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Financial Forecasting and Risk Analysis: Economic Variables Impact on Banks Performance Using Statistical and Machine Learning Models |
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PP: 1- 16 |
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doi:10.18576/jsap/140101
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Author(s) |
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Hiba A. A. Hussin,
Abdelsamie E. Tayfor,
Khaled A. H. Mohmmed,
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Abstract |
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| Financial forecasting using deep knowledge and linear regression methods is explored in this study, which looks at the association between financial metrics and bank presentation from 2003 to 2012. By analyzing a complete dataset, the research identifies macroeconomic variables such as interest rates and rise, together with crucial success drivers such as GDP growth, leverage, and liquidness ratios. We can investigate linear and non-linear connections by mixing several mechanism learning models, such as ARIMA and Random Woodland. However, different banks risk profiles and strategic prospects can be better understood through situation analysis and PCA-based clustering. The findings prove that rising GDP is a robust measure of economic wealth, but inflation cuts into pays. Institutions that are vulnerable to interest rate variations benefit from interest rates. Gathering shows that various banks adopt separate financial strategies, and scenario analysis shows that monetary results are very sensitive to changes in influence. By faithfully depicting financial doubt, the proposed models demonstrate pliability even during economically unstable times like the 2008 monetary crisis. This research emphasizes the importance of proactive risk organization and specialized forecasting tactics; upcoming studies should use hybrid modeling methodologies and join more macroeconomic variables. In the ever-changing world of finance, these advances aim to improve the correctness of predictions and planned decision-making.
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