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Using Deep Learning to Predict Stock Movements Direction in Emerging Markets: The Case of Qatar Stock Exchange

المؤلفAlhazbi, Saleh
المؤلفBen Said, Ahmed
المؤلفAl-Maadid, Alanoud
تاريخ الإتاحة2023-01-17T06:57:08Z
تاريخ النشر2020
اسم المنشور2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1109/ICIoT48696.2020.9089616
معرّف المصادر الموحدhttp://hdl.handle.net/10576/38501
الملخصDeep 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.
راعي المشروعACKNOWLEDGMENT 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.
اللغةen
الناشرInstitute of Electrical and Electronics Engineers Inc.
الموضوعdeep learning
emerging markets
Qatar stock exchange
stock prediction
العنوانUsing Deep Learning to Predict Stock Movements Direction in Emerging Markets: The Case of Qatar Stock Exchange
النوعConference Paper
الصفحات440-444


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