4 papers in the archive (2023–2026), average empirical rigor 7.2/10, average math complexity 5.2/10. Quadrant mix: Holy Grail ×3, Street Traders ×1.
Works mostly in Machine Learning for Market Forecasting, Portfolio Optimization, NLP & LLMs in Finance, Market Simulation & Agent-Based Models.
Frequent co-authors: Stefan Zohren (4), Wee Ling Tan (2), Kunihiro Miyazaki (1), Takanobu Kawahara (1), Trent Spears (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 present an end-to-end deep learning framework for systematic options trading that directly embeds hedging behavior through explicit control of portfolio-level risk exposures. While neural networks trained to optimize risk-adjusted performance have been shown to outperform traditional rules-based
We introduce Spatio-Temporal Momentum strategies, a class of models that unify both time-series and cross-sectional momentum strategies by trading assets based on their cross-sectional momentum features over time. While both time-series and cross-sectional momentum strategies are designed to systema
The Black-Litterman model extends the framework of the Markowitz Modern Portfolio Theory to incorporate investor views. We consider a case where multiple view estimates, including uncertainties, are given for the same underlying subset of assets at a point in time. This motivates our consideration o
The advancement of large language models (LLMs) has accelerated the development of autonomous financial trading systems. While mainstream approaches deploy multi-agent systems mimicking analyst and manager roles, they often rely on abstract instructions that overlook the intricacies of real-world wo