Kamalov, FiruzSmail, LindaGurrib, Ikhlaas2021-02-282021-02-28©20202020-12Kamalov, F., Smail, L., & Gurrib, I. (2020, December). Forecasting with Deep Learning: S&P 500 index. In 2020 13th International Symposium on Computational Intelligence and Design (ISCID) (pp. 422-425). IEEE. https://doi.org/10.1109/ISCID51228.2020.00102978-172818446-3https://doi.org/10.1109/ISCID51228.2020.00102http://hdl.handle.net/20.500.12519/346This conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in 2020 13th International Symposium on Computational Intelligence and Design (ISCID) (2020), available online at: https://doi.org/10.1109/ISCID51228.2020.00102Stock price prediction has been the focus of a large amount of research but an acceptable solution has so far escaped academics. Recent advances in deep learning have motivated researchers to apply neural networks to stock prediction. In this paper, we propose a convolution-based neural network model for predicting the future value of the S&P 500 index. The proposed model is capable of predicting the next-day direction of the index based on the previous values of the index. Experiments show that our model outperforms a number of benchmarks achieving an accuracy rate of over 55%. ©2020 IEEEenPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.Convolutional neuronsdeep learningSp 500 predictionTime-series forecastingElectronic tradingForecastingIntelligent computingNeural networksAccuracy rateLarge amountsNeural network modelStock predictionsStock price predictionConference 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.