3 papers in the archive (2023–2026), average empirical rigor 7/10, average math complexity 5/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in NLP & LLMs in Finance, Factor Investing & Asset Pricing, Portfolio Optimization, Reinforcement Learning for Trading.
Frequent co-authors: Bingyang Wang (1), Grant Johnson (1), Maria Hybinette (1), Srijan Sood (1), Kassiani Papasotiriou (1), Marius Vaiciulis (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%).
This paper investigates whether artificial intelligence can enhance stock clustering compared to traditional methods. We consider this in the context of the semi-strong Efficient Markets Hypothesis (EMH), which posits that prices fully reflect all public information and, accordingly, that clusters b
Portfolio Management is the process of overseeing a group of investments, referred to as a portfolio, with the objective of achieving predetermined investment goals. Portfolio optimization is a key component that involves allocating the portfolio assets so as to maximize returns while minimizing ris
Time series forecasting is important across various domains for decision-making. In particular, financial time series such as stock prices can be hard to predict as it is difficult to model short-term and long-term temporal dependencies between data points. Convolutional Neural Networks (CNN) are go