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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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    1. Rebeca Jiménez‐Rodríguez & Amalia Morales‐Zumaquero, 2020. "BRICS: How important is the exchange rate pass‐through?," The World Economy, Wiley Blackwell, vol. 43(3), pages 781-793, March.
    2. Hao Chen & Qiulan Wan & Yurong Wang, 2014. "Refined Diebold-Mariano Test Methods for the Evaluation of Wind Power Forecasting Models," Energies, MDPI, vol. 7(7), pages 1-14, July.
    3. Lin, Yu & Lu, Qin & Tan, Bin & Yu, Yuanyuan, 2022. "Forecasting energy prices using a novel hybrid model with variational mode decomposition," Energy, Elsevier, vol. 246(C).
    4. Huseyin Ince & Ali Fehim Cebeci & Salih Zeki Imamoglu, 2019. "An Artificial Neural Network-Based Approach to the Monetary Model of Exchange Rate," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 817-831, February.
    5. El Shazly, Mona R. & El Shazly, Hassan E., 1999. "Forecasting currency prices using a genetically evolved neural network architecture," International Review of Financial Analysis, Elsevier, vol. 8(1), pages 67-82.
    6. Solat, Karo & Tsang, Kwok Ping, 2021. "Forecasting exchange rates with elliptically symmetric principal components," International Journal of Forecasting, Elsevier, vol. 37(3), pages 1085-1091.
    7. Mona Shazly & Hassan Shazly, 1999. "Forecasting currency prices using a genetically evolved neural network architecture," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 5(1), pages 148-148, February.
    8. Zhou, Jin & Li, Haiqi & Zhong, Wanling, 2021. "A modified Diebold–Mariano test for equal forecast accuracy with clustered dependence," Economics Letters, Elsevier, vol. 207(C).
    9. Wei Huang & K. K. Lai & Y. Nakamori & Shouyang Wang, 2004. "Forecasting Foreign Exchange Rates With Artificial Neural Networks: A Review," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 3(01), pages 145-165.
    10. Meese, Richard A. & Rogoff, Kenneth, 1983. "Empirical exchange rate models of the seventies : Do they fit out of sample?," Journal of International Economics, Elsevier, vol. 14(1-2), pages 3-24, February.
    11. Lahmiri, Salim & Bekiros, Stelios, 2019. "Cryptocurrency forecasting with deep learning chaotic neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 35-40.
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