VALUING EMERGING MARKET EQUITIES – THE NEW …



VALUING EMERGING MARKET EQUITIES – THE NEW EMPIRICAL EVIDENCEBy Niso AbuafNiso Abuaf is Clinical Professor of Finance and Economics at Pace University; and Chief Economist and Strategist at Ramirez and Co., in New York, New York.I would like to thank Saichalee Bee Limaphichat for invaluable research assistance.Though practitioners and academics rely on similar conceptual frameworks when valuing international equities in general and emerging market equities in particular, they emphasize different aspects of the framework. In contrast to academics, practitioners adjust discount rates as opposed to cash flows, and use the US as opposed to the global equity market risk premium.Abuaf (2011) summarizes the arguments on the academic and practitioner sides, lets the data do the judging, and presents evidence that US dollar returns on emerging market equities (ADRs) primarily are a function of returns on the broad US equity market (e.g., the S&P 500) and on the corresponding country’s credit default swap (CDS) spreads. Because CDSs are standardized contracts that are far more liquid than dollar denominated emerging market bonds, in the current market environment they are better indicators of emerging-market risk. As such, I use them in my empirical work. In this paper, I update the evidence presented in Abuaf (2011) with a slight variation. In particular, I regress returns on ADRs, versus returns on the S&P 500 (both represented as log differences) and changes in CDS spreads, which is the variation presented in this paper. As such, the coefficient of the S&P500 would be interpreted as the traditional β, and the coefficient of the CDS would be interpreted as the modified duration of equities (semi-elasticity) with respect to CDS. I mathematically demonstrate why the modified duration equals the P/E multiple. When presenting the empirical results, I visually test whether the modified duration of equities vs. CDS spreads approximate the P/E multiples.When the worldwide privatization boom began in the late 1980s, sellers, buyers, and financial intermediaries realized that they needed a framework within which to price assets in disparate regions of the world. Unfortunately, standard international corporate finance theory could offer little assistance, primarily because it argued that when valuing, for example, telephone assets in Mexico, one should account for Mexican risk by adjusting the expected cash flows and then discounting these cash flows by applying a weighted average cost of capital (WACC). This approach was similar to valuing telephone assets residing in the US, but not applicable to international valuations as analysts had no rational way of adjusting cash flows to reflect country risk, such as Mexico’s.As an alternative method, Abuaf and Chu (1991, 1994) and Abuaf, Chu, Czapla, Lawley and Thadani (1997) recommend a pragmatic strategy of adjusting the cost of equity, leading to a related adjustment to the WACC. Intuitively, in a virtually integrated global capital market, the risk associated with the Mexican telephone-asset example consists of two building blocks:A US telephone-asset risk, andThe risk associated with an investment in Mexico.The above statements are simplifications that practitioners have used and continue to use. In theory, the correct approach would be to model the risk of the telecommunications industry worldwide, which in all likelihood would be modeled as returns on the global capital markets, adjusted by a global telecom beta. A further adjustment might be needed in that Mexico’s telecom beta might be different than a global or US telecom beta, because of the life-cycle maturity, or other industry characteristics, of the Mexican telecom industry versus its global counterparts. The literature’s approach to these questions is not monolithic, as summarized below:Literature SurveyThis Journal has had a long history of publishing articles related to valuing international equities and investments, particularly as they relate to emerging market investments. For example, since the early 1990s various authors have made the following salient points:Stulz (1995). “The increasing synchronization (or correlation) of both real international business activity and world financial markets is partly offsetting the benefits of global diversification.”Lessard (1996). “How managers adjust for risk (whether by