3 papers in the archive (2023–2025), average empirical rigor 7.3/10, average math complexity 3.8/10. Quadrant mix: Street Traders ×3.
Works mostly in Crypto Markets & DeFi, Risk Management & Tail Risk, Fixed Income & Interest Rates, NLP & LLMs in Finance.
Frequent co-authors: Antonio Briola (2), Fabio Caccioli (2), David Vidal-Tomás (1), Sabrina Aufiero (1), Tesfaye Salarin (1), Silvia Bartolucci (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 investigates the evolving link between cryptocurrency and equity markets in the context of the recent wave of corporate Bitcoin (BTC) treasury strategies. We assemble a dataset of 39 publicly listed firms holding BTC, from their first acquisition through April 2025. Using daily logarithmi
We investigate the response of shareholders to Environmental, Social, and Governance-related reputational risk (ESG-risk), focusing exclusively on the impact of social media. Using a dataset of 114 million tweets about firms listed on the S&P100 index between 2016 and 2022, we extract conversations
This paper investigates the causes of the FTX digital currency exchange’s failure in November 2022. We identify the collapse of the Terra-Luna ecosystem as the pivotal event that triggered a significant decrease in the exchange’s liquidity. Analysing on-chain data, we report that FTX heavily relied