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5 The Weighted Average Cost of Capital (WACC)
5.1 Introduction and main goals of the section
WACC is generally recognised as the best way to evaluate the allowed return on the capital in- vested.^1 It is relevant for regulatory purposes as it is one of the main elements in defining cost ori- ented prices, carrying out price/margin squeeze tests^2 and implementing the regulatory accounting obligations. It is relevant as a benchmark on return on investment and for applying claw-back mechanisms in state aid procedures.
The WACC plays an important role in setting cost-oriented regulated prices because it determines the reasonable rate of return on the capital employed. Regulated prices should provide the regu- lated firm with the opportunity to finance (efficient) investment and provide access seekers with ef- ficient “build-vs-buy” price signals. An increase (decrease) in WACC will, other things equal, in- crease (decrease) regulated prices. Even minor changes in the WACC can influence the regulated prices significantly given that the telecommunications sector is very capital intensive^3.
The WACC estimation is forward looking even when based on historical information. As such, es- timating the WACC requires regulatory judgement to assess whether current or historical evidence is more relevant on a forward-looking basis.
This implies that when calculating the WACC for wholesale price regulation purposes, NRAs can- not focus only on theory; a practical view on the estimation process is also necessary to take into account regulatory objectives and previous regulatory decisions.
It is possible to estimate the parameters in the WACC formula in different ways, and NRAs may take different approaches according to elements such as national economic conditions, availability of data, the degree of wholesale and retail competition, regulatory goals/strategy etc. BEREC con- siders that NRAs should have flexibility to take a practical approach to estimating WACC that sup- ports national circumstances
When NRAs calculate the WACC they may take into account general regulatory principles^4 such as predictability, transparency, and consistency, sending efficient price and investment signals etc..
Predictability limits uncertainty and thereby risks to the industry that is characterized by long term investment.
Transparency helps to obtain acceptance from the industry. In practical terms, this means the use of
- reliable and well documented data sources,
(^1) Commission Recommendation on 20 September 2010 on regulated access to Next Generation Networks (NGA). (^2) BoR (14) 190. (^3) For the local access market, for example, a 1% change in the WACC could change regulated wholesale prices by 5- 10%. 4 2009/140/EU
- proven methodologies that can be verified and
- financial theory that is established and accepted by the market.
Consistency ensures that the calculation of the parameter values is in line with both theory, empiri- cal findings and regulatory frameworks which helps WACCs converging methodologies across countries.
BEREC acknowledges that the WACC is not only a matter of wholesale price regulation; it can provide, together with the regulatory framework in charge and the overall remedies imposed a sig- nal directly to investors who invest in multiple EU-countries. In such a sense, a more homogenous WACC methodology estimation process can increase confidence of investors providing a better signal - in this case limiting the risk of distorting investments in telecommunications infrastructure between member states.
Specifically, in relation to the last relevant element a question arises on the advantages of a strong harmonisation of the parameters used for WACC estimation among European countries. To this respect, a cost/benefit trade-off should be carefully weighed against the desired consistency over time and stability within a member state. It needs to be assessed whether the costs of adjustment due to a change of methods within a country are higher than the potential benefit of a harmo- nised/common methodology.
Over the years BEREC has collected information about the way WACC is estimated by all NRAs in regulated markets. In BoR (13) 110, an extensive survey has been carried out on the subject of WACC confirming that nearly all NRAs use the CAPM (Capital Asset Pricing Model) to evaluate the equity rate of return from which they derive the WACC as a weighted average of the cost of debt and the cost of equity focusing the analysis on the fixed and mobile markets i. e. Market 4/ and Market 7/2007.^5 In general, NRAs that estimate the WACC for fixed markets also use the same values for fixed termination markets and leased lines with any differences usually the result of timing differences between market reviews.
In the Regulatory Accounting Report 2016 (BoR (16) 159) BEREC decided to update and expand the information collected for the WACC section in terms of quality, quantity and scope, providing an update of the 2013 Annex report as well as providing new benchmarks about WACC parameter es- timation and methodologies.
Before going to go into 2017 analysis BEREC would like to briefly mention the Brattle study. On 18 July 2016 the Commission published a study carried out by The Brattle Group and titled “Review of approaches to estimate a reasonable rate of return for investments in telecoms networks in regula- tory proceedings and options for EU harmonisation”.^6 This study analyses in detail the approaches NRAs use to determine the rate of return in regulatory environments providing guidance to a “con- verging” methodological approach for estimating parameters.
