3 papers in the archive (2025), average empirical rigor 8/10, average math complexity 6.5/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in Market Microstructure, Market Simulation & Agent-Based Models, Machine Learning for Market Forecasting, High-Frequency Trading & Optimal Execution.
Frequent co-authors: Kang Li (2), Stefan Zohren (2), Jakob Foerster (2), Valentin Mohl (1), Sascha Frey (1), Reuben Leyland (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%).
Simulating limit order books (LOBs) has important applications across forecasting and backtesting for financial market data. However, deep generative models struggle in this context due to the high noise and complexity of the data. Previous work uses autoregressive models, although these experience
While investment funds publicly disclose their objectives in broad terms, their managers optimize for complex combinations of competing goals that go beyond simple risk-return trade-offs. Traditional approaches attempt to model this through multi-objective utility functions, but face fundamental cha
Agent-based modelling (ABM) approaches for high-frequency financial markets are difficult to calibrate and validate, partly due to the large parameter space created by defining fixed agent policies. Multi-agent reinforcement learning (MARL) enables more realistic agent behaviour and reduces the numb