1. 11-0 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Corporate Finance Ross  Westerfield  Jaffe Sixth Edition 11 Chapter Eleven An Alternative View of Risk and Return: The APT Prepared by Gady Jacoby University of Manitoba and Sebouh Aintablian American University of Beirut

2. 11-1 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Chapter Outline 11.1 Factor Models: Announcements, Surprises, and Expected Returns 11.2 Risk: Systematic and Unsystematic 11.3 Systematic Risk and Betas 11.4 Portfolios and Factor Models 11.5 Betas and Expected Returns 11.6 The Capital Asset Pricing Model and the Arbitrage Pricing Theory 11.7 Parametric Approaches to Asset Pricing 11.8 Summary and Conclusions

3. 11-2 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited 11.1 Factor Models: Announcements, Surprises, and Expected Returns The return on any security consists of two parts. 1) the expected or normal return: the return that shareholders in the market predict or expect 2) the unexpected or risky return: the portion that comes from information that will be revealed. Examples of relevant information: –Statistics Canada figures (e.g., GNP) –A sudden drop in interest rates –News that the company’s sales figures are higher than expected

4. 11-3 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited 11.1 Factor Models: Announcements, Surprises, and Expected Returns A way to write the return on a stock in the coming month is:

5. 11-4 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited 11.1 Factor Models: Announcements, Surprises, and Expected Returns Any announcement can be broken down into two parts, the anticipated or expected part and the surprise or innovation: Announcement = Expected part + Surprise. The expected part of any announcement is part of the information the market uses to form the expectation, R of the return on the stock. The surprise is the news that influences the unanticipated return on the stock, U.

6. 11-5 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited 11.2 Risk: Systematic and Unsystematic A systematic risk is any risk that affects a large number of assets, each to a greater or lesser degree. An unsystematic risk is a risk that specifically affects a single asset or small group of assets. Unsystematic risk can be diversified away. Examples of systematic risk include uncertainty about general economic conditions, such as GNP, interest rates, or inflation. On the other hand, announcements specific to a company, such as a gold mining company striking gold, are examples of unsystematic risk.

7. 11-6 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited 11.2 Risk: Systematic and Unsystematic Systematic Risk; m Nonsystematic Risk;  n  Total risk; U We can break down the risk, U, of holding a stock into two components: systematic risk and unsystematic risk: 

8. 11-7 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited 11.2 Risk: Systematic and Unsystematic Systematic risk is referred to as market risk. m influences all assets in the market to some extent. Is specific to the company and unrelated to the specific risk of most other companies.

9. 11-8 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited 11.3 Systematic Risk and Betas The beta coefficient, , tells us the response of the stock’s return to a systematic risk. In the CAPM,  measured the responsiveness of a security’s return to a specific risk factor, the return on the market portfolio. We shall now consider many types of systematic risk.

10. 11-9 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited 11.3 Systematic Risk and Betas For example, suppose we have identified three systematic risks on which we want to focus: 1.Inflation 2. GDP growth 3.The dollar-pound spot exchange rate, S($,£) Our model is:

11. 11-10 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Systematic Risk and Betas: Example Suppose we have made the following estimates:  I = -2.30  GDP = 1.50  S = 0.50. Finally, the firm was able to attract a “superstar” CEO and this unanticipated development contributes 1% to the return.

12. 11-11 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Systematic Risk and Betas: Example We must decide what surprises took place in the systematic factors. If it was the case that the inflation rate was expected to be 3%, but in fact was 8% during the time period, then F I = Surprise in the inflation rate = actual – expected = 8% - 3% = 5%

13. 11-12 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Systematic Risk and Betas: Example If it was the case that the rate of GDP growth was expected to be 4%, but in fact was 1%, then F GDP = Surprise in the rate of GDP growth = actual – expected = 1% - 4% = -3%

14. 11-13 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Systematic Risk and Betas: Example If it was the case that dollar-pound spot exchange rate, S($,£), was expected to increase by 10%, but in fact remained stable during the time period, then F S = Surprise in the exchange rate = actual – expected = 0% - 10% = -10%

15. 11-14 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Systematic Risk and Betas: Example Finally, if it was the case that the expected return on the stock was 8%, then

16. 11-15 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited 11.4 Portfolios and Factor Models Now let us consider what happens to portfolios of stocks when each of the stocks follows a one-factor model. We will create portfolios from a list of N stocks and will capture the systematic risk with a 1-factor model. The i th stock in the list have returns:

17. 11-16 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Relationship Between the Return on the Common Factor & Excess Return Excess return The return on the factor F If we assume that there is no unsystematic risk, then  i = 0

18. 11-17 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Relationship Between the Return on the Common Factor & Excess Return Excess return The return on the factor F If we assume that there is no unsystematic risk, then  i = 0

19. 11-18 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Relationship Between the Return on the Common Factor & Excess Return Excess return The return on the factor F Different securities will have different betas

20. 11-19 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Portfolios and Diversification We know that the portfolio return is the weighted average of the returns on the individual assets in the portfolio:

21. 11-20 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Portfolios and Diversification The return on any portfolio is determined by three sets of parameters: In a large portfolio, the third row of this equation disappears as the unsystematic risk is diversified away. 1.The weighed average of expected returns. 2.The weighted average of the betas times the factor. 3.The weighted average of the unsystematic risks.

22. 11-21 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Portfolios and Diversification So the return on a diversified portfolio is determined by two sets of parameters: 1.The weighed average of expected returns. 2.The weighted average of the betas times the factor F. In a large portfolio, the only source of uncertainty is the portfolio’s sensitivity to the factor.

23. 11-22 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited 11.5 Betas and Expected Returns The return on a diversified portfolio is the sum of the expected return plus the sensitivity of the portfolio to the factor.

24. 11-23 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Relationship Between  & Expected Return The relevant risk in large and well-diversified portfolios is all systematic, because unsystematic risk is diversified away. If shareholders are ignoring unsystematic risk, only the systematic risk of a stock can be related to its expected return.

25. 11-24 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Relationship Between  & Expected Return Expected return  A B C D SML

26. 11-25 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited 11.6 The Capital Asset Pricing Model and the Arbitrage Pricing Theory APT applies to well diversified portfolios and not necessarily to individual stocks. With APT it is possible for some individual stocks to be mispriced---not lie on the SML. APT is more general in that it gets to an expected return and beta relationship without the assumption of the market portfolio. APT can be extended to multifactor models.

27. 11-26 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited Multi-factor APT Example: A Canadian study (Otuteye, CIR 1991) with five factors: 1.the rate of growth in industrial production 2.the changes in the slope of the term structure of interest rates 3.the default risk premium for bonds 4.inflation 5.The value-weighted return on the market portfolio (TSE 300)

28. 11-27 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited 11.7 Empirical Approaches to Asset Pricing Both the CAPM and APT are risk-based models. There are alternatives. Empirical methods are based less on theory and more on looking for some regularities in the historical record. Be aware that correlation does not imply causality. Related to empirical methods is the practice of classifying portfolios by style e.g., –Value portfolio –Growth portfolio

29. 11-28 McGraw-Hill Ryerson © 2003 McGraw–Hill Ryerson Limited 11.8 Summary and Conclusions The APT assumes that stock returns are generated according to factor models such as:  As securities are added to the portfolio, the unsystematic risks of the individual securities offset each other. A fully diversified portfolio has no unsystematic risk.  The CAPM can be viewed as a special case of the APT.  Empirical models try to capture the relations between returns and stock attributes that can be measured directly from the data without appeal to theory.