Convex optimisation solutions tend to be unstable, to the point of entirely offsetting the benefits of optimisation. For example, in the context of financial applications, it is known that portfolios optimised in sample often underperform the naïve (equal weights) allocation out of sample. This instability can be traced back to two sources:
- noise in the input variables; and
- 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 optimisation method that is robust to signal induced instability.
