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AuthorAlhazbi, Saleh
AuthorBen Said, Ahmed
AuthorAl-Maadid, Alanoud
Available date2023-01-17T06:57:08Z
Publication Date2020
Publication Name2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
ResourceScopus
URIhttp://dx.doi.org/10.1109/ICIoT48696.2020.9089616
URIhttp://hdl.handle.net/10576/38501
AbstractDeep learning approaches have been utilized to predict stocks. In this study, we use convolutional neural network (CNN) to predict stocks direction in Qatar stock exchange (QE) as a case of emerging markets. Prediction in emerging markets is more challenging than in developed ones because they have higher volatility rate. They are influenced by developed markets and by other external factors including oil price. In this study, we aim to use these external factors to improve the accuracy of the prediction in QE. In addition to historical data, we include data of SP index, Nikkei index, and oil price in the features of our mode. It is found that using these external factors improves the accuracy of the prediction by 10%. 2020 IEEE.
SponsorACKNOWLEDGMENT This research was made possible by: Qatar University Grant no. QUCP-CBE-2018-1, and NPRP award [NPRP10-0131-170-300] from Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
Subjectdeep learning
emerging markets
Qatar stock exchange
stock prediction
TitleUsing Deep Learning to Predict Stock Movements Direction in Emerging Markets: The Case of Qatar Stock Exchange
TypeConference Paper
Pagination440-444
dc.accessType Abstract Only


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