The Implied Cost of Capital: A New Approach

Hou, Kewei; Dijk, Mathijs A. Van; Zhang, Yinglei

doi:10.2139/ssrn.1343516

Cited by 66 publications

(191 citation statements)

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“…However, subsequent studies demonstrate that realised returns are a noisy proxy for expected returns (e.g., Fama and French, 2002). Among all of the possibilities, we focus on the implied cost of equity capital (hereafter, ICC) that does not rely on biased realised returns or on asset pricing models (Hou et al, 2012) because its reasoning is based on a variation of the Edwards-Bell-Ohlson accounting model (Edwards and Bell, 1961;Ohlson, 1995).…”

Section: Cost Of Equity Measurementmentioning

confidence: 99%

Economic Consequences of Key Performance Indicators' Disclosure Quality

et al. 2014

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Starting from 2006, UK listed companies are required to analyse their performance by using Key Performance Indicators (KPIs) in specific sections of their annual reports and the UK Accounting Standard Board (ASB) provides companies with guidelines for the best practice regarding KPI disclosure. Motivated by the possible effects of the KPI disclosure quality, we examine their potential economic consequences for a sample of UK listed firms for the period 2006 to 2010. Our sample consists of 448 firm-year observations. We first develop a measure for the quality of the KPI disclosure based on the ASB's guidelines. We then test the economic consequences of financial and nonfinancial KPIs disclosure quality both separately and combined into one variable. Our findings, after conducting various sensitivity tests, suggest that only the disclosure quality of financial KPIs matters. We find a significantly negative (weakly positive) relationship between disclosure quality of financial KPIs and the implied cost of capital (firm value). These results inform regulatory bodies as well as the academic literature about the potential economic consequences of this type of disclosure.

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Section: Cost Of Equity Measurementmentioning

confidence: 99%

Economic Consequences of Key Performance Indicators' Disclosure Quality

et al. 2014

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“…Specifically, the median quantile regression generates the following results: (i) β 50 1 is 0.94 consistent with mean reversion in accounting rates of return (e.g., Penman 1991;Fama and French 2000); (ii) β 50 2 is −0.01 consistent with loss makers having lower levels of future profitability (e.g., Hou et al 2012); (iii) β 50 3 is −0.14 consistent with faster mean reversion in profitability for loss making firms (e.g., Beaver et al 2012); (iv) β 50 4 is −0.02 consistent with the welldocumented negative relation between accruals and future firm performance (e.g., Sloan 1996;Richardson et al 2006); (v) β 50 5 is 0.02 consistent with dividend-paying firms having higher levels of future profitability (e.g., Hou et al 2012); and (vi) β 50 6 is 0.26 also consistent with firms with higher dividend payout having higher levels of profitability (e.g., Hou et al 2012).…”

Section: Appendix I: Variable Definitionsmentioning

confidence: 93%

“…Our model resembles the one of Hou et al (2012), with the exception that we forecast return on net operating assets (RNOA), instead of return on equity (ROE), and therefore do not include leverage as an explanatory variable and scale all variables by the average balance of net operating assets (NOA), rather than by the average balance of book equity. All variables used in the estimation are described in Appendix I.…”

Section: Appendix I: Variable Definitionsmentioning

confidence: 99%

Asset volatility

2017

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We examine whether fundamental measures of volatility are incremental to market-based measures of volatility in (i) predicting bankruptcies (out of sample), (ii) explaining cross-sectional variation in credit spreads, and (iii) explaining future credit excess returns. Our fundamental measures of volatility include (i) historical volatility in profitability, margins, turnover, operating income growth, and sales growth; (ii) dispersion in analyst forecasts of future earnings; and (iii) quantile regression forecasts of the interquartile range of the distribution of profitability. We find robust evidence that these fundamental measures of volatility improve out-of-sample forecasts of bankruptcy and help explain cross-sectional variation in credit spreads. This suggests that an analysis of credit risk can be enhanced with a detailed analysis of fundamental information. As a test case of the benefit of volatility forecasting, we document an improved ability to forecast future credit excess returns, particularly when using fundamental measures of volatility.

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“…As far as cost of capital is concerned, according to the literature concerning ICC estimation methods (among others: Hou et al 2012, Li & Mohanram 2014, the valuation method developed in this paper allows to extrapolate the cost of bank assets using the following formula:…”

Section: Methodsmentioning

confidence: 99%

Leverage, Cost of Capital and Bank Valuation

Beltrame

Caselli

Previtali

2018

J. Fin. Mngt. Mar. Inst.

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In this paper, we present a model that demonstrates the e®ect of debt on cost of capital and value in the case of banking¯rms. Using a static partial equilibrium setting, both in a steady state and steady growth scenario, we derive a bank-speci¯c valuation metric which separately attributes value to assets and debt cash°ows in the form of a liquidity premium and taxshield. We run our model on a sample of the largest 26 European banks from 2003 to 2016 nding that the value contribution of debt bene¯ts to enterprise value is large and persistent. Further from our model, we derived an implied cost of capital (ICC) measure¯nding consistent results with capital asset pricing model (CAPM). The theoretical framework we present is helpful to address bank debt bene¯ts valuation and to reconcile equity and asset side approaches.

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The Implied Cost of Capital: A New Approach

Cited by 66 publications

References 0 publications

Economic Consequences of Key Performance Indicators' Disclosure Quality

Economic Consequences of Key Performance Indicators' Disclosure Quality

Asset volatility

Leverage, Cost of Capital and Bank Valuation

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