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I examine a factor pricing model for stock returns. The factors are returns on physical investment, inferred from investment data via a production function. I examine the model's ability to explain variation in expected returns across assets and over time. The model is not rejected. It performs about as well as the CAPM and the Chen, Roll, and Ross factor model, and it performs substantially better than a simple consumption-based model. I also provide an easy technique for estimating and testing dynamic, conditional asset pricing models--one simply includes factors and returns scaled by instruments in an unconditional estimate--and for comparing such models.