Abstract
Recent studies show that the pricing information in standard test portfolios is not sufficient to discriminate between asset pricing models. In this paper, we develop a novel approach to test models on a large cross-section of several thousand portfolios. Our large-scale approach relies on a simple estimation procedure, is widely applicable, and allows for formal comparison tests. Empirically, our approach uncovers striking performance differences between models. While the models are all misspecified, the human capital and conditional CAPMs largely reduce the level of mispricing, consistent with the prediction that labor income shocks and business cycle variations are primary concerns for investors.
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