Mutual Fund Ratings Research Articles

Implementation of Decision Support in Mutual Fund Investment Selection using MOORA

This research focuses on the application of the Multi-Objective Optimization method based on Ratio Analysis (MOORA) as part of the Decision Support System (DSS) in the selection of mutual fund investments in Indonesia. The purpose of this study is to help novice investors who often find it difficult to choose mutual funds due to lack of knowledge. Considering that the number of investors continues to increase, especially during and after the pandemic, this research becomes relevant and important. The MOORA method was chosen because of its ease and flexibility in handling various criteria compared to other Multiple Criteria Decision Making (MCDM) methods. The five criteria taken as calculation material are return, risk, cost, liquidity, and reputation of the investment manager. The results showed that the MOORA method is effective in providing objective and data-driven investment recommendations. Considering various relevant criteria and weights, MOORA can provide mutual fund ratings that match investors' preferences and risk tolerance. Thus, this research successfully achieved its goal of assisting novice investors in choosing mutual funds. These results suggest that MOORA can be an important part of DSS in the context of mutual fund investing.

Analisis Komparatif Kinerja Reksa Dana Syariah dan Reksa Dana Konvensional di Masa Pandemi

This study aims to compare and see the performance of Islamic mutual funds and conventional mutual funds during the pandemic. This study uses a type of research with a quantitative approach. The samples used in this study were conventional mutual funds and Islamic mutual funds. The source of data in this study is secondary data from the Net Asset Value per unit (NAV/unit) of sharia mutual funds and conventional mutual funds available on the OJK website. The analytical method used is the comparative method and the average difference test using the independent sample t-test method, with the SPSS program and the Sharpe method to determine the best performance of stock mutual fund ratings. The results show that the performance of conventional stock mutual funds with an average Sharpe index of 0.026 is superior to the performance of Islamic mutual funds with an average Sharpe index of 0.021, while based on the independent sample t-test test, it is proven that there is no significant difference between the performance of Islamic stock mutual funds and performance of conventional equity mutual funds, with a 2-tailed sig value of 0.498 > 0.05.

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.

What Do Capture Ratios Really Capture in Mutual Fund Performance?

Many well-known mutual fund companies as well as mutual fund rating services such as Morningstar have recently reported “capture ratios” to help investors evaluate mutual fund performance. These ratios give investors a sense of how a fund has performed in certain market conditions. For example, there is an “upside market capture ratio” that shows a mutual fund’s past performance in up-markets. Similarly, there is a “downside market capture ratio” which provides the fund’s past performance in down-markets. In this article we use mutual fund data from 1990–2019 to analyze these capture ratios and examine how well they predict future fund performance. We find evidence that capture ratios are quite overrated. First, they do not seem to “capture” manager ability but rather just the beta of the portfolio. Second, when we use a measure of manager skill created by combining capture ratios, we find that this measure of skill is actually negatively and significantly related to future fund performance over periods longer than one year. Based on our results, investors should be cautious when using capture ratios to measure or predict performance. <b>Key Findings</b> ▪ This article finds that the relationship between the capture ratios and beta is significantly and strongly positive. This suggests that those using capture ratios as evidence of manager skill may be misattributing performance. ▪ This article also finds that the skill measure, which is the difference between a fund’s upside and downside capture ratios, is significantly and negatively related to three-year and five-year out-of-sample alpha. This suggests that longer-term investors do not benefit from investing in funds with higher skill. ▪ The results presented in this article suggest that mutual fund companies should be cautious when touting their capture ratios as evidence of manager skill, and investors should be cautious not to misinterpret the capture ratios as evidence of manager skill.

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

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

Mutual Fund Rating Prediction using Proportional Odds Logistic Regression with Imbalanced Class

Mutual funds ratings given by rating agencies, are very popular and helps new/first time investors to select and invest in funds based on the ratings a fund takes without going through the detailed portfolio. However sometimes these ratings could be biased or incorrect or in favor of specific fund and it could affect an investor decision. New investors face a lot of problems while investingand choosing mutual funds due to poor professional advice and lack of right tools and resources to assess a funds true performance. To overcome the problem of incorrect rating and to help an investor to choose the funds wisely using machine learning, we have attempted to predict the rating and classify mutual funds using proportional odds logistic regression which classifies funds intorating classes from 1 to 5 with 5 being the high rated fund and 1 being the low rated fund. While some prior studies have suggested methods of using clustering to classify based on performances using Supervised/Unsupervised learning, this paper deals with supervised learning forpredicting the ratings using the mutual fund financial ratios and also handles imbalanced classes.To handle imbalance class problem in a multi-class setting, we propose a new class balancing hybrid methodology of using EM and Gauss-Smote sampling that significantly improves the rating prediction

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.

