1 School of Business, Stevens Institute of Technology, Hoboken, NJ 07030, USA.
2 Department of Economics, Syracuse University, Syracuse, New York.
3 Department of Marketing Science and Business Analytics, Katz Graduate School of Business, University of Pittsburgh, PA, USA.
4 Department of Statistics, Rutgers University, 110 Frelinghuysen Rd Piscataway, NJ 0885, USA.
5 School of Management, Yale University, 165 Whitney Avenue, New Haven, CT 06511, USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 23(01), 2309-2321
Received on 09 June 2024; revised on 21 July 2024; accepted on 24 July 2024
This paper explores the application of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks for stock price prediction over a 10-day horizon. The study aims to compare the predictive performance of these two deep learning architectures within the context of financial forecasting. Utilizing historical stock data from the CAC40 dataset, which represents a capitalization-weighted measure of the 40 most significant stocks on the Euronext Paris, we train and evaluate RNN and LSTM models to forecast future stock prices. Our results demonstrate the superior performance of LSTM networks in capturing the intricate temporal dependencies inherent in stock price data. Compared to standard RNNs, LSTM models exhibit higher accuracy and provide more reliable forecasts over the 10-day prediction period. The specialized memory cells and gating mechanisms in LSTM networks enable them to effectively identify both short-term changes and long-term patterns in stock prices, thus outperforming traditional RNN architectures. This enhanced ability to model the complex dynamics of stock market data underscores the potential of LSTM networks to improve investment decision-making, risk management, and the overall efficiency of financial markets. The insights gained from this study contribute to the growing body of knowledge on the application of deep learning in finance and investment, offering valuable guidance for practitioners and researchers seeking to harness the power of advanced algorithms for stock market prediction and optimization.
Stock market prediction; Recurrent neural networks; LSTM; Deep learning; CAC40 index; Investment strategies; Financial forecasting
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Arash Tashakkori, Niloufar Erfanibehrouz, Shahin Mirshekari, Abolfazl Sodagartojgi and Vatsal Gupta. Enhancing stock market prediction accuracy with recurrent deep learning models: A case study on the CAC40 index. World Journal of Advanced Research and Reviews, 2024, 23(1), 2309-2321. Article DOI: https://doi.org/10.30574/wjarr.2024.23.1.2156