New Constructs' Friday Webinar: Intelligent Capital Allocation - 7 July 23

We have a minute before we get started. I was wondering if you would tell the story of this quote.

Oh, this is actually from the Harvard Business School and MIT SLOW case study. When meeting with and talking with the professors after they'd done all the work and Boiling down what it is we do differently or better than everyone else. And Charlie said, it's that New Constructs understands the underlying economic consequence of all the accounting data. That was the distinguishing factor. No one had ever really put it in those words to me before. And I thought that was interesting.

It's really nice to have somebody study it very closely from the outside.

The best thing about that study, you know, people would often say, hey, why don't you do a big study against Compustat data and show how your data's better? I'm like, And if I did, would you believe me? If the study came from us, no one would believe it. So when Harvard Business School got in touch and said they might be interested in doing it, I was like, yeah, let's do it.

Let's make it happen. Awesome. It's great. It's so succinct.

I really, really like it. Thank you.

Do we have quorum? Participants are still coming in, but—

It's time.

It's time. Let's get started. Welcome to our regular Friday webinar here at New Constructs, Intelligent Capital Allocation. You've got a great group with you today. David Trainer, our CEO, Haakon Solt, Hunter Anderson, our special guest, Kyle Geske II, and myself. And we're excited to get started. Here's our disclaimers and disclosures. And the agenda for today, it is quite a bit of information that we want to get through with you today. Welcome and announcements. We have some great special guests. This week is Hunter Anderson, and we have 2 more guests coming the next 2 weeks. So please stay tuned and join us for the next 2 weeks. They're going to be really, really interesting. And we are going to take a summer break from the Friday webinar. The Friday webinar won't be happening from July 28th to August 25th. So we'll be on hiatus for the webinar for those dates. As usual, the agenda is to focus on your topics and questions. So please do use the Q&A feature in Zoom and ask questions throughout so that we can make sure that we get to them. We definitely want to get to your questions. As they come up, please don't hold them. They're very important to us. If you're out on Society with us, if you're a member of the Society of Intelligent Investors, know that you can get the replay links there, but you can also get the slides there in PDF. If you have any questions about how to join the Society of Intelligent Investors, you can email support@newconstructs.com and we can walk you through it. Or you can go to newconstructs.com. WAC Society and find out about it there as well. We didn't meet last week, so I did put all of last week's research here. A number of pieces of research that came out of Kyle's team, model portfolio updates, long updates, short updates, several position closes for some big ones. You'll definitely want to make sure to go out and check those out. And then from this week, again, a couple of model portfolio updates, most dangerous and most attractive, focus list long update. And of course, 2Q23, the earnings meet beat report, which we'll be talking about for a second later as well. I'm going to go ahead and hand this over to David to talk about interesting headlines.

Thank you, Tim. Not a lot to update on, on the Bloomberg front. Things are still going well there and we're progressing. We're going to have some news hopefully in the next couple of weeks, but not much more from there. And then when it comes to other headlines, what we're talking about these days is bringing back a self-directed product to our clients. We realize that even if you're an institutional client, or institutional investor, professional investor, you might not want to jump in the deep end right away. And so we're going to have some starter packages or intro packages that will allow users to get more familiar with us and then hopefully move to the more sophisticated products. The side message here is that a lot of our legacy products are going to need to change. Our legacy Gold, Platinum, and Pro. We stopped offering those over 6 months ago, and we did that because they contain enormous amounts of institutional-level data. That's something that we think is important to move away from for a variety of reasons. Then the future of the fund management business. I will, I'll share my screen here as this will actually jump us over into Circle to talk about a couple of items. By the way, if you didn't see Cody's interview with ChatGPT, you gotta check that out because that was, that's hilarious. This is super funny. He's asking ChatGPT to try to do the work we do and it doesn't really work out.

It's pretty funny.

I love Jerry's comment real quickly on the cheat sheet. You know, is it accurate?

No.

