VALUING EMERGING MARKET EQUITIES – THE NEW EMPIRICAL EVIDENCE
By Niso Abuaf
Niso 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.
1
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, and
The 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
2
characteristics, of the Mexican telecom industry versus its global counterparts. The literature’s
approach to these questions is not monolithic, as summarized below:
Literature Survey
This 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)
3
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:
o
Industry betas in emerging markets are similar to industry betas in the US.
o
CDS spreads are statistically significantly correlated with ADR returns.
o
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.
4
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.
A. Why γ is Related to (or Approaches) the P/E Multiple
In 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
d (P/E)/(P/E)/dcds= -1/( ke-g+cds).
(3)
(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 Results
Tables 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
5
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:
A. Rejected Countries
Argentina. 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
6
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 Results
1. Brazil. 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.
2. Chile. Nine out of twelve “γ”s are statistically significant, with 6/12 R2 s exceeding 25%.
3. China. Twelve out of 20 “γ”s are statistically significant and 16/20 R2 s exceed 24%.
4. Italy. Twelve out of 16 “γ”s are statistically significant and 12/16 R2 s exceed 24%.
5. 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.
6. Russia. With 11 observations, all “γ”s statistically significant, and 10/11 R2 s, above 24%, I
would characterize the Russian results as strong.
7. 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.
8. 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.
7
9. 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.
10. 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 Industry
The 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.
8
Table I. Brazilian ADRs vs. S&P 500 and CDS, 1 Jan 2010 - 21 Jun 2013
S&P 500 and CDS Regressions
P/E ratio
Company
Industry
S&P500
5 Y CDS
Adjusted
Forward P/E
( Ticker)
Group
(t-statistic)
(t-statistic)
R2
(LTM P/E)
AMBEV-PRF ADR
Beverages
0.51
-7.81
0.28
(ABV)
(4.33)
(-3.10)
AMBEV-ADR
Beverages
0.51
-8.09
0.24
(ABV/C)
(3.85)
(-2.86)
PETROBRAS-SP ADR
Oil&Gas
0.76
-21.19
0.50
(PBR/A)
(5.15)
(-6.76)
PETROBRAS SA-ADR
Oil&Gas
0.88
-19.13
0.48
(PBR)
(5.62)
(-5.71)
VALE SA-SP P ADR
Iron/Steel
1.02
-12.73
0.47
(VALE/P)
(6.98)
(-4.11)
VALE SA-SP ADR
Iron/Steel
1.10
-11.94
0.49
(VALE)
(7.55)
(-3.84)
ITAU UNIBANC-ADR
Banks
1.09
-13.37
0.53
(ITUB)
(7.85)
(-4.54)
BRADESCO-ADR
Banks
0.96
-13.99
0.55
(BBD)
(7.61)
(-5.23)
BANCO DO BRA-ADR
Banks
0.74
-19.91
0.42
(BDORY)
(4.37)
(-5.53)
BANCO SANTANDER
Banks
1.01
-15.79
0.47
(BSBR)
(6.41)
(-4.72)
TELEFONICA B-ADR
Telecommunications
0.32
-13.50
0.30
(VIV)
(2.53)
(-4.99)
BRASIL FOODS-ADR
Food
0.83
-10.17
0.39
(BRFS)
(5.95)
(-3.41)
GERDAU SA-ADR
Iron/Steel
1.38
-11.42
0.50
(GGB)
(8.36)
(-3.25)
PAO ACUCAR-ADR
Food
0.50
-17.59
0.37
(CBD)
(3.34)
(-5.54)
ULTRAPAR PA-ADR
Chemicals
0.49
-10.81
0.36
(UGP)
(4.33)
(-4.51)
CPFL ENERGIA-ADR
Electric
0.35
-13.11
0.35
(CPL)
(3.06)
(-5.39)
SABESP-ADR
Water
0.53
-14.53
0.33
(SBS)
(3.64)
(-4.67)
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.
