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.
Highlights
There are many studies on the importance of financial intermediaries in the existing literature
This study aimed to investigate the relationship between bank loans and a set of bankspecific characteristics, macroeconomic indicators, and global factors in Turkey between 2002Q4 and 2019Q2
The study finds that the random forest model has the lowest predicting error, and it has the best out of sample fit measure
Summary
There are many studies on the importance of financial intermediaries in the existing literature. Financial intermediaries help lenders to invest their wealth into activities that yield smooth returns and help borrowers to increase their real asset holdings (Brainard and Tobin 1963). Owing to problems such as asymmetric information and numerous financial frictions in financial markets, the financial intermediaries’ role becomes more critical. Diamond (1984) finds that financial intermediaries are delegated monitors that specialize in minimizing potential information-based problems between borrowers and lenders. Boyd and Prescott (1986) claim that financial intermediaries reduce the cost of collecting and processing information regarding the borrower’s creditworthiness. Financial intermediaries effectively convert large amounts of savings into profitable investments (Levine 2005; Clark 2017)
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