Forecasting the Financial Times Stock Exchange Bursa Malaysia Kuala Lumpur Composite Index Using Geometric Brownian Motion

Authors

  • Teoh Yeong Kin Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Suzanawati Abu Hasan Universiti Teknologi MARA, Perlis Branch, Arau Campus
  • Nashni Hamdan Universiti Teknologi MARA, Perlis Branch, Arau Campus

Keywords:

FTSE, FBMKLCI, geometric Brownian motion, MAPE

Abstract

In Malaysia, Financial Times Stock Exchange (FTSE) of Bursa Malaysia Kuala Lumpur Composite Index (FBMKLCI) provides charts, companies’ profile and other market data to help the local and foreign i nvestors to make decisions involving their investments. Until now, there have been a lot of investors who faced losses due to making wrong investments at wrong times. The objective of this study is to forecast FBMKLCI for a one - month period using different periods of data. Besides, this study finds the suitable length of period when the forecasted values are the most accurate for FBMKLCI. Geometric Brownian motion (GBM) of stochastic calculus is used to predict the future indices. The results showed that th e forecasted FBMKLCI needed 1 to 20 weeks of input data to come out with the best values. The forecasted FBMKLCI will only be accurate within 4 weeks; after that the values will diverge. Since the average value of MAPE for eight different forecasted values is 1.54%, GBM can be used to predict the future FBMKLCI.

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Published

2017-03-30

How to Cite

Kin, T. Y., Abu Hasan, S., & Hamdan, N. (2017). Forecasting the Financial Times Stock Exchange Bursa Malaysia Kuala Lumpur Composite Index Using Geometric Brownian Motion. Journal of Computing Research and Innovation, 2(1), 45–49. Retrieved from //crinn.conferencehunter.com/index.php/jcrinn/article/view/29

Issue

Section

General Computing