3 papers in the archive (2023–2026), average empirical rigor 4.2/10, average math complexity 2.5/10. Quadrant mix: Philosophers ×2, Street Traders ×1.
Works mostly in NLP & LLMs in Finance, Machine Learning for Market Forecasting, Risk Management & Tail Risk.
Frequent co-authors: Gabin Taibi (1), Branka Hadji Misheva (1), Michael Seigne (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%).
This paper introduces an algorithmic framework for conducting systematic literature reviews (SLRs), designed to improve efficiency, reproducibility, and selection quality assessment in the literature review process. The proposed method integrates Natural Language Processing (NLP) techniques, cluster
We delve into the intricate world of share buy-backs, a strategic corporate capital allocation tool that has gained significant prominence over the past few decades. Despite being the subject of extensive research and debate, the execution phase of these transactions remains an underexplored area. T
Machine learning and deep learning have become increasingly prevalent in financial prediction and forecasting tasks, offering advantages such as enhanced customer experience, democratising financial services, improving consumer protection, and enhancing risk management. However, these complex models