22.11
(25.43)
22.11
(25.43)
6.70
(12.02)
6.70
(12.02)
6.04
(22.18)
6.04
(22.18)
9.19
(11.93)
9.31
(11.89)
5.81
(6.51)
9.29
(10.96)
12.19
(12.36)
22.33
(45.11)
14.86
(21.41)
20.57
(22.56)
22.98
(24.45)
14.52
(16.80)
7.10
(10.35)
9
Table II. Chilean ADRs vs. S&P 500 and CDS, 1 Jan 2010 - 21 Jun 2013
S&P 500 and CDS Regressions
Company
Industry
S&P500
5 Yr CDS
Adjusted
P/E ratio
Forward
P/E
( Ticker)
Group
(t-statistic)
(t-statistic)
R2
(LTM P/E)
Chemicals
0.80
(7.05)
0.66
(5.75)
0.81
(6.61)
0.53
(5.33)
0.65
(5.57)
0.61
(4.61)
0.32
(2.46)
0.30
(2.28)
0.62
(2.13)
0.39
(2.68)
0.65
(4.77)
0.46
(3.24)
-14.80
(-4.10)
-9.15
(-2.51)
-6.58
(-1.69)
-12.36
(-3.88)
-12.07
(-3.26)
-17.67
(-4.17)
-12.04
(-2.90)
-14.01
(-3.30)
-7.20
(-0.78)
-6.71
(-1.46)
-11.29
(-2.61)
-11.97
(-2.65)
0.45
16.99
(23.27)
13.51
(14.66)
12.90
(16.38)
14.60
(27.24)
12.70
(15.21)
22.53
(883.06)
16.33
(22.29)
16.33
(22.29)
17.71
(20.98)
11.89
(14.88)
8.47
(10.35)
19.81
(23.20)
QUIMICA Y-SP ADR
(SQM)
BANCO CHILE-ADR
(BCH)
BANCO SANTAN-ADR
(BSAC)
ENDESA-ADR (CHL)
(EOC)
ENERSIS SA-ADR
(ENI)
LATAM AIRLIN-ADR
(LFL)
EMBOT ANDINA-ADR
(AKO/A)
EMBOT ANDINA-ADR
(AKO/B)
CERVEZAS-ADR
(CCU)
CORPBANCA SA-ADR
(BCA)
PROVIDA-ADR
(PVD)
VINA CONCHA-ADR
(VCO)
Notes: See Table I.
Banks
Banks
Electric
Electric
Airlines
Beverages
Beverages
Beverages
Banks
Investment
Companies
Beverages
0.31
0.33
0.35
0.33
0.33
0.15
0.16
0.05
0.10
0.26
0.18
10
Table III. Chinese ADRs vs. S&P 500 and CDS, 1 Jan 2010 - 21 Jun 2013
S&P 500 and CDS Regressions
P/E ratio
Company
Industry
S&P500
5 Yr CDS
Adjusted
Forward P/E
( Ticker)
Group
(t-statistic)
(t-statistic)
R2
(LTM P/E)
Oil&Gas
0.92
(8.85)
0.79
(6.41)
0.69
(5.89)
0.69
(6.07)
1.04
(8.31)
0.87
(8.01)
0.71
(5.04)
0.99
(5.57)
0.83
(5.41)
0.63
(4.11)
0.92
(7.61)
1.32
(6.85)
0.64
(3.82)
0.57
(3.99)
1.16
(6.42)
1.08
(6.75)
1.34
(7.85)
0.92
(4.79)
1.03
(5.71)
1.16
(7.15)
-5.76
(-1.99)
-18.38
(-5.29)
-15.02
(-4.60)
-18.57
(-5.85)
-12.06
(-3.45)
3.01
(0.99)
-11.17
(-2.83)
-9.08
(-1.82)
-9.75
(-2.27)
-26.39
(-6.11)
2.11
(0.62)
-6.29
(-1.17)
-1.91
(-0.41)
0.83
(0.21)
-19.96
(-3.96)
-27.58
(-6.16)
-8.06
(-1.68)
-11.03
(-2.06)
-3.33
(-0.66)
-21.84
(-4.79)
0.43
8.93
(14.02)
5.18
(6.10)
5.05
(6.50)
4.76
