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An Effective Hybrid Approach for Forecasting Currency Exchange Rates

Author

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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
    Ph.D. Program in Biomedical Engineering, Kaohsiung Medical University, Kaohsiung 80708, Taiwan)

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.

Suggested Citation

  • Mei-Li Shen & Cheng-Feng Lee & Hsiou-Hsiang Liu & Po-Yin Chang & Cheng-Hong Yang, 2021. "An Effective Hybrid Approach for Forecasting Currency Exchange Rates," Sustainability, MDPI, vol. 13(5), pages 1-29, March.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:5:p:2761-:d:510193
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    References listed on IDEAS

    as
    1. Fan, Guo-Feng & Peng, Li-Ling & Hong, Wei-Chiang, 2018. "Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model," Applied Energy, Elsevier, vol. 224(C), pages 13-33.
    2. Maurice Obstfeld & Kenneth Rogoff, 2001. "The Six Major Puzzles in International Macroeconomics: Is There a Common Cause?," NBER Chapters, in: NBER Macroeconomics Annual 2000, Volume 15, pages 339-412, National Bureau of Economic Research, Inc.
    3. Jain, Anshul & Biswal, P.C., 2016. "Dynamic linkages among oil price, gold price, exchange rate, and stock market in India," Resources Policy, Elsevier, vol. 49(C), pages 179-185.
    4. Eichenbaum, Martin & Rebelo, Sérgio & Burnside, Craig & Kleshchelski, Isaac, 2006. "The Returns to Currency Speculation," CEPR Discussion Papers 5883, C.E.P.R. Discussion Papers.
    5. Pierdzioch, Christian & Rülke, Jan-Christoph, 2015. "On the directional accuracy of forecasts of emerging market exchange rates," International Review of Economics & Finance, Elsevier, vol. 38(C), pages 369-376.
    6. Cheung, Yin-Wong & Chinn, Menzie D. & Pascual, Antonio Garcia & Zhang, Yi, 2019. "Exchange rate prediction redux: New models, new data, new currencies," Journal of International Money and Finance, Elsevier, vol. 95(C), pages 332-362.
    7. Saeed Nosratabadi & Amir Mosavi & Puhong Duan & Pedram Ghamisi, 2020. "Data Science in Economics," Papers 2003.13422, arXiv.org.
    8. Xinyi Li & Yinchuan Li & Yuancheng Zhan & Xiao-Yang Liu, 2019. "Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation," Papers 1907.01503, arXiv.org.
    9. Tillmann, Peter, 2016. "Unconventional monetary policy and the spillovers to emerging markets," Journal of International Money and Finance, Elsevier, vol. 66(C), pages 136-156.
    10. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    11. repec:bla:scandj:v:78:y:1976:i:2:p:200-224 is not listed on IDEAS
    12. Mauro Bernardi & Leopoldo Catania, 2014. "The Model Confidence Set package for R," Papers 1410.8504, arXiv.org.
    13. Ince, Onur, 2014. "Forecasting exchange rates out-of-sample with panel methods and real-time data," Journal of International Money and Finance, Elsevier, vol. 43(C), pages 1-18.
    14. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," OSF Preprints yc6e2, Center for Open Science.
    15. Amat, Christophe & Michalski, Tomasz & Stoltz, Gilles, 2018. "Fundamentals and exchange rate forecastability with simple machine learning methods," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 1-24.
    16. Barbara Rossi, 2013. "Exchange Rate Predictability," Journal of Economic Literature, American Economic Association, vol. 51(4), pages 1063-1119, December.
    17. Xiao-Yang Liu & Zhuoran Xiong & Shan Zhong & Hongyang Yang & Anwar Walid, 2018. "Practical Deep Reinforcement Learning Approach for Stock Trading," Papers 1811.07522, arXiv.org, revised Jul 2022.
    18. Dick, Christian D. & MacDonald, Ronald & Menkhoff, Lukas, 2015. "Exchange rate forecasts and expected fundamentals," Journal of International Money and Finance, Elsevier, vol. 53(C), pages 235-256.
    19. Yuchen Zhang & Shigeyuki Hamori, 2020. "The Predictability of the Exchange Rate When Combining Machine Learning and Fundamental Models," JRFM, MDPI, vol. 13(3), pages 1-16, March.
    20. Nevin Cavusoglu & Andre R. Neveu, 2015. "The Predictive Power of Survey‐Based Exchange Rate Forecasts: Is there a Role for Dispersion?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(5), pages 337-353, August.
    21. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    22. Hyndman, Rob J. & Koehler, Anne B. & Snyder, Ralph D. & Grose, Simone, 2002. "A state space framework for automatic forecasting using exponential smoothing methods," International Journal of Forecasting, Elsevier, vol. 18(3), pages 439-454.
    23. Peng Yaohao & Pedro Henrique Melo Albuquerque, 2019. "Non-Linear Interactions and Exchange Rate Prediction: Empirical Evidence Using Support Vector Regression," Applied Mathematical Finance, Taylor & Francis Journals, vol. 26(1), pages 69-100, January.
    24. Sermpinis, Georgios & Theofilatos, Konstantinos & Karathanasopoulos, Andreas & Georgopoulos, Efstratios F. & Dunis, Christian, 2013. "Forecasting foreign exchange rates with adaptive neural networks using radial-basis functions and Particle Swarm Optimization," European Journal of Operational Research, Elsevier, vol. 225(3), pages 528-540.
