4 papers in the archive (2025–2026), average empirical rigor 7.6/10, average math complexity 5.6/10, 2 with linked code. Quadrant mix: Holy Grail ×3, Street Traders ×1.
Works mostly in NLP & LLMs in Finance, Reinforcement Learning for Trading, Market Simulation & Agent-Based Models, Portfolio Optimization.
Frequent co-authors: Li Zhao (3), Rui Sun (3), Zuo Bai (3), Zuoyou Jiang (2), Daxin Jiang (2), Cheng Hua (2).
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%).
Signal decay and regime shifts pose recurring challenges for data-driven investment strategies in non-stationary markets. Conventional time-series and machine learning approaches, which rely primarily on historical correlations, often struggle to generalize when the economic environment changes. Whi
Reinforcement Learning (RL) has enabled Large Language Models (LLMs) to achieve remarkable reasoning in domains like mathematics and coding, where verifiable rewards provide clear signals. However, extending this paradigm to financial decision is challenged by the market’s stochastic nature: rewards
The empirical results have shown that firstly, with one-week holding period and reinvesting, for SSE Composite Index stocks, the highest p-ratio investment strategy produces the largest annualized rate of return; and for NYSE Composite Index stocks, all the three strategies with both one-week and on
In financial trading, large language model (LLM)-based agents demonstrate significant potential. However, the high sensitivity to market noise undermines the performance of LLM-based trading systems. To address this limitation, we propose a novel multi-agent system featuring an internal competitive