raising the discount rate or reducing expected cash flows) should depend primarily on whether the risks are ‘systematic’ or instead are ‘diversifiable’ by world capital markets.”Godfrey and Espinoza (1996). “A credit spread is added to the US dollar risk-free rate to reflect transfer risks; and (2) a country-specific business volatility premium is used to reflect risks associated with the local business environment.”Keck, Levengood, and Longfield (1998). “While partly segmented markets may in fact have some different types of risk, the primary driver of cost-of-capital differences is likely to be ‘risk-price differentials’: the same risk priced differently.”Stulz (1999). “If the extra risk premium is used to compensate for country risks, then it must be demonstrated that those risks are not diversifiable and that shareholders charge a risk premium to hold those risks.”Schramm and Wang (1999). An important judgment call relates to “ what the base portfolio should be – the home-country market (assuming markets are segmented) or the global market (assuming integrated markets).”O’Brien (1999) suggests: “a formula for converting a firm’s cost of equity from US dollars into another currency.”Sabal (2004). “This paper argues that the traditional practitioners’ approach of incorporating a country risk premium is not appropriate, mainly because country risk is neither the same for all projects nor totally systematic, and there is no reason for it to be closely related to the spread on the government bonds of the country concerned.”Estrada (2007). “Although much has been published about discount rates in emerging markets, there is probably a long way to go until a convergence of opinions finally arises. “Soenen and Johnson (2008). “ When valuing projects in emerging economies, we recommend use of the CAPM adjusted for political risk and a measure of co-movement (country beta) between foreign and US stock markets. In the long run, increased international capital market integration can be expected to move country betas toward unity. But in the meantime, corporate planners should consider making the necessary adjustments to the CAPM.”Garcia-Sachez, Preve, and Sarria-Allende (2010). “Current methodologies based on adjustments of the discount rate present several problems. On the one hand, the practice of adding country risk to CAPM estimates violates the spirit of the model, according to which discount rates should reflect only ‘symmetric’ (or two-sided), non-diversifiable risks. Country risk, however, is not symmetric and may be at least partially diversifiable (though in respect to the latter, some models of dynamic correlations across countries give some support to the alternative view). On the other hand, popular techniques usually view the impact of emerging market country risk as the same for all firms and industries. It is easy, however, to find differences in business fundamentals or strategies that justify the opposite view: namely, that the effect of country risk on corporate values should depend on company- or industry-specific characteristics.”Pereiro (2010). “The choice of a cost of equity model for an emerging-market firm is very personal: it depends on how conceptually sound the model looks to the analyst, and on her view on which risks can – and which cannot – be diversified away by the investor.”Anshuman, Martin, and Titman (2011). “But in practice, the political risk associated with this type of investment [emerging markets] is typically accounted for implicitly by adjusting the investment’s required rate of return or the discount rate.”My bottom line. As stated in the Introduction, my bias has been to adjust the discount rate primarily because I have believed that adjusting cash flows is ad hoc and also because I have never believed that country risk is fully diversifiable. In light of the diversity of opinion reflected above, I will let the data do the judging and determine whether:Industry betas in emerging markets are similar to industry betas in the US.CDS spreads are statistically significantly correlated with ADR returns.Industries have differing CDS sensitivities, possibly due to differential political and country risk exposures of differing industries.I. Theory To reiterate, I take a pragmatic approach and move towards letting the data do the talking. My own intuition is that country risk is not fully diversifiable, and should be incorporated in the