Anticipating Credit Developments with Regularization and Shrinkage Methods: Evidence for Turkish Banking Industry

In this paper, we propose the use of regularization and shrinkage methods to address the variable selection problem in predicting credit growth. Using data from the 10 largest Turkish banks and a broader set of macro-financial predictors for the period 2012-2023, we find that the models generated by the Least Absolute Shrinkage and Selection Operator (LASSO) method have superior predictive power (lower level of forecast errors) for bank-level total credit growth compared to alternative factor-augmented models through recursive out-of-sample forecasting exercises. Our baseline findings remain intact against alternative choices of the tuning parameter and LASSO specifications. In addition to the dynamics of the total credit growth, the improvement in prediction accuracy is evident for commercial credit growth at all horizons, while the effect is limited to short-term horizons for consumer credit growth. Furthermore, additional robustness checks show that the baseline results do not vary considerably against different sample coverage and benchmark models. In the subsequent analyses, we utilize the LASSO method to synthesize the “residual credit” indicator as a proxy for excessive credit movements deviating from the level implied by macro-financial dynamics. In the scope of a case study, using this indicator as an input for local projection estimates, we show that recent inflationary pressures have resulted in excessive lending activity, which is not fully explained by macro-financial dynamics, in the period 2020-2023.