Morningstar Star Ratings Research Articles

Morningstar Star ratings and the performance, risk and flows of European bond mutual funds

In this paper we evaluate the relation between Morningstar Star ratings and the performance, risk and flows of European bond funds over the period 2006–2019 based on a comprehensive and survivorship-free dataset comprising 939 mutual funds. Fund performance is evaluated by net and gross returns, as well as alphas based on single and multi-factor models. Our results show that well-rated mutual funds exhibit superior performance even after controlling for differences in fund characteristics although they exhibit high risks both in terms of volatility and value-at-risk. We also find strong evidence of fund performance persistence and rating persistence. Finally, Morningstar Star ratings are also useful for explaining inflows and outflows. Funds with higher ratings attract more flows even if we control for prior performance.

Meeting new peers: The effects of Morningstar category reassignment on fund flows and star ratings

We investigate how the reassignment of a fund's Morningstar category affects fund flow and Morningstar star rating. We find that funds assigned to a different category gain positive abnormal flows and this effect is significant mainly for high-rated funds. Category reassignment does not improve a fund's star rating on average, and flows are less responsive to a star-rating change if the rating change is likely to be driven by category reassignment. The positive abnormal flows captured by high-rated funds after category reassignment are consistent with a visibility story: some investors filter funds by Morningstar category and star rating, and category reassignment makes a fund more visible to a new group of investors if the fund is highly rated. In contrast, a low-rated fund is likely to be selected only by investors who do not refer to the fund's Morningstar information and, hence, gains little visibility from category reassignment. We also find evidence that more sophisticated investors are more likely to consider not only fund rating but also fund category when evaluating fund performance.

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.

Redefining quality investing

In this paper we evaluate the relationship between quality investing combined with Economic Moat, ESG (Environmental, Social and Governance) and analyst opinions over the period 2014-2020 (28 quarters) based on a dataset comprising 803 US stocks rated by Morningstar. Performance is evaluated mainly in terms of alphas (six factor model). Our results show that quality stocks measured by ROIC exhibit superior performance. The incorporation of competitive advantages (Morningstar’s Economic Moat) allows a better discrimination among the classic high-quality strategies. Investment in stocks with quality and high ESG entails the payment of a premium but buying quality companies with Economic Moat makes up for this negative aspect. The Morningstar Star Rating is not significant when we control the performance by, for instance, sector or size and considering Economic Moat, but it is the average Price-to-Price target (Analyst consensus). Our results show that conventional quality strategies can be improved via the incorporation of a company's competitive advantages and, above all, by Analyst consensus regarding the potential for stock revaluation.

Relative Importance of Sustainability Measures and Costs in Mutual Fund Selection

Impact investing has become a mainstream investment practice, especially for millennial investors. More financially savvy than their Generation X predecessors and with an increased concern for environmental and social justice issues, Millennials are transforming socially responsible investing from unconventional to commonplace. This article examines the relationship between sustainability and mutual fund returns (both historical and anticipated) to identify beneficial information that socially responsible investors can use in making investment decisions. Using Morningstar sustainability ratings, star ratings, and analyst ratings, the authors find a very low correlation between sustainability and fund returns. Sustainability ratings do not vary across Morningstar star ratings or analyst ratings; nor do Morningstar star or analyst ratings vary within high or low sustainability groupings. The practical relevance of these findings is that socially responsible investors do not have to sacrifice financially when investing with a positive social inclination. However, the authors document the ability to enhance sustainable investment returns by limiting fund costs in the form of load charges and expense ratios. Low costs are associated with higher Morningstar star ratings and forward-looking analyst ratings. Hence, it is possible to “do well” financially by selecting less expensive funds with high sustainability ratings. TOPICS:ESG investing, mutual fund performance Key Findings • The practical relevance of these findings is that socially responsible investors do not have to sacrifice financially when investing with a positive social inclination. • The authors document the ability to enhance sustainable investment returns by limiting fund costs in the form of load charges and expense ratios. • Hence, it is possible to “do well” financially by selecting less expensive funds with high sustainability ratings.

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.

