This paper studies the role of f luctuations in the aggregate consumption-wealth ratio for predicting stock returns. Using U.S. quarterly stock market data, we find that these f luctuations in the consumption-wealth ratio are strong predictors of both real stock returns and excess returns over a Treasury bill rate. We also find that this variable is a better forecaster of future returns at short and intermediate horizons than is the dividend yield, the dividend payout ratio, and several other popular forecasting variables. Why should the consumption-wealth ratio forecast asset returns? We show that a wide class of optimal models of consumer behavior imply that the log consumption-aggregate wealth~human capital plus asset holdings! ratio summarizes expected returns on aggregate wealth, or the market portfolio. Although this ratio is not observable, we provide assumptions under which its important predictive components for future asset returns may be expressed in terms of observable variables, namely in terms of consumption, asset holdings and labor income. The framework implies that these variables are cointegrated, and that deviations from this shared trend summarize agents' expectations of future returns on the market portfolio. UNDERSTANDING THE EMPIRICAL LINKAGES between macroeconomic variables and financial markets has long been a goal of financial economics. One reason for the interest in these linkages is that expected excess returns on common stocks appear to vary with the business cycle. This evidence suggests that stock returns should be forecastable by business cycle variables at cyclical frequencies. Indeed, the forecastability of stock returns is well documented. Financial indicators such as the ratios of price to dividends, price to earnings, or dividends to earnings have predictive power for excess returns over a Treasury-bill rate. These financial variables, however, have been most successful at predicting returns over long horizons. Over horizons spanning the 815 length of a typical business cycle, stock returns have typically been found to be only weakly forecastable. 1 Moreover, traditional macroeconomic variables have proven especially dismal as predictive variables.The question of whether expected returns vary at cyclical frequencies and with macroeconomic variables is also pertinent to the debate over why excess returns are predictable. One possibility is that financial markets are inefficient. Alternatively, predictable variation in returns could simply ref lect the rational response of agents to time-varying investment opportunities, possibly driven by cyclical variation in risk aversion~e.g., Sundaresañ 1989!, Constantinides~1990!, Campbell and Cochrane~1999!! or in the joint distribution of consumption and asset returns. If these rational explanations are correct, it is reasonable to expect that key macroeconomic variables should perform an important function in forecasting excess stock returns. As yet, however, there is little empirical evidence that real macroeconomic variables perform ...

This paper merges two independent projects, Campbell and Lettau (1999) and Malkiel and Zu (1999). Campbell and Lettau are grateful to Sangjoon Kim for his contributions to the first version of their paper, Campbell, Kim and Lettau (1994). We thank two anonymous referees and René Stulz for useful comments. Jung-Wook Kim and Matt Van Vlack provided able research assistance. The views are those of the authors and do not necessarily reflect those of the Federal Reserve Bank of New York, the Federal Reserve System or the National Bureau of Economic Research. Any errors and omissions are the responsibility of the authors.

This paper explores the ability of theoretically-based asset pricing models such as the CAPM and the consumption CAPM referred to jointly as the (C)CAPM to explain the cross-section of average stock returns. Unlike many previous empirical tests of the (C)CAPM, we specify the pricing kernel as a conditional linear factor model, as would be expected if risk premia vary over time. Central to our approach is the use of a conditioning variable which proxies for fluctuations in the log consumption-aggregate wealth ratio and is likely to be important for summarizing conditional expectations of excess returns. We demonstrate that such conditional factor models are able to explain a substantial fraction of the cross-sectional variation in portfolio returns. These models perform much better than unconditional (C)CAPM specifications, and about as well as the three-factor Fama-French model on portfolios sorted by size and book-to-market ratios. This specification of the linear conditional consumption CAPM, using aggregate consumption data, is able to account for the difference in returns between low book-to-market and high book-to-market firms and exhibits little evidence of residual size or book-to-market effects. (JEL G10, E21) 9 This debate is borne out in several recent papers; for example, see Daniel and Titman (1997, 1998) and Davis, Fama and French (forthcoming). 10 See Harrison and Kreps (1979).

