Randomized Signature Methods in Optimal Portfolio Selection

Erdinc Akyildirim, Matteo Gambara, Josef Teichmann and Syang Zhou
Additional contact information
Erdinc Akyildirim: University of Bradford
Matteo Gambara: INAIT SA
Josef Teichmann: ETH Zurich; Swiss Finance Institute
Syang Zhou: ETH

No 24-79, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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 constrast 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: Machine Learning; Randomized Signature; Drift estimation; Returns forecast; Portfolio Optimization; Path-dependent Signal (search for similar items in EconPapers)
JEL-codes: C21 C22 G11 G14 G17 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2024-01
New Economics Papers: this item is included in nep-cmp
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2479

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