A Morningstar analysis of the average industry glidepath shows it will meet most retirees' spending needs, and funds with significantly different asset allocations have delivered similar returns in recent years. Other factors may contribute to these investments' relative success over the long term. A new Morningstar study of data on the firms offering the target date series suggests a tie between better stewardship practices and stronger risk-adjusted performance.
This methodology document addresses the return calculation for fixed-rate and zero-coupon bonds. The methodology does not currently address other types of bonds such as floating-note bonds, mortgage-backed and asset-backed bonds, Treasury Inflation-Protected Securities, and so on.
ETF managed portfolios are investment strategies that typically have more than 50% of portfolio assets invested in ETFs. Primarily available as separate accounts, they represent one of the fastest-growing segments of the managed account universe.
This article addresses the issue of the alleged superiority of risk-factor-based asset allocations over the more traditional asset-class-based asset allocation.
Total portfolio refers to a portfolio with a strategic asset-allocation policy consisting of multiple asset classes implemented with various investment managers. This document outlines several methods of attributing performance, including the classic approaches of Brinson, Hood, and Beebower and Brinson and Fachler, the principles upon which today's performance attribution methodologies are founded. It describes Morningstar's recommended method, the top-down geometric method.
Research that has led to what is known as the "low volatility anomaly" in cross-sectional stocks from a similar universe indicates that volatility is not compensated with a "volatility" premium. We find evidence of a risk premium, but it depends on the definition or measure of risk. "Tail risk" measures the probability of having significant losses and should be what investors care about the most. We investigated several risk measures, including volatility and tail risk, and found that volatility is not compensated but tail risk is compensated with higher expected return in both U.S. and non-U.S. equity funds.
The Global Fund Investor Experience report measures the experiences of mutual fund investors in 24 countries in North America, Europe, Asia, and Africa. Morningstar researchers evaluated countries in four categories—regulation & taxation, disclosure, fees & expenses, and sales & media.
Morningstar has conducted qualitative, analyst-driven research on 529 college-savings plans since 2004 and has transitioned to a new ratings scale. The Morningstar Analyst Rating® for 529 College-Savings Plans is the summary expression of our forward-looking analysis of a 529 college-savings plan.
Recent literature indicates that a liquidity investment style – the process of investing in relatively less liquid stocks within the liquid universe of publicly traded stocks – has led to excess returns relative to size, value, and momentum. We examine whether this style can be uncovered not just at the stock level, but at the mutual fund level.
The Similar Funds tool will generate a list of investments that are similar to a user-specified offering. This methodology is based upon several factors including the category, special criteria, portfolio allocation, and performance of the fund provided.
After-tax returns are measures of fund performance that take into account the taxes a hypothetical investor pays on fund distributions and capital gains. This methodology document addresses Morningstar’s interpretation of the 2001 Securities and Exchange Commission guidelines and changes that arose from the Jobs and Growth Tax Relief Reconciliation Act.
This article reviews the motivation for multiplying by root 12 and explains why it does not apply to returns. It then shows the derivation of the correct method to help the reader understand why it is in fact correct.
This paper proposes a new methodology to model ETF premiums and, for the first time, present empirical data on the time-series evolution of ETF premiums and discounts.
Morningstar Target-Date Fund Series Ratings and Research Reports are designed to help individual investors, financial advisors, plan sponsors, and other interested fiduciaries make informed decisions when evaluating a series of target-date funds. Target-date funds are marketed as a one-decision, comprehensive investment option for investors' retirement savings.
In this paper we introduce a model that takes into account current bond yields and allows them to “drift” 3 toward a higher value during retirement using an autoregressive model based primarily on historical relationships between asset classes. This approach can better replicate the actual bond returns a current or near retiree can expect during retirement both now and in the future.
Social Security (SS) is the largest source of retirement income for most Americans. This paper provides the reader with an overview of the SS retirement system and offers insight into key factors that should be considered when determining when to begin receiving SS retirement benefits.
The rating methodology described herein applies to the new issue of a resecuritization of one or more U.S. nonagency RMBS securities. In a typical Morningstar rated resecuritization, one or more securities are pledged to a trust as eligible collateral. All principal and interest collections from the pledged securities, or underlying securities, are paid to the resecuritization trust and distributed to the resecuritization securities in accordance with the resecuritization waterfall. Losses are generally allocated in reverse sequential order with the most junior tranche allocated losses first.
