3 papers in the archive (2025), average empirical rigor 8.2/10, average math complexity 6/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in Market Microstructure, High-Frequency Trading & Optimal Execution, Machine Learning for Market Forecasting, Reinforcement Learning for Trading.
Frequent co-authors: Mihai Cucuringu (2), Stefan Zohren (2), Jakob Foerster (2), Anisoara Calinescu (2), Valentin Mohl (1), Sascha Frey (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
This study focuses on forecasting intraday trading volumes, a crucial component for portfolio implementation, especially in high-frequency (HF) trading environments. Given the current scarcity of flexible methods in this area, we employ a suite of machine learning (ML) models enriched with numerous
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