4 papers in the archive (2025), average empirical rigor 7/10, average math complexity 5.9/10. Quadrant mix: Holy Grail ×1, Lab Rats ×1, Street Traders ×2.
Works mostly in Volatility Modeling & Forecasting, Factor Investing & Asset Pricing, NLP & LLMs in Finance, Market Microstructure.
Frequent co-authors: Jose Aguilera-Toste (1), Vinayak Lahiri (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%).
Financial markets exhibit an apparent paradox: while directional price movements remain largely unpredictable–consistent with weak-form efficiency–the magnitude of price changes displays systematic structure. Here we demonstrate that real-time order-flow entropy, computed from a 15-state Markov tran
We test whether simple, interpretable state variables-trend and momentum-can generate durable out-of-sample alpha in one of the world’s most liquid assets, gold. Using a rolling 10-year training and 6-month testing walk-forward from 2015 to 2025 (2,793 trading days), we convert a smoothed trend-mome
We document a high-performing cross-sectional equity factor that achieves out-of-sample Sharpe ratios above 13 through regime-conditional signal activation. The strategy combines value and short-term reversal signals only during stock-specific drift regimes, defined as periods when individual stocks
Large language models (LLMs) produce fluent but unsupported answers - hallucinations - limiting safe deployment in high-stakes domains. We propose ECLIPSE, a framework that treats hallucination as a mismatch between a model’s semantic entropy and the capacity of available evidence. We combine entrop