Mathematical Finance & Financial Data Science Seminar

Conditional Portfolio Optimization - Adapting Capital Allocations to Market Regimes via Machine Learning

Speaker: Ernest Chan, Founder and CEO, Inc.

Location: Online Zoom access provided to registrants

Date: Tuesday, November 1, 2022, 5:30 p.m.


Conditional Portfolio Optimization is a portfolio optimization technique that adapts to market regimes via machine learning. Traditional portfolio optimization methods take summary statistics of historical constituent returns as input and produce a portfolio that was optimal in the past, but may not be optimal going forward. Machine learning can condition the optimization on a large number of market features and propose a portfolio that is currently optimal. We call this Conditional Portfolio Optimization (CPO). Applications on portfolios in vastly different markets suggest that CPO can outperform traditional optimization methods under varying market regimes.


Speaker Bio:

Ernest Chan (Ernie) is the founder and CEO of, a machine learning SaaS. He started his career as a machine learning researcher at IBM's T.J. Watson Research Center's Human Language Technologies group, which produced some of the best-known quant fund managers. He later joined Morgan Stanley's Data Mining and Artificial Intelligence group, which was founded by Prof. Vasant Dhar of NYU. He is the founder and non-executive chairman of QTS Capital Management, a quantitative CPO/CTA. He obtained his Ph.D. in physics from Cornell University and his B.Sc. physics from the University of Toronto. 


This event is free, but requires registration.  Please click here to register.  You will then receive the Zoom link by email about a day or so before the event.