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Title: Trading macro-cycles of foreign exchange markets using hybrid models
Authors: Ling, Joseph Zhi Bin
Tsui, Albert K. 
Zhang, Zhaoyong
Keywords: Forecasting exchange rate
Hybrid forecast model
Multilayer feedforward neural network
Trading macro-cycles
Issue Date: 1-Sep-2021
Publisher: MDPI
Citation: Ling, Joseph Zhi Bin, Tsui, Albert K., Zhang, Zhaoyong (2021-09-01). Trading macro-cycles of foreign exchange markets using hybrid models. Sustainability (Switzerland) 13 (17) : 9820. ScholarBank@NUS Repository.
Rights: Attribution 4.0 International
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. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
Source Title: Sustainability (Switzerland)
ISSN: 2071-1050
DOI: 10.3390/su13179820
Rights: Attribution 4.0 International
Appears in Collections:Staff Publications

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