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Randomized Signature Methods in Optimal Portfolio Selection

Author

Listed:
  • Erdinc Akyildirim

    (University of Bradford)

  • Matteo Gambara

    (INAIT SA)

  • Josef Teichmann

    (ETH Zurich; Swiss Finance Institute)

  • Syang Zhou

    (ETH)

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.

Suggested Citation

  • Erdinc Akyildirim & Matteo Gambara & Josef Teichmann & Syang Zhou, 2024. "Randomized Signature Methods in Optimal Portfolio Selection," Swiss Finance Institute Research Paper Series 24-79, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2479
    as

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    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4676478
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    Citations

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    Cited by:

    1. Francesca Biagini & Lukas Gonon & Niklas Walter, 2024. "Universal randomised signatures for generative time series modelling," Papers 2406.10214, arXiv.org, revised Sep 2024.

    More about this item

    Keywords

    Machine Learning; Randomized Signature; Drift estimation; Returns forecast; Portfolio Optimization; Path-dependent Signal;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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