An Empirical Analysis of Loan Loss Provisioning Behaviour of U.S. Commercial Banks - Khan, Tasneema (2022)
This study investigates the Loan Loss Provisioning (LLP) behaviour of U.S. commercial banks over a period of 2003 to 2020. Using an unbalanced panel of 429 commercial banks with 4951 observations, this study analyzes the business cycle, income smoothing, capital management, and bad management and skimping hypotheses. To represent the bad management hypothesis, the cost efficiency, estimated by the Battese and Coelli (1995) Stochastic Frontier Analysis approach is used. The efficiency scores are then incorporated into the LLP regression, which is estimated using the two-step System GMM (SGMM) model. Since previous years’ LLPs are likely to affect the LLPs of the current year, the dynamic panel data is used for which the SGMM is proved to be suitable. With respect to the efficiency score, the overall mean cost efficiency is found to be 86% implying that the U.S. banks could reduce their costs by 14% and still be able to produce the same level of output. Over the sample period, the mean efficiency ranges from 81.4% to 89.3%. However, it shows a significant drop around the global financial crisis of 2007-2009 and the recent COVID-19 pandemic. The major findings reveal that the LLPs of the U.S. commercial bank show procyclical behaviour. This behaviour suggests that banks are likely to increase their LLPs when the economy slows down. With respect to capital management, the model provides strong support for this hypothesis. Therefore, bank managers use LLPs to maintain the regulatory capital. However, the empirical results do not provide any evidence for the income smoothing hypothesis. Furthermore, it is evidenced that skimping rather than bad management affects the LLPs of U.S. commercial banks. Further, this study analyzes the signalling hypothesis, which is found to exist for the U.S. commercial banks. This analysis contributes to the current literature in several ways. The inclusion of the bad management along with the three most widely used hypotheses makes the model more comprehensive. Moreover, the sample period covers the most recent year data including both the global financial crisis and the COVID-19 pandemic.