Finance

C H A P T E R 13 Industry Analysis*

After you read this chapter, you should be able to answer the following questions:

• Is there a difference between the returns for alternative industries during specific time periods? What is the

implication of these results for industry analysis?

• Is there consistency in the returns for individual industries over time? What do these results imply regarding

industry analysis?

• Is the performance for firms within an industry consistent? What is the implication of these results for industry and

company analysis?

• Is there a difference in risk among industries? What are the implications of these results for industry analysis?

• What happens to risk for individual industries over time? What does this imply for industry analysis?

• What are the stages in the industrial life cycle, and how does the stage in an industry’s life cycle affect the sales

estimate for an industry?

• What are the five basic competitive forces that determine the intensity of competition in an industry and, thus, its

rate of return on capital?

• How does an analyst determine the value of an industry using the DDM, assuming constant growth or two-stage

growth?

• How does an analyst determine the value of an industry using the free cash flow to equity (FCFE) model, assuming

constant growth or two-stage growth?

• What are the steps involved in estimating earnings per share for an industry?

• How does the procedure for estimating the operating profit margin differ for the aggregate market versus an

industry?

• What is involved in a macroanalysis of the industry earnings multiplier?

• What are the steps in the microanalysis of an industry earnings multiplier?

• How do you determine if an industry’s estimated multiplier is relatively high or low?

• How do analysts compare relative valuation ratios such as P/BV, P/CF, and P/S to comparable market ratios?

• How do industries differ in terms of what dictates their return on assets?

• What are some of the unique factors that must be considered in global industry analysis?

When asked about his or her job, a securities analyst typically will reply that he or she

is an oil analyst, a retail analyst, or a computer analyst. A widely read trade publica-

tion, The Institutional Investor, selects an All-American analyst team each year based

on industry groups. Investment managers talk about being in or out of the metals, the

autos, or the utilities. This constant reference to industry groups is because most pro-

fessional investors are extremely conscious of differences among alternative industries

and organize their analyses and portfolio decisions according to industry groups.

*The authors acknowledge input to the discussions on “The Business Cycle and Industry Sectors” and “Structural

Economic Changes” provided by Professor Edgar Norton of Illinois State University.

413

We acknowledge the importance of industry analysis as a component of the three-

step fundamental analysis procedure initiated in Chapter 11. Industry analysis is the

second step as we progress toward selecting specific firms and stocks for our invest-

ment portfolio. In Chapter 12, we did a macroanalysis and valuation of the stock mar-

ket to decide whether the expected rate of return from investing in common stocks

was equal to or greater than our required rate of return—that is, should we over-

weight, market weight, or underweight stocks? In this chapter, we analyze different

industries to determine if the intrinsic value of an industry is equal to or greater

than its market price. Based on this relationship, we decide how to weight the industry

in our stock portfolio. In Chapter 14, we analyze the individual companies and stocks

within alternative industries.

In the first section, we discuss the results of several studies that identify the bene-

fits and uses of industry analysis. Similar to the market analysis that considered both

macroeconomic factors and microvaluation, we consider several industry macroanaly-

sis topics, such as the differential impact of the business cycle on alternative industries,

how various structural economic changes affect different industries, understanding an

industry’s life cycle, and evaluating the competitive environment in an industry. This

macroanalysis is crucial to an overall understanding of what factors determine risk

and return in an industry.

Following this, the bulk of the chapter considers the microvaluation of the retail

industry including a demonstration of both the cash flow models and the relative val-

uation ratios introduced earlier in Chapter 11. We conclude the chapter with a discus-

sion of global industry analysis, because many industries transcend U.S. borders and

compete on a worldwide basis.

13.1 WHY DO INDUSTRY ANALYSIS? Investment practitioners perform industry analysis because they believe it helps them isolate investment opportunities that have favorable return-risk characteristics. We likewise have re- commended it as part of our three-step, top-down investment analysis approach. What exactly do we learn from an industry analysis? Can we spot trends in industries that make them good investments? Are there unique patterns in the rates of return and risk measures over time in different industries? In this section, we survey the results of studies that addressed the follow- ing set of questions designed to pinpoint the benefits and limitations of industry analysis:

• Is there a difference between the returns for alternative industries during specific time periods?

• Will an industry that performs well in one period continue to perform well in the future? That is, can we use past relationships between the market and an individual industry to predict future trends for the industry?

• Is the performance of firms within an industry consistent over time?

Several studies also considered questions related to risk:

• Is there a difference in the risk for alternative industries? • Does the risk for individual industries vary, or does it remain relatively constant over time?

Based on the results of these studies, we come to some general conclusions about the value of industry analysis. In addition, this assessment helps us interpret the results of our subsequent industry valuation.

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13.1.1 Cross-Sectional Industry Performance To find out if the rates of return among different industries varied during a given period (e.g., during the year 2012), researchers compared the performance of alternative industries during a specific time period. Similar performance during specific time periods for different industries would indicate that industry analysis is not necessary. For example, assume that during 2012, the aggregate stock market experienced a rate of return of 10 percent and the returns for all industries were bunched between 9 percent and 11 percent. If this similarity in performance persisted over time, you might question whether it was worthwhile to do industry analysis to find an industry that would return 11 percent when random selection would provide a return of about 10 percent (the average return).

Studies of the annual performance by numerous industries found that different industries have consistently shown wide dispersion in their rates of return. A specific example is the year 2010. As shown in Exhibit 13.1, although the aggregate stock market (the S&P 500) experienced a percent price change of about 12.78, the industry performance ranged from 90.51 percent (Platinum and Precious Metals) to −18.82 percent (Renewable Energy Equipment). The consis- tency of these wide-ranging results is confirmed in Exhibit 13.2 for the recent 11 years—the

Exhibit 13.1 How the Dow Jones U.S. Industry Groups Fared During 2010 (12/31/2009 to 12/31/2010)

Top Ten Performers Percent Change Bottom Ten Performers

Percent Change

1. Platinum and Precious Metals 90.51 1. Renewable Energy Equipment −18.82

2. Gambling 68.70 2. Farming & Fishing −14.22

3. Automobiles 66.27 3. Alternative Fuels −12.84

4. Commercial Vehicles and Tracks 62.48 4. Mortgage Finance −11.03

5. Auto Parts 56.08 5. Aluminum −3.39

6. Hotels 52.71 6. Alternative Electricity −2.22

7. General Mining 50.20 7. Defense −1.24

8. Nonferrous Metals 48.47 8. Consumer Finance −1.07

9. Travel and Tourism 47.68 9. Pharmaceuticals −0.93

10. Real Estate Services 44.11 10. Specialized Consumer Services 1.56

Source: Prepared by authors using data from The Wall Street Journal.

Exhibit 13.2 Annual Range of Industry Performance Relative to the Aggregate Market: 2000–2010

Year S&P 500 High Industry Percent Change Low Industry

Percent Change

Total Range (Percent)

2000 −10.14 Tobacco 85.92 Consumer Services −65.52 151.44

2001 −13.04 Consumer Services 57.12 Gas Utilities −71.60 128.72

2002 −23.37 Precious Metals 40.48 Pipelines −66.04 106.52

2003 26.38 Mining 156.68 Fixed Line Commun. −3.78 160.46

2004 8.99 General Mining 97.15 Semiconductors −21.65 118.80

2005 3.00 Oil & Gas Expl. 64.22 Automobiles −38.97 103.19

2006 13.63 Steel 61.66 Home Construction −20.69 82.35

2007 3.53 Heavy Construction 83.05 Home Construction −55.86 138.91

2008 −38.49 Brewers 37.71 Full Line Insurance −93.50 131.21

2009 23.45 Travel and Tourism 209.81 Renewable Energy Eq. −14.86 224.67

2010 12.78 Platinum and Prec. Metals 90.51 Renewable Energy Eq. −18.82 109.33

Average: 89.48 −42.84 132.33

Source: Prepared by authors using data from The Wall Street Journal.

Chapter 13: Industry Analysis 415

average high industry change is over 89 percent and the average low change is almost −43 per- cent. These results imply that industry analysis is important and necessary to uncover these sub- stantial performance differences—that is, industry analysis helps identify both unprofitable and profitable opportunities.

13.1.2 Industry Performance over Time Another set of studies questioned whether industries that perform well in one time period would perform well in subsequent time periods, or outperform the aggregate market in the later time period. Investigators found almost no association in industry performance year to year or over sequential rising or falling markets.

These time-series studies imply that past performance alone does not project future indus- try performance. The results do not, however, negate the usefulness of industry analysis. They simply confirm that variables that affect industry performance change over time, and each year you must estimate the current intrinsic value for each industry based on future estimates of relevant variables and compare this to its current market price.

13.1.3 Performance of the Companies within an Industry Other studies examined whether there is consistency in the performance of companies within an industry. If all the firms within an industry performed consistently during a specified time period, you would not need to do company analysis. Specifically, industry analysis alone would be enough because given a profitable industry you would know that all the stocks in that industry would do well.

These studies typically have found wide dispersion in the performance among companies in most industries. Studies by Meyers (1973) and Livingston (1977) provided evidence of a stron- ger industry effect in specific industries, such as oil or autos, but also showed that the industry effect has been declining over time.

Implication of Dispersion within Industries Some observers have contended that industry analysis is useless because all firms in an industry do not move together. Obviously, consistent firm performance in an industry would be ideal, as noted, because you would not need to do company analysis. For industries that have a strong, consistent industry influence, such as oil, gold, steel, autos, and railroads, company analysis is less critical than industry analysis.

The fact that there is not a strong industry influence across firms in most industries means that a thorough company analysis is necessary. Still, industry analysis is valuable because it is much eas- ier to select a superior company from a good industry than to find a good company in a poor in- dustry. By selecting the best stocks within a strong industry, you avoid the risk that your analysis and selection of the best company in the industry will be offset by poor industry performance.

13.1.4 Differences in Industry Risk Although numerous studies have examined industry returns, few studies have examined indus- try risk measures. The studies on industry risk investigated two questions: (1) Does risk differ among industries during a given time period? (2) Are industry risk measures stable over time? The studies of risk dispersion found a wide range of risk among different industries and the differences in industry risk typically widened during rising and falling markets. The results on risk stability were positive—the risk measures for individual industries over time were reasonably stable.

Therefore, although risk measures for different industries showed substantial dispersion during a period of time, individual industries’ risk measures are stable over time. Hence, the dispersion means that the analysis of industry risk is necessary, but this analysis of risk is useful when estimating the future risk for an industry.

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13.1.5 Summary of Research on Industry Analysis The conclusions of the studies dealing with industry analysis are:

• During any time period, the returns for different industries vary within a wide range, which means that industry analysis is an important part of the investment process.

• The rates of return for individual industries vary over time, so we cannot simply extrapo- late past industry performance into the future.

• The rates of return of firms within industries also vary, so analysis of individual compa- nies in an industry is a necessary follow-up to industry analysis.

• During any time period, different industries’ risk levels vary within wide ranges, so we must examine and estimate the risk factors for alternative industries.

• Risk measures for different industries remain fairly constant over time, so the historical risk analysis is useful when estimating future risk.

13.1.6 Industry Analysis Process An important question is: How should you structure your industry analysis? In our previous analysis of the economy and the aggregate equity market, we contended that it is necessary to examine the macroeconomy for two related reasons. First, although the security markets tend to move ahead of the aggregate economy, we know that security markets reflect the strength or weakness of the economy. Second, most of the variables that determine value for the security markets are macrovariables such as interest rates, GDP growth, and corporate earnings. There- fore, our analysis of the aggregate equity market contained two components—one dealing with macrovariables such as leading indicators and monetary policy, and a second facet being mi- croanalysis of specific variables that affect market valuation.

The point is, the industry analysis process is similar—first is a macroanalysis of the industry to determine how this industry relates to the business cycle and what economic variables drive this industry. This macroanalysis will make the microvaluation easier when we use the several valuation techniques introduced earlier. As noted, macroanalysis of the industry will make the estimation of the major valuation inputs (a discount rate and the expected growth for earnings and cash flows) easier and more accurate.

The specific macroanalysis topics are:

1. The business cycle and industry sectors 2. Structural economic changes and alternative industries 3. Evaluating an industry’s life cycle 4. Analysis of the competitive environment in an industry

13.2 THE BUSINESS CYCLE AND INDUSTRY SECTORS Economic trends can and do affect industry performance. Our objective is to monitor the economy and gauge how any new information on our economic outlook will impact the short-run and long-run valuation of our industry. Recall that in order to beat the market on a risk-adjusted basis, our forecasts must differ from the market consensus and we must be cor- rect more often than not.

Economic trends can take two basic forms: cyclical changes that arise from the ups and downs of the business cycle, and structural changes that occur when the economy is undergo- ing a major change in how it functions. For example, excess labor or capital may exist in some sectors, whereas shortages of labor and capital exist elsewhere. The “downsizing” of corporate America during the 1990s, transitions from socialist to market economies in Eastern Europe, and the transition in the United States from a manufacturing to a service economy are all

Chapter 13: Industry Analysis 417

examples of structural change. 1

Industry analysts must examine structural economic changes for the implications they have for an industry under review.

While industry performance is related to the stage of the business cycle, the real challenge is that every business cycle is different and those who look only at history miss the evolving trends that will determine future market and industry performance.

