Applying Markovitz’s portfolio theory to

company’s products portfolio analysis and Porter’s Five Forces Model industry’s

profitability analysis

David M. Atencia November 29, 2016

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Table of figures

Figure 1. Example of companies’ portfolio management analyses (not exhaustive). ...... 3

Figure 2. Graphical representation of Porter's five forces model ........................................ 4

Figure 3. Potential risk-return relationship (risk-return profile) for the portfolio of the

company. ..................................................................................................................................... 8

Figure 4. Potential risk-return relationship for the portfolio of the company

for different correlation coefficients (ρ =-1, 0, +1). ................................................................ 9

Figure 5. Impacts in the portfolio’s risk-return relationship when changing the risk-return

characteristics. ............................................................................................................................ 9

Figure 6. The WACC level and the in the portfolio’s risk-return relationship. ................. 10

Figure 7. Evolution of the current company’s products portfolio with increase in sales. 11

Figure 8. Impact on the company’s products portfolio risk-return profile with the

introduction of a new product (Product D). ........................................................................... 12

Figure 9. Graphical representation of Porter's five forces analysis. ................................. 14

Figure 10. Potential risk-return relationship for the industry. ............................................. 15

Figure 11. Potential risk-return relationship for a industry with two competitors. ........... 16

Figure 12. Impact on expected industry’s WACC with changes in competitor expected

return. ......................................................................................................................................... 17

Figure 13. Changes in risk-return relationship for the industry with a new entrant. ....... 18

Figure 14. Changes in risk-return relationship for the industry with strengthening power

of suppliers.. .............................................................................................................................. 19

Figure 15. Changes in risk-return relationship for the industry with strengthening power

of buyers. ................................................................................................................................... 20

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1. Executive summary This study provides a first analysis and evaluation of applying Markovitz’s Modern Portfolio Theory (MPT) to companies’ products portfolio performance. The same approach is used to assess industries’ profitability and risk based on Porter’s Five Forces Model (FFM). The analyses are based on mean-variance calculations to establish the risk-return profile and evaluate different scenarios. Results show that a company’s product portfolio can be characterized in terms of risk-return metrics, increasing the financial metrics and framework to assess performance that companies use for their portfolios. Additionally, the Modern Portfolio Theory equations showed to be a useful frame to establish an industry’s profitability-risk profile considering the shaping forces according to Porter’s model. As a result, the company’s products portfolio analysis shows that MPT allows to compare the company’s Weighted Average Cost of Capital (WACC) to the expected return and establish a risk level for the WACC accordingly to the company’s products portfolio risk-return relationship; analyze the impact in risk-return terms of the current company’s product portfolio with an increase/decrease in sales of one or several products; study the impact of the introduction/disinvestment in one or several products in the company’s products portfolio; segregate the products’ risk-return profile by categories and/or geographies; and test the sensibility of the risk-return relationship of the company’s products portfolio to changes in one or several variables in the expected return of one or several products. In the Porter’s Five Forces Model analysis, Markovitz’s framework allows to assess an industry’s profitability and associated risk and the impact of each of the forces (supplier power, buyer power, competitive rivalry, the threat of substitution, the threat of new entry). The study is developed using basic formulations and not applying real/detailed data to the equations. So, further analyses could be elaborated using real market data and compared with the results obtained here.

2. Introduction Company’s products portfolio analysis In any industry is easy to see company’s presentations in investors’ seminary showing analyses about the company’s brands/products’ portfolio and their category/geography mix. Normally, statements such as “very diversified and balanced portfolio” are commonly used, and sales contribution graphics per product and geography are shown. Also, categories’ analysis is done considering the position of the company, product in each different category of the market and growth/sales/profit breakdowns are developed. Additionally, companies also develop a holistic framework of key metrics, such as growth, margin, capital efficiency, capital allocation, etc, to assess performance, and these indicators are used to analyze growth strategies, identify market drivers, establish in which products/category/geography mixes to spend on marketing or in which products/categories to allocate capital expenditure resources (see Figure 1).

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Figure 1. Example of companies’ portfolio management analyses (not exhaustive).

