Forecasting significant stock price changes using neural networks

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dc.contributor.authorKamalov, Firuz
dc.date.accessioned2020-06-15T09:09:52Z
dc.date.available2020-06-15T09:09:52Z
dc.date.copyright2020
dc.date.issued2020
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this article is published in Neural Computing and Applications (2020), available online at: https://doi.org/10.1007/s00521-020-04942-3en_US
dc.description.abstractStock price prediction is a rich research topic that has attracted interest from various areas of science. The recent success of machine learning in speech and image recognition has prompted researchers to apply these methods to asset price prediction. The majority of literature has been devoted to predicting either the actual asset price or the direction of price movement. In this paper, we study a hitherto little explored question of predicting significant changes in stock price based on previous changes using machine learning algorithms. We are particularly interested in the performance of neural network classifiers in the given context. To this end, we construct and test three neural network models including multilayer perceptron, convolutional net, and long short-term memory net. As benchmark models, we use random forest and relative strength index methods. The models are tested using 10-year daily stock price data of four major US public companies. Test results show that predicting significant changes in stock price can be accomplished with a high degree of accuracy. In particular, we obtain substantially better results than similar studies that forecast the direction of price change. © 2020, Springer-Verlag London Ltd., part of Springer Nature.en_US
dc.identifier.citationKamalov, F. (2020). Forecasting significant stock price changes using neural networks. Neural Computing and Applications. https://doi.org/10.1007/s00521-020-04942-3en_US
dc.identifier.issn09410643
dc.identifier.urihttps://doi.org/10.1007/s00521-020-04942-3
dc.identifier.urihttp://hdl.handle.net/20.500.12519/216
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relationAuthor Affiliation :Kamalov, F., Department of Electrical Engineering, Canadian University of Dubai, Dubai, United Arab Emirates
dc.rightsLicense to reuse the abstract has been secured from Springer Nature and Copyright Clearance Center
dc.rights.holderCopyright : © 2020, Springer-Verlag London Ltd., part of Springer Nature.
dc.rights.urihttps://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=679462ac-da50-4e76-927f-24cfcd4a52b9
dc.subjectCNNen_US
dc.subjectLSTMen_US
dc.subjectNeural networksen_US
dc.subjectRSIen_US
dc.subjectStock price forecastingen_US
dc.subjectConvolutional neural networksen_US
dc.subjectDecision treesen_US
dc.subjectFinancial marketsen_US
dc.subjectFintechen_US
dc.subjectForecastingen_US
dc.subjectImage recognitionen_US
dc.subjectLearning algorithmsen_US
dc.subjectMachine learningen_US
dc.subjectMultilayer neural networksen_US
dc.subjectSpeech recognitionen_US
dc.subjectBenchmark modelsen_US
dc.subjectHigh degree of accuracyen_US
dc.subjectNeural network classifieren_US
dc.subjectNeural network modelen_US
dc.subjectPublic companyen_US
dc.subjectRelative strength indexen_US
dc.subjectResearch topicsen_US
dc.subjectStock price predictionen_US
dc.subjectElectronic tradingen_US
dc.titleForecasting significant stock price changes using neural networksen_US
dc.typeArticleen_US
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