3 papers in the archive (2025–2026), average empirical rigor 7/10, average math complexity 4.7/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in NLP & LLMs in Finance, Market Simulation & Agent-Based Models, Machine Learning for Market Forecasting.
Frequent co-authors: Mingyang (2), Yida (2), Huiwen (2), Yiming Lu (2), Wei Jin (2), Sedigheh Mahdavi (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%).
Financial LLM agents are often evaluated by comparing their end-to-end returns with those of a baseline and testing the paired difference against zero. This measures whether deploying the agent changes realized performance, but it does not isolate event-selection skill. An agent that frequently chan
Large Language Models (LLMs) have been employed in financial decision making, enhancing analytical capabilities for investment strategies. Traditional investment strategies often utilize quantitative models, fundamental analysis, and technical indicators. However, LLMs have introduced new capabiliti