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

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ÖZGÜR Ö., Karagol E. T., ÖZBUĞDAY F. C.

FINANCIAL INNOVATION, vol.7, no.1, 2021 (SSCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 7 Issue: 1
  • Publication Date: 2021
  • Doi Number: 10.1186/s40854-021-00237-1
  • Journal Name: FINANCIAL INNOVATION
  • Journal Indexes: Social Sciences Citation Index (SSCI), Scopus
  • Keywords: Bank lending, Machine learning techniques, Decision trees, Turkey, MONETARY-POLICY, PASS-THROUGH, OIL PRICES, FINANCIAL INTERMEDIATION, CREDIT, DETERMINANTS, BEHAVIOR, CHANNEL, DEMAND, TURKEY
  • Ankara University Affiliated: Yes

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.