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Consumption-Based Asset Pricing After 25 Years Douglas T. Breeden* *Dean and William W. Priest Professor of Finance, Duke University, Fuqua School of Business Reference notes, tables and graphs for June 20, 2005 Western Finance Association Talk

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2 Perspective and Goal of the Paper Rip Van Winkle (Austin Powers?) academic career: Intertemporal consumption, portfolio theory and asset pricing research 1976-1989. Left Duke 1992 to build Smith Breeden, did applied research on mortgages and corporate bonds. Returned to academia in 2000. Dean at Duke 2001-present, with usual IQ drop of Dean. Paper will be a follow-up look at the numbers for some major results of consumption-based asset pricing that I began working on 25 years ago. There are many areas for future research that we’ll see.

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3 Preceding Work on Intertemporal Asset Pricing and the Term Structure Markowitz (1952), Sharpe (1964), Lintner (1965) and Mossin (1966) developed diversification and the market-based CAPM. Samuelson (1969), Merton (1969, 1971, 1973), Hakannson (1970), Fama (1970), Pye (1972) and Long (1975) pioneered intertemporal investments. Hirshleifer (1970 book), Cox, Ingersoll and Ross (1985) and Garman (1976) on term structure.

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4 First Decade of Selected Research on Consumption-Based Asset Pricing Rubinstein (1976 BJEMS), Breeden-Litzenberger (1978 JB), Lucas (1978 Ec), Breeden (1979 JFE) Hall (1978), Breeden (1980), Stulz (1981), Grossman-Shiller (1982), Marsh-Rosenfeld (1982), Mankiw-Shapiro (1986), Mehra-Prescott (1985), Wheatley (1986), Hansen-Singleton (1982, 1983), Ferson (1983), Breeden (1984),Gibbons-Ferson. Chen, Roll and Ross (1986), Grossman, Melino, Shiller (1987), Campbell-Shiller (1988), Breeden, Gibbons and Litzenberger(1989) Term Structure: Garman (1976), Cox, Ingersoll, Ross (1985), Breeden (1986), Harvey (1988, 1989, 1991), Dunn-Singleton (1986), Sundaresan (1989)

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5 Second Decade of Selected Research on Consumption-Based Asset Pricing Constantinides (1990), Abel (1990), Epstein-Zin(1991), Hansen- Jagannathan (1992), Cochrane(1991,1994,1996), Campbell (1991) Campbell-Shiller(1990), Shanken (1990), Fama (1991), Kandel- Stambaugh(1991),Ferson-Constantinides (1991), Mankiw-Zeldes (1991), Fama-French (1992), Brennan-Schwartz-Lagnado(1997) Heaton (1995), Elton-Gruber-Blake (1995), He-Modest (1995), Constantinides-Duffie (1996), Jagannathan-Wang (1996), Campbell-Cochrane (1999), Campbell-Viceira (1999), Ferson- Harvey(1999)

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6 Third Decade of Selected Research on Consumption-Based Asset Pricing Campbell (2000), Heaton-Lucas (2000), Lettau-Ludwigson (2001a,b), Santos-Veronesi (2001),Brav-Constantinides-Geczy (2002), Wachter (2002), Barberis-Huang-Santos (2003) Verdelhan (2003), Lustig-Verdelhan(2004), Piazzesi-Schneider- Tuzel (2003),Bansal-Yaron(2005), Bansal- Dittmar,Lundbad(2005),Bansal-Dittmar-Kiku (2004) Jagannathan and Wang (2004), Parker-Julliard (2005), Campbell-Vuolteenaho (2004),Hansen-Heaton-Li(2005) In total, 179 articles with “consumption, asset pricing” in the abstract, far more than mentioned here. Apologies.

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7 Consumption Based Asset Pricing Outline of Paper 1. Consumption and marginal utility. 2. Consumption risks of corporate profits & cash flows. Capital budgeting. 3. Consumption betas vs. market betas for industries. 4. Term structure slope and consumption growth. 5. Risk and return and the “Maximum Correlation Portfolio” for consumption. 6. Consumption deviations from wealth and the investment and income opportunity sets. 7. Volatility of family consumption and the “Equity Premium Puzzle.”

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I.Consumption and Marginal Utility

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9 Consumption and Marginal Utility Some likely statistical indicators of times when the marginal utility of $1 is quite high, are the following: 1. Unemployment rate is increasing. 2.Job growth is less rapid than normal. 3. Businesses are failing more often, risky bonds’ yield spreads are high. 4. Banks are charging off more loans.

