4 papers in the archive (2025–2026), average empirical rigor 8.2/10, average math complexity 6.5/10, 2 with linked code. Quadrant mix: Holy Grail ×3, Street Traders ×1.
Works mostly in Market Microstructure, High-Frequency Trading & Optimal Execution, Market Simulation & Agent-Based Models, Reinforcement Learning for Trading.
Frequent co-authors: Kang Li (4), Stefan Zohren (4), Anisoara Calinescu (4), Valentin Mohl (3), George Nigmatulin (3), Mihai Cucuringu (3).
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
Autoregressive foundation models offer state-of-the-art capabilities to simulate the market by generating the message flow of limit order books (LOBs). Yet, without understanding their inner workings, research directions are hard to narrow and practical use in real-world trading environments remains
Realistic market impact is essential for generative limit order book (LOB) models to serve as world models for trading agents. If a simulator misprices this mechanism, agents learn policies that are optimal in the simulator but wrong in the real market. Existing metrics assess generated streams pure
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