Research‎ > ‎

Predictability and the Cross-Section of Expected Returns: A Challenge for Asset Pricing Models

In asset pricing models with state variables excess returns on arbitrary assets are typically predictable via the price-dividend ratio and the variance risk premium of the aggregate stock market. We propose a simple empirical test for the ability of such a model to explain the cross-section of expected returns by sorting stocks based on the sensitivity of expected returns to these quantities. Models with only one uncertainty-related state variable, like the habit model or the long-run risks model, cannot pass this test. However, even extensions with more state variables mostly fail. We derive criteria for the market prices of risks a model has to satisfy to produce expected return patterns in line with the data.