Forecasting with Deep Learning: S&P 500 index

Firuz Kamalov, Linda Smail and Ikhlaas Gurrib

Papers from arXiv.org

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%.

Date: 2021-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fmk and nep-for
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