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Enhancement of Neural Networks Model’s Predictions of Currencies Exchange Rates by Phase Space Reconstruction and Harris Hawks’ Optimization

Author

Listed:
  • Haider A. Khan

    (University of Denver)

  • Shahryar Ghorbani

    (Sakarya University)

  • Elham Shabani

    (University of Tabriz)

  • Shahab S. Band

    (National Yunlin University of Science and Technology)

Abstract

Predictions of variations in exchange rates of other currencies to a vehicle currency such as the Dollar (USD) are vital in order to reduce the risks for international transactions. In this study, we use a heuristic algorithm of Harris Hawks’ optimization (HHO) along with phase space reconstructions (PSRs) coupled to the ANN (PSR-ANNHHO) to predict the daily data of GBP/USD and CAD/USD exchange rates. In this new hybrid model, unlike the previous ones, the input of the model is based on the two parameters of time delay and the embedding dimension. The HHO algorithm increases the performance of ANN, which has can model non-linear systems in a natural manner. The performance of the PSR-ANNHHO model can be compared with the ANN and the ANN hybridized with metaheuristic Algorithm of Innovative Gunner (AIG) model (ANN-AIG). The Modified Diebold–Mariano test indicates the statistical difference between the accuracy of the models. Based on the statistical measures and graphs, the PSR-ANNHHO model predicts exchange rates considerably better than stand-alone ANN and ANN-AIG model in each case. Hence, implementing PSR along with using the heuristic algorithms could increase the accuracy of the model. This model’s precise performance supports the case for it to be employed to predict future exchange rate variations, in order to decrease transactions risks in the global markets.

Suggested Citation

  • Haider A. Khan & Shahryar Ghorbani & Elham Shabani & Shahab S. Band, 2024. "Enhancement of Neural Networks Model’s Predictions of Currencies Exchange Rates by Phase Space Reconstruction and Harris Hawks’ Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 835-860, February.
    • Handle: RePEc:kap:compec:v:63:y:2024:i:2:d:10.1007_s10614-023-10361-y
    DOI: 10.1007/s10614-023-10361-y
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    References listed on IDEAS

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