4 papers in the archive (2026), average empirical rigor 8.4/10, average math complexity 5.5/10. Quadrant mix: Holy Grail ×3, Street Traders ×1.
Works mostly in Crypto Markets & DeFi, NLP & LLMs in Finance, Volatility Modeling & Forecasting, Factor Investing & Asset Pricing.
Frequent co-authors: Andrew Maksakov (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 study investigates whether cryptocurrency whitepaper narratives align with empirically observed market factor structure. We construct a pipeline combining zero-shot NLP classification of 38 whitepapers across 10 semantic categories with CP tensor decomposition of hourly market data (49 assets,
We introduce the Aggregated Systemic Risk Index (ASRI), comprising four weighted sub-indices: Stablecoin Concentration Risk (30%), DeFi Liquidity Risk (25%), Contagion Risk (25%), and Regulatory Opacity Risk (20%). Using data from DeFi Llama, Federal Reserve FRED, and on-chain analytics, we validate
We investigate whether cryptocurrency markets differentiate between infrastructure failures and regulatory enforcement at the return level, complementing a companion conditional variance analysis that finds 5.7 times larger volatility impacts from infrastructure events (p = 0.0008). Using event-leve
Using the Crypto Fear & Greed Index and Bitcoin daily data, we document that sentiment extremity predicts excess uncertainty beyond realized volatility. Extreme fear and extreme greed regimes exhibit significantly higher spreads than neutral periods – a phenomenon we term the “extremity premium.” E