Machine Learning Asset Allocation (Presentation Slides)

35 Pages Posted: 18 Oct 2019 Last revised: 1 Jun 2020

See all articles by Marcos Lopez de Prado

Marcos Lopez de Prado

Cornell University - Operations Research & Industrial Engineering; Abu Dhabi Investment Authority; True Positive Technologies

Date Written: October 15, 2019


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:

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

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

Suggested Citation

López de Prado, Marcos and López de Prado, Marcos, Machine Learning Asset Allocation (Presentation Slides) (October 15, 2019). Available at SSRN: or

Marcos López de Prado (Contact Author)

Cornell University - Operations Research & Industrial Engineering ( email )

237 Rhodes Hall
Ithaca, NY 14853
United States


Abu Dhabi Investment Authority ( email )

211 Corniche Road
Abu Dhabi, Abu Dhabi PO Box3600
United Arab Emirates


True Positive Technologies ( email )

United States


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