The study has been thoroughly examined by BEREC since one of the main goals of EC in tender- ing the research has been to promote a methodological approach in Europe to estimate WACC avoiding high discrepancies that could lead to weakening the single market.
Chapter 5 of the 2017 RA report will survey WACC values, benchmarking final rates, and method- ologies of single parameters within the WACC formula computed by NRAs for the estimation of
(^5) Annex to the 2013 RA Report “Cost of Capital in Europe – Cost of Capital Parameters in 27 European Countries” (Data as of 1st January 2012) 6 https://publications.europa.eu/it/publication-detail/-/publication/da1cbe44-4a4e-11e6-9c64-01aa75ed71a
A nominal WACC includes the impact of inflation. The conversion from a nominal WACC to a real one should be done by using the well-known Fisher equation :
WACC_real = (1+Wacc_nominal )/(1+Inflation rate)-1.
The choice of a nominal or real WACC is related to the used price base. Generally, NRAs, when using bottom-up models, can include inflation via price evolution of assets, in that case a real WACC is required for estimating the cost of capital, whereas in the opposite case a nominal WACC is needed to avoid double counting of the inflation.
It is relevant to consider that the conversion from nominal to real WACC should be consistent with the estimation of a pre– or post-tax calculation of the WACC. In line with the concept that taxes are always paid in nominal terms, a pre-tax WACC should be derived from an after tax WACC ex- pressed in nominal terms and not in real terms. So a pre-tax real WACC should be derived from nominal pre-tax WACC.
A first methodological element in the WACC calculation to be considered is how the cost of equity is determined. The most common approach used by NRAs is the Capital Asset Pricing Model (CAPM)^8 , which is a linear single factor model that provides a measure of the relationship between perceived market risk and expected returns from the point of view of the market investor. The CAPM makes the assumption that the rate of return should be positively correlated with a market risk.
The rate of return of a market stock can be expressed as the sum of the Risk Free Rate (r (^) f) plus a Market Risk Premium. The model explains that the equity risk of a stock market can be expressed as a linear relation between the beta of the market stock and the Equity Risk Premium (rm - r f).
The beta provides an estimation of the component of non-diversifiable risk of a market stock as- suming the investor has a perfectly diversified portfolio. The beta of a market stock is expressed as the ratio between the co-variance of the rate of return of the market stock with respect to the rate of return of the whole market portfolio and the variance of the market portfolio itself: cov(Ri,Rm)/σ^2(Rm). When beta is equal to 1, then the rate of return of a stock market is equal to the rate of return of the whole market portfolio. A beta grater (lower) than one means that the rate of return of a market stock has a greater (lower) systematic risk with respect to the whole market portfolio.
The following analysis focuses on CAPM since this is the methodology NRAs use. Other methods for estimating the cost of equity have not been considered (such as: Arbitrage Pricing Theory (APT); Fama-French model^9 ; Empirical capital asset pricing model^10 ; Market derived CAPM^11 ; To- tal Market Return (TRM)).
(^8) William F. Sharpe “Capital Asset Prices: A theory of Market Equilibrium under Conditions of Risk” Vol. 19, No. 3 (Sep., 1964), pp. 425- (^9) The Fama and French is a three-factor model that can be thought of either as a special case of APT or as an en- hancement of CAPM. The model has three factors: market factor, company size factor, and book/market value factor. While this model has been, to some extent, supported by the results of certain empirical studies, there has been a con-
The questionnaire asked NRAs to provide information on the following main parameters: i) Risk Free Rate; ii) Cost of Debt; iii) Beta; iv) Equity Risk Premium; v) Gearing; vi) Tax. Information was collected both on methodologies and values, for decisions currently in force as well as past deci- sions. Specifically, the questionnaire relates to WACC decisions in market 3a of the Recommenda- tion; in case “not applicable/not available”, data related to other fixed markets have been consid- ered (fixed termination or market 3b).
Table 1 – WACC parameters
Parameters 1 Risk Free Rate 2 Equity Risk Premium 3 Beta 4 Cost of debt 5 Gearing 6 Tax rate 7 Wacc Nominal pre-tax
Source: BEREC 2017
In table 2 the year of information provided about WACC calculation is reported for each country as well as their frequency of updating.
31 NRAs replied to the questionnaire providing information on WACC methodologies and values applied to market 3a in the 2008-2017 period.^12 Most of the NRAs (21) update WACC in line with their market analysis or when pricing decision are taken. In this case, market-specific WACCs may be in force for 2 or more years. Some NRAs update yearly (10), but in some cases the update comes into force only when new pricing decisions are taken.