Maximizing College Savings through the Selection of Low-Cost 529 Plan Portfolios

Parents and grandparents saving for descendent children and grandchildren’s college expenses seek to leave as large a nest egg as possible. As with most investments, the objective is to reach a high terminal value without taking excessive risk. In the case of college savings funds, a larger terminal value will expand the set of options available to the college-bound student. State and federal governments have worked together to make saving for college less onerous through the creation of tax-advantaged 529 plans. Analysis of the entire universe of 529Ps with 10 years of data reveals that a significant factor in differentiating between better and worse 529P investment performance is their expenses and their loads. Among the 529Ps with the lowest 25% of expenses, the authors find significantly better returns, lower standard deviation, and no appreciable impact on risk as measured by beta. Selection of 529Ps without loads also increases returns without increasing standard deviation or systematic risk. Risk-adjusted returns using standard deviation, negative return variance, or beta all demonstrate the value of avoiding high expenses and load/commission fees. Morningstar star ratings were also found to be higher, and downside capture ratios were found to be lower for the lower expense 529Ps.

Information Effects of Changes to Analystss Recommendations: Morningstar Star Ratings Changes for Stocks

We study the nature and impact of ratings changes for individual stocks provided to investors by Morningstar, Inc. Morningstar’s recommendations follow negative momentum for upgrades and positive momentum for downgrades. When ratings change, upgraded stocks experience positive abnormal returns, while downgraded stocks experience negative abnormal returns. Morningstar recommendations not only impact stock prices at announcement, but statistically significant abnormal returns occur over the following 30 trading days. Additional variables tracked by Morningstar, such as economic moat and uncertainty, explain variation in abnormal returns associated with ratings change announcements. Overall, the results suggest that Morningstar analysts provide valuable information to investors.

The performance of US equity mutual funds

The paper examines the performance of US no-load equity mutual funds. Fund performance is derived using stochastic frontier analysis for a flexible functional form. This analysis allows us to derive parametric estimates of efficiency scores for each fund in our sample for the first time in the literature. Our results indicate that US no-load equity funds display varying levels of efficiency over time but also depending on size and on investment style. Robustness analysis reaffirm the efficiency scores remain consistent across different selections of inputs and outputs as well as the underlying distribution of the return. Having estimated each fund’s efficiency in the sample we unveil their underlying dynamics, also with respect to risk and operational characteristics such as flows, assets, and Morningstar star ratings. Panel-VAR estimations reveal that the response of funds’ efficiency to a shock in risk is positive and substantial. Some evidence of reverse causality is also observed. Finally, we extend our analysis to investigate the relationship between funds performance and key covariates across subgroups defined by size.

Morningstar Analytical Mutual Fund Measures and Selection Model

First, this study reviews Morningstar analytical grading measures used by investors to choose mutual funds. These measures include Morningstar Star Ratings, Analyst Ratings, Total Pillar Ratings, Upside and Downside Capture Ratios, and Stewardship Ratings. Second, the study provides results of research that assesses effectiveness of Morningstar fund grading measures. Third, a model using selected Morningstar fund grading measures (and data) is applied to selection of Vanguard domestic actively managed equity funds.

Morningstar Mutual Fund Measures and SelectionModel

The first purpose of this study is to review the Morningstar analytical grading measures used by investors to select mutual funds. The grading measures include Morningstar star ratings, analyst ratings, total pillar ratings, upside and downside capture ratios, and stewardship ratings. The second purpose is to provide the results of studies that assess the effectiveness of Morningstar grading measures. The third purpose is to propose a model that systematically applies selected Morningstar measures to ease investor choice of equity funds. In this case, the model is applied to Vanguard domestic actively managed equity funds, less sector funds. The effectiveness of this model is best-tested using future performance of each selected fund over three-, five-, and ten-year periods.

Selection of Vanguard Actively Managed Stock Funds Using Morningstar Measures

First, this study reviews Morningstar analytical grading measures used by investors to choose mutual funds. These measures include Morningstar Star Ratings, Analyst Ratings, Total Pillar Ratings, Upside and Downside Capture Ratios, and Stewardship Ratings. Second, the study provides results of research that assesses effectiveness of Morningstar fund grading measures.Third, a model is proposed that systematically applies selected Morningstar measures to ease investor choice of equity funds. In this case, the model is applied to Vanguard domestic actively managed equity funds, less sector funds. The effectiveness of this model is best-tested using future performance of the selected Vanguard domestic actively managed equity funds over three-, five-, and ten-year periods.