Switching industry groups over the course of a business cycle is known as a rotation strategy. When trying to determine which industry groups will benefit from the next stage of the business cycle, investors need to monitor economic trends and changes in industry characteristics.

Exhibit 13.3 presents a stylized graphic of which industry groups typically perform well in the different stages of the business cycle. For example, toward the end of a recession, financial stocks rise in value because investors anticipate that banks’ earnings will rise as both the economy and loan demand recover. Brokerage houses become attractive investments because their sales and earn- ings are expected to rise as investors trade securities, businesses sell debt and equity, and there are more mergers during the economic recovery. These industry expectations assume that when the re- cession ends there will be an increase in loan demand, housing construction, and security offerings.

Once the economy begins its recovery, consumer durable firms that produce items such as cars, personal computers, refrigerators, lawn tractors, and snow blowers become attractive in- vestments because a reviving economy will increase consumer confidence and disposable income. Once businesses recognize the economic recovery, they think about modernizing, ren- ovating, or purchasing new equipment to satisfy rising demand and reduce costs. Thus, capital goods industries such as heavy equipment manufacturers, machine tool makers, and airplane manufacturers become attractive.

Cyclical industries whose sales rise and fall along with general economic activity are attractive investments during the early stages of an economic recovery because of their high degree of operat- ing leverage, which means that they benefit greatly from the sales increases during an economic expansion.

2 Industries with high financial leverage likewise benefit from rising sales volume.

3

Exhibit 13.3 The Stock Market and the Business Cycle

E C

O N O

MIC C Y C

L E

T r o u g h

P e a k

Financial

Stocks Excel

Capital

Goods Excel

Basic

Industries

Excel

Consumer

Staples Excel

Consumer

Durables

Excel

Source: Susan E. Kuhn, “Stocks Are Still Your Best Buy,” Fortune, March 21, 1994, 140. © 1994 Time

Inc. All rights reserved.

1 An excellent discussion of structural changes in the U.S. economy and the implications of these changes for the busi-

ness cycle, the stock market, and some specific industries is contained in Dudley and McKelvey (1997). 2 As discussed in Chapter 1, operating leverage arises from the existence of fixed costs in a firm’s operating structure.

Industries with large fixed expenses will have high degrees of operating leverage. This means a small percentage

change in sales can result in a large percentage change in operating income. 3 As noted in Chapter 10, financial leverage arises from fixed financial costs (that is, interest expense) in a firm’s

capital structure. Industries that have extensive debt financing (such as banks or utilities) will have net income that

is sensitive to small changes in operating income.

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Traditionally, toward the business cycle peak, inflation increases as demand starts to out- strip supply. Basic materials industries such as oil, metals, and timber, which transform raw materials into finished products, become investor favorites. Because inflation has little influ- ence on the cost of extracting these products and the firms in these industries can increase prices, these industries experience higher profit margins.

During a recession, some industries do better than others. Consumer staples, such as pharma- ceuticals, food, and beverages, outperform other sectors during a recession because, although overall spending may decline, people still spend money on necessities so these “defensive” indus- tries generally maintain their values—that is, they will experience minimal declines. Similarly, if a weak domestic economy causes a weak currency, industries that export to growing economies benefit because their goods become more cost competitive.

We have identified certain industries that are typically attractive investments over the busi- ness cycle. Remember, you should not invest based only upon the current economic environ- ment because the efficient market has already incorporated current economic news into security prices. Rather, you must forecast important economic variables and invest accordingly. The following subsections consider how changes in several important economic variables may affect different industries.

13.2.1 Inflation As noted in several chapters, higher inflation is generally negative for stocks because it causes higher market interest rates and more uncertainty about future prices and costs, and it harms firms that cannot pass through cost increases. Although these adverse effects are true for most industries, there are some industries that benefit from inflation. Natural resource industries ben- efit if their production costs do not rise with inflation, because their output will likely sell at higher prices. Industries with high operating leverage benefit because many of their costs are fixed in nominal (current dollar) terms, whereas revenues increase with inflation. Industries with high financial leverage may also gain, because their debts are repaid in cheaper dollars.

13.2.2 Interest Rates Financial institutions, including banks, are typically adversely impacted by higher rates because they find it difficult to pass on these higher rates to customers (i.e., lagged adjustment). High interest rates clearly harm the housing and the construction industry, but they might benefit industries that supply the do-it-yourselfer. High interest rates also benefit retirees whose in- come is dependent on interest income. A study by Reilly, Wright, and Johnson (2007) exam- ines the interest rate sensitivity of a large cross-section of industries and types of stocks.

13.2.3 International Economics Both domestic and international events affect the value of the U.S. dollar. A weaker U.S. dollar helps U.S. industries because their exports become comparatively cheaper in overseas markets, while the goods of foreign competitors become more expensive in the United States. A stron- ger dollar has an opposite effect. Economic growth in world regions or specific countries ben- efits industries that have a large presence in those areas. The creation of free trade zones, such as the European Community and the North American Free Trade Zone, assist industries that produce goods and services that previously faced quotas or tariffs in partner countries.

13.2.4 Consumer Sentiment Because it comprises about two-thirds of GDP, consumption spending has a large impact on the economy. Optimistic consumers are more willing to spend and borrow money for expen- sive goods, such as houses, cars, new clothes, and furniture. Therefore, the performance of consumer cyclical industries will be affected by changes in consumer sentiment and by consu- mers’ willingness and ability to borrow and spend money.

Chapter 13: Industry Analysis 419

13.3 STRUCTURAL ECONOMIC CHANGES AND

ALTERNATIVE INDUSTRIES Influences other than the economy are part of the business environment. Demographics, changes in technology, and political and regulatory environments also can have a significant effect on the cash flow and risk prospects of different industries.

13.3.1 Demographics In the past 50 years, the United States has had a baby boom and a baby bust and is now en- joying a baby boomlet as members of the baby boom generation (those born between the end of World War II and the early 1960s) have children. The influx of the baby boom and the “graying of the baby boom” have had a large impact on U.S. consumption, from advertising strategies to house construction to concerns over social security and health care. The study of demographics includes much more than population growth and age distributions. Demo- graphics also includes the geographical distribution of people, the changing ethnic mix in a society, and changes in income distribution. Therefore, industry analysts need to carefully study demographic trends and project their effect on different industries.

During the period from 1990 to 2011, the fastest-growing age groups in the United States were teens, those in their forties and fifties, and those over 70; among the declining groups were those between ages 18 and 24. As of the early 2000s, more than one in eight Americans are 65 years of age or older. The changing age profile of Americans has implications for re- source availability, namely, a possible shortage of entry-level workers leading to an increase in labor costs and difficulty in finding qualified persons to replace the retiring baby boomers. The aging U.S. population also affects U.S. savings patterns, because people in the 40 to 60 age bracket usually save more than younger people. This is good for the financial services industry, which offers assistance to those who want to invest their savings. Alternatively, fewer younger workers and more “saving seniors” may have a negative impact on some industries, such as the retailing industry.

13.3.2 Lifestyles Lifestyles deal with how people live, work, form households, consume, enjoy leisure, and edu- cate themselves. Consumer behavior is affected by trends and fads. The rise and fall of de- signer jeans, chinos, and other styles in clothes illustrate the sensitivity of some markets to changes in consumer tastes. The increase in divorce rates, dual-career families, population shifts away from cities, and computer-based education and entertainment have influenced nu- merous industries, including housing, restaurants, automobiles, catalog shopping, services, and home entertainment. From an international perspective, some U.S.-brand goods—from blue jeans to movies—have a high demand overseas. They are perceived to be more in style and perhaps higher quality than items produced domestically. Several industries have benefited from this positive brand reputation.

13.3.3 Technology Technology can affect numerous industry factors, including the product or service and how it is produced and delivered. There are numerous examples of changes due to technological innovations. For example, demand has fallen for carburetors on cars because of electronic fuel-injection technology. The engineering process has changed because of the advent of computer-aided design and manufacturing. Perpetual improvement of designs in the semicon- ductor and microprocessor industry has made that industry a difficult one to evaluate.

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Innovations in process technology allowed steel minimills to grow at the expense of large steel producers. Advances in technology allow some plant sites and buildings to generate their own electricity, bypassing their need for power from the local electric utility. Trucks have reduced railroads’ market share in the long-distance carrier industry. The information superhighway is becoming a reality and encouraging linkages between telecommunications and cable television systems. Changes in technology have spurred capital spending in technological equipment as a way for firms to gain competitive advantages. The future effects of the Internet will be astronomical.

The retailing industry is a wonderful example of how an industry can use new technology. Some forecasters envision relationship merchandising, in which customer databases will allow closer links between retail stores and customer needs. Rather than market research on aggre- gate consumer trends, specialized retailers offer products that consumers desire in preferred locations. Technology allows retailers to become more organizationally decentralized and geo- graphically diversified. Major retailers use barcode scanning, which speeds the checkout pro- cess and allows the firm to track inventory and customer preferences. Credit cards allow firms to track customer purchases and send customized sales announcements. Electronic data interchange (EDI) allows the retailer to electronically communicate with suppliers to order new inventory and pay accounts payable. Electronic funds transfer allows retailers to move funds quickly and easily between local banks and headquarters.

It is essential for an analyst to become aware of how technology can be a benefit or a threat to an industry.

13.3.4 Politics and Regulations Because political change reflects social values, today’s social trend may be tomorrow’s law, reg- ulation, or tax. The industry analyst needs to project and assess political changes relevant to the industry under study.

Some regulations and laws are based on economic reasoning. Due to utilities’ positions as natural monopolies, their rates must be reviewed and approved by a regulatory body.

4 Some

regulation involves social ends. For example, the Food and Drug Administration protects con- sumers by reviewing new drugs. Public and worker safety concerns spurred creation of the Consumer Product Safety Commission, the Environmental Protection Agency, and OSHA. Notably, heavy regulation of an industry can increase a firm’s costs but also can restrict entry into the industry.

Regulatory changes have affected numerous industries. An example is the numerous regula- tions and inspections introduced to protect against terrorist attacks. Changing regulations and technology are bringing participants in the financial services industry—banking, insurance, in- vestment banking, and investment services—together.

Regulations and laws affect international commerce. International tax laws, tariffs, quotas, embargoes, and other trade barriers have a significant effect on some industries and global commerce.

Again, an interesting example is how the retail industry is affected by numerous regulatory factors. First, the minimum-wage law impacts many retail employees. A second factor is em- ployer-paid health insurance, which dramatically impacts the labor costs of service industries, such as retailing. Third, because goods must first be delivered to the stores, regulations that affect the cost of shipping by airplane, ship, or truck will affect retailers’ costs. Finally, lower tariffs and quotas will allow retailers to offer imported goods at lower prices (e.g., Walmart), which will expand their international production (outsourcing).

4 Technology can change natural monopolies. We mentioned earlier how some firms are generating their own electri-

cal power. Another example is that, currently, numerous states are allowing electric utilities to compete for customers.

Chapter 13: Industry Analysis 421

13.4 EVALUATING THE INDUSTRY LIFE CYCLE An insightful analysis when predicting industry sales and trends in profitability is to view the industry over time and divide its development into stages similar to those that humans prog- ress through: birth, adolescence, adulthood, middle age, old age. The number of stages in this industry life cycle analysis can vary based on how much detail you want. A five-stage model would include:

1. Pioneering development 2. Rapid accelerating growth 3. Mature growth 4. Stabilization and market maturity 5. Deceleration of growth and decline

Exhibit 13.4 shows the growth path of sales during each stage. The vertical scale in logs reflects rates of growth, whereas the arithmetic horizontal scale has different widths representing dif- ferent, unequal time periods. To estimate industry sales, you must predict the length of time for each stage. This requires answers to such questions as: How long will an industry grow at an accelerating rate (Stage 2)? How long will it be in a mature growth phase (Stage 3) before its sales growth stabilizes (Stage 4) and then declines (Stage 5)?

Besides being useful when estimating sales, this industry life cycle analysis also provides insights into profit margins and earnings growth, although these profit measures may not parallel the sales growth. The profit margin series typically peaks very early in the total cycle and then levels off and declines as competition is attracted by the early success of the industry.

Exhibit 13.4 Life Cycle of an Industry

Stage 1

Pioneering

Development

Stage 2

Rapid

Accelerating

Growth

Stage 3

Mature

Growth

Stage 4

Stabilization

and Market

Maturity

Stage 5

Deceleration

of Growth and

Decline

Time

Net Sales

Log Scale 32

16

8

4

2

0

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The following is a brief description of how these stages affect sales growth and profits:

1. Pioneering development. During this start-up stage, the industry experiences modest sales growth and very small or negative profits. The market for the industry’s product or service during this stage is small, and the firms incur major development costs.

2. Rapid accelerating growth. During this rapid growth stage, a market develops for the prod- uct or service and demand becomes substantial. The limited number of firms in the indus- try face little competition, and firms can experience substantial backlogs and very high profit margins. The industry builds its productive capacity as sales grow at an increasing rate and the industry attempts to meet excess demand. High sales growth and high profit margins that increase as firms become more efficient cause industry and firm profits to explode (i.e., profits can grow at over 100 percent a year because of the low earnings base and the rapid growth of sales and margins).