discount rate. However, this intuition is not written in stone, and as Keynes famously stated “if the data change, I will change my mind.” I also agree with the above authors who opine that different industries might bear different amounts of country risk. Again, as we shall see later on, I will let the data judge that. As stated in Abuaf (2011), the practical approach to estimating the cost of an emerging market equity is similar to estimating extended Capital Asset Pricing Models (CAPM) models or frameworks that academics have introduced such as the arbitrage pricing theory (APT), the initial, or the extended Fama-French models. Stated differently, the introduction of a political risk premium variable (CDS) is in the same spirit as the above extensions of the CAPM. In this spirit, I postulate that the cost of equity, ke , can be estimated by an extended CAPM:Ke =dlog (ADR) = c + β(dlogS&P500) + γ(ΔCDS), (1)where c is a constant (a purist would argue that c equals the risk-free rate minus β times the risk-free rate), dlog represents log differences, approximating percentage changes, ADR represents the levels of emerging market stock prices being investigated, β is the traditional CAPM constant, S&P500 is the level of the broad US market stock index, γ is the modified duration of ADRs vs. CDS (aka semi elasticity), Δ represents first differences, and CDS is represented as a real number. Why γ is Related to (or Approaches) the P/E MultipleIn a traditional perpetuity growth model:P/E = 1/(ke-g+cds) (2)where g represents the perpetual growth rate. Taking derivatives on both sides: d (P/E) = - (ke-g+cds)-2 cds, and (3) d (P/E)/(P/E)/dcds= -1/( ke-g+cds). (4)I have therefore shown that γ should theoretically equal the negative P/E multiple in the absence of a political risk premium. That is, in theory, γ equals the negative of the P/E multiple for an equity that has no country risk. In principle: ke = risk-free rate + β(equity market risk premium) +α (CDS), (5)where α represents the proportion of CDS risk borne by the specific industry.II. The Empirical ResultsTables I-X summarize my empirical results. In choosing the countries, I wanted to make sure that various regions of the world such as Latin America, the Asia Pacific region, and Europe were well represented. My results include two European countries, Italy and Spain, which strictly speaking should not be counted as emerging market countries. I have, nonetheless, included them because the European financial crisis has underscored the political and country risk associated with investing in these countries. Other criteria were that each country should have at least ten ADRs, and that the results should be meaningful. In this regard, I rejected:Rejected CountriesArgentina. I would characterize the results for Argentina as weak with 6/13 “γ”s as statistically significant. None of the Argentine “γ”s approach their theoretically expected P/E multiples. Moreover, the Argentine R2s are by and large weak with only 5/13 exceeding 25%. These weak results may be due to Argentina’s troubled political economy and its declining importance in the global landscape.Greece. Having only five observations, and respectively median γ, R2, and P/E’s of 0.85, 36%, and 7.44x, I would characterize the Greek results as weak, possibly due to the small sample size and everything else that has been happening in Greece.Indonesia. Having only six observations, and respectively median γ, R2, and P/E’s of 7.41, 21%, and 14.24x, I would characterize the Indonesian results as weak, possibly due to the small sample size. Nonetheless, a typical Indonesian “γ”s is statistically indistinguishable from its corresponding P/E’s, as predicted by theory.Israel. Having only eleven observations, and respectively median γ, R2, and P/E’s of 5.96, 12%, and 10.24x, I would characterize the Israeli results as weak, possibly due to the small sample size. Nonetheless, a typical Israeli “γ” is still statistically indistinguishable from its corresponding P/E, as predicted by theory.Japan. With 20 observations, has respectively median γ, R2, and P/E’s of 3.61, 28%, and 12.32x, I would characterize the Japanese results as weak. Most of th“γ”s (17/20) for Japan are statistically insignificant. Nonetheless, the “γ” of a global brand such as Nissan is statistically indistinguishable from its corresponding P/E, as predicted by theory.Malaysia. With only two observations, the