  We present convincing empirical results on the application of Randomized
Signature Methods for non-linear, non-parametric drift estimation for a
multi-variate financial market. Even though drift estimation is notoriously ill
defined due to small signal to noise ratio, one can still try to learn optimal
non-linear maps from data to future returns for the purposes of portfolio
optimization. Randomized Signatures, in contrast to classical signatures, allow
for high dimensional market dimension and provide features on the same scale.
We do not contribute to the theory of Randomized Signatures here, but rather
present our empirical findings on portfolio selection in real world settings
including real market data and transaction costs.
