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Methodology Documents

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Methodology Documents

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.
Methodology Documents

Morningstar methodology paper explaining the load adjusted return calculations.
Methodology Documents

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.
Methodology Documents

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.
Methodology Documents

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.
Methodology Documents

This iteration of Morningstar's first energy-based quantitative model explores the improvements proposed in the first quantitative modeling white paper.
Methodology Documents

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.
Methodology Documents

Morningstar are introducing 'representative cost' fields, that will contain the best information on the re-occurring cost that are charged via the fund itself, so would not include one-off cost or cost charged by third parties such as advisers or platforms, nor one-off costs charged on entry or exit.
Methodology Documents

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.
Methodology Documents

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.
Methodology Documents

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.
Methodology Documents

Here is the methodology for this designation, which is assigned to portfolios that have low carbon-risk scores and low levels of fossil-fuel exposure.
Methodology Documents

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.
Methodology Documents

Methodology paper explaining how Morningstar calculates the average bond price for a portfolio.
Methodology Documents

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.
Methodology Documents

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.
Methodology Documents

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.
Methodology Documents

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.
Methodology Documents

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.
Methodology Documents

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.
Methodology Documents

We draw on results from a survey of U.S. adults to create a model of financial health. Using demographic, psychographic, emotional, and behavioral variables, we show that financial behavior and emotional well-being are affected by two simple mental factors that advisors can target to better help their clients achieve financial health.
Methodology Documents

An $8.7 billion inflow is impressive for a category group that’s usually in outflow territory. Investors poured $27.6 billion into passive U.S.-equity funds last month, more than doubling the previous month’s $12.7-billion inflow. U.S. GDP growth has been at around 3.0% for two quarters in a row, and Washington has recently been focused on passing a tax bill that would significantly reduce corporate tax rates and make it easier for businesses to increase profitability.
Methodology Documents

Despite expectations of rising rates, taxable bond was once again the most popular category group in September with $34.9 billion in flows overall, significantly higher than the $27.5 billion it had received in August. In a reversal from the previous month, passive taxable-bond flows surpassed active ones: $20.5 billion versus $14.4 billion.
Methodology Documents

Taxable bond remained the leading category group in August with $27.5 billion in flows overall. Unlike in June and July, however, active taxable-bond flows surpassed passive ones: $14.1 billion versus $13.3 billion.
Methodology Documents

Morningstar category averages are designed to represent the average return of funds within their category over time. Morningstar creates a category average daily total return index series, or TRI, as well as monthly, quarterly, and annual averages of return and non-return data.
Methodology Documents

Morningstar provides data on individual securities registered in dozens of countries, but researchers evaluating those investments often have a need to study groups of investments as they're branded in a local market.