results are extremely weak with R2s approaching zero.Philippines. With only two observations, the results are extremely weak with one R2 at 22%, and the other approaching zero. Nonetheless, Philippine “γ”s are statistically indistinguishable from their corresponding P/E’s, as predicted by theory.Portugal. Having only five observations, and respectively median γ (only two statistically significant), R2, and P/E’s of 0.79, 19%, and 23.83x, I would characterize the Portuguese results as weak, possibly due to the small sample size and everything else that has been happening in Portugal.Thailand. With only five observations, the results are extremely weak with R2’s approaching zero.Turkey. Having only five observations, and respectively median γ (only three statistically significant), R2, and P/E’s of 11.59, 28%, and 12.81x, I would characterize the Turkish results as modest, primarily because the median γ is virtually equal to the median P/E.Regarding data frequency and the use of daily versus weekly data, I find that, with two and a half years-long data, the bottom line does not change appreciably whether we use daily or weekly data. B. Analysis of ResultsBrazil. I would characterize the results for Brazil as strong with all “γ”s as statistically significant. The median Brazilian “γ”s approach their theoretically expected median P/E multiples. Specifically, the median Brazilian γ is 13.37 while the median Brazilian forward P/E is 9.31. Given a typical standard error of a Brazilian γ of around four, I can confidently say that Brazilian “γ”s are statistically indistinguishable from their corresponding P/E’s, as predicted by theory. Moreover, the Brazilian R2s are by and large strong with all exceeding 24%, and 7/17 approaching, or exceeding 50%. These strong results may be due to Brazil’s growing importance in the global landscape.Chile. Nine out of twelve “γ”s are statistically significant, with 6/12 R2 s exceeding 25%.China. Twelve out of 20 “γ”s are statistically significant and 16/20 R2 s exceed 24%.Italy. Twelve out of 16 “γ”s are statistically significant and 12/16 R2 s exceed 24%.Mexico. Eighteen out of 20 “γ”s are statistically significant, and 15/20 R2 s aexceed 24%. A typical Mexican “γ” is statistically indistinguishable from its corresponding P/E, as predicted by theory.Russia. With 11 observations, all “γ”s statistically significant, and 10/11 R2 s, above 24%, I would characterize the Russian results as strong. South Africa. With 20 observations, all “γ”s statistically significant, and 15/20 R2 s greater than 24%, I would characterize the South African results as strong. South Korea. With 10 observations, only one “γ” statistically significant and 5/10 R2 s greater than 24%, I would characterize the South Korean results as weak, possibly because political risk premium has not varied much in this country. Spain. With 11 observations, eight “γ”s statistically significant and 8/11 R2 s greater than 24%, I would characterize the Spanish results as strong, possibly due to the European crisis.Industry Betas. Broadly speaking, as I compare betas reported in Tables I-IX versus betas reported in Table X, I would say that ADR betas are not statistically indistinguishable from US industry betas, as intuitively expected. C. Estimating Equity Costs by Country and by IndustryThe theory and empirical results presented in the preceding sections suggest that we can estimate equity costs by country and by industry, as suggested in equation (5). I present the results in Table XI, where I assume that the US risk free rate is 4% (slightly higher than the current 30-Year Treasury Bond, and that the US equity market risk premium is 7%. If we assume that the forward-looking P/E multiple of the S&P 500 is 14.5x, by equation (2)the cost of equity for the S&P 500 would be 11.9%, assuming a 5% perpetual growth rate for the S&P 500, approximately justifying my assumptions.I deduce the α’s from the regression estimates of the sensitivities of the various industry groups to CDS spreads. Similarly, I obtain the β’s from the regression estimates for the UUS. As expected, utilities have one of the lowest kes, primarily because of their low β, and despite their modest exposure to country/political risk. On the other hand, energy stocks and financials have the highest cost of equities primarily because of their high betas, and high country/political risk exposures. Ceteris paribus, cost