The role of air quality in analyst bias

Professor Dasgupta and his collaborators, Shi Yushui and Xia Ying of Monash University, and Wu Weili of Central University of Finance and Economics, conducted their research using Chinese data, examining how fund managers assess information from their familiar analysts and other analysts in their portfolio decision-making process. They also tested their findings using US data and found consistent results.

The researchers examined the Crystal Ball Awards for Sell-Side Analysts in China from 2011 to 2019, which allow fund managers to vote for their preferred analysts. Analysts who received a fund manager’s vote in the prior year were considered connected to that manager. Hence, the researchers call them “connected analysts”.

The researchers analysed how fund managers used information from these connected analysts. Specifically, they looked at companies in a fund’s portfolio that the connected analysts have visited physically and issued earnings forecasts within 15 days of their visits. Fund analysts often make visits to corporations as part of their research and analysis process.

Although it is commonly assumed that fund managers use analyst information in their portfolio decisions, proving this causality is challenging due to other variables that can influence them. Therefore, the researchers analysed how exogenous shocks, such as air quality during corporate visits, affected the analysts’ forecasts and, eventually, fund managers’ decisions.

Air quality changes are highly unpredictable and influenced by unrelated factors, making it a suitable instrument for isolating the impact of biases that arise. Fund managers normally do not take part in the same site visits. “Air quality on a given day at a firm’s location can affect an analyst’s outlook or mood the forecast the analyst issues of the firm’s earnings per share,” Professor Dasgupta 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.

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.

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.