3. Mature growth. The success in Stage 2 has satisfied most of the demand for the industry goods or service. Thus, future sales growth may be above normal, but it no longer acceler- ates. For example, if the overall economy is growing at 8 percent, sales for this industry might grow at an above normal rate of 15 percent to 20 percent a year. Also, the rapid growth of sales and the high profit margins attract competitors to the industry, causing an increase in supply and lower prices, which means that the profit margins begin to de- cline to normal levels.

4. Stabilization and market maturity. During this stage, which is probably the longest phase, the industry growth rate declines to the growth rate of the aggregate economy or its indus- try segment. During this stage, investors can estimate growth easily because sales correlate highly with an economic series. Although sales grow in line with the economy, profit growth varies by industry because the competitive structure varies by industry, and by individual firms within the industry because the ability to control costs differs among companies. Competition produces tight profit margins, and the rates of return on capital (e.g., return on assets, return on equity) eventually become equal to or slightly below the competitive level.

5. Deceleration of growth and decline. At this stage of maturity, the industry’s sales growth declines because of shifts in demand or growth of substitutes. Profit margins continue to be squeezed, and some firms experience low profits or even losses. Firms that remain prof- itable may show very low rates of return on capital. Finally, investors begin thinking about alternative uses for the capital tied up in this industry.

Although these are general descriptions of the alternative life cycle stages, they should help you identify the stage your industry is in, which in turn should help you estimate its potential sales growth and profit margin. Obviously, everyone is looking for an industry in the early phases of Stage 2 and hopes to avoid industries in Stage 4 or Stage 5. Comparing the sales and earnings growth of an industry to similar growth in the economy should help you identify the indus- try’s stage within the industrial life cycle.

13.5 ANALYSIS OF INDUSTRY COMPETITION Similar to the sales forecast that can be enhanced by the analysis of the industrial life cycle, an industry earnings forecast should be preceded by the analyses of the competitive structure for the industry. Specifically, a critical factor affecting the profit potential of an industry is the in- tensity of competition in the industry, as Porter (1980a, 1980b, 1985) has discussed.

13.5.1 Competition and Expected Industry Returns Porter’s concept of competitive strategy is described as the search by a firm for a favorable competitive position in an industry. To create a profitable competitive strategy, a firm must

Chapter 13: Industry Analysis 423

first examine the basic competitive structure of its industry because the potential profitability of a firm is heavily influenced by the profitability of its industry. After determining the com- petitive structure of the industry, you examine the factors that determine the relative competi- tive position of a firm within its industry. In this section, we consider the competitive forces that determine the competitive structure of the industry. In the next chapter, our discussion of company analysis considers the factors that determine the relative competitive position of a firm within its industry.

Basic Competitive Forces Porter believes that the competitive environment of an industry (the intensity of competition among the firms in that industry) determines the ability of the firms to sustain above-average rates of return on invested capital. As shown in Exhibit 13.5, he suggests that five competitive forces determine the intensity of competition and that the rel- ative effect of each of these five factors can vary dramatically among industries.

1. Rivalry among the existing competitors. For each industry analyzed, you must judge if the rivalry among firms is currently intense and growing, or polite and stable. Rivalry increases when many firms of relatively equal size compete in an industry. When estimating the num- ber and size of firms, be sure to include foreign competitors. Further, slow growth causes competitors to fight for market share and increases competition. High fixed costs stimulate the desire to sell at the full capacity, which can lead to price cutting and greater competition. Finally, look for exit barriers, such as specialized facilities or labor agreements. These can keep firms in the industry despite below-average or negative rates of return.

Beyond these general conditions that impact competition, the following four factors will likewise affect the competitive environment within an industry:

Exhibit 13.5 Forces Driving Industry Competition

Potential

Entrants

Industry

Competitors

Rivalry among

Existing Firms

Suppliers Buyers

Substitutes

Threat of Substitute Products or Services

Bargaining Power of BuyersBargaining Power of Suppliers

Threat of New Entrants

Source: Reprinted with the permission of The Free Press, an imprint of Simon & Schuster, Inc., from

Competitive Strategy: Techniques for Analyzing Industries and Competitors by Michael E. Porter.

Copyright © 1980, 1998 by The Free Press. All rights reserved.

424 Part 4: Analysis and Management of Common Stocks

2. Threat of new entrants. Although an industry may have few competitors, you must deter- mine the likelihood of firms entering the industry and increasing competition. High bar- riers to entry, such as low current prices relative to costs, keep the threat of new entrants low. Other barriers to entry include the need to invest large financial resources to compete as well as the availability of capital. Also, substantial economies of scale give a current in- dustry member an advantage over a new firm. Further, entrants might be discouraged if success in the industry requires extensive distribution channels that are hard to build be- cause of exclusive distribution contracts. Similarly, high costs of switching products or brands, such as those required to change a computer or telephone system, keep competi- tion low. Finally, government policy can restrict entry by imposing licensing requirements or limiting access to materials (lumber, coal). Without some of these barriers, it might be very easy for competitors to enter an industry, increasing the competition and driving down potential profit margins and rates of return.

3. Threat of substitute products. Substitute products limit the profit potential of an industry because they limit the prices firms in an industry can charge. Although almost everything has a substitute, you must determine how close the substitute is in price and function to the product in your industry. As an example, the threat of substitute glass containers hurt the metal container industry. Glass containers kept declining in price, forcing metal con- tainer prices and profits down. In the food industry, consumers constantly substitute be- tween beef, pork, chicken, and fish. The more commodity-like the product, the greater the competition and the lower the profit margins.

4. Bargaining power of buyers. Buyers can influence the profitability of an industry because they can bid down prices or demand higher quality or more services by showing a pro- pensity to switch among competitors. Buyers become powerful when they purchase a large volume relative to the sales of a supplier (e.g., Walmart, Home Depot). The most vulnera- ble firm is a one-customer firm that supplies a single large manufacturer, as is common for auto parts manufacturers or software developers. Buyers will be more conscious of the costs of items that represent a significant percentage of the firm’s total costs. This con- sciousness increases if the buying firm is feeling cost pressure from its customers. Also, buyers who know a lot about the costs of supplying an industry will bargain more intensely—for example, when the buying firm supplies some of its own needs and buys from the outside.

5. Bargaining power of suppliers. Suppliers can alter future industry returns if they increase prices or reduce the quality of the product or the services they provide. The suppliers are more powerful if there are few of them and if they are more concentrated than the indus- try to which they sell. Their power increases if they supply critical inputs to several indus- tries for which few, if any, substitutes exist. In this instance, the suppliers are free to change prices and services they supply to the firms in an industry. When analyzing sup- plier bargaining power, be sure to consider labor’s power within each industry.

An investor needs to analyze these competitive forces to determine the intensity of the competition in an industry and assess the effect of this competition on the industry’s long- run profit potential. You should examine each of these factors and develop a relative competi- tive profile for each industry. It is important to consistently update this analysis of an indus- try’s competitive environment over time because an industry’s competitive structure can and will change over time.

13.6 ESTIMATING INDUSTRY RATES OF RETURN At this point, we have determined that industry analysis helps an investor select profitable in- vestment opportunities, and we have completed a thorough macroanalysis of the industry. Our next question is: How do we go about valuing an industry? Again, we consider the two equity

Chapter 13: Industry Analysis 425

valuation approaches introduced in Chapter 11—the present value of cash flows and the rela- tive valuation ratios.

Although our investment decision is always the same, the form of the comparison depends on which valuation approach is being used. In the case of the present value of cash flow tech- niques, we compare the present value of the specified cash flow and the prevailing value of the index and determine if we should underweight, equal weight, or overweight this global indus- try in our portfolio.

When we value the industry using the alternative valuation ratios, an important addition to the analysis is that we also need to compare our industry ratios to the market ratios presented in Chapter 12.

To demonstrate industry analysis, we use Standard and Poor’s retailing index to represent industry-wide data. This retailing index (hereinafter referred to as the RET industry) contains about 30 individual companies from several retailing sectors including two drugstores. There- fore, it should be reasonably familiar to most observers, and it is consistent with the subse- quent company analysis of Walgreens.

13.6.1 Valuation Using the Reduced Form DDM Recall that the reduced form DDM is:

13.1 Pi = D1

k − g

where:

Pi = the price of Industry i at Time t D1 = expected dividend for Industry i in Period 1 equal to D0ð1 þ gÞ

k = the required rate of return on the equity for Industry i g = the expected long-run growth rate of earnings and dividend for Industry i

As always, the two major estimates for any valuation model are k and g. We will discuss each of these at this point and also use these estimates subsequently when applying the two-step price/earnings ratio technique for valuation.

Estimating the Required Rate of Return (k) Because the required rate of return (k) on all investments is influenced by the risk-free rate and the expected inflation rate, the differentiat- ing factor in this case is the risk premium for the retailing industry versus the market. In turn, we discussed the risk premium in terms of fundamental factors, including business risk (BR), financial risk (FR), liquidity risk (LR), exchange rate risk (ERR), and country (political) risk (CR). Alternatively, you can estimate the risk premium based on the CAPM, which implies that the risk premium is a function of the systematic risk (beta) of the asset. Therefore, to de- rive an estimate of the industry’s risk premium, you should examine the BR, FR, LR, ERR, and CR for the industry and compare these industry risk factors to those of the aggregate market. Alternatively, you can compute the systematic risk (beta) for the industry and compare this to the market beta of 1.0. Prior to calculating an industry beta, we briefly discuss the industry’s fundamental risk factors.

Business risk is a function of relative sales volatility and operating leverage. As we will see when we examine the sales and earnings for the industry, the annual percentage changes in retailing sales were less volatile than aggregate sales as represented by PCE. Also, the OPM (operating profit margin) for retail stores was less volatile than for the S&P Industrials Index. Therefore, because both the sales and the OPM for the retailing industry have been less volatile than the market, operating profits are substantially less volatile. This implies that the business risk for the retailing industry is below average.

426 Part 4: Analysis and Management of Common Stocks

The financial risk for this industry is difficult to judge because of widespread use of operat- ing leases for stores in the industry that are not included on the balance sheet. As a result, the reported data on debt to total capital or interest coverage ratios indicate that the FR for this industry is substantially below the market. Assuming substantial use of long-term lease con- tracts, when these are capitalized the retailing industry probably has financial risk about equal to the market. While data are not available to capitalize leases for the industry, we showed how to do this in Chapter 10 and will demonstrate it for a company in Chapter 14.

To evaluate an industry’s market liquidity risk, you must estimate the liquidity risk for all the firms in the industry, and derive a composite view. The fact is, there is substantial varia- tion in market liquidity among the firms in this industry, ranging from Walgreens and Walmart, which are fairly liquid to small specialty retail chains, which are relatively illiquid. A conservative view is that the industry has above-average liquidity risk.

Exchange rate risk (ERR) is the uncertainty of earnings due to changes in exchange rates faced by firms in this industry that sell outside the United States. The amount of ERR is determined by what proportion of sales is non-U.S., how these sales are distributed among countries, and the exchange rate volatility for these countries. This risk could range from an industry with very limited international sales (e.g., a service industry that is not involved overseas) to a global industry (e.g., the chemical or pharmaceutical industry). For a truly global industry, you need to examine the distribution of sales among specific countries because the exchange rate risk varies among countries based on the volatility of exchange rates with the U.S. dollar. The ERR for the retailing industry would be relatively low because sales and earnings for most retailing firms are attributable to activity within the United States.

The existence of country risk (CR) is likewise a function of the proportion of foreign sales, the specific foreign countries involved, and the stability of the political/economic system in these countries. As noted, there is very little CR in the United Kingdom and Japan, but there can be substantial CR in China, Russia, and South Africa. Again, for the retailing industry, country risk would be relatively low because of limited foreign sales.

In summary, for the retailing industry business risk is definitely below average, financial risk is at best equal to the market, liquidity risk is above average, and exchange rate risk and country risk are fairly low for most retail firms. The consensus is that the fundamental risk for the RET industry should be slightly lower than for the aggregate market.

The systematic risk for the retailing industry is computed using the market model as follows:

13.2 % Δ RETt = αi + βið% Δ S&P 500tÞ

where:

% Δ RETt = the percentage price change in the retailing ðRETÞ index during month t αi = the regression intercept for the RET industry

βi = the systematic risk measure for the RET industry equal to Covi;m=σ 2 m

To derive an estimate for the RET industry, the model specified was run with monthly data for the five-year period of 2006 to 2010. The results for this regression are as follows:

αi = 0:008 R 2 = 0:69

βi = 1:10 DW = 1:86 t-value = 5:45 F = 68:50

The systematic risk (β = 1.10) for the RET industry is a little above unity, indicating an above market risk industry (i.e., risk slightly above the market). These results suggest that the system- atic risk for this industry is a little above the prior estimate of fundamental risk factors (BR, FR, LR, ERR, CR).

Chapter 13: Industry Analysis 427

Translating this systematic risk into a required rate of return estimate (k) calls for using the security market line model as follows:

13.3 ki = RFR + βiðRm − RFRÞ

Recall that in Chapter 12 we derived three estimates for the required market rate of return based on alternative risk premiums (0.048 − 0.085 − 0.108). For our purposes here, it seems that the midpoint is reasonable—that is, a nominal RFR of 0.032 and an Rm of 0.072. This, combined with a beta for the industry at 1.10, indicates the following:

k = 0:032 + 1:10ð0:04Þ

= 0:032 + 0:044

= 0:076 = 7:60%

For ease of computation, we will use a k of 8.0%. The fundamental risk analysis indicates risk slightly below the market and the CAPM estimate implies risk slightly above average. We pre- fer the 8 percent estimate for k in an environment of historically low Treasury rates.