And I think the benefit of new constructs is that you don't have to cheat. We do all the work. You get the full monty of analysis, research details. We're doing all that, so you don't need a cheat sheet. I mean, the best cheat sheet in the world is our rating, which is that green is good and red is bad, and that boils it all down for you. This is the comment I was pointing to in terms of the future of the fund management business. And I wanted to talk about this because we've recently received a lot of attention from venture capitalists, private equity firms who seem to have quite suddenly woken up to this idea that the data business is changing and that the investment industry is ripe for a lot of change in particular around this idea that how are they going to be profitable In a world where so much money has gone into index funds or passive vehicles away from active, because as most everybody in this business knows, 75% of the time active funds underperform every year. And then the number of funds that outperform over 2 years, so 25 make it 1 year. The number of funds that make it 2 years is 4%, 3 years is less than 1%. There's been a lot of excess fees paid for underperformance. Our big takeaway here is that the number of firms that need ways to differentiate and create alpha is much larger than the number of firms that actually provide data. And when you take the fundamental data sector or industry in particular, which is where we are, emerging data providers, new constructs. There's really no one else out there in the world that can provide this value-added alpha-generating fundamental data. Everybody for decades has relied on unscrubbed regular data, and it's just been the standard table stakes that people have used. And for a long time, the fund industry and fund companies have been able to get away with that because they've been able to cut costs by getting bigger and bigger, larger and larger, greater economies of scale. And we think the amount of time or the amount of opportunity remaining in scale advantages and getting bigger distribution is next to none. So they got nothing left to grow profits or maintain profits anymore besides really creating a differentiated product in a market that's become incredibly commoditized. And we are in the catbird seat and providing a very clear and direct way to generate incremental alpha. That they need, and there's no one else that can do it. So that's my prediction is that all firms will have to be eventually users of this superior fundamental data, not just because it generates alpha, but because once it becomes really more well-known, how can any money manager make a straight-faced argument that they're able to fulfill their fiduciary duty of care if they're knowingly ignoring What is proven superior fundamental data. I don't think the compliance department's going to want to take the risk that you make an investment decision based on the proven inferior data and that blows up, you underperform, and you're getting shareholder lawsuits because they're saying, why were you driving at night without your headlights? Why did you have your head in the sand? That positions new constructs really well in the coming days and months. We're really excited to see more folks, especially in the institutional private markets, recognizing that. The other thing I want to touch on really quick before we get to the main attraction is this interesting question about Moderna. And by the way, we loved that Eric posted the question and questions for webinars. No way we're missing that because he put it in the right spot with good timing. Eric here was asking a question about Moderna, how we thought earnings were too low, Street earnings were too low, and how it would beat. And really the heart of the question was, hey, why don't you measure earnings distortion scores based on invested capital instead of total assets in the denominator? And I made a quick comment down here. The actual definition for earnings distortion is core earnings minus net income, not economic earnings minus net income. There's a big difference there. There's a whole paper on core earnings, and that's an apples-to-apples number for Wall Street folks. When the professors were doing the study, I suggested, hey, try using invested capital instead of total assets, and they liked total assets better. Performance was better. Surprised. Great question. One of the ones I came up with exactly right away, and apparently the normalization effect of total assets. was superior to invested capital. The adjustments weren't really helping for the normalization. And by normalization, I mean we needed to divide earnings distortion by a number that would make the score apples to apples because just because you're a big company, your earnings distortion number dollar value might be a lot larger, even if it's not as impactful. as a lower number because that lower number might be for a company so much smaller that that smaller dollar value would just be more impactful to the overall business. And total assets was just apparently just a better way to capture firm size because we may make adjustments to reduce or increase invested capital relative to total assets that weren't necessarily conducive toward really creating a relative ranking and comparison. So great, awesome question. But trust me, the professors were out to get as much idiosyncratic alpha or show as much idiosyncratic alpha as they could. And so they went with what showed the most alpha.

Okay.

It's time for me to introduce Hunter Anderson. Who is our senior financial analyst. Hunter's been with us for 7 years. He is a guru on a lot of levels, but the top one or the most important one, the crown jewel, is that Hunter is the creator of the credit ratings. He did the work to actually bring this idea to fruition, and I wanted to make sure, because Hunter's going off to a big fancy business school in New York City in a few months. So he's effectively graduating from New Constructs to new and bigger things. And I wanted to have a chance for him to share more details about our credit rating system and how that came about. He's done some incredible work here to create what I believe to be the best credit ratings in the world. There are a lot of really cool, unique innovations. that we bring to credit ratings that no one else in the world has. And without any further ado, I'm going to pass it over to Hunter to go through what those are.

Thank you, David. So as David said, I'm a senior analyst here at New Constructs, and I was very lucky to be a part of the credit rating process from its infancy to the product that you have today. It's a pleasure and honor here to go through some of the details and to take you guys through the origins to how we got to where we are today. Here's the table of contents and the purpose of this meeting is to Start with what the origins of the credit ratings were, why we decided to create them, and then go through the incremental processes that we went through to get to the final product, and then give you guys a little bit of the final results that we ultimately saw. So originally we outsourced our credit ratings. We relied on legacy providers like S&P Global, Fitch, and used them mostly to get a debt yield. That was used in our WACC calculation. So for a long time, it really wasn't our expertise. It wasn't a focus of our plans. And it was just faster, simpler, and easier for us to do that. Now, we knew that outsourcing it came with accepting inferior products. For a long time, that was something that we were comfortable with. At some point, we realized that the problems with legacy ratings were just too substantial for us to ignore. They're based on unscrubbed data. They don't go into the footnotes to the extent that we do. And one of the big things here also is that they have conflicts of interest. They have a lot of conflicts of interest that make it so that their ratings are not as objective as they should be.

