Commission Watch

The industry requires new analytical tools to incorporate the realities of today's higher risk operating and investment environment into the equity allowance process.

 

During the past 10 years, capital investment in regulated electric utility assets has slowed considerably. While a tremendous amount of capital was committed to new unregulated generation, capital investment in the regulated business has not kept pace with depreciation. With a "return to basics" mantra now common in the industry, coupled with the recognition of under-investment and heightened reliability concerns, most companies are now facing significant capital expenditure programs. According to the Energy Information Administration, the investment required to replace aging infrastructure and keep pace with growing energy demand amounts to $400 billion over the next 20 years.¹ In addition to these impending capital expenditures, most utilities have experienced a significant escalation of operating costs in the recent past, resulting in decreased operating margins.

Because of these factors, many utilities are considering or have filed for rate relief. For many companies, this represents the first such filing in several years. However, due to the low interest rate environment, companies filing new cases risk living with a much lower return on equity. As Figure 1 shows, authorized returns have trended down during the last decade, and some recent awards have reached single digits.

While allowed returns have decreased, risk in the regulated utility business has increased. In fact, investor risk, as measured by standard deviation in earned return on ratebase, has doubled since the mid-1990s. (See Figure 2, p.27.) If risk is increasing, why are allowed ROEs trending down? The overall decrease in interest rates is the likely driver of this downward trend. However, the traditional methods of determining ROE are contributing factors. Critically, these approaches do not reflect the rising level of industry risk, nor do they empirically account for firm-specific risks, which directly affect an investor's requirement for equity returns.

The question, as posed by an author in this publication earlier this year (Feb. 15, 2003), becomes: "Is there a better approach for estimating the appropriate equity allowance for a particular utility than the traditional methods we have relied upon for the past 30 years?" Can more rigor and insight into the underlying business risks and operating conditions be infused into the determination of appropriate rates of return?

Ernst and Young LLP believes it is possible, and the company has developed a rigorous approach for estimating an equity allowance that is based on differentiating and highlighting the increased risks in the industry and the idiosyncratic risks faced by a particular company. Why is this relevant? To raise the magnitude of capital necessary to support the infrastructure needs of America's electric industry over the next two decades, equity returns will need to be commensurate with the risks of the industry. If not, investors will allocate capital to companies offering superior return-risk profiles, and management teams will continue to abstain from capital investments in regulated assets and deploy excess capital in the payment of dividends and debt reduction.

Do Current Methodologies Work?

All methodologies for imputing cost of capital require assumptions. Currently, commissioners rely on the following methodologies for determining a particular firm's cost of equity:

Historically the discounted cash flow (DCF) method has been the backbone of rate case testimonies, and it is the preferred method among state commissions for computing a cost of equity. However, both the DCF method and the other traditional cost-of-equity calculations have fundamental limitations. The DCF method, for example, is an effective means to measure cost of equity in a company/industry that has stable growth rates, leverage ratios, and dividend payouts; but it cannot account for volatility of future earnings and is incapable of addressing changes in firm or industry risk profile over the course of time.

The Risk Premium and Comparable Earnings methods, similarly, are academically sound, but each under-compensates for risk, measured as a function of increasing or decreasing volatility of future cash flows.

The fourth method, the capital asset pricing model (CAPM) approach, best addresses the issue of volatility as a source of investor risk. However, many difficulties exist in applying CAPM theory to a utility holding company, and several academic studies questioned the ability of the CAPM to enable the estimation of equity cost on a forward-looking basis.

These weaknesses have rarely been exposed in the ratemaking forum because of the relative stability prevalent in the utilities industry prior to the restructuring activities of the past decade. The utility industry had been adopted as a case study for optimal use of the DCF and other traditional methods. However, recent upheavals in the fuels and power markets, coupled with the uncertainty over RTOs, provider-of-last-resort responsibility, and the future of competition have added elements of risk that did not exist in previous years. As a result, the use of the same methodologies and same underlying assumptions has created a significant problem. In fact, the standard deviation of industry returns² for the regulated portion of the business has increased from approximately 2 percent to 4 percent (a doubling of risk), yet traditional methods are suggesting the only impact on allowed return should be the result of lower yields on government bonds. Intuitively, this does not make sense. Why are current methodologies ineffective in accounting for changes in volatility?