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10 United States Unemployment Rate EOQ, 1960-Q4 2004

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11 U.S. Real Consumption Growth Last Four Quarters Percent, 1960-2004 Q4

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12 Unemployment Changes (6mo) and Consumption Growth 1959-2004

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13 Unemployment Changes (6mo) and Real S&P 500 Returns: 1959-2004

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14 Unemployment Rate vs. Consumption and Stock Prices 1959-2004 (6 month changes) Independent Variable Slopet-statisticCRSQ S&P 500-0.004-0.65-0.01 Total Real Consumption -0.13-5.560.25 NDS Consumption -0.15-4.580.18

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15 Employment Growth vs. Consumption and Stock Prices 1959-2004 (6 month changes) Employment Growth vs. Consumption and Stock Prices 1959-2004 (6 month changes) Independent Variable Slopet-statisticCRSQ S&P 500-0.000-0.00-0.02 Total Real Consumption 0.313.270.18 NDS Consumption 0.493.680.22

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16 Conclusion on Marginal Utility Consumption’s percentage changes likely represent more correctly changes in the marginal utility of $1 to individuals than do changes in real stock prices. This is no failing of stock prices, for as the present value of future dividends, they should reflect future profit growth and changing risks and risk aversion.

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II. Consumption and Market Risks of Corporate Cash Flows

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18 Remember the Discounted Cash Flow Approach to Valuation? We teach our students to value an asset by discounting expected cash flows at their proper risk-adjusted discount rates. Breeden-Litzenberger (Oct 1978, J. Business) derived risk-adjusted discount rates in a multiperiod economy with power utility, jointly lognormal cash flows. Correct discount rates were derived as the Consumption-based CAPM.

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19 Consumption and Market Risks: Earnings, Cash Flows vs. Stock Prices Major problem applying CCAPM to stock prices is imprecision of consumption beta estimates vs. very precise market betas. Breeden paper presented at the French Finance Ass’n at U. Paris Dauphine on “Capital Budgeting with Consumption” June 1989 showed opposite results for earnings risks. Updated slides follow. Consumption risks are more precisely estimated for earnings than are market risks, making CCAPM use natural for capital budgeting. Just in textbooks, e.g., Elton-Gruber.

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20 NIPA U.S Real BT Earnings Growth vs Total Consumption: Annual Data: 1930-2003 (Excl. 1939-1947)

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21 NIPA U.S Real BT Earnings Growth vs NDS Consumption: Annual Data: 1930-2003 (Excl. 1939-1947)

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22 NIPA U.S Real BT Earnings Growth vs S&P 500 Real Return: Annual Data: 1930-2003 (Excl. 1939-1947)

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23 NIPA U.S Real BT Earnings Growth vs Total Consumption: Postwar Annual Data: 1948-2003

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24 NIPA U.S Real BT Earnings Growth vs S&P 500 Real Return: Postwar Annual Data: 1948-2003

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25 NIPA Profits and Cash Flows: Average RSQ vs. SP500 and Consumption

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26 Conclusion on Cash Flow Risks As we teach our students and in practice, it is both more correct and more intuitive to measure cash flow risks in terms of sensitivity to fluctuations in aggregate real consumption, rather than in terms of their relationship to stock market fluctuations. Of course, if P/E multiples were constant, stock prices would be proportionate to earnings, and 1% higher earnings would give 1% higher stock prices. However, in reality, stocks’ price/earnings multiples fluctuate also with interest rates, economic risk and with growth prospects.

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III. Relative Consumption Betas For Industries Are Different From Their Market Betas

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28 Market Betas vs. Consumption Betas: Estimation Procedure Stock market betas are estimated from industry returns data from Professor Kenneth French’s website, using quarterly data from 1948-2004. Consumption betas are from NIPA “coarse industries” quarterly profit data, using 2-quarter percentage changes in real profits. Actual calculation is of changes in real profits/employee compensation vs. real consumption growth, divided by average profits/employee compensation.

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29 Risks by Industry 1948-2004: Part 1 Market Betas vs. Consumption Betas IndustryBeta vs S&P500 Beta vs TotCons Relative C-Beta Beta vs NdsCon Relative C-Beta Utilities 0.61-0.4-0.10-0.20 Oil 0.76-1.4-0.351.60.40 Food & Bev’ge 0.841.80.452.90.70 U.S. Total 1.004.01.004.31.00 Banks 1.031.20.301.70.40

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30 Risks By Industry 1948-2004: Part 2 Market Betas vs. Consumption Betas IndustryBeta vs SP 500 Beta vs TotCons Relative C-Beta Beta vs NdsCon Relative C-Beta Motor Vehicles 1.0824.56.1015.13.50 Retail 1.09 7.01.709.22.20 Whole- sale 1.102.80.703.60.90 Durables 1.1611.22.8010.42.40 Construc- tion 1.235.21.308.52.00

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31 Market Betas vs. Relative Consumption Betas for Selected Industries Semiannual data 1948-2004. Stock returns on stocks, Profits on Consumption.

