Uncovering Mutual Fund Private Information with Machine Learning

43 Pages Posted: 2 Dec 2020 Last revised: 31 Mar 2021

See all articles by Alan L. Zhang

Alan L. Zhang

Georgia State University - J. Mack Robinson College of Business

Date Written: October 17, 2020

Abstract

This paper implements natural language processing (NLP) models and neural networks to predict mutual fund performance using the textual information disclosed in mutual fund shareholder letters. Informed funds identified by the prediction model deliver superior abnormal returns and are more likely to receive an upgrade in Morningstar ratings. Informed funds also attract greater flows in three days and up to 24 months after the disclosure of shareholder letters, especially when their disclosure has greater investor attention, suggesting that investors recognize the information from the qualitative disclosure. The machine learning model shows that informed funds tend to discuss sector specializations, portfolio risk taking, big picture of the financial market, and mixed strategies across assets. Collectively, this study shows that mutual fund disclosure contains rich, value-relevant textual information that can be analyzed by state-of-the-art machine learning models and help investors identify informed funds.

Keywords: Machine Learning, Mutual Fund Performance, Disclosure, Textual Analysis, Shareholder Letters, Fund Flows

JEL Classification: C45, G11, G14, G23

Suggested Citation

Zhang, Alan L., Uncovering Mutual Fund Private Information with Machine Learning (October 17, 2020). Available at SSRN: https://ssrn.com/abstract=3713966 or http://dx.doi.org/10.2139/ssrn.3713966

Alan L. Zhang (Contact Author)

Georgia State University - J. Mack Robinson College of Business ( email )

35 Broad Street NW
Suite 1242
Atlanta, GA 30303
United States

HOME PAGE: http://alan-lzhang.github.io/

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