VOLUME 22 | NUMBER 4 | FALL 2010

Journal of APPLIED CORPORATE A MORGAN STANLEY PUBLICATION

In This Issue: Payout Policy and Communicating with Investors

Financial Planning and Investor Communications at GE 8 Keith Sherin, General Electric (With a Look at Why We Ended Earnings Guidance)

The Value of Reputation in and Investment Banking 18 Jonathan Macey, Yale Law School (and the Related Roles of Regulation and Market Efficiency)

Maintaining a Flexible Payout Policy in a Mature Industry: 30 James Ang, Florida State University, and The Case of Crown Cork and Seal in the Connelly Era Tom Arnold, C. Mitchell Conover, and Carol Lancaster, University of Richmond

Is Carl Icahn Good for (Long-Term) Shareholders? 45 Vinod Venkiteshwaran, Texas A&M University-Corpus Christi, A Case Study in Shareholder Activism and Subramanian R. Iyer and Ramesh P. Rao, Oklahoma State University

Drexel University Center for Corporate Governance Roundtable on 58 Panelists: Scott Bauguess, U.S. Securities and Exchange Risk Management, Corporate Governance, and the Commission; Jim Dunigan, PNC Asset Management Group; Search for Long-Term Investors Damien Park, Hedge Fund Solutions; Patrick McGurn, Risk Metrics; Don Chew, Morgan Stanley. Moderated by Ralph Walkling, Drexel University.

Blockholders Are More Common in the United States Than You Might Think 75 Clifford G. Holderness, Boston College

Private Equity in the U.K.: Building a New Future 86 Mike Wright, Center for Management Buy-out Research and EMLyon, and Andrew Jackson and Steve Frobisher, PAConsulting Group Limited and Center for Management Buy-out Research

Should Asset Managers Hedge Their “Fees at Risk”? 96 Bernd Scherer, EDHEC , London

Measuring Corporate Liquidity Risk 103 Håkan Jankensgård, Lund University

The Beta Dilemma in Emerging Markets 110 Luis E. Pereiro, Universidad Torcuato Di Tella The Beta Dilemma in Emerging Markets by Luis E. Pereiro, Universidad Torcuato Di Tella*

he Capital Asset Pricing Model1 (“CAPM” industry betas, you may find that some markets simply have henceforth) has provided both equity investors no companies quoting in certain industries, and therefore, T and corporate officers making direct investment no industry betas will be available at the domestic level. decisions with a way to estimate present values For example, neither Brazil nor Russia has locally-quoted and expected returns on investments. The CAPM framework biotechnology companies. In other markets, the local price provides financial practitioners with a measure of beta (or series available to compute betas are unacceptably short— “systematic risk”) for entire stock markets, for industry sub- therefore, betas will be unreliable. In still others, even if you sectors, and for individual equities. do find a reliable industry beta, it may still not fairly reflect Betas represent the way a particular asset’s returns (or an the risk of its industry if the sector in question weighs heavily industry’s returns) co-vary with the returns of a broad market and disproportionately in the local market’s capitalization. index. Typically, the beta of a U.S. equity is measured against In Argentina, for example, a single industry, oil, accounts changes in the Standard & Poor’s 500 stock index. Betas for 40% of total domestic market cap; in South Africa, the of equities in other countries are usually measured against mining sector makes up close to 24% of local market cap; changes in local stock market indices. in Slovenia, the pharmaceutical industry (itself made up of a CAPM can provide a useful estimate of a firm’s cost of single company) represents more than one third of the local capital even if that firm does not have publicly traded shares market cap. In such cases, the beta of the sector at the top of to provide a measure of risk. Groups of similar firms (perhaps the market cap list is reflectingmarket , rather than industry, in the same industry) that do have listed shares can provide a risk, and will not be a fair gauge of the true sector’s risk.2 good proxy for the risk a private firm faces. To avoid the data predicament, many value appraisers When combined with (1) some “risk-free rate” (usually simply resort to using U.S. betas as surrogates for emerging assumed to be the yield on the relevant sovereign debt), (2) a market (EM) betas. In Argentina, 67% of financial advisors “market risk premium” (the annualized rate by which local and private equity funds do so when valuing local compa- equity investors have outperformed local debt investors over nies; only 14% of them apply some kind of corrective process long periods of time) and (3) a company-specific leverage to U.S. data before computing a domestic cost of capital.3 factor, the beta of an equity can tell an investor what rate of Unfortunately, U.S. beta fans bump on the other side of the return that investment should achieve. dilemma against an equivalence predicament—namely, are This same rate should also function as a “hurdle rate” local industry betas equivalent across borders? corporate decision-makers use to assess the desirability of a Intuition suggests they need not be, simply because an particular direct investment and for someone trying to price EM industry could well be in a different stage of develop- a private asset in an emerging market. ment than its U.S. counterpart—and thus command a very In principle, the CAPM is universally applicable to invest- different level of risk.4 Demand for, say, soft drinks could be ments. more sensitive to an economic recession in Latvia than in In practice though, decision-makers face at least two the U.S.; the risk—and therefore the beta—of the soft drink serious predicaments in applying CAPM logic to emerging industry in Latvia should then be larger. Industry betas may market investment assets. also differ across borders due to dissimilar levels of operating The first is a data predicament—that is, relevant local data and financial leverage. Despite the intuition that cross-border may not exist, or it can turn out to be undependable or atypi- differences in domestic betas may exist, we still lack the empir- cal. If you try to use a pricing model based on local (domestic) ical research needed for verification and, as a consequence, the

* I would like to thank Aswath Damodaran, Martín González-Rozada, Javier Estrada, 1. Sharpe, William F. (1964). “Capital Asset Prices—A Theory of Market Equilibrium Nora Sánchez and Gustavo Vulcano for useful comments and suggestions. Lucía Freira Under Conditions of Risk.” Journal of Finance XIX (3): 425–42. provided valuable research assistance. The views expressed below and any errors that 2. For additional examples, see Zenner and Akaydin (2002). may remain are entirely my own. 3. Pereiro (2006). 4. Damodaran (2002); Shapiro (2003).