The dataset used for the following analysis takes into consideration 65 observations on all 7 pa- rameters previously listed. Specifically 10 NRAs provided one WACC updates, 8 NRAs provided two WACC updates and 13 NRAs provided three WACC updates.
All values provided by NRAs are consistent with their final nominal pre-tax WACC calculation meaning that in some cases parameters contain also some country specific premium added to the cost of Equity and attributed mainly to RFR, ERP or Beta in line with the information provided to the RA-EWG.
siderable debate on whether the risk premium associated with the two additional factors (company size and book/market value) are statistically significant. 10 Jensen, Michael C. and Black, Fischer and Scholes, Myron S., The Capital Asset Pricing Model: Some Empirical Tests. Michael C. Jensen, STUDIES IN THE THEORY OF CAPITAL MARKETS, Praeger Publishers Inc., 1972. Availa- ble at SSRN: 11 https://ssrn.com/abstract= J. McNulty, T.D. Yeh, W.S. Schulze, M. H. Lubatkin “What’s your real cost of capital?” Harvard Business Review 2002 (^12) Only EE said that the final WACC value is obtained using a benchmark from other NRAs, not applying directly a formu- la.
Figure 1 – WACC-Nominal Pre-tax^13
Source: BEREC 2017
In figure 2 the average year-by-year adopted nominal pre-tax WACC values are shown to have a better understanding on the influence of the time when the decisions were taken on the estimation on the nominal WACC values. The currently in force average value comes from averaging values that are in use at the date of questionnaire’s replies (independent from the year of the decision).^14
(^13) UK WACC for 2017 comes from March 2017 WLA Consultation. This caveat applies to each parameter. (^14) For DE the WACC for 2017 is the corresponding real pre-tax WACC equal to 5.76%.
Figure 2 – WACC-Nominal Pre-tax 2008-
Source: BEREC 2017
We note a reduction over time of the average WACC estimation for the last four years (only NRAs that updated WACC values from 2014 were considered). In order to explore the WACC parameters relevance with respect to the WACC values according to the formula and the dataset collected by NRAs, we carried out i) a sensitivity analysis and ii) a re- gression exercise. Figure 3 shows the outcomes of the sensitivity analysis (Beta and ERP lines overlie since they have the same coefficient)^15.
Figure 3 – Sensitivity analysis on WACC formula
(^15) The sensitivity analysis is as follows. We take, for each parameter the arithmetic average of the survey, which is de- scribed in the next Sections. We let each parameter vary by keeping constant the others. Each parameter in the picture may vary by +/- 20%. We may conclude that, in a realistic parameter space, linear approximation of variation (i.e. a con- stant partial derivative also for tax parameter) is accurate. Just when varying taxes more than +100% the WACC curve may exhibit a nonlinear trend on this single parameter variation.
5.2.1 Risk Free Rate
Definition and general financial theory
The risk free rate is the return which an investor can expect to gain from investments which do not carry any risk: it measures the expected return on an investment free of default and systematic risk, it reflects the time value of money understood as the compensation that investors require in order to invest today in favour of future consumption. The risk free rate is a relevant element for es- timating the cost of equity in the CAPM model.
RFR is generally informed by reference to government bond yields. The nominal RFR can be in- formed by yields on nominal government bonds while the real RFR can be informed by yields on index-linked government bonds. A real RFR can be translated into a nominal RFR by using the Fisher equation and an assumption about inflation. From the introduction of the Euro up until the crisis of 2009, nominal government bond yields in the Eurozone economies have been at similar levels – see Figure 5(a). This trend changed from 2009 onwards when, due to the global financial crisis, the difference between country bond yields increased. In figure 5(b) the relative standard deviation of the average for 10 year spot rate bond for all countries is represented, showing a sig- nificant increase. At the same time, the Quantitative Easing Policy of BCE reduced the average level of the bond yields from mid-2012 onwards.
Figure 5 – Yield difference average yield (a) / average and standard deviation (b)
(a)
(b)
Source: BEREC 2017 on ECB publicly available data
It is generally recognised that the domestic bond yield is the correct approach to include a country risk premium in the cost of equity, moreover, it provides a good approximation of the nominal Risk Free Rate considering the fact that the domestic bond yield reflects inflation expectations in a for- ward looking perspective.
The inclusion of a country specific risk premium in the risk-free rate seems may be one way to cap- ture the specific effect that the financial crisis had on the required return for regulated assets, as it incorporates part of the additional country risk premium that may be required by equity investors with regards to the macro environment of a specific country. The domestic bond yield will generally compensate for country specific (incl. regulatory) risks. Also, in the event of an economic downturn or crisis the risk of increased default payments, bad debt and network abandonment can increase the systematic and non-diversifiable risk.
