Machine Learning Asset Allocation (Presentation Slides)

36 Pages Posted: 18 Oct 2019

See all articles by Marcos Lopez de Prado

Marcos Lopez de Prado

Cornell University - Operations Research & Industrial Engineering; True Positive Technologies

Date Written: October 15, 2019

Abstract

Convex optimization solutions tend to be unstable, to the point of entirely offsetting the benefits of optimization. For example, in the context of financial applications, it is known that portfolios optimized in sample often underperform the naïve (equal weights) allocation out of sample.

This instability can be traced back to two sources: (1) noise in the input variables; and (2) signal structure that magnifies the estimation errors in the input variables.

There is abundant literature discussing noise induced instability. In contrast, signal induced instability is often ignored or misunderstood.

We introduce a new optimization method that is robust to signal induced instability.

For additional details, see the full paper at: https://ssrn.com/abstract=3469961.

Keywords: Monte Carlo, convex optimization, de-noising, clustering, shrinkage

JEL Classification: G0, G1, G2, G15, G24, E44

Suggested Citation

López de Prado, Marcos, Machine Learning Asset Allocation (Presentation Slides) (October 15, 2019). Available at SSRN: https://ssrn.com/abstract=3469964 or http://dx.doi.org/10.2139/ssrn.3469964

Marcos López de Prado (Contact Author)

Cornell University - Operations Research & Industrial Engineering ( email )

237 Rhodes Hall
Ithaca, NY 14853
United States

HOME PAGE: http://www.orie.cornell.edu

True Positive Technologies ( email )

NY
United States

HOME PAGE: http://www.truepositive.com

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