Price change: cash flow or discount rate?

1. Price change: cash flow or discount rate?

2. Why do prices vary so much?

4. Introduction • 1970s view: • Expected returns don’t move much over time — stocks are unpredictable. • Prices move on news of cashflow (dividend). • CAPM works pretty well. • Beta derives from the covariance of cashflows with market cashflows.

5. Introduction • All are dramatically different now. 1. Expected returns move a lot over time — stocks are predictable. (Long run, business cycle correlation) 2. Prices move on news of discount rate changes. 3. We understand the cross-section with multifactor models. (a) A larger number of characteristics other than beta are associated with expected returns (b) To the extent we understand those patterns, expected returns line up with nonmarket betas

6. Introduction 4. Betas derive from the covariance of discount rates with market discount rates. 5. Facts are pushing us to the “risk premium” view of the world, as opposed to the “constant expected return, cashflow” view from the 1970s. 6. These are the facts underlying theoretical modeling.

7. Old Facts

8. New View of facts

9. Why D/P forecasts long horizon returns?

10. Predictability of Dividend growth • P/D “should” forecast a dividend rise. Price high relative to current dividends should mean that future dividends will be higher. • Dividend growth is not predictable! The point estimates are the “wrong” sign!

11. Do “low” prices mean / reveal high returns?

12. “Predictability” ↔ time-varying expected returns

13. Inefficiency? • Does this mean markets are “inefficient”? Is this an invitation to “buy low and sell high?” • Not necessarily. Time varying risk premia are possible. • Are expected returns higher in good times or in bad times? (Bad, why?) business-cycle related time-varying risk premium is certainly possible.

14. Campbell-Shiller linearization of the one-period return • 小写字母代表大写字母的对数 • Intuition: higher returns come from higher prices (higher valuations p-d), lower initial prices, or higher dividends.

15. The Campbell-Shiller present value identity • If both Δd and r are unforecastable, p−d is constant. If p-d varies at all, something must be forecastable. The fact that d-p varies means that we do not live in an iid world. (Plus no bubbles)

17. A Pervasive Phenomenon • Stocks. Dividend yields forecast returns, not dividend growth. • Treasuries. A rising yield curve signals better 1-year returns for long-term bonds, not higher future interest rates. Fed fund futures signal returns, not changes in the funds rate. • Bonds. Much variation in credit spreads over time and across firms or categories signals returns, not default probabilities. • Foreign exchange. International interest rate spreads signal returns, not exchange rate depreciation. • Houses. High price/rent ratios signal low returns, not rising rents or prices that rise forever.

19. Common element: business cycle • low prices, high returns in recessions. High prices, low returns in booms

20. Multivariate Challenges: More variables

21. Understanding prices: short and long-run forecasts • Cay:消费财富比率

22. The cross section 5

23. Value effect and factor

24. Value (size, and bond factors)

25. The Multidimensional Challenge • (Market, value, size), momentum, accruals, equity issues, beta-arbitrage, credit risk, bond & equity market timing, carry trade, put writing, “liquidity provision,”... 1. Which of these are independently important for E(Re )? (“multiple regression”) 2. Does E(Re ) spread correspond to new factors? 3. Do we need all the new factors? Or again, fewer factors than E(Re ) characteristics? 4. Why do prices move? –Long run. • How to approach such a highly multidimensional problem?

26. Asset Pricing on Characteristics/Uni…cation 1. Portfolio sorts are really cross-sectional regressions

27. Asset Pricing on Characteristics/Uni…cation

28. Theory classifi…cation

29. Consumption/habits

30. Investment and Q

31. Challenges for theories • Pervasive, coordinated risk premium in all markets, especially unintermediated • Mean returns are associated with comovement. • Strong correlation with macroeconomics

32. “Arbitrages”

33. “Arbitrages”

34. Price and volume in the tech “bubble

35. Bonds： –a cautionary tale

36. Stocks (your endowment) in the crisis

37. Alphas, betas, and performance evaluation • A hedge fund manager said, “‘Exotic beta’ is my alpha. I understand those systematic factors and know how to trade them. My clients don’t.”

38. Conclusion • Discount rates vary over time and across assets a lot more than you thought • Empirical: how. Theoretical: why. Applications: at all. • We’’ve only started • How do you ask the right question?