110 Journal of Applied Corporate Finance • Volume 22 Number 4 A Morgan Stanley Publication • Fall 2010 equivalence predicament keeps haunting the analyst. risk features across EMs are so contrasting to those in the This article proposes a solution to the beta dilemma. The U.S., that we feel justified in grouping EMs into a single, data predicament is negotiated first, by computing domestic distinctive asset class—which is exactly what international industry betas for a long list of EMs considered as a unique asset portfolio managers have been doing for years. class. The procedure allows us to find a sizable number of betas To illustrate the aggregation procedure, we defined EMs per industry, for a large number of industries, thus automati- as a broad asset class comprising 81 stock markets,8 and cally diluting the data predicament. We next attend to the collected the betas of all their public firms by January 1st, equivalence problem by formally testing whether domestic 2010. Betas had been computed by Bloomberg over a 5-year EM and U.S. industry betas are fair proxies for each other; window by regressing weekly U.S.-dollar based returns on such tests will clearly inform value appraisers on when it is the most important local index. For the U.S., the original sensible to assume equivalence—and when it isn’t. data came from ValueLine, betas being computed over a 5-year window by regressing weekly returns against the Solving the Data Predicament NYSE Composite Index. We purified the data by eliminat- To calculate the cost of equity of an emerging-market company, ing companies in financial distress (as signaled by a zero many value appraisers resort to CAPM-based models that or negative book value of equity, or by a negative book- employ domestic industry betas (see Exhibit 1, Panel A for a to-equity ratio), and finally screened out companies with brief review). These models, however, work well only if a local nil or negative betas, which are useless for cost-of-capital industry beta is available, is reliable, and is truly representa- computations. tive of its sector. In many EMs, alas, such betas are simply Observable firm betas reflect the current financial lever- not there, and analysts choose to use as a reference the beta of age of the firms in question, and are therefore financially a different emerging market, whose industry plausibly shares levered betas. From these we cleansed the effect of financial the risk-return pattern of the target’s. In many cases, though, leverage by computing unlevered firm betas: this strategy will still be to no avail: local betas in the alternate country may still be lacking, undependable, or atypical. Unlevered Beta = Levered Beta / [1+ (1-T) x D/E] (1) The data predicament can be approached by aggregating EMs into a single asset class. Such grouping, which allows where T is the corporate tax rate, and D/E is the market- for finding a large enough number of valid industry betas, is marked financial leverage of the firm in question.9 We are predicated on the fact that, in contrast to the U.S., emerging interested in unlevered betas since these are, for private firms, stock markets share a number of risk-related characteristics: the standard starting point in cost-of-capital computations: small absolute and relative size, low liquidity, higher leverage, the unlevered beta is re-levered with the D/E ratio of the firm heavy concentration, and high volatility. China, nowadays the under and introduced into any of the models avail- center of gravity of Asian markets, represents less than 19% able for cost-of-capital calculations. of U.S. market cap; , less than 9%; and Brazil, less than Going a step further, we computed total firm betas. A 6%. The importance of EMs in their respective economies is total beta gauges the total risk of a stock (i.e., that stock’s likewise smaller: the median weight of the stock market as a systematic plus unsystematic risks); it is defined as the full percentage of GNP is about 40% in EMs; the figure in the volatility of the stock relative to the market’s: U.S.: 180%. Smallness goes hand in hand with lower liquid- ity, which in EMs is only one third of the U.S.’s.5 The median Total Beta= [Standard Deviation of Firm’s Stock market debt-to-assets ratio is 16.6% in EMs and 15.4% in the Returns / Standard Deviation of Market’s Returns] (2) U.S. Add to that concentration—which can and often does lead to price manipulation: the top 10 largest firms in EMs We are interested in total beta because it is a most useful risk represent about 56% of total market cap; in the NYSE, the measure for undiversified investors—e.g., private-firm owners figure is 23%.6 Finally, EM returns march alongside large whose wealth is concentrated into a single stock and thus bear volatilities: the long-term annual return in the U.S. is 8.1%, the handicap of nil diversification. Also, total beta works well at an annual volatility of 16.2%; the return figure for EMs is as a maximum extreme reference for partially diversified inves- about 9.1% a year—at an annual volatility of 34.8%.7 Similar tors—i.e., those whose portfolios comprise just a few stocks

5. Based on data from Lesmond (2005) and Lesmond, Ogden and Trzcinka (1999). Jordan, Kazakhstan, Kenya, Kuwait, Latvia, Lebanon, Liberia, Lithuania, Malawi, Malay- 6. Pereiro (2001a). sia, Mauritius, Mexico, Morocco, Namibia, Nicaragua, Nigeria, Oman, Pakistan, Pales- 7. Computed over 1920–1996 by Goetzmann and Jorion (1999). tine, Panama, Paraguay, Peru, Philippines, Poland, Qatar, Romania, Russia, Saudi Ara- 8. The class included, in fact, a mixture of emerging, frontier, and less-developed bia, Serbia, Singapore, Slovakia, Slovenia, South Africa, South Korea, Sri Lanka, stock markets: Argentina, Bahamas, Bahrain, Bangladesh, Bermudas, Bolivia, Botswa- Swaziland, Taiwan, Thailand, Trinidad, Tunisia, Turkey, U.A.E., Ukraine, Uruguay, Ven- na, Brazil, Bulgaria, Cayman, Chile, China, Colombia, Costa Rica, Croatia, Cyprus, ezuela, Vietnam, Zambia, and Zimbabwe. We call the whole class “emerging” for sim- Czech Republic, Dominican Republic, Ecuador, Egypt, El Salvador, Estonia, Fiji, Ghana, plicity. Guatemala, Hong Kong, Hungary, India, Indonesia, Iran, Israel, Ivory Coast, Jamaica, 9. This is the classic Hamada’s (1972) formula.

