RELOCALIZACIÓN DE DIQUES

The following chapter describes my choice of key variables of the cost of capital to investigate the relevance of ESG disclosure for debt and equity investors.

7.3.3.1 Expected Cost of Equity

I use four key variables to capture the expected cost of equity to empirically test the relevance of ESG disclosure for equity investors: Bloomberg's cost of equity, cost of equity based on

the capital asset pricing model, cost of equity based on Fama and French's three-factor model, and cost of equity based on Carhart's four-factor model. To compute all alternative versions of the cost of equity, I obtain data from Bloomberg, Graham and Harvey's survey data on the Equity Risk Premium104, Kenneth French's Data Library105, and Thomson Reuters Datastream. My first dependent variable is based on Bloomberg's cost of equity. Bloomberg's cost of equity in a given year equals the risk-free rate plus the company's beta coefficient multiplied by the expected equity market premium. Bloomberg estimates a company's beta coefficient using the capital asset pricing model (See e.g. Equation 4 in Chapter 3.6.3.2. 'Regression Analysis') based on weekly observations over the previous two years. My remaining dependent variables are based on my own computations and represent alternative versions of the expected cost of equity and differ somewhat from Bloomberg's cost of equity.

My second dependent variable is the expected cost of equity based on Graham and Harvey's annual surveys on the expected market premium. The expected cost of equity equals the risk-free rate plus a company's beta coefficient multiplied by the expected equity market premium which I obtain from Graham and Harvey's annual surveys on the expected equity risk premium based on survey data of US Chief Financial Officers from S&P 500 companies. Using survey data of expected equity risk premia is similar to using analyst earnings per share forecasts (EPS) commonly used to compute implied cost of equities (See e.g. Dhaliwal et al., 2011, 2012; El Ghoul et al., 2011; Plumlee et al., 2010). I infer a company's beta coefficient by estimating the capital asset pricing model based on daily, weekly, and monthly observations using Equation 4 in Chapter 3.6.3.2. 'Regression Analysis'). I use daily, weekly, and monthly data frequencies to increase the robustness of my estimations as well as to avoid known biases such as the trading frequency bias with short-interval data (See e.g. Dimson, 1979; Roll, 1981; Scholes and Williams, 1977).

My third dependent variable expands the second dependent variable and adds a company's size coefficient multiplied by the size premium as well as a value coefficient multiplied by the value premium. I infer the additional size and value coefficients using Fama and French's three factor model (See e.g. Equation 5 in Chapter 3.6.3.2. 'Regression Analysis').

104 _{Graham and Harvey (2015) conduct and publish quarterly surveys of US Chief Financial Officers from S&P} 500 companies to obtain the expected risk premium. Graham and Harvey's (2015) survey data on the expected equity risk premium goes back to 1996 and is available online on SSRN and the following website: http://www.cfosurvey.org/past-results-2015.html).

My fourth dependent variable expands the third dependent variable once more and adds a company's momentum coefficient multiplied by the momentum premium. I infer the additional momentum coefficient using Carhart's four factor model (See e.g. Equation 6 in Chapter 3.6.3.2. 'Regression Analysis'). I re-estimate and calculate each of my dependent variables using daily, weekly, and monthly data frequencies. Overall, I produce nine expected cost of equities inferred from three asset pricing models including CAPM, Fama and French, and Carhart (See Equations 4 to 6 in Chapter 3.6.3.2. 'Regression Analysis').

My expected cost of equity models relative to implied cost of equity models require fewer assumptions about earnings forecasts. Even the simplest implied cost of equity model (see e.g. Easton's, 2004 implied cost of equity based on the Price-Earnings-Growth Ratio) requires at least two year ahead positive earnings forecasts (El Ghoul et al., 2011). In addition, companies require positive actual earnings per share figures. Companies with negative earnings forecasts will be either excluded from the sample or "replaced by the value implied by a 6% return on assets" (El Ghoul et al., 2011:2402). Excluding companies with negative earnings forecasts considerably reduces the sample size and representativeness of the sample. Thus, to overcome the shortcomings of the implied cost of equity model by shortening my sample size due to companies' negative earnings forecasts, I use Graham and Harvey's survey data on the expected equity risk premium to infer my expected cost of equities. This ensures that my initial sample size stays intact.

A second shortcoming of the implied cost of equity approach is a common bias called the analyst's forecast optimism bias. It is common among analysts to provide overly optimistic company earnings forecasts, which could introduce a bias in the implied cost of equity estimate (El Ghoul et al., 2011; Kothari, 2001). Controlling for this bias is not always straigthforward. Thus, my approach to compute the expected cost of equity based on Graham and Harvey's survey data on the expected equity risk premium is a more conservative approach.

7.3.3.2 Cost of Debt

I use one key variable to capture the cost of debt to test the relevance of ESG disclosure for debt investors: corporate yield spreads. The corporate yield spread of a certain bond class represents the average yield spread between corporate bonds and government bonds. Bloomberg computes a 'debt adjustment factor' which reflects a company's credit rating in the calculation of the cost of debt. I collect the cost of debt variable directly from Bloomberg.

Bloomberg's cost of debt variable is available on an annual basis at the end of each calendar year.