of equity increases with CDS spreads. Table I. Brazilian ADRs vs. S&P 500 and CDS, 1 Jan 2010 - 21 Jun 2013??S&P 500 and CDS RegressionsP/E ratioCompanyIndustry S&P500 5 Y CDSAdjustedForward P/E( Ticker)Group(t-statistic)(t-statistic) R2(LTM P/E)AMBEV-PRF ADRBeverages 0.51-7.810.2822.11(ABV)(4.33)(-3.10)(25.43)AMBEV-ADRBeverages 0.51-8.090.2422.11(ABV/C)(3.85)(-2.86)(25.43)PETROBRAS-SP ADROil&Gas 0.76-21.190.506.70(PBR/A)(5.15)(-6.76)(12.02)PETROBRAS SA-ADROil&Gas 0.88-19.130.486.70(PBR)(5.62)(-5.71)(12.02)VALE SA-SP P ADRIron/Steel 1.02-12.730.476.04(VALE/P)(6.98)(-4.11)(22.18)VALE SA-SP ADRIron/Steel 1.10-11.940.496.04(VALE)(7.55)(-3.84)(22.18)ITAU UNIBANC-ADRBanks 1.09-13.370.539.19(ITUB)(7.85)(-4.54)(11.93)BRADESCO-ADRBanks 0.96-13.990.559.31(BBD)(7.61)(-5.23)(11.89)BANCO DO BRA-ADRBanks 0.74-19.910.425.81(BDORY)(4.37)(-5.53)(6.51)BANCO SANTANDERBanks 1.01-15.790.479.29(BSBR)(6.41)(-4.72)(10.96)TELEFONICA B-ADRTelecommunications 0.32-13.500.3012.19(VIV)(2.53)(-4.99)(12.36)BRASIL FOODS-ADRFood0.83-10.170.3922.33(BRFS)(5.95)(-3.41)(45.11)GERDAU SA-ADRIron/Steel 1.38-11.420.5014.86(GGB)(8.36)(-3.25)(21.41)PAO ACUCAR-ADRFood0.50-17.590.3720.57(CBD)(3.34)(-5.54)(22.56)ULTRAPAR PA-ADRChemicals 0.49-10.810.3622.98(UGP)(4.33)(-4.51)(24.45)CPFL ENERGIA-ADRElectric 0.35-13.110.3514.52(CPL)(3.06)(-5.39)(16.80)SABESP-ADRWater0.53-14.530.337.10(SBS)(3.64)(-4.67)(10.35)Notes: Data are weekly; The ADR and the S&P 500 variables are in natural log differences. ? The CDS variable is in differences expressed as a real number. LTM stands for trailing last twelve months. S.D. represents standard deviation. Results are ranked by market cap.Source: Bloomberg.?????Table II. Chilean ADRs vs. S&P 500 and CDS, 1 Jan 2010 - 21 Jun 2013??S&P 500 and CDS RegressionsP/E ratioCompanyIndustry S&P500 5 Yr CDSAdjustedForward P/E( Ticker)Group(t-statistic)(t-statistic) R2(LTM P/E)QUIMICA Y-SP ADRChemicals0.80-14.800.4516.99(SQM)(7.05)(-4.10)(23.27)BANCO CHILE-ADRBanks 0.66-9.150.3113.51(BCH)(5.75)(-2.51)(14.66)BANCO SANTAN-ADRBanks 0.81-6.580.3312.90(BSAC)(6.61)(-1.69)(16.38)ENDESA-ADR (CHL)Electric0.53-12.360.3514.60(EOC)(5.33)(-3.88)(27.24)ENERSIS SA-ADRElectric0.65-12.070.3312.70(ENI)(5.57)(-3.26)(15.21)LATAM AIRLIN-ADRAirlines 0.61-17.670.3322.53(LFL)(4.61)(-4.17)(883.06)EMBOT ANDINA-ADRBeverages 0.32-12.040.1516.33(AKO/A)(2.46)(-2.90)(22.29)EMBOT ANDINA-ADRBeverages 0.30-14.010.1616.33(AKO/B)(2.28)(-3.30)(22.29)CERVEZAS-ADRBeverages 0.62-7.200.0517.71(CCU)(2.13)(-0.78)(20.98)CORPBANCA SA-ADRBanks 0.39-6.710.1011.89(BCA)(2.68)(-1.46)(14.88)PROVIDA-ADRInvestment 0.65-11.290.268.47(PVD)Companies(4.77)(-2.61)(10.35)VINA CONCHA-ADRBeverages0.46-11.970.1819.81(VCO)(3.24)(-2.65)(23.20)Notes: See Table I.?????Table III. Chinese ADRs vs. S&P 500 and CDS, 1 Jan 2010 - 21 Jun 2013??S&P 500 and CDS RegressionsP/E ratioCompanyIndustry S&P500 5 Yr CDSAdjustedForward P/E( Ticker)Group(t-statistic)(t-statistic) R2(LTM P/E)PETROCHINA -ADROil&Gas0.92-5.760.438.93(PTR)(8.85)(-1.99)(14.02)IND & COMM-ADRBanks 0.79-18.380.435.18(IDCBY)(6.41)(-5.29)(6.10)CHINA CONSTR-ADRBanks 0.69-15.020.385.05(CICHY)(5.89)(-4.60)(6.50)BANK OF CHIN-ADRBanks 0.69-18.570.444.76(BACHY)(6.07)(-5.85)(5.57)CNOOC LTD-ADROil&Gas1.04-12.060.457.26(CEO)(8.31)(-3.45)(9.44)CHINA PETRO-ADROil&Gas0.873.010.306.42(SNP)(8.01)(0.99)(9.60)CHINA LIFE-ADRInsurance 0.71-11.170.2513.97(LFC)(5.04)(-2.83)(52.18)CHINA SHENH-ADRCoal 0.99-9.080.247.47(CSUAY)(5.57)(-1.82)(11.12)TENCENT HOLD-ADRInternet 0.83-9.750.2525.57(TCEHY)(5.41)(-2.27)(28.76)PING AN INSU-ADRInsurance0.63-26.390.3711.39(PNGAY)(4.11)(-6.11)(20.64)CHINA TELECO-ADRTelecommunications 0.922.110.2812.99(CHA)(7.61)(0.62)(19.26)BAIDU INC-SP ADRInternet 1.32-6.290.2918.39(BIDU)(6.85)(-1.17)(20.96)WANT WANT-ADRFood 0.64-1.910.1026.38(WWNTY)(3.82)(-0.41)(32.89)HUANENG POWR-ADRElectric 0.570.830.098.24(HNP)(3.99)(0.21)(14.79)YANZHOU COAL-ADRCoal 1.16-19.960.378.38(YZC)(6.42)(-3.96)(8.18)JIANGXI COPP-ADRMining 1.08-27.580.488.13(JIXAY)(6.75)(-6.16)(11.01)CHINA OILFIE-ADROil&Gas Services1.34-8.060.369.40(CHOLY)(7.85)(-1.68)(12.67)AIR CHINA-SP-ADRAirlines 0.92-11.030.219.76(AIRYY)(4.79)(-2.06)(13.79)LENOVO GROUP-ADRComputers 1.03-3.330.2112.55(LNVGY)(5.71)(-0.66)(16.12)ALUMINUM COR-ADRMining 1.16-21.840.44n/a(ACH)(7.15)(-4.79)(n/a)Notes: See Table I.?????Table IV. Italian ADRs vs. S&P 500 and CDS, 1 Jan 2010 -21 Jun 2013??S&P 500 and CDS RegressionsP/E ratioCompanyIndustry S&P500 5 Yr