Data Analysis for Investments

1.5 units

The objective is to obtain an in-depth understanding of major empirical issues in investments and to develop implementation skills. Based on recent advances, students are required to learn the facts, theories, and associated statistical tools for analyzing financial data with Python, along with optional tutorials and code in R and MATLAB. The topics include portfolio optimization, factor models, factor investing, Bayesian and shrinkage estimations, principal analysis, predictability, big data tools, asset allocation, stock screening, performance evaluation, anomalies, limits to arbitrage, behavioral finance, and the Black-Litterman model. Students may not earn credit in both FIN 5321 and FIN 6321.