Are these analyses flaw? No, quite the contrary. Undoubtedly, all these analyses are right and must be developed and managed. To put it simple, a company’s value depends on its portfolio of products. So, one of the biggest challenges faced by companies is to decide how to invest and in which categories/products to invest to have a “very diversified and balanced portfolio”. But what do “diversified” and “balanced” mean. According to the Cambridge Dictionary “balanced” means: considering all sides or opinions equally, or, another definition, containing an equal amount or number of similar things or people. And “diversified” means: including a range of different types of products, investments, etc. in order to reduce risk or increase the chances of success. One important question to answer by managers is to decide whether to invest in a few existing individual or new products, evaluating each in isolation, or take a portfolio approach. A “portfolio approach” means evaluating individual products in relation to their contribution to the returns characteristics of the whole portfolio of products. "Portfolio diversification” helps to avoid disastrous investment outcomes. This benefit is most convincingly illustrated by examining what may happen when companies relay all their value in one product, this is they are not diversified. So, the same way that financial assets are generally defined by their risk and return characteristics, products in any company’s portfolio should be characterized in the same way. Comparison along these two dimensions simplifies the process of managing existing products or creating new ones among different options. The expression “risk–return trade-off” refers to the positive relationship between expected risk and return. In other words, a higher return is not possible to attain in efficient markets/sectors/industries and over long periods of time without accepting higher risk. Expected returns should be greater for products with greater risk. Thus, by taking a diversified portfolio approach, managers can spread away some of the risk and be concerned about the risk–return trade-off of their portfolio. The portfolio approach

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provides a way to reduce the risk associated with companies’ investment decisions without necessarily decreasing their expected rate of return. The concept of diversification has been around for a long time and has a great deal of intuitive appeal. However, the actual theory underlying this basic concept and its application to investments only emerged in 1952 with the publication of Harry Markowitz’s classic article on portfolio selection. It’s with this risk–return trade-off and diversification approach that the analyses will be developed. Porter’s Five Forces Model industry’s profitability analysis Porter’s Five Forces Analysis (see Figure 2) is an important tool for assessing the potential for profitability in an industry. This tool was created by Harvard Business School professor, Michael Porter, to analyze the attractiveness and likely-profitability of an industry. Since publication, it has become one of the most important business strategy tools.

Industry’s profitability framework

Figure 2. Graphical representation of Porter's five forces model

The forces that shape competition in any industry, and its profitability, can be summarized in five components:

Supplier Power: The power of suppliers to drive up the prices of inputs; Buyer Power: The power of your customers to drive down prices; Competitive Rivalry: The strength of competition in the industry; The Threat of Substitution: The extent to which different products and

services can be used in place of existing ones; The Threat of New Entry: The ease with which new competitors can enter the

market if they see that good profits are made in the industry. The goal of this methodology is to analyze the attractiveness and sustainable likely-profitability of an industry in the long term. However, the main “hurdle” of this methodology is the lack of specific formulation to assess profitability and risk values associated with the analysis framework. As stated above, a company’s value depends on its portfolio of products and it is possible to establish risk-return pair of values to the company. So, the same way that financial assets are generally defined by their risk and return characteristics, companies competing in a specific industry should be characterized in the same way. This approach allows to assess the industry’s risk-

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return profile taking into account the impact of the five forces that shape competitiveness.

3. Modern Portfolio Theory: Basic formulation A major reason that portfolios can effectively reduce risk is that combining securities whose returns do not move together provides diversification. Although portfolio diversification generally does reduce risk, it does not necessarily provide the same level of risk reduction during times of severe market turmoil as it does when the economy and markets are operating ‘normally’. In fact, if the economy or markets fail totally (which has happened numerous times around the world), then diversification is a false promise. In the face of a worldwide contagion, diversification was ineffective, as illustrated at the end of 2008. The concept of diversification has been around for a long time and has a great deal of intuitive appeal. However, the actual theory underlying this basic concept and its application to investments only emerged in 1952 with the publication of Harry Markowitz’s classic article on portfolio selection. The article provided the foundation for what is now known as modern portfolio theory (MPT). The main conclusion of MPT is that investors should not only hold portfolios but should also focus on how individual securities in the portfolios are related to one another. In addition to the diversification benefits of portfolios to investors, the work of William Sharpe (1964), John Lintner (1965), and Jack Treynor (1961) demonstrated the role that portfolios play in determining the appropriate individual asset risk premium (i.e., the return in excess of the risk-free return expected by investors as compensation for the asset’s risk). In addition to avoiding a potential disaster associated with over investing in a single product, portfolios also generally offer equivalent expected returns with lower over-all volatility of returns—as represented by a measure such as standard deviation. The aim of this analysis is not to demonstrate the Modern Portfolio Theory formulation, but its potential application to company’s portfolio products analysis/management. It is considered that the reader is familiar with this theory, so the formulas that underlie the theory will be presented quite straight forward. It is also consider that the reader has got knowledge of basic statistical concepts such as expected return (E(·)), variance

