4 papers in the archive (2025–2026), average empirical rigor 7.5/10, average math complexity 7.1/10. Quadrant mix: Holy Grail ×4.
Works mostly in Machine Learning for Market Forecasting, Factor Investing & Asset Pricing, Commodities & Energy Markets, NLP & LLMs in Finance.
Frequent co-authors: Alban Etienne (2), Jean-Jacques Ohana (2), Béatrice Guez (2), Ethan Setrouk (2), Thomas Jacquot (2), Harris Buchanan (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 presents a novel hierarchical framework for portfolio optimization, integrating lightweight Large Language Models (LLMs) with Deep Reinforcement Learning (DRL) to combine sentiment signals from financial news with traditional market indicators. Our three-tier architecture employs base RL
Commodity Trading Advisors (CTAs) have historically relied on trend-following rules that operate on vastly different horizons from long-term breakouts that capture major directional moves to short-term momentum signals that thrive in fast-moving markets. Despite a large body of work on trend followi
Recent work has emphasized the diversification benefits of combining trend signals across multiple horizons, with the medium-term window-typically six months to one year-long viewed as the “sweet spot” of trend-following. This paper revisits this conventional view by reallocating exposure dynamicall
Trend-following strategies underpin many systematic trading approaches yet struggle under nonstationary and nonlinear market regimes. We propose an LSTM-based framework to forecast next-day trend differences ($Δ_t$) for the top 30 S&P 500 equities, validated across market cycles (2005–2025). Key c