Why We ❤️ Strategy Data

Imagine you are looking for a Caribbean vacation. There is a luxury resort brand you love, and you want to compare availability, prices, and amenities across locations. You could do your research island by island, combing through the options until you find your favorite brand. Or you could go to a travel website, use its search utility to find the resort you want across the locations you are considering, and begin comparing resorts instantly.

Many asset managers go about their investment research like our hypothetical vacationer. They start with a fund manager and an investment strategy that they like, then hunt through vehicles and domiciles looking for the right investment. That wasn’t especially onerous when the choice was between a mutual fund and an ETF. With the rise of alternative investments, a fund manager might choose to execute their strategy through a closed-end fund, an open-end fund, a separately managed account, a unit trust, a collective investment trust, or an uncollateralized debt instrument.

From a data perspective, the way to solve this problem is to abstract the investment strategy—an investment decision-making process and portfolio construction approach that is agnostic of vehicle or domicile—from the vehicle itself. When we do, each becomes discoverable independently of each other. When integrated into a broader data-management system, tagging a fund with a strategy identifier can improve data integration across an entire portfolio.

Morningstar’s strategy data sits above investment-level data, identifying and linking together managed investments that employ the same strategy. Those investments may span multiple investment types, regions, markets, and advisor/subadvisor relationships. With strategy data, asset managers can screen, filter, and analyze one or more strategies, and link and view the complete range of investments that employ the same strategy. 

As well as linking investments horizontally, strategy data can also inherit investment-level data from below (such as categories, fund performance and fees), achieving a second layer of data integration. With this capability, asset managers can search for all strategies within a particular category of investment fund or analyze the aggregate performance of a strategy across different vehicles. Morningstar strategy data is comprehensive: we track 150,000 separate strategies, covering 98.5% of the Morningstar managed investments universe.

Take an asset manager who wants to add real estate investments to a model portfolio. A search for managed investment funds in the “Global Real Estate,” “Real Estate” or “Static Real Estate” categories yields 5,981 results. A search for investment strategies that inherit the same three categories from the funds they are linked to yields 226 strategies across 128 asset management firms.

The asset manager decides to narrow their search further by focusing on real-estate strategies from Cohen & Steers, an asset manager they follow and admire. They find 11 distinct real estate strategies that cut across three different types of investment vehicles: mutual funds, SMAs and interval funds (see table).

Morningstar’s strategy data includes a growing set of derived analytics linking strategy-level and investment-level data. For instance, in cases where a strategy is shared across multiple vehicles, Strategy Performance Source can identify the vehicle with the longest continuous gross performance stream. In addition, when a particular fund manager offers a spectrum of solutions for investors, we link this family of strategies (which share the same management team, portfolio processes and investment philosophies, but typically differ by asset allocation, risk targets, and related benchmarks) using a Strategy Series ID, providing further data integration.

As well as aggregating, organizing, and associating vehicle-level data related to a specific strategy or designated group of strategies, asset managers can use strategy data to:

• Formulate a policy portfolio consisting of an optimal mix of investment strategy sleeves to target for investment.

• Identify strategies that pass search criteria by conducting strategy comparison.

• Conduct focused analysis on a strategy to confirm if the manager’s stated approach has translated to expected performance outcomes.

• See the differences between a strategy’s available vehicles to determine which is best to incorporate into a portfolio.

• Insert a chosen vehicle for each strategy into a custom model and benchmark it against the investment policy.

• Communicate a strategy-oriented story in client materials.

Unlike Morningstar categories, which we assign to managed investments based on our own best judgment of the fund’s peer group, strategy data is designed to reflect the intention of the fund manager. We create the data both qualitatively (by analyzing the fund’s stated objective) and quantitatively (by analyzing the fund’s portfolio holdings and the analytics we derive from them).

Sometimes, our analysis does not align to the fund manager’s stated intention. For example, the fund might declare a U.S. large cap growth strategy, whereas our analysis of its holdings consistently put it in a different style box. This can be useful for a manager to understand as they refine their positioning—just as a vacation resort which markets itself first as a wellness refuge might be curious to learn that its customers come principally for the bar and buffet.