Analytics for Portfolio Optimization . 3 credits

Catalog description

This course introduces advanced quantitative and analytical methods for portfolio construction, optimization, and capital allocation in modern financial institutions. It connects optimization theory, data analytics, and computational tools to support portfolio decisions under uncertainty. Students learn how modern optimization and data‑driven financial modeling help evaluate risk, return, and resilience across portfolios. Topics include mean–variance optimization, downside‑risk and robust portfolio design, risk‑parity and index‑tracking models, factor‑based and constrained formulations, and adaptive approaches using statistical and machine‑learning techniques. The course emphasizes practical implementation and institutional considerations such as estimation error, model risk, regulatory limits, and stress testing. Hands‑on Python work supports empirical analysis, scenario evaluation, and portfolio analytics in enterprise risk and financial operations. Offered by Systems Engr & Operations Rsch . Limited to two attempts.