CDSAdjustedForward P/E( Ticker)Group(t-statistic)(t-statistic) R2(LTM P/E)ENI SPA-ADROil&Gas 1.24-3.560.698.94( E )(13.92)(-5.51)(16.38)ENEL SPA - ADRElectric0.86-6.980.537.56(ENLAY)(6.72)(-7.55)(34.11)INTESA SAN- ADRBanks 1.26-10.840.5811.97(ISNPY)(7.24)(-8.61)(13.00)LUXOTTICA GR-ADRHealthcare-Products1.00-1.300.5526.42(LUX)(11.67)(-2.10)(26.56)SAIPEM SPA-ADROil&Gas Services 1.33-3.700.35n/a(SAPMY)(6.90)(-2.66)(14.29)TELECOM ITAL-ADRTelecommunications0.47-6.560.324.78(TI/A)(3.16)(-6.06)(n/a)TELECOM ITAL-ADRTelecommunications0.80-6.460.424.78(TI)(5.44)(-6.12)(n/a)ATLANTIA SPA-ADRCommercial Services0.48-6.130.2413.38(ATASY)(2.72)(-4.77)(11.09)FIAT SPA-ADRAuto Manufacturers1.89-3.130.2818.81(FIATY)(6.43)(-1.48)(13.25)DAVIDE CAMPA-ADRBeverages0.392.060.0117.91(DVDCY)(1.57)(1.14)(21.48)LOTTOMATICA-ADREntertainment 0.19-4.600.1212.58(GTKYY)(1.07)(-3.65)(12.74)FINMECCANICA-ADRAerospace/Defense1.22-6.840.358.00(FINMY)(5.57)(-4.34)(n/a)MEDIASET SPA-ADRMedia 1.08-8.100.4496.96(MDIUY)(5.90)(-6.11)(n/a)ITALCEMENTI-ADRBuilding Materials1.54-5.910.52n/a(ITALY)(9.02)(-4.78)(n/a)GENTIUM SPA-ADRPharmaceuticals 0.910.780.04n/a(GENT)(2.96)(0.35)(n/a)NATUZZI SPA-ADRHome Furnishings0.810.170.08n/a(NTZ)(3.60)(0.1)(n/a)Notes: See Table I.?????Table V. Mexican ADRs vs. S&P 500 and CDS, 1 Jan 2010 -21 Jun 2013??S&P 500 and CDS RegressionsP/E ratioCompanyIndustry S&P500 5 Yr CDSAdjustedForward P/E( Ticker)Group(t-statistic)(t-statistic) R2(LTM P/E)AMERICA MO-ADR ATelecommunications0.89-8.290.4610.64(AMOV)(7.28)(-3.16)(12.40)AMERICA MO-ADR LTelecommunications0.89-7.770.4710.64(AMX)(7.43)(-3.02)(12.40)WAL-MART MEX-ADRRetail 0.68-8.550.3223.91(WMMVY)(4.94)(-2.93)(32.23)FOMENTO ECON-ADRBeverages 0.54-12.600.4223.65(FMX)(4.68)(-5.08)(22.35)GRUPO MODELO-ADRBeverages0.60-5.020.2329.75(GPMCY)(4.40)(-1.73)(30.41)COCA-COLA F-ADRBeverages0.40-9.290.2425.15(KOF)(3.11)(-3.43)(29.14)GRUPO F INBU-ADRBanks 0.30-23.640.3418.24(GPFOY)(1.68)(-6.23)(29.43)GRUPO F BANO-ADRBanks 0.84-17.510.4312.42(GBOOY)(5.01)(-4.90)(17.83)GRUPO TELEV-ADRMedia 0.88-8.270.4920.47(TV)(7.66)(-3.39)(18.66)CEMEX SAB-SP ADRBuilding Materials1.80-18.720.56n/a(CX)(8.50)(-4.13)(n/a)GRUPO CARSO-ADRHolding 0.46-18.450.2117.87(GPOVY)Companies-Divers(2.08)(-3.94)(18.93)KIMBERLY-CLA-ADRHousehold 0.67-7.310.2826.35(KCDMY)Products/Wares(4.75)(-2.43)(24.98)GRUPO AEROPO-ADREngineering&0.75-9.310.3219.63(ASR)Construction(4.97)(-2.89)(21.16)GRUPO AEROPO-ADREngineering&0.55-9.320.2819.25(PAC)Construction(4.04)(-3.18)(22.03)PROMOTORA Y-ADREngineering&0.38-10.690.1424.94(PUODY)Construction(2.20)(-2.86)(18.02)GRUPO SIMEC-ADRIron/Steel 0.71-14.030.4112.16(SIM)(4.91)(-4.56)(12.18)GRUMA SAB-ADRFood 0.91-6.920.2216.28(GMK)(4.46)(-1.59)(16.06)EMP ICA-ADREngineering&1.22-12.930.4312.39(ICA)Construction(6.49)(-3.22)(14.64)INDUS BACHOC-ADRFood 0.03-10.160.0910.38(IBA)(0.18)(-3.15)(8.26)GRUPO AEROPO-ADREngineering&0.77-7.320.2517.93(OMAB)Construction(4.52)(-2.01)(17.08)Notes: See Table I.?????Table VI. Russian ADRs vs. S&P 500 and CDS, 1 Jan 2010 - 21 Jun 2013??S&P 500 and CDS RegressionsP/E ratioCompanyIndustry S&P500 5 Yr CDSAdjustedForward P/E( Ticker)Group(t-statistic)(t-statistic) R2(LTM P/E)GAZPROM-ADROil&Gas0.76-11.940.51n/a(OGZPY)(5.00)(-6.33)(6.12)LUKOIL OAO-ADROil&Gas 0.78-7.580.51n/a(LUKOY)(6.46)(-5.00)(7.90)MMC NORILSK ADRMining 0.69-11.080.44n/a(NILSY)(4.32)(-5.58)(15.34)SURGUTNEFTEG-ADROil&Gas 0.83-9.750.45n/a(SGTZY)(5.14)(-4.85)(5.96)GAZPROM NEFT-ADROil&Gas 0.94-6.420.38n/a(GZPFY)(5.67)(-3.09)(9.17)MOBILE TELES-ADRTelecommunications0.60-8.050.38n/a(MBT)(4.18)(-4.52)(7.12)TATNEFT-ADROil&Gas 0.31-14.460.28n/a(OAOFY)(1.43)(-5.39)(7.26)ROSTELECOM-ADRTelecommunications0.78-10.480.31n/a(ROSYY)(3.59)(-3.89)(10.82)POLYUS G-SP ADRMining0.23-8.500.10n/a(OPYGY)(0.96)(-2.81)(14.08)SURGUTN-ADR PREFOil&Gas 0.58-11.790.42n/a(SGTPY)(3.64)(-5.88)(5.96)MECHEL-SPON ADRIron/Steel1.45-18.720.50n/a(MTL)(5.53)(-5.70)(4.46)Notes: See Table I.?????Table VII. South African ADRs vs. S&P 500 and CDS, 1 Jan 2010 - 21 Jun 2013??S&P 500 and CDS RegressionsP/E ratioCompanyIndustry S&P500 5 Yr CDSAdjustedForward P/E( Ticker)Group(t-statistic)(t-statistic) R2(LTM P/E)MTN GROUP-ADRTelecommunications0.51-12.520.3713.42(MTNOY)(3.73)(-5.53)(16.31)SASOL LTD-SP ADRChemicals 1.07-5.860.609.03(SSL)(10.67)(-3.56)(8.10)NASPERS-N ADRMedia 0.81-11.130.4325.17(NPSNY)(5.75)(-4.77)(33.27)KUMBA IRON-ADRIron/Steel 1.00-13.960.479.57(KIROY)(6.11)(-5.20)(14.98)ANGLO AMERIC-ADRMining 