Bank Asset Risk: Evidence from Early-Warning Models

I. INTRODUCTION The recent U.S. financial difficulties serve as a reminder that accurately analyzing a bank's vulnerability to financial distress remains difficult. Although most observers of the U.S. banking difficulties would agree that banks' risk postures played an important role in the crisis, the ability to distinguish accurately the links between particular bank investments and bank performance is still developing. A better understanding of these links would enhance the ability to identify the risk profile of banks and provide policy insights into the relative merits of different tools of supervision. Early warning models used to predict bank failures provide a useful framework for analyzing the role of asset risk in the recent difficulties. Most previous studies of early-warning models concentrate on alternative econometric methods and the choice of explanatory variables. These include discriminant analysis models (Sinkey, 1975; 1978), logit models (Avery and Hanweck, 1984; Rose and Kolari, 1985; Pantalone and Platt, 1987), proportional-hazards models (Whalen, 1991), and two-stage models (Demirguc-Kunt, 1989b; Gajewski, 1990; and Thomson, 1992), all of which choose slightly different variables. This paper extends the previous literature by explicitly testing the usefulness of a number of risk measures in early-warning models. The evidence could help policymakers to assess the relative value of using available accounting data to measure bank risk. The paper estimates early-warning models of bank failure using data on all U.S. banks between 1985 and 1991. The models themselves follow previous models in order to facilitate comparison with other models currently in use. The paper also explores the stability of these early-warning models over time. Because the United States experienced a high rate of bank failures for a number of years, its banks provide a rich data set for repeated estimations. To test coefficient stability explicitly, the data set differs from the usual by holding the time-to-failure interval constant while estimating the models across different time periods. The results indicate that a more-precise measure of risk, such as the alternative measure defined in the analysis here as the within-sample information method, can improve the predictive accuracy of an early-warning model relative to models using traditional risk measures. Thus, bank regulatory agencies can use this technique to improve upon one of their tools of supervision, the early-warning model. However, the results also show that early-warning model coefficient estimates and the predictive accuracy of the models vary across time. The coefficient changes are statistically and economically significant. This variability suggests that it is difficult to draw conclusions about the constancy of relationships between individual components of a bank's portfolio and its vulnerability to failure. The variation of coefficients raises some questions about the precision with which bank regulations can quantify and measure bank risk. A recent trend is to tie risk to specific accounting variables, especially those reflecting a bank's risk posture. One example is the Basel Accord on risk-based capital, an international agreement that uses the amount of assets a bank holds in various categories of assets as a guide to the bank's health. This approach is based on the premise that stable, predictable relationships exist between the level of bank risk and these variables. If the relationships between the chosen variables and bank health are in fact stable, then such regulations will aid in identifying bank risk and will be a useful advance in regulation. But if the relationships are not stable over time, then such regulation will be imprecise in identifying the risk of bank failure, and this regulatory approach must be supplemented with other approaches. II. ESTIMATION OF THE EARLY-WARNING MODELS A. Choice of Risk Measure in an Early-Warning Model To investigate the effectiveness of different measures of asset risk, each of the empirical models in this paper contains the same explanatory variables except for the risk measures included. …