4 papers in the archive (2024–2026), average empirical rigor 8.5/10, average math complexity 5.5/10. Quadrant mix: Holy Grail ×2, Street Traders ×2.
Works mostly in Factor Investing & Asset Pricing, NLP & LLMs in Finance, High-Frequency Trading & Optimal Execution, Crypto Markets & DeFi.
Frequent co-authors: Nicola Borri (4), Yukun Liu (4), Xi Wu (2), Denis Chetverikov (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 study 380 trillion tokens of realized AI consumption across more than four hundred LLMs. We build a high-frequency AI factor and show that a long-short strategy based on firms’ AI exposure earns significantly positive returns. The average strategy return is larger based on intensive, frontier-ori
We propose a new non-linear single-factor asset pricing model $r_{it}=h(f_{t}λ_{i})+ε_{it}$. Despite its parsimony, this model represents exactly any non-linear model with an arbitrary number of factors and loadings – a consequence of the Kolmogorov-Arnold representation theorem. It features only o
We organize existing empirical regularities of cryptocurrencies into seven stylized facts and analyze cryptocurrencies through the lens of empirical asset pricing. We find important similarities with traditional markets–risk-adjusted performance so far is broadly comparable, and the cross-section o
The European Union Emission Trading System is a prominent market-based mechanism to reduce emissions. While the theory is well understood, we are the first to study the whole cap-and-trade mechanism as a financial market. Analyzing the universe of transactions in 2005-2020 (more than one million rec