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potential variation in the calculated COE of more than 1,100 basis points, even before considering the variation in earnings growth forecasts.
The usual method to address daily stock price volatility is to base DCF calculations on an average of recent stock prices. 4 While this violates the theoretical EMH, it reduces (but does not eliminate) the variation in the computed cost of equity. How many trading days to use in computing an average stock price is arbitrary-the greater the number of trading days used, the lower the potential variation. Graph 3 (p.15), for example, shows the probability distributions of the utility's calculated COE, using the same initial 5 percent dividend yield and constant 5 percent earnings growth forecast, based on average stock prices taken over 10-, 20-, and 30-trading-day periods.
Even assuming 30 trading days (6 weeks) are used to average stock prices, the underlying volatility of the utility's stock leads to a 150 basis point variation in the utility's COE. If 10 trading days are used, the variation in the dividend yield is more than 250 basis points. Clearly, such variability could have a tremendous impact on a utility's financial well-being when determining the utility's allowed return.
Of course, compounding this variation are variations in earnings forecasts themselves. Greater volatility in stock prices will lead to greater uncertainty about future earnings. That uncertainty, expressed in more disparate earnings forecasts, may adversely affect a utility's bond rating by raising default risk, further pressuring cash flows, and introducing still more uncertainty into future earnings forecasts. None of this bodes well for relying solely on the DCF. 5
DCF & Earnings Growth Uncertainty
In the traditional DCF form, the cost of equity equals the sum of the current dividend yield and a forecast of long-term earnings growth. Although different versions of the DCF attempt to address potential differences between short-term and long-term forecasts of earnings growth rates, there is generally little discussion of how those forecasts are determined.
Separate earnings growth forecasts are not typically developed for use in DCF studies. Instead, analyst forecasts developed by well-known financial analysis firms (e.g., Thompson-I/B/E/S, Zack's, Value Line) are used. Typically, these firms provide projections of earnings growth looking out five years. As an example, consider the earnings growth data presented in Table 1 (p. 18). This table lists all of the electric and natural gas utilities tracked by the Value Line Investment Survey for which long-term earnings growth forecasts were published by Thompson-I/B/E/S at the end of December 2002. For each utility with three or more estimates, the mean, high, low, and median estimates are presented.
The table shows the percentage point spread between high and low earnings growth estimates, and the ratio of that percentage point spread to the mean earnings growth rate. For example, Thompson-I/B/E/S reports on the results of 10 separate analyst estimates for Allegheny Energy. The mean long-term earnings growth forecast is 6.2 percent, the spread between the high and low growth estimates is 13 percent (15%-2%), and the ratio of the spread to the mean growth estimate is 2.1 (13/6.2).