(5.57)
7.26
(9.44)
6.42
(9.60)
13.97
(52.18)
7.47
(11.12)
25.57
(28.76)
11.39
(20.64)
12.99
(19.26)
18.39
(20.96)
26.38
(32.89)
8.24
(14.79)
8.38
(8.18)
8.13
(11.01)
9.40
(12.67)
9.76
(13.79)
12.55
(16.12)
n/a
(n/a)
PETROCHINA -ADR
(PTR)
IND & COMM-ADR
(IDCBY)
CHINA CONSTR-ADR
(CICHY)
BANK OF CHIN-ADR
(BACHY)
CNOOC LTD-ADR
(CEO)
CHINA PETRO-ADR
(SNP)
CHINA LIFE-ADR
(LFC)
CHINA SHENH-ADR
(CSUAY)
TENCENT HOLD-ADR
(TCEHY)
PING AN INSU-ADR
(PNGAY)
CHINA TELECO-ADR
(CHA)
BAIDU INC-SP ADR
(BIDU)
WANT WANT-ADR
(WWNTY)
HUANENG POWR-ADR
(HNP)
YANZHOU COAL-ADR
(YZC)
JIANGXI COPP-ADR
(JIXAY)
CHINA OILFIE-ADR
(CHOLY)
AIR CHINA-SP-ADR
(AIRYY)
LENOVO GROUP-ADR
(LNVGY)
ALUMINUM COR-ADR
(ACH)
Notes: See Table I.
Banks
Banks
Banks
Oil&Gas
Oil&Gas
Insurance
Coal
Internet
Insurance
Telecommunications
Internet
Food
Electric
Coal
Mining
Oil&Gas Services
Airlines
Computers
Mining
0.43
0.38
0.44
0.45
0.30
0.25
0.24
0.25
0.37
0.28
0.29
0.10
0.09
0.37
0.48
0.36
0.21
0.21
0.44
11
Table IV. Italian ADRs vs. S&P 500 and CDS, 1 Jan 2010 -21 Jun 2013
S&P 500 and CDS Regressions
P/E ratio
Company
Industry
S&P500
5 Yr CDS
Adjusted
Forward P/E
( Ticker)
Group
(t-statistic)
(t-statistic)
R2
(LTM P/E)
Oil&Gas
1.24
(13.92)
0.86
(6.72)
1.26
(7.24)
1.00
(11.67)
1.33
(6.90)
0.47
(3.16)
0.80
(5.44)
0.48
(2.72)
1.89
(6.43)
0.39
(1.57)
0.19
(1.07)
1.22
(5.57)
1.08
(5.90)
1.54
(9.02)
0.91
(2.96)
0.81
(3.60)
-3.56
(-5.51)
-6.98
(-7.55)
-10.84
(-8.61)
-1.30
(-2.10)
-3.70
(-2.66)
-6.56
(-6.06)
-6.46
(-6.12)
-6.13
(-4.77)
-3.13
(-1.48)
2.06
(1.14)
-4.60
(-3.65)
-6.84
(-4.34)
-8.10
(-6.11)
-5.91
(-4.78)
0.78
(0.35)
0.17
(0.1)
0.69
8.94
(16.38)
7.56
(34.11)
11.97
(13.00)
26.42
(26.56)
n/a
(14.29)
4.78
(n/a)
4.78
(n/a)
13.38
(11.09)
18.81
(13.25)
17.91
(21.48)
12.58
(12.74)
8.00
(n/a)
96.96
(n/a)
n/a
(n/a)
n/a
(n/a)
n/a
(n/a)
ENI SPA-ADR
(E)
ENEL SPA - ADR
(ENLAY)
INTESA SAN- ADR
(ISNPY)
LUXOTTICA GR-ADR
(LUX)
SAIPEM SPA-ADR
(SAPMY)
TELECOM ITAL-ADR
(TI/A)
TELECOM ITAL-ADR
(TI)
ATLANTIA SPA-ADR
(ATASY)
FIAT SPA-ADR
(FIATY)
DAVIDE CAMPA-ADR
(DVDCY)
LOTTOMATICA-ADR
(GTKYY)
FINMECCANICA-ADR
(FINMY)
MEDIASET SPA-ADR
(MDIUY)
ITALCEMENTI-ADR
(ITALY)
GENTIUM SPA-ADR
(GENT)
NATUZZI SPA-ADR
(NTZ)
Notes: See Table I.
