Abstract
In the constant trade-off between accurate modelling of stock returns and maintaining a practical number of risk factors there is clear incentive to prematurely suggest optimal solutions. Contemporary model comparison techniques prompt the revisiting of conclusions, Bayesian techniques being especially helpful to reflect the true role the observed data plays in supporting choice. As the literature enters a period of machine learning, and potential over-fitting, this is a timely reminder to give more heed to the true applicability of any proclamation on improvement in asset pricing fit.
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