Learning from Prospectuses
55 Pages Posted: 15 Jun 2021 Last revised: 19 Jan 2022
Date Written: December 24, 2021
We study qualitative information disclosures by mutual funds when investors learn from such disclosures in addition to past performance. We show theoretically that fund managers with specialized strategies optimally choose to disclose detailed strategy descriptions, while those with standardized strategies provide generic descriptions. Generic descriptions lead to benchmarking errors by investors who confuse factor returns and skill, resulting in higher fund flow uncertainty. While all managers dislike this uncertainty, those with above-average factor exposures also benefit from the errors on balance and thus grow larger. We find evidence for this trade-off in the data, using a comprehensive dataset of fund prospectuses: funds with more informative descriptions are smaller and more specialized, exhibit higher flow- performance sensitivity, and show lower correlation between size and flow volatility. Investors in these funds make fewer benchmarking errors, and the effects are more pronounced for funds with shorter return histories.
Keywords: mutual funds, fund prospectus, information disclosure, fund benchmarks, machine learning
JEL Classification: G23, G11, D83
Suggested Citation: Suggested Citation