3 papers in the archive (2026), average empirical rigor 8.2/10, average math complexity 5.5/10. Quadrant mix: Holy Grail ×1, Street Traders ×2.
Works mostly in Machine Learning for Market Forecasting, NLP & LLMs in Finance, Volatility Modeling & Forecasting, Crypto Markets & DeFi.
Frequent co-authors: Zheqi Fan (3), Yikuan Huang (2), Kaiqi Hu (2), Meng Melody Wang (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 whether model-based spot volatility estimators extracted from traded options data enhance the predictive power of the Heterogeneous Autoregressive (HAR) model for realized volatility. Specifically, we infer spot volatility under the rough stochastic volatility model via an iterative two-s
LLM agents are promising tools for empirical discovery, but their flexibility can also turn discovery into uncontrolled search. We study how to use agents under a reproducible protocol through cryptocurrency factor discovery. Our framework casts the task as sequential hypothesis search: an agent rea
Text-based financial networks are increasingly used to study cross-stock return predictability. A common approach constructs links from similarities in firms’ disclosure embeddings, but such networks often contain spurious edges because textual proximity does not necessarily imply economic connectio