FORECASTING KUALA LUMPUR COMPOSITE INDEX: EVIDENCE OF THE ARTIFICIAL NEURAL NETWORK AND ARIMA

ABSTRACT: The aim of this paper is to use, compare, and analyze two forecasting technique: namely Auto  Regressive  Integrated  Moving  Average(ARIMA)  and  Artificial  Neural Network(ANN) using Kuala Lumpur Composite Index(KLCI) in Malaysia. Artificial Neural Network is used because of its popularity of capturing the volatility patterns in nonlinear time series while ARIMA used since it is a standard method in the forecasting tool. Daily data of Kuala Lumpur Composite Index from 4 January 1999 to 26 September 2005 is used. ANN training with “early stopping” technique is investigated. We foundthat the deviation  or error showed in the ANN technique is much less than that in ARIMA. Hence ANN can be used as a good forecaster engine for univariate time series model. It can predict  nonlinear time series using the pattern of  the past data. The proposed technique may help government, decision makers and planners especially in Malaysia.

Keyword:  Auto  Regressive  Integrated  Moving  Average(ARIMA),  artificial  neural network(ANN) and Kuala Lumpur Composite Index(KLCI)

Author: Raditya Sukmana & Mahmud Iwan Solihin

Journal Code: jpmanajemengg070016

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