Learning from Prospectuses
41 Pages Posted: 15 Jun 2021
Date Written: June 2021
We study qualitative information disclosure by mutual funds when investors learn from these disclosures in addition to past performance. We show theoretically that fund managers with specialized strategies optimally choose to disclose detailed strategy descriptions, while managers with standardized strategies provide generic descriptions. Generic descriptions lead to errors in benchmarking by investors and thus higher volatility in capital flows. While all fund managers dislike such volatility, those with above-average factor exposures also benefit from benchmarking errors as investors incorrectly ascribe factor returns to managerial skill. The model generates a number of predictions that we are able to test empirically using a comprehensive dataset of fund prospectuses. Consistent with the model’s predictions, funds with standardized strategies include more boilerplate in their descriptions, grow larger and have lower flow-performance sensitivity, despite having greater flow volatility.
Keywords: mutual funds, fund prospectus, information disclosure, fund benchmarks, machine learning
JEL Classification: G23, G11, D83
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