Machine Learning Asset Allocation (Presentation Slides)
36 Pages Posted: 18 Oct 2019
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: https://ssrn.com/abstract=3469961.
Keywords: Monte Carlo, convex optimization, de-noising, clustering, shrinkage
JEL Classification: G0, G1, G2, G15, G24, E44
Suggested Citation: Suggested Citation