Stock price forecast with deep learning

Firuz Kamalov, Linda Smail and Ikhlaas Gurrib

Papers from arXiv.org

Abstract: In this paper, we compare various approaches to stock price prediction using neural networks. We analyze the performance fully connected, convolutional, and recurrent architectures in predicting the next day value of S&P 500 index based on its previous values. We further expand our analysis by including three different optimization techniques: Stochastic Gradient Descent, Root Mean Square Propagation, and Adaptive Moment Estimation. The numerical experiments reveal that a single layer recurrent neural network with RMSprop optimizer produces optimal results with validation and test Mean Absolute Error of 0.0150 and 0.0148 respectively.

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