In financial theory different approaches are applied for estimating a country risk premium in order to determine the cost of equity: i) using a government country bond yield for the RFR; ii) using a spread between company bond working in national market that suffers a financial crisis and com- pany bond working in market that doesn’t suffer a financial crisis;^19 iii) adjusting the ERP estimated in normal conditions through the ratio of equity market volatility of a country suffering a financial crisis and equity market volatility of a market that doesn’t suffer a financial crisis.
The use of domestic bonds is generally not recommended in case of illiquidity, in fact in this case the spread between the real risk free rate (i. e. German Government Bonds) cannot represent a compensation of the systematic risk (non-diversifiable risk) due to macroeconomic conditions of the country, but just an illiquidity premium. Illiquidity measures can be obtained using trading vol- ume, trading frequency (the number of trades executed within a specified interval, without regard to trade size, etc.). However, “relatively” simple indicators of market activity based on trading volumes
(^19) While the cost of equity compensates investors for a different set of risks than the cost of debt, using data from debt markets can still provide some insight on the country risk premium. Intuitively, the country risk premium on equity would be expected to be at least as big as the country risk premium on debt, since equity-holders are the residual claimants on a firm’s cash flows (Oxera http://www.autorita.energia.it/allegati/docs/15/275-15oxera.pdf).
Figure 6 – Nominal Risk Free Rate
Source: BEREC RA database 2017
The following table compares the main approaches used by NRAs to estimate the RFR (the an- swers were based on a set of pre-defined alternatives as reported in the table). As an example for the “main methodology” indicator, the following assumptions have been considered.
Main methodology Domestic bond
Refers to the use of own country bond
Country specific bond
Refers to the use of a specific bond from a dif- ferent country
Other A mix of methodologies and judgement is used to derive an estimate
Benchmarking the RFR is estimated by referenced to RFR values used by other NRAs
Table 2 – RFR methodology survey RA Report 2017
Source: BEREC 2017
Next we provide some highlights from the questionnaires. As a preliminary output, most of NRAs use a nominal estimation of the RFR without evaluating a real risk-free rate. A real risk-free rate is estimated by 7 NRAs (CH, IE, IS, LU, NO, PL, UK).
All the indicators identified by the questionnaire show a quite consistent approach in terms of the main methodology used for estimating the RFR. The averaging window is the only factor where a variety of approaches are taken by NRAs.^21
Combining the approaches in terms of general methodology (geographical scope) and time win- dows, i. e. the more differentiated parameters to estimate the RFR, the following statistics emerged (figure 7).
(^21) In table 2, replies of “7 years” (SE, LU) have been included in the closer category “5 years”.
In general, NRAs that use domestic bonds as a methodology for estimating the RFR together with a less than one-year time window motivated the choice in terms of consistency with a forward look- ing approach with respect to the financial situation. In this case the deviation from the spot rate is a way to overcome short term volatility. It should be considered that the frequency of updating the WACC can have an influence on the approach used: among the 8 NRAs that use short time win- dows, 5 update the WACC yearly (ES, HU, LT, PL, SK). On the other end, out of the 8 NRAs that use a longer time window only one NRAs updates the WACC yearly (DK).
NRAs that use domestic bonds and a time window average of more than 5 years explained as their motivations the imposition of some “regulatory objective”, thus granting predictability, consistency and transparency, and overcoming the effects of quantitative easing.^22
In the last case, the choice of averaging bond windows seems to be related to adjusting the level of the risk-free rate (by including a country risk premium when this is not included in other way). That is to say, within the current period of very low yields, the aim is to place more emphasis on longer data series aiming at mitigating the risk of underestimating the WACC.
In summary, the main motivations behind the choice of averaging windows are: i) to maintain regu- latory predictability (e.g. consistent approach over time, or taking long term averages to limit varia- tions between market reviews); ii) to avoid putting too much weight on factors which may distort current yields (e.g. QE); iii) consistency with regulatory period; iv) consistency with investment life cycle.
In order to see how predictability and consistency principles are interpreted by NRAs in practical terms, the questionnaire asked about the motivation behind the change (if any) of main methodol- ogies in recent years. Only few NRAs stated to have changed their approach over the years for es- timating the RFR (FI, FR, IT). The motivation for changing was to overcome instability in the final value of the WACC that could have occurred from the application of previous methodologies due to the low level of the domestic bond rate (FI) with respect to previous regulatory periods, or the vola- tility experienced due to the global financial crisis (FR and IT).