Electric
Banks
Healthcare-Products
Oil&Gas Services
Telecommunications
Telecommunications
Commercial Services
Auto Manufacturers
Beverages
Entertainment
Aerospace/Defense
Media
Building Materials
Pharmaceuticals
Home Furnishings
0.53
0.58
0.55
0.35
0.32
0.42
0.24
0.28
0.01
0.12
0.35
0.44
0.52
0.04
0.08
12
Table V. Mexican ADRs vs. S&P 500 and CDS, 1 Jan 2010 -21 Jun 2013
S&P 500 and CDS Regressions
P/E ratio
Company
Industry
S&P500
5 Yr CDS
Adjusted
Forward P/E
( Ticker)
Group
(t-statistic)
(t-statistic)
R2
(LTM P/E)
AMERICA MO-ADR A
(AMOV)
AMERICA MO-ADR L
(AMX)
WAL-MART MEX-ADR
(WMMVY)
FOMENTO ECON-ADR
(FMX)
GRUPO MODELO-ADR
(GPMCY)
COCA-COLA F-ADR
(KOF)
GRUPO F INBU-ADR
(GPFOY)
GRUPO F BANO-ADR
(GBOOY)
GRUPO TELEV-ADR
(TV)
CEMEX SAB-SP ADR
(CX)
GRUPO CARSO-ADR
(GPOVY)
KIMBERLY-CLA-ADR
(KCDMY)
GRUPO AEROPO-ADR
(ASR)
GRUPO AEROPO-ADR
(PAC)
PROMOTORA Y-ADR
(PUODY)
GRUPO SIMEC-ADR
(SIM)
GRUMA SAB-ADR
(GMK)
EMP ICA-ADR
(ICA)
INDUS BACHOC-ADR
(IBA)
GRUPO AEROPO-ADR
(OMAB)
Notes: See Table I.
Telecommunications
0.89
(7.28)
0.89
(7.43)
0.68
(4.94)
0.54
(4.68)
0.60
(4.40)
0.40
(3.11)
0.30
(1.68)
0.84
(5.01)
0.88
(7.66)
1.80
(8.50)
0.46
(2.08)
0.67
(4.75)
0.75
(4.97)
0.55
(4.04)
0.38
(2.20)
0.71
(4.91)
0.91
(4.46)
1.22
(6.49)
0.03
(0.18)
0.77
(4.52)
-8.29
(-3.16)
-7.77
(-3.02)
-8.55
(-2.93)
-12.60
(-5.08)
-5.02
(-1.73)
-9.29
(-3.43)
-23.64
(-6.23)
-17.51
(-4.90)
-8.27
(-3.39)
-18.72
(-4.13)
-18.45
(-3.94)
-7.31
(-2.43)
-9.31
(-2.89)
-9.32
(-3.18)
-10.69
(-2.86)
-14.03
(-4.56)
-6.92
(-1.59)
-12.93
(-3.22)
-10.16
(-3.15)
-7.32
(-2.01)
0.46
10.64
(12.40)
10.64
(12.40)
23.91
(32.23)
23.65
(22.35)
29.75
(30.41)
25.15
(29.14)
18.24
(29.43)
12.42
(17.83)
20.47
(18.66)
n/a
(n/a)
17.87
(18.93)
26.35
(24.98)
19.63
(21.16)
19.25
(22.03)
24.94
(18.02)
12.16
(12.18)
16.28
(16.06)
12.39
(14.64)
10.38
(8.26)
17.93
(17.08)
Telecommunications
Retail
Beverages
Beverages
Beverages
Banks
Banks
Media
Building Materials
Holding
Companies-Divers
Household
Products/Wares
Engineering&
Construction
