Machine learning approach to drivers of bank lending: evidence from an emerging economy

Önder Özgür (), Erdal Karagol and Fatih Cemil Ozbugday

Financial Innovation, 2021, vol. 7, issue 1, 1-29

Abstract: Abstract 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.

Keywords: Bank lending; Machine learning techniques; Decision trees; Turkey (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:fininn:v:7:y:2021:i:1:d:10.1186_s40854-021-00237-1

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DOI: 10.1186/s40854-021-00237-1

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