Journal of Applied Corporate Finance • Volume 22 Number 4 A Morgan Stanley Publication • Fall 2010 111 and thus bear the handicap of partial, or incomplete, diver- apply, without further regrets, a pricing model that employs sification; for these investors, true betas will lie somewhere a straight U.S. beta (see Exhibit 1, Panel B, for a brief review between regular and total betas. (Since totally or partially of these models). undiversified investors are legion,10 analysts have developed several models to deal with their special diversification status; Solving the Equivalence Predicament some appear in Exhibit 1, Panel A.) Another way to solve the data predicament would be to Total betas can be computed from regular betas by using apply U.S. domestic betas directly to EMs; but this strategy the following equation: is fraught with danger because local industry betas may not be equivalent across asset classes.12 The last column in Exhibit Total Unlevered Beta = [Unlevered Beta / Rho] (3) 2 shows that close to 80% of industry betas are not equiva- lent across classes. Consequently, we can justify using U.S. where Rho is the correlation coefficient of the stock’s return betas as sensible surrogates for domestic EM betas for only a against the market’s.11 Rho is a simple measure of how handful of industries. sensitive a particular stock’s return is to general economic Look next at the results reported in the last column conditions, as reflected in the stock market. of Exhibit 3: total betas turn out to be significantly differ- The next-to-last step was to group betas and total betas ent between asset classes for about 30% of the sample. The according to industry. Since Bloomberg and Value Line classify overall conclusion is telling: U.S. betas should not be directly industries differently, we constructed our own set of catego- applied to EMs unless cross-border beta equivalence has been ries by checking out each company’s SIC/NAICS number adequately proven in the first place. and, when necessary, resorting to individual company reports, to properly sort each firm into the right industry. Fickle Risk, Beta Waves Finally, we computed industry medians for betas and total A closer look at Exhibit 2 reveals a striking find: all industry betas. The median is a standard measure of centrality widely betas that are significantly different across classes are smaller used in corporate valuation because it is impervious to outliers in EMs. In fact, the median of medians in EMs is 0.37—less (in contrast to arithmetic means; outliers frequently crop up than half the U.S. norm of 0.84. Since differences in develop- in beta samples). After discarding sectors with fewer than 10 ment, or in operating leverage, may produce larger or smaller firms, we were left with 66 industries whose betas and total betas across asset classes, our consistent find of smaller betas in betas appear in Exhibits 2 and 3, respectively. EMs is somewhat troubling. Is there a problem in the data? Courtesy of the aggregation procedure, the value The problem is not in the data, but in the fact that appraiser can simply look into our tables to find a plausible betas and total betas are inter-temporally unstable risk industry beta for an emerging market. Since our betas have measures—i.e., they change over time. A stock’s risk, as been computed against local market indexes, they should be noted, has several components: the degree of operating reflecting domestic industry risk fairly well, thus being the leverage; the potential obsolescence of the product or service perfect fit for models that employ local betas. offered; the strength of the competition; the position of The aggregation solution works well, of course, only if one the industry in its life cycle (which affects the sensitivity of believes that the industry beta of the average EM is a good the demand to economic conditions); the vulnerability of match for its analogue in the specific emerging market (say, operations to external financing via credit or equity (in a Latvia) in which the target company operates. If not, one dry market, financing is difficult or impossible); and even may well be facing an industry whose risk-return dynamics investor sentiment. All these components are bound to vary (and beta) are instead akin to those of developed markets; being as time elapses, and so are risk metrics—betas and total the U.S. a good proxy for the latter, in such cases one could betas.13

10. The existence of imperfectly diversified investors has been reported on: majority used only if betas are normally distributed in both asset classes. We used the Shapiro- shareholders in family-owned companies (Moskowitz and Vissing-Jorgensen, 2002); Wilk (S-W) test to test for such dual normality and applied the t-test only to those indus- venture capitalists (Norton and Tenenbaum, 1993; Schertler, 2001); angel investors tries in which the condition verified. (By definition, the t-test tests differences between (Freear, Sohl and Wetzel, 1997; Pereiro, 2001b); individuals investing in the U.S. stock means, not medians; yet in normally-distributed data, mean and median coincide be- market (Blume and Friend, 1975; Lease, Lewellen and Schlarbaum, 1976; Barber and cause of symmetry, therefore testing means is equivalent to testing medians—which is Odean, 2000; Huberman, 2001; Ivkovic and Weisbenner, 2003); corporate employees our goal). Where dual normality was not present, we applied a non-parametric test—the (Benartzi, 2001); and international portfolio managers (Karolyi and Stulz, 2002). Wald-Wolfovitz (W-W) Runs, a tool that tests not only the location (median) of the distri-

11. Let RT= Alpha + Beta. RM; where RT is the total return of the stock and RM the bution but also its shape—hence being a very comprehensive test. The choice of the S-W 2 market return. V(RT)= Beta . V(RM), where V is the variance. On the other hand, V(RT) and W-W tests is predicated on the fact that they work better for small samples—fewer can be de-composed in one systematic and one unsystematic risk components, like this: than 2,000—which are the norm in industry beta datasets. A good tip is to use always 2 2 2 2 V(RT)= R .V(RT) + (1-R ). V(RT). Using both identities: V (RT)= Beta . V(RM) = Rho . exact tests of significance, since these can handle small samples, sparse tables, and 2 2 V(RT), from where V(RT) = Beta . V(RM)/Rho . Extracting the square root in both terms: unbalanced designs—features that are likely to appear in many an industry.

Standard Deviation (RT) = (Beta/Rho). Standard Deviation(RM). From where the beta of 13. Financial leverage is also a risk component, but we have cleansed its effects from the total return, or total beta, is (Beta/Rho). our data by working with unlevered betas. 12. Testing for equivalence is akin to testing the difference of medians. The most powerful difference-of-medians test is Pearson’s t-test but, being parametric, it can be

