive trading simulations. However, current evaluations of real-time trading performance overlook a critical failure mode: severe behavioral instability in