Estimating the Expected Growth Rate (g) Recall that earnings and dividend growth are determined by the retention rate and the return on equity:

g = f (Retention Rate and Return on Equity)

Again, we employ the three components of ROE:

Net Profit

Equity =

Net Income

Sales ×

Sales

Total Assets ×

Total Assets

Equity

= Profit

Margin ×

Total Asset Turnover

× Financial Leverage

We examine each of these variables in Exhibit 13.6 to determine if they imply a different ex- pected growth rate for RET versus the aggregate market (S&P Industrials Index).

Earnings Retention Rate The data in Exhibit 13.6 indicate that the RET industry has a higher retention rate than the market (78 percent versus 63 percent), which implies a potentially higher growth rate, all else being the same (i.e., equal ROE).

Return on Equity Because the return on equity is a function of the net profit margin, total asset turnover, and financial leverage, these three variables are examined individually.

Historically, the data in Exhibit 13.6 show that the net profit margin for the S&P Industrials Index series has been consistently higher than the margin for the RET industry. This is not surprising because retail firms typically have lower profit margins but higher asset turnover.

As expected, the total asset turnover (TAT) for the RET industry was higher than the average industrial company. In Exhibit 13.6, the average TAT for the S&P Industrials Index was 0.86 ver- sus 1.74 for the RET industry. Beyond the overall difference, as shown in Exhibits 13.6 and 13.7, the spread between the two series increased over the period because the industrials TAT experi- enced an overall decline, while the TAT for the RET industry experienced an overall increase. Multiplying the PM by the TAT indicates the industry’s return on total assets (ROTA).

5

Net Income

Sales ×

Sales

Total Assets =

Net Income

Total Assets

The results in Exhibit 13.6 indicate that the return on total assets (ROTA) for the S&P In- dustrials Index series went from 3.27 percent in 1993 to 5.12 percent in 2009 and averaged

5 The reader is encouraged to read Appendix 13B to this chapter, which contains a discussion of an article by Selling and

Stickney (1989), wherein they analyze the components of ROA and relate this to an industry’s economics and its strategy.

428 Part 4: Analysis and Management of Common Stocks

E x h i b i t 1 3 . 6

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% N e t P ro fi t M a rg in

% T o ta l A ss e t T u rn o ve r

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0 .8 4

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2 .5 2

1 2 .2 2

9 .5 4

2 0 0 9

N /A

N /A

6 0 .4 4

7 2 .5 5

6 .2 1

3 .5 7

0 .8 1

1 .5 9

5 .1 2

5 .7 3

2 .6 3

2 .4 5

1 3 .3 0

1 3 .8 9

M e a n

2 3 .3 1

2 3 .1 2

6 3 .4 9

7 8 .4 6

5 .9 3

3 .5 7

0 .8 6

1 .7 4

4 .7 2

6 .0 2

3 .0 3

2 .6 1

1 5 .3 1

1 5 .7 6

S o u rc e : S ta n d a rd a n d P o o r’ s A n a ly s ts

’ H a n d b o o k .

Chapter 13: Industry Analysis 429

4.72 percent, whereas the ROTA for the RET industry went from 4.07 percent to 5.73 percent and averaged 6.02 percent. Clearly, the industry ROTA results were superior on average to S&P Industrial results.

The final component is the financial leverage multiplier (total assets/equity). As shown in Exhibits 13.6 and 13.8 both leverage multipliers experienced declines over the 17-year period—

Exhibit 13.7 Time-Series Plot of Total Asset Turnover for the S&P Industrials

and Retailing: 1993–2009

0.00

0.50

1.00

1.50

2.00

2.50

0.00

0.50

1.00

1.50

2.00

2.50

1993 1995 1997 1999 2001 2003 2005 2007 2009

Years

A s

s e

ts /E

q u

it y

( F

in a

n c

ia l

L e

v e

ra g

e )

RetailIndustrials

Source: Standard and Poor’s Analysts’ Handbook.

Exhibit 13.8 Time-Series Plot of Financial Leverage for the S&P Industrials

and Retailing: 1993–2009

2.00

2.20

2.40

2.60

2.80

3.00

3.20

3.40

3.60

3.80

2.00

2.20

2.40

2.60

2.80

3.00

3.20

3.40

3.60

3.80

1993 1995 1997 1999 2001 2003 2005 2007 2009

Years

A ss

e ts

/E q

u it

y (

F in

a n

ci a

l L

e v

e ra

g e

)

Retai lIndustri al s

Source: Standard and Poor’s Analysts’ Handbook.

430 Part 4: Analysis and Management of Common Stocks

the industrials by 27 percent and the RET industry by 30 percent. Although the higher financial leverage multiplier implies greater financial risk for the S&P Industrials Index series, recall that the financial leverage for the RET industry is understated because the widely used leases are not capitalized.

This final value of ROE in Exhibits 13.6 and 13.9 indicate that the best annual ROE varied by year until the last seven years when the RET industry was higher, except for 2008 when its profit margin experienced a large decline. The average annual ROEs were quite close—15.31 percent for the industrials, 15.76 percent for the RET industry. These average percentages are quite consistent with what would be derived from multiplying the averages of the components from Exhibit 13.6 as follows:

ROE ESTIMATE BASED ON TOTAL PERIOD AVERAGES (1993–2009)

Profit

Margin

Total Asset

Turnover

Total Assets/

Equity ROE

S&P Industrials Index 5.93 × 0.86 × 3.03 = 15.45

RET Industry 3.57 × 1.74 × 2.61 = 16.21

Although examining the long-term historical trends and the averages for each of the com- ponents is important, you should not forget that expectations of future performance will deter- mine the ROE value for the industry, and these expectations probably will be heavily influenced by the recent years. To get more information regarding recent performance, it is in- sightful to examine the results for the recent five-year period (2005–2009) as follows:

ROE ESTIMATE BASED ON RECENT FIVE-YEAR AVERAGES (2005–2009)

Profit

Margin

Total Asset

Turnover

Total Assets/

Equity ROE

S&P Industrials Index 6.17 × 0.86 × 2.75 = 14.59

RET Industry 4.08 × 1.68 × 2.33 = 15.97

Exhibit 13.9 Time-Series Plot of Return on Equity for the S&P Industrials

and Retailing: 2005–2009

5.00

7.50

10.00

12.50

15.00

17.50

20.00

5.00

7.50

10.00

12.50

15.00

17.50

20.00

1993 1995 1997 1999 2001 2003 2005 2007 2009

Years

R e

tu rn

o n

E q

u it

y

Industri al s Retai l

Source: Standard and Poor’s Analysts’ Handbook.

Chapter 13: Industry Analysis 431

Notably, using the recent results, the ROE results for the RET industry are still superior to the industrials with a slightly larger margin. Combining these recent ROE results with alternative retention rates provides the following growth estimates:

GROWTH ESTIMATES BASED ON RECENT ROE WITH HISTORICAL

AND RECENT RETENTION RATES

Recent*

ROE

Hist.**

RR

Est.

g

Recent*

ROE

Recent*

RR

Est.

g

S&P Industrials Index 14.59 0.63 9.19 14.59 0.62 9.05

RET Industry 15.97 0.78 12.46 15.97 0.76 12.14

*Recent five-year average.

**Total period average.

Given the decrease in g for the industry when we consider the recent results, it is probably appropriate to use a growth estimate for the RET industry that is below the long-run historical estimate—that is, we will assume a near-term growth rate of 11 percent.

Combining the Estimates At this point, we have the following estimates:

k = 0:080 g = 0:11

D0 = $6:28 ðEstimated 2010 DividendsÞ D1 = $6:28 × 1:11= $6:97 ðEstimated Dividend for 2011Þ

Because of the inequality between k and g (this g is above k and above the long-run market norm of about 7 percent and probably cannot be sustained), we need to evaluate this industry using the temporary growth company model discussed in Chapter 12. We will assume the fol- lowing growth pattern:

2011–2013 0.11

2014–2016 0.09

2017–2019 0.07

2020–onward 0.06

Using these estimates of k and this growth pattern, the computation of value for the industry using the DDM is contained in Exhibit 13.10.

Exhibit 13.10 Dividend Discount Calculations

Year Estimated Dividend Discount Factor @ 8% Present Value of Dividends

2011 6.97 — —

2012 7.74 0.9259 7.17

2013 8.59 0.8573 7.36

2014 9.36 0.7938 7.43

2015 10.20 0.7350 7.50

2016 11.12 0.6806 7.57

2017 11.90 0.6302 7.50

2018 12.73 0.5835 7.43

2019 13.62 0.5402 7.36

Continuing Valuea 722.00 0.5402 390.02

Total Value $449.34

a Constant Growth Rate = 7%

Continuing Value: D1

k − g = 13:62(1:06)

0:08−0:06 = 14:44

0:02 =722

432 Part 4: Analysis and Management of Common Stocks

These computations imply a value of $449.34, compared to the stock price for the industry index of about $430 in early 2011. Therefore, according to this valuation model and these k and g estimates, this industry is currently about 4 percent undervalued. As will be shown, a fairly small change in the k − g spread can have a large effect on the estimated value.

If we assumed a constant growth rate of 6 percent from the beginning and a D1 of $6.97, the value would be as follows:

P = 6:97

ð0:08 − 0:06Þ =

6:97

0:02 = $348:50

Notably, this valuation likewise implies the industry is overvalued, even though it is a low esti- mate of value since it assumes the base growth rate of 6 percent from the beginning. The point is, the industry is undervalued assuming strong growth in the next few years, but also overva- lued assuming basic economic growth. Clearly, both valuations are dependent on the growth estimate.

13.6.2 Industry Valuation Using the Free Cash Flow to Equity (FCFE) Model We initially define the FCFE series and present the series for the recent 17-year period, along with an estimate for 2010 in Exhibit 13.11. Given these data, we consider the historical growth rates for the components and for the final FCFE series as inputs to estimating future growth for the valuation models. You will recall that FCFE is defined (measured) as follows:

Net Income + Depreciation expense − Capital expenditures − Change in working capital − Principal debt repayments + New debt issues

As noted, the FCFE data inputs and final annual value of FCFE for the RET industry for the period 1993–2010 is contained in Exhibit 13.11, along with 5-year and 10-year growth rates of the components. Using this data, we derive an estimate using the FCFE model under two scenar- ios: (1) a constant growth rate from the present, and (2) a two-stage growth rate assumption.

The Constant Growth Rate FCFE Model We know that the constant growth rate model requires that the growth rate (g) be lower than the required rate of return (k), which we have specified as 8.00 percent. In the current case, this is difficult because the 10-year growth rate exceeds this. Still, in order to use the model, we assume a 10 percent growth in 2011 and 6 percent long-run growth in subsequent years. The result is as follows:

g = 0:06 ðLong-Run Growth Beginning in 2012Þ

k = 0:080

FCFE ð2010Þ = $21:83

FCFE ð2011Þ = $21:83ð1:10Þ = $24:01 = FCFE0

V = FCFE1

k − g

= 24:01 ð1:06Þ

0:080 − 0:060 =

24:01

0:02

= $1,200

Chapter 13: Industry Analysis 433

This $1,200 value exceeds the industry stock price of $430 that prevailed in early 2011. This implies that the industry is substantially undervalued and should be overweighted in the portfolio. Notably, even if we assumed the long-run growth rate was only 5 percent, the estimated value would be $800, which would still imply that the industry is undervalued.

The Two-Stage Growth FCFE Model As before, we assume a period of above-average growth for several years followed by a second period of constant growth at 6 percent. The period of above-average growth will be as follows based on an estimated initial 9 percent growth rate of FCFE, which is lower than what was used for dividends because the FCFE series has been fairly erratic over the past 17 years, including several years when the FCFE was negative.

2011 9%

2012 9%

2013 8%

2014 8%

2015 7%

2016 7%

2017–onward 6%

If we assume a k of 8.0 percent and an FCFE of $21.83 in 2010, $23.79 in 2011, and $25.93 in 2012, the value for the industry is as shown in Exhibit 13.12. These results are very encourag- ing for the industry because the computed value of $1,368 is substantially above the recent market price of about $430. This apparent undervaluation would indicate that the industry should be overweighted in the portfolio.

