3 papers in the archive (2025–2026), average empirical rigor 7.8/10, average math complexity 5.5/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in Machine Learning for Market Forecasting, Fixed Income & Interest Rates, Portfolio Optimization, Risk Management & Tail Risk.
Frequent co-authors: Thomas Gottron (1), Andrea Colombo (1), Jörg Reddig (1), Annalauro Ianiro (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%).
A growing empirical literature suggests that equity-premium predictability is state dependent, with much of the forecasting power concentrated around recessionary periods (Henkel et al., 2011; Dangl and Halling, 2012; Devpura et al., 2018). I study U.S. stock return predictability across economic re
In Aarab (2020), I examine U.S. stock return predictability across economic regimes and document evidence of time-varying expected returns across market states in the long run. The analysis introduces a state-switching specification in which the market state is proxied by the slope of the yield curv
Micro-structural models of contagion and systemic risk emphasize that shock propagation is inherently multi-channel, spanning counterparty exposures, short-term funding and roll-over risk, securities cross-holdings, and common-asset (fire-sale) spillovers. Empirical implementations, however, often r