Date of Conferral
Doctor of Business Administration (D.B.A.)
A liquidity shortfall in the United States triggered the bankruptcy of several large commercial banks, and bank failures continue to occur, with 50 banks failing between 2013 and 2015. Therefore, it is critical banking regulators understand the correlates of financial performance measures and the potential for banks to fail. In this study, binary logistic regression was employed to assess the theoretical proposition that banks with higher nonperforming loans, lower Tier 1 leverage capital, and higher noncore funding dependence are more likely to fail. Archival data ranging from 2012-2015 were collected from 250 commercial banks listed on the Federal Deposit Insurance Corporation's website. The results of the logistic regression analyses indicated the model was able to predict bank failure, X2(3, N = 250) = 218.86, p < .001. Nonperforming loans, Tier 1 leverage capital, and noncore funding were all statistically significant, with Tier 1 leverage capital (Î² = -1.485), p < .001) accounting for a higher contribution to the model than nonperforming loans (Î² = .354, p < .001) and noncore funding dependence (Î² = -.057, p = .015). The implication for positive social change of this study includes the potential for bank regulators to enhance job security, wealth creation, and lending within the community by working with bank managers to develop more timely corrective action plans to alleviate the risk of bank failure.
Pruitt, Helen, "Predicting Bank Failure Using Regulatory Accounting Data" (2017). Walden Dissertations and Doctoral Studies. 4167.