Forecasting with Deep Learning: S&P 500 index

Autor:	Kamalov, Firuz, Smail, Linda, Gurrib, Ikhlaas
Rok vydání:	2021
Předmět:	Quantitative Finance - Statistical Finance Computer Science - Machine Learning
Druh dokumentu:	Working Paper
DOI:	10.1109/ISCID51228.2020.00102
Popis:	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%. Comment: Published in: 2020 13th International Symposium on Computational Intelligence and Design (ISCID)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2103.14080 Zobrazit plný text záznamu View this record from Arxiv