An Effective Hybrid Approach for Forecasting Currency Exchange Rates

Mei-Li Shen, Cheng-Feng Lee, Hsiou-Hsiang Liu, Po-Yin Chang and Cheng-Hong Yang
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Mei-Li Shen: Department of Tourism Management, National Kaohsiung University of Science and Technology, Kaohsiung 824004, Taiwan
Cheng-Feng Lee: Department of Business Administration, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
Hsiou-Hsiang Liu: Department of Tourism Management, National Kaohsiung University of Science and Technology, Kaohsiung 824004, Taiwan
Po-Yin Chang: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan
Cheng-Hong Yang: Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 807618, Taiwan

Sustainability, 2021, vol. 13, issue 5, 1-29

Abstract: Accurately forecasting the movement of exchange rates is of interest in a variety of fields, such as international business, financial management, and monetary policy, though this is not an easy task due to dramatic fluctuations caused by political and economic events. In this study, we develop a new forecasting approach referred to as FSPSOSVR, which is able to accurately predict exchange rates by combining particle swarm optimization (PSO), random forest feature selection, and support vector regression (SVR). PSO is used to obtain the optimal SVR parameters for predicting exchange rates. Our analysis involves the monthly exchange rates from January 1971 to December 2017 of seven countries including Australia, Canada, China, the European Union, Japan, Taiwan, and the United Kingdom. The out-of-sample forecast performance of the FSPSOSVR algorithm is compared with six competing forecasting models using the mean absolute percentage error (MAPE) and root mean square error (RMSE), including random walk, exponential smoothing, autoregressive integrated moving average (ARIMA), seasonal ARIMA, SVR, and PSOSVR. Our empirical results show that the FSPSOSVR algorithm consistently yields excellent predictive accuracy, which compares favorably with competing models for all currencies. These findings suggest that the proposed algorithm is a promising method for the empirical forecasting of exchange rates. Finally, we show the empirical relevance of exchange rate forecasts arising from FSPSOSVR by use of foreign exchange carry trades and find that the proposed trading strategies can deliver positive excess returns of more than 3% per annum for most currencies, except for AUD and NTD.

Keywords: exchange rates; machine learning; forecasting; particle swarm optimization (PSO); support vector machines (SVM) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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