Machine learning is considered a powerful technique in many applications such as classification, clustering, recognition and prediction. Deep learning is a modern, vital and superior machine learning that gives stunning performance, especially with huge data. Stock market price prediction is the process of determining the future value of a prospect of a financial instrument traded in the market, to gain a great profit a successful prediction must be conducted, in order to achieve that machine learning is used, in this article, two approaches are proposed to predict the stock market prices and movement using two datasets, the first approach employs two machine learning models (J48 & logistic regression) while the second approach based on recurrent neural network (proposed long short term memory (LSTM) model). The proposed LSTM architecture is designed and trained with inefficient optimizer, tuned hyperparameters and a good choice dropout ratio to avoid overfitting. The aim of this article is to conduct an experimental comparison between the classical machine learning approach (J48 & logistic regression) and deep learning represented by LSTM. The experimental results show that the proposed approach of LSTM outperforms other approaches with the two datasets in predicting the price and movement of the stock market.
