Technical analysis efficiency enhancement in moving average indicator through artificial neural network / Muhamad Sukor Jaafar
The technical approach to investment is essentially a reflection of the idea that prices move in trends which are determined by the changing attitudes of investors toward a variety of economy, monetary, political and psychological forces (Pring, 2001). The response of stock prices toward the changes...
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| Format: | Thesis |
| Language: | English |
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2017
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| Online Access: | https://ir.uitm.edu.my/id/eprint/21615/ |
| _version_ | 1848805292550127616 |
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| author | Jaafar, Muhamad Sukor |
| author_facet | Jaafar, Muhamad Sukor |
| author_sort | Jaafar, Muhamad Sukor |
| building | UiTM Institutional Repository |
| collection | Online Access |
| description | The technical approach to investment is essentially a reflection of the idea that prices move in trends which are determined by the changing attitudes of investors toward a variety of economy, monetary, political and psychological forces (Pring, 2001). The response of stock prices toward the changes in economic variables vary from one to another hence, it makes trading decision to be very complex (Darie et. al., 2011). Efficiency refers to the ability to produce an acceptable level of output using costminimizing input ratios (Farrel, 1957). Thus, in technical analysis, efficiency refers to the ability of the indicators to indicate a good timing of entry and out of the market with profit. And levels of efficiencies are showed by actual output ratios versus expected output ratios (Shao and Lin, 2001). The higher the actual output ratios against the expected output ratios, the higher the efficiency level of the indicators. This research investigates several technical indicator and found none of the indicators reached the efficiency level. To improve the level, this study apply the Artificial Neural Network model that capable to learn the price and the moving average pattern and suggest a new pattern better than the previous one in term of efficiency. This research found that the improvements are not just to the efficiency but also increase number of trading as per selected period hence increase the changes of investor to enter and exit from the market with possibility of a better profit as compared to traditional technical analysis. |
| first_indexed | 2025-11-14T22:08:43Z |
| format | Thesis |
| id | uitm-21615 |
| institution | Universiti Teknologi MARA |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T22:08:43Z |
| publishDate | 2017 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | uitm-216152025-07-17T04:37:18Z https://ir.uitm.edu.my/id/eprint/21615/ Technical analysis efficiency enhancement in moving average indicator through artificial neural network / Muhamad Sukor Jaafar Jaafar, Muhamad Sukor Electronic data processing. Information technology. Knowledge economy. Including artificial intelligence and knowledge management The technical approach to investment is essentially a reflection of the idea that prices move in trends which are determined by the changing attitudes of investors toward a variety of economy, monetary, political and psychological forces (Pring, 2001). The response of stock prices toward the changes in economic variables vary from one to another hence, it makes trading decision to be very complex (Darie et. al., 2011). Efficiency refers to the ability to produce an acceptable level of output using costminimizing input ratios (Farrel, 1957). Thus, in technical analysis, efficiency refers to the ability of the indicators to indicate a good timing of entry and out of the market with profit. And levels of efficiencies are showed by actual output ratios versus expected output ratios (Shao and Lin, 2001). The higher the actual output ratios against the expected output ratios, the higher the efficiency level of the indicators. This research investigates several technical indicator and found none of the indicators reached the efficiency level. To improve the level, this study apply the Artificial Neural Network model that capable to learn the price and the moving average pattern and suggest a new pattern better than the previous one in term of efficiency. This research found that the improvements are not just to the efficiency but also increase number of trading as per selected period hence increase the changes of investor to enter and exit from the market with possibility of a better profit as compared to traditional technical analysis. 2017 Thesis NonPeerReviewed text en https://ir.uitm.edu.my/id/eprint/21615/7/21615.pdf Jaafar, Muhamad Sukor (2017) Technical analysis efficiency enhancement in moving average indicator through artificial neural network / Muhamad Sukor Jaafar. (2017) PhD thesis, thesis, Universiti Teknologi MARA. <http://terminalib.uitm.edu.my/21615.pdf> |
| spellingShingle | Electronic data processing. Information technology. Knowledge economy. Including artificial intelligence and knowledge management Jaafar, Muhamad Sukor Technical analysis efficiency enhancement in moving average indicator through artificial neural network / Muhamad Sukor Jaafar |
| title | Technical analysis efficiency enhancement in moving average indicator through artificial neural network / Muhamad Sukor Jaafar |
| title_full | Technical analysis efficiency enhancement in moving average indicator through artificial neural network / Muhamad Sukor Jaafar |
| title_fullStr | Technical analysis efficiency enhancement in moving average indicator through artificial neural network / Muhamad Sukor Jaafar |
| title_full_unstemmed | Technical analysis efficiency enhancement in moving average indicator through artificial neural network / Muhamad Sukor Jaafar |
| title_short | Technical analysis efficiency enhancement in moving average indicator through artificial neural network / Muhamad Sukor Jaafar |
| title_sort | technical analysis efficiency enhancement in moving average indicator through artificial neural network / muhamad sukor jaafar |
| topic | Electronic data processing. Information technology. Knowledge economy. Including artificial intelligence and knowledge management |
| url | https://ir.uitm.edu.my/id/eprint/21615/ |