3 papers in the archive (2025), average empirical rigor 6.3/10, average math complexity 6.7/10. Quadrant mix: Holy Grail ×3.
Works mostly in Market Simulation & Agent-Based Models, Machine Learning for Market Forecasting, NLP & LLMs in Finance, Reinforcement Learning for Trading.
Frequent co-authors: Rory Baggott (2), Namid Stillman (2), Dimitrios Emmanoulopoulos (1), Justin Lyon (1), Namid R. Stillman (1), Andreea Bacalum (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%).
Large language models (LLMs) are increasingly deployed in agentic frameworks, in which prompts trigger complex tool-based analysis in pursuit of a goal. While these frameworks have shown promise across multiple domains including in finance, they typically lack a principled model-building step, relyi
The recent application of deep learning models to financial trading has heightened the need for high fidelity financial time series data. This synthetic data can be used to supplement historical data to train large trading models. The state-of-the-art models for the generative application often rely
Execution algorithms are vital to modern trading, they enable market participants to execute large orders while minimising market impact and transaction costs. As these algorithms grow more sophisticated, optimising them becomes increasingly challenging. In this work, we present a reinforcement lear