Exhibit 13.11 Components of Free Cash Flow to Equityholders for the Retail Industry

Year Net

Income Depreciation Expense

Capital Expenditures

Working Capital

Change in Working Capital

Principal Repayment

Debt Issuance

Total FCFE

1993 5.55 2.76 7.37 23.18 N/A N/A N/A N/A

1994 5.92 3.05 8.51 25.52 2.34 2.11 0.23

1995 5.26 3.77 8.93 30.50 4.98 7.41 2.53

1996 6.43 4.27 8.28 29.54 −0.96 0.36 3.74

1997 7.29 4.80 9.06 29.99 0.45 1.17 3.75

1998 9.66 5.24 10.51 28.02 −1.97 1.64 4.72

1999 10.65 6.01 12.93 24.67 −3.35 3.26 10.34

2000 9.50 6.52 15.60 23.24 −1.43 2.73 4.58

2001 9.83 7.25 15.76 33.66 10.42 4.09 −5.01

2002 13.20 7.00 14.23 33.21 −0.45 3.53 9.95

2003 19.16 9.11 15.70 51.11 17.90 3.57 −1.76

2004 20.47 10.57 17.98 56.02 4.91 10.05 −1.90

2005 20.07 11.38 19.44 45.27 −10.75 2.21 24.97

2006 27.96 13.05 23.83 48.27 3.00 16.29 30.47

2007 26.94 14.46 26.99 48.13 −0.14 23.85 38.40

2008 14.03 15.99 22.17 45.43 −2.70 2.95 13.50

2009 20.65 16.30 13.22 56.83 11.40 13.25 −0.92

2010(e) 24.00 17.00 20.00 60.00 3.17 4.00 21.83

5-year Growth Rate* 0.58% 8.65% −6.40%

10-year Growth Rate 11.74% 15.00% −1.53%

*Growth rates are meaningful for Net Income, Depreciation, and CapEx and do not include the estimates for 2010.

Source: Adapted from the Standard and Poor’s Analysts’ Handbook.

434 Part 4: Analysis and Management of Common Stocks

Notably, the alternative present value of cash flow models have generated the following intrinsic values:

Model Computed Value

Constant growth DDM $348

Two-stage growth DDM $449

Constant growth FCFE $1,200

Two-stage growth FCFE $1,368

While there is a wide range of computed intrinsic values, three of four of them indicated that this industry is undervalued given its recent market price of $430.

13.7 INDUSTRY ANALYSIS USING THE RELATIVE VALUATION

APPROACH This section contains a discussion and demonstration of the relative valuation ratio techniques: (1) price/earnings ratios (P/E), (2) the price to book value ratios (P/BV), (3) price to cash flow ratios (P/CF), and (4) price to sales ratios (P/S). We begin with the detailed demonstration of the P/E ratio approach, which provides a specific intrinsic value that equals an estimate of future earnings per share and an industry multiple.

The analysis of the other relative valuation ratios is also more meaningful because we can compare the industry valuation ratios to the market valuation ratios while considering what factors affect the specific valuation ratios.

13.7.1 The Earnings Multiple Technique Again, the earnings multiple technique involves (1) a detailed estimation of future earnings per share, and (2) an estimate of an appropriate earnings multiplier (P/E ratio) based on a consid- eration of P/E determinants derived from the DDM.

Estimating Earnings per Share To estimate earnings per share, you must start by estimat- ing sales per share. We initially describe three techniques that provide help and insights for the sales estimate. Next, we derive an estimate of earnings per share, which implies a net profit margin for the industry. As in Chapter 12, we begin the EPS estimate by estimating the oper- ating profit margin and operating profits. Then we subtract estimates of interest expense and then apply a tax rate to arrive at an estimate of earnings per share.

Exhibit 13.12 Computation of Retail Industry Equity Value Using the FCFE

Model and Two-Stage Growth

Year FCFE Discount

Factor @ 0.080 Present Value

2012 25.93 0.9259 24.01

2013 28.00 0.8573 24.00

2014 30.24 0.7938 24.00

2015 32.36 0.7350 23.78

2016 34.62 0.6806 23.56

Continuing Value 1,835.00 0.6806 01,248.90

Total Present Value $1,368.25

Continuing Value:  34:62(1:06)

0:08−0:06 = 36:70

0:02 =1,835

Chapter 13: Industry Analysis 435

Forecasting Sales per Share Recall that the macroanalysis of the industry included the follow- ing: (1) how the industry is impacted by the business cycle, (2) what structural changes have occurred within the industry, and (3) where the industry is in its life cycle, Based on this, the an- alyst would have a strong start regarding an industry sales estimate. We consider two general es- timation techniques (time series and input-output analysis) and a primary technique that should be used for almost all industries (i.e., a specific analysis of the industry-economy relationship).

Time-Series Analysis A simple time-series plot of the sales for an industry can be very infor- mative regarding the pattern and the rate of growth for industry sales. Analyzing this series with an overlay of business cycle periods (expansions and recessions) and notations regarding major events will provide further insights. Finally, for many industries, you can extrapolate the time series to derive an estimate of sales. For industries that have experienced consistent growth, this can be an important estimate, especially if it is a new industry that has not yet developed a history with the economy. If the sales growth has been at a constant rate, you should use a semi-log scale where the constant growth shows as a straight line.

Input-Output Analysis Input-output analysis provides useful insights regarding the outlook for an industry by identifying an industry’s suppliers and customers. This will indicate (1) the future demand from customers, and (2) the ability of suppliers to provide the goods and services required by the industry. The goal is to determine the long-run sales outlook for the industry’s suppliers and its major customers. In the case of global industries, you must include worldwide suppliers and customers.

Industry-Economy Relationships The most rigorous and useful analysis involves comparing sales for an industry with one or several economic series that are related to the goods and ser- vices produced by the industry. The specific question is: What economic variables influence the demand for this industry? You should think about numerous factors that impact industry sales, how these economic variables will impact demand, and how they might interact. The following example demonstrates this industry-economy technique for the retailing industry (RET).

Demonstrating a Sales Forecast The RET industry includes retailers of basic necessities, in- cluding pharmaceuticals, medical supplies, and nonmedical products, such as food and cloth- ing. Therefore, we want a series that (1) reflects broad consumption expenditures, and (2) gives weight to food and clothing. The economic series we consider are personal consumption expenditures (PCE) and PCE food, clothing, and shoes. Exhibit 13.13 contains the aggregate and per-capita values for these two series.

These time-series data indicate that personal consumption expenditures (PCE) have experi- enced reasonably steady growth of about 7.7 percent a year during this period, while PCE food, clothing, and shoes has grown at a slower rate of about 4.6 percent. As a result, food, clothing, and shoes as a percentage of all PCE has declined from 14.1 percent in 1993 to only 11.0 percent in 2009. Obviously, as an analyst, you would be encouraged because even though sales of food, clothing, and shoes have grown slower than overall PCE, retail sales have grown faster than both of them at about 14.5 percent.

The scatterplot in Exhibit 13.14 indicates a strong linear relationship between retail sales per share and sales of food, clothing, and shoes. Although not shown, there also is a good relationship with PCE. Therefore, if you can accurately estimate changes in these economic series, you should be able to estimate sales for RET.

As the industry being analyzed becomes more specialized, you need a more unique economic series that reflects the demand for the industry’s unique product. The selection of several appro- priate economic series demonstrates an analyst’s knowledge and innovation. Also, industry sales are often dependent on several components of the economy, and in such a case you should con- sider a multivariate model that would include two or more economic series. For example, if you are analyzing the tire industry, you would need to consider new-car production, new-truck pro- duction, and a series that would reflect the replacement tire demand for both cars and trucks.

436 Part 4: Analysis and Management of Common Stocks

You also should consider per-capita personal consumption expenditures. Although aggregate PCE increases each year, there also is an increase in the aggregate population, so the increase in PCE per capita (the average PCE for each adult and child) will be less than for the aggregate series. For example, during 2007, aggregate PCE increased about 5.2 percent, but per-capita PCE

Exhibit 13.13 S&P Retail Sales and Various Economic Series: 1993–2009

Per Capita

Year Retail Sales ($/Share)

Personal Consumption

Expenditures ($ Bn)

PCE Food, Clothing, and Shoes ($ Bn) PCE ($)

PCE Food, Clothing, and Shoes ($)

Food, Clothing, and Shoes as % of PCE

1993 173.89 4483.6 632.6 21904 2430.44 14.11

1994 197.21 4750.8 659.5 22466 2503.27 13.88

1995 208.15 4987.3 674.9 22803 2531.62 13.53

1996 232.82 5273.6 701.4 23325 2600.53 13.30

1997 251.33 5570.6 722.3 23899 2646.19 12.97

1998 276.12 5918.5 744.3 24861 2695.24 12.58

1999 302.62 6342.8 784.7 25923 2809.24 12.37

2000 329.76 6830.4 818.3 26939 2897.48 11.98

2001 352.27 7148.8 837.6 27385 2935.50 11.72

2002 355.62 7439.2 848.4 27841 2944.47 11.40

2003 383.89 7804.0 880.1 28357 3026.01 11.28

2004 444.61 8285.1 928.2 29072 3162.50 11.20

2005 476.54 8819.0 980.5 29771 3309.94 11.12

2006 580.68 9322.7 1028.1 30341 3437.86 11.03

2007 605.18 9806.3 1076.3 30757 3563.61 10.98

2008 598.70 10104.5 1109.3 30394 3639.07 10.98

2009 578.46 10001.3 1100.1 29770 3577.76 11.00

Compound Annual

Growth Rate 14.54% 7.69% 4.62% 2.24% 2.95% −1.38%

Source: Bureau of Economic Analysis.

Exhibit 13.14 Scatterplot of Retail Sales per Share and PCE—Food, Clothing, and Shoes:

1993–2009

100

200

300

400

500

600

700

100

200

300

400

500

600

700

600 800 1000 1200

PCE—Food, Clothing, and Shoes ($ Bn)

R e

ta il

S a

le s

p e

r S

h a

re $

Chapter 13: Industry Analysis 437

increased only 1.0 percent. Finally, an analysis of the relationship between percent change in an economic variable and percent changes in industry sales will indicate how the two series move together and would be sensitive to any changes in the relationship. Using annual percentage changes provides the following regression model:

13.4 %Δ Industry Sales = αi + βi(%Δ in Economic Series)

The size of the βi coefficient should indicate how closely the two series move together. Assum- ing the intercept (αi) is close to zero, a slope (βi) value of 1.00 would indicate relatively equal percent changes (e.g., a 10 percent increase in PCE typically is associated with a 10 percent increase in industry sales). A βi larger than unity would imply that industry sales are more volatile than the economy is. This analysis and the levels relationship reflected in Exhibit 13.14 would help you find an economic series that closely reflects the demand for the indus- try’s products; it also would indicate the form of the relationship.

As indicated, there was a good relationship between retail sales and PCE—Food, Clothing, and Shoes. The specific regression relationship was:

13.5 % Δ Retail Sales = − 0:05 + 2:52ð%Δ PCE—Food, Clothing, and ShoesÞ r2 = 0:37

Given the regression results, the specific sales forecast begins with an estimate of aggregate PCE growth for the years in question (i.e., 2010 and 2011). Because of the importance of PCE as a component of the economy, several estimates of PCE are available. The next step is to estimate the proportion of PCE spent on food, clothing, and shoes that has declined from 14.11 percent to 11 percent. You apply an estimate of this proportion to the PCE estimate and derive an estimate of the percent change in PCE—Food, Clothing, and Shoes that is used in the retail store sales regression model.

To demonstrate this process, we examined several economic sources that indicated that nominal PCE increased by 5.1 percent in 2010 (to $10,511 billion) and the projection was for an increase in 2011 of 6.3 percent to $11,173 billion. Regarding the percent of PCE spent on food, clothing, and shoes, we expect this proportion to increase slightly with the slow recovery of the economy—we estimate 11.02 percent in 2010 and 11.04 percent in 2011. This implies values for PCE—Food, Clothing, and Shoes of $1,158 billion in 2010 and $1,233 billion in 2011, which implies growth in this series of 5.3 percent in 2010 and 6.5 percent in 2011. Using these percentages in the Equation 13.5 regression provides an estimate of retail stores sales growth of 8.4 percent for 2010 ($627) and 11.38 percent for 2011 ($698). These sales growth rates are conservative relative to the long-run results in this industry (which was over 14 per- cent a year), but reflect the sluggish economic recovery in 2010 and 2011.

Forecasting Earnings per Share After the sales forecast, it is necessary to estimate the in- dustry’s profitability based on an analysis of the industry income statement. An analyst should also benefit from the prior macroanalysis that considered where the industry is in its life cycle, which impacts its profitability. How does this industry relate to the business cycle, and what does this imply regarding profit margins at this point in the cycle? Most important, what did you conclude regarding the competitive environment in the industry, and what does this mean for the profitability of sales?

Industry Profit Margin Forecast Similar to the aggregate market, the net profit margin is the most volatile and the hardest margin to estimate directly. Alternatively, it is suggested that you begin with the operating profit margin (EBIT/Sales) and then estimate interest expense and the tax rate.

The Industry’s Operating Profit Margin Recall that in the market analysis, we analyzed the fac- tors that should influence the economy’s operating profit margin, including capacity utilization,

438 Part 4: Analysis and Management of Common Stocks

unit labor cost, inflation, and net exports. The most important variables were capacity utilization and unit labor cost. We cannot do such an analysis for most industries because these variables (capacity utilization and unit labor cost) typically are not available for individual industries. As an alternative, we can assume that movements in these industry profit margin variables are re- lated to movements in similar economic variables. For example, if we assume an increase in ca- pacity utilization for the aggregate economies, there is probably a comparable increase in utilization for the auto industry or the chemical industry. The same could be true for unit labor cost. If there is a stable relationship between these variables for the industry and the economy, we would expect a relationship to exist between the profit margins for the industry and the economy. Although it is not necessary that the relationship be completely linear, it is important for the relationship (whatever it is) to be generally stable.

