3 papers in the archive (2026), average empirical rigor 7.8/10, average math complexity 6.2/10. Quadrant mix: Holy Grail ×3.
Works mostly in Machine Learning for Market Forecasting, NLP & LLMs in Finance, Reinforcement Learning for Trading.
Frequent co-authors: Mohammad Al Ridhawi (3), Mahtab Haj Ali (3).
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%).
Forecast evaluation in finance has relied on aggregate accuracy metrics and predictive-accuracy tests built on point-forecast errors. These instruments evaluate forecast outputs but cannot evaluate the process of forecast generation, which is increasingly relevant as forecasting systems become agent
Stock markets exhibit regime-dependent behavior where prediction models optimized for stable conditions often fail during volatile periods. Existing approaches typically treat all market states uniformly or require manual regime labeling, which is expensive and quickly becomes stale as market dynami
Stock market prediction presents considerable challenges for investors, financial institutions, and policymakers operating in complex market environments characterized by noise, non-stationarity, and behavioral dynamics. Traditional forecasting methods, including fundamental analysis and technical i