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Forecasting of Currency Exchange Rate Using Artificial Neural Network: A Case Study of Solomon Island Dollar

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PRICAI 2019: Trends in Artificial Intelligence (PRICAI 2019)

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Abstract

The use of neural network models for currency exchange rate forecasting has received much attention in recent time. In this paper, we propose an exchange rate forecasting model based on artificial neural network. We tested our model on forecasting the exchange rate of Solomon Islands Dollar against some major trading currencies of the country such as, Australian Dollar, Great Britain Pound, Japanese yen, and Euro. We compared the performance of our model with that of the single exponential smoothing model; the double exponential smoothing with trend model; and Holt-Winter multiplicative and additive seasonal and multiple linear regression model. The performance of the models was measured using the error function, root mean square error (RMSE). The empirical result reveals that the proposed model is more efficient and accurate in forecasting currency exchange rate in comparison to the regression and time series models.

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References

  • Abhyanker, A., Copeland, L.S., Wong, W.: Uncovering nonlinear structure in real-time stock-market indexes: the S&P 500, The Dax, the Nikkei 225, and the FTSE-100. J. Bus. Econ. Stat. 15, 1–14 (1997)

    Google Scholar 

  • Ahmed, S., Khan, M.G.M., Prasad, B.: Forecasting Tala/USD and Tala/AUD of Samoa using AR (1), and AR(4): a comparative study. Math. Comput. Contemp. Sci., 178–186 (2013)

    Google Scholar 

  • CBSI: CBSI quarterly review, vol. 17. Central Bank of Solomon Islands, Honiara, June 2005

    Google Scholar 

  • CBSI: CBSI quarterly review, vol. 27. Central Bank of Solomon Islands, Honiara, June 2014

    Google Scholar 

  • Egrioglu, E., Aladag, H.C., Yolcu, U.: Comparison of architect selection criteria in analysing long memory time series. Adv. Time Ser. Forecast. 3, 18–25 (2012)

    Article  Google Scholar 

  • Gencay, R.: Linear, non-linear and essential Foreign exchange rate prediction with simple technical trading rules. J. Int. Econ. 47, 91–107 (1999)

    Article  Google Scholar 

  • Huang, W., Lai, K.K.: Forecasting Foreign exchange rates with artificial neural networks: a review. Int. J. Inf. Technol. Decis. Mak. 3, 145–165 (2004)

    Article  Google Scholar 

  • Hyndman, R.J., Athanasopoulos, G.: Forecasting: Principles and Practice. OTexts (2014)

    Google Scholar 

  • Kadilar, C., Muammer, S., Aladag, H.C.: Forecasting the exchange rate series with ANN: the case of Turkey. Istanb. Univ. Econ. Stat. E-J. 9, 17–29 (2009)

    Google Scholar 

  • Kamruzzaman, J., Sarker, R.A.: ANN-based forecasting of Foreign currency exchange rates. Neural Inf. Process. Lett. Rev. 3, 49–58 (2004)

    Google Scholar 

  • Kuan, C.M., Liu, T.: Forecasting exchange rates using feedforward and recurrent neural networks. J. Appl. Econ. 10, 347–364 (1995)

    Article  Google Scholar 

  • Lee, C.L., Boon, H.T.: Macroeconomic factors of exchange rate volatility evidence from four neighbouring ASEAN economies. Stud. Econ. Financ. 24, 266–285 (2007)

    Article  Google Scholar 

  • Leung, M.T., Chen, A.S., Daouk, H.: Forecasting exchange rates using general regression neural networks. Comput. Oper. Res. 27, 1093–1110 (2000)

    Article  Google Scholar 

  • Maniatis, P.: Forecasting the exchange rate between Euro and USD: probabilistic approach versus ARIMA and exponential smoothing techniques. J. Appl. Bus. Res. 28, 171–192 (2012)

    Article  Google Scholar 

  • Meese, R.A., Rogoff, K.: Empirical exchange rate models of the seventies. Do they fit out of sample? J. Int. Econ. 14, 3–24 (1983)

    Article  Google Scholar 

  • Pradhan, R.P., Kumar, R.: Forecasting exchange rate in India: an application of artificial neural network model. J. Math. Res. 2, 111–117 (2010)

    Article  Google Scholar 

  • Tambi, M.K.: Forecasting exchange rate: a univariate out of sample approach. IUP J. Bank Manag. 0(2), 60–74 (2005)

    Google Scholar 

  • Walczak, S.: An empirical analysis of data requirements for financial forecasting with neural networks. J. Manag. Inf. Syst. 17, 203–222 (2001)

    Article  Google Scholar 

  • Wu, W.P., Yang, H.L.: Forecasting New Taiwan/United States dollar exchange rate using neural network. Bus. Rev. 7, 63–69 (2007)

    Google Scholar 

  • Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with artificial neural networks: The state of the art. Int. J. Forecast. 14, 35–62 (1998)

    Article  Google Scholar 

  • Zhang, G.P.: Time series forecasting using a hybrid ARIMA and neural network model. Neurocomputing 50, 159–175 (2003)

    Article  Google Scholar 

Download references

Acknowledgement

The authors would like to thank Mr. Ali Homelo from the Central Bank of Solomon Islands for providing the daily exchange rate data and the information on the basket of currencies.

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Correspondence to M. G. M. Khan .

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Kimata, J.D., Khan, M.G.M., Sharma, A., Rashid, M.A., Tekabu, T. (2019). Forecasting of Currency Exchange Rate Using Artificial Neural Network: A Case Study of Solomon Island Dollar. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11672. Springer, Cham. https://doi.org/10.1007/978-3-030-29894-4_58

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  • DOI: https://doi.org/10.1007/978-3-030-29894-4_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29893-7

  • Online ISBN: 978-3-030-29894-4

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