3 papers in the archive (2025–2026), average empirical rigor 7.2/10, average math complexity 7.7/10. Quadrant mix: Holy Grail ×3.
Works mostly in Reinforcement Learning for Trading, NLP & LLMs in Finance, Market Microstructure, Factor Investing & Asset Pricing.
Frequent co-authors: Junjie Zhao (1), Chengxi Zhang (1), Chenkai Wang (1), Xintong Liu (1), Chenkai Zhang (1), Yanzhao Ma (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%).
Reinforcement learning (RL) has successfully automated the complex process of mining formulaic alpha factors, for creating interpretable and profitable investment strategies. However, existing methods are hampered by the sparse rewards given the underlying Markov Decision Process. This inefficiency
Trade-based manipulation (TBM) undermines the fairness and stability of financial markets drastically. Spoofing, one of the most covert and deceptive TBM strategies, exhibits complex anomaly patterns across multilevel prices, while often being simplified as a single-level manipulation. These pattern
Formulaic alpha factor mining searches for symbolic expressions that transform market data into predictive signals. Recent LLM-based evolutionary factor mining methods use large language models as semantic variation operators. Under the conventional LLM-EA paradigm, stochastic variation and fitness-