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
