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Future Trends and Opportunities in Financial Forecasting with Deep Learning
Mohamed-Amine EL khayati. INREDD Research Laboratory for Innovation Responsibility and Sustainable Development, Cadi Ayyad University, Marrakesh, Morocco.
Charaf Saidi. INREDD Research Laboratory for Innovation Responsibility and Sustainable Development, Cadi Ayyad University, Marrakesh, Morocco.
Resumen/Abstract
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Resumen / Abstract
In the rapidly evolving domain of financial forecasting for businesses, organizations, and educational institutions, deep learning holds immense potential for generating precise predictions and optimizing financial strategies. This paper aims to explore and elucidate the future trends and opportunities offered by deep learning techniques in financial forecasting. By providing a comprehensive analysis of the applications and benefits of deep learning, this paper contributes valuable insights that can enhance both academic research and professional practice. Key areas of focus include enhanced data analysis, improved risk assessment, portfolio optimization, fraud detection, real-time financial analysis, natural language processing, and automated financial systems. Additionally, this paper discusses the limitations and ethical considerations associated with implementing deep learning in financial forecasting. The findings highlight how deep learning can transform financial management practices, improve decision-making processes, and foster innovation in both organizational and educational contexts. This paper serves as a valuable resource for researchers and practitioners seeking to leverage advanced AI techniques to navigate the complexities of financial forecasting.
Palabras Clave/Keywords
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Palabras Clave / Keywords
Deep Learning, Financial Forecasting, Risk Assessment, Automated Financial Systems.
Referencias/References
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Referencias / References
[1] You, Y.: Forecasting Stock Price: A Deep Learning Approach with LSTM and Hyperparameter Optimization. Highlights in Science, Engineering and Technology 85, 328–338 (2024).
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[4] Michańków, J., Kwiatkowski, Ł., Morajda, J.: Combining Deep Learning and GARCH Models for Financial Volatility and Risk Fore-casting (2023).
[5] Gensler, G., Bailey, L.: Deep Learning and Financial Stability. SSRN Electronic Journal (2020).
[6] Tseng, K.-K., Ou, C., Huang, A., Lin, R.F.-Y., Guo, X.: Financial Analysis with Deep Learning. In: Proceedings of 2019 International Conference, pp. 545–552 (2019).
[7] Zhang, Y., Jiang, Z., Peng, C., Zhu, X., Wang, G.: Management Analysis Method of Multivariate Time Series Anomaly Detection in Financial Risk Assessment. Journal of Organizational and End User Computing 36(1), 1–19 (2024).
[8] Karn, A.L., et al.: B-LSTM-NB Based Composite Sequence Learning Model for Detecting Fraudulent Financial Activities. Malaysian Journal of Computer Science, 30–49 (2022).
[9] Yildiz, K., Dedebek, S., Okay, F.Y., Simsek, M.U.: Anomaly Detection in Financial Data Using Deep Learning: A Comparative Analy-sis. In: 2022 Innovations in Intelligent Systems and Applications Conference (ASYU), pp. 1–6. IEEE (2022).
Cómo citar/How to cite
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Cómo citar / How to cite
EL khayati, M. A., y Saidi, C. (2024). Future Trends and Opportunities in Financial Forecasting with Deep Learning. En C. Rusu et al., (1ª ed.), Transformación digital en la educación: innovaciones y desafíos desde los campus virtuales (pp. 31-33). Huelva (España): United Academic Journals (UA Journals). https://doi.org/10.54988/uaj.000027.005
