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...

Full description

Saved in:
Bibliographic Details
Main Author: Jaafar, Muhamad Sukor
Format: Thesis
Language:English
Published: 2017
Subjects:
Online Access:https://ir.uitm.edu.my/id/eprint/21615/1/TP_MUHAMAD%20SUKOR%20JAAFAR%20BM%2017_5.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
id my-uitm-ir.21615
record_format uketd_dc
spelling my-uitm-ir.216152018-09-26T03:39:36Z Technical analysis efficiency enhancement in moving average indicator through artificial neural network / Muhamad Sukor Jaafar 2017 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 https://ir.uitm.edu.my/id/eprint/21615/ https://ir.uitm.edu.my/id/eprint/21615/1/TP_MUHAMAD%20SUKOR%20JAAFAR%20BM%2017_5.pdf text en public phd doctoral Universiti Teknologi MARA Faculty of Business and Management
institution Universiti Teknologi MARA
collection UiTM Institutional Repository
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
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
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
granting_institution Universiti Teknologi MARA
granting_department Faculty of Business and Management
publishDate 2017
url https://ir.uitm.edu.my/id/eprint/21615/1/TP_MUHAMAD%20SUKOR%20JAAFAR%20BM%2017_5.pdf
_version_ 1783733758057250816