(Var(·)), risk (represented by ), correlation coefficient (ρ), covariance (Cov(·,·)) and

basic relationships among these variables. Consider a company’s portfolio of N products and Ii designating each of the investments associated with the different products in the company’s portfolio, with i=1,2,3,…,N. So, every product in the portfolio has associated a weight (wi) calculated as Ii/IT, with IT = ΣIi for i=1,2,3,…,N. Likewise, for every product it is defined an

expected return ( ). Every expected return can be defined as a function of its sales level (ni) price (Pi), Costs (Ci), exchange rates (FXi), taxes (Ti), interests rates (ri) and any other variables necessary to establish the expected return. When several individual products are combined into the portfolio, we can compute the portfolio return as a

weighted average of the returns in the portfolio. The portfolio return ( ) is simply a weighted average of the returns of the individual products,

(1)

(2)

(3)

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(4)

(5)

(6)

with defined by

) (7) Additionally, to fully characterize a product, it is necessary to associate a measure of risk. Variance is a measure of the volatility or the dispersion of returns. Higher variance suggests less predictable returns and therefore a more risky product. Variance is measured as the average squared deviation from the mean,

(8)

was defined as a function of different variables in (7). So, the variance will be also a function depending on these variables,

(9) These means that the volatility associated to each product will depend on the volatility (variances) of the different variables that define and impact on the products’ expected return. This simple observation will be useful for later analyses when analyzing the performance of the portfolio. Then, the risk associated to the product will be established using the standard deviation,

(10)

Jointly with other key metrics such as growth, margin, capital efficiency, capital allocation, etc, defining the risk associated with each product gives a better understanding of the product’s performance. It is necessary to remember that for each product, and for the portfolio of products as we will see later, there is “risk–return trade-off”. In other words, a higher return is not possible to attain in efficient markets and over long periods of time without accepting higher risk. For example, any company launching a new product (not existing previously in the market) that turns out to be successful (profitable in terms defined by the company) will probably be copied by other companies, impacting in the launching company’s expected return (in relative and absolute terms) of the product. Like the portfolio’s return, we can calculate the portfolio’s variance. When computing the variance of the portfolio’s return, standard statistical methodology can be used by finding the variance of the full expression of portfolio return. Although the return of a portfolio is simply a weighted average of the returns of each product, this is not the case with the standard deviation of a portfolio (unless all products’ expected returns are perfectly correlated, that is, correlation equals one). Then, considering that the share of each product in the company’s portfolio (i.e its weight in the portfolio’s expected return) doesn’t changes and it is perfectly known, the variance can be expressed more generally as,

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(11)

(12)

(13)

(14)

The same way that was considered for an individual product of the portfolio, the risk associated to the portfolio of products of the company will be established using the standard deviation,

(15)

With a risk-return perspective it is possible to analyze the impact of each product in the company’s risk-return profile and also whether the products have a diversification effect. To examine how a portfolio with many products works and the ways in which we can reduce the risk of the portfolio, assume that the portfolio has equal weights (1/N)

for all N products. In addition, assume that and are the average variance and average covariance. Given equal weights and average variance/covariance, we can rewrite the portfolio variance as,

(16)

(17)

(17) shows that as N becomes large, the first term on the right side with the denominator of N becomes smaller and smaller, implying that the contribution of one asset’s variance to portfolio variance gradually becomes negligible. The second term, however, approaches the average covariance as N increases. It is reasonable to say that for portfolios with a large number of products, covariance among the assets accounts for almost all of the risk for the portfolio of products. The analysis becomes more instructive and interesting if we assume that all assets in the portfolio have the same variance and the same correlation among assets. In that case, the portfolio risk can then be rewritten as,

(18)

The first term in (18) under the root sign becomes negligible as the number of assets in the portfolio increases leaving the second term (correlation) as the main determining factor for portfolio risk. If the assets are unrelated to one another, the portfolio can have close to zero risk. In the next section, we review these concepts to learn how portfolios can be diversified.

