Morningstar Ratings Research Articles

Investor response to Morningstar's ratings, category information, and alpha in the Japanese mutual fund market

Mutual fund investors evaluate fund managers' skills before making investment decisions. Previous studies worldwide examined the rationale behind retail investors' investment decisions and found that investors reward performance by net fund flow. A Japanese study found that investors respond to alpha but do not risk factor-related returns. Surprisingly, the result would mean that Japanese investors are more sophisticated than US investors because the literature reported that US investors respond to factor-related returns without distinguishing them from alpha. We explore the background of this result in the Japanese market. This study focuses on the effects of Morningstar's fund ratings, its categories, and salient gross returns, which are readily available to investors unlike alpha. We find the following results: (1) Morningstar's rating does not substitute alpha, indicating that our result differs from the US results. (2) Investors respond only to high category excess returns, but it is a unique effect different from alpha. (3) Salient gross returns do not have a concrete relation with alpha. Moreover, we find solid convex relationships between alpha and fund flows. Investors' responses are limited to a few extreme flows; other than these, they do not respond to alpha. This extreme flow can explain the reactions to alpha on average reported in previous studies. In addition, we find that the category excess return has similar features. The solid convexity reveals that the Japanese mutual fund market is far from sophisticated, consistent with the literature. These results reveal the importance of fund distributors' behavior and information disclosure regulations.

Practical Applications of Importance of Costs in Target Date Fund Selection Using Three Morningstar Ratings

In <b><i>Importance of Costs in Target Date Fund Selection Using Three Morningstar Ratings</i></b>, from the Spring 2021 issue of <b><i>The Journal of Retirement</i></b>, authors <b>C. Edward Chang</b> (of <b>Missouri State University</b>), <b>Thomas M. Krueger</b> (of <b>Texas A&amp;M University-Kingsville</b>), and <b>H. Doug Witte</b> (of <b>Missouri State University</b>) studied the factors influencing the rating scales that people use to pick investments. Target-date funds (TDFs) are popular because they automatically diversify investments and reallocate to conservative assets as retirement approaches. Investors need tools to pick good TDFs from the thousands available. The study focused on Morningstar’s different rating systems: 1) star ratings, 2) Analyst Ratings (ARs), and 3) the new computer-generated Quantitative Ratings (QRs). It found that TDFs with low expense ratios receive higher ratings across the board. However, human analysts give higher ARs to larger and newer TDFs and do not penalize for poor past performance, while the computer does penalize for poor performance and does not favor funds by age. Therefore, ARs and QRs are not comparable, but TDFs with any higher ratings have lower costs and are positioned to outperform.

How do corporate bond investors measure performance? Evidence from mutual fund flows

Which factor model do investors in corporate bonds use? We examine this question by tracking investors’ decisions to invest in actively managed corporate bond mutual funds with a revealed preference approach. Our main result is that all bond factor models are dominated by the simple Sharpe ratio and Morningstar ratings. For all major corporate bond mutual fund styles, the Sharpe ratio explains fund flows better than alphas from bond factor models. Since the Sharpe ratio (and to some extent also Morningstar ratings) can be easily manipulated in bond markets, our findings have potentially severe implications for all market participants.

Mismarking in Mutual Funds

We study mismarking of newly purchased odd lot and two classes of round lot structured product positions in mutual funds. Such mismarking artificially inflates net asset values and overstates cumulative returns. Applied to funds launched after January 2010, a simulation-tested mismarking fund filter identifies 12 Highly Questionable funds managing $75 billion. The performance of these funds matches closely the predicted pattern of mismarking: extremely high alpha and skewness, particularly immediately after launch. We show that structured product mismarking can seriously inflate return-since-inception metrics. We also provide evidence consistent with return smoothing for one quarter of the sample structured product funds. The inflated performance metrics benefit fund managers through significantly higher Sharpe ratios, Morningstar ratings, and asset growth but cause material losses to later investor cohorts. This paper was accepted by Agostino Capponi, finance. Funding: P. Schuster gratefully acknowledges funding by the Deutsche Forschungsgemeinschaft [Grant SCHU 3049/1-1]. Supplemental Material: Data and the online appendices are available at https://doi.org/10.1287/mnsc.2022.4366 .