On the failure of mutual fund industry regulation

Mutual funds grant retail investors access to professional asset management and facilitate exposure to financial markets. The academic literature and regulators have traditionally focused on issues such as portfolio diversification, performance, liquidity, and management fees in attempts to analyze and improve market efficiency. Scarce attention has been paid to market risk management. There is unanimity on this issue throughout the world. The lack of regulatory attention creates a gap, which is partially covered by mutual fund rating agents and asset management analysts. Those agents base their ratings on various rating methodologies — which engenders a wide array of difficulties, especially for retail investors. We employ proprietary data on historic mutual fund ratings in Israel and show that retail investors do not necessarily benefit from this diversity of opinions. Furthermore, we find that the voluntary implementation of quantitative risk measurement techniques by certain mutual funds tends to be associated with fewer outflows and greater inflows in these funds. Interestingly, the application of (backward-looking) value-at-risk analysis is associated with fewer outflows, while (forward-looking) stress-testing techniques are associated with greater inflows. Given the similarity of mutual fund industry environments across the globe, our results have worldwide applicability.

On the Failure of Mutual Fund Industry Regulation

Mutual funds grant retail investors access to professional asset management and facilitate exposure to financial markets. The academic literature and regulators have traditionally focused on issues such as portfolio diversification, performance, liquidity, and management fees in attempts to analyze and improve market efficiency. Scarce attention has been paid to market risk management. There is unanimity on this issue throughout the world. The lack of regulatory attention creates a gap, which is partially covered by mutual fund rating agents and asset management analysts. Those agents base their ratings on various rating methodologies — which engenders a wide array of difficulties, especially for retail investors. We employ proprietary data on historic mutual fund ratings in Israel and show that retail investors do not necessarily benefit from this diversity of opinions. Furthermore, we find that the voluntary implementation of quantitative risk measurement techniques by certain mutual funds tends to be associated with fewer outflows and greater inflows in these funds. Interestingly, the application of (backward-looking) value-at-risk analysis is associated with fewer outflows, while (forward-looking) stress-testing techniques are associated with greater inflows. Given the similarity of mutual fund industry environments across the globe, our results have worldwide applicability.

Do mutual fund ratings provide valuable information for retail investors?

PurposeRetail investors use information provided by mutual fund rating agencies to make investment decisions. This paper aims to examine whether the ratings provide useful information to retail investors by analyzing the rating migration and closure risk of mutual funds that received Morningstar’s mutual fund ratings from 2005 to 2012.Design/methodology/approachThe research design differentiates between buy-and-hold investment strategies and dynamic investment strategies. To assess the information content of mutual fund ratings for buy-and-hold investment strategies, the rating migration based on the first and the last mutual fund rating during two-, four-, six- and eight-year horizons is determined. With respect to dynamic investment strategies, the number of rating changes per fund on a monthly basis during these time horizons is calculated.FindingsMutual fund rating persistence is low or even inexistent, in particular, during longer time periods. Only for lower-rated funds, the rating appears to indicate higher risk of fund closure. In addition, mutual funds face a large number of up to 38 monthly rating changes in the eight-year window.Originality/valueMutual fund rating persistence has hardly been analyzed for funds offered to retail investors so far. This paper clearly points out that because of the extensive rating migration and the high number of monthly rating changes, retail investors barely benefit from using mutual fund ratings.

Does Fund Managers'Ability Vary with Mutual Fund Rating in India?

Does Fund Managers'Ability Vary with Mutual Fund Rating in India?

Mutual Fund Ratings and Efficiency: An Examination of the Relationship

Morning Star is a rating agency that rates mutual funds based on past performance. The best performing funds are rated 5* while the lowest performing ones are rated 1*. Investors rely heavily on these ratings for making their investment decision. Funds with high rating tend to attract millions of dollars more than those rated lower. The highly rated funds also charge higher management fees as compared to the lower rated funds. Very few studies have examined whether the higher ratings and fees are justified by better performance. In this paper we use a Data Envelopment Based measure of efficiency to examine whether the funds rated 5* are more efficient than funds that are rated 3* by Morning Star ratings. We use returns as the sole output and standard deviation and expense ratio as the two inputs. The results of the study indicate that by and large the Morning Star 5* funds are more efficient than the 3* funds. Another significant find is that small cap funds are generally more efficient than large caps. This implies that even within a particular rating category small funds may be a better investment. The findings of the paper are significant as they provide evidence that ratings are a useful tool for investors in the selection of funds.

