Stock price forecast with deep learning
Source of Publication
2020 International Conference on Decision Aid Sciences and Application, DASA 2020
© 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.
Business | Computer Sciences
convolutional neurons, deep learning, recurrent neurons, SP 500 prediction, time-series forecasting
Kamalov, Firuz; Smail, Linda; and Gurrib, Ikhlaas, "Stock price forecast with deep learning" (2020). All Works. 4075.
Indexed in Scopus