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Measuring the Ex Ante Beta 2039 Calculating a Beta Coefficient Using Ex Ante Returns Ex Ante means forecast… You would use ex ante return data if historical rates of return are somehow not indicative of the kinds of returns the company will produce in the future. A good example of this is Air Canada or American Airlines, before and after September 11, 2001. After the World Trade Centre terrorist attacks, a fundamental shift in demand for air travel occurred. The historical returns on airlines are not useful in estimating future returns. In this slide set The beta coefficient The formula approach to beta measurement using ex ante returns – – – – – Ex ante returns Finding the expected return Determining variance and standard deviation Finding covariance Calculating and interpreting the beta coefficient The Beta Coefficient Under the theory of the Capital Asset Pricing Model total risk is partitioned into two parts: – – Systematic risk Unsystematic risk Total Risk of the Investment Systematic Risk Unsystematic Risk Systematic risk is the only relevant risk to the diversified investor The beta coefficient measures systematic risk The Beta Coefficient – the formula Covariance of Returns between th e stock and the market Variance of the Market Returns Cov(R s R M ) Beta Var(R M ) Beta The Term – “Relevant Risk” What does the term “relevant risk” mean in the context of the CAPM? – It is generally assumed that all investors are wealth maximizing risk averse people – It is also assumed that the markets where these people trade are highly efficient – In a highly efficient market, the prices of all the securities adjust instantly to cause the expected return of the investment to equal the required return – When E(r) = R(r) then the market price of the stock equals its inherent worth (intrinsic value) – In this perfect world, the R(r) then will justly and appropriately compensate the investor only for the risk that they perceive as relevant…hence investors are only rewarded for systematic risk…risk that can be diversified away IS…and prices and returns reflect ONLY systematic risk. The Proportion of Total Risk that is Systematic Each investor varies in the percentage of total risk that is systematic Some stocks have virtually no systematic risk. – – Such stocks are not influenced by the health of the economy in general…their financial results are predominantly influenced by company-specific factors An example is cigarette companies…people consume cigarettes because they are addicted…so it doesn’t matter whether the economy is healthy or not…they just continue to smoke Some stocks have a high proportion of their total risk that is systematic – – Returns on these stocks are strongly influenced by the health of the economy Durable goods manufacturers tend to have a high degree of systematic risk The Formula Approach to Measuring the Beta Cov(R s R M ) Beta Var(R M ) You need to calculate the covariance of the returns between the stock and the market…as well as the variance of the market returns. To do this you must follow these steps: • Calculate the expected returns for the stock and the market • Using the expected returns for each, measure the variance and standard deviation of both return distributions • Now calculate the covariance • Use the results to calculate the beta Ex ante return data (a sample) An set of estimates of possible returns and their respective probabilities looks as follows: Possible Future State of the Economy Probability Boom Normal Recession 0.25 0.5 0.25 Possible Possible Returns on Returns on the Stock the Market 0.28 0.17 -0.14 0.2 0.11 -0.04 The Total of the Probabilities must equal 100% This means that we have considered all of the possible outcomes in this discrete probability distribution Possible Future State of the Economy Probability Boom Normal Recession 0.25 0.50 0.25 1.00 Possible Possible Returns on Returns on the Stock the Market 0.28 0.17 -0.14 0.2 0.11 -0.04 Measuring Expected Return on the stock From Ex Ante Return Data The expected return is weighted average returns from the given ex ante data (1) (2) (3) (4) Possible Future State of the Economy Probability Boom Normal Recession 0.25 0.50 0.25 Possible Returns on the Stock (4) = (2)*(3) 0.28 0.17 -0.14 Expected return on the stock = 0.07 0.085 -0.035 0.12 Measuring Expected Return on the market From Ex Ante Return Data The expected return is weighted average returns from the given ex ante data (1) (2) (3) (4) Possible Future State of the Economy Probability Boom Normal Recession 0.25 0.50 0.25 Possible Returns on the Market (4) = (2)*(3) 0.2 0.11 -0.04 0.05 0.055 -0.01 Expected return on the market = 0.095 Measuring Variances, Standard Deviations from Ex Ante Return Data Using the expected return, calculate the deviations away from the mean, square those deviations and then weight the squared deviations by the probability of their occurrence. Add up the weighted and squared deviations from the mean and you have