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“It Looks All the Same to Me”: Cross-Index Training for Long-Term Financial Series Prediction

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Machine Learning, Optimization, and Data Science (LOD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14505))

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Abstract

We investigate a number of Artificial Neural Network architectures (well-known and more “exotic”) in application to the long-term financial time-series forecasts of indexes on different global markets. The particular area of interest of this research is to examine the correlation of these indexes’ behaviour in terms of Machine Learning algorithms cross-training. Would training an algorithm on an index from one global market produce similar or even better accuracy when such a model is applied for predicting another index from a different market? The demonstrated predominately positive answer to this question is another argument in favour of the long-debated Efficient Market Hypothesis of Eugene Fama.

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Authors and Affiliations

  1. University of Bedfordshire, Park Square, Luton, LU1 3JU, UK

    Stanislav Selitskiy

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  1. Stanislav Selitskiy
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Correspondence to Stanislav Selitskiy .

Editor information

Editors and Affiliations

  1. University of Catania, Catania, Catania, Italy

    Giuseppe Nicosia

  2. Newcastle University, Newcastle upon Tyne, UK

    Varun Ojha

  3. University of Oxford, Oxford, UK

    Emanuele La Malfa

  4. University of Cambridge, Cambridge, UK

    Gabriele La Malfa

  5. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  6. Dana-Farber Cancer Institute, Boston, MA, USA

    Renato Umeton

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Selitskiy, S. (2024). “It Looks All the Same to Me”: Cross-Index Training for Long-Term Financial Series Prediction. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14505. Springer, Cham. https://doi.org/10.1007/978-3-031-53969-5_26

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  • DOI: https://doi.org/10.1007/978-3-031-53969-5_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53968-8

  • Online ISBN: 978-3-031-53969-5

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