Published: 2024-04-01
Implications of Deep Learning for Stock Market Forecasting
DOI: 10.35870/ijsecs.v4i1.2281
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Abstract
This research explores the effectiveness of using deep learning in predicting stock market movements. This research uses rigorous methods to bring out the performance of deep learning models, compare them with traditional methods, and identify critical factors that influence stock market predictions. The research results show that deep learning models, especially LSTM and CNN-LSTM architectures, can achieve satisfactory levels of accuracy and outperform traditional methods by capturing patterns in complex stock market data. In addition, this research identifies external and internal factors that influence predictions of stock market movements. This research's practical and theoretical implications highlight the potential of deep learning in improving investment decision-making and understanding financial market dynamics. Recommendations for future research include exploration of advanced deep learning techniques, integration with traditional methods, emphasis on risk management strategies, continuous evaluation of model performance, and provision of training and education to encourage analysts and investors to adopt this technology. By implementing these recommendations, the potential of deep learning models in financial analysis can be optimized, ultimately improving market efficiency and investment returns.
Keywords
Deep Learning ; Stock Market Movements ; Prediction ; Financial Analysis ; LSTM
Article Information
This article has been peer-reviewed and published in the International Journal Software Engineering and Computer Science (IJSECS). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
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Issue: Vol. 4 No. 1 (2024)
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Section: Articles
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Published: %750 %e, %2024
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License: CC BY 4.0
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Copyright: © 2024 Authors
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DOI: 10.35870/ijsecs.v4i1.2281
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Supendi
Management Study Program, Faculty of Social Economics, Universitas Linggabuana PGRI Sukabumi, Sukabumi City, West Java Province, Indonesia
Devi Kumala
Digital Business Study Program, Faculty of Economics, Universitas Muhammadiyah Aceh, Banda Aceh City, Aceh Province, Indonesia
Maria Lusiana Yulianti
Accounting Study Program, Faculty of Economics and Business, Universitas Winaya Mukti, Bandung City, West Java Province, Indonesia
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