The Morningstar Rating™is a quantitative assessment of a fund's past performance--both return and risk--as measured from one to five stars. It uses focused comparison groups to better measure fund manager skill. The Morningstar Rating is intended for us as the first step in the fund evaluation process. A high rating is not a sufficient basis for investment decisions.
This paper explores the potential benefit an investor (i.e., consumer) can achieve by postponing deferrals into a 401(k) plan and paying off credit card debt instead. Through an analysis we determine that an investor who first pays off a credit card and then saves for retirement, versus just making the minimum credit card payment, can potentially increase his or her 401(k) balance at retirement.
In this updated and expanded report, we build upon our original examination of synthetic exchange-traded funds (ETFs) in Europe, highlighting recent progress made by providers towards increasing the degree of investor protection within their products and the level of transparency around them.
In this paper, we will discuss proprietary Morningstar ETF data points, which can serve as tools to measure the true cost of ETF ownership. These data points help quantify the investor experience of owning an ETF and allow for better fund selection. Selecting the right ETF involves more than just picking the cheapest fund within a category. At the same time, combining all of these factors to determine the best ETF can be overwhelming. This is why we have begun quantifying the ETF investor experience.
This paper explores important differences in the definitions of the expected replacement ratio and elaborate how, like any type of projection involving multiple variables over an extended duration, replacement rate estimates estimates can vary materially based on different assumptions and provide varying levels of insight. Based on this analysis, we would strongly suggest plan fiduciaries be cautious with respect to these projections before basing any decisions on the values.
The sole aim of this paper is to explain why it’s so important for closed-end funds to improve their transparency and release full portfolio holdings on a more frequent basis than has been the case historically.
The purpose of this study is to first establish a framework to evaluate different withdrawal strategies and second to use that framework, in conjunction with Monte Carlo simulations, to determine the optimal withdrawal strategies for various case studies. To establish the framework, we introduce a new metric, the “Withdrawal Efficiency Rate” (WER), which measures the relative efficiency of various withdrawal strategies.
This document addresses the methodology for The Morningstar Category™ classifications for hedge funds. Morningstar supports 31 hedge-fund categories, which map into the following six broad category groups: directional equity, relative value, directional debt, global/derivatives, event, and multistrategy.
This article notes that benchmark-relative comparisons will not tell you if the glide path itself is “good.” We believe the evolving asset allocation is going to be the primary driver of performance and have the greatest effect on the level of returns and income realized by the investor. We also explore the appropriate use of a target date benchmark, and include a concrete example of how to benchmark a target date fund / fund family.
In this paper, we define what the equity risk premium is and how it can be used in equilibrium and beat-the-market contexts. We also discuss various other premiums in the market that represent the differential returns of the many different asset classes and investment styles in the market. To make sound investment decisions, it is important to have good estimates of these premiums.
Some people argue that investors should allocate assets entirely to bonds, not only because bonds are the safer investment but also because they believe bonds will outperform stocks over the long run. In other words, if bonds can deliver higher returns than stocks with less risk, why bother with stocks? We look into this viewpoint.
This article explains how the Morningstar Dividend Composite Index and the Morningstar Dividend Leaders Index are constructed. The weighting system used in these indexes is designed so that the indexes are highly macro consistent, investable and have low turnover.
The rating methodology described herein will generally be applied to the new issuance and surveillance of ratings of U.S. residential mortgage-backed securities by Morningstar Credit Ratings, LLC (“Morningstar”). A rating that is assigned based on this methodology should provide market participants with a risk benchmark that can be used to gauge the relative default risk of a security against that of other RMBS securities. This methodology includes a new section on mortgage insurance analysis and clarifying edits. It supersedes the criteria published in May 2015 and is in effect immediately.
Mean-variance optimization has been the standard for creating asset allocation strategies. In Morningstar's asset allocation optimization methodology document, we describe the limitations of MVO and discusses asset-class modeling and portfolio optimization techniques that overcome some of these shortcomings.
This document describes the methodology behind the Morningstar Analyst Rating™ for U.S. closed-end funds; the summary expression of our forward-looking analysis of a fund. Ratings are assigned globally on a five-tier scale. Our global analyst team has identified five key areas that we believe are crucial to predicting the future success of funds: People, Parent, Process, Performance, and Price.