Blurred stars: Mutual fund ratings in the shadow of conflicts of interest

Using unique data on trading commission payments to mutual fund rating companies (MFRCs) by mutual funds in China, this paper investigates whether the conflicts of interest arising from trading commission payments bias MFRCs’ mutual fund star ratings and hence affect their informativeness. We find the rating of a mutual fund is more optimistic when the MFRC either (i) receives trading commission fees from the mutual fund or (ii) can potentially receive fees in the future. The paper further shows that the usefulness of ratings in terms of predicting a fund’s future performance is negatively impacted by conflicts of interest. There is also evidence that investors can see through the problem, responding less enthusiastically (in terms of fund flows) to the ratings of conflicted MFRCs. We further find that the introduction of a rating qualification system that aims to improve mutual fund rating quality exacerbates the rating bias.

The Performance of Thai Mutual Funds: A 5-Star Morningstar Mutual Fund Rating

The Performance of Thai Mutual Funds: A 5-Star Morningstar Mutual Fund Rating

Frontier-based vs. traditional mutual fund ratings: A first backtesting analysis

We explore the potential benefits of a series of existing and new non-parametric convex and non-convex frontier-based fund rating models to summarize the information contained in the moments of the mutual fund price series. Limiting ourselves to the traditional mean-variance portfolio setting, we test in a simple backtesting setup whether these efficiency measures fare any better than more traditional financial performance measures in selecting promising investment opportunities. The evidence points to a remarkable superior performance of these frontier models compared to most, but not all traditional financial performance measures.

Evaluating the Rating of Stiftung Warentest: How Good Are Mutual Fund Ratings and Can They Be Improved?

AbstractWe test the abilities of the Stiftung Warentest fund rating system to predict future fund performance among German registered funds for six equity categories: Germany, Euro‐Zone, Europe, North America, Pacific and World. Stiftung Warentest is a consumer protection agency and a major provider of fund ratings in Germany. Our empirical analysis documents predictive abilities of the rating system. The reason is that measures of past performance are positively related to future performance in several of these markets, even after controlling for momentum. Measures of fund activity (e.g., Active Share) are also helpful to predict performance, in particular to identify likely future losers. Hence, they should be taken into consideration by consumers when selecting a fund and rating agencies when revising current rating methodologies.

Mutual Fund Ratings and Performance Persistence

This paper studies the persistence of mutual fund performance. Academic research often focuses on fund returns, sometimes adjusted for style and market cap biases. Because fund rating systems play a central role in the asset management industry, we consider another approach in this paper. Using a Markov modeling of these ratings, we illustrate that the persistence of the performance is relatively poor with respect to the time horizon of investors. We show that two facts may explain these results. First, the rating system is not necessarily time-homogeneous. Second, the importance of style is crucial when comparing the ratings of mutual funds. However, we show that it is extremely dicult to characterize quantitatively the style of a mutual fund. We conclude that fund selection is more art than science, and that quantitative analysis must be combined with qualitative insight.

The Predictive Performance of Morningstar's Mutual Fund Ratings

With currently more than 17,000 mutual funds available, many investors are looking for tools to identify the best performing mutual funds. The most well-known fund rating system is provided by Morningstar Inc. This study investigates in a thorough empirical analysis the predictive performance of mutual fund ratings given by Morningstar over the course of a 10 year period beginning March 1995. From this analysis it becomes clear that the predictive performances of the different rating systems used by Morningstar do not beat a random walk. Furthermore, our results show that the latest amendment to the rating system, the introduction of 64 categories over four different asset classes, has reduced the predictive performance of the rating system as a whole.

Morningstar Mutual Fund Ratings Redux

We perform an extensive examination of how the new Morningstar rating system, introduced in June 2002, predicts future fund performance. Specifically, we examine all domestic equity funds that were rated by Morningstar as of June 30, 2002. We then examine the performance of these funds over the next three years, July 2002-June 2005. Using four different performance metrics, adjustments for loads, and three different methodologies for dealing with survivorship bias, we find widespread support for the notion that the new Morningstar rating system can predict future performance, at least within the first three years out-of-sample. Specifically, we find that higher rated funds, for the most part, significantly outperform lower rated funds. Moreover, the effect is relatively monotonic as even the next to lowest rated funds (2-star funds) significantly outperform the lowest rated funds (1-star funds). These results are quite different from those of Blake and Morey (2000) and Morey (2002b) which show that the older Morningstar rating system did not predict future performance well.

Kiss of Death: A 5-Star Morningstar Mutual Fund Rating?

We examine the effect that an initial 5-star Morningstar mutual fund rating has on future fund performance, strategy, risk-taking, expenses, and portfolio turnover. Using a sample of diversified domestic equity funds from the 1990s we find that three-years after a fund received its initial 5-star rating, fund performance severely falls off. This result is robust across different performance measures and different samples of funds. We also find that after receiving their initial 5-star rating, the risk levels of funds rise and that the funds are not able to load on momentum stocks as well as they did before receiving the 5-star rating. These results suggest that funds, to some degree, alter their portfolios after receiving a 5-star rating and that investors should be very wary about using the 5-star rating as a signal of future 3-year performance.