Looking at the distribution of the “time windows” used by NRAs in 2013-2017, the period when the number of NRAs that have updated their WACC is higher, it seems that there is a growth in prefer- ence with respect to a time windows >=5 years.
At the same time the average Risk Free Rate estimated in the same period shows higher values than European bonds leading to more stable values than the ones obtainable from the spot estima- tion of the parameter at the basis of RFR estimation (fig 9).
(^22) One NRA (DE) declared that a high fluctuation of the regulatory WACC over time is not in line with the requirements of the law. Therefore an exponential smoothing procedure has been used since 2009. The procedure’s goal is to achieve fairness in the long run without having instability and unpredictability while, in the short run, it allows the regulator to stick to the chosen estimation procedures for the WACC even in years when the procedure leads to unexpected results. This exponential smoothing consist in weighting the current estimation by 30%, while 70% is the weight attributed to the WACC estimated in previous period.
Figure 8 – RFR: distribution of methodology
Source: BEREC 2017
Figure 9 – RFR/European spot bond rate
Source: BEREC 2017
Looking at Quantitative easing, only one NRA takes this explicitly into account (BE)^23. In two other cases (FR and UK) quantitative easing is indirectly taken into account without an explicit adjust- ment.. One NRA (UK), even without making an explicit adjustment to time windows for this effect, explains that QE is one reason for preferring longer term average yields rather than spot rates.
(^23) The RFR in this case is based on a composed Bloomberg index.
ally is multi-year and much longer than one year, arguments for geometric average premiums may become stronger. So the use of arithmetic, geometric or hybrid approaches can all be supported by financial theory in order to obtain reliable information on the country’s market risk premium.
Another method for evaluating the ERP is based on a survey done via interviews with investors and CEOs about their expectations and their forward-looking perspective of market stock premi- ums. There is a widespread view that this methodology may have some drawbacks concerning its reliability when estimate the ERP for regulatory purposes. Specifically, replies collected in surveys might have been affected too much by recent outcomes of the stock market providing an overesti- mate or an underestimate of the ERP due to short term events. Moreover, the survey approach, in some cases, can be less consistent with the estimation of the RFR as it takes into account a gen- eral view on premium that weights expectation on both treasury bill (short term) and bonds (long terms).
An alternative approach is to use a dividend growth model (DGM) that is based on the estimation of actual share prices and returns per share. The use of the DGM is a common technique for esti- mating the cost of equity for a company or an index making use of short-run data. In its simplest form the DGM can be written as follows.
The DGM estimates the cost of equity by computing the discount rate that equates a stock’s cur- rent market price with the present value of all future expected dividends. In a simple (one-stage) DGM it is assumed that there is a constant expected growth rate of dividends for all future years.
Given this assumption, the stock is valued at a price P0 derived as follows:
(1) R =D0*(1+g)/P0+g Where:
- D0 is the dividend per share at period 0;
- R is the post-tax cost of equity;
- g is the dividend per share growth rate (assumed constant); and
- P0 is equal to the share price at period 0 (measured at ex-dividend date).
Equation (1) states that a firm’s cost of equity is equal to: i) its prospective dividend yield (expected next period dividend per share divided by stock price on the ex-dividend date of the previous divi- dend paid out) plus ii) the long-term expected rate of growth in its dividend.
The ERP is obtained subtracting from the cost of equity (R) an estimation of the Risk Free Rate.
In the described model a single stage of growth rate is assumed.
A more complex approach is a multi-stage DGM with the hypothesis that the growth rate cannot be constant over time: the growth rate of a company may be higher at the early stage and stabilising in the future (see figure below).
In case of a linear H-model the implied cost of equity can be written in a closed formula:
[ n a n ] n
0
0 (1 g ) H (g g ) g
P
D
R = ⋅ + + ⋅ − +
In this case two different growth rates have to be estimated at the beginning of the period (g_a) and after 2H years (g_n).
The estimation of the growth rate is mainly based on the view of financial analysts and in this re- spect is more of a subjective analysis, in line with the one provided in the survey approach.
Main output from the survey. From the replies to the 2017 questionnaire the following statistics emerge. 28
2017 Average Median Standard Deviation
Relative Standard Deviation
Maximum Minimum
Equity risk premium ERP (31 NRAs) (^) 5.77% 5.15% 2.12% 36.82% 15.26% 3.00%
(^28) The data represented include a country risk premium in ERP value provided by NRAs to be consistent with the final WACC estimation (CY, IE, EL).