112 Journal of Applied Corporate Finance • Volume 22 Number 4 A Morgan Stanley Publication • Fall 2010 How, exactly, have betas been evolving lately? Exhibit In summary, betas and total betas do change over time 4 lets us peek at the answer: between 2007 and 2010, the with the gyrations of stock markets, and this warrants a stern median of medians for U.S. industry betas remained fairly caveat: if we employ the aggregation procedure to solve the stable (it climbed slightly from 0.83 to 0.85, but the change data predicament, we must take the precaution to update our was statistically insignificant); while the median in EMs datasets from time to time. dropped sharply—from 0.94 to 0.39, a highly significant difference. In other words, EMs became less risky on average Asset Pricing with the Beta Solution over that period. The choice of a cost of equity model for an emerging-market Panels B1 and B2 in Exhibit 4 graphically reinforce firm is very personal: it depends on how conceptually sound our argument: the beta probability distribution—or beta the model looks to the analyst, and on her view on which risks ‘wave’—stays in about the same place in the U.S., but shifts can—and which cannot—be diversified away by the investor. dramatically to the left (i.e., to less risky terrain) in EMs. It The choice done, the analyst faces the challenge of finding a is this shift, precisely, that may be accountable for our finding plausible beta to plug into the selected model—and at this that all industry betas were, as of the dawn of 2010, smaller in point is when the solution to the beta dilemma becomes help- EMs than in the U.S. ful (see Exhibit 5). As for total industry betas, some turn out to be larger Those who prefer using local pricing models but are in the U.S., and some others don’t; but in any case, U.S. unable to find plausible local betas in the emerging market, median total beta increased significantly—going from 1.66 in can use the industry beta of (a) another EM, suspected to January 2007 to 2.36 in January 2010. In consonance, Panels have a similar risk-return industry dynamics (and, as long as B3 and B4 in Exhibit 4 illustrate how the total beta wave in such beta is available, reliable, and representative); or (b), as the U.S. displaces heavily to the right, i.e., to riskier terrain. we have argued, the beta of the whole EM class. Relying on The reason behind this shift has almost certainly been the U.S. betas head-on is, we explained, a dangerous approach, subprime credit crisis.14 In stark contrast, the median total since betas may be significantly different across classes. beta for EMs decreased over the same period—shifting signifi- If the risk-return dynamics of the industry in the target cantly from 2.44 to 2.02. EM is suspected to be different from that of the EM class as How come U.S. total beta increased while U.S. beta a whole, why not assume that such market is rather follow- remained stable? The answer lies in the evolution of ing the dynamics of developed markets? Being the U.S. the Rho—the correlation. Recall that beta equals total beta epitome of such, using a U.S.-beta-based pricing model will divided by Rho. Over the time stretch under analysis, U.S. be a fair strategy to apply in such occasions. All in all, solving total beta went up while Rho went down—in such a way the beta dilemma clarifies when it is sensible to use a local, that beta was left virtually unscathed. Not in EMs, though: and when a U.S.-based, asset pricing model. here, while Rho likewise decreased over time, on balance, beta decreased significantly. Now, since Rho went down luis e. pereiro is Professor of Finance at the Business School, Univer- in both the U.S. and EMs, it seems that in both asset classes, sidad Torcuato Di Tella in Buenos Aires, Argentina. He is also the author the idiosyncratic, or non-diversifiable portion of total of Valuation of Companies in Emerging Markets: A Practical Approach, risk prevailed over the market-related portion (i.e., that John Wiley & Sons (2002). measured by beta).

14. The first symptoms of the credit crunch appeared in January/February 2007, when the ABX index, which tracks credit default-swaps based on bonds backed by sub- prime mortgages, started deteriorating heavily. The fallout became more evident by April 2007, when New Century Financial, the largest subprime lender in the U.S., filed for bankruptcy. January 1, 2007 thus seems to be a sensible pre-crisis comparison point.

Journal of Applied Corporate Finance • Volume 22 Number 4 A Morgan Stanley Publication • Fall 2010 113 References Lesmond, D., Ogden, J., Trzcinca, C. (1999). “A New Barber B.M. and T. Odean (2000). “Trading is Hazardous to Estimate of Transaction Costs.” Review of Financial Studies, Your Wealth: The Common Stock Investment Performance of 12, 1113–1141. Individual Investors.” Journal of Finance, 55, 773–806. Lessard, D. (1996). “Incorporating Country Risk in the Benartzi, S. (2001). “Excessive Extrapolation and the Valuation of Offshore Projects.”Journal of Applied Corporate Allocation of 401(k) Accounts to Company Stock.” Journal Finance, 9, 52–63. of Finance, 56, 1747–1764. Mariscal, J.O. and K. Hargis (1999). “A Long-Term Blume, M. and I. Friend (1975). “The Asset Structure Perspective on Short-Term Risk.” Goldman Sachs Invest- of Individual Portfolios and Some Implications for Utility ment Research. Functions.” Journal of Finance, 30, 585–603. Moskowitz, T.J. and A. Vissing-Jorgensen (2002). Damodaran, Aswath (2002). Investment Valuation. John “The Returns to Entrepreneurial Investment: A Private Wiley & Sons, New York. Equity Premium Puzzle?” American Economic Review, 92, Freear, J., Sohl, J.E. and W.E. Wetzel, Jr. (1997). “The 745–778. Informal Venture Capital Market: Milestones Passed and the Norton, E. and B.H. Tenenbaum (1993). “The Effects of Road Ahead.” in Sexton, D.L. & R.W. Smilor (Eds.), Entre- Venture Capitalists’ Characteristics on the Structure of the preneurship 2000, Upstart Publishing Company, Chicago. Venture Capital Deal.” Journal of Small Business Management Goetzmann, W.N. and P. Jorion (1999). “Re-Emerging (October), 32–41. Markets.” Journal of Financial and Quantitative Analysis, 34, Pereiro, L.E. (2006). “The Practice of Investment Valua- 1–31. tion in Emerging Markets: Evidence from Argentina.” Journal Hamada, R.S. (1972). “The Effect of the Firm’s Capital of Multinational Financial Management, 16, 160–183. Structure on the Systematic Risk of Common Stocks.” Journal Pereiro, L.E. (2001a). “The Valuation of Closely-Held of Finance, 27, 435–452. Companies in Latin America.” Emerging Markets Review, 2, Huberman, G. (2001). “Familiarity Breeds Investment.” 330–370. Review of Financial Studies, 14, 659–680. Pereiro, L.E. (2001b). “Tango and Cash: Entrepreneurial Ivkovic, Z. and S. Weisbenner (2003). “Local Does as Finance and Venture Capital in Argentina.” Venture Capital, Local Is: Information Content of Geography of Individual 3, 291–308. Investor’s Common Stock Investments.” NBER Working Schertler, A. (2001). “Venture Capital in Europe’s Paper No. 9685. Common Market: A Quantitative Description.” EIFC– Karolyi, G.A. and R.M. Stulz (2002). “Are Financial Technology and Finance Working Papers 4, United Nations Assets Priced Locally or Globally?” NBER Working Paper University, Institute for New Technologies. No. W8994. Shapiro, A.C. (2003). Multinational Financial Manage- Lease, R.C., Lewellen, W.G. and G.G. Schlarbaum ment (Seventh Edition), John Wiley & Sons, New York. (1976). “Market Segmentation: Evidence on the Individual Zenner, M. and Akaydin, E. (2002). “A Practical Investor.” Financial Analysts Journal (Sep-Oct), 53–60. Approach to the International Valuation and Capital Alloca- Lesmond, D.A. (2005). “Liquidity of Emerging Markets.” tion Puzzle.” Global Corporate Finance Papers, Salomon Journal of , 77, 411–452. Smith Barney.