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Both textbook economics and common sense teach us that the value of household wealth should be related to consumer spending. At the same time, movements in asset values often seem disassociated with important movements in consumer spending, as episodes such as the 1987 stock market crash and the contraction in equity values that occurred in the fall of 1998 suggest. An important first step in understanding the consumption-wealth linkage is determining how closely the two variables are actually correlated, and whether there exist important movements in asset values that are not associated with changes in consumption. This paper provides evidence that a surprisingly small fraction of the variation in household net worth is related to variation in aggregate consumer spending. We use empirical techniques that allow us to quantify the relative importance of permanent and transitory innovations in the variation of consumer spending and wealth and find that transitory shocks dominate postwar variation in wealth, while permanent shocks dominate variation in aggregate consumption. Although transitory innovations are found to have little influence on consumer spending, they have long-lasting effects on wealth, exhibiting a half life of a little over two years. The findings suggest that most macro models-which make no allowance for transitory variation in wealth that is orthogonal to consumption-are likely to misstate both the timing and magnitude of the consumption-wealth linkage.

This paper uses a disaggregated approach to study the volatility of common stocks at the market, industry, and firm levels. Over the period 1962-97 there has been a noticeable increase in firm-level volatility relative to market volatility. Accordingly, correlations among individual stocks and the explanatory power of the market model for a typical stock have declined, while the number of stocks needed to achieve a given level of diversification has increased. All the volatility measures move together countercyclically and help to predict GDP growth. Market volatility tends to lead the other volatility series. Factors that may be responsible for these findings are suggested.

This paper explores the ability of theoretically-based asset pricing models such as the CAPM and the consumption CAPM-referred to jointly as the (C)CAPM-to explain the cross-section of average stock returns. Unlike many previous empirical tests of the (C)CAPM, we specify the pricing kernel as a conditional linear factor model, as would be expected if risk premia vary over time. Central to our approach is the use of a conditioning variable which proxies for fluctuations in the log consumption-aggregate wealth ratio and is likely to be important for summarizing conditional expectations of excess returns. We demonstrate that such conditional factor models are able to explain a substantial fraction of the cross-sectional variation in portfolio returns. These models perform much better than unconditional (C)CAPM specifications, and about as well as the three-factor Fama-French model on portfolios sorted by size and book-tomarket ratios. This specification of the linear conditional consumption CAPM, using aggregate consumption data, is able to account for the difference in returns between low book-to-market and high book-to-market firms and exhibits little evidence of residual size or book-to-market effects. (JEL G10, E21) 1989), Hodrick (1992), Lamont (1998) and Lettau and Ludvigson (2001).7 Pioneering theoretical work in this area includes Sundaresan 1989;Constantinides 1990; Campbell and Cochrane 1999a. 8 This methodology builds off of the work of Ferson, Kandel, and Stambaugh (1987); Harvey (1989); and Shanken (1990) who call for scaling the conditional betas themselves (rather than the factors directly) in a cross sectional linear regression model where market betas are expected to vary over time.

Evidence of stock return predictability by financial ratios is still controversial, as documented by inconsistent results for in-sample and out-of-sample regressions and by substantial parameter instability. This paper shows that these seemingly incompatible results can be reconciled if the assumption of a fixed steady-state mean of the economy is relaxed. We find strong empirical evidence in support of shifts in the steady-state and propose simple methods to adjust financial ratios for such shifts. The in-sample forecasting relationship of adjusted price ratios and future returns is statistically significant and stable over time. In real-time, however, changes in the steady-state make the in-sample return forecastability hard to exploit out-of-sample. The uncertainty of estimating the size of steady-state shifts rather than the estimation of their dates is responsible for the difficulty of forecasting stock returns in real-time. Our conclusions hold for a variety of financial ratios and are robust to changes in the econometric technique used to estimate shifts in the steady-state.