Predictability and the cross-section of expected returns: A challenge for asset pricing models - Christian Schlag; Michael Semenischev; Julian Thimme (Leibniz Institute for Financial Research SAFE, 2020)
Many modern macro finance models imply that excess returns on arbitrary assets are 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 conditions under which models would be able to produce expected return patterns in line with the data and discuss various examples.
Keywords: G12; E44; D81; 330; asset pricing; cross-section of stock returns; predictability