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dc.contributor.authorFazeli, Arvand
dc.contributor.authorHoughten, Sheridan
dc.date.accessioned2022-11-11T14:20:58Z
dc.date.available2022-11-11T14:20:58Z
dc.date.issued2019-12
dc.identifier.citationA. Fazeli and S. Houghten, "Deep Learning for the Prediction of Stock Market Trends," 2019 IEEE International Conference on Big Data (Big Data), 2019, pp. 5513-5521, doi: 10.1109/BigData47090.2019.9005523.en_US
dc.identifier.urihttp://hdl.handle.net/10464/16913
dc.description.abstractIn this study, deep learning will be used to test the predictability of stock trends. Stock markets are known to be volatile, prices fluctuate, and there are many complicated financial indicators involved. Various data including news or financial indicators can be used to predict stock prices. In this study, the focus will be on using past stock prices and using technical indicators to increase the performance of the results. The goal of this study is to measure the accuracy of predictions and evaluate the results. Historical data is gathered for Apple, Microsoft, Google and Intel stocks. A prediction model is created by using past data and technical indicators were used as features in the model. The experiments were performed by using long short-term memory networks. Different approaches and techniques were tested to boost the performance of the results. To prove the usability of the final model in the real world and measure the profitability of results backtesting was performed. The final results show that while it is not possible to predict the exact price of a stock in the future to gain profitable results, deep learning can be used to predict the trend of stock markets to generate buy and sell signals.en_US
dc.publisherIEEEen_US
dc.rights.urihttps://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
dc.source2019 IEEE International Conference on Big Data (Big Data)
dc.subjectDeep learningen_US
dc.subjectStock marketen_US
dc.titleDeep Learning for the Prediction of Stock Market Trendsen_US
dc.typeArticleen_US
dc.identifier.doi10.1109/bigdata47090.2019.9005523
refterms.dateFOA2022-11-11T14:20:59Z


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