Ciamac Moallemi On Quant Investing, Machine Learning and Trading Styles

This episode is sponsored by Masterworks.

Ciamac is Professor of Business in the Decision, Risk, and Operations Division of the Graduate School of Business at Columbia University, where he has been since 2007. He also develops quantitative trading strategies at Bourbaki LLC, a quantitative investment advisor. A high school dropout, he received degrees at MIT, Cambridge, and Stanford. In this podcast, we discuss:

1. Types of quant investing – prediction vs risk premia.
2. Why machine learning is impacting finance more slowly than other domains (like vision and text).
3. The pros and cons of using linear regressions.
4. The advantages of machine learning in non-linear and complex markets.
5. How to think about alternative and big data.
6. Portfolio construction and combining signals.
7. The importance of incorporating costs.
8. Understanding time horizons of different markets.
9. The trend to winner-takes-all with quant investors.
10. Why bitcoin and crypto technology is special.
11. Books that influenced Ciamac: The Elements of Statistical Learning (Hastie and Tibshirani), Dynamic Programming and Optimal Control: books 1 and 2 (Bertsekas), Active Portfolio Management (Grinold and Kahn).

You can follow Ciamac on Twitter here and his work here