Selected Malaysia stock predictions using artificial neural network

Selected Malaysia stock predictions using artificial neural network

Stock market prediction is one of the fascinating issues of stock market research. Accurate stock prediction becomes the biggest challenge in investment industry because the distribution of stock data is changing over the time. In this study, the feedforward backpropagation neural network with Leven...

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Bibliographic Details
Published in:Proceedings of 2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009
Main Author: Bahrun P.N.; Taib M.N.
Format: Conference paper
Language:English
Published: 2009
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349926148&doi=10.1109%2fCSPA.2009.5069265&partnerID=40&md5=6e3d09cd136f9c079311fa0820bb7ee8
id 2-s2.0-70349926148
spelling 2-s2.0-70349926148
Bahrun P.N.; Taib M.N.
Selected Malaysia stock predictions using artificial neural network
2009
Proceedings of 2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009


10.1109/CSPA.2009.5069265
https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349926148&doi=10.1109%2fCSPA.2009.5069265&partnerID=40&md5=6e3d09cd136f9c079311fa0820bb7ee8
Stock market prediction is one of the fascinating issues of stock market research. Accurate stock prediction becomes the biggest challenge in investment industry because the distribution of stock data is changing over the time. In this study, the feedforward backpropagation neural network with Levenberg-Marquardt training algorithm is used. Selected Malaysian stocks, namely Maybank and Tenaga, were modeled and simulated for trading using four trading strategies. The results show that ANN provide a highly accurate model for the stocks also realises profitable systems using all four trading strategies. ©2009 IEEE.


English
Conference paper
author Bahrun P.N.; Taib M.N.
spellingShingle Bahrun P.N.; Taib M.N.
Selected Malaysia stock predictions using artificial neural network
author_facet Bahrun P.N.; Taib M.N.
author_sort Bahrun P.N.; Taib M.N.
title Selected Malaysia stock predictions using artificial neural network
title_short Selected Malaysia stock predictions using artificial neural network
title_full Selected Malaysia stock predictions using artificial neural network
title_fullStr Selected Malaysia stock predictions using artificial neural network
title_full_unstemmed Selected Malaysia stock predictions using artificial neural network
title_sort Selected Malaysia stock predictions using artificial neural network
publishDate 2009
container_title Proceedings of 2009 5th International Colloquium on Signal Processing and Its Applications, CSPA 2009
container_volume
container_issue
doi_str_mv 10.1109/CSPA.2009.5069265
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349926148&doi=10.1109%2fCSPA.2009.5069265&partnerID=40&md5=6e3d09cd136f9c079311fa0820bb7ee8
description Stock market prediction is one of the fascinating issues of stock market research. Accurate stock prediction becomes the biggest challenge in investment industry because the distribution of stock data is changing over the time. In this study, the feedforward backpropagation neural network with Levenberg-Marquardt training algorithm is used. Selected Malaysian stocks, namely Maybank and Tenaga, were modeled and simulated for trading using four trading strategies. The results show that ANN provide a highly accurate model for the stocks also realises profitable systems using all four trading strategies. ©2009 IEEE.
publisher
issn
language English
format Conference paper
accesstype
record_format scopus
collection Scopus
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