Ambiguity Aversion and Mutual Funds: Evidence from China

I provide empirical evidence of ambiguity averse investors&amp;rsquo; behaviour in Chinas mutual funds market. My analysis is motivated by the substantial uncertainty in China&amp;rsquo;s mutual funds market, and theoretical research of decision indicates that investors would be more ambiguity averse when face higher uncertainty. The most substantial implication of the empirical research is that investors tend to place more weight on the worst signal. Across multiple horizons, fund flows will also display more sensitivity to the worst performance. I also conduct robustness test about the different rank funds by Morningstar rating and compare the positive and negative performance during the minimum performance period.

Enhancing Equity Index Mutual Fund Returns Using Cost and Morningstar Ratings Information

Although equity index mutual funds (EIMFs) are often viewed as a commodity, we analyze the impact that differences in expenses and other fund characteristics have on both past returns and forward-looking ratings. Morningstar evaluates past performance with their “star” rating and evaluates future fund prospects using analyst ratings (ARs) and quantitative ratings (QRs). Funds with low expense ratios and no loads earn higher returns without adding proportionally higher risk. Morningstar analysts carefully consider fund fees in their evaluations and assign much higher ARs to lower-cost funds. Interestingly, although loads initially appear to negatively impact these ratings, once expense ratios are controlled for, analysts seem to be favorably disposed to funds with loads. Regression analysis further suggests that ARs are also positively influenced by Morningstar ratings and fund size. QRs are positively influenced by past performance, but negatively correlated with loads and expense ratios. EIMF age has little influence on AR and QR levels, though the likelihood of an AR rating increases with time since inception. Similar results occur in both the US and foreign equity markets. Expense ratios, loads, past performance, and size can all be used as fund selection tools that aid in maximizing investor wealth.

Ratings-Driven Demand and Systematic Price Fluctuations

Abstract We show that mutual fund ratings generate correlated demand that creates systematic price fluctuations. Mutual fund investors chase fund performance via Morningstar ratings. Until June 2002, funds pursuing the same investment style had highly correlated ratings. Therefore, rating-chasing investors directed capital into winning styles, generating style-level price pressures, which reverted over time. In June 2002, Morningstar reformed its methodology of equalizing ratings across styles. Style-level correlated demand via mutual funds immediately became muted, significantly altering the time-series and cross-sectional variation in style returns.

Don’t Take Their Word For It: The Misclassification of Bond Mutual Funds

We provide evidence that bond fund managers misclassify their holdings, and that these misclassifications have a real and significant impact on investor capital flows. In particular, many funds report more investment grade assets than are actually held in their portfolios to important information intermediaries, making these funds appear significantly less risky. This results in pervasive misclassification across the universe of US fixed income mutual funds. The problem is widespread - resulting in up to 31.4% of funds being misclassified with safer profiles, when compared against their true, publicly reported holdings. “Misclassified funds” – i.e., those that hold risky bonds, but claim to hold safer bonds – appear to on-average outperform the low-risk funds in their peer groups. Within category groups, “Misclassified funds” moreover receive higher Morningstar Ratings (significantly more Morningstar Stars) and higher investor flows due to this perceived on-average outperformance. However, when we correctly classify them based on their actual risk, these funds are mediocre performers. These Misclassified funds also significantly underperform precisely when junk-bonds crash in returns. Misreporting is stronger following several quarters of large negative returns. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Importance of Costs in Target Date Fund Selection Using Three Morningstar Ratings