114 Journal of Applied Corporate Finance • Volume 22 Number 4 A Morgan Stanley Publication • Fall 2010 Appendix

Exhibit 1 CAPM-Based Cost of Equity Models Used in Emerging Markets Valuation

Panel A Models Using Local (Domestic) Betas

Model Description

1. Local CAPM (L-CAPM); CE = Rf US + RC + BLL . (RM L – Rf L) adapted from Pereiro (2001a) where CE is the cost of equity capital, Rf US is the U.S. risk-free rate, RC is a country risk premium, BLL is the beta of a

comparable local company (or industry) computed against a local (i.e., emerging) market index, and RM L the return of the

local stock market. The country risk premium RC is usually computed as the spread of dollar-denominated sovereign bonds over American T-bonds of similar denomination, yield and term. This model fits apartially diversified investor: the use of beta suggests the investor is fully diversified at the domestic company (or industry) level, but unable to diversify at the country level.

2 2. Adjusted Local CAPM (AL-CAPM); CE = Rf US+ RC + BLL . (RM L – Rf L) . (1 - R ) adapted from Pereiro (2001a) where R2 may be thought of as the amount of variance in the equity volatility of the target company that is explained by country risk. This model is an improvement over the Local CAPM, since the last term compensates for the double-counting

of country risk implied in using both RC and RM L in the same equation. Again, the model fits a partially diversified investor: well-diversified at the local firm (or industry) level, but unable to diversify at the country level.

2 3. Full Risk Adjusted Local CAPM CE = Rf US+ RC + BT LL . (RM L – Rf L) . (1 - R )

(FRAL-CAPM) where BTLL is the total beta of a comparable local company (or industry) computed against a local market index. BT is computed as the standard deviation of the return of a local comparable company (industry), divided by the standard deviation of the return of the local market. This model is conceptually equivalent to the AL-CAPM but applies instead to fully undiversified investors, since a total beta is used.

4. Full Risk Hybrid Local CAPM CE = Rf US+ BTLL . (RM US – Rf US) . BT L,US (FRHL-CAPM) In this model, country risk is incorporated by calibrating the U.S. market risk premium to the local (emerging) market via

a total country beta, or BTL,US, equal to the standard deviation of returns in the local equity market divided by the standard

deviation of returns in the U.S. market. Since the RC factor is not used, no correction for risk double-counting is needed. The model is hybrid in the sense that it combines EM data with U.S. data. Like the FRAL-CAPM, this model applies to fully undiversified investors—i.e., to those unable to diversify company, industry, and country risk.

5. Goldman-Sachs (G-S) Model; CE = Rf US + R C + B LL. (RM US – Rf US) . BT L,US . (1 – R) + RId adapted from Mariscal and Hargis where RM US is the return of the U.S. stock market index, and R is the correlation of dollar returns between the local stock market (1999) and the sovereign bond used to measure country risk. While (1-R) is used to alleviate the problem of double counting country

risk, the problem is not fully solved, since the model also includes BTL,US in the same equation. A special feature of the model is

RId, an idiosyncratic risk premium related to the special features of the target firm (e.g., specific firm credit rating as embodied in its corporate debt spread, industry cyclicality, percentage of revenues coming from the target country, etc). This model fits a

partially diversified investor: the use of LLB suggests the investor is well diversified at the local company (or industry) level, but unable to diversify at the country level.

6. Gamma Model; Damodaran (2002) CE = Rf US + R C . Gamma + B LL . (RM US – Rf US)

where Gamma is a firm-specific exposure to country risk ranging from zero to one, and LLB is the local company beta computed against a local market index. The exposure factor Gamma could be, for instance, the percentage of revenues to

the parent firm coming from the local (emerging) market. The use of the CR factor suggests, again, that the investor is unable

to diversify country risk; yet the use of BLL suggests the investor is able to diversify company (or industry) risk.

Journal of Applied Corporate Finance • Volume 22 Number 4 A Morgan Stanley Publication • Fall 2010 115

Panel B Models Using U.S. Betas

Model Description

7. Lessard´s Model Lessard (1996) CE = Rf US + RC + BCL,US . BUS . (RM US – Rf US)

where RC is a country risk premium that includes the chance of expropriatory actions, payment difficulties

and other risks, BCL,US is the country beta (the relative sensitivity of the returns of the local stock market

to the U.S. market’s), and BUS is the beta of a U.S.-based project whose risk pattern is comparable to the offshore project. The country risk premium can be computed as a sovereign bond yield spread against U.S. treasuries, as an OPIC insurance premium, or indirectly derived from political risk ratings. The simultaneous

use of RC and BCL,US without any further correction suggests the model double-counts country risk. The use of a U.S. beta suggests the investor is well-diversified at the firm (or industry) level, but unable to diversify at the country level. This model (and all other models within this panel) implicitly assumes that a U.S. firm (or industry) beta is a plausible surrogate for the local EM beta.

2 8. Adjusted Hybrid CAPM (AH-CAPM) CE = Rf US+ RC + BCL,US . BUS . (RM US – Rf US) . (1 – R )

(adapted from Pereiro, 2001a) where BUS is the average unlevered beta of comparable companies quoting in the U.S. market, relevered with the financial structure of the target company, and 2R is the coefficient of determination of the regression between the equity volatility of the local market against the variation in country risk. Conceptually similar to Lessard’s model, but attempts to correct for the double counting of country risk by including the (1 - R2) factor. R2 can be thought of as the amount of variance in the volatility of the local equity market that is explained by country risk. Like Lessard’s, this model applies to an investor that is well-diversified at the firm (or industry) level, but unable to diversify at the country level.

9. Full Risk Hybrid U.S. CAPM (FRHUS-CAPM) CE = Rf US+ BTUS . (RM US – Rf US) . BT L,US This model is identical to the FRHL-CAPM, except that it uses a total U.S. beta instead of a total local beta.

BTUS is the total beta of a comparable U.S. company (or industry) computed against the U.S. market index. Like the FRHL-CAPM, this model incorporates country risk by calibrating the U.S. market risk premium to the local

(emerging) market via the total country beta BTL,US. In this aspect, the model is similar to the G-S model; but in contrast, the Rc factor is not used here, and that’s why no correction for risk double counting is needed. Like the FRAL- and FRHL-CAPMs, this model applies to fully undiversified investors—i.e., to those unable to diversify company, industry, and country risks. The model uses a total U.S. beta which, as this article purports, may not necessarily be a good proxy for its emerging market counterpart.

