Search results

The purpose of this paper is to examine the impact of the March 28, 1979 accident at the Three Mile Island (TMI) nuclear power facility on the risk and return of General Public Utilities (TMI's owner) and other electric utilities heavily invested in nuclear power facilities. The results have implications for the returns required by investors and, therefore, on the economic viability of nuclear power for electricity generation. A recent article by Bowen, Castanias, and Daley used cumulative abnormal residual (CAR) analysis to examine the impact of the TMI accident on the electric utility industry. However, as has been shown by Larcker, Gordon, and Pinches, CAR results may be misleading due to systematic risk changes and autocorrelation in the data. Intervention analysis is proposed as an alternative to CAR analysis. This paper compares the results of a traditional cumulative abnormal residual analysis with the results of intervention analysts. It is found that the CAR analysis indicates abnormal negative returns occurred immediately after the TMI accident. However, intervention analysis shows the assumptions necessary for the CAR method to be appropriate are violated. When adjustments are made for a shift in systematic risk and autocorrelation, no abnormal returns are observed for GPU, for other utilities with nuclear facilities or for non‐nuclear utilities. These results are in conflict with those reported by BCD.

Earlier research found that firms with the highest distress risk have low book‐to‐market (B/M) ratios and low returns. This paper aims to examine the robustness of those's results and provide further evidence that high distress‐risk firms do not enjoy the same high returns earned by high B/M firms and that distress risk is unlikely to explain the Fama and French high‐minus‐low (HML) B/M factor.

A distress‐risk measure, distressed‐minus‐solvent (DMS), is calculated and a range of zero investment distress‐risk trading strategies is investigated. Value‐ and equal‐weighted portfolios are examined both with negative book‐equity firms and without. These most distressed firms have low or negative B/M values and would either not be included in the Fama and French sample or included in the low B/M portfolio.

The paper finds that the DMS factor is negative and significant, and none of the zero investment strategies earns significantly positive returns.

The findings suggest that exposure to distress risk does not earns investors a positive risk premium. It appears that over the period examined, market inefficiencies drive the market value and returns of high distress‐risk firms.

The distress‐risk premium is shown to be negative and, therefore, cannot be driven by bankruptcy risk alone. The negative premium is not consistent with a financial distress explanation for the Fama and French HML factor.

This study employs a multivariate GARCH model to investigate the relative sensitivities of the first and the second moment of bank stock return distribution to the short‐term and long‐term interest rates and their respective volatilities. Three portfolios are formed representing the money center banks, large banks, and small banks, respectively. Estimation and testing of hypotheses are carried out for each of the three portfolios separately. The sample includes daily data over the 1988‐2000 period. Several hypotheses are tested within the multivariate GARCH specification. These include the hypotheses of: (i) insensitivity of bank stock return to the changes in the short‐term and long‐term interest rates, (ii) insensitivity of bank stock returns to the changes in the volatilities of short‐term and long‐term interest rates, and (iii) insensitivity of bank stock return volatility to the changes in the short‐term and long‐term interest rate volatilities. The findings indicate that short‐term and long‐term interest rates and their volatilities do exert significant and differential impacts on the return generation process of the three bank portfolios. The magnitudes and the direction of the effect are model‐specific namely that they depend on whether the short‐term or the long‐term interest rate level is included in the mean return equation. These findings have implications on bank hedging strategies against the interest rate risk, regulatory decisions concerning risk‐based capital requirement, and investor’s choice of a portfolio mix.

Using various proxies for the firms' return on equity (ROE) and retention ratios (b) the authors calculate 36 sustainable growth rates, on a rolling basis, for a comprehensive set of firms over a 52-year period. The authors then assess the ability of these different sustainable growth rates to predict the actual, out-of-sample, five-year growth rates of the firms' earnings.

The authors compare the forecast to determine which method of estimating ROE and b produce the lowest mean-squared-errors and then determine the estimation method that works best for firms with different characteristics and for firms in different industries.

Overall, using the median ROE of all firms in the market and the 5-year average of the specific firm's retention ratio produces the lowest, statistically significant, forecast errors. Variations are documented based on firm characteristics, including dividend payout, level of ROE and industry.

The findings can guide practitioners in using the best earnings forecasting method.

Financial textbooks seem universally to suggest that one method of estimating the growth rate of a firm's earnings is to calculate the “sustainable growth rate” by multiplying the firm's ROE by the firm's b. At the same time, multiple methods of proxying for both ROE and b have been suggested; therefore, it is an interesting and useful empirical question, which, heretofore, has not been addressed in the literature, as to which estimation of the sustainable growth rate best approximates the actual future growth of the firm's earnings. The findings can guide practitioners in using the best earnings forecasting method.

This study attempts to determine whether the level and volatility of interest rates affect the equity returns of commercial banks. Short‐term, intermediate‐term, and long‐term interest rates are used. Volatility is defined as the conditional variance of respective interest rates and is generated by using the ARCH estimation procedure. Two sets of models are estimated. The basic models attempt to determine the effect of contemporaneous and lagged interest rate volatility on bank equity returns, while the extended models incorporate additional contemporaneous macroeconomic variables. Contemporaneous interest rate volatility has little explanatory power, while lagged volatilities do possess some explanatory power, with the lag length varying depending on the interest rate series used and the time period examined. The results from the extended model suggest that the long‐term interest rate affects bank equity returns more adversely than the short‐term or the intermediate‐term interest rates. The findings establish the relevance of incorporating macroeconomic variables and their volatilities in models determining bank equity returns.

This paper aims to examine the impact of stock market liberalization on efficiency of the stock markets in Latin America.

Daily stock indices from Latin American countries, including Brazil, Mexico, Chile, Peru, Jamaica and Trinidad and Tobago, are used in the analysis. To examine the impact of stock market liberalization on efficiency, the authors use several approaches, including the runs test, Chow–Denning multiple variation ratio test, Wright variance ratio test, the martingale hypothesis test and the stochastic dominance (SD) test, on the above Latin American stock market indices.

The authors find that stock market liberalization does not improve stock market efficiency in Latin America.

This investigation is among the first to examine the impact of stock market liberalization on the efficiency of the stock markets. It is among the first to examine the impact of stock market liberalization on the efficiency of the Latin American stock markets. It is also among the first to apply the martingale hypothesis test and a SD approach on issue about efficient market.