3 papers in the archive (2024–2025), average empirical rigor 8.2/10, average math complexity 2.5/10. Quadrant mix: Street Traders ×3.
Works mostly in NLP & LLMs in Finance, Machine Learning for Market Forecasting.
Frequent co-authors: Shuaiyu Chen (1), T. Clifton Green (1), Huseyin Gulen (1), Edward Li (1), Min Shen (1), Zhiyuan Tu (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%).
We examine how large language models (LLMs) interpret historical stock returns and compare their forecasts with estimates from a crowd-sourced platform for ranking stocks. While stock returns exhibit short-term reversals, LLM forecasts over-extrapolate, placing excessive weight on recent performance
We show that while anonymization effectively obscures firm identity, it significantly reduces the power of textual understanding, thereby diminishing models’ ability to extract meaningful economic signals from financial texts. This information loss is particularly severe when numerical and object en
Large language models (LLMs) promise to democratize financial analysis by reducing information-processing costs. Yet equal access does not ensure equal outcomes, as the locus of friction may shift from processing information to evaluating model outputs. We study GPT’s earnings forecasts following co