Although target date mutual funds (TDFs) have only been around since the mid-1990s, they are now common vehicles for retirement investing. Sixty percent of US companies now automatically funnel employees’ non-directed retirement funds into TDFs, which account for nearly one-quarter of all savings US workers have in 401(k)s. Helping investors pick among TDFs, Morningstar rates past risk-adjusted performance using a star system and provides forward-looking evaluations of more than 2,500 TDFs using either Analyst Ratings or a newly implemented machine-learning-based Quantitative Ratings. Morningstar assigns Analyst Ratings to a smaller subset of TDFs that tend to have been in existence the longest and have the largest size. We find that TDFs with lower fees have significantly higher star ratings than their higher expense counterparts. No-load TDFs have significantly higher star ratings than their load-charging counterparts. In assessing future fund prospects, TDFs with low expense ratios are favored by both analysts and artificial intelligence. TDFs without load charges have significantly better Quantitative Ratings than their load charging counterparts. TDFs with Quantitative Ratings tend to be smaller, younger, and have poorer prior performance than TDFs with Analyst Ratings. <b>TOPICS:</b>Long-term/retirement investing, mutual funds/passive investing/indexing, information providers/credit ratings <b>Key Findings</b> ▪ Target date funds (TDFs) with low expenses and without load charges have significantly higher Morningstar star ratings of past risk-adjusted performance. These same low-cost funds have higher Morningstar Analyst ratings and Quantitative ratings, which estimate future fund prospects of performance. ▪ Regression results suggest Morningstar’s two forward-looking fund ratings are not perfect substitutes. Analyst ratings weight fund expense and fund age more heavily (both negatively) while the computer-generated Quantitative ratings are more positively influenced by past fund performance. ▪ Morningstar’s star ratings, Analyst ratings, and Quantitative ratings all suggest that retirement investing will result in a larger nest egg when attention is restricted to no-load, low-expense TDFs. Additionally, the ratings suggest investors should favor larger but relatively younger funds.

Performance of Portfolios Based on the Expected Utility-Entropy Fund Rating Approach.

Yang and Qiu proposed and reframed an expected utility–entropy (EU-E) based decision model. Later on, a similar numerical representation for a risky choice was axiomatically developed by Luce et al. under the condition of segregation. Recently, we established a fund rating approach based on the EU-E decision model and Morningstar ratings. In this paper, we apply the approach to US mutual funds and construct portfolios using the best rating funds. Furthermore, we evaluate the performance of the fund ratings based on the EU-E decision model against Morningstar ratings by examining the performance of the three models in portfolio selection. The conclusions show that portfolios constructed using the ratings based on the EU-E models with moderate tradeoff coefficients perform better than those constructed using Morningstar. The conclusion is robust to different rebalancing intervals.

Star rating, fund flows and performance predictability: evidence from Norway

This paper studies the effect of Morningstar ratings on fund flows and fund performance predictability using a proprietary data set of equity funds from Norway. Controlling for a number of variables proxying for fund and firm visibility, we find that fund flows respond asymmetrically to changes in Morningstar ratings. Specifically, 4- and 5-star rated funds get more flows, and funds upgraded to 5-star get significantly more flows not only in the next month, but also over the following 12 months after the rating change. Downgraded funds suffer outflows, but the results only become statistically significant when fund performance falls to a 2-star rating. We also find evidence of long-term performance predictability for top-rated funds. As the mutual fund industry develops worldwide, our results suggest that Morningstar has been successful in bringing its brand name to markets outside the USA, and that Morningstar ratings are a valuable tool for helping investors make strong investment decisions.

Does Active Fee Predict Mutual Fund Flow? - Price Sensitivity of Demand for Active Management

Active fee is the ratio between the excess cost of active management over the index alternative and the fund's activity level. We suggest a simple model that explains active capital allocations in the presence of time-varying active fee. We show that investors respond in accordance with the model's prediction of a negative relationship between active fee and subsequent flows. Our findings can be interpreted as negative price elasticity of demand for active management, where one standard deviation change in active fee translates into a shift in net flow of 83.4 bps. This effect implies a 42% change in the unconditional expected yearly flow. The time-series relation is driven by both the activity level and the excess cost of active management. The result also holds after controlling for Morningstar Ratings.

Quality and Product Differentiation: Theory and Evidence from the Mutual Fund Industry

We study product differentiation in the mutual fund industry. We design a model in which funds with heterogeneous perceived quality can choose their level of product differentiation. In equilibrium, high quality funds choose broad market designs (i.e., low differentiation) appealing to many investors, while low quality funds adopt niche designs (i.e., high differentiation) that investors either love or loath. Using as a measure of fund differentiation the degree of textual uniqueness of investment strategy description in fund prospectuses, we confirm empirically that funds with lower expected performance tend to differentiate more. We use the issuance of Morningstar rating to previously unrated funds as an exogenous shock to perceived quality to identify the economic mechanism. We find that funds receiving a low rating increase their product differentiation. The effect is mainly concentrated on funds run by small management companies, a feature associated with lower performance. This increase in product differentiation makes funds more likely to survive. It also has a market-level impact on the menu of funds available to investors.

