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Comparison Between Box-Jenkins and Holt in Forecasting
Kuala Lumpur Composite Index (KLCI)
Muhammad Rasydan Bin Khalid
Supervisor: Dr. Nurfadhlina Binti Abdul Halim
Bachelor of Science (Financial Mathematics)
School of Informatics and Applied Mathematics
The Kuala Lumpur Composite Index (KLCI) serves as a achievement benchmark of
Malaysia current economic condition. It’s consist of 30 major shares in Bursa Malaysia.
The main objective of this study is to determine the best fit approach between Box-
Jenkins and Holt methodology in forecasting the KLCI for year 2017. The sample data
from year 2015 until 2016 was obtained from Investing.com. This study shows that
ARIMA (1,1,0) is the best model for Box-Jenkins method while there is no suitable for
Holt in forecasting the KLCI index for 2017. This model was chosen because it has the
smallest MSE score of 116.117 and MAPE score of 6.66%. The KLCI index performance
for the year 2017 is forecast in the decreasing trend.
829 | UMT UNDERGRADUATE RESEARCH DAY 2018