3 papers in the archive (2025), average empirical rigor 4.8/10, average math complexity 4.5/10. Quadrant mix: Lab Rats ×1, Philosophers ×1, Street Traders ×1.
Works mostly in Market Simulation & Agent-Based Models, Market Microstructure, Machine Learning for Market Forecasting, High-Frequency Trading & Optimal Execution.
Frequent co-authors: Juho Kanniainen (3), Anubha Goel (1), Alexandros Iosifidis (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 rapid development of sophisticated machine learning methods, together with the increased availability of financial data, has the potential to transform financial research, but also poses a challenge in terms of validation and interpretation. A good case study is the task of classifying financial
In this work we show how generative tools, which were successfully applied to limit order book data, can be utilized for the task of imitating trading agents. To this end, we propose a modified generative architecture based on the state-space model, and apply it to limit order book data with identif
There are multiple explanations for stylized facts in high-frequency trading, including adaptive and informed agents, many of which have been studied through agent-based models. This paper investigates an alternative explanation by examining whether, and under what circumstances, interactions betwee