Stock Market Prediction Using a Hybrid of Deep Learning Models

Abstract

Financial markets play an essential role in developing modern society and enabling the deployment of economic resources. This study focuses on predicting stock prices using deep learning models. In particular, the daily closing prices of two different stocks from the Casablanca Stock Market Viz Bank of Africa and Itissalat Al-Maghrib (IAM) are considered. The datasets were pre-processed and passed through the Long Short-Term Memory (LSTM), Multi-Layer Perceptron (MLP), and Convolutional Neural Networks (CNN) models. The models’ performances were compared based on the performance evaluation metrics, viz: mean squared error (MSE) and root mean squared error (RMSE) and Mean Absolute Error (MAE). The paper proposes a novel hybrid model. The hybrid design of the model improves its predictive power as the results of the Hybrid network performance surpassed all the other models.