    25. Bakshi, Gurdip & Panayotov, George, 2013. "Predictability of currency carry trades and asset pricing implications," Journal of Financial Economics, Elsevier, vol. 110(1), pages 139-163.
    26. Fu, Sibao & Li, Yongwu & Sun, Shaolong & Li, Hongtao, 2019. "Evolutionary support vector machine for RMB exchange rate forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 692-704.
    27. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," MetaArXiv haf2v, Center for Open Science.
    28. Nhi N.Y.Vo & Xue-Zhong He & Shaowu Liu & Guandong Xu, 2019. "Deep Learning for Decision Making and the Optimization of Socially Responsible Investments and Portfolio," Published Paper Series 2019-3, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
    29. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    30. Yongquan Dong & Zichen Zhang & Wei-Chiang Hong, 2018. "A Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting," Energies, MDPI, vol. 11(4), pages 1-21, April.
    31. Apergis, Nicholas & Chatziantoniou, Ioannis & Cooray, Arusha, 2020. "Monetary policy and commodity markets: Unconventional versus conventional impact and the role of economic uncertainty," International Review of Financial Analysis, Elsevier, vol. 71(C).
    32. Ronald Macdonald & Mark P. Taylor, 1992. "Exchange Rate Economics: A Survey," IMF Staff Papers, Palgrave Macmillan, vol. 39(1), pages 1-57, March.
    33. Jordà, Òscar & Taylor, Alan M., 2012. "The carry trade and fundamentals: Nothing to fear but FEER itself," Journal of International Economics, Elsevier, vol. 88(1), pages 74-90.
    34. Kharrat, Sabrine & Hammami, Yacine & Fatnassi, Ibrahim, 2020. "On the cross-sectional relation between exchange rates and future fundamentals," Economic Modelling, Elsevier, vol. 89(C), pages 484-501.
    35. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    36. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," Thesis Commons auyvc, Center for Open Science.
    37. Beckmann, Joscha & Czudaj, Robert L. & Arora, Vipin, 2020. "The relationship between oil prices and exchange rates: Revisiting theory and evidence," Energy Economics, Elsevier, vol. 88(C).
    38. Dahlquist, Magnus & Hasseltoft, Henrik, 2020. "Economic momentum and currency returns," Journal of Financial Economics, Elsevier, vol. 136(1), pages 152-167.
    39. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," LawArXiv kczj5, Center for Open Science.
    40. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," EdArXiv 5dwrt, Center for Open Science.
    41. Uz Akdogan, Idil, 2020. "Understanding the dynamics of foreign reserve management: The central bank intervention policy and the exchange rate fundamentals," International Economics, Elsevier, vol. 161(C), pages 41-55.
    42. Amir Mosavi & Pedram Ghamisi & Yaser Faghan & Puhong Duan, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Papers 2004.01509, arXiv.org.
    43. Menzie D. Chinn & Michael J. Moore, 2011. "Order Flow and the Monetary Model of Exchange Rates: Evidence from a Novel Data Set," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 43(8), pages 1599-1624, December.
    44. Mosavi, Amir & Faghan, Yaser & Ghamisi, Pedram & Duan, Puhong & Ardabili, Sina Faizollahzadeh & Hassan, Salwana & Band, Shahab S., 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," OSF Preprints jrc58, Center for Open Science.
    45. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," SocArXiv 9vdwf, Center for Open Science.
    46. Dongling Huang & Lan Luo, 2016. "Consumer Preference Elicitation of Complex Products Using Fuzzy Support Vector Machine Active Learning," Marketing Science, INFORMS, vol. 35(3), pages 445-464, May.
    47. 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.
    48. Idil Uz Akdogan, 2020. "Understanding the dynamics of foreign reserve management: The central bank intervention policy and the exchange rate fundamentals," International Economics, CEPII research center, issue 161, pages 41-55.
    49. Sadaf Shamshoddin & Jameel Khader & Showkat Gani, 2020. "Predicting consumer preferences in electronic market based on IoT and Social Networks using deep learning based collaborative filtering techniques," Electronic Commerce Research, Springer, vol. 20(2), pages 241-258, June.
    50. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
    51. Ahmed, Shamim & Liu, Xiaoquan & Valente, Giorgio, 2016. "Can currency-based risk factors help forecast exchange rates?," International Journal of Forecasting, Elsevier, vol. 32(1), pages 75-97.
    52. Li-Yeh Chuang & Yu-Da Lin & Hsueh-Wei Chang & Cheng-Hong Yang, 2012. "An Improved PSO Algorithm for Generating Protective SNP Barcodes in Breast Cancer," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-9, May.
    53. Zhipeng Liang & Hao Chen & Junhao Zhu & Kangkang Jiang & Yanran Li, 2018. "Adversarial Deep Reinforcement Learning in Portfolio Management," Papers 1808.09940, arXiv.org, revised Nov 2018.
    54. Mussa, Michael, 1979. "Empirical regularities in the behavior of exchange rates and theories of the foreign exchange market," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 11(1), pages 9-57, January.
    55. Altan, Aytaç & Karasu, Seçkin & Bekiros, Stelios, 2019. "Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 325-336.
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