Engineering&
Construction
Engineering&
Construction
Iron/Steel
Food
Engineering&
Construction
Food
Engineering&
Construction
0.47
0.32
0.42
0.23
0.24
0.34
0.43
0.49
0.56
0.21
0.28
0.32
0.28
0.14
0.41
0.22
0.43
0.09
0.25
13
Table VI. Russian ADRs vs. S&P 500 and CDS, 1 Jan 2010 - 21 Jun 2013
S&P 500 and CDS Regressions
P/E ratio
Company
Industry
S&P500
5 Yr CDS
Adjusted
Forward P/E
( Ticker)
Group
(t-statistic)
(t-statistic)
R2
(LTM P/E)
Oil&Gas
0.76
(5.00)
0.78
(6.46)
0.69
(4.32)
0.83
(5.14)
0.94
(5.67)
0.60
(4.18)
0.31
(1.43)
0.78
(3.59)
0.23
(0.96)
0.58
(3.64)
1.45
(5.53)
-11.94
(-6.33)
-7.58
(-5.00)
-11.08
(-5.58)
-9.75
(-4.85)
-6.42
(-3.09)
-8.05
(-4.52)
-14.46
(-5.39)
-10.48
(-3.89)
-8.50
(-2.81)
-11.79
(-5.88)
-18.72
(-5.70)
0.51
n/a
(6.12)
n/a
(7.90)
n/a
(15.34)
n/a
(5.96)
n/a
(9.17)
n/a
(7.12)
n/a
(7.26)
n/a
(10.82)
n/a
(14.08)
n/a
(5.96)
n/a
(4.46)
GAZPROM-ADR
(OGZPY)
LUKOIL OAO-ADR
(LUKOY)
MMC NORILSK ADR
(NILSY)
SURGUTNEFTEG-ADR
(SGTZY)
GAZPROM NEFT-ADR
(GZPFY)
MOBILE TELES-ADR
(MBT)
TATNEFT-ADR
(OAOFY)
ROSTELECOM-ADR
(ROSYY)
POLYUS G-SP ADR
(OPYGY)
SURGUTN-ADR PREF
(SGTPY)
MECHEL-SPON ADR
(MTL)
Notes: See Table I.
Oil&Gas
Mining
Oil&Gas
Oil&Gas
Telecommunications
Oil&Gas
Telecommunications
Mining
Oil&Gas
Iron/Steel
0.51
0.44
0.45
0.38
0.38
0.28
0.31
0.10
0.42
0.50
14
Table VII. South African ADRs vs. S&P 500 and CDS, 1 Jan 2010 - 21 Jun 2013
S&P 500 and CDS Regressions
P/E ratio
Company
Industry
S&P500
5 Yr CDS
Adjusted
Forward P/E
( Ticker)
Group
(t-statistic)
(t-statistic)
R2
(LTM P/E)
MTN GROUP-ADR
(MTNOY)
SASOL LTD-SP ADR
(SSL)
NASPERS-N ADR
(NPSNY)
KUMBA IRON-ADR
(KIROY)
ANGLO AMERIC-ADR
(AGPPY)
SHOPRITE-ADR
(SRHGY)
ANGLOGOLD AS-ADR
(AU)
ABSA GROUP-ADR
(AGRPY)
IMPALA PLAT-ADR
(IMPUY)
NEDBANK GROU-ADR
(NDBKY)
SANLAM LTD-ADR
(SLLDY)
GOLD FIELDS-ADR
(GFI)
BIDVEST GRP-ADR
(BDVSY)
TIGER BRANDS-ADR
(TBLMY)
EXXARO RE-SP ADR
(EXXAY)
IMPERIAL HLD-ADR
(IHLDY)
MASSMART HLDGS
(MMRTY)
HARMONY GOLD-ADR
(HMY)
AFRICAN BK -ADR
(AFRVY)
PPC LTD-ADR
(PPCYY)
Notes: See Table I.
