Expected Loan Loss Provisioning: An Empirical Model
The Accounting Review · 2022 · Volume 97 · Issue 7
Abstract
The new accounting standard requires that financial institutions estimate expected credit losses on their loan portfolios. The predictability of long-term losses, however, remains an open question. We develop a model that predicts long-term loan losses and incorporates adjustments for macroeconomic forecasts. The model combines cross-sectional predictions with a high-dimensional dynamic factor model that tracks aggregate losses over the business cycle. The model predicts long-term losses out-of-sample with significantly greater accuracy than theHarris et al. (2018)model and several other alternatives. It is also more effective at detecting bank failures. We use the model to estimate the present value of expected losses and the expected loss overhang for a given bank-quarter. The estimated present values subsume information in reported allowances and in fair value disclosures about long-term losses; the evidence is also consistent with loss overhang distorting banks' decisions. The model provides a useful benchmark to study loan loss provisioning. JEL Classifications:G21; M40; M41.
Keywords
expected long-term credit losses, CECL, prediction, aggregate credit losses, machine learning, expected loss overhang
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