ARMA model as an ideal stock identification

Chin, Chan Ann (2010) ARMA model as an ideal stock identification. Universiti Malaysia Sabah. (Unpublished)


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The global economic crisis 2008 brought the KLCI (Kuala Lumpur Composite Index) sharply decreased 38.9%. This case was frightening most of the investors. The investors do not sure what stocks they should buy or sell. In order to pacify the investors' sentiment, this dissertation can be the guideline to the investors. This study primarily focused on the forecasting of three blue chip stocks that chosen from three different sectors by using time series. This study chose ARMA (Autoregressive Moving Average) model as the forecasting method. Firstly, the paper used transformation to transform the nonstationary data to the stationary form. The eight selection criteria are used to choose the best model. After the best model had been obtained, transform the transformed data to the stock price form and compare the forecasting stock price with the actual stock price. Then, calculate the MAPE (Mean Absolute Percentage Error) to ensure accuracy of the best model is reliable. This study is significant to choose the ideal stocks and prove the accuracy of the ARMA model. Therefore, the investors able to choose their ideal stocks to invest and the analysts can use ARMA model as the tools to forecast stock price.

Item Type: Academic Exercise
Uncontrolled Keywords: forecasting, blue chip stocks, time series, Autoregressive Moving Average Model
Subjects: Q Science > QA Mathematics
Divisions: SCHOOL > School of Science and Technology
Depositing User: ADMIN ADMIN
Date Deposited: 15 Jul 2016 08:27
Last Modified: 20 Nov 2017 03:39

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