Can We Use Financial Data to Predict Bank Failure in 2009?

Shirley (Min) Liu ()
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Shirley (Min) Liu: Ness School of Management and Economics, South Dakota State University, Brookings, SD 57007, USA

JRFM, 2024, vol. 17, issue 11, 1-30

Abstract: This study seeks to answer the question of whether we could use a bank’s past financial data to predict the bank failure in 2009 and proposes three new empirical proxies for loan quality (LQ), interest margins (IntMag), and earnings efficiency (OIOE) to forecast bank failure. Using the bank failure list from the Federal Deposit Insurance Corporation (FDIC) database, I match the banks that failed in 2009 with a control sample based on geography, size, the ratio of total loans to total assets, and the age of banks. The model suggested by this paper could predict correctly up to 94.44% (97.15%) for the failure (and non-failure) of banks, with an overall 96.43% prediction accuracy, ( p = 0.5). Specifically, the stepwise logistic regression suggests some proxies for capital adequacy, assets/loan risk, profit efficiency, earnings, and liquidity risk to be the predictors of bank failure. These results partially agree with previous studies regarding the importance of certain variables, while offering new findings that the three proposed proxies for LQ, IntMag, and OIOE statistically and economically significantly impact the probability of bank failure.

Keywords: bank failure; financial data; capital adequacy; interest margin; loan quality; default risk; liquidity risk; earnings; logit (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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