4. Company’s products portfolio risk-return analysis To continue the analysis, and for simplicity to handle the results, we will consider a company initially with only 2 products (product A and product B), with defined risk-return variables. The previous expressions for the portfolio of products simplify to the following ones,

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(19)

(20)

(21)

Considering the different options for the products’ weight in the company’s portfolio, this is from a portfolio containing only one of the products to all the possible combinations of the weights (relative investment) of the products, the risk-return relationship (risk-return profile) for the portfolio can be represented graphically (see Figure 3).

Figure 3. Potential risk-return relationship (risk-return profile) for the portfolio of the company.

As can be seen in Figure 3, the extreme points of the curve are represented considering a portfolio with only one of the products of the company (Point A and Point B). Between these two points, there all the hypothetical portfolio of products depending on the weight of each product. Point C represents a theoretical current portfolio of the company defined by its products’ expected returns and risks. So, what we can see is that the combination of products in the company’s portfolio results in a higher expected return than a 100% product B portfolio but in less return when compared to a 100% Product A portfolio. However, because of the risk diversification effect, the current’s product portfolio risk is less than that of a 100% Product A portfolio. In the risk-return relationship, an important factor is the correlation coefficient, defined for two random variables (x,y) as,

(22)

with,

(23) Depending on the value of correlation coefficent (from -1 to 1), the relative position for Point C among the options (risk-return profile) and the potential risk-return scenarios change (see Figure 4). Depending on the value of correlation coefficient, the risk

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Products Portfolio risk

100% Product A

100% Product B

Current company’s

products portfolio

A

B

C

100% Product A

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associated to the current company’s products portfolio (Point C) reduces to a minimum (ρ = -1), keeping the expected return. The more negatively correlated the two products of the company are, the more the risk associated reduces.

Figure 4. Potential risk-return relationship for the portfolio of the company

for different correlation coefficients (ρ =-1, 0, +1).

For two given products, accepting that the expected return and risk sources and values don’t change, the portfolio’s risk-return relationship doesn’t change. Consequently, this means that different portfolio scenarios (risk-returns associated) can be analyzed. However, changes in the risk-return values in any of the products of the company’s portfolio will change also the portfolio’s risk-return relationship. As an example, Figure 5 shows how the portfolio’s risk-return relationship modifies (without changing the correlation coefficient) when for Product A changes: 1) the expected return and 2) the risk associated. But remember, in practice changes in return should trigger changes in risk and vice versa. So, a combination of changes in both variables should happen at the same time.

Figure 5. Impacts in the portfolio’s risk-return relationship when changing the risk-return characteristics.

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Products Portfolio risk

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Products Portfolio risk

100% Product A

100% Product B

Current company’s

products portfolio

A

B

C

ρ = 1 ρ = 0 ρ = -1

100% Product B

A

C

B

100% Product A

Current company’s

products portfolio

Increase in

expected return

Increase in risk

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As shown in Figure 5, changing Product A’s expected return changes the current company’s products portfolio expected return proportionally to its weight in the portfolio. An increase in Product A’s expected return will increase the current company’s products portfolio expected return and, consequently, a decrease in Product A’s expected return will decrease the current company’s products portfolio expected return. On the other hand, changing Product A’s risk also changes the current company’s products portfolio risk. An increase in Product A’s risk will increase the current company’s products portfolio risk and, consequently, a decrease in Product A’s risk will decrease the current company’s products portfolio risk. However, this time the changes are not only proportionally to its weight in the portfolio (see proportion factor in (24)),

(24)

Once we have characterized the risk-return profiles of the company’s products and established the potential risk-return relationship of the company’s products portfolio, we can develop different applications/analysis:

Compare the company’s expected Weighted Average Cost of Capital (WACC) to the expected return of the company’s portfolio and establish a risk level for the WACC accordingly to the company’s products portfolio risk-return relationship;

Analyze the impact in risk-return terms of the current company’s product portfolio with an increase/decrease in sales of one or several products;

Study the impact of the introduction/disinvestment in one or several products in the company’s products portfolio;

Segregate the products’ risk-return profile by categories and/or geographies; Test the sensibility of the risk-return relationship of the company’s products portfolio

to changes in one or several variables in the expected return.

