This paper presents new evidence on the predictability of aggregate stock market returns by a labor market based economic indicator–the unemployment gap–which is defined as the difference between the actual unemployment rate and its estimated trend. The unemployment gap emerges as a strong predictor of both raw stock returns and excess stock returns, in the U.S. and in international data. In addition, fluctuations in the unemployment gap are tightly linked to cyclical variation in risk preferences. A widening of the unemployment gap, i.e. a rise in the unemployment rate relative to its trend, is associated with a rise in risk aversion and higher excess returns. The results support the view that the expected equity risk premiums arise as a rational response of economic agents to changes in the investment opportunities.

We apply an empirical approximation of the intertemporal capital asset pricing model (ICAPM) to show that cross-sectional dispersion in currency returns can be rationalized by differences in currency excess returns’ sensitivities to the market return’s cash-flow news component. This finding echoes recent explanations of the value and growth stock market anomaly. The distinction between cash-flow news and discount-rate news is key to jointly explain average stock and currency returns. Our analysis reveals the presence of a common source of systematic risk in stock and foreign currency returns that is reflected in the market return’s cash-flow news component.

This paper introduces a novel consumption‐based variable, cyclical consumption, and examines its predictive properties for stock returns. Future expected stock returns are high (low) when aggregate consumption falls (rises) relative to its trend and marginal utility from current consumption is high (low). We show that the empirical evidence ties consumption decisions of agents to time variation in returns in a manner consistent with asset pricing models based on external habit formation. The predictive power of cyclical consumption is not confined to bad times and subsumes the predictability of many popular forecasting variables.