FIN 5321
Data Analysis for Investments
The objective is to obtain an in-depth understanding of some of the major empirical issues in investments and to gain the implementation skills. Based on recent advances, students are required to learn the facts, theories and the associated statistical tools to analyze financial data with Python, and with some optional tutorial and codes 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 Black-Litterman model.
Instructors
Reviews
One of the best professors in Olin. The M&A class is extremely practical and useful for competitive finance interviews. Overall, a fantastic experience.
12/13/2024
He only reads from his slides. I also noticed that the course mainly focuses on academic models rather than practical applications. When I asked if we could incorporate real cases or current news into the class, he told me to ask ChatGPT. I'm not sure what he meant by that—does he think ChatGPT can teach better than he can?!
10/25/2024
Professor Guofu's course is very interesting. The content is a bit difficult if you don't have a basic understanding. It is more about application rather than mathematical reasoning, so it is acceptable. There are code examples shared, which is very interesting.
7/2/2024
2-4 hrs/week
Zhou has such weird grading. Material not hard w/ coding background
5/23/2024
Dear two posters below me - I think your comments say more about you than the professor. PS. Guofu is hilarious, has comprehensive notes, is very helpful outside of class, and is one of the top finance professors (research and teaching) at WashU. He will NOT be fired.
11/27/2009
terrible professor i went to half the classes and got nothing out of them and wondered to myself why i ever went in the first place
1/15/2007