🔮 Want to learn what’s in store for data in 2022? Read 9 data trends and predictions for 2022 and beyond.
Skip to main content

Bayesian Covariance for Portfolio Optimization

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 Margenot, Academia and Data Science Lead at Quantopian, uses probabilistic programming to compute probability distributions on the covariance of a set of assets. This yields a more robust estimate of their variation and factors uncertainty into how we calculate weights for a portfolio of assets.

You can find the slides here.

Max Margenot Headshot
Max Margenot

Academia and Data Science Lead at Quantopian

Hands-on learning experience

Companies using DataCamp achieve course completion rates 6X higher than traditional online course providers

Learn More

Upskill your teams in data science and analytics

Learn More

Join 2.000+ companies and 80% of the Fortune 1000 who use DataCamp to upskill their teams.

Don’t just take our word for it.