David Trainer and Hunter explain how their credit-rating framework adjusts for sector and company size, finding that universal thresholds fail because real estate and healthcare have different balance-sheet structures. Large-cap firms get higher ratings with weaker metrics, while small-caps trend toward worse ratings using the same standards.
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Are numerical ranks based on traditional or new construct scrubbed data? I want to be really clear because it's complicated.
The idea was basically to try to recreate the legacy provider ratings using what we call traditional metrics, and then to apply the thresholds derived from that to our better adjusted version. And so that our ratings are, if you were to apply our better data to what we presume to be the legacy provider thresholds, what would you get? And that was our approach. And we had to figure out how we wanted to map that out. And we determined that debt as a percentage of total in terms of each tier was how we wanted to do that. So the chart on the left just shows you the conversion. If you were looking at S&P and Fitch and Moody's, what it means Based on our internal ratings. And so everything makes sense. I would say below unattractive, you start to get into very speculative territory. And then obviously very unattractive is really reserved for the worst of the worst, the companies that are on the verge of bankruptcy effectively. But I want to go into more detail on the next slide about this legacy provider rating. I wanted to talk about some of the key findings here that we saw. What you can see is that most of the debt, so really how to read this is if you were to take all of the companies that were rated very attractive or AAA from S&P, and you were to sum up all of their debt, and you were to do that for each one of these 5 categories all the way down to the D S&P rating, and then you were to take each tier and divide it by the total. So you're getting the percentage of the total for each tier. What would you get? And what's interesting is that you would see the majority, 40% of all the debt in the universe out there is attractive, and then 35% is neutral. So the vast majority is attractive or neutral. And what that really tells you is that the vast majority of companies are getting what we would call attractive or neutral, but what would be typically called upper medium grade or lower medium grade. That is the vast majority of ratings that the traditional providers are giving out. And you could also see from this that large-cap companies get preferential treatment. And the way that we know that is because The companies with the most debt are the companies that are largest. So if you're an Apple, you might have $10 to $20 billion of debt based on your total capital. And if you're a $500 million company, you're gonna have far less. The largest companies in the world have the most debt. The fact that 40% of the total debt is in tier 2 indicates that there's a large cap preferential treatment. And really that makes a lot of sense. If you're a larger company, you're less risky, you're more blue chip, stable, predictable. You have more access to equity markets to raise capital if you need to. It just makes more sense that large-cap companies would have preferential treatment and get better ratings, even if their underlying metrics are worse than a small-cap company. And that's the trends that we were seeing, and that's a lot of the stuff that we had to incorporate in our actual metrics as well in our credit rating system. And this slide talks a little bit more about that. So in the beginning, we tried to apply a universal set of thresholds to all companies, and what we found was that the results were Pretty horrible. They were unsatisfactory to say the least. And this makes sense because every sector has inherently different balance sheets and inherently different businesses. So you think about real estate and utility companies, they're much more prone to having debt, whereas healthcare and technologies borrow less. They raise more money through the equity markets. But we had to think to ourselves, well, if it's the best performing real estate company, the most stable, the largest, the highest quality real estate company, should they receive a worse credit rating than a microcap technology company that just doesn't have any debt? We determined that that didn't make any sense. Universal thresholds just really aren't going to apply. And we also noticed that there was a difference, as I mentioned earlier, between market cap sizes. So large-cap companies have easier thresholds to meet for very attractive ratings than small-cap companies. And then we also saw that there was differences between sectors. So this was a really important distinction and a key finding for us during this process, and that's ultimately why we decided to split the credit ratings into individual groups that are based on both sector and market cap.
Just to take a second here, this innovation with respect to normalizing around sectors and market caps is absolutely brilliant. There's no way to have this consistent credit rating system applied across sectors and across multiple different size companies without what Hunter figured out here in order to create different thresholds and different ranking criteria for different market cap companies and different sector groups. Absolutely brilliant. And figuring out how to get that, those distributions based on those different groups. Amazing. Amazing. And to figure out which groups you needed to do it for required a ton of really cool, really awesome analysis. I remember going through it and, and really just being so pleased that we cracked the code on that. And I think it's a big part of what makes what we did special and part of why I wanted Hunter to come on here and talk about it because he did some amazing work figuring that out.
Yeah, absolutely. Thank you, David. This chart highlights a lot of that too. You can just see the disparity for each of the 5 metrics if you were to take an average and apply them to different sectors. Some of these are pretty extreme, the healthcare negative 70 interest coverage ratio. So that also shows that these ratios can be driven by the extremes. So it's very difficult to determine thresholds that apply to each individual sector, let alone to apply thresholds that apply for all of them. So that's really all this slide is meant to show you. It's just how volatile these things can be. And if you look at debt per capital, which is probably the most stable out of all the 5, you can even still see what I was mentioning earlier, where real estate has an average debt-to-capital of 0.48, healthcare has 0.21. Now that isn't to say that the highest quality real estate company doesn't deserve a very attractive credit rating just because it has a higher inherent amount of debt, because that's just what the business structure implies. So that this was stuff that we were looking at when we were trying to determine Market caps and sector groups. This is just the overall credit rating distribution. So this is what we ended with, and this is the total number of companies. What you can see is that our total number of companies leans a little bit more neutral and attractive, whereas we don't give out the attractive, very attractive as often, but when it's attractive, very attractive, it's likely to be a larger cap company. So one distinction here is that this isn't the debt percentage total, so it's not going to correlate with the chart I showed you earlier, but this is the total count, the total number. So it's a little bit not normally distributed. It's, I believe, tailed there. But I think the last important thing that I'll note here is if you were to look at these things from large cap to small cap, you'll start to see how it moves from very attractive to more very unattractive as it gets more difficult for small cap companies to get better ratings. than the mid-cap and large-cap. And that's by design, and that's what we intended.
It's very intuitive. I mean, that the smaller-cap companies are going to be more heavily debt-ridden. If you were to look at all this analysis, especially the group stuff that Hunter put together, at the end of the day, the results are intuitive. It makes sense. And I think that that's an enormous amount of value add to go through hundreds of thousands of filings. Thousands and thousands of companies over multiple periods, over multiple years, quarters, enormous amounts of data and results. And to come up with something that actually makes sense systematically, consistently across thousands of companies, it's amazing. It was a really tremendous piece of work. And we don't talk about our credit ratings that much, but I'm really happy that we had a chance to spend some special time on that today because Hunter did some tremendous work. bringing this into fruition.