116 Journal of Applied Corporate Finance • Volume 22 Number 4 A Morgan Stanley Publication • Fall 2010 Exhibit 2 Betas: Industry Medians in Emerging Markets and the U.S. Market, 2010

This exhibit shows industry medians of unlevered betas for 66 sectors as of January 1, 2010. Industries with betas normally distributed in both EM and U.S. data have been tested with the t-test; all others have been tested with the Wald-Wolfovitz (W-W) Runs test. Significances of 0.1 or smaller lead to reject the null hypothesis that median betas in EMs and the U.S. are not statistically different. The median of medians in EMs, 0.37, is signifi- cantly different from the U.S.’s, 0.84 (W-W statistic: -7.864; significance: 0.00).

Unlevered Betas Tests of Normality: Test of the Difference of Medians: Shapiro-Wilk Significance Significances

Industry NEM NUS BetaEM BetaUS EMBetas USBetas Both t-test W-W Test Medians normal? different?

Advertising 26 18 0.40 0.90 0.00 0.09 No - 0.01 Yes

Aerospace/Defense 21 51 0.31 0.80 0.01 0.04 No - 0.00 Yes

Air transportation 43 34 0.37 0.53 0.00 0.34 No - 0.50 No

Apparel/Textile 325 37 0.32 0.90 0.00 0.65 No - 0.00 Yes

Auto parts 156 35 0.35 1.05 0.00 0.05 No - 0.00 Yes

Auto/Truck 45 15 0.31 0.73 0.00 0.55 No - 0.01 Yes

Beverages 84 27 0.31 0.58 0.00 0.71 No - 0.02 Yes

Biotechnology 28 54 0.41 0.90 0.12 0.31 Yes 0.00 0.01 Yes

Building materials 97 39 0.29 0.83 0.00 0.27 No - 0.01 Yes

Cable TV/TV/Radio 43 13 0.46 0.66 0.00 0.30 No - 0.78 No

Chemical-Basic 248 13 0.35 1.12 0.00 0.34 No - 0.00 Yes

Chemical-Diversifed 48 25 0.31 0.97 0.00 0.46 No - 0.00 Yes

Chemical-Specialty 171 65 0.37 0.88 0.00 0.81 No - 0.00 Yes

Coal 37 17 0.61 1.33 0.00 0.84 No - 0.05 Yes

Computer-Hardware/Equipment 189 86 0.43 0.94 0.00 0.42 No - 0.00 Yes

Computer-Software/Services 405 228 0.52 0.85 0.00 0.00 No - 0.00 Yes

Construction-Heavy/Engineering 396 12 0.40 1.20 0.00 0.94 No - 0.13 No

Construction-Residential/Commercial 185 25 0.30 0.36 0.00 0.02 No - 0.49 No

Cosmetics/Personal care 25 13 0.39 0.82 0.03 0.19 No - 0.02 Yes

Educational services 18 25 0.31 0.63 0.05 0.04 No - 0.04 No

Electric utility 175 60 0.24 0.44 0.00 0.00 No - 0.00 Yes

Electrical equipment 258 63 0.40 0.95 0.00 0.81 No - 0.00 Yes

Electronics 457 148 0.44 0.89 0.00 0.00 No - 0.00 Yes

Entertainment 48 49 0.36 0.69 0.00 0.36 No - 0.02 Yes

Environmental 39 50 0.58 0.65 0.00 0.13 No - 0.15 No

Financial services-Brokerage/Investment banking 103 22 0.46 0.99 0.00 0.02 No - 0.03 Yes

Financial services-Diversified 83 125 0.28 0.71 0.00 0.03 No - 0.00 Yes

Food-Processing 303 82 0.32 0.61 0.00 0.35 No - 0.08 Yes

Food-Retail/Supermarkets 25 15 0.36 0.59 0.00 0.83 No - 0.02 Yes

Food-Wholesalers 16 13 0.44 0.44 0.12 0.15 Yes 0.82 0.52 No

Furniture/Home decoration 80 28 0.36 0.85 0.00 0.95 No - 0.02 Yes

Hotel/Casino 168 46 0.40 0.82 0.00 0.17 No - 0.07 Yes

Journal of Applied Corporate Finance • Volume 22 Number 4 A Morgan Stanley Publication • Fall 2010 117 Exhibit 2 continued Unlevered Betas Tests of Normality: Test of the Difference of Medians: Shapiro-Wilk Significance Significances

Industry NEM NUS BetaEM BetaUS EMBetas USBetas Both t-test W-W Test Medians normal? different? Household products 75 20 0.27 0.71 0.00 0.33 No - 0.00 Yes

