Stock price forecast with deep learning

dc.contributor.authorKamalov, Firuz
dc.contributor.authorSmail, Linda
dc.contributor.authorGurrib, Ikhlaas© 2020
dc.descriptionThis conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in 2020 International Conference on Decision Aid Sciences and Application (DASA) (2020), available online at:
dc.description.abstractIn this paper, we compare various approaches to stock price prediction using neural networks. We analyze the performance fully connected, convolutional, and recurrent architectures in predicting the next day value of SP 500 index based on its previous values. We further expand our analysis by including three different optimization techniques: Stochastic Gradient Descent, Root Mean Square Propagation, and Adaptive Moment Estimation. The numerical experiments reveal that a single layer recurrent neural network with RMSprop optimizer produces optimal results with validation and test Mean Absolute Error of 0.0150 and 0.0148 respectively. © 2020 IEEE.en_US
dc.identifier.citationKamalov, F., Smail, L., & Gurrib, I. (2020, November). Stock price forecast with deep learning. In 2020 International Conference on Decision Aid Sciences and Application (DASA) (pp. 1098-1102). IEEE.
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relationAuthors Affiliations : Kamalov, F., Canadian University Dubai, Department of Electrical Engineering, Dubai, United Arab Emirates; Smail, L., College of Natural and Health Sciences, Zayed University, Dubai, United Arab Emirates; Gurrib, I., Canadian University Dubai, Department of Electrical Engineering, Dubai, United Arab Emirates
dc.relation.ispartofseries2020 International Conference on Decision Aid Sciences and Application (DASA);
dc.rightsPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.
dc.rights.holderCopyright : ©2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectconvolutional neuronsen_US
dc.subjectdeep learningen_US
dc.subjectrecurrent neuronsen_US
dc.subjectSP 500 predictionen_US
dc.subjecttime-series forecastingen_US
dc.subjectDecision support systemsen_US
dc.subjectElectronic tradingen_US
dc.subjectFinancial marketsen_US
dc.subjectGradient methodsen_US
dc.subjectMultilayer neural networksen_US
dc.subjectNetwork layersen_US
dc.subjectStochastic systemsen_US
dc.subjectMean absolute erroren_US
dc.subjectMoment estimationen_US
dc.subjectNumerical experimentsen_US
dc.subjectOptimization techniquesen_US
dc.subjectRoot Mean Squareen_US
dc.subjectStochastic gradient descenten_US
dc.subjectStock price forecastsen_US
dc.subjectStock price predictionen_US
dc.subjectRecurrent neural networksen_US
dc.titleStock price forecast with deep learningen_US
dc.typeConference Paperen_US
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