To help investors better analyze managed funds along the increasing number of factor dimensions, we introduce the Morningstar Factor Profile, a scientific and intuitive tool to capture the factor exposures of investment portfolios.
Morningstar is making revisions on the industry group and industry level in response to market and technology changes. The revised system calibrates the classifications to align more closely with industry standards, clarifies industry definitions, and better reflects industry trends to allow for a more accurate grouping of companies based on market behavior.
Morningstar enhances the current Morningstar Sustainability Rating methodology by replacing Sustainalytics' company ESG Rating with its ESG Risk Rating.
Morningstar is enhancing the current Morningstar Sustainability Rating methodology by replacing Sustainalytics' company ESG Rating with its ESG Risk Rating.
Target-date funds are designed to be a simple, all-in-one solution that is relatively easy for investors to use. However, these simplifying features can make target-date funds tricky to evaluate. Morningstar Target-Date Fund Series Reports ("the Reports") are designed to help individual investors, financial advisors, consultants, plan sponsors, and other interested fiduciaries make informed decisions when selecting a series of target-date funds.
The ability to model the risk of a portfolio is paramount to making investment decisions that maximize utility. Our fundamental factor-based methodology provides a way to forecast risk, but, more important, it provides an intuitive interpretation of the mechanics behind the forecast.
In 2018, we released our fund flow models into Morningstar's Investor Pulse tool starting with the U.S. market and expanding globally. Based off "The Fall of Funds" paper, we are implementing the probability of fund shutdown model so these insights can be gleaned in a live setting. The first fund shutdown model implemented is for the U.S. equity market.
Morningstar’s goal for this data set is to consistently provide country level geographic segment data that can be used to calculate a company or portfolio’s economic exposure to different geographic regions.
The following document explains the circumstances under which Morningstar considers funds and the indexes they track eligible for collection of Index Strategy, Index Weighting, and Strategic Beta Group. The document also details how assignment is determined and regularly reviewed. This document represents Morningstar’s position on the subject; it is not a summary of local regulations.
Morningstar developed the Morningstar Equity Comparables system to give investors and financial professionals an objective benchmark for comparing companies. Morningstar Equity Comparables is genuinely different to other industry classification schemes. We start from the bottom up with comparable companies, as opposed to the top down with sector definitions. For every pair of companies, we determine how similar they are–anywhere from closely comparable to distantly related based on automated analysis of the companies' own business description. We automatically analyse the text of the business description and work out whether companies are talking about similar things as they describe their businesses. Businesses described in similar terms are comparable.
This iteration of Morningstar's first energy-based quantitative model explores the improvements proposed in the first quantitative modeling white paper.
This Morningstar tool calculates the impact of user-specified macroeconomic and financial system shocks on forecast factor exposures and volatilities. The user can specify multiple shocks at different points in time and calculate the subsequent expected risk and return of the portfolio.
The Market-Driven Scenario Analysis tool allows users to select a market index and to specify the percentile dividing the range of the return distribution to determine the impact of user-specified market shocks on factor exposures, portfolio returns, and Value at Risk.
This paper seeks to outline the methodology Morningstar uses in calculating the expected future yield as of the portfolio date, from the holdings of the portfolio. The measure is based on Forward Dividend Yield (forward-yield) for stocks and the yield-to-maturity of the fixed income portfolio.
We agree that there's much to be skeptical of with the original robo-advisor business model. In fact, we wrote in 2015 about the challenging economics, how many stand-alone robo-advisors wouldn't survive, and robo-advisors integrated with established financial institutions would leapfrog the early leaders. However, new business models are addressing the three faults of the original robo-advisor model: high client acquisition costs, ongoing costs of servicing clients, and low revenue yield on client assets. Lead-generation tools and strategic partnerships are reducing acquisition costs, while building for scale and operating leverage eventually solves service costs. Revenue-enhancement strategies underpin much of our optimism for select robo-advisors becoming profitable. Upselling to human advice, ancillary service offerings, and incorporating proprietary products in portfolios are key revenue drivers.
We don't see robo-advisors disrupting moaty financial institutions. Instead, we expect established financial institutions will co-opt the technology and user experience improvements of robo-advisors to expand their own businesses. Among the asset management, wealth management, and online brokerage firms that we cover, we believe Invesco, Credit Suisse, and UBS are trading at the most attractive valuations.
Fiduciary responsibility and increased regulations require investors to analyze and
understand their risk exposures in a carbon-constrained future. To help investors address this challenge, Morningstar has introduced portfolio carbon metrics that measure the risk that companies in a portfolio face from the transition to a low-carbon economy.
Profit margins have expanded rapidly over the past three decades. That, combined with strong optimism for future growth, has driven market earnings multiples to extreme levels. We view overall stock market valuations as stretched, with close to 60% of
our universe trading above our fair value estimates as of October 2017. While we do expect a partial reversion to the historical valuations of the past as economic pressures build, the durability and defensibility of the largest firms’ competitive positions suggest that profitability levels will not fully revert to historical midcycle levels.
All-time high flows, paired with positive returns, lifted assets in target-date mutual funds above $1 trillion in 2017. This year's report covers recent developments in the competitive landscape, and then it highlights noteworthy considerations for target-date investors in five areas: Price, Performance, Parent, People, and Process.
U.S. President Donald Trump recently shook up global financial markets by announcing plans to enact import tariffs of 25% on steel and 10% on aluminum. The exact form these tariffs will take remains unclear. We've updated our forecasts and fair value estimates based on the expectation of a targeted approach. A blanket tariff covering all imports would be far more severe and, in turn, far more beneficial to U.S. steel and aluminum producers. The consequences for U.S. metal users, while significant in aggregate, are far more diffuse, impacting industries from aerospace to aluminum cans. Accordingly, our long-term forecasts and fair value estimates for these companies aren't meaningfully affected. Harmful second order effects, including retaliation by U.S. trade partners, are possible, but we have not assumed major moves in our base case forecasts. Perhaps the most damaging of potential reactions would be cancellation of significant Boeing aircraft orders by Chinese customers.
To receive safe harbor regulatory protections, defined-contribution plan sponsors must, among other things, follow a prudent process for selecting a qualified default investment alternative. This includes considering the specific demographics of the participants when making a QDIA selection. Unfortunately, regulations do not provide much of a blueprint on how to consider demographics. This paper presents a framework for using participant data to help determine which type of QDIA may be most appropriate, including which glide path best fits the demographics of plan participants, when a custom glide path may make sense, what the “pivot” age should be for a hybrid QDIA, and when managed accounts may be necessary for meeting the unique needs of a plan.
This methodology addresses the assumptions and formulas used in calculating the Total Return.
This methodology is only applicable to interest rate series that are updated at a daily frequency.
It is intended for interest rate indices with one or less years to maturity.
Taxable bond was once again the Morningstar category group with the largest inflows last
month, as investors sought to lock in the gains of an almost-nine-year bull market and
continued to rebalance from stocks to bonds.
