The Effect of Shadow Banking on Financial Stability Using Support Vector Machine: An International Evidence

Myvel Nabil
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Myvel Nabil: Ain Shams University

A chapter in Transformational Trends in Finance, Banking, and Economics, 2025, pp 189-203 from Springer

Abstract: Abstract This chapter investigates the impact of shadow banking on financial stability annually from 2000 to 2021 for 68 countries, which is divided into four groups according to the level of income. Shadow banking index has been measured by the ratio of the assets’ value of nonbank financial intermediaries divided by the total assets’ value of the entire financial system, while the financial stability is measured by Z-score and an aggregate banking stability index for countries. Further, this chapter employs panel data regression and support vector regression (SVR) to study the factors that could affect financial stability and compare the results with those obtained using the regression method. Using panel data, the findings indicate that there is a negative impact of shadow banking on financial stability, most notably by Z-score. Further, the results reveal that the model based on the SVR approach with the Radial Basis Function kernel type performs better in prediction, compared to the regression model. This can be more elaborated through further research to investigate the effect of shadow banking on stock market returns under economic and political conditions and various methods.

Keywords: Aggregate banking stability index (ABSI); Financial stability; Non-bank financial intermediation; Prediction; Shadow banking; Support vector machines (SVMs); Z-score (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-81532-4_10

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DOI: 10.1007/978-3-031-81532-4_10

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