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Removing predictable analyst forecast errors to improve implied cost of equity estimates

Abstract

Prior research documents a weak association between the implied cost of equity inferred from analyst forecasts and realized returns. It points to predictable errors in analyst forecasts as a possible cause. We show that removing predictable errors from analyst forecasts leads to a much stronger association between implied cost of equity estimates obtained from adjusted forecasts and realized returns after controlling for cash flow news and discount rate news. An estimate of implied risk premium based on the average of four commonly used methods after making adjustments for predictable errors exhibits strong correlations with future realized returns as well as the lowest measurement error. Overall, our results confirm the validity of implied cost of equity estimates as measures of expected returns. Future research using implied cost of equity should remove predictable errors from implied cost of capital estimates and then average across multiple metrics.

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Notes

  1. In a recent theory paper, Hughes et al. (2009) show that when the expected return is stochastic, the implied cost of equity can differ from expected return. Lambert (2009) however suggests that the expected difference between implied cost of equity and expected returns might not be a first-order effect.

  2. We do not estimate implied cost of equity using the dividend discount model as in Botosan and Plumlee (2002) and Brav et al. (2005) for two reasons. First, these models rely on target prices and forecasts of dividends that are available only for a small subset of firms. Second, bias in target future prices is less clearly understood than bias in earnings forecasts, as the latter has been the focus of much research.

  3. Easton and Monahan (2010) evaluate these two approaches and conclude that ranking implied RP metrics based on their correlation with risk factors is illogical because the use of accounting-based implied RP metrics implicitly assumes that the factors determining expected returns are either unknown or cannot be estimated reliably.

  4. Reconciling these two streams of research, Easterwood and Nutt (1999) show that analysts react differently based on the nature of the earnings news, by under-reacting to extreme bad news and overreacting to extreme good news.

  5. Payout is estimated as the ratio of indicated annual dividend from I/B/E/S (iadiv) to actual earnings (fy0a). If it cannot be estimated from I/B/E/S, payout is calculated as the ratio of annual dividend (Compustat #21) to net income before extraordinary items (Compustat #18). If earnings are negative, payout is estimated as the ratio of earnings to 6 % of total assets (Compustat #6).

  6. Industry median ROE is estimated as the median of all ROEs from firms in the same industry defined using the Fama and French (1997) classification over the past 5 years with positive earnings and book values, where ROE is defined as the ratio of net income before extraordinary items (Compustat #18) to lagged total common shareholders’ equity (Compustat #60).

  7. Note from Eq. 1 that the expression for RPOJ contains a square root. When short-term growth is very low [less than (γ−1)], this expression can be negative, and hence no valid estimate can be inferred.

  8. Easton and Monahan (2005) use continuously compounded expected returns [that is, log (1 + ret)], while we use the untransformed metrics. Results are virtually identical if we also continuously compound the implied RP metrics and realized returns.

  9. Accruals are defined as earnings before extra-ordinary items (Compustat #18) minus cash from operations (Compustat #308) scaled by lagged total assets (Compustat #6). For years prior to 1988, we use the balance sheet approach to calculating accruals. See Sloan (1996) for details. Results are unchanged if we use the balance sheet approach for the entire period.

  10. Accruals may represent mispricing that neither markets (Sloan 1996) nor analysts (Bradshaw et al. 2001) understand. We hence rerun the error prediction regressions without accruals. Results are essentially unchanged.

  11. We reran the error prediction methodology with only the variables with significant coefficients. Results are essentially unchanged. We do not use this as our default approach as doing so might impose a look-ahead bias.

  12. To reduce the influence of outliers, we winsorize PSURP1 and PSURP2 at the 0.5 and 99.5% level annually. Results are unchanged if we truncate extreme values instead of winsorizing.

  13. Sometimes the adjusted estimates of two-year-ahead EPS are below the adjusted estimates of one-year-ahead EPS, that is, two-year growth is negative. In that case, we set the short-term growth rate (STG) used for ARPOJ and ARPPEG equal to the forecasted long-term growth rate (LTG).

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Acknowledgments

We would like to thank the editor Peter Easton, two anonymous referees, Jim Ohlson, Steve Monahan and the seminar participants at the Columbia-NYU Joint Seminar, Indian School of Business Accounting Conference, Ohio State University, and Washington University –St. Louis for their useful comments. We would like to thank Maria Ogneva for providing us with the code to estimate the measurement error variables used in the paper. Partha Mohanram would like to thank the Social Sciences and Humanities Research Council (SSHRC) of Canada for their generous financial support.

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Correspondence to Partha Mohanram.

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Mohanram, P., Gode, D. Removing predictable analyst forecast errors to improve implied cost of equity estimates. Rev Account Stud 18, 443–478 (2013). https://doi.org/10.1007/s11142-012-9219-2

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Keywords

  • Implied cost of capital
  • Implied cost of equity
  • Analyst forecasts
  • Realized returns
  • Expected returns
  • Predictable errors

JEL Classification

  • M41
  • G12
  • G31
  • G32