Stock Market Prediction Using a Hybrid of Deep Learning Models
DOI :
https://doi.org/10.61549/ijfsem.v2i2.111Mots-clés :
Deep learning, Stock market prediction, Financial markets, Financial time series, Hybrid modelsRésumé
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.Téléchargements
Publiée
2023-05-22
Comment citer
Yinka-Banjo, C. ., Akinyemi , M., & Er-rabbany, B. (2023). Stock Market Prediction Using a Hybrid of Deep Learning Models. International Journal of Financial Studies, Economics and Management, 2(2), 1–16. https://doi.org/10.61549/ijfsem.v2i2.111
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(c) Tous droits réservés Chika Yinka-Banjo, Mary Akinyemi , Bouchra Er-rabbany 2023

Ce travail est disponible sous licence Creative Commons Attribution - Pas d'Utilisation Commerciale - Pas de Modification 4.0 International.