Recognizing Loan Losses in Banks: An Examination of Alternative Approaches

I investigate the accounting rules for loan loss recognition in banks and ways to improve them. The FASB in June 2016 issued a new rule, effective in December 2019, that will replace current GAAP with a model which will allow banks to use broader information in estimating loan loss allowance. To empirically examine the current GAAP and the new model, I exploit the differences in the information sets allowed under the old and the new rules. Using a methodology that combines micro data and machine learning techniques, I provide evidence that it is possible to construct a loan loss recognition model that outperforms the current GAAP without expanding the information set beyond that permitted under the current rule. I find that expanding this model’s information set does not significantly improve its performance. I further show that the difference between my model predicted allowance and the actual allowance recognized by banks is economically meaningful. Using my model banks would have recognized larger losses as they entered the financial crisis. My results provide a novel method to evaluate aspects of the new accounting rule before it comes into effect. They suggest that the way information is used, rather than the use of broader information set in itself improves the estimates of loan loss allowance. These findings raise the question of whether discretion enhances the quality of accounting in my setting.