Conference Agenda

Active exchange-traded funds (AETFs) are booming despite massive outflows from traditional active mutual funds (AMFs). This growth is not driven by performance; AETFs persistently underperform their AMF peers. Instead, AETFs capitalize on their intraday tradability to attract an attention-driven retail clientele. We document a novel U-shaped flow-performance relation where AETF investors chase extreme short-term returns—both positive and negative. Because long-term underperformance is not penalized with outflows, AETF managers are incentivized to rely on elevated risk-taking to generate these attention-grabbing returns. Overall, AETFs function less as skill-based investment products and more as high-risk attention assets.

This paper documents substantial asset `reclassification' in the mutual fund industry, exceeding $450 billion in 2021. These reclassification events do not involve investor flows; instead, mutual fund assets are simply converted into twin investment vehicles, such as separate accounts or collective investment trusts. Analyzing the implications of asset reclassification for the mutual fund literature, we find that these events distort inferred mutual fund flows without reflecting actual asset movements at the investment product level. Failing to account for asset reclassification in flow-based regression analyses can lead to biased estimates, as it resembles a non-classical measurement error. We first analyze scenarios in which mutual fund flows serve as a dependent variable, focusing on flow-performance sensitivity. A regression utilizing reclassification-adjusted quarterly flows demonstrates a 40-100% greater flow-performance sensitivity for mutual funds with twin vehicles than one employing unadjusted flows. We then examine cases when flows serve as an independent variable, as in `smart money' tests, where measurement error artificially inflates true estimates.