4 papers in the archive (2023–2025), average empirical rigor 3.8/10, average math complexity 7/10. Quadrant mix: Holy Grail ×1, Lab Rats ×3.
Works mostly in Portfolio Optimization, Risk Management & Tail Risk, Machine Learning for Market Forecasting.
Frequent co-authors: Deb Narayan Barik (3), Vidya Sagar G (1), Shifat Ali (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%).
This paper presents a machine learning driven framework for sectoral stress testing in the Indian financial market, focusing on financial services, information technology, energy, consumer goods, and pharmaceuticals. Initially, we address the limitations observed in conventional stress testing throu
In this article, we consider the problem of a bank’s loan portfolio in the context of liquidity risk, while allowing for the limited liability protection enjoyed by the bank. Accordingly, we construct a novel loan portfolio model with limited liability, while maintaining a threshold level of haircut
We present a novel approach for the bank’s decision problem, incorporating Limited Liability in the objective function. Accordingly, we consider continuous time models, with and without Limited Liability. We compare the solutions of these two models to demonstrate the effect of inclusion of Limited
This paper studies the bank dynamic decision problem in the intermediate time step for a discrete-time setup. We have considered a three-time-step model. Initially, the banks raise money through debt and equity and invest in different types of loans. It liquidates its assets and raises new funds at