Neglected Risks in Mutual Fund Performance Measurement: An Additional Cost to Stock-Picking

This paper takes a closer look at utility based performance measurement proposed by Goetzmann et al. (2007) and used in popular Morningstar star ratings. Utility based per- formance measures offer a very intuitive way of risk correction and are hard to manipulate. They require, however, a proper benchmark measure to filter out lucky funds. I propose to use the Daniel et al. (1997) (DGTW) characteristic based benchmark portfolios as bench- marks for the utility based performance measure. I find that the DGTW selection measure consistently overestimates the manager’s selection skills in certainty equivalent terms, and that this overestimation can be decomposed into an idiosyncratic and a systematic compo- nent. In diversified fund portfolios, the certainty equivalent selection measure is, on average, 87bps higher than in undiversified fund portfolios. The remaining undiversifiable risks cost 47bps per year in certainty equivalent terms and can be explained in part be imprecise correction for known systematic risk factors, in part by unknown but undiversifiable risk factors. The certainty equivalent measure captures risk particularly well in years with high moment realizations of the CRSP value weighted index.

S&P 500 Index Mutual Funds: Diverse Expenses and Performance Characteristics

For investor and institutional class index mutual funds that track the S&P 500 Index, there are just 25 funds with statistically low expense ratios (management fee findings are found above). However, there are only five index funds - all investor class - with statistically very high and extremely high expense ratios. Thus, these results contain both bad and good news for investors. Unfortunately, the complete story of high expense ratios for S&P 500Index funds finds more bad news. The Sharpe ratio, Jensen’s alpha, annualized total return, Morningstar Star ratings, and average net assets are all statistically, negatively correlated with index funds with statistically high expense ratios. Further, portfolio turnover and 12b-1 fees are statistically, positively correlated with index funds with statistically high expense ratios.

What is the Wind Behind this Sail? Can Fund Managers Successfully Time Their Investment Styles?

There is a considerable body of literature that examines the behaviour of institutional investors as a potential source of market price movement. Most existing studies focus on the market timing abilities of active fund managers and find mixed evidence for their fund timing skills. However, few studies have investigated fund manager timing abilities within segments of the market, such as factor timing and sector timing. This study investigates the style timing behaviour of US domestic equity funds existing at any time during the period 1992-2002. Specifically, I examine the timing activities of actively managed mutual funds within different market segments based on such established systematic risk factors as size, book-to-market, momentum, and across different fund styles such as aggressive growth, growth and income, and small company funds, etc. Mutual fund timing strategy can be viewed as the fund manager's response to his/her private information regarding future factor premiums. Instead of directly observing how fund managers make their timing decisions, an alternative approach is to look at the direct outcomes of their decisions, which are related to the factor timing loadings derived from a factor timing model. I significantly expand on the work of Bollen and Busse (2001) and Volkman (1999) by combining systematic risk factors unique to equity markets with timing factors unique to actively managed portfolios. Within this empirical timing-activity evaluation framework, I additionally investigate fund timing behaviour in the context of Morningstar star rating performance record, investment objectives, fund age, turnover, and load expense, etc. This paper is an original contribution to the literature of fund timing activities, which seeks to contribute to our understanding in terms of investigating mutual fund managers' timing strategies with respect to specific systematic risk factors and their evolution over time. This research has important implications both for extant asset pricing theories and for practitioners especially in the evaluation of portfolio performance and investigation of fund managers' timing activities.

Star Power: The Effect of Monrningstar Ratings on Mutual Fund Flow

AbstractWe apply an event-study methodology on over 10,000 Morningstar star rating changes and find that Morningstar has subsantial independent influence on the investment allocation decisions of retail mutual fund investors. It is the discrete change in the star rating itself and not the change in the underlying performance measures that drives frow. We document econnomically and statistically significant positive abnormal flow following rating upgrades, and negative abnormal flow following rating downgrades. In contrast to the cross-sectional flow performance literature, we find evidence of investor punishment of performance declines, some of which is evident immediately in the month of the rating change.

Identification and Performance of Equity Mutual Funds with High Management Fees and Expense Ratios