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IV. The Term Structure of Interest Rates as a Predictor of Economic Growth

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33 Theory: Slope of the Term Structure Optimally Related to Changes in Real Economic Growth Breeden’s (1986, JFE) article on “Consumption, Production, Inflation and Interest Rates: A Synthesis”, generalized Garman (1976), Rubinstein (1976), Fisher (1907) and Hirshleifer (1970) and derived and illustrated optimal relations of the term structure with expected consumption growth and its volatility. Higher expected consumption growth is consistent with higher real rates. Higher volatility is consistent with lower real rates. With forecasted declines in expected growth, the term structure should have a negative slope. A positive slope should presage an expected strengthening in economic growth.

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34 Tests and Uses of the Term Structure Slope to Forecast Changes in Economic Growth Harvey (JFE 1988, 1989,1991,1993) tested Breeden’s equilibrium model and found it to be quite powerful, forecasting economic growth better than most professional economists and working in many countries. Reflecting this research, in 1996, the slope of the term structure was added as a variable in the Index for Leading Economic Indicators. Dotsey (1998) of the Federal Reserve Bank of Richmond found a negative term structure slope gave 18 true quarterly signals and only 2 false signals of recession in quarterly growth during the 1955-1995 period.

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35 US Yield Curve Inverts Before Last Six US Recessions (5-year US Treasury bond - 3-month US Treasury bill) Source: Campbell R. Harvey, Professor, Duke University % Real annual GDP growth Yield curve slope Recession Correct 2 Recessions Correct Recession Correct Yield curve accurate in recent forecast Recession Correct Annual GDP growth or Yield Curve Data though 1/12/03

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36 Slope of the Term Structure Predicts Real Consumption Growth 1959-2004

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37 Forecasts of Growth from Term Spread Quarterly Data, 1953 : 1978

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38 Forecasts of Growth from Term Spread Quarterly Data, 1979 : 2004

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39 Global Term Structure Slopes: 10 Year-3 Month Ends of Years 1989-2003

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V. Consumption Risk and Returns and the Maximum Correlation Portfolio

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41 Maximum Correlation Portfolio Elements S&P 500, Baa-Treasury Bonds, Term Spread Breeden, Gibbons and Litzenberger (JF, 1989) proved that the CCAPM also holds with regard to betas measured against the maximum correlation portfolio for consumption. Three broad traded markets are for (1) stocks, (2) Government bonds and (3) corporate bonds. Consumption relates to each of these through effects of wealth, the term structure, and relation of credit risk to the economic cycle, respectively.

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42 Maximum Correlation Portfolio Semiannual Data (Dec-Jun) 1960-2004 PCETot Slope PCETot t-stat PCENDS Slope PCENDS t-stat S&P 500.06853.30.05493.55 dBaa -10 Yr Treas (bp sprd) -1.54-3.06-0.85-2.27 Lagged 3Yr-3Mo TS Slope 0.702.74 RSQ=.29 0.392.03 RSQ=.24

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“Consumption Risk and the Cross-Section of Expected Returns”, Parker-Julliard (JPE, 2005) Consumption betas measured by contemporaneous covariances of assets’ returns and consumption growth, fail to explain the dispersion in risk premiums across assets. Parker-Julliard measure ultimate consumption risk as covariation of return with current and future consumption growth. Ultimate consumption betas, therefore, measure the exposure of asset returns to long- run risks in consumption.

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Consumption Risk and Expected Returns Parker-Julliard (JPE, 2005) continued Parker-Julliard argue that ultimate consumption risk measures are much more robust to measurement errors in consumption, as well as slow and costly adjustments of consumption to returns. Using post-war quarterly data on 25 Fama-French portfolios sorted by Book Equity/Market Equity and Size, they show ultimate consumption risk measures are able to explain from 44% to 73% of the variation in expected returns.

