3 papers in the archive (2025–2026), average empirical rigor 6.3/10, average math complexity 7.5/10, 1 with linked code. Quadrant mix: Holy Grail ×2, Lab Rats ×1.
Works mostly in Reinforcement Learning for Trading, Crypto Markets & DeFi, Risk Management & Tail Risk, Machine Learning for Market Forecasting.
Frequent co-authors: Alexander Ardaiz (1), Varun Budati (1), Qingyu Li (1), Chiranjib Mukhopadhyay (1), Abolfazl Bayat (1), Arshia Abolghasemi (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%).
Score-based diffusion methods usually perturb data with Brownian Gaussian noise, corresponding to a light-tailed forward stochastic differential equation (SDE). For market time series this SDE can be a poor abstraction: limit order book (LOB) order flow is heavy-tailed, bursty, and occasionally jump
Deep reinforcement-learning policies for order execution can vary substantially across training seeds, so apparent architectural gains may reflect favourable training realisations rather than reproducible properties of the architecture. We evaluate vanilla Double Deep Q-Learning (DDQL), K-means-part
Recent advances in quantum computing have demonstrated its potential to significantly enhance the analysis and forecasting of complex classical data. Among these, quantum reservoir computing has emerged as a particularly powerful approach, combining quantum computation with machine learning for mode