3 papers in the archive (2026), average empirical rigor 7.5/10, average math complexity 7.2/10. Quadrant mix: Holy Grail ×3.
Works mostly in Portfolio Optimization, Machine Learning for Market Forecasting.
Frequent co-authors: Anubha Goel (2), Rupendra Yadav (1), Aparna Mehra (1), Vrinda Dhingra (1), Juho Kanniainen (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%).
The mean-variance model remains the most prevalent investment framework, built on diversification principles. However, it consistently struggles with estimation errors in expected returns and the covariance matrix, its core parameters. To address this concern, this research evaluates the performance
Topological Data Analysis (TDA), an emerging field in investment sciences, harnesses mathematical methods to extract data features based on shape, offering a promising alternative to classical portfolio selection methodologies. We utilize persistence landscapes, a type of summary statistics for pers
Index tracking, also known as passive investing, has gained significant traction in financial markets due to its cost-effective and efficient approach to replicating the performance of a specific market index. This review paper provides a comprehensive overview of the various modeling approaches and