Forecasting with Deep Learning: S&P 500 index

• URL: http://arxiv.org/abs/2103.14080v1
• Date: Sun, 21 Mar 2021 11:51:49 GMT
• Title: Forecasting with Deep Learning: S&P 500 index
• Authors: Firuz Kamalov, Linda Smail, Ikhlaas Gurrib
• Abstract summary: We propose a convolution-based neural network model for predicting the future value of the S&P 500 index. Experiments show that our model outperforms a number of benchmarks achieving an accuracy rate of over 55%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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%.

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