Kamalov, FiruzSmail, LindaGurrib, Ikhlaas2021-03-012021-03-01© 20202020-11-08Kamalov, 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. https://doi.org/10.1109/DASA51403.2020.9317260978-172819677-0https://doi.org/10.1109/DASA51403.2020.9317260http://hdl.handle.net/20.500.12519/349This 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: https://doi.org/10.1109/DASA51403.2020.9317260In 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.enPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.convolutional neuronsdeep learningrecurrent neuronsSP 500 predictiontime-series forecastingBackpropagationDecision support systemsElectronic tradingFinancial marketsForecastingGradient methodsMultilayer neural networksNetwork layersOptimizationStochastic systemsMean absolute errorMoment estimationNumerical experimentsOptimization techniquesRoot Mean SquareStochastic gradient descentStock price forecastsStock price predictionRecurrent neural networksConference PaperCopyright : ©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.