0.65-14.610.3730.11(AGPPY)(3.99)(-5.41)(n/a)SHOPRITE-ADRFood 0.30-15.520.3426.34(SRHGY)(2.07)(-6.40)(24.82)ANGLOGOLD AS-ADRMining0.48-6.740.1510.46(AU)(2.86)(-2.41)(10.45)ABSA GROUP-ADRBanks0.10-13.590.269.73(AGRPY)(.69)(-5.97)(13.36)IMPALA PLAT-ADRMining 0.92-12.660.4326.35(IMPUY)(5.74)(-4.79)(19.74)NEDBANK GROU-ADRBanks0.51-10.570.379.40(NDBKY)(4.08)(-5.13)(11.42)SANLAM LTD-ADRInsurance 0.65-9.630.4514.22(SLLDY)(5.70)(-5.13)(14.98)GOLD FIELDS-ADRMining0.41-10.240.1911.94(GFI)(2.38)(-3.61)(27.16)BIDVEST GRP-ADRHolding0.43-6.870.1715.16(BDVSY) Companies-Divers (2.85)(-2.75)(12.35)TIGER BRANDS-ADRFood 0.48-11.820.4016.58(TBLMY)(3.89)(-5.83)(16.17)EXXARO RE-SP ADRCoal0.93-8.880.319.88(EXXAY)(5.10)(-2.95)(12.06)IMPERIAL HLD-ADRHolding 0.28-16.550.3311.46(IHLDY)Companies-Divers (1.80)(-6.44)(10.98)MASSMART HLDGSRetail 0.53-12.580.1324.64(MMRTY)(1.94)(-2.80)(29.90)HARMONY GOLD-ADRMining 0.57-8.200.1313.02(HMY)(2.65)(-2.31)(13.54)AFRICAN BK -ADRDiversified 0.56-15.180.316.13(AFRVY)Finan Serv(3.12)(-5.14)(9.65)PPC LTD-ADRBuilding Materials0.37-10.770.2413.11(PPCYY)(2.47)(-4.30)(17.90)Notes: See Table I.?????Table VIII. South Korean ADRs vs. S&P 500 and CDS, 1 Jan 2010 - 21 Jun 2013??S&P 500 and CDS RegressionsP/E ratioCompanyIndustry S&P500 5 Yr CDSAdjustedForward P/E( Ticker)Group(t-statistic)(t-statistic) R2(LTM P/E)POSCO-ADRIron/Steel 1.21-1.620.4111.07(PKX)(11.19)(-0.62)(10.79)KOREA ELEC P-ADRElectric 0.78-0.400.16n/a(KEP)(5.85)(-0.13)(n/a)SHINHAN FINA-ADRDiversified 1.261.540.37n/a(SHG)Finan Serv (10.37)(0.53)(37.61)KB FINANCIAL-ADRDiversified 1.383.320.44n/a(KB)Finan Serv (11.91)(1.20)(22.65)LG DISPLAY-ADRElectronics 1.497.630.3813.95(LPL)(10.42)(2.22)(388.13)SK TELECOM-ADRTelecommunications 0.61-1.290.1510.19(SKM)(5.75)(-0.51)(8.55)KT CORP-ADRTelecommunications 0.72-1.570.16n/a(KT)(5.96)(-0.54)(12.02)WOORI FINANC-ADRDiversified 1.666.770.40n/a(WF)Finan Serv (10.82)(1.84)(25.71)WEBZEN INC-ADRInternet -0.130.40-0.01n/a(WZENY)(-0.42)(0.05)(n/a)GRAVITY CO-ADRInternet 0.66-3.060.05n/a(GRVY)(3.26)(-0.63)(2.69)Notes: See Table I.?????Table IX. Spanish ADRs vs. S&P 500 and CDS, 1 Jan 2010 - 21 June 2013??S&P 500 and CDS RegressionsP/E ratioCompanyIndustry S&P500 5 Yr CDSAdjustedForward P/E( Ticker)Group(t-statistic)(t-statistic) R2(LTM P/E)INDITEX-ADRRetail 0.87-2.730.3722.88(IDEXY)(7.20)(-3.05)(27.24)BANCO SANTAN-ADRBanks 0.99-9.910.5810.37(SAN)(6.81)(-9.26)(26.44)TELEFONICA-ADRTelecommunications 0.84-6.970.599.22(TEF)(7.69)(-8.60)(11.71)BANCO BILBAO-ADRBanks 1.09-10.610.619.71(BBVA)(7.28)(-9.61)(28.56)IBERDROLA SA-ADRElectric 0.99-7.580.5810.06(IBDRY)(7.93)(-8.24)(9.14)REPSOL SA-ADROil&Gas 1.22-6.080.579.44(REPYY)(9.21)(-6.21)(9.83)RED ELECTRIC-ADRElectric0.74-2.120.2510.69(RDEIY)(5.52)(-2.13)(10.30)ENAGAS-ADRGas0.69-4.720.3911.01(ENGGY)(5.72)(-5.26)(10.15)BANKINTER-ADRBanks 0.36-1.600.0013.35(BKNIY)(.86)(-0.51)(9.14)ABENGOA SA-ADREngineering&0.87-2.530.0210.18(ABGOY)Construction (1.88)(-0.74)(10.39)GAMESA CORP-ADRElectrical 1.06-4.040.0634.90(GCTAY)Compo&Equip(2.41)(-1.24)N/ANotes: See Table I.?????Table X. US Industry Beta vs. S&P 500, 1 Jan 2010 - 21 June 2013 (Continued)?S&P500 RegressionsIndustry S&P500 Adjusted( Ticker)(t-statistic) R2Consumer Discretionary1.060.90( S5COND )(40.65)Automobiles1.520.57( S5AUTO )(15.42)Home Furnishings1.320.57( S5HOMF )(15.53)Consumer Services0.840.73( S5HOTR )(22.18)Media1.120.84( S5MEDA )(31.30)Movies & Entertainment1.190.79( S5MOVI )(26.09)Retailing0.990.75( S5RETL )(23.51)Automotive Retail0.660.32( S5AUTR )(9.78)Consumer Staples0.530.64( S5CONS )(18.11)Food & Staples Retailing0.580.57( S5FDSR )(15.62)Beverages0.510.44( S5BEVG )(12.05)Food Products0.500.52( S5FDPR )(14.26)Household & Personal Products0.470.38( S5HOUS )(10.59)Energy1.260.84( S5ENRS )(30.70)Oil & Gas Equipment & Services1.640.67( S5OILE )(19.30)Coal & Consumable Fuels1.930.50( S5CCSF )(13.41)Financials1.260.86( S5FINL )(33.04)Banks1.270.71( S5BANK )(20.92)Diversified Financials1.420.76( S5DIVF )(23.91)??