Progress Accuracy of CPO Price Prediction: Evidence from ARMA Family and Artificial Neural Network Approach
Authors: Abdul Aziz Karia & Imbarine Bujang
Journal: International Research Journal of Finance and Economics
ISSN/EISSN: 1450-2887 Year: 2011 Issue: 64
Publisher: EuroJournals Publishing
Pages: 14
Abstract
Journal: International Research Journal of Finance and Economics
ISSN/EISSN: 1450-2887 Year: 2011 Issue: 64
Publisher: EuroJournals Publishing
Pages: 14
Abstract
Oil palm can be considered as one major contributor of developing countries by not only bringing the capital investment but also technology, foreign workers, and management knowledge. On the other hand, some thinkers assume that oil palm can become future direction of the world economy. Therefore the race of oil palm industry has become increasing competitive. However, price of oil palm keeps fluctuating over time. Due to the instability price of oil palm, there is risk and uncertainties that would be faced by tree-corps farmers if they depend too much on this agriculture product. Indeed, oil palms are more volatile compare to other commodities like cocoa, soybean and rubber at the international market. Tree-corps farmers, shareholder, traders, government and producer should consider the uncertainty and high risk in palm oil business world. Therefore, we develop CPO price prediction by using ARMA family and artificial neural network (ANN) using data stretching from 3rd January 2006 to 30th December 2010. Besides, there is also a problem in determining the forecasting techniques. Every forecasting technique has its pros and cons. Such as ARIMA which is not compatible to be implemented in the nonlinear time series data. Whereas ANN, generates more forecasted error in linear time series data. If worst come to worst, there is difficulty in determining the linearity of time series data in order to implement Box-Jenkins and ANN. Therefore, it leads to the risk of unreliable and forecasting error. In this research paper, we forecasted the daily, weekly and monthly basis of the CPO price. Moreover, we selected power of forecasting technique based from the lower root mean squared (RMSE), and mean square error (MSE)
Keywords: Box-Jenkins (BJ); ANN; RMSE; MSE; Crude Palm Oil; NAR.
JEL Classification Codes: C18 and C45
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
