Randomized Signature Methods in Optimal Portfolio Selection
Portfolio Management
2023-12-29 v1 Artificial Intelligence
Machine Learning
Pricing of Securities
Abstract
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.
Keywords
Cite
@article{arxiv.2312.16448,
title = {Randomized Signature Methods in Optimal Portfolio Selection},
author = {Erdinc Akyildirim and Matteo Gambara and Josef Teichmann and Syang Zhou},
journal= {arXiv preprint arXiv:2312.16448},
year = {2023}
}