A fierce debate has erupted in the utility policy community, with battle lines drawn within FERC itself. In the effort to improve system efficiency, two competing alternatives stand out: to build...
the industry has doubled (or has been halved), how much of that change in risk is applicable to each utility operating company in the sector, and what does it mean for the return investors require for investing in that business?
A high-level outline of this approach is described below.
Step 1: Identify specific factors affecting utility returns:
- Identify risks in three categories: regulatory, franchise, and asset; and
- Identify specific risk factors and define representative metrics for each category.
Step 2: Quantify the impact of these factors and identify relationships at the firm-specific level:
- Collect several years of data for all operating companies in the industry over a specific size threshold;
- Specify a model and perform regression analysis; 7 and
- Use variables that are statistically significant to build an industry normalized predictive model.
Step 3: Quantify risk at the firm-specific level:
- Utilize the predictive model at the firm-specific level;
- Analyze actual vs. predicted results based upon actual year variables and coefficients; and
- Differentiate between externally driven risk impacts and risks within management control.
Step 4: Determine an appropriate return-risk ratio for the industry:
- Analyze historical relationships between allowed return and risk factors within the industry; and
- Determine an appropriate return-risk ratio for the electric utility industry, , an industry Sharpe Ratio.
Step 5: Compute the cost of equity capital based upon specific operating factors and a consistent return-risk measure:
- Using the inputs determined above, compute equity allowance from a standard return-risk model for the specific firm.
What Are the Results?
After investing considerable resources in identifying the appropriate risk measures, constructing the model, and analyzing the data, several very interesting (and some mundane) observations can be made. Eleven specific measures of idiosyncratic risk facing a company have a statistically significant 8 impact on a firm's actual return, which when compared to the industry at large, create a firm level relative risk profile. Some measures are obvious, while others are more intriguing. The most significant observation, however, is the strong negative correlation between new plant investment and return-another point of confirmation for the justification of under-investment in the delivery infrastructure due to insufficient return prospects.
With the model specified correctly, an analysis of the external risk factors at the firm-specific level is conducted. This is based on an assessment of predicted return relative to actual return. For example, if a firm is allowed an 11.7 percent return on equity and actually earns 9.9 percent, but has a predicted return of 10.7 percent, the firm would have only a 55.5 percent exposure to the external risk factors identified by the model. The remaining 44.5 percent is due to unexplained factors, firm-specific strategic decisions, or model error. This ratio is important in linking the overall increase in risk observed in the industry to specific external risk factors faced by a firm. It is important to note that firms should not be rewarded for risk that can be measured but not explained.
In evaluating the appropriate return-risk ratio for the industry, an historical look at a modified Sharpe ratio 9 for the industry is appropriate (see Figure