Randomized Signature Methods in Optimal Portfolio Selection
This work addresses portfolio selection for financial investors, but it is incremental as it applies an existing method to a new domain without theoretical contributions.
The paper tackled the problem of drift estimation for portfolio optimization in financial markets by applying Randomized Signature Methods to learn non-linear maps from data to future returns, achieving convincing empirical results with real market data and transaction costs.
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.