Industrial services 186 115 0.32 0.72 0.00 0.42 No - 0.00 Yes

Information services 16 19 0.67 0.85 0.67 0.16 Yes 0.22 0.16 No

Internet 89 99 0.66 0.93 0.01 0.05 No - 0.01 Yes

Machinery 278 93 0.39 0.93 0.00 0.16 No - 0.00 Yes

Maritime transportation 118 47 0.33 0.57 0.00 0.06 No - 0.16 No

Medical services 52 102 0.31 0.75 0.01 0.08 No - 0.00 Yes

Medical supplies 48 176 0.48 0.81 0.00 0.00 No - 0.00 Yes

Metal fabricating 45 26 0.31 1.03 0.00 0.29 No - 0.01 Yes

Mining 168 68 0.53 1.16 0.00 0.25 No - 0.00 Yes

Natural gas 47 72 0.40 0.54 0.00 0.02 No - 0.03 Yes

Office Equipment/Supplies 29 16 0.36 0.77 0.06 0.48 No - 0.00 Yes

Oilfield services/equipment 106 93 0.38 1.06 0.00 0.35 No - 0.00 Yes

Oil-Integrated 19 24 0.69 0.98 0.97 0.17 Yes 0.01 0.20 No

Oil-Producing 33 126 0.69 0.98 0.22 0.16 Yes 0.08 0.74 No

Packaging/Container 85 24 0.26 0.76 0.00 0.31 No - 0.00 Yes

Paper/Forest products 104 29 0.32 0.76 0.00 0.29 No - 0.00 Yes

Pharmaceuticals 224 179 0.38 0.85 0.00 0.50 No - 0.00 Yes

Pharmacy services 24 16 0.33 0.71 0.00 0.09 No - 0.00 Yes

Printing/Publishing 66 18 0.33 0.85 0.00 0.83 No - 0.08 Yes

Real Estate-Services/Development 516 13 0.36 0.55 0.00 0.55 No - 0.72 No

Recreation/Leisure time 50 46 0.37 0.72 0.00 0.90 No - 0.00 Yes

Retail Store 184 159 0.42 0.86 0.00 0.00 No - 0.00 Yes

Retail-Automotive 21 14 0.20 0.76 0.02 0.08 No - 0.00 Yes

Retail-Restaurant 32 55 0.46 0.78 0.48 0.33 Yes 0.00 0.10 Yes

Semiconductor 247 98 0.51 1.04 0.00 0.00 No - 0.00 Yes

Semiconductor equipment 87 13 0.49 1.05 0.00 0.41 No - 0.00 Yes

Shoe 22 17 0.51 0.94 0.11 0.98 Yes 0.00 0.78 No

Steel 199 31 0.36 1.10 0.00 0.12 No - 0.00 Yes

Telecom-Equipment 198 81 0.53 0.85 0.00 0.11 No - 0.00 Yes

Telecom-Services 86 87 0.37 0.66 0.00 0.26 No - 0.35 No

Telecom-Wireless/Cellular/Satellite 49 38 0.37 1.00 0.00 0.04 No - 0.00 Yes

Trucking 39 31 0.28 0.85 0.00 0.21 No - 0.01 Yes

Water utility 32 13 0.23 0.49 0.00 0.00 No - 0.01 Yes

Total (66 industries) 7,919 3,591

Minimum 0.20 0.36

Maximum 0.69 1.33

Mean of Medians 0.39 0.82

Median of Medians 0.37 0.84

118 Journal of Applied Corporate Finance • Volume 22 Number 4 A Morgan Stanley Publication • Fall 2010 Exhibit 3 Total Betas: Industry Medians in Emerging Markets and the U.S. Market, 2010

This exhibit shows median unlevered total betas for 66 industries as of January 1, 2010. All industries in the exhibit have non-normally distributed betas in EM, the U.S. or both; and have therefore been tested with the Wald-Wolfovitz (W-W) Runs test. Significances of 0.1 or smaller lead to reject the null hypothesis that median total betas in EMs and the U.S. are not statistically different. The median of medians in EMs, 2.16, is not significantly different from the U.S.’s, 2.45 (W-W statistic: -5.24; significance: 0.30).

Unlevered Total Betas Tests of Normality: Test of the Difference of Shapiro-Wilk Significance Medians: Significances

Industry NEM NUS Total Total EMTotal Betas USTotal Betas Both W-W Test Medians

BetaEM BetaUS normal? different? Advertising 26 14 2.36 3.25 0.23 0.00 No 0.50 No

Aerospace/Defense 20 45 2.79 2.39 0.00 0.00 No 0.14 No

Air transportation 41 28 1.44 1.89 0.00 0.00 No 0.75 No

Apparel/Textile 291 34 2.34 2.89 0.00 0.00 No 0.40 No

Auto parts 145 28 2.15 2.88 0.00 0.51 No 0.03 Yes

Auto/Truck 41 15 2.59 1.87 0.00 0.15 No 0.51 No

Beverages 75 25 1.82 1.83 0.00 0.00 No 0.17 No

Biotechnology 25 45 2.74 3.53 0.00 0.00 No 0.05 Yes

Building materials 87 32 2.14 2.36 0.00 0.05 No 0.43 No

Cable TV/TV/Radio 40 11 1.96 1.73 0.00 0.07 No 0.30 No

Chemical-Basic 226 12 2.39 3.04 0.00 0.29 No 0.80 No

Chemical-Diversifed 46 23 2.55 2.29 0.03 0.00 No 0.03 Yes

Chemical-Specialty 153 58 2.29 2.46 0.00 0.00 No 0.56 No

Coal 35 16 4.17 2.78 0.00 0.62 No 0.63 No

Computer-Hardware/Equipment 175 79 3.33 3.46 0.00 0.00 No 0.45 No

Computer-Software/Services 372 163 2.66 2.85 0.00 0.00 No 0.55 No

Construction-Heavy/Engineering 362 11 2.14 2.84 0.00 0.53 No 0.73 No

Construction-Residential/Commercial 168 21 2.01 1.34 0.00 0.59 No 0.84 No

Cosmetics/Personal care 24 12 2.09 2.07 0.00 0.00 No 0.50 No

Educational services 15 22 2.35 2.70 0.05 0.77 No 0.21 No

Electric utility 155 59 1.56 0.87 0.00 0.00 No 0.00 Yes

Electrical equipment 238 58 2.63 2.75 0.00 0.00 No 0.34 No

Electronics 430 133 2.96 3.05 0.00 0.00 No 0.31 No

Entertainment 42 39 2.86 2.71 0.00 0.00 No 0.29 No

Environmental 39 37 2.79 2.81 0.00 0.03 No 0.13 Yes

Financial services-Brokerage/Investment banking 94 21 1.87 2.58 0.00 0.00 No 0.09 Yes

Financial services-Diversified 79 103 1.13 2.40 0.00 0.00 No 0.00 Yes

Food-Processing 271 74 1.70 1.79 0.00 0.00 No 0.48 No

Food-Retail/Supermarkets 25 12 1.81 1.70 0.00 0.25 No 0.81 No

Food-Wholesalers 15 13 2.08 1.55 0.05 0.02 No 0.09 Yes

Furniture/Home decoration 71 26 2.37 2.87 0.00 0.00 No 0.49 No

Hotel/Casino 153 41 1.91 2.44 0.00 0.57 No 0.28 No

Household products 66 20 2.17 1.83 0.00 0.05 No 0.42 No

Journal of Applied Corporate Finance • Volume 22 Number 4 A Morgan Stanley Publication • Fall 2010 119 Exhibit 3 continued Unlevered Total Betas Tests of Normality: Test of the Difference of Shapiro-Wilk Significance Medians: Significances

Industry NEM NUS Total Total EMTotal Betas USTotal Betas Both W-W Test Medians

BetaEM BetaUS normal? different? Industrial services 169 99 2.12 2.34 0.00 0.00 No 0.22 No