(Continued)Table X. US Industry Beta vs. S&P 500, 1 Jan 2010 - 21 June 2013(Continued)?S&P500 RegressionsIndustry S&P500 Adjusted( Ticker)(t-statistic) R2Capital Markets1.370.77( S5CAPM )(24.26)Insurance1.080.85( S5INSU )(31.48)Consumer Finance1.210.65( S5CFIN )(18.55)Health Care0.700.75( S5HLTH )(23.19)Health Care Equipment & Supplies0.840.72( S5HCEQ )(21.70)Biotechnology0.670.37( S5BIOT )(10.74)Pharmaceuticals0.560.53( S5PHAR )(14.46)Industrials1.180.94( S5INDU )(51.50)Aerospace & Defense1.080.86( S5AERO )(33.90)Construction & Engineering1.490.73( S5CSTE )(21.88)Commercial & Professional Services0.910.79( S5COMS )(26.23)Transportation1.080.80( S5TRAN )(26.64)Airlines1.000.37( S5AIRL )(10.33)Information Technology1.080.84( S5INFT )(30.76)Internet Software & Services1.040.53( S5INSSX )(14.29)Technology Hardware & Equipment1.140.68( S5TECH )(19.44)Computers & Peripherals1.140.52( S5CMPE )(14.08)Materials1.280.84( S5MATR )(30.36)Chemicals1.170.83( S5CHEM )(29.29)??(Continued)Table X. US Industry Beta vs. S&P 500, 1 Jan 2010 - 21 June 2013?S&P500 RegressionsIndustry S&P500 Adjusted( Ticker)(t-statistic) R2Construction Materials1.350.35( S5CSTM )(9.85)Metals & Mining1.520.60( S5METL )(16.64)Steel1.810.62( S5STEL )(17.17)Telecommunication Services0.590.46( S5TELS )(12.45)Utilities0.550.47( S5UTIL )(12.71)Electric Utilities0.490.39( S5ELUTX )(10.79)Notes: See Table I.??Table XI. International Cost of Equity EstimatesIII. ConclusionWith a few exceptions such as Argentina, Greece, Malaysia, Philippines, South Korea, and Thailand, most of our results support the hypothesis that ADR returns are significantly dependent on their respective CDSs, in addition to returns of the broad market. Most countries have most of their R2s exceeding 24%-25%, frequently approaching 40%, and CDS durations statistically indistinguishable from their respective P/E multiples as predicted by theory. Moreover, ADR betas seem statistically indistinguishable from US industry betas, and using daily versus weekly data does not significantly alter our broad conclusions, except for a few special cases. ReferenecesAbuaf, Niso, 2011. “Valuing Emerging Market Equities – The Empirical Evidence.” Journal of Applied Finance, 21, 2, pp. 123-141.Abuaf, Niso, and Quyen Chu, 1991. “The Executive’s Guide to International Capital Budgeting.” Series in Global Corporate Finance, Salomon Brothers, June. Abuaf, Niso, Quyen Chu, Chad Czapla, and Suvir Thadani, 1997. “The International Cost of Capital – The Empirical Evidence.” Series in Global Corporate Finance, Salomon Smith Barney, June. Anshuman, V. Ravi, John Martin, and Sheridan Titman, 2011. “Accounting for Sovereign Risk When Investing in Emerging Markets.” Journal of Applied Corporate Finance, 23, 2, pp. 41-49.Estrada, Javier, 2007. “Discount Rates in Emerging Markets: Four Models and an Application.” Journal of Applied Corporate Finance, 19, 2, pp. 72-77.Garcia-Sanchez, Javier, Lorenzo Preve, and Virginia Sarria-Allende, 2010. “Valuation in Emerging Markets: a Simulation Approach.” Journal of Applied Corporate Finance, 22, 2, pp. 100-108.Godfrey, Stephen, and Ramon Espinosa, 1996. “A Practical Approach to Calculating Costs of Equity for Investments in Emerging Markets.” Journal of Applied Corporate Finance, 9, 3, pp. 80-89.Keck, Tom, Eric Levengood, and Al Longfield, 1998. “Using Discounted Cash Flow Analysis in an International Setting: A Survey of Issues in Modeling the Cost of Capital.” Journal of Applied Corporate Finance, 11, 3, pp. 82-99.Lessard, D., 1996. “Incorporating Country Risk in the Valuation of Offshore Projects.” Journal of Applied Corporate Finance, 9, 52-63.O’Brien, Thomas J., 1999. “The Global CAPM and a Firm’s Cost of Capital in Different Currencies.” Journal of Applied Corporate Finance, 12, 3, pp. 73-79.Sabal, Jaime, 2004. “Discount Rates in Emerging Markets: A Guide.” Journal of Applied Corporate Finance, 16, 2-3, pp. 155-166.Schramm, Ronald M., and Henry N. Wang, 1999. “Measuring the Cost of Capital in an International CAPM Framework.” Journal of Applied Corporate Finance, 12, 3, pp. 63-72. Soenen, Luc, and Robert Johnson, 2008. “The Equity Market Risk Premium and Valuation of Overseas Investments.” Journal of Applied Corporate Finance, 20, 2, pp. 113-121.Stulz, Rene M., 1999. “Globalization, Corporate Finance, and the Cost of Capital.” Journal of Applied Corporate Finance, 12, 3, pp. 8-25.Stulz, Rene M., 1995. “Globalization of Capital Markets and the Cost of Capital: The Case of Nestle.” Journal of Applied Corporate Finance, 8, 3, pp. 30-38.Pereiro, Louis, E., 2010. “The Beta Dilemma in Emerging Markets.” Journal of Applied Corporate Finance, 22, 4, pp. 110-122. Zenner, Mark, and Ece Akaydin, 2002. “A Practical Approach to the International Valuation and Capital Allocation Puzzle.” Global Corporate Finance Papers, Salomon Smith Barney. ................
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