Telecommunications
0.51
(3.73)
1.07
(10.67)
0.81
(5.75)
1.00
(6.11)
0.65
(3.99)
0.30
(2.07)
0.48
(2.86)
0.10
(.69)
0.92
(5.74)
0.51
(4.08)
0.65
(5.70)
0.41
(2.38)
0.43
(2.85)
0.48
(3.89)
0.93
(5.10)
0.28
(1.80)
0.53
(1.94)
0.57
(2.65)
0.56
(3.12)
0.37
(2.47)
-12.52
(-5.53)
-5.86
(-3.56)
-11.13
(-4.77)
-13.96
(-5.20)
-14.61
(-5.41)
-15.52
(-6.40)
-6.74
(-2.41)
-13.59
(-5.97)
-12.66
(-4.79)
-10.57
(-5.13)
-9.63
(-5.13)
-10.24
(-3.61)
-6.87
(-2.75)
-11.82
(-5.83)
-8.88
(-2.95)
-16.55
(-6.44)
-12.58
(-2.80)
-8.20
(-2.31)
-15.18
(-5.14)
-10.77
(-4.30)
0.37
13.42
(16.31)
9.03
(8.10)
25.17
(33.27)
9.57
(14.98)
30.11
(n/a)
26.34
(24.82)
10.46
(10.45)
9.73
(13.36)
26.35
(19.74)
9.40
(11.42)
14.22
(14.98)
11.94
(27.16)
15.16
(12.35)
16.58
(16.17)
9.88
(12.06)
11.46
(10.98)
24.64
(29.90)
13.02
(13.54)
6.13
(9.65)
13.11
(17.90)
Chemicals
Media
Iron/Steel
Mining
Food
Mining
Banks
Mining
Banks
Insurance
Mining
Holding
Companies-Divers
Food
Coal
Holding
Companies-Divers
Retail
Mining
Diversified
Finan Serv
Building Materials
0.60
0.43
0.47
0.37
0.34
0.15
0.26
0.43
0.37
0.45
0.19
0.17
0.40
0.31
0.33
0.13
0.13
0.31
0.24
15
Table VIII. South Korean ADRs vs. S&P 500 and CDS, 1 Jan 2010 - 21 Jun 2013
S&P 500 and CDS Regressions
P/E ratio
Company
Industry
S&P500
5 Yr CDS
Adjusted
Forward P/E
( Ticker)
Group
(t-statistic)
(t-statistic)
R2
(LTM P/E)
Iron/Steel
1.21
(11.19)
0.78
(5.85)
1.26
(10.37)
1.38
(11.91)
1.49
(10.42)
0.61
(5.75)
0.72
(5.96)
1.66
(10.82)
-0.13
(-0.42)
0.66
(3.26)
-1.62
(-0.62)
-0.40
(-0.13)
1.54
(0.53)
3.32
(1.20)
7.63
(2.22)
-1.29
(-0.51)
-1.57
(-0.54)
6.77
(1.84)
0.40
(0.05)
-3.06
(-0.63)
0.41
11.07
(10.79)
n/a
(n/a)
n/a
(37.61)
n/a
(22.65)
13.95
(388.13)
10.19
(8.55)
n/a
(12.02)
n/a
(25.71)
n/a
(n/a)
n/a
(2.69)
POSCO-ADR
(PKX)
KOREA ELEC P-ADR
(KEP)
SHINHAN FINA-ADR
(SHG)
KB FINANCIAL-ADR
(KB)
LG DISPLAY-ADR
(LPL)
SK TELECOM-ADR
(SKM)
KT CORP-ADR
(KT)
WOORI FINANC-ADR
(WF)
WEBZEN INC-ADR
(WZENY)
GRAVITY CO-ADR
(GRVY)
Notes: See Table I.
