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Randomized Signature Methods in Optimal Portfolio Selection

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  • Erdinc Akyildirim
  • Matteo Gambara
  • Josef Teichmann
  • Syang Zhou

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.

Suggested Citation

  • Erdinc Akyildirim & Matteo Gambara & Josef Teichmann & Syang Zhou, 2023. "Randomized Signature Methods in Optimal Portfolio Selection," Papers 2312.16448, arXiv.org.
    • Handle: RePEc:arx:papers:2312.16448
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    References listed on IDEAS

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    14. Owen Futter & Blanka Horvath & Magnus Wiese, 2023. "Signature Trading: A Path-Dependent Extension of the Mean-Variance Framework with Exogenous Signals," Papers 2308.15135, arXiv.org, revised Aug 2023.
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    Cited by:

    1. Federico M. Bandi & Roberto Ren`o & Sara Svaluto-Ferro, 2025. "Local signature-based expansions," Papers 2504.06351, arXiv.org.
    2. Giovanni Ballarin & Jacopo Capra & Petros Dellaportas, 2025. "Multi-Horizon Echo State Network Prediction of Intraday Stock Returns," Papers 2504.19623, arXiv.org.

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