Forecasting with deep learning: S&P 500 index

Document Type

Conference Proceeding

Source of Publication

Proceedings - 2020 13th International Symposium on Computational Intelligence and Design, ISCID 2020

Publication Date

12-12-2020

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%.

ISBN

978-1-7281-8446-3

ISSN

2473-3547

Publisher

Institute of Electrical and Electronics Engineers Inc.

First Page

422

Last Page

425

Disciplines

Computer Sciences

Keywords

Electronic trading, Forecasting, Intelligent computing, Neural networks, Accuracy rate, Large amounts, Neural network model, Stock predictions, Stock price prediction, Deep learning

Scopus ID

85100383631

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Green: A manuscript of this publication is openly available in a repository

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