Electric
Diversified
Finan Serv
Diversified
Finan Serv
Electronics
Telecommunications
Telecommunications
Diversified
Finan Serv
Internet
Internet
0.16
0.37
0.44
0.38
0.15
0.16
0.40
-0.01
0.05
16
Table IX. Spanish ADRs vs. S&P 500 and CDS, 1 Jan 2010 - 21 June 2013
S&P 500 and CDS Regressions
P/E ratio
Company
Industry
S&P500
5 Yr CDS
Adjusted
Forward P/E
( Ticker)
Group
(t-statistic)
(t-statistic)
R2
(LTM P/E)
Retail
0.87
(7.20)
0.99
(6.81)
0.84
(7.69)
1.09
(7.28)
0.99
(7.93)
1.22
(9.21)
0.74
(5.52)
0.69
(5.72)
0.36
(.86)
0.87
(1.88)
1.06
(2.41)
-2.73
(-3.05)
-9.91
(-9.26)
-6.97
(-8.60)
-10.61
(-9.61)
-7.58
(-8.24)
-6.08
(-6.21)
-2.12
(-2.13)
-4.72
(-5.26)
-1.60
(-0.51)
-2.53
(-0.74)
-4.04
(-1.24)
0.37
22.88
(27.24)
10.37
(26.44)
9.22
(11.71)
9.71
(28.56)
10.06
(9.14)
9.44
(9.83)
10.69
(10.30)
11.01
(10.15)
13.35
(9.14)
10.18
(10.39)
34.90
N/A
INDITEX-ADR
(IDEXY)
BANCO SANTAN-ADR
(SAN)
TELEFONICA-ADR
(TEF)
BANCO BILBAO-ADR
(BBVA)
IBERDROLA SA-ADR
(IBDRY)
REPSOL SA-ADR
(REPYY)
RED ELECTRIC-ADR
(RDEIY)
ENAGAS-ADR
(ENGGY)
BANKINTER-ADR
(BKNIY)
ABENGOA SA-ADR
(ABGOY)
GAMESA CORP-ADR
(GCTAY)
Notes: See Table I.
Banks
Telecommunications
Banks
Electric
Oil&Gas
Electric
Gas
Banks
Engineering&
Construction
Electrical
Compo&Equip
0.58
0.59
0.61
0.58
0.57
0.25
0.39
0.00
0.02
0.06
17
Table X. US Industry Beta vs. S&P 500, 1 Jan 2010 - 21 June 2013 (Continued)
S&P500 Regressions
Industry
S&P500
Adjusted
( Ticker)
(t-statistic)
R2
Consumer Discretionary
1.06
0.90
( S5COND )
(40.65)
Automobiles
1.52
( S5AUTO )
(15.42)
Home Furnishings
1.32
( S5HOMF )
(15.53)
Consumer Services
0.84
( S5HOTR )
(22.18)
Media
1.12
( S5MEDA )
(31.30)
Movies & Entertainment
1.19
( S5MOVI )
(26.09)
Retailing
0.99
( S5RETL )
(23.51)
Automotive Retail
0.66
( S5AUTR )
(9.78)
Consumer Staples
0.53
( S5CONS )
(18.11)
Food & Staples Retailing
0.58
( S5FDSR )
(15.62)
Beverages
0.51
( S5BEVG )
(12.05)
Food Products
0.50
( S5FDPR )
(14.26)
Household & Personal Products
0.47
( S5HOUS )
(10.59)
Energy
1.26
( S5ENRS )
(30.70)
Oil & Gas Equipment & Services
1.64
( S5OILE )
(19.30)
Coal & Consumable Fuels
1.93
( S5CCSF )
(13.41)
Financials
1.26
( S5FINL )
(33.04)
Banks
1.27
( S5BANK )
(20.92)
Diversified Financials
1.42
( S5DIVF )
(23.91)
0.57
0.57
0.73
0.84
0.79
0.75
0.32
0.64
0.57
0.44
0.52
0.38
0.84
0.67
0.50
0.86
0.71
0.76
(Continued)
18
Table X. US Industry Beta vs. S&P 500, 1 Jan 2010 - 21 June 2013(Continued)
S&P500 Regressions
Industry
S&P500
Adjusted
( Ticker)
(t-statistic)
R2
Capital Markets
1.37
0.77
( S5CAPM )