Weighted Average Cost of Capital (WACC) analysis

A risk-return approach in the company’s products portfolio allows to analyze and to compare the portfolios’ expected return-risk profile with the expected Weighted Average Cost of Capital (WACC) of the company. Additionally, it is possible to associate a level of risk when targeting a specific WACC given the company’s current risk-return relationship (see Figure 6).

Figure 6. The WACC level and the in the portfolio’s risk-return relationship.

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Products Portfolio risk

100% Product B

Current company’s

products portfolio

A

B

100% Product A

C

σWACC

Current company’s

products portfolio Expected company’s

WACC

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As shown in Figure 6, introducing the company’s expected WACC level as an expected return allows comparing the performance of our products portfolio and the expected WACC. In this example, the current company’s products portfolio expected return (Point C) would be above the expected WACC, so the company is creating value through its products portfolio performance. At the same time, it can be seen that the responsible for creating that excess of value is Product A and that Product B is underperforming. Moreover, using the risk-return relationship of the company’s products portfolio it is also possible to settle a risk for the companies expected WACC (σWACC) accordingly with the company’s products profile and characteristics (in risk-return terms).

Figure 7. Evolution of the current company’s products portfolio with increase in sales.

Increase/decrease in sales Consider that for example Product A has an unexpected increase in sales (nA). As established in (7), besides sales, the expected return depends on other variables such as price, costs, etc. However, considering that the other variables remain the same, with no changes, the impact on the expected return of Product A will be an increase and, consequently, an increase in the expected return of the company’s products portfolio (see Figure 7) . The same reasoning can be done for a decrease in sales for any of the products. Introduction/disinvestment of products One of the most interesting analyses that can be done with the risk-return portfolio approach is assessing impact of the introduction/disinvestment of a product. In the case of disinvestment, considering that we have only two products in our portfolio, the result is very simple: the new risk-return values for the company’s products portfolio will be that of the remaining product. In our example, let’s consider that the company disinvest in Product B (the less profitable one, however the less risky). So, the new current company’s products portfolio risk-return values (Product C) will be the same as that of Product A. Let’s consider now that the company wants to invest and introduce a new product (Product D). Let’s consider too that the existing products (Product A and Product B) represent each 50% of the company’s investments and that the company wants to invest an equal amount of money in Product D. So, as a result, each of the products

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Products Portfolio risk

A 100% Product A

100% Product B

Current company’s

products portfolio

B

C

Increase in sales for

Product A

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will have approximately a 33% weight in the new company’s portfolio. Product A and Product B correlation coefficient equals -0,5 and Product D isn’t correlated with any of them (ρAD=0 and ρBD=0). This is, Product D doesn’t modifies risk through the covariance with Product A or Product B. Finally, consider that return and risk for the new product (Product D) are the average values of Product A`s and Product B’s expected return and risk, respectively. This last premise is to facilitate the understanding of the impacts. Product D’s could take any values for expected return and risk.

Figure 8. Impact on the company’s products portfolio risk-return profile with the introduction of a new

product (Product D).

The results are presented in Figure 8. Small points in the figure represent all potential risk-return scenarios considering Product A, Product B and Product D. The dashed line represents the new company’s products portfolio risk-return relationship (minimum-variance frontier in Markowitz’s Modern Portfolio Theory) including Point D. Because we decided to establish Product D’s expected return as the average expected return of Point A and Point B’s expected return, the new company’s products portfolio keeps the same expected return. However, because Product D’s risk value is the average risk between Product A and Product B and because the correlation coefficient between Product D and both Product A and Product B, the new company’s products portfolio risk reduces (moves to the left). As presented, these results will vary depending on Product D’s characteristics (risk-return profile). However, this methodology permits to analyze the impact of introducing/disinvesting in any product of the company. Categories and/or geographies analyses Another application of this methodology is to segregate the risk-return company’s products portfolio profile by categories and/or geographies. Let’s remember how the total expected return for the company’s products portfolio is calculated,

