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.
The Morningstar Manager Benchmarks contain three distinct series of benchmarks based on key peer grouping factors such as Morningstar Institutional Category, equity style, investment strategy, and market capitalization. Each series has a unique set of construction rules to determine constituency, so that peer grouping is a precise combination of Morningstar Institutional Category and fund attribute data.
This paper outlines our method of calculating the Fixed-Income Ownership Zone, which is where the duration range and the credit quality range intersect in the Morningstar Style Box.
Morningstar® Investor Return™ (also known as dollar-weighted return) measures how the average investor fared in a fund over a period of time. Investor return incorporates the impact of cash inflows and outflows from purchases and sales and the growth in fund assets.
It is well known that the normal distribution model fails to describe the fat tails of markets. The Lévy stable distribution model, meanwhile, has fat tails but leads to an infinite variance, thus complicating risk estimation. This study introduces truncated Lévy flight (TLF) — a better distribution model that has fat tails, finite variance, and more importantly, scaling properties. The paper uses TLF to estimate the downside risk of a variety of asset classes.
The importance of asset allocation has been the subject of considerable debate and misunderstanding. What seems like an easy question or topic on the surface is actually quite complicated and filled with nuance. In an article written by Thomas Idzorek, James Xiong, Roger Ibbotson, and Peng Chen, “The Equal Importance of Asset Allocation and Active Management” for the Financial Analysts Journal, they pinpoint one of the primary sources of confusion surrounding the importance of asset allocation. This article presents that paper's key insights.
The 2011 Industry Survey updates much of the data from the 2010 edition while providing new features on asset allocation, performance attribution, and portfolio composition — particularly among retirement-income funds, the terminal funds in many target-date series.
This paper provides a brief summary on the importance of asset allocation, while noting that nowhere near 90 percent of the variation in returns is caused by the specific asset allocation mix. It highlights that most time-series variation comes from general market movement, with active management having about the same impact on performance as a fund’s specific asset allocation policy.
What is the relative importance of asset allocation policy versus active portfolio management in explaining variability in performance? This paper shows that, with market movements removed, asset allocation and active management assume equal importance in determining portfolio return differences within a peer group. It also examines period-by-period cross-sectional results to reveal why researchers using the same regression technique can get widely different results.
We investigate the impact of mean-conditional value-at-risk (M-CVaR) optimizations that take into account fat tails and skewness on optimal asset allocation. In a series of controlled optimizations, we compare optimal asset allocation weights obtained from the traditional mean-variance optimizations with those from M-CVaR. The study provides useful insights for
designing optimal asset allocation mixes when investor preferences go beyond mean and variance.
This paper studies a variety of asset allocation issues associated with deferred variable annuities with guaranteed minimum withdrawal benefits for life (VA+GMWB) with the goal of developing a framework for the construction of optimal retirement portfolios.
The idea of an economic moat refers to how likely companies are to keep competitors at bay for an extended period. Morningstar calculates an average economic moat score for mutual funds by using the economic moat ratings assigned to the fund’s stock holdings. Economic moat is calculated for all universes.
This article discusses the level of overlap among style index providers, and the implications for investors using style-based indexes. It explores the differences in performance within style families and the serious ramifications for investors’ portfolio building activities. It explains the potential for indexes and index funds to help investors by laying the basis for performance attribution, portfolio construction, and better manager evaluation.
After another market crash, advisors question whether Modern Portfolio Theory is the best way to tackle asset allocation. We asked two experts to debate its merits.
This document describes the rationale for, and the formulas and procedures used in, calculating the Morningstar Rating™ (star rating) for funds domiciled or available for sale in Europe, Hong Kong, Singapore, Taiwan, and the United States.The Morningstar Rating has the following key characteristics: 1) The peer group for each fund’s rating is its Morningstar Category™; 2) ratings are based on funds’ risk-adjusted returns.
This survey measures the experiences of mutual fund investors in 16 countries in North America, Europe, and Asia. Aiming to promote best practices for investors, we rated companies across six categories—Investor Protection, Transparency in Prospectus and Shareholder Reports, Transparency in Sales Practices and Media, Taxation, Fees and Expenses, and Distribution/Choice—and added the cumulative category scores to produce an overall country grade.
This paper studies the role of infrastructure in a strategic asset allocation. It addresses two critical questions: is infrastructure an asset class; and if so, what might be an appropriate asset allocation range. Though adding infrastructure to a diversified portfolio led to only a marginal improvement in the efficient frontier, unconstrained historical and forward-looking optimizations resulted in significant infrastructure allocations.
This document is a supplement to the main methodology, presenting how the holdings weights of the portfolio and the benchmark are calculated under various exception cases. This paper expands on the single period definition described in the Morningstar® Equity Performance Attribution Methodology document to accommodate for user-specified time periods that may not begin on a date that has holdings data.
This paper studies the impact of liability-driven investing on asset allocation policy with an emphasis on U.S commercial real estate and Non-U.S. commercial real estate allocations. This study consists of two parts – a historical analysis and a forward-looking analysis. It uses a relatively robust opportunity set of 14 asset classes that is indicative of the type of opportunity set used by a sophisticated investor.
Morningstar undertook an initiative to incorporate the effect of short positions and derivatives in a portfolio's descriptive statistics. This involved recalculating current and historical portfolio statistics to better capture the exposures provided by these instruments. This document describes how the portfolio statistics changed and gives examples of the types of funds affected.
