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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Main Author:	Jaafar, Muhamad Sukor
Format:	Thesis
Language:	English
Published:	2017
Subjects:	Electronic data processing. Information technology. Knowledge economy. Including artificial intelligence and knowledge management
Online Access:	http://ir.uitm.edu.my/id/eprint/21615/ http://ir.uitm.edu.my/id/eprint/21615/1/TP_MUHAMAD%20SUKOR%20JAAFAR%20BM%2017_5.pdf

id	uitm-21615
recordtype	eprints
spelling	uitm-216152018-09-26T03:39:36Z http://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 http://ir.uitm.edu.my/id/eprint/21615/1/TP_MUHAMAD%20SUKOR%20JAAFAR%20BM%2017_5.pdf Jaafar, Muhamad Sukor (2017) Technical analysis efficiency enhancement in moving average indicator through artificial neural network / Muhamad Sukor Jaafar. PhD thesis, Universiti Teknologi MARA.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
topic	Electronic data processing. Information technology. Knowledge economy. Including artificial intelligence and knowledge management
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
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.
format	Thesis
author	Jaafar, Muhamad Sukor
author_facet	Jaafar, Muhamad Sukor
author_sort	Jaafar, Muhamad Sukor
title	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_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_sort	technical analysis efficiency enhancement in moving average indicator through artificial neural network / muhamad sukor jaafar
publishDate	2017
url	http://ir.uitm.edu.my/id/eprint/21615/ http://ir.uitm.edu.my/id/eprint/21615/1/TP_MUHAMAD%20SUKOR%20JAAFAR%20BM%2017_5.pdf
first_indexed	2023-09-18T23:06:57Z
last_indexed	2023-09-18T23:06:57Z
_version_	1777418546701139968

Technical analysis efficiency enhancement in moving average indicator through artificial neural network / Muhamad Sukor Jaafar

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