(25)

(26)

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Products Portfolio risk

A 100% Product A

B 100% Product B

D 100% Product D Current company’s

products portfolio C

New company’s

products portfolio

C’

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(27)

(28)

These expressions can be rearranged considering products by geography, for instance. If we group products by geographies, expected return for the category can be calculated as the average expected return for all the products in that region,

(29)

with NA being the total number of products in geography A. So the expected return for products in geography A can be represented as

(30)

If the company operates in M geographies, then the company’s products portfolio expected return in terms of geography can be represented as

(31)

(32)

(33)

(33) expresses the company’s products portfolio expected return in terms of geography. Analogously, the risk can also be expressed in terms of geography. Let’s consider the following expression for the portfolio’s variance,

(34)

As the expected return can be grouped by geographies, the corresponding variances can also be grouped,

(35)

(35) can be interpreted as follows: the total variance of the company’s products portfolio is a weighted average of the variances of the geographies (

) plus a “cross-border” variance between each of the geographies and the other geographies (sum of the covariance terms in each category

). So, the risk for the

company’s products portfolio will be,

(36)

This kind of analysis can be useful to establish risk-return profiles by geographies for the company’s products. The same analysis, with an analog reasoning, can be developed for a category analysis. Sensibility analysis of variables A particular case for this analysis was presented in the Increase/decrease in sales section.

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) (37)

(38) Sensibility analyses can be carried out for any of the variables that impact on the expected return and risk. These analyses can be performed considering one variable at a time or taking into account several variables.

5. Industry’s profitability(return)-risk analysis Porter’s model works by looking at the strength of five important forces that affect competition and that effectively impact on the industry’s profitability: Supplier Power: The power of suppliers to drive up the prices of inputs; Buyer Power: The power of your customers to drive down prices; Competitive Rivalry: The strength of competition in the industry; The Threat of Substitution: The extent to which different products and services

can be used in place of existing ones; The Threat of New Entry: The ease with which new competitors can enter the

market if they see that good profits are made in the industry. A summary of different drivers/causes that can be considered when analyzing each of the forces in the industry’s analysis is presented in Figure 9.

Figure 9. Graphical representation of Porter's five forces analysis.

Understanding the competitive forces, and their underlying causes, reveals the base of an industry’s current profitability while providing a framework for anticipating and influencing competition (and profitability) over time.

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But the big question is how to model the industry’s profitability and its associated risk? The answer is using the Markovitz’s Modern Portfolio Theory. Consider that in an

industry exist N players and each one has got an expected return ( ) and an associated variance,

, defining a certain level of risk defined by

, with i=1,2,3,…,N. As establish before for a company’s products portfolio, an

industry can be modeled as a portfolio that includes all the assets in the industry, in this case all the companies that play in that specific industry. So, on the one hand, the expected return (profitability) of that industry can be established as the weighted average of the returns of the different players. The weight for each company in the industry will be established considering the company’s invested capital (equity and debt) relative to the total invested capital in the industry (i.e. the sum of the invested capital in the companies of the industry). So, every company in the industry has associated a weight (wi) calculated as Ii/IT, with IT = ΣIi for i=1,2,3,…,N. And, on the other hand, the risk associated to the expected return of the industry is defined by the combined variance of the expected returns of all the players in the industry. It’s also useful to remember at this point, that the expected return and, hence, variance/risk of the players can be modeled using several variables such as its sales level (ni) price (Pi), Costs (Ci), exchange rates (FXi), taxes (Ti), interests rates (ri), etc. Putting all together, an industry’s expected return-risk profile could be defined with the following equations,

(39)

(40)

(41)

(42)

) (43)

(44)

Figure 10. Potential risk-return relationship for the industry.

