What Should Investors Care About? Mutual Fund Ratings by Analysts vs. Machine Learning Technique

70 Pages Posted: 1 Oct 2020 Last revised: 8 May 2021

See all articles by Si Cheng

Si Cheng

Chinese University of Hong Kong - Department of Finance

Ruichang Lu

Department of Finance, Guanghua School of Management, Peking University

Xiaojun Zhang

Peking University

Date Written: May 8, 2021

Abstract

We examine two forward-looking mutual fund ratings provided by Morningstar: the analyst rating produced by human analysts and the quantitative rating generated by machine learning technique. The analyst rating identifies outperforming funds, while the quantitative rating fails to do so—such a difference is mostly due to the selection of analyst coverage. Moreover, the tone in the analyst report contains incremental soft information in predicting fund performance. Finally, retail investors do not follow analyst recommendations, but instead chase the quantitative rating. The overall evidence highlights the importance of mutual fund analysts in information production and implies a capital misallocation problem in mutual fund investment.

Keywords: Analyst Rating, Quantitative Rating, Mutual Funds, Information Provision, Fund Flows, Machine Learning

JEL Classification: G11, G23

Suggested Citation

Cheng, Si and Lu, Ruichang and Zhang, Xiaojun, What Should Investors Care About? Mutual Fund Ratings by Analysts vs. Machine Learning Technique (May 8, 2021). Institute of Global Finance Working Paper, Available at SSRN: https://ssrn.com/abstract=3702749 or http://dx.doi.org/10.2139/ssrn.3702749

Si Cheng (Contact Author)

Chinese University of Hong Kong - Department of Finance ( email )

12/F, Cheng Yu Tung Building
No.12, Chak Cheung Street
Shatin, N.T.
Hong Kong

HOME PAGE: http://www.bschool.cuhk.edu.hk/staff/cheng-si/

Ruichang Lu

Department of Finance, Guanghua School of Management, Peking University ( email )

Beijing
China

Xiaojun Zhang

Peking University ( email )

No. 5 Yiheyuan Road
Haidian District
Beijing, Beijing 100871
China

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
220
Abstract Views
1,132
rank
166,827
PlumX Metrics