INVESTMENT OBJECTIVES AND FACTORS THAT INFLUENCE THE DISAPPEARANCE OF SPANISH MUTUAL FUNDS

This paper analyses which variables influence the disappearance of mutual funds in the Spanish market and whether these variables vary depending on the investment objectives. The following variables are tested: age, size, investment flows, return, volatility, Sharpe ratio, Morningstar rating, and fund family. The Kaplan-Meier estimator and an extension of the Cox model, the Andersen-Gill model are used and the results indicate that the impact of some variables on survival capacity is different depending on the fund’s investment objectives. The originality of this article is twofold. The analysis of disappearance takes the investment objectives of the mutual funds into account and a new variable, the Morningstar rating, is introduced. Moreover, no previous study examines survival capacity in the Spanish market according to different investment objectives. In Spain, mutual funds are highly concentrated because most of them are in the hands of a small number of banks who also control the country’s largest fund families. This characteristic not only makes the Spanish market an interesting one for analysis, but it also means that the results of this paper are significant for mutual fund investors.

Non-Fundamental Demand and Style Returns

We show that mutual fund ratings generate correlated demand that creates systematic price fluctuations. Mutual fund investors chase fund performance via Morningstar ratings. Until June 2002, funds pursuing the same investment style had highly correlated ratings. Therefore, rating-chasing investors directed capital into winning styles, generating style-level price pressures, which reverted over time. In June 2002, Morningstar reformed its methodology of equalizing ratings across styles. Style-level correlated demand via mutual funds immediately became muted, significantly altering the time-series and cross-sectional variation in style returns. Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.

Uncovering Mutual Fund Private Information with Machine Learning

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.

Influences on mutual fund performance: comparing US and Europe using qualitative comparative analysis

This study examines the conditions that lead mutual funds to underperform or outperform competitors. Using fuzzy-set qualitative comparative analysis (fsQCA), we draw upon extensive research on fund returns to affirm and extend earlier discoveries. Fund performance (Morningstar ratings), features of the funds themselves, and characteristics of the fund managers are considered. Positive Morningstar star and analyst ratings are necessary conditions for funds to generate value (measured by Jensen’s alpha). Funds with low management fees and low ongoing fees have attractive Sharpe ratios and high returns. Likewise, large funds with good Morningstar ratings have good Sharpe ratios and returns, often when fund managers have short tenures.

Mismarking Fraud in Mutual Funds

We study potential fraudulent mismarking of newly purchased odd lot and two classes of round lot positions in structured products. Such mismarking artificially inflates net asset values and overstates cumulative returns. Applied to mutual funds launched after January 2010, a simulation-tested mismarking fund filter identifies 12 Highly Questionable funds managing $75 billion. The performance of these funds matches closely the predicted pattern of mismarking: extremely high alpha and skewness, particularly immediately after launch. We show that structured-product mismarking can seriously inflate return-since-inception metrics. We also provide evidence consistent with return smoothing in at one quarter of the sample structured product funds. The inflated performance metrics benefit fund managers through significantly higher Morningstar ratings and asset growth, but cause material losses to later investor cohorts.

The Effect of Rating Thresholds on Portfolio Pumping and Market Quality

This paper reveals that mutual fund managers manipulate Morningstar star ratings by inflating their month-end portfolio values when they are likely to finish the month near rating cutoffs. This star rating manipulation is more pronounced among funds with greater incentives and abilities to pump their portfolios and manipulate star ratings. Following heightened regulatory scrutiny, portfolio pumping to manipulate star ratings has largely migrated from quarter/year-ends to less prominent month-ends. Improving star ratings, portfolio pumping increases fund flows, especially in the month of a rating upgrade. Placebo tests exploiting the June 2002 change in Morningstar rating methodology yield expected null effects.

Manipulation-Proof Performance Measurement

Morningstar ratings are important determinants of fund flows. We derive the finite-sample distribution of the underlying Morningstar risk-adjusted return (MRAR) and show that it exhibits high variance and bias. High variance explains the observed low absolute persistence of Morningstar ratings. The bias is increasing in fund volatility, perversely rewarding funds for taking on risk. This explains why 1-star ratings are more persistent than 5-star ratings. These theoretical findings are confirmed in a bootstrap analysis of the joint empirical distribution of mutual fund returns. Lastly, as an expected utility measure, the MRAR is meaningful at the portfolio-level but not the fund-level.