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Industry risk

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Figure 10 shows the graphical representation for equations (40) and (42). The points represent all the possible combinations for the industry’s risk-expected return (profitability) scenarios. The continuous line represents the new company’s products portfolio risk-return relationship (minimum-variance frontier in Markowitz’s Modern Portfolio Theory). As was done before, for simplicity, we will consider that there are only two competitors in an industry (Company A and Company B). The previous expressions for the

industry’s expected return ( and risk ( ) simplify to the following ones,

(45)

(46)

(47)

(48)

Figure 11 shows the potential risk-return industry’s profile considering two competitors with given specific characteristics (values) for expected return, risk and correlation coefficient (Point A and point B). Point C identifies the current industry’s position in terms of risk and profitability considering competitors’ risk-return profiles and weight (market share in terms of investments). Considering that the industry’s risk-return relationship applies, changes in the forces that drive competition can be modeled and their impact in the industry’s profitability (and risk) established.

Figure 11. Potential risk-return relationship for a industry with two competitors.

The expected return (profitability) associated with the industry’s current position can be understood as the expected Weighted Average Cost of Capital (WACC) for the industry as a whole. Because the investment associated with each company considers equity investments and debt investments, the expected return for the industry can be associated with a WACC measure. At the same time, the risk associated with the industry’s current position would be the risk associated with the industry’s profitability and consequently with the industry’s expected WACC. This doesn’t mean that all players target that specific expected WACC but it is the average value for that industry in the current conditions. Changes in any of the players current conditions (expected

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Industry risk

Profitability

100% Product A

100% Company B

Current industry’s

position

A

B

C

100% Company A

σIndustry

RIndustry

Ris

k

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return, risk or even correlation coefficient with other competitors) will change the industry’s current position.

Figure 12. Impact on expected industry’s WACC with changes in competitor expected return.

As an example, in Figure 12 is shown how a change in Company A’s expected return impacts in the expected profitability (WACC) of the industry. Because the change is only in Company A’s expected return (no changes in terms of risk, weight or correlation coefficient), the current industry’s position is only affected in terms of return. So, an increase in Company’s A expected return increases the expected WACC for the industry keeping the same level of risk for that expected WACC. But how changes in the shaping forces of the industry can impact in the expected risk-return profile and its current position? As mentioned before, there are five forces that shape the industry competitiveness, hence its expected return and risk: the threat of new entry, the power of suppliers, the power of buyers, the threat of substitutes, rivalry among existing competitors. Changes in any of the forces can happen independently or simultaneously. However, for analysis purposes, the effects of each force will be presented independently. The threat of new entry New entrants to an industry introduce new capacity (offer for the market) and target to gain market share, putting downward pressure on prices and costs. It also increases the level of investments necessary to compete (investments in marketing, R&D, new technologies, etc). Therefore, the threat of new entrants tends to lower the potential profit of an industry. Let’s consider an industry with only two competitors (see Figure 11). A new entrant in the industry’s risk-return relationship and its current industry’s position (in terms of risk and expected return) would have two impacts: firstly, the weight (industry’s share in terms of investments) of each competitor would be redistributed, so the risk-return relationship for the industry would be modified. And secondly, the return for the existent players could be expected to be lower. Changes in risk terms for the existing competitors in the industry should be expected, but these changes would depend on how prices and costs are affected. So changes in existing competitors’ risk will not be represented here, but should also be expected.

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A potential modeling for this case is shown in Figure 13. As did in the Company’s products portfolio risk-return analysis, let’s consider that the existing companies (Company A and Company B) represent each 50% of the industry’s investments and that the new entrant wants to invest an equal amount of money to develop its business. So, as a result, each of the products will have approximately a 33% weight in the industry. Company A and Company B correlation coefficient equals -0,5 and Company D isn’t correlated with any of them (ρAD=0 and ρBD=0). This is, company D doesn’t modifies risk through the covariance with Company A or Company B. Finally, consider that because of the threat of the new entrant, Company A and Company B reduce their expected return (trough prices reduction) 10%. Then, for simplicity, we will assume that expected return and risk for Company D are the average values of Company A`s and Company B’s expected return and risk, respectively.

Figure 13. Changes in risk-return relationship for the industry with a new entrant.

