Machine learning approach to drivers of bank lending: evidence from an emerging economy
| Autor: | Erdal Tanas Karagöl, Onder Ozgur, Fatih Cemil Ozbugday |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Turkey
Decision trees Decision tree Machine learning computer.software_genre lcsh:K4430-4675 Management of Technology and Innovation Bank lending ddc:650 0502 economics and business lcsh:Finance lcsh:HG1-9999 Economics 050207 economics Emerging markets lcsh:Public finance 050208 finance business.industry 05 social sciences Nonparametric statistics Artificial intelligence business Machine learning techniques computer Finance |
| Zdroj: | Financial Innovation, Vol 7, Iss 1, Pp 1-29 (2021) |
| ISSN: | 2199-4730 |
| Popis: | The study analyzes the performance of bank-specific characteristics, macroeconomic indicators, and global factors to predict the bank lending in Turkey for the period 2002Q4–2019Q2. The objective of this study is first, to clarify the possible nonlinear and nonparametric relationships between outstanding bank loans and bank-specific, macroeconomic, and global factors. Second, it aims to propose various machine learning algorithms that determine drivers of bank lending and benefits from the advantages of these techniques. The empirical findings indicate favorable evidence that the drivers of bank lending exhibit some nonlinearities. Additionally, partial dependence plots depict that numerous bank-specific characteristics and macroeconomic indicators tend to be important variables that influence bank lending behavior. The study’s findings have some policy implications for bank managers, regulatory authorities, and policymakers. |
| Databáze: | OpenAIRE |
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