Information services 15 17 1.75 2.18 0.00 0.30 No 0.98 No

Internet 86 80 2.95 3.33 0.00 0.00 No 0.08 Yes

Machinery 251 92 2.42 2.42 0.00 0.00 No 0.46 No

Maritime transportation 112 34 1.72 1.13 0.00 0.00 No 0.49 No

Medical services 49 89 1.45 2.47 0.00 0.00 No 0.01 Yes

Medical supplies 45 157 2.29 2.91 0.00 0.00 No 0.27 No

Metal fabricating 42 20 1.65 2.50 0.00 0.53 No 0.18 No

Mining 156 52 2.78 3.47 0.00 0.00 No 0.01 Yes

Natural gas 48 68 1.28 1.05 0.00 0.00 No 0.40 No

Office Equipment/Supplies 27 15 2.14 2.27 0.00 0.42 No 0.86 No

Oilfield services/equipment 100 83 2.07 2.59 0.00 0.00 No 0.06 Yes

Oil-Integrated 18 24 1.95 2.01 0.04 0.31 No 0.49 No

Oil-Producing 32 93 2.21 2.77 0.00 0.00 No 0.35 No

Packaging/Container 77 20 1.69 1.90 0.00 0.69 No 0.00 Yes

Paper/Forest products 98 26 1.68 1.90 0.00 0.38 No 0.20 No

Pharmaceuticals 199 144 2.25 3.46 0.00 0.00 No 0.74 No

Pharmacy services 20 10 2.27 1.92 0.00 0.02 No 0.36 No

Printing/Publishing 61 16 2.27 2.61 0.00 0.70 No 0.59 No

Real Estate-Services/Development 480 8 2.09 1.55 0.00 0.76 No 0.65 No

Recreation/Leisure time 45 41 2.42 2.17 0.00 0.00 No 0.75 No

Retail Store 169 148 2.07 2.83 0.00 0.00 No 0.29 No

Retail-Automotive 20 14 1.57 1.80 0.00 0.03 No 0.02 Yes

Retail-Restaurant 26 52 2.48 2.44 0.00 0.00 No 0.53 No

Semiconductor 225 86 3.82 3.07 0.00 0.04 No 0.05 Yes

Semiconductor equipment 83 13 2.51 2.62 0.00 0.00 No 0.14 No

Shoe 22 17 1.62 2.52 0.01 0.06 No 0.29 No

Steel 182 27 1.81 2.64 0.00 0.77 No 0.62 No

Telecom-Equipment 191 69 2.74 2.89 0.00 0.00 No 0.09 Yes

Telecom-Services 80 69 1.48 2.04 0.00 0.00 No 0.37 No

Telecom-Wireless/Cellular/Satellite 47 32 1.41 2.79 0.00 0.00 No 0.00 Yes

Trucking 35 30 2.28 2.01 0.00 1.00 No 0.00 Yes

Water utility 29 11 3.21 1.11 0.00 0.00 No 0.28 No

Total (66 industries) 7,284 3,086

Minimum 1.13 0.87

Maximum 4.17 3.53

Mean of Medians 2.22 2.39 Median of Medians 2.16 2.45

120 Journal of Applied Corporate Finance • Volume 22 Number 4 A Morgan Stanley Publication • Fall 2010 Exhibit 4 The Evolution of Industry Betas, 2007–2010

Panel A of this exhibit shows the evolution of the industry medians for three parameters: beta, total beta, and correlation. Since over time some companies enter stock markets, while others exit them, analyzing all companies quoting at a given time may yield a distorted view of the parameters’ evolution. To avoid this survivorship bias, the parameters have been computed for groups of identical companies within each asset class. The U.S. panel comprised 2,265 firms in 76 industries; the EM panel, 2,765 firms in 74 industries. Differences between distributions’ medians were tested with the Wald-Wolfovitz (W-W) test, whereby significances of 0.1 or smaller imply that the distributions’ location and shape are significantly different between asset classes. anelP B graphs the evolution of beta and total beta waves in each asset class.

Panel A

W-W Test, 2007 vs. 2010

Jan 2007 Jan 2010 W-W statistic Significance

Unlevered Beta U.S. 0.83 0.85 0.977 0.84 (median of medians) EM 0.94 0.39 -9.073 0.00 W-W Test, U.S. vs. EM W-W statistic 0.166 -8.52 Significance 0.57 0.00 Unlevered total beta U.S. 1.66 2.36 -1.953 0.03 (median of medians) EM 2.44 2.02 -4.619 0.00 W-W Test, U.S. vs. EM W-W statistic -3.112 -2.456 Significance 0.00 0.01 Correlation (Rho) U.S. 0.48 0.36 -4.720 0.00 (median of medians) EM 0.38 0.19 -6.104 0.00 W-W Test, U.S. vs. EM W-W statistic -3.603 -7.045 Significance 0.00 0.00

Panel B

B1. U.S. Industry Betas: 2007 (back) vs. 2010 (front) B2. EM Industry Betas: 2007 (back) vs. 2010 (front) 25% 40%

20% 30% 15% 20% 10%

Probability 10%

5% Probability

0% 0% 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 Median Unlevered Beta Median Unlevered Beta

B3. U.S. Total Industry Betas: 2007 (back) vs. 2010 (front) B4. EM Total Industry Betas: 2007 (back) vs. 2010 (front)

20% 50%

15% 40% 30% 10% 20% 5% Probability Probability 10% 0% 0% 0.0 0.3 0.5 0.8 1.0 1.3 1.5 1.8 2.0 2.3 2.5 2.8 3.0 3.3 3.5 0.0 0.3 0.5 0.8 1.0 1.3 1.5 1.8 2.0 2.3 2.5 2.8 3.0 3.3 3.5

Median Unlevered Total Beta Median Unlevered Total Beta

Journal of Applied Corporate Finance • Volume 22 Number 4 A Morgan Stanley Publication • Fall 2010 121 Exhibit 5 Choosing an Asset Pricing Model in Emerging Markets (EMs)

Yes: Use local industry Yes: beta of EM Use local industry Is a good beta of EM ...a local EM Yes: industry beta No: industry beta Use industry available in the EM? Is there another beta of EM class EM with a similar No: industry dynamics Is the industry and good beta? dynamics in the Choose a pricing EM class a good model No: proxy for that of that uses... Assimilate the Yes: the EM EM to a developed Use U.S. industry in questions? market and use beta Is the U.S.’s a pricing model industry dynamics a with a U.S. ...a U.S. industry beta industry beta good proxy for that No: of the EM in question? Consider using a pricing model with a local EM industry beta

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