• ISSN: 2301-3567 (Print), 2972-3981 (Online)
    • Abbreviated Title: J. Econ. Bus. Manag.
    • Frequency: Quarterly
    • DOI: 10.18178/JOEBM
    • Editor-in-Chief: Prof. Eunjin Hwang
    • Executive Editor: Ms. Fiona Chu
    • Abstracting/ Indexing:  CNKIGoogle ScholarCrossref
    • E-mail: joebm.editor@gmail.com
JOEBM 2015 Vol.3(7): 699-703 ISSN: 2301-3567
DOI: 10.7763/JOEBM.2015.V3.269

Modeling Gold Price via Artificial Neural Network

Hossein Mombeini and Abdolreza Yazdani-Chamzini

Abstract—Developing a precise and accurate model of gold price is critical to manage assets because of its unique features. In this paper, artificial neural network (ANN) model have been used for modeling the gold price, and compared with the traditional statistical model of ARIMA (autoregressive integrated moving average). The three performance measures, the coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE), are utilized to evaluate the performances of different models developed. The results show that the ANN model outperforms ARIMA model, in terms of different performance criteria during the training and validation phases.

Index Terms—ANN, gold price, forecasting, ARIMA.

Hossein Mombeini is with the Faculty of finance, Islamic Azad University, Dubai branch, Dubai, UAE (e-mail: h.mombeini@yahoo.com).
Abdolreza Yazdani-Chamzini is with the South Tehran Branch, Islamic Azad University, Tehran, Iran (e-mail: abdalrezaych@gmail.com).


Cite: Hossein Mombeini and Abdolreza Yazdani-Chamzini, "Modeling Gold Price via Artificial Neural Network," Journal of Economics, Business and Management vol. 3, no. 7, pp. 699-703, 2015.

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