Chapter Thirteen Return, Risk, and the Security Market Line © 2003 The McGraw-Hill Companies, Inc. All rights reserved. 13.1 Key Concepts and Skills • • • • • • Know how to calculate expected returns Understand the impact of diversification Understand the systematic risk principle Understand the security market line Understand the risk-return trade-off Be able to use the Capital Asset Pricing Model (CAPM) Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.2 Chapter Outline • • • • • • • • Expected Returns and Variances Portfolios Risk: Systematic and Unsystematic Diversification and Portfolio Risk Systematic Risk and Beta The Security Market Line The SML and the Cost of Capital: A Preview Arbitrage Pricing Theory Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.3 Expected Returns 13.1 • Expected returns are based on the probabilities of possible outcomes • In this context, “expected” means average if the process is repeated many times • The “expected” return does not even have to be a possible return • n = total number of states, p = probability that state i occurs, R = return in state i n E ( R) pi Ri i 1 Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.4 Expected Returns – Example 1 • Suppose you have predicted the following returns for stocks C and T in three possible states of nature. What are the expected returns? – – – – State Boom Normal Recession Probability 0.3 0.5 0.2 C 0.15 0.10 0.02 T 0.25 0.20 0.01 • RC = .3(.15) + .5(.10) + .2(.02) = .099 = 9.9% • RT = .3(.25) + .5(.20) + .2(.01) = .177 = 17.7% Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.5 Variance and Standard Deviation • Variance (σ²) and standard deviation (σ) still measure the volatility of returns • You can use unequal probabilities for the entire range of possibilities • Weighted average of squared deviations n σ 2 pi ( Ri E ( R)) 2 i 1 Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.6 Variance and Standard Deviation – Example 1 • Consider the previous example. What is the variance and standard deviation for each stock? • Stock C 2 = .3(.15-.099)2 + .5(.1-.099)2 + .2(.02-.099)2 = .002029 = .045 • Stock T 2 = .3(.25-.177)2 + .5(.2-.177)2 + .2(.01-.177)2 = .007441 = .0863 Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.7 Portfolios 13.2 • A portfolio is a collection of assets • An asset’s risk and return is important in how it affects the risk and return of the portfolio • The risk-return trade-off for a portfolio is measured by the portfolio expected return and standard deviation, just as with individual assets • The sum of risks of individual assets does not equal the risk of the portfolio Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.8 Example: Portfolio Weights • Suppose you have $15,000 to invest and you have purchased securities in the following amounts. What are your portfolio weights in each security? – – – – $2000 of ABC $3000 of DEF $4000 of GHI $6000 of JKL •ABC: 2/15 = .133 •DEF: 3/15 = .2 •GHI: 4/15 = .267 •JKL: 6/15 = .4 Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.9 Portfolio Expected Returns • The expected return of a portfolio is the weighted average of the expected returns for each asset in the portfolio • w = the weight of asset j in the portfolio m E ( RP ) w j E ( R j ) j 1 Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.10 Example: Expected Portfolio Returns • Consider the portfolio weights computed previously. If the individual stocks have the following expected returns, what is the expected return for the portfolio? – ABC: 19.65% – DEF: 8.96% – GHI: 9.67% – JKL: 8.13% • E(RP) = .133(19.65) + .2(8.96) + .267(9.67) + .4(8.13) = 10.24% Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.11 Portfolio Variance • Compute the portfolio return for each state: RP = w1R1 + w2R2 + … + wmRm • Compute the expected portfolio return using the same formula as for an individual asset • Compute the portfolio variance and standard deviation using the same formulas as for an individual asset Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.12 Example: Portfolio Variance • Consider the following information – – – – Invest 50% of your money in Asset A State Probability A B Portfolio Boom .5 70% 10% 7.3% Bust .5 -20% 30% 12.8% • What is the expected return and standard deviation for each asset? • What is the expected return and standard deviation for the portfolio? Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.13 Portfolio Variance Asset A E( R)= 0.5 (0.7) + 0.5 (-0.2) = 0.35 – 0.1 = 0.25 σ²= 0.5 (0.7 - 0.25)² + 0.5 (-0.2 - 0.25)² = 0.2025 σ= 0.45 Asset B E( R)= 0.5 (0.1) + 0.5 (0.3) = 0.05 + 0.15 = 0.2 σ²= 0.5 (0.1 - 0.2)² + 0.5 (0.3 - 0.2)² = 0.01 σ= 0.1 Portfolio E( R)= 0.5 (0.25) + 0.5 (0.2) = 0.125 + 0.1 = 0.225 σ²= 0.5 (0.25 – 0.225)² + 0.5 (0.2 – 0.225)² = 0.000625 σ= 0.025 Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.14 Another Way to Calculate Portfolio Variance • Portfolio variance can also be calculated using the following formula: P2 xL2 L2 xU2 U2 2 xL xU CORR L ,U L U Assuming that the correlatio n between A and B is - 1.00, we have P2 0.52 0.2025 0.52 0.01 2 0.5 0.5 -1.00 0.45 0.1 P2 0.030625 Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.15 Figure 13.1 – Different Correlation Coefficients Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.16 Figure 13.1 – Different Correlation Coefficients Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.17 Figure 13.1 – Different Correlation Coefficients Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.18 Figure 13.2 – Graphs of Possible Relationships Between Two Stocks Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.19 Diversification • There are benefits to diversification whenever the correlation between two stocks is less than perfect (p < 1.0) • Figure 13.4 Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.20 Terminology • Feasible set (also called the opportunity set) – the curve that comprises all of the possible portfolio combinations • Efficient set – the portion of the feasible set that only includes the efficient portfolio (where the maximum return is achieved for a given level of risk, or where the minimum risk is accepted for a given level of return) • Minimum Variance Portfolio – the possible portfolio with the least amount of risk Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.21 Quick Quiz II • Consider the following information – – – – State Boom Normal Recession Probability .25 .60 .15 X 15% 10% 5% Z 10% 9% 10% • What is the expected return and standard deviation for a portfolio with an investment of $6000 in asset X and $4000 in asset Y? Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.22 Systematic Risk 13.4 • Risk that cannot be diversified away through portfolio formation (rewarded with return) • Risk factors that affect a large number of assets • Also known as non-diversifiable risk or market risk • Includes such things as changes in GDP, inflation, interest rates, etc. Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.23 Unsystematic Risk • Risk that can be diversified away through portfolio formation (no reward of return) • Risk factors that affect a limited number of assets • Also known as diversifiable risk, unique risk and asset-specific risk • Includes such things as labor strikes, shortages, etc. Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.24 Diversification 13.5 • Portfolio diversification is the investment in several different asset classes or sectors • Diversification is not just holding a lot of assets • For example, if you own 50 internet stocks, you are not diversified • However, if you own 50 stocks that span 20 different industries, then you are diversified Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.25 Table 13.8 – Portfolio Diversification Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.26 The Principle of Diversification • Diversification can substantially reduce the variability of returns without an equivalent reduction in expected returns • This reduction in risk arises because worse than expected returns from one asset are offset by better than expected returns from another • However, there is a minimum level of risk that cannot be diversified away and that is the systematic portion Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.27 Figure 13.6 – Portfolio Diversification Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.28 Diversifiable (Unsystematic) Risk • The risk that can be eliminated by combining assets into a portfolio • Synonymous with unsystematic, unique or asset-specific risk • If we hold only one asset, or assets in the same industry, then we are exposing ourselves to risk that we could diversify away • The market will not compensate investors for assuming unnecessary risk Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.29 Total Risk • Total risk = systematic risk + unsystematic risk • The standard deviation of returns is a measure of total risk • For well diversified portfolios, unsystematic risk is very small • Consequently, the total risk for a diversified portfolio is essentially equivalent to the systematic risk Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.30 Systematic Risk Principle 13.6 • There is a reward for bearing risk • There is not a reward for bearing risk unnecessarily • The expected return on a risky asset depends only on that asset’s systematic risk since unsystematic risk can be diversified away Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.31 Measuring Systematic Risk • How do we measure systematic risk? – We use the beta coefficient to measure systematic risk. i is the percent change in asset i’s return for a 1% change in the market portfolio’s return. • What does beta tell us? – A beta of 1 implies the asset has the same