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Financial Prediction Approaches: A Comprehensive Review
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 past few years, financial prediction has become one of the most important themes in market research and, subsequently, in scientific studies on Machine Learning and Data Analysis. Competent estimation of such fi-nancial results as budget needs, revenue trends, and risk assessment is crucial for strategic decisions in educational institutions. This paper gives an overview of different methodologies for financial prediction, focusing on key techniques and their respective advantages and challenges in relation to their implementation within educational management. By providing a foundational understanding, this paper is targeted at practitioners in the field and future researchers for further exploration and development. The results should provide a basis for practical application and academic inquiry, improving the accuracy and reliability of financial predictions in educational settings. Thus, this review is designed to bridge the gap between theoretical innovations and practical applications, which may be used to refine financial management strategies within the education sector.
Palabras Clave/Keywords
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Palabras Clave / Keywords
Financial Prediction, Machine Learning, Data Analysis, Educational Finance, Budget Forecasting, Risk Management.
Referencias/References
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Referencias / References
[1] Zhang, Z.: Analysis of Financial Management and Decision-Making in Institutions of Higher Learning Based on Deep Learning Algorithm. Mobile Information Systems 2022, 1–10 (2022).
[2] Afriyie, A.O.: Financial Sustainability Factors of Higher Education Institutions: A Predictive Model (2015).
[3] Abdulla, Y.Y., Al-Alawi, A.I.: Advances in Machine Learning for Financial Risk Management: A Systematic Literature Review. In: 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), pp. 531–535. IEEE, (2024).
[4] Ampountolas, A.: Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins. Forecasting 5(2), 472–486 (2023).
[5] Liu, S., Wu, K., Jiang, C., Huang, B., Ma, D.: Financial Time-Series Forecasting: Towards Synergizing Performance and Interpretability within a Hybrid Machine Learning Approach (2023).
[6] Yavasani, R., Wang, H.: Comparative Analysis of LSTM, GRU, and ARIMA Models for Stock Market Price Prediction. Journal of Student Research 12(4) (2023).
[7] Ge, C., Xie, J.: Application of Grey Forecasting Model Based on Improved Residual Correction in the Cost Estimation of University Education. International Journal of Emerging Technologies in Learning (iJET) 10(8), 30 (2015).
[8] Shen, Y., Ma, X., Sun, Y., Du, S.: Prediction of University Fund Revenue and Expenditure Based on Fuzzy Time Series with a Periodic Factor. PLoS One 18(5), e0286325 (2023).
[9] Song, Y., Du, H., Piao, T., Shi, H.: Research on Financial Risk Intelligent Monitoring and Early Warning Model Based on LSTM, Transformer, and Deep Learning. Journal of Organizational and End User Computing 36(1), 1–24 (2024).
[10] Topaloğlu, G., Kalaycı, T.A., Pekel, K., Akay, M.F.: Revenue Forecast Models Using Hybrid Intelligent Methods. International Journal of Mathematics and Computer in Engineering 2(1), 117–124 (2024).
Cómo citar/How to cite
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Cómo citar / How to cite
El khayati, M. A., y Saidi, C. (2024). Financial Prediction Approaches: A Comprehensive Review. En C. Rusu et al., (1ª ed.), Transformación digital en la educación: innovaciones y desafíos desde los campus virtuales (pp. 59-63). Huelva (España): United Academic Journals (UA Journals). https://doi.org/10.54988/uaj.000027.010
