Ernest Chan (Ernie) is the founder and chief scientific officer of Predictnow.ai, a machine learning SaaS and consultancy for risk management and adaptive optimization. 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 was also one of the first few employees of Morgan Stanley’s AI group. He is the founder and non-executive chairman of QTS Capital Management, a quantitative CPO/CTA, and the acclaimed author of several books on quantitative trading, all published by Wiley. He obtained his Ph.D. in physics from Cornell University and his B.Sc. in physics from the University of Toronto.
Founder and chief scientific officer of Predictnow.ai
Abstract
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.
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