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

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.

Forecasting nonperforming loans using machine learning

“…The Rsq values suggest that the NPL forecasting accuracy of the random forest model is 76.10%. The result is consistent with several previous studies, such as Ozgur et al (2021), Bonato et al (2023), and Akyildirim et al (2021), who also documented the suitability of the random forest model in different financial series forecasting.…”

“…Our conclusion is also in line with earlier studies on machine learning‐based models. For example, Ozgur et al (2021), Bonato et al (2023), and Akyildirim et al (2021) report better output with the random forest model in different forecasting scenarios.…”

Predicting cash holdings using supervised machine learning algorithms

“…Recently, machine learning algorithms have frequently been used as prediction tools even in finance, especially for price prediction, financial risk management, financial services, and decision making (Xiao and Ke 2021 ). To predict bank lending, we used and compared various machine learning algorithms, such as panel regression, tree regression, RF, and XGBoost (Ozgur et al 2021 ). Moreover, on-site supervision and self-supervision approaches are compared using machine learning approaches like the RF algorithm (Antunes 2021 ).…”

“…They revealed that the RF method provides the best predictive models and that incorporating disclosure tone variables into the predictive model with financial variables enhances the accuracy and quality of these models. Ozgur et al ( 2021 ) used machine learning techniques (i.e., XGBoost, regression tree, boosting, bootstrap aggregating, RF, and extra-trees) to predict bank lending behavior. They documented that RF is the best predictive model.…”

Predicting abnormal trading behavior from internet rumor propagation: a machine learning approach

“…Finally, we boosted the combined classification tree using a random forest (cf. Brieman et al 1984), which has been used in finance for things like predicting bank lending (Ozgur et al 2021). A random forest is made up of several random trees that have been trained using bootstrapped observations from the training data (Shmueli 2016).…”