3 papers in the archive (2025–2026), average empirical rigor 5.7/10, average math complexity 5.5/10. Quadrant mix: Holy Grail ×2, Philosophers ×1.
Works mostly in Machine Learning for Market Forecasting, Crypto Markets & DeFi, Risk Management & Tail Risk, NLP & LLMs in Finance.
Frequent co-authors: Akash Sedai (2), Francesca Medda (2), Thomas Schroeder (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%).
Classical correlation and rolling PCA summarize market dependence through covariance spectra, but they do not provide a unified operator representation for entropy, purity-based mixing, and standardized structural deviations built from rolling multi-feature trajectories. We propose the Quantum Netwo
Quantum computing is becoming strategically relevant to finance because several core financial bottlenecks are already defined by combinatorial search, expectation estimation, rare-event analysis, representation learning, and long-horizon cryptographic resilience. This review examines that landscape
Recent advances in large language models, tool-using agents, and financial machine learning are shifting financial automation from isolated prediction tasks to integrated decision systems that can perceive information, reason over objectives, and generate or execute actions. This paper develops an i