The operating profit margin (OPM) for the S&P Industrials Index and the RET industry is presented in Exhibit 13.15. The time-series plot in Exhibit 13.16 indicates that the S&P Indus- trials Index OPM experienced a steady increase from 1993 through 2000, followed by a decline during the recession year 2001. The margins slowly recovered from 2002 through 2007, but had small declines during the recession in 2008–2009. The RET industry OPM likewise experi- enced a fairly steady increase that peaked in 1999, followed by small declines in 2000 and 2001, a recovery in 2002 to a peak in 2005, gradual declines through 2008, and a small recov- ery in 2009. The regression analysis of the relationship between the OPM for the market and industry was not useful, so it is not discussed. Apparently, the best estimate for the RET indus- try is the OPM time-series plot using what we know about the changing competitive environ- ment and profit trends in the retail business. The point is, any quantitative analysis should be a supplement to the macroanalysis of the competitive environment for the industry.

Regression analysis and the time-series analysis are both useful tools, but neither technique should be applied mechanically. You should be aware of any unique factors affecting the indus- try, such as price wars, contract negotiations, building plans, or foreign competition. These unique events can significantly impact an estimate of the gross profit margin or a range of industry profit margins (optimistic, pessimistic, most likely).

Beyond this estimation of the near-term OPM, it is important to consider the long-term profitability of the industry based on its competitive structure.

Industry Interest Expense An industry’s interest expense will be a function of its financial leverage and interest rates. As shown in Exhibit 13.17, interest expense for the RET industry has always been relatively low and stable compared to the S&P Industrials Index. Because there is not a relationship between the two interest expense series, an analyst would need to derive his/ her own estimate based on two separate estimates: (1) changes in the amount of debt outstand- ing for this industry, and (2) the trend in interest rates (will they increase or decline?).

Estimating Interest Expense The historical data needed to estimate interest expense are in Exhibit 13.17, and we can use the following steps:

1. Estimate the annual total asset turnover (TAT) for the RET industry. 2. Use your 2011 sales and TAT forecasts to estimate total assets. 3. Estimate the long-term (interest-bearing) debt as a percentage of total assets for the RET

industry. 4. Based on your estimate of total assets and the percent of long-term debt to total assets,

estimate the dollar value of long-term debt. 5. Estimate the annual interest cost as a percentage of long-term debt based upon prior esti-

mates of market yields. 6. Estimate interest expense based on the following:

(Estimated Interest Cost of Debt) × (Estimated Long-Term Debt)

Chapter 13: Industry Analysis 439

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S o u rc e : S ta n d a rd a n d P o o r’ s A n a ly s ts

’ H a n d b o o k .

440 Part 4: Analysis and Management of Common Stocks

For example, our sales estimate of $698 and a TAT that has averaged about 1.63 over the recent years imply total assets of $428 next year. Long-term interest-bearing debt has averaged about 20 percent of total assets for the RET industry recently, so we will use that percent for 2011, which implies debt of $85.60 (0.20 × $428). In turn, interest expense as a percentage of long-term debt

Exhibit 13.16 Time-Series Plot of the Operating Profit Margin for the S&P Industrials and

Retail Industries: 1993–2009

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

18.00

1993 1995 1997 1999 2001 2003 2005 2007 2009

Years

O p

e ra

ti n

g P

ro &

t M

a rg

in %

Industri al s Retai l

Exhibit 13.17 Components for Deriving Specific Estimates for Interest Expense for the

Retail Industry: 1993–2009

Year Sales Total Assets Total Asset Turnover LT Debt

LT Debt as % of Total Assets

Interest Expense

Interest Exp. as % of LT Debt

1993 173.89 131.73 1.32 23.59 17.91 2.43 10.30

1994 197.21 136.95 1.44 25.01 18.26 1.90 7.60

1995 208.15 131.74 1.58 32.63 24.77 2.81 8.61

1996 232.82 127.22 1.83 32.62 25.64 3.01 9.23

1997 251.33 135.85 1.85 34.54 25.42 2.60 7.53

1998 276.12 140.88 1.96 34.65 24.60 2.99 8.63

1999 302.62 149.81 2.02 36.15 24.13 2.76 7.63

2000 329.76 163.25 2.02 36.99 22.66 3.09 8.35

2001 352.27 176.14 2.00 43.54 24.72 3.18 7.30

2002 355.62 189.16 1.88 44.96 23.77 2.72 6.05

2003 383.89 229.87 1.67 47.76 20.78 3.33 6.97

2004 444.61 267.84 1.66 41.13 15.36 2.72 6.61

2005 476.54 280.32 1.70 44.08 15.73 2.66 6.03

2006 580.68 317.31 1.83 59.17 18.65 3.26 5.51

2007 605.18 360.23 1.68 78.44 21.78 4.39 5.60

2008 598.70 369.57 1.62 79.06 21.39 4.60 5.82

2009 578.46 363.81 1.59 71.63 19.69 4.75 6.63

Means 1.74x 21.48% 7.32%

Source: Standard and Poor’s Analysts’ Handbook.

Chapter 13: Industry Analysis 441

(the industry’s approximate debt cost) during the recent period has been between 4 and 5 percent. Based on the declines in market interest rates during 2010 and early 2011, we would estimate a debt rate of 4.50 percent. This interest rate estimate combined with our long-term debt estimate of $85.60 implies interest expense of $3.85 (0.045 × $85.60).

Industry Tax Rate As you might expect, tax rates differ between industries. An extreme exam- ple would be the oil industry, in which heavy depletion allowances cause lower taxes. Gener- ally, you can assume that tax law changes are consistent across industries. To see if this is valid, you need to examine the relationship of tax rates over time for your industry and the aggregate market to determine if you can use regression analysis in your estimation process. Alternatively, a time-series plot could provide a useful estimate.

Exhibit 13.19 indicates that the RET industry tax rate has been between 31 and 35 percent and has been at or below the lower boundary in recent years. Therefore, we would use 32 per- cent, although you still need to consider pending national legislation and unique industry tax factors. Using this estimated tax rate, you multiply the EBT per share value by (1 − 0.32) to get your estimate of earnings per share (EPS).

In addition to estimating EPS, you also should examine the industry’s net profit margin as a check on your EPS estimate. A time-series plot of the net profit margin series for the industry and the S&P Industrials Index is contained in Exhibit 13.20. Notably, both series experienced an overall increase of about 2 percent, but the RET industry has been less volatile.

An Industry Earnings Estimate Example To help you understand the procedure, the follow- ing is an estimate of earnings per share for the RET industry. A practicing analyst would use this example as an initial estimate that would be modified based on his or her industry knowl- edge, current events, and expectations of unique factors.

Given that the best relationship was between RET industry sales and PCE—Food, Clothing, and Shoes, we estimated PCE and PCE—Food, Clothing, and Shoes as a percent of PCE and derived a RET sales estimate of $698 per share.

Exhibit 13.18 Time-Series Plot of Interest Expense for the Industrials and

Retail Industry: 1993–2009

0.00

5.00

10.00

15.00

20.00

25.00

0.00

5.00

10.00

15.00

20.00

25.00

1993 1995 1997 1999 2001 2003 2005 2007 2009

Years

In te

re st

E x

p e

n se

$ /S

h a

re

Industri al s Retai l

442 Part 4: Analysis and Management of Common Stocks

As noted earlier, the OPM for the RET industry recovered from the recession in 2002 and experienced strong performance during 2003 through 2007. Following a decline during the 2008 recession, the margin recovered to almost 6 percent in 2009. We estimate a further re- covery to 6.50 percent in 2010 and 7.00 percent in 2011, which implies operating profits in 2011of $48.86 (0.07 × $698).

Exhibit 13.19 Time-Series Plot of Effective Tax Rates for the Industrials

and Retail Industry: 1993–2009

25.00

27.00

29.00

31.00

33.00

35.00

37.00

39.00

41.00

43.00

25.00

27.00

29.00

31.00

33.00

35.00

37.00

39.00

41.00

43.00

1993 1995 1997 1999 2001 2003 2005 2007 2009

Years

E +

e ct

iv e

T a

x R

a te

s %

Industri al s Retai l

Source: Standard and Poor’s Analysts’ Handbook.

Exhibit 13.20 Time-Series Plot of the Net Profit Margin for the Industrials

and Retail Industry: 1993–2009

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

1993 1995 1997 1999 2001 2003 2005 2007 2009

Years

N e

t P

ro &

t M

a rg

in %

Industri al s Retai l

Chapter 13: Industry Analysis 443

Given the declining yields during 2010 and the flat interest rates envisioned during 2011, our estimate of interest expense was $3.85 in 2011. Thus, EBT would be $45.01 (48.86 − 3.85).

As noted earlier, the tax rate for the RET industry has consistently been in the 31–35 per- cent range in recent years, and 32 percent seems appropriate for 2011. This implies net income of $30.61 (45.01 × 0.68) and a net profit margin for the RET industry of 4.39 percent (30.61/ 698), which is similar to the 2007 margin.

Given an estimate of the industry’s net income per share (for simplicity, we will round the EPS estimate to $31 per share), the next step is to estimate the earnings multiplier for this industry.

Estimating an Industry Earnings Multiplier In this section, we discuss how to estimate an industry earnings multiplier using two alternative techniques: macroanalysis and microanal- ysis. In macroanalysis, you examine the relationship between the multiplier for the industry and the market. In microanalysis, you estimate the multiplier by examining the specific vari- ables that influence it: (1) the dividend-payout ratio, (2) the required rate of return for the in- dustry (k), and (3) the expected growth rate of earnings and dividends for the industry (g).

Macroanalysis of an Industry Multiplier: Why a Relationship? This subsection considers the relationship between the earnings multiplier (P/E ratio) for an industry to the P/E for the ag- gregate market. We expect a relationship based on the variables that influence the multiplier— the required rate of return, the expected growth rate of earnings and dividends, and the divi- dend-payout ratio. Specifically, as discussed, the required rate of return (k) is a function of the nominal risk-free rate (which is the same for all investments) plus a risk premium, which is the major reason for differences in k. Also, while the risk premium may differ between the market and an industry, any changes in the risk premium for the market and the industry are probably related.

Clearly, the rate of growth (g) for an industry may differ from that of the market and is a major reason for the difference in the level of the P/E ratio. Still, changes in the growth expec- tations for many industries are related to changes in g for the market and other industries be- cause all of these growth rates are driven by macroeconomic growth factors that affect the overall market and most industries. Therefore, changes in the P/E ratio for the aggregate mar- ket and for alternative industries are caused by changes in the k − g spread. Because, these two variables have several components that move together, we expect an overall (macro) relation- ship between changes in an industry’s P/E and the market P/E ratio.

An examination of the relationship between the P/E ratios for 71 S&P industries and the S&P Industrials Index by Reilly and Zeller (1974) indicated a significant positive relationship between percentage changes in P/E ratios for most industries examined. Notably, because the significance of the relationship differed between alternative industries and the market, it is nec- essary to evaluate the quality of the relationship between the P/E ratios for a specific industry and the market before using this technique.

The results in Exhibits 13.6 and 13.21 for the RET industry indicate a relatively close rela- tionship between the P/Es for the market and the RET industry. In fact, the P/E ratios have been very similar during six of the last seven years. Still, a crucial question is: Why has the RET industry multiplier been so similar to the market and should this similarity continue? The following microanalysis should help us answer this question.

Microanalysis of an Industry Multiplier In Chapter 12, we estimated a future market earnings multiplier by estimating a range of values for the three variables (i.e., dividend payout, k, and g) that determine the multiplier and derived a range of P/E ratios that were used with our EPS estimate to compute a range of intrinsic values for the market index.

For the industry multiplier, we want to take advantage of the prior work on the stock mar- ket multiplier. Because the variables that affect the multipliers are the same, we can compare

444 Part 4: Analysis and Management of Common Stocks

our outlook for the three variables that determine the industry and market earnings multiplier. Hence, we can determine whether the industry earnings multiplier should be above, below, or equal to the market multiplier and can estimate a specific industry P/E ratio.

Industry Multiplier versus the Market Multiplier Recall from Exhibit 13.21 that the P/E ratios for the RET industry and the market generally moved together during the recent seven years, except for 2007. An analysis of the earnings multiplier factors should help us determine if this is an appropriate relationship.

Comparing Dividend-Payout Ratios The data in Exhibit 13.6 indicate that the retention rate for retail stores has consistently been higher than for the market (over 78 percent vs. about 63 percent). This indicates a higher dividend payout for the S&P Industrials Index, which alone implies a higher multiplier for the S&P Industrials Index.

Comparing the Required Rates of Return Earlier, we estimated the industry’s required rate of return (k) assuming a beta of 1.10 for the RET industry. Notably, this was not consistent with the fundamental risk characteristics of the industry that indicated slightly below average risk. In turn, this SML beta implied a k of 7.60 percent that we rounded to 8.00 percent, which compares to the stock market k derived in Chapter 12 of 8.00 percent. This specific variable indicates that the industry and market P/E should be approximately the same.

