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Abstract
Factor-based performance attribution seeks to evaluate the components of a portfolio’s return, studying the contribution that each factor makes to the total return. But biases in the analysis can lead to several common errors, such as misclassifying factor returns as asset-specific contributions (or vice versa) or misestimating the actual factor exposure.
Robert Stubbs of Axioma and Vishv Jeet of Burgiss have developed a hybrid approach that uses cross-sectional estimates and adjusts them if there is a systematic bias over time. In Adjusted Factor-Based Performance Attribution , Stubbs and Jeet examine the methodology behind performance analytics and explain how their adjusted model can provide deeper insight into the real drivers of investment performance.
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