In this episode, we take a deep dive into quantitative investing with Michael Robbins, author of the new book “Quantitative Asset Management: Factor Investing and Machine Learning for Institutional Investing.” We discuss data science and machine learning, factor investing, risk management, the qualities of a good back test and a lot more.
- 01:58 – Why Michael wrote the book
- 04:11 – Is it better if the math or the finance comes first?
- 07:13 – What is data science?
- 10:39 – The best use of quantitative strategies
- 11:33 – The long-term impact of machine learning on investing
- 16:25 – Stacking premia and the equity risk premium
- 20:24 – The criteria Michael would use to evaluate a quantitative manager
- 25:09 – What makes a good investing factor?
- 30:13 – Does factor timing work?
- 33:44 – The different types of models
- 37:51 – Is value investing dead?
- 44:32 – What makes a good back test?
- 45:52 – Evolving an investment strategy over time
- 53:01 – The importance of risk management
- 54:39 – The one lesson Michael would teach the average investor