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VI. Consumption Deviations from Wealth as a Predictor of Income and Investment Opportunities

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46 Consumption Deviations from Wealth Predict Income and Investment Opportunities Breeden (1984, JET) showed with relative risk aversion >1, investors’ consumption levels are positively related to income and investment opportunities. (If RRA<1, reverse hedging occurs.) In June 1989, at the French Finance Association in Paris and in 1991 at Harvard, Breeden paper on “Capital Budgeting with Consumption”, argued that as consumption optimally is a function of wealth, income and investment opportunities, consumption fluctuations orthogonalized for wealth effects should be indicators of the income and investment opportunity set. Test results presented then are updated as follows:

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47 Consumption Growth Predicted by Stock Returns Quarterly Data, 1949 – 2003 Dependent VariableSlope Coefficients Adj. RSQ CurrentLag1Lag2 PCE Total0.00990.02360.0233 t-stat1.343.193.16 Robust t-stat1.752.984.330.09 Dependent VariableSlope Coefficients Adj. RSQ CurrentLag1Lag2 PCE NDS0.00870.01430.0089 t-stat2.053.352.08 Robust t-stat2.543.582.660.08

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48 Consumption Growth Deviations and the Income and Investment Opportunity Set The lagged values of the residuals from the above regressions are examined for predictive ability with regard to income, wages and corporate profits. Specifically, we regress the growth rate of each variable on its own lag and the lagged consumption residuals.

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49 Consumption Deviations Predict Real Personal Income Growth: 1989 Results Results in 1989 Breeden paper (Quarterly 1950-1988): Personal Income Growth (t)= =.0054+.36 PI(t-1) +.31(PCETotal Residual) (t=4.84) (t=3.44) RSQ=.27 =.0054 +.35 PI(t-1) +.47 (PCE NDS Residual) (t=4.67) (t=3.52) RSQ=.27

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50 Consumption Deviations Predict Real Personal Income Growth: 2004 Results Updating tests quarterly from 1949-2003: Personal Income Growth (t)= = -.125 PI(t-1) +.33(PCETotal Residual) (t=-1.38) (t=3.89) Adj RSQ=.06 = -.133 PI(t-1) +.56 (PCE NDS Residual) (t=-1.52) (t=4.90) Adj RSQ=.05

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51 Consumption Deviations Predict Real Wage Growth Using quarterly data from 1949-2003: Real Wage Growth (t)= = 0.29 RW(t-1) +.25 (PCETotal Residual) (t=1.93) (t=3.22) Adj RSQ=.15 = 0.26 RW(t-1) +.58 (PCE NDS Residual) (t=1.72) (t=4.15) Adj RSQ=.15

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52 Consumption Deviations Predict Unemployment Rate Changes Using semiannual data from 1949-2003: 6-Mo. Change in Unemployment Rate (t)= = -0.21 (PCETotal Residual) (t=3.62) Adj RSQ=.10 = -0.26 (PCE NDS Residual) (t=3.17) Adj RSQ=.08

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53 Consumption Deviations Predict Real GDP Growth Using quarterly data from 1949-2003: Real GDP Growth (t)= = 0.26 GDP(t-1) +.16 (PCETotal Residual) (t=3.26) (t=1.54) Adj RSQ=.11 = 0.23 GDP(t-1) +.55 (PCE NDS Residual) (t=3.22) (t=3.41) Adj RSQ=.16

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54 Consumption Deviations from Wealth and the Investment Opportunity Set Positive consumption deviations precede (1949-2003 Q): Lower average stock returns and Lower volatility (squared returns) Higher corporate profits Correlations of Lagged Consumption Residuals: PCETot t PCE NDS t S&P 500 Real Return (Mean) -0.10 -1.5 -0.14 -2.1 S&P 500 Squared Return -0.15 -2.2 -0.18 -2.7 S&P 500 Earnings/Share 0.29 4.5 0.23 3.5

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55 Conclusion on Consumption and the Income and Investment Opportunity Sets Test results show strongly that, as consumption and portfolio theory predict, consumption choices do reflect knowledge about future income and investment opportunities. High consumption relative to wealth is followed by high wage and personal income growth, and by higher corporate profits and reduced volatility of investment returns. Low consumption/wealth reflects weak income opportunities, lower profits and higher risks. Lettau-Ludwigson’s (2001a,b) results confirm some of these effects.

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V.Volatility of Individual Consumption vs. Aggregate Consumption and the Equity Premium Puzzle See also: Heaton-Lucas (1996 JPE) and Brav, Constantinides, Geczy (2002 JPE)

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57 Volatility of Family Consumption: Consumer Expenditure Survey,1980 Q1-1998Q2 Vol(NIPA Total Consumption)=1.6%. Vol(Sample Aggregate=6.9%) Low (1/3) Income Middle (1/3) Income High (1/3) Income Small (<=2 persons) 11.0%15.4%19.1% Medium (2- 4 persons) 8.9%9.3%14.2% Large (>4 persons) 8.9%8.5%14.6%

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58 Equity Premium Puzzle and Consumption Volatility Individual consumption volatility is 5-10 times larger than measured volatility of NIPA aggregate consumption. High income individuals own the most assets, have highest consumption volatility. Reasons include incomplete markets and optimal incentives for labor choices. Marginal utility is indeed related to that consumption volatility and likely helps explain the equilibrium equity risk premium.

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