4 papers in the archive (2025–2026), average empirical rigor 7.4/10, average math complexity 7.6/10. Quadrant mix: Holy Grail ×4.
Works mostly in Factor Investing & Asset Pricing, Commodities & Energy Markets.
Frequent co-authors: Jean-Jacques Ohana (4), Eric Benhamou (4), Thomas Jacquot (4), Alban Etienne (2), Béatrice Guez (2), Beatrice Guez (2).
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 research agents can generate and monitor trading rules faster than a human can document the resulting selection process. Candidates appear at data-dependent times, weak rules are abandoned, and promising ones are watched until they cross a reporting threshold. We introduce PROMOTE, a proto
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
Different learning systems can make nearly the same trade when they share data, objectives, or representations. The market then sees decision concentration even if the models look diverse. We call exposure to this shared direction a signal collision. A linear-impact game shows the basic externality: