Physical description 
1 electronic text (p. 241364) : PDF digital file. 
Series 
Foundations and trends in accounting, 15540650 ; v. 2, issue 4, p. 241364


Foundations and trends in accounting (Online), 15540650 ; v. 2, issue 4, p. 241364.

Notes 
Title from PDF t.p. (viewed on April 5, 2009). 
Bibliography 
Includes bibliographical references (p. 359364). 
Contents 
Abstract  Introduction  Valuing the firm  The discounted cash flow valuation model  A simple example  The residual operating income valuation model  Reverse engineering  The algebra of the derivation of the accountingbased valuation models  The derivation of the residual operating income valuation model  Summary  Changing the focus to the valuation of equity and introducing reverse engineering  The dividend capitalization model  A simple example  The residual income valuation model  Reverse engineering the residual income valuation model  The importance of simultaneously estimating both the implied expected rate of return and the implied expected growth rate  Formal derivation of the residual income valuation model  The importance of the cleansurplus assumption  Summary  Reverse engineering the abnormal growth in earnings valuation model: PE ratios and PEG ratios  The abnormal growth in earnings valuation model  Formal derivation of the abnormal growth in earnings valuation model  The connection between the abnormal growth in earnings valuation model and the residual income valuation model  A simple example  What is abnormal growth in earnings  The concept of economic earnings  What is growth in abnormal growth in earnings  Special case: PE ratios  PE ratios and PEG ratios  Stock recommendations based on the PEG ratio  The modified PEG ratio  The PEG ratio  The GODE and Mohanram modification  Conclusion regarding modifications  Summary  Reverse engineering the residual income valuation model to obtain firmspecific estimates of the implied expected rate of return  Reverse engineering the residual income valuation model  Approaches to the problem of growth rates beyond the forecast horizon  Advantages/disadvantages  Gebhardt et al (2001)  Why fade to the industry median returnonequity  What is the appropriate industry comparison group  Claus and Thomas (2001)  Growth at rf 3%  Growth stemming from accounting conservatism is not zero  Firmspecific estimates are unlikely to be meaningful when the same growth rate is applied to all firms  A model that fades to the cost of capital  Summary  Reverse engineering the abnormal growth in earnings valuation model to obtain portfoliolevel estimates of the implied expected rate of return  A method for simultaneously estimating the rate of change in abnormal growth in earnings and the expected rate of return  A word of caution  Bias in estimates of the expected rate of return based on the peg ratio  An illustration: P&G  The regressionbased estimate for P&G  An illustration: large sample evidence of the effect of assumptions about longterm growth in earnings  The importance of high rsquare in the Easton (2004) regression  An example: the DJIA as of December 31, 2004  Summary  Reverse engineering the residual income valuation model to obtain portfoliolevel estimates of the implied expected rate of return  Simultaneously estimating the rate of growth in residual income and the expected rate of return that are implied by market prices, book value, and forecasts of earnings  Earnings aggregation  Example of earnings aggregation: P&G  The ETSS iterative procedure  An illustration: large sample evidence of the effect of assumptions about longterm growth in residual income  The tradeoff between using just one quarter or just one year of earnings and using all available forecast data  Simultaneously estimating the rate of growth in residual income and the expected rate of return that are implied by market prices, book value, and current earnings  A key issue is the implicit assumption that the accounting data summarize the payoffs about which the investor is concerned when determining the value of the stock  Which earnings  A need for caution  Valueweighted estimates of the implied expected rate of return  Summary  Methods for assessing the quality/validity of firmspecific estimates  The motivation for estimating accountingbased estimates of the expected rate of return at the firm level  Do the estimates of ex ante expected return explain ex post realized return  Correlated omitted variables bias  Using realized return as a measure of validity is at odds with the motivation for using accountingbased estimates  The components of realized returns  A method for evaluating estimates of expected returns  All components of realized returns are measured with error  Correlations with realized return as the method for evaluating expected return proxies  Evaluation based on the regression of the estimates of the expected rate of return on commonly used risk proxies  The regression of the firmspecific estimate of the return premium on risk proxies  Shortcomings of the regression approach  Illustration of spurious effects  The role of correlations with risk proxies  The importance of focusing on measurement error  Summary  Extant firmspecific estimates are poor  Comparison with the riskfree rate and other descriptive statistics  Correlation with realized returns  The measurement error variance of the estimates of the expected rate of return  Ranking of the methods for estimating the expected rate of return  Summary  Bias in estimates of the expected rate of return due to bias in earnings forecasts  Bias matters  An ex ante measure of optimism  Bias is the difference between estimates based on forecasts of earnings and estimates based on earnings realizations  Ex ante and ex post measures of bias  Ex ante determination of the effect of bias  Ex post determination of the effect of bias  Ex ante and ex post comparison  Empirical estimate of the ex ante bias  Empirical estimate of ex post bias  Which earnings are related to prices? does the market see through the forecast bias  Summary  Dealing with shortcomings in firmspecific estimates  Methods for mitigating the effects of measurement error  Grouping  Variables that may be used to form groups  Empirical evidence of the effects of grouping  Instrumental variables  Variables that may be used as instruments  Empirical evidence of the effectiveness of instrumental variables  The errors in variables problem may be less severe for some subsets of the data  The relevance of the vast literature on analysts' forecast errors  Subsamples where the error and/or bias may be less  Reducing the error and/or reducing the effects of the error: an example  Methods for dealing with, socalled, "sluggish" forecasts  Critique of methods  Reducing the forecast error by the predicted value from a regression of forecast errors on various firm characteristics  Combining timeseries forecasts and analysts' forecasts  Summary  Methods for determining the effect of a phenomenon of interest on the cost of capital  Examples of phenomena studied in the extant literature  The most common methodology  A method for comparing expected rates of return across groups of stocks  Controlling for effects other than the effect of interest  Introducing controls in the dummy variable regression  Additional dummy variables or interaction terms  Matchedsample design  The firm as its own control  Matching and the firm as its own control: the dummy variable regression  Expected growth rates are determined by the data  Summary  Data issues. 

Misalignment of prices, book values, and earnings forecasts  An example: P&G  A close look at the timeline for these forecasts  The earnings forecast may be for a fiscal period that has ended  Book value will not be known until the earnings announcement date  Forecasted book value as the anchor  One option: calculate implied expected rates of return based on forecasts obtained at year end and based on yearend prices  Disadvantages of using reverse engineering based on prices at a particular point in time  Determining virtual forecasted book values and virtual forecasted earnings at any date: the method proposed by Daske et al. (2006)  An example: P&G  Estimating earnings to the estimation date  Estimating virtual book value  Estimating earnings for the remainder of the fiscal period  Discount daily  An alternative: adjust prices  Summary  Some thoughts on future directions  Other sources of earnings forecasts: the data  Mitigating errors and bias  Refocus on operations references updates. 
Restrictions 
Restricted to subscribers or individual document purchasers. 
Summary 
Estimating the Cost of Capital Implied by Market Prices and Accounting Data focuses on estimating the expected rate of return implied by market prices, summary accounting numbers, and forecasts of earnings and dividends. Estimates of the expected rate of return, often used as proxies for the cost of capital, are obtained by inverting accountingbased valuation models. The author describes accounting based valuation models and discusses how these models have been used, and how they may be used, to obtain estimates of the cost of capital. 
Notes 
Peter Easton (2009) "Estimating the Cost of Capital Implied by Market Prices and Accounting Data", Foundations and Trends in Accounting: Vol. 2: No 4, pp 241364. 
Other formats 
Also available in print. 
System notes 
Mode of access: World Wide Web. 

System requirements: Adobe Acrobat reader. 
Subject 
Capital costs  Econometric models.


Rate of return  Econometric models.


Valuation  Econometric models.

ISBN 
9781601981974 (electronic) 

9781601981943 (print) 
Standard Number 
10.1561/1400000009 