Comparing the Expected Growth Rate (g) Previously, we estimated an industry growth rate in connection with the present value of cash flow models. Using the relationship:

g = Retention Rate ðbÞ × Return on Equity ðROEÞ

= ðbÞ × ðROEÞ

we estimated a g of over 11 percent based on historical and recent results. This 11 percent growth rate estimate compares to the growth rates for the S&P Industrials

Index of between 10 and 11 percent (using the long-run retention rate or the recent five-year

Exhibit 13.21 Time-Series Plot of the Annual Average Future Earnings

Multiplier for the Industrials and Retail Industry: 1993–2009

0

5

10

15

20

25

30

35

40

45

50

0

5

10

15

20

25

30

35

40

45

50

1993 1995 1997 1999 2001 2003 2005 2007

Years

F u

tu re

E a

rn in

g s

M u

lt ip

li e

r

Industri al s Retai l

Chapter 13: Industry Analysis 445

retention rate). Notably, the growth rates implied for both the industry (11 percent) and the market (10–11 percent) appear too high for any long-term period—that is, we typically use continuing growth rates of 5 to 6 percent. Therefore, while we would not use the higher growth rates for long-run estimates, the comparison appears valid for our purposes here. Spe- cifically, the industry has a slightly higher g than the market, which implies that based on this specific variable alone that the industry multiple would be a little higher than the market multiple.

In summary, a comparison of the dividend-payout ratios indicates that the market P/E ratio should be higher. The required returns are approximately equal and the growth rate compari- son barely favors the industry multiple. Therefore, overall the analysis suggests that the indus- try multiple should be similar to the market, which is consistent with the recent relationship. The discussion in Chapter 12 concluded that the forward market multiple should be about 20 times, which implies an industry multiplier in the 18 to 22 range.

Estimate of Industry Value Based upon an earlier estimate of industry earnings per share ($31.00) and an estimate for an industry earnings multiple in the 18 to 22 range imply a range of intrinsic values as follows:

Optimistic Multiple: 22 × 31:00 = $682 Expected Multiple: 20 × 31:00 = $620 Pessimistic Multiple: 18 × 31:00 = $558

Given a current market price for the industry index of about $430, all of these results indicate the industry is underpriced. Put another way, while we envisioned P/Es of between 18 and 22, the RET industry is currently priced at about 14 times.

13.8 OTHER RELATIVE VALUATION RATIOS Now we need to consider the other three relative valuation ratios (P/BV, P/CF, and P/S) and compare their performance over time relative to similar ratios for the aggregate stock market (the S&P Industrials Index).

Again, the calculations employ the average annual price and future book value, cash flow, and sales. The input data derived for the industry and the S&P Industrials Index (from Chap- ter 12) with 10-year growth rates for each of these variables are contained in Exhibit 13.22. Notably, the subsequent comparison indicates that the compound growth rate for every RET industry variable is higher than the growth rate for the same aggregate market variable.

Exhibit 13.23 contains the four relative valuation ratios for the RET industry and for the S&P Industrials Index, along with the ratio of the annual industry valuation ratio divided by the market valuation ratio. For each valuation ratio, you should examine the long-run indus- try-market relationship, including any changes in this relationship. Subsequently, the goal is to explain the overall relationship and consider any changes that have occurred and whether these changes in the relationship can be explained based upon the factors that should deter- mine the relative valuation ratio. The comparative P/E ratios will not be discussed again be- cause we previously concluded they should be approximately equal.

13.8.1 The Price/Book Value Ratio Exhibit 13.24 shows the overall increase in the price/book value ratio experienced by both the market and the industry to a peak of about five times in 1999 followed by a brief trough in 2003, after which they are virtually equal. Notably, the relationship between the industry and the market has typically shown the market P/BV ratio larger than or equal to the RET industry ratio. The ratios were equal in 2008 at the lowest values since 1993.

446 Part 4: Analysis and Management of Common Stocks

E x h i b i t 1 3 . 2 2

In p u t s fo

r R e la

t iv

e V a lu

a t io

n R a t io

s fo

r R e t a il

a n d

In d u s t r ia

ls : 1 9 9 3 – 2 0 0 9

R e ta il

In d u st ri a ls

Y e a r

M e a n P ri c e

p e r S h a re

E P S

C a sh

Fl o w

p e r S h a re

B o o k V a lu e

p e r S h a re

S a le s p e r

S h a re

D iv id e n d s

p e r S h a re

M e a n P ri c e

p e r S h a re

E P S

C a sh

Fl o w

p e r S h a re

B o o k V a lu e

p e r S h a re

S a le s p e r

S h a re

D iv id e n d s

p e r S h a re

1 9 9 3

1 1 5 .9 4

5 .5 5

8 .2 1

3 7 .1 8

1 7 3 .8 9

1 .5 7

5 2 0 .2 1

3 1 .0 2

5 8 .0 0

1 9 2 .2 4

6 2 2 .1 2

1 2 .5 1

1 9 9 4

1 0 9 .6 7

5 .9 2

9 .2 6

4 0 .5 3

1 9 7 .2 1

1 .7 1

5 3 6 .5 2

3 6 .6 4

7 2 .3 9

2 1 1 .3 9

6 5 3 .7 5

1 3 .0 1

1 9 9 5

1 0 8 .8 5

5 .2 6

8 .8 4

4 3 .2 5

2 0 8 .1 5

1 .6 9

6 3 8 .9 7

4 3 .3 6

7 9 .2 7

2 2 7 .5 0

7 0 6 .1 3

1 3 .9 6

1 9 9 6

1 1 7 .4 6

6 .4 3

1 0 .9 4

4 6 .7 5

2 3 2 .8 2

1 .4 5

7 9 5 .0 1

4 1 .6 9

8 2 .8 9

2 3 9 .0 0

7 2 7 .4 0

1 5 .5 8

1 9 9 7

1 5 2 .5 0

7 .2 9

1 2 .3 6

5 1 .6 6

2 5 1 .3 3

1 .5 9

1 0 0 6 .1 2

4 4 .4 3

8 3 .7 3

2 4 8 .0 9

7 5 0 .7 1

1 6 .7 2

1 9 9 8

2 3 0 .7 8

9 .6 6

1 4 .7 6

5 3 .3 4

2 7 6 .1 2

1 .6 2

1 2 8 5 .7 3

3 6 .9 6

8 7 .3 8

2 6 5 .0 8

7 5 0 .4 8

1 7 .2 8

1 9 9 9

3 3 5 .2 2

1 0 .6 5

1 6 .9 3

6 0 .7 2

3 0 2 .6 2

1 .7 4

1 6 5 1 .8 2

4 6 .2 8

9 7 .7 3

3 0 2 .4 5

8 1 2 .0 0

1 7 .4 0

2 0 0 0

3 2 0 .0 7

9 .5 0

1 5 .9 7

6 5 .4 8

3 2 9 .7 6

1 .6 7

1 6 9 2 .8 5

5 3 .5 6

1 0 5 .0 9

3 3 8 .0 2

8 5 3 .8 6

1 6 .5 9

2 0 0 1

3 0 1 .4 8

9 .8 3

1 6 .2 9

7 0 .5 4

3 5 2 .2 7

1 .7 0

1 3 6 3 .3 0

3 7 .4 1

6 8 .5 8

4 0 1 .5 5

8 1 1 .0 4

1 5 .8 5

2 0 0 2

3 0 9 .9 0

1 3 .2 0

2 0 .2 1

7 7 .7 6

3 5 5 .6 2

1 .8 3

1 1 3 3 .0 5

5 6 .5 1

6 1 .5 9

3 1 1 .4 5

7 8 1 .6 5

1 6 .1 8

2 0 0 3

3 1 7 .6 1

1 9 .1 6

2 8 .4 0

1 1 6 .5 3

3 8 3 .8 9

2 .8 5

1 0 9 7 .2 7

4 9 .5 2

9 0 .8 8

3 6 4 .2 0

8 4 7 .3 8

1 7 .2 3

2 0 0 4

4 1 3 .3 8

2 0 .4 7

3 1 .1 6

1 2 4 .7 2

4 4 4 .6 1

2 .9 4

1 2 9 4 .4 7

6 5 .2 2

1 0 4 .7 6

4 0 5 .9 8

9 4 4 .3 6

1 8 .7 5

2 0 0 5

4 4 3 .5 8

2 0 .0 7

3 6 .0 7

1 3 9 .3 7

4 7 6 .5 4

3 .2 8

1 3 7 5 .3 7

7 2 .4 1

1 2 1 .3 6

4 5 0 .0 0

1 0 4 3 .6 1

2 1 .5 8

2 0 0 6

4 6 4 .2 4

2 7 .9 6

4 1 .2 1

1 5 9 .1 6

5 8 0 .6 8

4 .6 3

1 4 9 2 .0 5

7 9 .1 1

1 3 4 .1 7

4 9 2 .4 8

1 1 2 3 .3 4

2 4 .2 8

2 0 0 7

4 7 1 .1 2

2 6 .9 4

4 1 .6 5

1 5 5 .3 9

6 0 5 .1 8

5 .5 4

1 6 9 0 .5 4

6 5 .3 8

1 3 2 .7 1

5 1 9 .5 3

1 2 1 1 .9 0

2 7 .2 3

2 0 0 8

3 1 8 .9 1

1 4 .0 3

3 0 .4 9

1 4 3 .7 2

5 9 8 .7 0

5 .7 1

1 3 5 8 .4 3

2 3 .4 3

1 2 2 .4 7

4 6 7 .7 3

1 3 2 6 .0 1

3 0 .2 1

2 0 0 9

3 2 3 .7 6

2 0 .6 5

3 7 .3 7

1 4 9 .8 5

5 7 8 .4 6

5 .6 7

1 1 8 8 .9 6

6 5 .7 9

1 3 2 .9 0

5 4 0 .5 9

1 1 6 3 .5 0

2 8 .5 7

1 0 -y e a r G ro w th R a te s

0 .1 2 %

1 1 .7 4 %

1 3 .4 0 %

1 2 .8 8 %

7 .5 4 %

2 3 .9 5 %

− 2 .9 8 %

2 .2 8 %

2 .6 5 %

5 .9 9 %

3 .6 3 %

7 .2 2 %

Chapter 13: Industry Analysis 447

E x h i b i t 1 3 . 2 3

R e la

t iv

e V a lu

a t io

n R a t io

s f o r t h e R e t a il

a n d

In d u s t r ia

l in

d u s t r ie

s : 1 9 9 3 – 2 0 0 8

P ri c e /E a rn in g s (t + 1)

P ri c e /B o o k V a lu e (t + 1)

P ri c e /C a sh

Fl o w

(t + 1)

P ri c e / S a le s (t + 1)

Y e a r

R e ta il

In d u st ri a ls

R a ti o R e t/ In d

R e ta il

In d u st ri a ls

R a ti o R e t/ In d

R e ta il

In d u st ri a ls

R a ti o R e t/ In d

R e ta il

In d u st ri a ls

R a ti o R e t/ In d

1 9 9 3

1 8 .7 0

1 4 .2 0

1 .3 2

2 .8 6

2 .4 0

1 .1 9

1 2 .5 2

7 .1 9

1 .7 4

0 .5 9

0 .8 0

0 .7 4

1 9 9 4

2 1 .6 7

1 2 .3 7

1 .7 5

2 .5 4

2 .3 1

1 .1 0

1 2 .4 1

6 .7 7

1 .8 3

0 .5 3

0 .7 6

0 .7 0

1 9 9 5

1 6 .3 2

1 5 .3 3

1 .0 6

2 .3 3

2 .6 3

0 .8 9

9 .9 5

7 .7 1

1 .2 9

0 .4 7

0 .8 8

0 .5 3

1 9 9 6

1 5 .5 4

1 7 .8 9

0 .8 7

2 .2 7

3 .1 4

0 .7 2

1 0 .3 3

9 .4 9

1 .0 9

0 .4 7

1 .0 6

0 .4 4

1 9 9 7

1 6 .0 2

2 7 .2 2

0 .5 9

2 .8 6

3 .7 1

0 .7 7

1 3 .6 3

1 1 .5 1

1 .1 8

0 .5 5

1 .3 4

0 .4 1

1 9 9 8

2 1 .1 3

2 7 .7 8

0 .7 6

3 .8 0

4 .1 5

0 .9 2

2 0 .9 9

1 3 .1 6

1 .5 9

0 .7 6

1 .5 8

0 .4 8

1 9 9 9

3 5 .5 1

3 0 .8 4

1 .1 5

5 .1 2

4 .8 0

1 .0 7

1 9 .6 5

1 5 .7 2

1 .2 5

1 .0 2

1 .9 3

0 .5 3

2 0 0 0

3 5 .4 1

4 5 .2 5

0 .7 8

4 .5 4

4 .9 3

0 .9 2

1 4 .9 2

2 4 .6 8

0 .6 0

0 .9 1

2 .0 9

0 .4 4

2 0 0 1

2 2 .8 2

2 4 .1 2

0 .9 5

3 .8 8

4 .3 4

0 .8 9

1 0 .9 1

2 2 .1 4

0 .4 9

0 .8 5

1 .7 4

0 .4 9

2 0 0 2

1 6 .0 7

2 2 .8 8

0 .7 0

2 .6 6

3 .0 8

0 .8 6

1 0 .1 9

1 2 .4 7

0 .8 2

0 .8 1

1 .3 4

0 .6 0

2 0 0 3

1 5 .4 3

1 6 .8 2

0 .9 2

2 .5 5

2 .6 3

0 .9 7

1 1 .4 6

1 0 .4 7

1 .0 9

0 .7 1

1 .1 6

0 .6 1

2 0 0 4

1 6 .7 5

1 7 .8 8

0 .9 4

2 .9 7

2 .8 8

1 .0 3

1 0 .7 6

1 0 .6 7

1 .0 1

0 .8 7

1 .2 4

0 .7 0

2 0 0 5

1 5 .7 6

1 7 .3 9

0 .9 1

2 .7 9

2 .7 5

1 .0 1

1 2 .1 2

1 0 .2 5

1 .1 8

0 .7 6

1 .2 2

0 .6 2

2 0 0 6

2 0 .4 8

1 9 .6 1

1 .0 4

3 .5 5

3 .0 7

1 .1 6

1 3 .2 5

7 .7 5

1 .7 1

0 .9 1

1 .3 2

0 .6 9

2 0 0 7

3 2 .6 2

2 9 .0 5

1 .1 2

3 .1 8

3 .7 8

0 .8 4

1 5 .0 1

8 .1 7

1 .8 4

0 .7 6

1 .3 3

0 .5 7

2 0 0 8

1 5 .3 5

1 8 .8 1

0 .8 2

2 .1 2

2 .1 3

0 .9 9

8 .4 8

6 .1 4

1 .3 8

0 .5 5

0 .9 9

0 .5 5

S o u rc e : S ta n d a rd a n d P o o r’ s A n a ly s ts

’ H a n d b o o k .

