3 papers in the archive (2024–2025), average empirical rigor 8.2/10, average math complexity 4.8/10. Quadrant mix: Holy Grail ×1, Street Traders ×2.
Works mostly in NLP & LLMs in Finance, Machine Learning for Market Forecasting, Fixed Income & Interest Rates.
Frequent co-authors: Yuntao Wu (3), Andreas Veneris (2), Ing-Haw Cheng (2), John Hull (2), Yoshio Nozawa (2), Ege Mert Akin (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%).
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 examine how textual features in earnings press releases predict stock returns on earnings announcement days. Using over 138,000 press releases from 2005 to 2023, we compare traditional bag-of-words and BERT-based embeddings. We find that press release content (soft information) is as informative
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.