Abstract

Most existing studies on forecasting exchange rates focus on predicting next-period returns. In contrast, this study takes the novel approach of forecasting and trading the longer-term trends (macro-cycles) of exchange rates. It proposes a unique hybrid forecast model consisting of linear regression, multilayer neural network, and combination models embedded with technical trading rules and economic fundamentals to predict the macro-cycles of the selected currencies and investigate the predicative power and market timing ability of the model. The results confirm that the combination model has a significant predictive power and market timing ability, and outperforms the benchmark models in terms of returns. The finding that the government bond yield differentials and CPI differentials are the important factors in exchange rate forecasts further implies that interest rate parity and PPP have strong influence on foreign exchange market participants.

RAS ID

37002

Document Type

Journal Article

Date of Publication

2021

Funding Information

Edith Cowan University - Open Access Support Scheme 2021

Sumitomo Foundation

School

School of Business and Law

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Publisher

MDPI

Identifier

Zhaoyong Zhang

https://orcid.org/0000-0001-9596-2648

Comments

Ling, J. Z. B., Tsui, A. K., & Zhang, Z. (2021). Trading macro-cycles of foreign exchange markets using hybrid models. Sustainability, 13(17), article 9820. https://doi.org/10.3390/su13179820

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Link to publisher version (DOI)

10.3390/su13179820