Prediction of Malaysian stock market movement using sentiment analysis

Prediction of Malaysian stock market movement using sentiment analysis

Financial and business news contain various information about different companies, stock markets and other financial information. This information could be useful for predicting the stock market movement. The aim of this study is to determine whether financial news could be used to predict the Malay...

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Published in:Journal of Physics: Conference Series
Main Author: Cheng Kuan L.; Akmar Ismail M.; Zayet T.M.A.; Mohamed Shuhidan S.
Format: Conference paper
Language:English
Published: Institute of Physics Publishing 2019
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077813983&doi=10.1088%2f1742-6596%2f1339%2f1%2f012017&partnerID=40&md5=2e92fba8c98dee76d4c592c86e836a57
id 2-s2.0-85077813983
spelling 2-s2.0-85077813983
Cheng Kuan L.; Akmar Ismail M.; Zayet T.M.A.; Mohamed Shuhidan S.
Prediction of Malaysian stock market movement using sentiment analysis
2019
Journal of Physics: Conference Series
1339
1
10.1088/1742-6596/1339/1/012017
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077813983&doi=10.1088%2f1742-6596%2f1339%2f1%2f012017&partnerID=40&md5=2e92fba8c98dee76d4c592c86e836a57
Financial and business news contain various information about different companies, stock markets and other financial information. This information could be useful for predicting the stock market movement. The aim of this study is to determine whether financial news could be used to predict the Malaysian stock market movement. The sentiment analysis and classification were done using Hybrid Naïve Bayes algorithm. The data for this study was collected from Genting Berhad for a period of 11 months. The method resulted in news classification accuracy of 68.75% and showed a correlation of 58.41% between historical stock price and the sentiment data. © Published under licence by IOP Publishing Ltd.
Institute of Physics Publishing
17426588
English
Conference paper
All Open Access; Gold Open Access
author Cheng Kuan L.; Akmar Ismail M.; Zayet T.M.A.; Mohamed Shuhidan S.
spellingShingle Cheng Kuan L.; Akmar Ismail M.; Zayet T.M.A.; Mohamed Shuhidan S.
Prediction of Malaysian stock market movement using sentiment analysis
author_facet Cheng Kuan L.; Akmar Ismail M.; Zayet T.M.A.; Mohamed Shuhidan S.
author_sort Cheng Kuan L.; Akmar Ismail M.; Zayet T.M.A.; Mohamed Shuhidan S.
title Prediction of Malaysian stock market movement using sentiment analysis
title_short Prediction of Malaysian stock market movement using sentiment analysis
title_full Prediction of Malaysian stock market movement using sentiment analysis
title_fullStr Prediction of Malaysian stock market movement using sentiment analysis
title_full_unstemmed Prediction of Malaysian stock market movement using sentiment analysis
title_sort Prediction of Malaysian stock market movement using sentiment analysis
publishDate 2019
container_title Journal of Physics: Conference Series
container_volume 1339
container_issue 1
doi_str_mv 10.1088/1742-6596/1339/1/012017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85077813983&doi=10.1088%2f1742-6596%2f1339%2f1%2f012017&partnerID=40&md5=2e92fba8c98dee76d4c592c86e836a57
description Financial and business news contain various information about different companies, stock markets and other financial information. This information could be useful for predicting the stock market movement. The aim of this study is to determine whether financial news could be used to predict the Malaysian stock market movement. The sentiment analysis and classification were done using Hybrid Naïve Bayes algorithm. The data for this study was collected from Genting Berhad for a period of 11 months. The method resulted in news classification accuracy of 68.75% and showed a correlation of 58.41% between historical stock price and the sentiment data. © Published under licence by IOP Publishing Ltd.
publisher Institute of Physics Publishing
issn 17426588
language English
format Conference paper
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
_version_ 1809677899963301888

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