An evaluation forecasting techniques in Kuala Lumpur Stock Exchange (KLSE) finance
The major conflict is regarding the quality of existing literatures in stock market. Evidence shows that some researchers’ supports on incorporating complexity forecasting models while some of them support applied simple forecasting model in forecasting. Up to now the existing studies still far from...
| Main Authors: | , , |
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| Format: | Article |
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TechScience Publications
2011
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| _version_ | 1796970233671450624 |
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| author | Ong, Tze San Lim, Hwee Chen Teh, Boon Heng |
| author_facet | Ong, Tze San Lim, Hwee Chen Teh, Boon Heng |
| author_sort | Ong, Tze San |
| collection | UPM |
| description | The major conflict is regarding the quality of existing literatures in stock market. Evidence shows that some researchers’ supports on incorporating complexity forecasting models while some of them support applied simple forecasting model in forecasting. Up to now the existing studies still far from completed. Hence, it had motivated researchers to find the best and the most accurate volatility forecasting models. This study aims to employ various types of forecasting models into Kuala Lumpur Stock Exchange (KLSE) Finance. This study uses
daily volatility of KLSE Finance stock prices from the
period 1 January 1991 to 31 December 2010. This aim of this
paper is to examine which of the model has the potential
and tend to provide the accuracy in forecasting samples. Forecasting models employed in this study include random walk, historical mean model, moving average model and simple regression model. This study uses error statistic to obtain the best forecasting models through the model comparison and rankings. There are four types of error statistic to evaluate the best forecasting models, namely Mean Error (ME); Mean Absolute Error (MAE); Root Mean Square Error (RMSE); and Mean Absolute Percent Error (MAPE). The result of this study shows that simple regression model is the best forecasting model to be implemented into KLSE Finance. |
| first_indexed | 2024-03-06T07:55:01Z |
| format | Article |
| id | upm.eprints-22799 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-03-06T07:55:01Z |
| publishDate | 2011 |
| publisher | TechScience Publications |
| record_format | dspace |
| spelling | upm.eprints-227992015-05-20T11:25:23Z http://psasir.upm.edu.my/id/eprint/22799/ An evaluation forecasting techniques in Kuala Lumpur Stock Exchange (KLSE) finance Ong, Tze San Lim, Hwee Chen Teh, Boon Heng The major conflict is regarding the quality of existing literatures in stock market. Evidence shows that some researchers’ supports on incorporating complexity forecasting models while some of them support applied simple forecasting model in forecasting. Up to now the existing studies still far from completed. Hence, it had motivated researchers to find the best and the most accurate volatility forecasting models. This study aims to employ various types of forecasting models into Kuala Lumpur Stock Exchange (KLSE) Finance. This study uses daily volatility of KLSE Finance stock prices from the period 1 January 1991 to 31 December 2010. This aim of this paper is to examine which of the model has the potential and tend to provide the accuracy in forecasting samples. Forecasting models employed in this study include random walk, historical mean model, moving average model and simple regression model. This study uses error statistic to obtain the best forecasting models through the model comparison and rankings. There are four types of error statistic to evaluate the best forecasting models, namely Mean Error (ME); Mean Absolute Error (MAE); Root Mean Square Error (RMSE); and Mean Absolute Percent Error (MAPE). The result of this study shows that simple regression model is the best forecasting model to be implemented into KLSE Finance. TechScience Publications 2011-11 Article PeerReviewed Ong, Tze San and Lim, Hwee Chen and Teh, Boon Heng (2011) An evaluation forecasting techniques in Kuala Lumpur Stock Exchange (KLSE) finance. International Journal of Business Management and Economic Research, 2 (6). pp. 382-390. ISSN 2229-6247 http://www.ijbmer.com/docs/volumes/vol2issue6/ijbmer2011020606.pdf |
| spellingShingle | Ong, Tze San Lim, Hwee Chen Teh, Boon Heng An evaluation forecasting techniques in Kuala Lumpur Stock Exchange (KLSE) finance |
| title | An evaluation forecasting techniques in Kuala Lumpur Stock Exchange (KLSE) finance |
| title_full | An evaluation forecasting techniques in Kuala Lumpur Stock Exchange (KLSE) finance |
| title_fullStr | An evaluation forecasting techniques in Kuala Lumpur Stock Exchange (KLSE) finance |
| title_full_unstemmed | An evaluation forecasting techniques in Kuala Lumpur Stock Exchange (KLSE) finance |
| title_short | An evaluation forecasting techniques in Kuala Lumpur Stock Exchange (KLSE) finance |
| title_sort | evaluation forecasting techniques in kuala lumpur stock exchange klse finance |
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