3 papers in the archive (2025), average empirical rigor 6/10, average math complexity 6.3/10. Quadrant mix: Holy Grail ×3.
Works mostly in Market Simulation & Agent-Based Models, Market Microstructure, Machine Learning for Market Forecasting, Reinforcement Learning for Trading.
Frequent co-authors: Namid Stillman (3), Ollie Olby (2), Perukrishnen Vytelingum (1), Jianfei Zhang (1), Dingqiu Zhu (1), Tao Chen (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%).
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
In this paper, we describe a novel agent-based approach for modelling the transaction cost of buying or selling an asset in financial markets, e.g., to liquidate a large position as a result of a margin call to meet financial obligations. The simple act of buying or selling in the market causes a pr