Join us for our webinar with DataCamp on Tuesday, January 15th at 11 am ET. Max Margenot, Quantopian’s lead data scientist, will be presenting “Bayesian Covariance for Portfolio Optimization.” You can register for free here.
Uncertainty quantified as probability is the rock upon which Bayesian inference is built. The instability of sample covariance matrices leads to major problems in Markowitz portfolio optimization. Max will use probabilistic programming to compute probability distributions on the covariance of a set of assets. This yields a more robust estimate of their variation and adds uncertainty into how we calculate weights for a portfolio of assets.
Following his presentation, Max will be answering questions from attendees. Don’t miss out, be sure to register today!