A soup-to-nuts preview of the next 12 months that touches on spinoffs and interest rates, climate change and New Source Review, the future of nuclear, investor returns, and natural-gas price...
The Fortnightly 100 Revisited: Do Utility Stock Prices Reflect Operational Efficiency?
management quality and effectiveness are subjective measures and are hard to quantify in a consistent way. Price of the product also has a bearing on how the utility is assessed, for it influences competitiveness probably more directly than any other factor. But it is important to keep in mind that effectiveness of management and product prices are strongly correlated with operating efficiency and their effects are already captured in the efficiency index. Their inclusion would therefore be redundant and would confound the statistical results of the regression analysis.
The Data. The historical data we used in estimating the parameters of the regression model covered the 1995-1997 period. The POWERDAT( Database, an electronic compilation of the information contained in Federal Energy Regulatory Commission Form 1 and other utility-specific financial data, was the primary source of information for this analysis.fn7 Additional financial data were obtained from the Bureau of Labor Statistics and FERC. As noted above, The Fortnightly 100 supplied the list of the 100 utilities analyzed and their efficiency ranks. However, our regression model in the present article was estimated using information on 77 utilities. Lack of complete and reliable information on financial indicators for some utilities prevented the inclusion of all 100 utilities in the sample.
Reading the Numbers:
What They Suggest
The statistical results of the estimated regression equation are summarized in Table 1. For each variable, figures in the table show the mean values for all variables included in the analysis during the 1995-1997 period, the estimated slope coefficient (beta), the t statistic and estimated elasticity at the mean.
The "mean values" show the average of the various explanatory variables, as well as that of the dependent (M/B ratio) variables. For each explanatory variable, the estimated coefficient measures the impact of a one-unit change in that variable on the M/B ratio. The t statistic is a measure of the statistical significance of the relationship between the explanatory variable and the M/B ratio. A value of greater than 2 in absolute terms generally indicates a statistically significant relationship. For each explanatory variable, the elasticity (ei) values are calculated at the mean as the product of the estimated coefficient and the ratio of the means of the dependent (y) and the explanatory (xi) variables. It measures the percentage change in the dependent variable that is likely to arise as the result of 1 percent change in the explanatory variable. Positive values of the elasticity indicate the particular explanatory variable has a positive impact on the M/B ratio (i.e., when the explanatory variable increases, so does the M/B ratio).
As can be seen in Table 1, all estimated parameters have the correct signs, supporting the hypothesized relationships between market-to-book ratio and its determinants. Judging by the values of the t statistics, these effects also seem to be significant from a statistical point of view. The three exceptions are the stock and liquidity ratios and fuel diversity. The estimation results also produce a relatively strong fit for the overall relationship with a multiple coefficient of determination (R2) of 0.64, showing that 64 percent of