3 papers in the archive (2023–2025), average empirical rigor 7.8/10, average math complexity 4.7/10. Quadrant mix: Holy Grail ×1, Street Traders ×2.
Works mostly in Machine Learning for Market Forecasting, NLP & LLMs in Finance, Factor Investing & Asset Pricing.
Frequent co-authors: Harry Mamaysky (2), Caden Lin (1), Kriste Krstovski (1), Paul Laliberte (1), Jimmy Qin (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%).
An increase in the novelty of news predicts negative stock market returns and negative macroeconomic outcomes over the next year. We quantify news novelty - changes in the distribution of news text - through an entropy measure, calculated using a recurrent neural network applied to a large news corp
Over the past 30 years, nearly all the gains in the U.S. stock market have been earned overnight, while average intraday returns have been negative or flat. We find that a large part of this effect can be explained through features of intraday and overnight news. Our analysis uses a collection of 2.
Large language models (LLMs), including ChatGPT, can extract profitable trading signals from the sentiment in news text. However, backtesting such strategies poses a challenge because LLMs are trained on many years of data, and backtesting produces biased results if the training and backtesting peri