• Discounted Cash Flow Method: The DCF method has two components: dividend yield and long-term earnings growth. Dividend yield is positively correlated with interest rates, and will either increase or decrease as a function of interest rate movements. Long-term earnings growth³ is nearly impossible to predict with so much fundamental uncertainty in the current regulatory environment. Since neither variable adequately accounts for changes in volatility, the traditional DCF method will not reflect any changes in the business environment across the industry, nor will it incorporate differences in operating conditions between firms. The result is an equity allowance that is not appropriate from a reward-risk perspective.
• Capital Asset Pricing Model: The key component of the CAPM, Beta, is a measure of the slope of the regression line of company returns versus market returns. In theory, this methodology should be best suited to address changes in the risk profile of the industry. However, most utility operating companies are part of larger holding companies (many of which are engaged in unregulated and/or unrelated businesses), making pure Beta calculations difficult. Also, in the recent past returns on utility stocks (inclusive of yield) have not demonstrated meaningful correlation with the returns on broad equity market indexes, such as the S&P 500.⁴
• Bond Yield Plus Risk Premium Analysis: Since this analysis relies on past spreads between bond yields and equity returns, the methodology has no ability to account for changes in the risk environment.
• Comparable Earnings Approach: Its circular logic is self-fulfilling as the analysis depends upon commission-set returns to determine the appropriate allowed return for the subject firm. If the commission-set returns for peer group firms have been computed using only the methodologies above, no analysis of change in the risk profile of the firm or the industry in which it competes can be incorporated.

In sum, traditional methods rely on the assumption that the electric utility industry has not and will not change. These methods assume growth rates will remain predictable, dividend payouts will continue ad infinitum, challenges faced by management remain constant, and market values will fluctuate only as a function of interest rates. These assumptions may have held true for a long period of time, but they are quite tenuous in an era of regulatory uncertainty and structural change. What is needed is a methodology that allows firms and commissions to:

• Identify factors affecting realized utility returns;
• Quantify the impact of these factors and assess significance at the firm-specific level;
• Quantify risk at the firm-specific level;
• Determine an appropriate return-risk ratio for firms in the industry from an investor's perspective; and
• Compute the cost of equity capital for a firm based upon specific operating characteristics and a consistent return-risk measure.

The Operating Approach to Equity Allowance

Many readers of this article will have an entrenched view of the appropriate method for establishing equity allowance and will have dismissed this next section before even reading it. This is quite unfortunate. Right now, the industry requires new analytical tools to incorporate the realities of today's operating and investment environment into the equity allowance process.⁵ Since no existing equity allowance methodology sufficiently compensates for these risks, Ernst & Young has developed an approach that is based on the financial concept of the Sharpe Ratio, which measures unit of additional reward per unit of risk.⁶

The fundamental concept behind using the Sharpe Ratio to calculate equity allowance is that it calculates required return directly from the level of risk of an industry, or a specific company. As such, if risk increases, reward needs to increase commensurately. Otherwise, investors will be exposed to too much risk relative to return and will therefore choose to invest elsewhere.

The key to this approach then is to accurately identify and assess the risks inherent in the industry, and more importantly, identify how much of the risk in the industry is applicable to each company. In other words, if the risk in 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 significant⁸ 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 ratio⁹ for the industry is appropriate (see Figure 3). As the chart illustrates, after several years of a consistent modified Sharpe ratio of approximately 2 to 2.5, this return-risk measure has plunged below 1.6 for four consecutive years.¹⁰ This clearly illustrates that for the industry as a whole, investors are taking on far more risk relative to return than they have in the past.

With the appropriate industry return-risk ratio specified and the firm specific external risk factor quantified, the equity allowance calculation for a particular company is then ready to be solved. Continuing the hypothetical example above and using 2.3 as the Sharpe ratio, the company-specific equity allowance would be 12.56 percent. This appears to be a healthy return when compared with recent rate case decisions. However, two things should be noted. First, the number calculated reflects the idiosyncratic risks of the company analyzed. As such, it is a fair return for this company, because the company faces greater risks than other utilities, and as such, requires the higher return to attract adequate investment dollars. Second, because this methodology reflects the individual risks of particular companies, it does not always produce 12 percent returns. For some companies, this methodology will show that they are less risky than the utility group as a whole, and therefore it will suggest a lower equity allowance.

To illustrate how this methodology compares to two of the more established methodologies, we analyzed two actual utility operating companies and calculated equity allowance using the DCF, Comparable Earnings, and the new Operating Approach (see Figure 4, p.29).

