Stock Market Prediction Using LSTM Recurrent Neural Network

By Adil Moghar, M’hamed Hamiche. ANT/EDI40, 2020

513 citations recorded by the source corpus.

Abstract It has never been easy to invest in a set of assets, the abnormally of financial market does not allow simple models to predict future asset values with higher accuracy. Machine learning, which consist of making computers perform tasks that normally requiring human intelligence is currently the dominant trend in scientific research. This article aims to build a model using Recurrent Neural Networks (RNN) and especially Long-Short Term Memory model (LSTM) to predict future stock market values. The main objective of this paper is to see in which precision a Machine learning algorithm can predict and how much the epochs can improve our model.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/32219e9dbd6db7ec075a7ef5c7b80d835b5b89f7