3 papers in the archive (2023–2025), average empirical rigor 4.3/10, average math complexity 5.8/10. Quadrant mix: Lab Rats ×2, Street Traders ×1.
Works mostly in Market Simulation & Agent-Based Models, Market Microstructure, Machine Learning for Market Forecasting.
Frequent co-authors: Mitja Steinbacher (2), Matjaz Steinbacher (2).
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 paper studies intraday price movement of stocks that is considered as an image classification problem. Using a CNN-based model we make a compelling case for the high-level relationship between the first hour of trading and the close. The algorithm managed to adequately separate between the two o
This study examines the impact of different computing implementations of clearing mechanisms on multi-asset price dynamics within an artificial stock market framework. We show that sequential processing of order books introduces a systematic and significant bias by affecting the allocation of trader
This paper explores the bifurcative dynamics of an artificial stock market exchange (ASME) with endogenous, myopic traders interacting through a limit order book (LOB). We showed that agent-based price dynamics possess intrinsic bistability, which is not a result of randomness but an emergent proper