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Understanding Morningstar's Quantitative Rating for Funds

Lee Davidson
Jeremy Glaser

Jeremy Glaser: For Morningstar, I'm Jeremy Glaser. We're launching the Morningstar Quantitative Rating for funds. I'm joined today by Lee Davidson, he leads our Morningstar quantitative research group, to look at why we've launched this and how investors can use it. 

Lee, thanks for joining me.

Lee Davidson: Thanks for having me.

Glaser: I guess the first question is really why we're launching this quantitative rating for funds. How is this different than, say, the star rating which also uses quantitative data?

Davidson: About five years ago, six years ago, we launched the Morningstar Analyst Rating for funds, which is actually probably the most important thing to understand to understand this quantitative rating system. The Morningstar Analyst Rating covers about 1,700 funds in the United States and Canada. It's forward-looking, which is different than the star rating, which tends to be backward-looking, and it really puts our analysts' best foot forward from an analytical perspective about what funds we think are going to perform the best. 

The Morningstar Quantitative Rating for funds is a complement, kind of a megaphone or an amplifier on that Morningstar Analyst Rating system, and it covers all of the funds that our Morningstar Analyst Rating system does not cover in a similar forward-looking, philosophically analogous fashion.

Glaser: How does this work? What inputs are going into this model?

Davidson: We've got a whole bunch of inputs going into this model. It's rooted in artificial intelligence, so what we're really trying to do is trying to understand the decision-making processes that our analysts go through. We believe that our analysts have a repeatable process. We train people in that process when they come in the door. It's rooted in data, it's rooted in rigor, and because of that, because it's data-driven, our analyst rating process is, we can look at the data that are about the funds that analysts cover and the decisions that our analysts have made and draw that connection using machine learning techniques, which is what we did with the quantitative rating. What that allows us to do is take a fund we've never covered before, look at the data about that fund, and provide a prediction or an estimate or a guess, a very educated guess, about what an analyst might do if they were to pick up coverage on that fund, and that prediction becomes the quantitative rating.

Glaser: What's some of that data that the machine's looking at?

Davidson: We're looking at fees, manager tenure, performance, firm success, firm fees--we're looking at a whole bunch of different data points that kind of correspond to the five pillars of the Morningstar methodology: Parent, Process, People, Performance, and Price.

Glaser: When this is all said and done, what comes out? What is the output of this model?

Davidson: It's going be a rating on the same scale that we're used to seeing from the analysts: Gold, Silver, Bronze, which are our recommended class of ratings; Neutral, which are the ones we think will probably perform in line with the market or with the category; and negative, those are things that are going to be impaired or potentially have a flaw. Those five ratings will be the same ratings that our quantitative ratings system uses, but they'll have a superscript Q to denote that it was arrived algorithmically.

Glaser: How should investors use this then? You said there's the recommended ones, is this kind of the start of a research process? What's the best way to think about that?

Davidson: I think investors are probably best to use this in the same way they use the analyst rating. It's a summary expression of our conviction and the likelihood that a fund's going to beat its peers on a full market cycle. Certainly, we don't have an analyst looking at these funds. It's an expectation. We definitely advise folks to use this with caution and incorporate other information into their investment decision-making process, but as a starting point, in a lot of tests that we've done, it's been very efficacious.

Glaser: Lee, thanks for being here today.

Davidson: Thank you.

Glaser: For Morningstar, I'm Jeremy Glaser, thanks for watching.