3 papers in the archive (2024–2026), average empirical rigor 7.3/10, average math complexity 5.7/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in NLP & LLMs in Finance, Machine Learning for Market Forecasting, Risk Management & Tail Risk, Reinforcement Learning for Trading.
Frequent co-authors: John Hull (3), Andreas Veneris (2), Charles Martineau (2), Yoshio Nozawa (2), Yuntao Wu (2), Jacky Chen (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%).
Options market makers absorb client order flow and must hedge the resulting inventory risk under trading frictions. We study whether an option hedging reinforcement learning (RL) agent trained on historical market data outperforms three alternatives in tail-risk protection: (i) traditional delta hed
News spreads rapidly across languages and regions, but translations may lose subtle nuances. We propose a method to align sentences in multilingual news articles using optimal transport, identifying semantically similar content across languages. We apply this method to align more than 140,000 pairs
We study the drivers of the Gilchrist and Zakrajšek (2012) excess bond premium (EBP) through the lens of the news. The monthly attention the news pays to 180 topics (Bybee et al., 2024) captures up to 80% of the variation in the EBP, and this component of variation forecasts macroeconomic movements.