Forecasting Malaysian Exchange Rate using Artificial Neural Network

  • Ikhwan Muzammil Amran
  • Anas Fathul Ariffin UNIVERSITI TEKNOLOGI MARA
Keywords: Artificial Neural Network, modelling, exchange rates, non-linear models

Abstract

In todays fast paced global economy, the accuracy in forecasting the foreign exchange rate or predicting the trend is a critical key for any future business to come. The use of computational intelligence based techniques for forecasting has been proved to be successful for quite some time. This study presents a computational advance for forecasting the Foreign Exchange Rate in Kuala Lumpur for Ringgit Malaysia against US Dollar. A neural network based model has been used in forecasting the days ahead of exchange rate. The aims of this research are to make a prediction of Foreign Exchange Rate in Kuala Lumpur for Ringgit Malaysia against US Dollar using artificial neural network and determine practicality of the model. The Alyuda NeuroIntelligence software was utilized to analyze and to predict the data. After the data has been processed and the structural network compared to each other, the network of 2-4-1 has been chosen by outperforming other networks. This network selection criteria are based on Akaike Information Criterion (AIC) value which shows the lowest of them all. The training algorithm that applied is Quasi-Netwon based on the lowest recorded absolute training error. Hence, it is believed that experimental results demonstrate that Artificial Neural Network based model can closely predict the future exchange rate.

References

Akaike, H. (1973). Information Theory and an Extension of the Maximum Likelihood Principle. International Symposium on Information Theory, Tsahkadsor, Armenia, USSR, September 2-8, 1971, 2222-2847.
Azeem, K., Asrar, Z., Qamar, F., & Aslam, M.F. (2017). Currency prospects of Malaysian Ringgit - current and future outlook on the basis of Malaysian economy. Research Journal of Finance and Accounting, 8(3), 101–106.
Butt, S., Ramakrishnan, S., Chohan, M. A, & Punshi, S. K. Prediction of Malaysian Exchange Rate using microstructure fundamental and commodities prices: A machine learning method. International Journal of Recent Technology and Engineering (IJRTE), 8(2). doi:10.35940/ijrte.B1189.0982S919
Chandrasekara, V., & Tilakaratne, C. (2009). Forecasting exchange rates using artificial neural networks. Sri Lankan Journal of Applied Statistics, 10, 187-201.
Erdogan, O., & Goksu, A. (2014). Forecasting Euro and Turkish Lira Exchange Rates with Artificial Neural Networks (ANN). International Journal of Academic Research in Accounting, Finance and Management Sciences, 4(4), 307–316.
Feng, L. H., & Lu, J. (2010). The practical research on flood forecasting based on artificial neural networks. Expert Systems with Applications, 37, 2974–2977.
Galeshchuk, S. (2016). Neural networks performance in exchange rate prediction. Neurocomputing, 172, 446–452.
Hegde, N. N., Nagananda M. S., & Harsha (2015). EEG signal classification using K- Means and Fuzzy C Means Clustering Methods Department of Medical Electronics Engineering. IJSTE -International Journal of Science Technology & Engineering, 2(1), 83–87.
Joe, L. A., Cong, L. C., San, L. P., Wal, N. J., & Chin, Y. M. (2016). Determinants of foreign exchange rate (Malaysia: 1991 Q1 – 2015 Q3), Universiti Tunku Abdul Rahman, Kampar, Perak, Malaysia. Retrieved May 20, 2019, from http://eprints.utar.edu.my/2371/1/FN-2016-1307619.pdf
Kadilar, C., Simsek, M., & Aladag, C. H. (2009). Forecasting the exchange rate series with ANN : The case of Turkey. Istanbul University Journal of Econometrics and Statistics, 9, 17–29.
Kamruzzaman, J., & Sarker, R. A. (2003). Forecasting of currency exchange rates using ANN: A case study. International Conference on Neural Networks and Signal Processing, 1, 793–797.
Leung, M. T., Chen, A.S., & Daouk, H. (2000). Forecasting exchange rates using General Regression Neural Networks. Computers & Operations Research, 27, 1093–1110.
Mida, J., & Horvarth R. (2013). Forecasting exchange rates: A VAR Analysis. Charles University, Prague, Czech Republic.
Philip, A. A., Taofiki, A. A., & Bidemi A. A. (2011). Artificial Neural Network Model for forecasting foreign exchange rate. World of Computer Science and Information Technology Journal, 1(3), 2221–741110.
Umezaki, S. (2007). Monetary policy in a small open economy: The case of Malaysia. The Developing Economies, 45(4), 437–464.
Yakob, N. A., & Yaacob, M. H. (2003). Behaviors of Malaysian Exchange Rates post September 2, 1998. Retrieved April 7, 2019, from https://www.ums.edu.my/fpep/files/71_OTHERS_2003.pdf
Published
2020-07-28