The analysis provides some interesting findings. First, the operating approach demonstrates that riskier companies merit higher equity allowance, while companies that have lower relative risk merit lower equity allowance. Second, since the DCF model does not focus specifically on measuring relative risk, the answer provided for these two companies is in contrast to their relative risk profile, suggesting a lower return for the company exposed to greater risk. Lastly, the risk premium method is unable to draw a distinction among the relative risk for these two companies, suggesting that investors require the same return, even though the companies have different risk profiles.

In addition to the more intuitive, and we would argue, more efficacious answers provided from the operating method, there are a few implementation issues that also work to its benefit.

• The operating method does not rely upon choosing and defending a peer group for the analysis. The model is built on using all utility operating companies over a size threshold. In the analysis above, 114 companies were used, significantly more than the handful used in many filings.
• There is no need to justify why a holding company Beta is applicable for the operating company. All the foibles of Beta are avoided in this approach.
• The operating approach does not require a traded stock price or dividend to calculate a dividend yield. Since both of these variables stem from the holding company, arguing about their applicability to the operating company is avoided.
• Since this approach uses 11 discrete variables to calculate an appropriate equity allowance, there is not an over-dependence on one variable for the answer like there is with the DCF approach. With the DCF approach, the range of outcomes is highly driven by what experts estimate for growth.

Another benefit that stems from the operating approach is that since the methodology quantifies the effects of various industry factors, companies can use the results of this analysis to have a dialogue with regulators regarding the financial impact of certain regulatory policies. It may be the case for instance that a company would be willing to accept a slightly lower equity allowance if the commission would consider changing a regulatory policy that increases the company's variability of returns.

In the real world there is no such thing as the perfect model for economic and financial events. This is also true when regulators set an allowed return. In these dynamic times, regulated utilities and their regulators should explore alternative methods. This is especially true when firms and regulators determine an allowed rate of return. Using the operating approach, an opportunity now exists to use a different, and academically sound, approach to analyzing the cost of capital for firms in the electric utility industry. Meaningful dialogue between firms and commissioners regarding the appropriate return-risk measure can open up a fresh new angle of analysis that has not been present within the ratemaking context. If firms and commissioners augment their current methodologies with the fresh perspective described above, progress can be made in attracting more capital to this industry, and more importantly, in improving the allocation of that capital to the appropriate infrastructure investments and to the firms managing the most risk.

1. Referenced in EPRI's Electricity Sector Framework for the Future, Volume I, p. 34 (Aug. 6, 2003).
2. Defined as Net Operating Profit After-Tax/Ratebase. For conservatism purposes, this measure eliminates the impact of highly leveraged capital structures. A three-year trailing average has been used to smooth the trend lines and reduce the impact of outliers.
3. Argument over the use of earnings growth or dividend growth is moot as the only long-term source of dividends is earnings. Also, since "long-term" growth rates are clearly required by the model because it assumes this rate in perpetuity, it is theoretically invalid for this rate to be greater than the long-term GDP growth rate.
4. An interesting topic of study as the movement in utility stock prices has become more dependent upon the movement in interest rates and "event-specific phenomena" such as regulation/deregulation decisions, severe weather, and plant shutdowns.
5. The well-known Professor Stewart Myers encourages this practice in a 1978 publication: "Use more than one model when you can. Because estimating the opportunity cost of capital is difficult, only a fool throws away useful information."
6. The Sharpe ratio is a measure of excess return per unit of excess risk defined as: (Expected Return - Risk Free Rate)/Standard Deviation of Returns.
7. For those who are statistically inclined, the model is (RORBc,t) = y0 + y1
   (Equity Allowance) y2(Regulatory Risk) + y3
   (Operational Risk) + y4(Franchise Risk) + y5
   (Managerial Factors) + et where;
   RORB = Return on Rate Base =
   (NOPATc,t / Rate Base)
   NOPATc,t = net operating profits after tax for firm c in year t
   y0 = universal constant
   y1 = coefficient for allowed return on Rate Base
   y2 = coefficient for regulatory risk
8. Most of these variables are significant at the 98 percent or greater level.
9. The graphic uses the average allowed ROE granted in decisions handed down during the calendar year for expected return and uses the standard deviation of returns across the industry for the calendar year.
10. We are looking to validate the appropriate range of target Sharpe ratio. Our research continues, but based on our work to date, a ratio between 2 and 2.5 seems appropriate.

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