Forecasting Malaysia Gold’s Price by using Neural Networks

Authors

  • Norpah Mahat Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Perlis, Kampus Arau
  • Aini Mardhiah Yusuf Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Perlis Kampus Arau
  • Siti Sarah Raseli Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Perlis Kampus Arau

DOI:

https://doi.org/10.24191/ji.v14i2.227

Keywords:

Malaysian gold price, forecasting, neural networks

Abstract

Gold and all kinds of gold alloys are commonly used in the manufacture of jewelry, coins and inexchange for trade in many countries. In addition, gold can conduct electricity efficiently and withstandcorrosion. This has made gold becomes an important industrial metal in the late 20th century. It is alsoimportant for the investors and public to know the trend of changes on gold’s price in order to assist themin making a good decision on their business. This research is done to forecast the Malaysia gold’s priceby using artificial Neural Network (NN). The forecasting models are implemented by using AlyudaNeurointelligence software. A monthly gold’s price data from January 2013 until March 2018 is used andapplied to the models and comparing their error measures. The results show that the Conjugate GradientAlgorithm (CGA) is chosen as the best neural network algorithm to forecast gold price since it has ahigher value of correlation and R square with the best architecture design [2-5-1]. Then, the future priceof gold starting from April 2018 until December 2018 is forecasted by using the best model. Keywords: Malaysian gold price, forecasting, neural networks

References

Alptekin, D., Alptekin, B. & Aladag, C. (2017). Forecasting Gold Prices with Fuzzy Time Series. An Official Journal of Turkish Fuzzy Systems Association, 8(2), 102-107.
Dharmaraja, S., Vineet, K. & Abhishek, M. (2019). Indian Stock Market Prediction Using Artificial Neural Networks on Tick Data. Financial Innovation 5(16). doi:10.1186/s40854-019-0131-7
Dr. Sindhu, D. (2013). A Study on Impact of Select Factors on the Price of Gold. IOSR Journal of Business and Management, 8(4), 84-93.doi: 10.9790/487x-0848493
IndexMundi-Malaysia-Country Profile (2018). https://www/indexmundi.com/malaysia/.
Lan, T., Chen, P., Chang, Y., & Chiu, W. (2015). An Investigation of the Relationship between Performance of Physical Fitness and Swimming Ability based on the Artificial Neural Network. Innovation in Design, Communication and Engineering - Meen, Prior & Lam (Eds), 181-183.
Mira, J. (2001). Bio-Inspired Applications of Connectionism (p. 283). Springer Science & Business Media
Mombeini, H. & Yazdani-Chamzini, A. (2015). Modeling Gold Price via Artificial Neural Network. Journal of Economics, Business and Management, 3(7), 699-703. http://dx.doi.org/10.7763/joebm.2015.v3.269
Sadig, M. (2018). Financial Time Series Prediction Using Artificial Neural Network Based on Levenberg-Marquardt Algorithm. Procedia Computer Science, 120(2017), 602-607. https://doi.org/10.1016/j.procs.2017.11.285
Zhao, J., Li, Y., Yu, X., & Zhang, X. (2014). Levenberg-Marquardt Algorithm for Mackey-Glass Chaotic Time Series Prediction. Discrete Dynamics in Nature and Society, 1-6. doi: 10.1155/2014/193758

Downloads

Published

2019-11-29