448 Part 4: Analysis and Management of Common Stocks

The relationship of generally equal ratios is reasonable because the P/BV ratio should reflect the ability of the market or an industry to earn a ROE that exceeds its cost of equity. During the last five years, the ROE for the industry has been larger than for the market, but the cost of equity for the industry is also higher (β = 1.10). This consistency between ROE and risk implies that the P/BV ratio for the industry should be similar to the market.

13.8.2 The Price/Cash Flow Ratio As shown in Exhibit 13.23 and Exhibit 13.25, the P/CF ratios peaked in the late 1990s, then declined to about 11 times; both series ended with their lowest values since 1993, with the industry multiple a little larger. The P/CF ratio is similar to the P/E ratio because it is driven by the growth rate and the stability of the cash flow series. As shown in Exhibit 13.22, the growth rate of the industry CF has consistently been higher than the market CF (i.e., over 14 percent versus about 5 percent). Also, the growth of the industry CF series was more consistent than the market’s growth. Therefore, the P/CF ratio relationship is in the right direction (the industry ratio is larger), and the difference in the P/CF ratio of about 38 percent seems justified by the substantial difference in the growth rate of CF and its consistency.

13.8.3 The Price/Sales Ratio As shown in Exhibits 13.23 and 13.26, the P/S ratio for the aggregate market has always been substantially higher in terms of the mean values (0.72 vs. 1.30), during the final year (0.55 vs. 0.99), or the mean industry/market ratio (0.57). The P/S ratio should be impacted by three fac- tors: (1) sales growth rate, (2) the uncertainty (risk) of sales growth, and (3) the profitability of sales (i.e., the net profit margin). The results indicate the following: (1) The industry experienced a higher rate of growth for sales during the recent 10-year period; (2) both the industry and the market experienced relatively low volatility in sales growth; and (3) the profit margin (PM)

Exhibit 13.24 Time-Series Plot of Price/Book Value Ratios (t + 1) for

Industrials and Retail: 1993–2008

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

5.50

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

5.50

1993 1995 1997 1999 2001 2003 2005 2007

Years

P r ic e /B o o k V a lu e R a ti o (

t + 1 )

Retail Industrials

Chapter 13: Industry Analysis 449

has always been higher for the market, but the difference in profit margins has declined in recent years. Therefore, the direction of difference is appropriate (the market should be higher because of the difference in the important PM), but one might question whether the size of the difference is too much.

Exhibit 13.25 Time-Series Plot of Price/Cash Flow Ratios (t + 1) for

Industrials and Retail: 1993–2008

0.50

5.50

10.50

15.50

20.50

25.50

30.50

0.50

5.50

10.50

15.50

20.50

25.50

30.50

1993 1995 1997 1999 2001 2003 2005 2007

Years

P r ic e /C a s h F lo w R a t io (

t + 1 )

Retai l Industri al s

Exhibit 13.26 Time-Series Plot of Price/Sales Ratios (t + 1) for Industrials

and Retail: 1993–2008

0.35

0.55

0.75

0.95

1.15

1.35

1.55

1.75

1.95

2.15

0.35

0.55

0.75

0.95

1.15

1.35

1.55

1.75

1.95

2.15

1993 1995 1997 1999 2001 2003 2005 2007

Years

P r ic e /S a le s R a t io (

t + 1 )

Retai l Industri al s

450 Part 4: Analysis and Management of Common Stocks

13.8.4 Summary of Industry/Market Ratios Exhibit 13.27 contains the four industry-market ratios for each of the valuation ratios. These results indicate that investors’ assessment of this industry relative to the market has declined slightly over time because all the industry-market relative ratios have declined. Only the price/CF ratio for the industry is above the market. Given that the industry has higher growth rates for all of these variables and generally lower risk characteristics, this indicates that possi- bly the industry is undervalued relative to the aggregate stock market.

13.9 GLOBAL INDUSTRY ANALYSIS Because so many firms are active in foreign markets and because the proportion of foreign sales is growing for many firms, it is necessary to consider the effects of foreign firms on in- dustry returns. To see why this is so, consider the auto industry. Besides Ford and General Motors, the auto industry for a global investor includes numerous firms from Japan, Germany, Italy, and Korea, among others. Thus, we must extend our analysis to include global factors.

While space does not permit an example, the following major factors need to be analyzed in this context:

• The macroenvironment in the major producing and consuming countries for an industry. This will impact demand from these countries.

• An overall analysis of the significant global companies in the industry, the products they produce, and how successful they are in terms of the DuPont three-component analysis.

• Regarding company analysis, what are the accounting differences by country and how do these differences impact the relative valuation ratios? Because of accounting differences, it is typically not possible to directly compare many ratios across countries but only to

Exhibit 13.27 Time-Series Plot of the Retail/Industrials Ratio for Retail

and Industrials: 1993–2008

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2.00

1993 1995 1997 1999 2001 2003 2005 2007

Years

R a t io ( t im e s )

Price/Cash Flow (t + 1) Price/Earnings (t + 1)

Price/Book Value (t + 1) Price/Sales (t + 1)

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examine them over time within a country. This problem should be reduced as the use of international accounting standards grows, as discussed in Chapter 10.

• What is the effect of currency exchange rate trends for the major countries? Significant currency changes can affect the demand for U.S. products from specific countries and also impact costs, assuming U.S. firms receive inputs from foreign firms.

Global industry analysis is growing in importance as documented by Cavaglia, Brightman, and Aked (2000). Specifically, prior research showed that country factors dominated industry fac- tors in terms of explaining equity returns, but the Cavaglia et al. (2000) study presented evidence that industry factors have been growing in importance and currently dominate coun- try factors. In summary, it is important to carry out industry analysis on a global scale.

SUMMARY • Several studies examined industry performance

and risk. They found wide dispersion in the per- formance of alternative industries during specified time periods, implying that industry analysis can help identify superior investments. They also showed inconsistent industry performance over time, implying that looking at only past perfor- mance of an industry has little value in projecting future performance. Also, the performance by firms within industries typically is not very consis- tent, so you must analyze the individual compa- nies in an industry following the industry analysis.

• The analysis of industry risk indicated wide dis- persion in the measures of risk for different indus- tries but strong consistency in the risk measure over time for individual industries. These results imply that risk analysis and measurement are use- ful and necessary. The good news is that past risk measures are useful when estimating future risk.

• We discussed and demonstrated both approaches to the valuation of the RET industry. The present value of cash flow models indicated a fairly wide range of values.

• The four relative valuation ratio techniques also provided a range of results, including the two- step earnings multiple technique where the multi- ple results surrounded the current market price. All of the relative valuation ratios declined over time relative to the market.

• Industry analysis needs to be carried out on a global scale and must evaluate the effects of world supply, demand, and cost components for an in- dustry but also different valuation levels due to ac- counting conventions. Finally, you must consider the impact of exchange rates on the total industry and the firms within it.

SUGGESTED READINGS Goodman, D. A., and John W. Peavy III. “Industry

Relative Price-Earnings Ratios as Indicators of In- vestment Returns,” Financial Analysts Journal 39, no. 2 (March–April 1983): 60–66.

Porter, Michael E. “How to Conduct an Industry Analysis.” In The Financial Analysts Handbook, 2nd ed., ed. Sumner N. Levine. Homewood, IL: Dow Jones-Irwin, 1988.

The following are proceedings from industry analysis seminars sponsored by the Association for Invest- ment Management and Research (AIMR):

Balog, James, ed. The Health Care Industry. Charlottesville, VA: AIMR, 1993.

Bhatia, Sanjiv, ed. The Consumer Staples Industry. Charlottesville, VA: AIMR, 1995.

Bhatia, Sanjiv, ed. The Media Industry. Charlottesville, VA: AIMR, 1996.

Petrie, Thomas, A., ed. The Oil and Gas Industries. Charlottesville, VA: AIMR, 1993.

Shasta, Theodore, ed. The Automotive Industry. Charlottesville, VA: AIMR, 1994.

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QUESTIONS 1. Briefly describe the results of studies that examined the performance of alternative indus-

tries during specific time periods, and discuss their implications for industry analysis. 2. Briefly describe the results of the studies that examined industry performance over time.

Do these results complicate or simplify industry analysis? 3. Assume all the firms in a particular industry have consistently experienced similar rates

of return. Discuss what this implies regarding the importance of industry and company analysis for this industry.

4. Discuss the contention that differences in the performance of various firms within an in- dustry limit the usefulness of industry analysis.

5. Several studies have examined the difference in risk for alternative industries during a specified time period. Describe the results of these studies, and discuss their implications for industry analysis.

6. What were the results when industry risk was examined during successive time periods? Discuss the implication of these results for industry analysis.

7. Assume the industry you are analyzing is in the fourth stage of the industrial life cycle. How would you react if your industry-economic analysis predicted that sales per share for this industry would increase by 20 percent? Discuss your reasoning.

8. Discuss at what stage in the industrial life cycle you would like to discover an industry. Justify your decision.

9. Give an example of an industry in Stage 2 of the industrial life cycle. Discuss your rea- soning for putting the industry in Stage 2 and any evidence that caused you to select this stage for the industry.

10. Discuss the impact of substitute products on the steel industry’s profitability. 11. Discuss the two variables that must be considered whether you are using the present

value of cash flow approach or the relative valuation ratio approach to valuation. Why are these variables relevant for either valuation approach?

12. List the three variables that are relevant when attempting to determine whether the earn- ings multiple (P/E ratio) for an industry should be higher, equal to, or lower than the market multiple.

13. Discuss when you would use the two-stage growth FCFE model rather than the constant growth model.

14. You are examining the P/CF ratio for an industry compared to the market and find that the industry ratio has always been at a discount to the market—for example, the industry- market ratio of ratios is about 0.80. Discuss the variable(s) you would examine to explain this difference or to justify an increase in the industry-market ratio.

PROBLEMS 1. Select three industries from Standard and Poor’s Analysts’ Handbook, or some other

source of industry data with different demand factors. For each industry, indicate what economic series you would use to predict the growth for the industry. Discuss why the economic series selected is relevant for this industry.

2. Prepare a scatterplot for one of the industries in Problem 1 of industry sales per share and observations from the economic series you suggested for this industry. Do this for the most recent 10 years using information available in the Analysts’ Handbook or another source. Based on the results of the scatterplot, discuss whether the economic series was closely related to this industry’s sales.

3. Based on an analysis of the results in Problem 2, discuss the stage of your industry in its life cycle.

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4. Evaluate your industry in terms of the five factors that determine an industry’s intensity of competition. Based on this analysis, what are your expectations about the industry’s prof- itability in the short run (1 or 2 years) and the long run (5 to 10 years)?

5. Using Standard and Poor’s Analysts’ Handbook or another source, plot the latest 10-year history of the operating profit margin for the S&P Industrials Index or another aggregate market series versus an industry of your choice. Is there a positive, negative, or zero correlation?

6. Using the Analysts’ Handbook or another source of industry data, calculate the means for the following variables of the S&P Industrials Index or another aggregate market series and the industry of your choice during the last 10 years: a. Price/earnings multiplier b. Retention rate c. Return on equity d. Equity turnover e. Net profit margin Briefly comment on how your industry and the S&P Industrials Index differ for each of the variables.

7. Prepare a table listing the variables that influence the earnings multiplier for your chosen industry and the market index series for the most recent 10 years. a. Do the average dividend-payout ratios for your industry and the market index differ?

How should the dividend payout influence the difference between the multipliers? b. Based on the fundamental factors, would you expect the risk for this industry to differ

from that for the market? In what direction, and why? Calculate the industry beta using monthly data for five years. Based on the fundamental factors and the computed systematic risk, how does this industry’s risk compare to the market? What effect will this difference in risk have on the industry multiplier relative to the market multiplier?

c. Analyze and discuss the different components of growth (retention rate, total asset turnover, total assets/equity, and profit margin) for your chosen industry and a market index during the most recent 10 years. Based on this analysis, how would you expect the growth rate for your industry to compare with the growth rate for the market in- dex? How would this difference in expected growth affect the multiplier?

454 Part 4: Analysis and Management of Common Stocks