3 papers in the archive (2025–2026), average empirical rigor 7.2/10, average math complexity 5/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in NLP & LLMs in Finance, Portfolio Optimization, Market Simulation & Agent-Based Models.
Frequent co-authors: Yuan Zhang (3), Chen Liu (3), Yifan Li (2), Zhengzhao Lai (2), Binh Minh An (1), Xiaolong Luo (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%).
Large language models (LLMs) and multi-agent systems (MAS) have shown promise in financial decision-making, yet existing evaluations focus on equity trading and primarily assess directional prediction, overlooking the structural complexity of derivative markets. Option trading introduces fundamental
Sparse portfolio optimization is a fundamental yet challenging problem in quantitative finance, since traditional approaches heavily relying on historical return statistics and static objectives can hardly adapt to dynamic market regimes. To address this issue, we propose Evolutionary Factor Search
Large Language Models (LLMs) excel at general code generation, yet translating natural-language trading intents into correct option strategies remains challenging. Real-world option design requires reasoning over massive, multi-dimensional option chain data with strict constraints, which often overw