Abstract
Cross-sectional asset pricing tests with GMM can generate spuriously high explanatory power for factor models when the moment conditions are specified such that they allow the estimated factor means to substantially deviate from the observed sample averages. In fact, by shifting the weights on the moment conditions, any level of cross-sectional fit can be attained. This property is a feature of the GMM estimation design and applies to strong as well as weak factors, and to all sample sizes and test assets. We reveal the origins of this bias theoretically, gauge its size using simulations, and document its relevance empirically.
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