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dc.contributor.authorFazeli, Arvand
dc.date.accessioned2019-09-13T18:30:56Z
dc.date.available2019-09-13T18:30:56Z
dc.identifier.urihttp://hdl.handle.net/10464/14506
dc.description.abstractDeep learning has shown great promise in solving complicated problems in recent years. One applicable area is finance. In 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. While the opinion of researchers differ about the predictability of stocks, it has been shown by previous empirical studies that some aspects of stock markets can be predictable to some extent. 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.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectdeep learningen_US
dc.subjectstocksen_US
dc.subjecttechnical analysisen_US
dc.titleUsing Deep Learning for Predicting Stock Trendsen_US
dc.typeElectronic Thesis or Dissertationen_US
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US
refterms.dateFOA2021-08-15T02:08:39Z


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