Forecasting bank supervisory ratings using securities market information. ‘Federal Reserve Bank of San Francisco

Abstract: Approximately once a year, bank supervisors in the United States conduct a comprehensive on-site inspection of a bank holding company and assign it a supervisory rating meant to summarize its overall condition. We develop an empirical forecasting model of these ratings that combines accounting and financial market data. We find that securities market variables, such as stock returns and changes in bond yield spreads, improve the model’s in-sample fit. Both equity and debt market variables appear to be useful for explaining upgrades and downgrades. We conclude that stock and bond market investors possess different, but complementary information about bank holding company condition. In an out-of-sample forecasting exercise, we find that the forecast accuracy of the model with both equity and debt variables is little different from the accuracy of a model based on accounting and lagged supervisory data alone. Acknowledgments: The views expressed here are those of the authors and not necessarily those of the Federal Reserve Bank of San Francisco or the Federal Reserve System. We thank Rob Bliss for generously sharing his bank holding company debt database with us. We thank Fred Furlong and seminar participants at the Bank of England fo