J. Kenton Zumwalt

An Empirical Study of the Interest Rate Sensitivity of Commercial Bank Returns: A Multi-Index Approach

Several recent studies of the capital asset pricing model were designed to improve the understanding of the pricing of capital assets by expanding the singlefactor market model to include macroeconomic information, industry influences and individual firm characteristics. Stone [20] has offered another means of expanding the market model. He has proposed a two-index model consisting of the traditional “equity market†index and a “debt market†index. Stone justified the model by arguing that individual equity securities exhibit varying degrees of sensitivity to interest rates and that the opportunity to invest in risky debt securities may represent an attractive alternative to riskless assets and risky equity securities. He indicated the incorporation of an index for the return on debt in the market model might improve its explanatory power for such securities as “…gold, bank, savings and loan, public utility, and similar stocks exhibiting considerable interest rate sensitivity [20, p. 710].â€

AN ANALYSIS OF RISK IN BULL AND BEAR MARKETS

In a recent article Fabozzi and Francis [3] presented the results of empirical tests designed to determine if the regression coefficients of the single-index market model were significantly different in bull and bear markets. Using three alternative definitions of bull and bear markets, Fabozzi and Francis (FF) concluded the coefficients of the single-index market model were not significantly different in the two types of markets.

Combining forecasts to improve earnings per share prediction: An examination of electric utilities

Abstract The purpose of this study is to investigate the efficacy of combining forecasting models in order to improve earnings per share forecasts. The utility industry is used because regulation causes the accounting procedures of the firms to be more homogeneous than other industries. Three types of forecasting models which use historical data are compared to the forecasts of the Value Line Investment Survey . It is found that predictions of the analysts of Value Line are more accurate than the predictions of the models which use only historical data. However the study also shows that forecasts of earnings per share can be improved by combining the predictions of Value Line with the predictions of other models. Specifically, the forecast error is the least when the Value Line forecast is combined with the forecast of the Brown-Rozeff ARIMA model.

Rate of Return - Rate Base Issues in Utility Regulation

ABSTRACT Despite the apparent simplicity of the reduced form DCF model [ke = (D1/P0 + g], the cost of equity measure emerging from most DCF analyses must be adjusted before being applied to a test year rate base to determine a utilitys required earnings. The failure of regulatory commissions to recognize the need for these adjustments provides a partial explanation of why utilities tend not to earn either their allowed or their required returns. A recent FERC case is used to demonstrate the impact that an incorrect implementation of the DCF methodology may have upon the estimation of a utilitys required earnings.

More on public information and stock price

Abstract In a recent issue of this journal [2] McCain and Millar examined whether “favorable” and unfavorable” stock analyses appearing in the Wall Street Journal column “Heard on the Street” could be used to predict one-day, seven-day, and six-month price movements of the affected stocks. In this note we question the conclusions of that study based upon methodological grounds.

How to determine the stability of beta values

A lthough the subject of beta stability has 22 2 8 ;; been examined in several previous studies, the focus of these studies has been more upon the stability of beta rankings rather than the stability of actual beta values. The purpose of this paper is to examine the stability of security betas in different risk classes, using an equal class interval beta classification scheme based upon numerical beta values instead of beta ranks. We use numerical beta class intervals because the numerical beta value is more important than the beta rank for portfolio management purposes. Although the stability of beta ranks is of interest, beta rankings of individual securities may change, with no change occurring in the actual beta values. Conversely, a change in the numerical values can occur with no change in the beta ranks. Previous studies using decile and pentile rankings imply that betas in both high and low beta groups are more stable than betas in the middle groups. This study examines the relative stability of betas in extreme beta groups compared to middle beta groups by comparing the ,stability of individual risk classes using, first, a decile classification method and, second, an equal class interval classification method. 5cl

Impact of alternative length estimation and prediction periods on the stability of security and portfolio betas

Abstract This study examines how the forecast errors of beta predictions are influenced by the following: 1) the length of the estimation period, 2) the length of the prediction period, 3) the size of the portfolio, and 4) the risk class of the security or portfolio. The mean-square error is utilized as the forecast error measure, and the components of the mean-square error (bias, inefficiency, and random error) are analyzed to determine the source of the forecast error.

A multivariate analysis of DISC election decision

ConclusionThis study found that the decision by management to establish a DISC unit may not be satisfactorily identified by the examination of the financial data on either anex ante orex post basis. However, examination of the responses to the questionnaires indicate differences in the perceptions of the management of the two groups are significantly different on a multivariate basis.A possible implication of this research effort is that the use of published financial data alone cannot adequately explain decisions made by management. Indeed, unless managements expectations are realized, use of published data alone may result in unwarranted conclusions.

Abstract: The Forecast Error Impact of Alternative Length Beta Estimation Periods, Adjustment Techniques, and Risk Classes

This paper examines empirically the relationship among the stability of security and portfolio betas and (1) the length of the sample period used to calculate betas, (2) beta adjustment techniques, and (3) beta magnitudes. Beta values are forecast using four models: (1) a naive model which assumes the beta value in period t + 1 is the same as in period t, (2) Blumes regression model, (3) a regression model used by Merrill Lynch, Pierce, Fenner and Smith, and (4) a Bayesian procedure suggested by Vasicek.