Because strong price competition does not characterize the mutual fund industry, we conclude that fund expenses are too high. This conclusion is consistent with the study’s empirical findings that very large and significant differences exist among specifically identified funds with respect to management fees and expense ratios. Yet, there remains the question of “so what” if mutual fund expenses are too high? No one forces investors to choose funds. We find the answer in the Investment Company Act of 1940 and its requirement that independent directors have a fiduciary duty with respect to the reasonableness of fund fees.We identify 67 specific mutual funds with statistically very high and 23 funds with extremely high management fees. In addition, we identify 66 funds with very high and 27 funds with extremely high expense ratios. The number of funds in each of these two management fee and expense ratio standard deviation classes each represents 1.5% of total sample funds. Investors should consider these findings carefully, as should fund researchers, financial columnists and writers, investment advisers, financial planners and regulators. We also examine the association of management fees and expense ratios to descriptive performance measures by Morningstar category overall across each of the four standard deviation classes. These measures are the Sharpe ratio, Jensen alpha, Morningstar star ratings, and five-year annualized total returns.Management fees have a mixed association with each of the performance measures. Each performance measure is higher in the extremely high management fee class ( 3σ) than in the above average management fee class (within 1σ). ANOVA tests only show significant differences for the Sharpe ratio and Jensen’s alpha Morningstar categories overall across the four standard deviation classes. A potential implication of these results is that higher management fees may add value to active portfolio management and contribute to improved performance measures. These findings add fuel to the continuing debate over active versus passive portfolio management. Expense ratios have the expected general negative associations with each of the performance measures. These findings are consistent with previous studies by Jensen (1968), Malkiel (1995), Gruber (1996), and Carhart (1997). Yet, ANOVA tests show significant differences only for the Morningstar star ratings and five-year annualized total returns by Morningstar category overall across the four standard deviation classes. Nonetheless, our results show that expense ratios maintain their negative associations with each of the performance measures despite the mixed associations of component management fees.

Fiduciary Selection and Monitoring of Investment Managers Under Daubert: Statistically Testing the Hypothesis that a Money Manager is Better or Worse than Random

Statutory fiduciary standards relevant to the selection of investment managers and the monitoring of investment decisions offer very little operational guidance. The guidance cautions little more than that the fiduciary exercise ordinary prudence, diversify and adhere to modern portfolio theory. No specific process or quantitative measures are defined. The most common approaches to satisfying the fiduciary's need for quantitative specificity, in one form or another, reduce to the mean-variance (risk-reward) metrics that underpin the Capital Asset Pricing Model (CAPM). Though widely employed, the array of risk adjusted performance measures such as the Sharpe Ratio and Morningstar's Star rating system provide only rankings and do not meet the Daubert judicial standard. The Supreme Court in the Daubert decision adopted the proposition that Scientific methodology should be based on generating hypotheses and them to see if they can be falsified... The generated hypotheses should be supported by an articulation of credible principles. There must be more than the ipse dixit of the proponent. Applying the mandate to the context of investment advice, the decision effectively requires that the tools of statistical inference be employed when offering opinion testimony as to the adequacy of an investment manager's performance or process. The solution presented in this paper is to reduce the manager's decision process to a binomial model and then perform a back test. The back testing results are in turn then used to accept or reject the hypothesis that the manager or strategy is more than random and will likely, at some level of statistical significance, outperform the appropriate benchmark. To illustrate the technique and provide operational specificity, a naive best performer strategy is back tested. Over the ten year period studied the strategy has a 20% annual return. Analysis of the illustrative best performer strategy concludes that the estimated success rate is between 51% and 68% with a 93% probability, rejecting the random hypothesis. An articulation of the causal mechanism is discussed. In the binomial model success is defined as the strategy out performing the benchmark in a sample trading period. Mapping the manager's performance to a binomial decision process admits the panoply of runs tests and non parametric analysis available for the binomial model. The results can then be used to monitor the consistency of future performance.

The Ranking Properties of the Morningstar Risk-Adjusted Rating

The study examines the ranking properties of the Morningstar risk-adjusted rating (RAR). We find that the RAR and the excess return from the CAPM regression yield similar star ratings. In contrast, we document systematic differences between the star ratings produced by the RAR and the excess return estimated from the Fama-French [1993] three factor model. Approximately 77% of domestic equity funds with a ten-year five-star Morningstar rating do not maintain their five stars under the null hypothesis of the Fama-French three factor model. The study concludes that the sole reliance upon the Morningstar star rating to select U.S. equity funds may not result in an optimal allocation across style categories for a multi-fund portfolio.

Star Power: The Effect of Morningstar Ratings on Mutual Fund Flows

We apply an event-study methodology on over 10,000 Morningstar star rating changes and find that Morningstar has substantial independent influence on the investment allocation decisions of retail mutual fund investors. It is the discrete change in the star rating itself, and not the change in the underlying performance measures that drives flow. We document economically and statistically significant positive abnormal flow following rating upgrades, and negative abnormal flow following rating downgrades. In contrast to the cross-sectional flow-performance literature, we find evidence of investor punishment of performance declines, some of which is evident immediately in the month of the rating change.