A potential modeling for this case is shown in Figure 13. As did in the Company’s products portfolio risk-return analysis, let’s consider that the existing companies (Company A and Company B) represent each 50% of the industry’s investments and that the new entrant wants to invest an equal amount of money to develop its business. So, as a result, each of the products will have approximately a 33% weight in the industry. Company A and Company B correlation coefficient equals -0,5 and Company D isn’t correlated with any of them (ρAD=0 and ρBD=0). This is, company D doesn’t modifies risk through the covariance with Company A or Company B. Finally, consider that because of the threat of the new entrant, Company A and Company B reduce their expected return (trough prices reduction) 10%. Then, for simplicity, we will assume that expected return and risk for Company D are the average values of Company A`s and Company B’s expected return and risk, respectively. Small points in Figure 13 represent all potential risk-return scenarios considering Company A, Company B and Company D. The dashed line represents the new company’s products portfolio risk-return relationship (minimum-variance frontier in Markowitz’s Modern Portfolio Theory) including Company D in the industry. Because of the premises used, the current industry’s position moves to the left and down. This is, the introduction of averaged-risk Company D in the industry, simultaneously with a downward pressure on market prices, lowers the expectations for profitability in the industry with a reduction of risk (diversification effect) because of Company D’s characteristics. Different results and behaviors in the risk-return profile can be obtained by changing the premises.

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The power of suppliers In some cases, powerful suppliers can create more value for themselves by charging higher prices, limiting quality or services, or shifting costs to industry participants. In some other cases, suppliers receive strong pressure from customers to continuously reduce costs and improve service level. In any case, an industry’s supplier power of bargaining with their customers impacts in the industry’s expected return and risk. At the same time, suppliers’ costs and negotiating power can be not evenly distributed among industry’s competitors. This is, market leaders will have better negotiating conditions (for example, because of sales share in the market) than the other competitors. So, market leaders could put pressure on costs keeping service levels agreements to limit risks. Additionally, industries with very profitable perspectives can trigger strong suppliers to enter the industry. This case would be the one presented in the previous section.

Figure 14. Changes in risk-return relationship for the industry with strengthening power of suppliers..

Figure 14 shows the example of how the industry’s risk-return relationship modifies when Company B’s supplier put pressure on the company’s costs. Let’s say that Company A position (risk-return values) is not affected because its leading position in the market. Considering that the risks remain the same, an increase in costs from suppliers in Company B translates in the current industry’s position moving down, hence reducing the industry’s profitability. The power of buyers Customers can force prices down by playing industry participants off against one another. Besides, demanding better quality or more service can also drive up costs, all at the expense of industry profitability. Buyers can be powerful if they have negotiating leverage relative to industry participants, especially if they are price sensitive. Figure 15 shows an example of the changes in the industry’s risk-return relationship and in its current position in risk-return terms when buyers can drive prices down. Putting pressure on prices, without changing costs, lowers expected returns for all players in the industry. In this case, Company A depicts a smaller sensibility with prices than Company B, whose expected return suffers the greatest reduction.

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Figure 15. Changes in risk-return relationship for the industry with strengthening power of buyers.

The above mentioned changes make the industry’s profitability go down and the industry’s risk-return relationship be modified. The threat of substitutes A substitute can produce a great change in the expected return (profitability) for an industry by a different means. When the threat of substitutes is high, industry profitability goes down because of diminishing sales or even because of putting a ceiling on prices. If an industry does not distance itself from substitutes through product performance, marketing, or other means, it will suffer in terms of profitability. So, substitutes can have, except for differentiation strategies among participants in the industry, a general impact on players’ expected return, driving down the expected profitability for the industry. The changes in the industry’s risk-return relationship would be similar to those resulting from an increase in the power of suppliers (see Figure 15) Rivalry among existing competitors Rivalry among existing competitors takes many familiar forms, including price discounting, new product introductions, advertising campaigns, and service improvements. High rivalry limits the profitability of an industry. The degree to which rivalry drives down an industry’s profit potential depends, first, on the intensity with which companies compete and, second, on the basis on which they compete. The strength of rivalry reflects not just the intensity of competition but also the basis of competition. The dimensions on which competition takes place, and whether rivals converge to compete on the same dimensions, have a major influence on profitability and the industry’s risk. Rivalry is especially destructive to profitability if it gravitates solely to price because price competition transfers profits directly from an industry to its customers. Price cuts are usually easy for competitors to see and match, making successive rounds of retaliation likely.

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