Economics & Finance,Innovation & Technology

Fund Analysis: A Problem of ‘Mutual’ Attraction

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New research shows naïve retail investors chase machine-led fund ratings while ignoring analysts’ outperforming predictions

Retail investors are increasingly relying on mutual funds to meet their long-term financial objectives. In the U.S., this group of investors hold about 89 percent of all mutual fund net assets, which in October 2020 totalled US$21.82 trillion, according to industry statistics.

Yet the findings of a study, What Should Investors Care About? Mutual Fund Ratings by Analysts vs. Machine Learning Technique show people are making retail investments while paying little attention to the quality of mutual fund predictions.

“The overall evidence highlights the importance of mutual fund analysts in providing information and shows that retail investors are just not investing in funds that produce the best returns,” says Si Cheng, Assistant Professor at the Department of Finance at The Chinese University of Hong Kong Business School.

Investors do not react to analyst ratings, but instead rely on backward-looking past performance and star ratings as well as quantitative ratings.

Prof. Si Cheng

Prof. Cheng, who jointly carried out the research with Profs. Ruichang Lu and Xiaojun Zhang at Guanghua School of Management at Peking University, analysed American financial service company Morningstar’s two forward-looking mutual fund rankings – the analyst rating and quantitative rating, the latter of which is based on a machine-learning model and which rates funds not covered by analysts.

Prof. Cheng says up to now there has been little academic focus on the relative predictive abilities of the analyst and quantitative ratings, which investors may follow when selecting mutual funds, so the new study is an attempt to put that right.

She and her colleagues found that while the analyst rating is able to identify outperforming funds, the quantitative rating fails to do so and that such a difference is mostly because analysts selectively cover high-quality funds that outperform the market.

“Moreover, the tone in an analyst report contains incremental information in predicting fund performance,” she says. “Yet, retail investors do not follow analyst recommendations, but instead chase the quantitative rating.”

She says the study’s findings offer many useful insights for people thinking of putting money into mutual funds. The two forward-looking ratings may appear to offer similar assessments, but may often provide very different conclusions. “Investors should be aware of such disparities, rather than naïvely believing the quantitative rating offers the same level of accuracy and information as the analyst rating,” she says.

Morningstar’s Ratings

Since 1985, Morningstar has offered investors a free backward-looking Star Rating service, ranked from 1 to 5 – based on mathematically derived and adjusted past-performance indicators compared with other funds in the same category.

However, its limitations in predicting future returns led to the 2011 introduction of the company’s Analyst Rating, generated from forward-looking analysis of funds on a rising five-tier scale: Negative, Neutral, and three positive ratings, i.e., Bronze, Silver and Gold.

A top-three rating shows analysts think highly of a fund. The differences between them correspond to the level of analyst conviction in a fund’s ability to outperform its benchmark and peers over time, despite its risks.

Financial services firm Morningstar developed a machine-learning model in 2017 to create its quantitative rating.

Analysts covering funds examine and rank them based on five important pillars – people, process, parent, performance and price – to predict its success in different market environments and highlight key developments in performance and portfolio holdings. In arriving at an analyst rating, they also produce an analyst report through interviewing key parent company executives, risk managers and traders.

Yet as the company’s analyst coverage is limited by the size of its team, it also developed a machine-learning model in 2017 to create its quantitative rating, which is analogous to the rating an analyst might assign to a fund if it were covered. This model also assesses the funds based on the five key pillars. Investors pay US$199 a year to use the two predictive ratings.

The study used monthly analyst ratings, quantitative ratings, and star ratings found on the Morningstar mutual fund database and manually downloaded analyst reports from Morningstar’s website. A final sample featured 3,256 actively managed U.S. equity funds, including 1,056 funds that have been covered by Morningstar analysts at least once.

Human or AI – Which Rating is Better?

Prof. Cheng says the study highlights the importance of analyst reports in providing unique and additional information and insights for retail investors. She also notes that when an analyst adopts a positive tone, it can improve a fund’s annual return.

The study shows analyst reports are even more informative at predicting a fund’s future returns when the tone is at odds with the analyst rating, she says. For example, Gold-rated funds with a more negative tone display a lower future performance, while Negative-rated funds with a more positive tone tend to rebound.

The study also investigates the reaction of mutual fund investors to Morningstar ratings. “We find that investors do not react to analyst ratings, but instead rely on backward-looking past performance and star ratings as well as quantitative ratings,” Prof. Cheng says.

Although analysts’ recommendations are largely ignored by retail mutual fund investors, institutional investors do take advantage of the valuable information provided by the analyst rating and report, for example by withdrawing from Gold-rated funds that have received an assessment with a more negative tone from analysts.

The study also analyses the summary section and the title of analyst reports instead of the full report and finds that only the tone in the full analyst report predicts returns that exceed those of similar funds. This suggests investors need to carefully read the whole report to obtain useful information.

However, investors tend to react strongly to the tone in the summary section and the title, but not in the full analyst report, Prof. Cheng says. This suggests mutual fund investors are not sophisticated in considering the information before them and making investment decisions and are likely to be influenced by the information that attracts their attention.

Pedestrians walk past a financial display board in Hong Kong, China. Researchers found that retail investors tended to chase the a machine-generated quantitative rating, rather than follow analyst recommendations.

She believes the study is the first to reveal the informational value of the analyst rating and analyst reports, and to highlight the importance of soft information, expressed as ideas and opinions, in mutual fund investment. The study’s findings suggest mutual fund analysts play an important role in acquiring and processing information as well as facilitating more efficient capital allocation across mutual funds.

“In future, an improved information environment could reduce the search cost in the mutual fund industry and, as a result, lead to a more efficient asset management market and financial market,” Prof. Cheng says.

The study also shows that the analyst rating is easy to access and follow in real time, so it should be easy for investors who rely on the star rating to switch to the analyst rating and improve their performance, she says.

Over-reliance on Fintech?

The findings also touch on the increasing adoption of financial technology (fintech) in the financial industry through the use of statistical methods and machine-learning techniques, such as in credit rating, financial advising and asset management.

Fintech can greatly reduce information production costs and enhance financial inclusion, but the study highlights one of the drawbacks, she says. The quantitative rating cannot be considered a like-for-like substitute for an analyst rating because of the selection of analysts’ coverage and the information value of analyst reports.

Cheng says the research also has implications for investor education and financial service provision. While individual investors can outsource their day-to-day portfolio management decisions to professional fund managers, the growing market size and variety of financial products mean that fund selection can be complicated.

The study’s findings show there is a need to offer continuous financial education to individual investors and inform them of the up-to-date, valuable financial services and tools, she says.