4 papers in the archive (2023–2025), average empirical rigor 8.2/10, average math complexity 7.5/10. Quadrant mix: Holy Grail ×4.
Works mostly in Portfolio Optimization, Machine Learning for Market Forecasting, Volatility Modeling & Forecasting, High-Frequency Trading & Optimal Execution.
Frequent co-authors: Greeshma Balabhadra (1), El Mehdi Ainasse (1), Weichuan Deng (1), Abolfazl Safikhani (1), Ronakdilip Shah (1), Jiaju Miao (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%).
Conditional Autoencoders (CAEs) offer a flexible, interpretable approach for estimating latent asset-pricing factors from firm characteristics. However, existing studies usually limit the latent factor dimension to around K=5 due to concerns that larger K can degrade performance. To overcome this ch
We propose a method for constructing sparse high-frequency volatility estimators that are robust against change points in the spot volatility process. The estimators we propose are $\ell_1$-regularized versions of existing volatility estimators. We focus on power variation estimators as they represe
We propose a gradient-free online ensemble learning algorithm that dynamically combines forecasts from a heterogeneous set of machine learning models based on their recent predictive performance, measured by out-of-sample R-squared. The ensemble is model-agnostic, requires no gradient access, and is
We introduce a unified framework for rapid, large-scale portfolio optimization that incorporates both shrinkage and regularization techniques. This framework addresses multiple objectives, including minimum variance, mean-variance, and the maximum Sharpe ratio, and also adapts to various portfolio w