Stock price forecast with deep learning

Document Type

Conference Proceeding

Source of Publication

2020 International Conference on Decision Aid Sciences and Application, DASA 2020

Publication Date

11-8-2020

Abstract

© 2020 IEEE. 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 SP 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.

ISBN

9781728196770

Publisher

IEEE

First Page

1098

Last Page

1102

Disciplines

Business | Computer Sciences

Keywords

convolutional neurons, deep learning, recurrent neurons, SP 500 prediction, time-series forecasting

Scopus ID

85100553403

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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