Consumption, Aggregate Wealth and Expected Stock Returns

Consumption, Aggregate Wealth and Expected Stock Returns

First draft: February 26, 1999, June 8, 1999 | Martin Lettau and Sydney Ludvigson
This paper investigates the role of detrended wealth in predicting stock returns. The authors define a transitory movement in wealth as a deviation from its shared trend with consumption and labor income. Using U.S. quarterly stock market data, they find that these trend deviations in wealth are strong predictors of both real stock returns and excess returns over a Treasury bill rate. The variable is also a better forecaster of future returns at short and intermediate horizons compared to other popular forecasting variables such as dividend yield, earnings yield, and dividend payout ratio. The authors argue that a wide class of optimal models of consumer behavior imply that the log consumption-aggregate wealth ratio forecasts the expected return on aggregate wealth, or the market portfolio. Although this ratio is not observable, the authors demonstrate that its important predictive components can be expressed in terms of observable variables: consumption, nonhuman wealth, 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. The paper also discusses the estimation of the trend relationship among consumption, labor income, and nonhuman wealth, and tests the model's implications for stock return forecasts. The results show that deviations from the shared trend in consumption, labor income, and assets are better described as transitory movements in asset wealth than as transitory movements in consumption or labor income. These deviations are found to be significant predictors of future stock returns, particularly at business cycle frequencies (1 to 5 quarters). The authors conclude that the detrended wealth measure provides information about future stock returns that is not captured by lagged values of other popular forecasting variables and displays its greatest predictive power for returns over business cycle frequencies.This paper investigates the role of detrended wealth in predicting stock returns. The authors define a transitory movement in wealth as a deviation from its shared trend with consumption and labor income. Using U.S. quarterly stock market data, they find that these trend deviations in wealth are strong predictors of both real stock returns and excess returns over a Treasury bill rate. The variable is also a better forecaster of future returns at short and intermediate horizons compared to other popular forecasting variables such as dividend yield, earnings yield, and dividend payout ratio. The authors argue that a wide class of optimal models of consumer behavior imply that the log consumption-aggregate wealth ratio forecasts the expected return on aggregate wealth, or the market portfolio. Although this ratio is not observable, the authors demonstrate that its important predictive components can be expressed in terms of observable variables: consumption, nonhuman wealth, 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. The paper also discusses the estimation of the trend relationship among consumption, labor income, and nonhuman wealth, and tests the model's implications for stock return forecasts. The results show that deviations from the shared trend in consumption, labor income, and assets are better described as transitory movements in asset wealth than as transitory movements in consumption or labor income. These deviations are found to be significant predictors of future stock returns, particularly at business cycle frequencies (1 to 5 quarters). The authors conclude that the detrended wealth measure provides information about future stock returns that is not captured by lagged values of other popular forecasting variables and displays its greatest predictive power for returns over business cycle frequencies.
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