Stock Market Prediction Using LSTM Recurrent Neural Network

2020/01/01 by Adil Moghar, Mhamed Hamiche · 5 citations
Decision Sciences · Engineering · #Energy Load and Power Forecasting #Forecasting Techniques and Applications #Stock Market Forecasting Methods

paper · doi:10.1016/j.procs.2020.03.049

openalex publication_date 2020/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

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.

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