Why Don’t All Troubled Banks Fail? Oshinsky, R. and Olin, V. (2006). Troubled Banks: Why Don't They All Fail?, FDIC Banking Review (Vol. 18, pp. 22). Michael Campbell Public Policy Analysis (ECON 539) 02/02/09

Introduction Many failure-prediction models and early- warning systems already exist. Focus has been on pairs of outcomes: –Failure vs. non-failure –Merger vs. consolidation Benefits to FDIC and bank researchers.

Prior Research Findings Specific findings include: failure results from low capital, embedded risky assets, poor management, low earnings, and low liquidity. Most findings are binary, i.e. this or that, e.g. failure v. non-failure, acquisition v. consolidation, etc.

Criticisms of Prior Research Rarely in life are possible outcomes binary. Benefits are diminished by not studying other possible outcomes. Data set includes all banks vs. only troubled banks. Limited prediction accuracy and inherent bias.

Question Under Study Troubled Banks: Why Don’t They All Fail?

Sample and Data Data sources: CAMALS ratings from regulatory agency and quarterly FDIC CALL reports. 1,996 banks on the FDIC problem-bank list from 1990 – Event pairing provided 3,747 observations. Observation events: Event 1 - CAMELS rating of a 4 or 5, and Event 2 – bank recovery, merger or consolidation, problems remain, or failure.

Sample and Data Dependent Variables: –Succeed, Fail, Remain Troubled, and Merge/Consolidate Independent Variables: –Capital Ratio, Asset Quality, Management Efficiency, Earnings Ratio, and Liquidity Ratio.

Logistic Regression Multi-State Model Univariate Trend Analysis: Used to determine whether prior- period financial characteristics differ by future bank state. One-way Analysis of Variance: Used to examine the financial characteristics of recovered banks versus banks in the other three states. Multinomial Logistic Estimating Procedure: U sed to model future bank states. The model simultaneously estimates three binary logits for comparison among the outcome categories. The general form of the model tested: Probability of State (k) i,t = F(Financial condition i,t-1, Economic conditions t ) (Probability of State (k)i,t is the probability that bank i will be in state k at time t)

Model Explained Multinomial Logistic Model is a regression model which generalizes logit regression by allowing more than two discreet outcomes. Used when the dependent variable(s) is nominal (succeed, fail, remain unchanged, merge/consolidate) and when the response is not ordinal (can’t be ordered 1, 2, 3…).

Model Assumptions Examples: –Each independent variable has a single value. –The dependent variable(s) cannot be perfectly predicted from the independent variables. –Collinearity is relatively low (improved predictability of a variable’s impact upon y).

Model’s Effectiveness Model’s effectiveness was evaluated by: –Comparing results to competing models. –Investigating the economic and statistical effect of the explanatory variables. –Verifying that banks with the highest predictability of failure actually did fail.

Results General agreement with researcher’s expectations: –Improved net income ratios associated with recovery state. –Reduced non-performing assets and expenses associated with recovery state. –Reduced exposure to volatile liabilities and assets associated with recovery state. –No expectations with financial ratios for merged or remain troubled states except that they’ll fall between succeeded and failed bank ratios. –Positive coefficient signs = success; Negative coefficient signs = failure.

Conclusions By deviating from previous models this study focused on only troubled banks resulting in greater predictability. This four-state model offers the FDIC and bank researchers more information: –Eliminates bias by excluding healthy banks. –Identifies alternative outcomes. –Enables better estimate of contingent loss reserve. –Better long-term strategic resource planning. The additional information can better assist the FDIC is long-term strategic planning.

Policy Implications Because the model shows that certain explanatory variables affect future bank states this can assist regulators in choosing policies that affect the likelihood that troubled banks can successfully resolve their own problems.