3 papers in the archive (2025), average empirical rigor 6.5/10, average math complexity 6.5/10. Quadrant mix: Holy Grail ×1, Lab Rats ×1, Street Traders ×1.
Works mostly in NLP & LLMs in Finance, Options & Derivatives Pricing, Crypto Markets & DeFi, Volatility Modeling & Forecasting.
Frequent co-authors: Yuntao Wu (3), Charles Martineau (2), Ege Mert Akin (1), Vincent Grégoire (1), Srisht Fateh Singh (1), Reina Ke Xin Li (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
This paper mathematically models a constant-function automated market maker (CFAMM) position as a portfolio of exotic options, known as perpetual American continuous-installment (CI) options. This model replicates an AMM position’s delta at each point in time over an infinite time horizon, thus taki