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Bank portfolio exposure to emerging markets and its effects on bank market value

Journal of Banking & Finance 2006 30(4), 1103-1126
This study estimates a model of banking company equity returns taking into consideration book value and market value measures of their exposure to emerging markets debt. In this estimation, general systematic market factors, such as the rate of return on the S&P500 stock index and yields on a constant maturity 5-year Treasury note, are held constant such that the exposure variables are accounting for effects due to banks’ exposure to emerging market debt. The results, although not uniform among banking companies, support the hypothesis that the extent of exposure to emerging market debt are factored into the valuation of banking company equity contemporaneously. The inclusion of a market value indicator adds to the explanation of equity returns of some banks. It is also clear that knowing the extent of the exposure on a book value basis is important information alone that may allow investors to take account of or evaluate the effects of changes in banking company equity valuation from LDC debt exposures. We also perform an event study for three major debt crises to determine whether the market recognizes the effects of these events on bank valuation. The event study results show that there is little information from identifying the time period of the crises on banking company equity returns. Explanations for this are that the information of these possible crises has been embedded in bank changes in exposure and that the market valuation of the emerging market debt is already accounted for by our model.

Is there cyclical bias in bank holding company risk ratings?

Journal of Banking & Finance 2008 32(7), 1297-1309
This paper examines whether bank holding company (BHC) risk ratings are asymmetrically assigned or biased over business cycles from 1986 to 2003. In a model of ratings determination which accounts for bank characteristics, financial market conditions, past supervisory information, and aggregate macro-economic factors, we find that bank exam ratings exhibit inter-temporal characteristics. First, exam ratings exhibit some evidence of examiner bias for several periods analyzed. When the business cycle turns, examiners sometime depart from standards that they set during the previous phases of the cycle. However, this bias is not widespread or systematic. Second, exam ratings exhibit some inertia. Our results suggest that examiners rate on the side of not changing (rather than upgrading or downgrading) an institution’s exam rating. Third, we find robust evidence of a secular trend towards more stringent examination BHC ratings standards over time.

Equity market information, bank holding company risk, and market discipline

Journal of Banking & Finance 2008 32(5), 807-819
For market discipline to be effective, market factors such as changes in firm equity and debt values and returns, must influence firm decision making. In banking, this can occur directly via bank management or indirectly though supervisory examinations and oversight influencing bank management. In this study, we investigate whether equity market variables can provide timely information and add value to accounting models that predict changes in bank holding company (BOPEC) risk ratings over the 1988–2000 period. Using a variety of equity market indicators, the findings suggest that one-quarter lagged market data adds forecast value to lagged financial statement data and prior supervisory information in the logistic regressions. Furthermore, using extensive out-of-sample testing for the years 2001–2003, we find: (1) that multiple models estimated over different phases of the business and banking cycles are superior to a single model for forecasting BOPEC rating changes; (2) that equity data adds economically significant power in forecasting BOPEC rating upgrades and performs well for identifying no changes; (3) that for downgrades, the accounting model forecasts the best; (4) that modeling the three possible risk ratings categories simultaneously (downgrade, no change and upgrade) minimizes both Type I and Type II classification errors; and (5) that using multiple models to forecast risk ratings enhances the overall percentage of correct classifications.