found the variance! (1) (2) Possible Future State of the Economy Probability Boom Normal Recession 0.25 0.50 0.25 (3) (4) Possible Returns on the Stock (4) = (2)*(3) 0.28 0.17 -0.14 Expected return on the stock = (5) Deviations (7) Squared Deviations Weighted and Squared Deviations 0.0256 0.0064 0.0025 0.00125 0.0676 0.0169 0.12 Variance = 0.02455 Standard Deviation = 0.156684 0.07 0.085 -0.035 0.16 0.05 -0.26 (6) Measuring Variances, Standard Deviations from Ex Ante Return Data Now do this for the possible returns on the market (1) (2) Possible Future State of the Economy Probability Boom Normal Recession 0.25 0.50 0.25 (3) (4) Possible Returns on the Market (4) = (2)*(3) 0.2 0.11 -0.04 Expected return on the market (5) Deviations (6) (7) Squared Deviations Weighted and Squared Deviations 0.105 0.011025 0.002756 0.015 0.000225 0.000113 -0.135 0.018225 0.004556 0.095 Variance = 0.007425 Standard Deviation = 0.086168 0.05 0.055 -0.01 Covariance The formula for the covariance between the returns on the stock and the returns on the market is: n Cov( Rs RM ) Pt ( Rs R s )( RM R m ) t 1 Covariance is an absolute measure of the degree of ‘co-movement’ of returns. The correlation coefficient is also a measure of the degree of co-movement of returns…but it is a relative measure…this is why it is on a scale from +1 to -1. Correlation Coefficient The formula for the correlation coefficient between the returns on the stock and the returns on the market is: Corr ( Rs RM ) Cov( Rs RM ) s M The correlation coefficient will always have a value in the range of +1 to 1. Measuring Covariances and Correlation Coefficients from Ex Ante Return Data Using the expected return (mean return) and given data measure the deviations for both the market and the stock and multiply them together with the probability of occurrence…then add the products up. (1) Possible Future State of the Economy Boom Normal Recession (2) (3) Prob. Possible Returns on the Stock 0.25 28.0% 0.50 17.0% 0.25 -14.0% Expected return on the stock = (4) (4) = (2)*(3) 0.07 0.085 -0.035 12.0% (5) (6) Possible Returns on the Market (6)=(2)*(5) 20.0% 11.0% -4.0% 0.05 0.055 -0.01 9.5% (7) (8) "(9) Deviations from the mean for the stock Deviations from the mean for the market (8)=(2)(6)(7 ) 16.0% 10.5% 5.0% 1.5% -26.0% -13.5% Covariance = 0.0042 0.000375 0.008775 0.01335 The Beta Measured Using Ex Ante Return Data Now you can plug in the covariance and the variance of the returns on the market to find the beta of the stock: Cov(R s R M ) .01335 Beta 1.8 Var(R M ) .007425 A beta that is greater than 1 means that the investment is aggressive…its returns are more volatile than the market as a whole. If the market returns were expected to go up by 10%, then the stock returns are expected to rise by 18%. If the market returns are expected to fall by 10%, then the stock returns are expected to fall by 18%. Lets Prove the Beta of the Market is 1.0 Let us assume we are comparing the possible market returns against itself…what will the beta be? (1) (2) (3) Possible Possible Future Returns State of the on the Economy Prob. Market Boom Normal Recession 0.25 0.50 0.25 20.0% 11.0% -4.0% Expected return on the market = (4) (4) = (2)*(3) 0.05 0.055 -0.01 9.5% (5) (6) Possible Returns on the Market (6)=(2)*(5) 20.0% 11.0% -4.0% 0.05 0.055 -0.01 9.5% (6) (7) (8) Deviations from the mean for the stock Deviations from the mean for the market (8)=(2)(6)(7 ) 10.5% 10.5% 1.5% 1.5% -13.5% -13.5% Covariance = 0.002756 0.000113 0.004556 0.007425 Since the variance of the returns on the market is = .007425 …the beta for the market is indeed equal to 1.0 !!! Proving the Beta of Market = 1 If you now place the covariance of the market with itself value in the beta formula you get: Cov(R M R M ) .007425 Beta 1.0 Var(R M ) .007425 How Do We use Expected and Required Rates of Return? Once you have estimated the expected and required rates of return, you can plot them on a SML and see if the stock is under or overpriced. % Return E(R) = 5.0% R(RX) = 4.76% SML E(RM)= 4.2% Risk-free Rate = 3% BM= 1.0 BX = 1.464 Since E(r)>R(r) the stock is underpriced. How Do We use Expected and Required Rates of Return? The stock is fairly priced if the expected return = the required return. This is what we would expect to see ‘normally’ or most of the time. % Return E(RX) = R(RX) 4.76% SML E(RM)= 4.2% Risk-free Rate = 3% BM= 1.0 BX = 1.464 Use of the Forecast Beta We can use the forecast beta, together with an estimate of the riskfree rate and the market premium for risk to calculate the investor’s required return on the stock using the CAPM: Required Return R f β j [E(rM ) R f ] Conclusions Analysts can make estimates or forecasts for the returns on stock and returns on the market portfolio. Those forecasts can be analyzed to estimate the beta coefficient for the stock. The required return on a stock can be calculated using the CAPM – but you will need the stock’s beta coefficient, the expected return on the market portfolio and the risk-free rate.