Measuring financial soundness around the world: A machine learning approach
| dc.contributor.author | Mertzanis, Charilaos | |
| dc.contributor.author | Cerchiello, Paola | |
| dc.contributor.author | Bitetto, Alessandro | |
| dc.date.accessioned | 2024-05-23T06:01:35Z | |
| dc.date.available | 2024-05-23T06:01:35Z | |
| dc.date.issued | 2023-01 | |
| dc.description | The importance of a sound financial system for sustained economic growth is well documented (Allen & Gale, 2000). A sound financial system supports economic activity by pooling and mobilizing savings for productive use, providing information on existing and potential investment opportunities, improving corporate governance and facilitating trading, diversification, and risk management. The 2007–8 financial crisis has underscored the importance of financial system resiliency in providing finance to households and business throughout the business cycle. | |
| dc.description.abstract | We use a fully data-driven approach and information provided by the IMF’s financial soundness indicators to measure the condition of a country’s financial system around the world. Given the nature of the measurement problem, we apply different versions of principal component analysis (PCA) to deal with the presence of strong cross-sectional and time dependence in the data due to unobserved common factors. Using this comprehensive sample and various statistical methods, we produce an alternative data-driven measure of financial soundness that provides policy makers and financial institutions with a monitoring and policy tool that is easy to implement and update. We validate our index by using alternative macroeconomic factors, confirming its predictive power. Our index captures important aspects of financial intermediation around the world. Keywords: Financial soundness, Data-driven, Cross-country, Policy framework, Principal Component Analysis, Random Forest | |
| dc.identifier.citation | Bitetto, A., Cerchiello, P., & Mertzanis, C. (2023). Measuring financial soundness around the world: A machine learning approach. International Review of Financial Analysis, 85, 102451. | |
| dc.identifier.doi | https://doi.org/10.1016/j.irfa.2022.102451 | |
| dc.identifier.uri | https://dspace.adu.ac.ae/handle/1/5360 | |
| dc.language.iso | en | |
| dc.publisher | Science Direct | |
| dc.title | Measuring financial soundness around the world: A machine learning approach | |
| dc.type | Article |