<p>A precise understanding of the risks driving portfolio performance is a key component of asset management. We show that classical methods used to estimate risk factor contributions can lead to important “misattributions” of performance. These misattributions are not well documented, in spite of their potentially significant impact on the understanding of investment strategies. We investigate the drivers of misattribution episodes, including model uncertainty, time-varying factor parameters and extreme data points, and propose analytical approaches to mitigate their effects. We also show that introducing more model complexity does not necessarily lead to better estimates. By addressing misattribution drivers, our research contributes to better-informed decisions and more effective risk management in dynamic market environments.</p>