Forecasting with Deep Learning: S&P 500 index

Date
2020-12
Authors
Kamalov, Firuz
Smail, Linda
Gurrib, Ikhlaas
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
Stock 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 IEEE
Description
This 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/ISCID51228.2020.00102
Keywords
Convolutional neurons, deep learning, Sp 500 prediction, Time-series forecasting, Electronic trading, Forecasting, Intelligent computing, Neural networks, Accuracy rate, Large amounts, Neural network model, Stock predictions, Stock price prediction
Citation
Kamalov, 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/ISCID51228.2020.00102