systematic risk as the overall market – A beta < 1 implies the asset has less systematic risk than the overall market – A beta > 1 implies the asset has more systematic risk than the overall market Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.32 Figure 13.7 – High and Low Betas Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.33 Table 13.10 – Beta Coefficients for Selected Companies Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.34 Example: Portfolio Betas • Consider the previous example with the following four securities – Security – ABC – DEF – GHI – JKL Weight .133 .2 .267 .4 Beta 3.69 0.64 1.64 1.79 • What is the portfolio beta? • .133(3.69) + .2(.64) + .267(1.64) + .4(1.79) = 1.773 Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.35 Beta and the Risk Premium 13.7 • Remember that the risk premium = expected return – risk-free rate • The higher the beta, the greater the risk premium should be • Can we define the relationship between the risk premium and beta so that we can estimate the expected return? – YES! Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.36 Figure 13.8A – Portfolio Expected Returns and Betas Rf Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.37 Reward-to-Risk Ratio: Definition and Example • The reward-to-risk ratio is the slope of the line illustrated in the previous example – Slope = (E(RA) – Rf) / (A – 0) – Reward-to-risk ratio for previous example = (20 – 8) / (1.6 – 0) = 7.5 – f = 0 • What if an asset has a reward-to-risk ratio of 8 (implying that the asset plots above the line)? • What if an asset has a reward-to-risk ratio of 7 (implying that the asset plots below the line)? Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.38 Market Equilibrium • In equilibrium, all assets and portfolios must have the same reward-to-risk ratio and they all must equal the reward-to-risk ratio for the market E ( RA ) R f A E ( RM R f ) M Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.39 Security Market Line • The security market line (SML) is the representation of market equilibrium • The slope of the SML is the reward-to-risk ratio: (E(RM) – Rf) / M • But since the beta for the market is ALWAYS equal to one, the slope can be rewritten • Slope = E(RM) – Rf = market risk premium Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.40 Figure 13.11 – Security Market Line Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.41 The Capital Asset Pricing Model (CAPM) • The capital asset pricing model defines the relationship between risk and return • E(RA) = Rf + A(E(RM) – Rf) • If we know an asset’s systematic risk, we can use the CAPM to determine its expected return • This is true whether we are talking about financial assets or physical assets Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.42 Factors Affecting Expected Return • Pure time value of money – measured by the risk-free rate • Reward for bearing systematic risk – measured by the market risk premium • Amount of systematic risk – measured by beta Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.43 Example - CAPM • Consider the betas for each of the assets given earlier. If the risk-free rate is 4.5% and the market risk premium is 8.5%, what is the expected return for each? Security Beta Expected Return ABC 3.69 4.5 + 3.69(8.5) = 35.865% DEF .64 4.5 + .64(8.5) = 9.940% GHI 1.64 4.5 + 1.64(8.5) = 18.440% JKL 1.79 4.5 + 1.79(8.5) = 19.715% Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.44 Example • Calculate the market risk premium on the asset i, and the expected return on asset i using the following information: i = 1.5, E(RM )= 15%, Rf =5% • Market risk premium = 15% - 5% = 10% • Risk premium on asset i = 1.5(15% - 5%) = 15% • Expected return on asset i = 5% + 1.5(15% - 5%) = 20% Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.45 Arbitrage Pricing Theory (APT) 13.9 • The major advantage of the APT model is that it can handle multiple factors not included in CAPM. • Like CAPM, the APT model assumes that stock returns depend on both expected and unexpected returns. • Unlike CAPM, the unexpected return in the APT model is related to several market factors. • Assuming those factors are unanticipated changes in inflation, GNP, and interest rates, the expected return would be written as: E( R) RF E( R1 RF ) 1 E( R2 RF ) 2 ... E( RK RF ) K Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved. 13.46 Summary 13.10 • There is a reward for bearing risk • Total risk has two parts: systematic risk and unsystematic risk • Unsystematic risk can be eliminated through diversification • Systematic risk cannot be eliminated and is rewarded with a risk premium • Systematic risk is measured by the beta • The equation for the SML is the CAPM, and it determines the equilibrium required return for a given level of risk Copyright © 2005 McGraw-Hill Ryerson Limited. All rights reserved.