Morningstar® Analyst Rating™ for is the summary expression of Morningstar's forward-looking analysis of a fund. Morningstar analysts assign the ratings on a five-tier scale with three positive ratings of Gold, Silver, and Bronze, a Neutral rating, and a Negative rating.
This paper establishes a flexible framework for estimating credit risk and illiquidity risk for guaranteed products, so their “true” risks are reflected in the inputs to asset-allocation-oriented optimizations. Ignoring or inaccurately estimating illiquidity risk and credit risk can lead to an unjustified preference for guaranteed products.
This paper examines the qualitative measures to determine if a target maturity benchmark is appropriate. It also discusses three relatively new quantitative measures: average absolute difference in glide paths, average historical tracking error, and average forward-looking (or current) tracking error.
This document presents our method of calculating the portfolio performance attribution: comparing a portfolio's performance with that of a benchmark and analyzing components of the excess return to explain the impact of various investment decisions.
In this paper, we document the changes in the implied (cross-sectional) glide paths of the major target date fund providers through time. Additionally, we introduce a new measure for tracking the stability, or perhaps we should say instability, of glide paths through time that we call the “Glide Path Stability Score.”
This document details the methodology for calculating the Morningstar® Stewardship Grade℠ for mutual fund firms, which is designed to help investors further research, identify, and compare fund companies that do a good job--or a poor job--of aligning their interests with those of fund shareholders.
The Morningstar Rating™ for exchange-traded funds uses the same methodology as Morningstar Rating for funds. Ratings are based on risk-adjusted returns for the three-, five-, and 10-year time periods, and then the overall rating is a weighed average of the available time period ratings.
The Morningstar Rating™ is a quantitative assessment of a fund's past performance--both return and risk--as measured from one to five stars. It uses focused comparison groups to better measure fund manager skill.
This survey measures the experiences of mutual fund investors in 22 countries in North America, Europe, Asia, and Africa. Aiming to promote best practices for investors, we rated companies across four categories—Regulation and Taxation, Disclosure, Fees and Expenses, and Sales and Media—and added the cumulative category scores to produce an overall country grade.
The financial crisis of 2008 has led many investors to search for tools that minimize downside risk. In our study, we explore one of the promising alternatives to mean-variance optimization (MVO) that incorporates non-normal return distributions—mean-conditional value at risk (M-CVaR) optimization—and gain insights into the ramifications of skewness and kurtosis for optimal asset allocations through a side-by-side comparison of MVO vs. M-CVaR.
This paper addresses the methodology behind Morningstar's procedure for assigning absolute ranks, the formula for percentile ranks, and the assigning of fractional ranks.
In this paper—the first of a series of articles—we discuss the options for and issues involved in creating a universal risk measure that can act as a useful guide for individual investors without misrepresenting the complexity of risk as a concept, with particular focus on the European Securities and Markets Authority's proposed Synthetic Risk Reward Indicator for the Key Investor Information Document.
While style-based investing remains a significant part of portfolio construction in the United States, many European investors have yet to embrace this approach. This article examines Morningstar’s new European style indexes to demonstrate that even over a period shorter than a decade, style effects can be important enough to matter to European stock investors.
This paper builds on previous research to investigate whether composites of mutual funds that hold stocks with high momentum outperform composites of mutual funds that hold stocks with low momentum. Additionally, we investigate if composites of mutual funds that hold low liquidity high momentum stocks outperform those that hold high liquidity low momentum stocks.
Adding longevity insurance to a portfolio is a delicate process. After running hundreds of cases, through Ibbotson's guaranteed product-type optimizer, Ibbotson created guidelines to help advisors and their clients set the optimal allocations in their retirement portfolios to insure an income for life. This article shows the primary factors advisors and clients must weigh to do it correctly, which depend on each client’s situation and goals.
Monte Carlo Simulation has become a standard tool of risk management and its latest incarnations offer several bold advances. We examine the historical use of Monte Carlo simulation in asset allocation analysis as well as three new technologies—interactive simulation, the Distribution String™, and cloud computing—making it more practical, interactive, and flexible.
Morningstar Institutional Credit Research enables institutional investors to access our large collection of research and analyses on companies and industries around the globe. Our analysts create detailed full five-year projected pro-forma financial statements for each company covered.
