Author ORCID Identifier
https://orcid.org/0000-0002-5584-170X
Date of Award
Summer 7-15-2021
Degree Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Finance
First Advisor
Dr. Vikas Agarwal
Second Advisor
Dr. Baozhong Yang
Third Advisor
Dr. Zhen Shi
Fourth Advisor
Dr. Sean Cao
Fifth Advisor
Dr. Wei Jiang
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.
DOI
https://doi.org/10.57709/24069435
Recommended Citation
Zhang, Liang, "Uncovering Mutual Fund Private Information with Machine Learning." Dissertation, Georgia State University, 2021.
doi: https://doi.org/10.57709/24069435
File Upload Confirmation
1