(24.26)
Insurance
1.08
( S5INSU )
(31.48)
Consumer Finance
1.21
( S5CFIN )
(18.55)
Health Care
0.70
( S5HLTH )
(23.19)
Health Care Equipment & Supplies
0.84
( S5HCEQ )
(21.70)
Biotechnology
0.67
( S5BIOT )
(10.74)
Pharmaceuticals
0.56
( S5PHAR )
(14.46)
Industrials
1.18
( S5INDU )
(51.50)
Aerospace & Defense
1.08
( S5AERO )
(33.90)
Construction & Engineering
1.49
( S5CSTE )
(21.88)
Commercial & Professional Services
0.91
( S5COMS )
(26.23)
Transportation
1.08
( S5TRAN )
(26.64)
Airlines
1.00
( S5AIRL )
(10.33)
Information Technology
1.08
( S5INFT )
(30.76)
Internet Software & Services
1.04
( S5INSSX )
(14.29)
Technology Hardware & Equipment
1.14
( S5TECH )
(19.44)
Computers & Peripherals
1.14
( S5CMPE )
(14.08)
Materials
1.28
( S5MATR )
(30.36)
Chemicals
1.17
( S5CHEM )
(29.29)
0.85
0.65
0.75
0.72
0.37
0.53
0.94
0.86
0.73
0.79
0.80
0.37
0.84
0.53
0.68
0.52
0.84
0.83
(Continued)
19
Table X. US Industry Beta vs. S&P 500, 1 Jan 2010 - 21 June 2013
S&P500 Regressions
Notes:
Industry
S&P500
Adjusted
( Ticker)
(t-statistic)
R2
Construction Materials
1.35
0.35
( S5CSTM )
(9.85)
Metals & Mining
1.52
( S5METL )
(16.64)
Steel
1.81
( S5STEL )
(17.17)
Telecommunication Services
0.59
( S5TELS )
(12.45)
Utilities
0.55
( S5UTIL )
(12.71)
Electric Utilities
0.49
( S5ELUTX )
(10.79)
0.60
0.62
0.46
0.47
0.39
See Table I.
Table XI. International Cost of Equity Estimates
CDS Risk
Allocation
US Risk Free Rate (%)
US Market Risk Premium (%)
CDS (%)
Industry
Consumer Discretionary
Consumer Staple
Energy
Financials
Health Care
Industrials
Information Technology
Materials
Telecommunications
Utilities
Brazil
Chile
China
1.90
0.97
1.05
Italy
Mexico
Russia
South
Africa
South
Korea
Spain
2.56
1.27
1.85
2.22
0.84
2.52
11.93
8.57
14.11
16.28
10.20
13.35
12.16
13.98
9.71
9.50
12.06
8.70
14.48
16.65
10.45
13.60
12.41
14.23
9.96
9.75
11.57
8.21
13.10
15.27
9.49
12.64
11.45
13.27
9.00
8.79
12.16
8.80
14.78
16.95
10.66
13.81
12.62
14.44
10.17
9.96
4.00
7.00
Beta
1.04
0.56
1.18
1.49
0.70
1.15
0.98
1.24
0.63
0.60
CDS Risk
0.35
0.35
1.00
1.00
0.70
0.70
0.70
0.70
0.70
0.70
Values in Percent
11.95
8.59
14.16
16.33
10.23
13.38
12.19
14.01
9.74
9.53
11.62
8.26
13.23
15.40
9.58
12.73
11.54
13.36
9.09
8.88
11.65
8.29
13.31
15.48
9.64
12.79
11.60
13.42
9.15
8.94
12.18
8.82
14.82
16.99
10.69
13.84
12.65
14.47
10.20
9.99
11.72
8.36
13.53
15.70
9.79
12.94
11.75
13.57
9.30
9.09
20
III. Conclusion
With 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.
21
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