We were using the third party for a while and we had to actually build in special logic in our weighted average cost of capital calculations to deal with ratings that made no sense. And the rating would make no sense in reference to the spread that we would add to the risk-free rate in order to calculate the cost of debt and the cost of capital, as Hunter was saying. But they made no sense. And prior to the financial crisis, it was really super clear to us that most of these ratings did not take into account the off-balance sheet debt from operating leases. It's phenomenal. This is what they do. They focus on debt, but they're not capturing this. And we would see companies when we adjusted for off-balance sheet debt that would have debt-to-total capital ratios upwards of 90%, but with AAA or AA ratings. This just makes no sense. Eventually we were forced to come up with our own credit rating. We wanted to do it for a long time. We needed somebody with the skills and intelligence of 100 to do that. And we're super excited to do it, especially post-financial crisis when we all saw what happened with respect to the credit ratings that led to so many people making bad investments in mortgage-backed securities. Because as anyone who watched The Big Short or read that book knows that The credit rating agencies were absolutely negligent. Absolutely negligent. There's no question. This article is one of them where Moody's was fined for failing to disclose the conflicts of interest that drove the negligence. That negligence being the same negligence that equity investors or equity analysts have on Wall Street, which is if we don't give this company a good rating, we're going to lose business because that company won't sell stock. Or debt through us. How do you quote from, from the movie about that?

They went in to ask her why the mortgage-backed securities were AAA. And she said, well, if we don't give them AAA, they'll go down the street and they'll, they'll go to our competitors and they'll give them AAA. Really massive conflict of interest.

Go to the Rolling Stone article. We'll talk about that for a second. It's another exposé on the conflicts of interest that led to unreliable credit ratings. We're really happy to address that. Head back to your slides, Hunter, and go to the solution there. It's not just that we've got better data. We're not conflicted. It's a big difference between what we do and they do. They have major conflicts of interest that have been proven to taint their ratings. We don't have any of that. Plus we got better data. Back to you, Hunter.

To echo what David just said, to start, our data is proven to be better. So our credit ratings are based on that better and reliable data. We're unconflicted. We don't get paid to give credit ratings to companies. That's typically how it's done elsewhere. Our ratings are free of those conflicts. They're unbiased, they're independent. We have significantly more coverage and that's only going to get more significant as we add more companies under coverage. So every single company that we add to coverage will get a credit rating as long as they have debt for now. Whereas S&P Global, Moody's, they're pretty limited in terms of their scope. Of their credit ratings. And really importantly also, we update ours every single quarter. So every single time that the company gets a new financial statement, a new quarterly document or an annual, we'll update our credit ratings. That is not something that you see with traditional credit providers. In order to get a new credit rating on a company, the company has to approach, say, S&P Global or Moody's seeking to get more debt themselves, and then the credit rating agencies will give them a new credit rating. So that leads to a lot of situations where a company's credit rating is very stale. So in our system, we noticed that something like, say, Microsoft, the last time they got a credit rating in our system at the time was back in 2009. And we were using that credit rating in 2020. So thankfully with Microsoft, they're still a great company and they got a great rating back then. So it didn't matter, but the possibility that our data was that stale was really alarming to us. That's something that we provide as well as more frequent updating.

Anybody who's a consumer of other credit research knows that it does not get updated very often at all. I know some of our partners on the self-directed channel, Schwab, TD, Interactive Brokers, E-Trade, those folks, they were frequently very interested in our research because for no other reason than the fact that what little they could get was updated very intermittently, if not at all, for months, quarters, years.

It's a big deal that we update it as often as we do. So when we were thinking about how to develop a credit rating, we had to think about the difference between equity investors and credit investors, because that distinction's really important. We have an equity rating system, but the credit rating system is going to have to be different in a lot of different ways, and that's based on what credit investors are looking for as opposed to what equity investors are looking for. But this slide really does highlight the difference between the two. So when you think about equity investors, they benefit from stock appreciation. So if the value of the company goes up and the stock goes up, they gain from that. Whereas a credit investor, they don't really participate in the stock appreciation. They don't benefit from the capital gains. Alternatively, they don't lose from the capital losses, but that's a distinction to keep in mind. Equity investors are also really investing in the future cash flows of the business. So you see that a lot with these companies that are IPOing lately. That are highly unprofitable, you really are only investing in those companies because you think 10, 20, 30 years down the line, they're going to turn into the next Amazon, the next Apple. Really, as an equity investor, that is what your focal point is. That's why we focus a lot on DCFs and future cash flows. Whereas a credit investor, you're really more focused on what are the cash flows today and what has the company done leading up to this point. And that's similar to just like a credit score that you might get individually. So if they look at your credit score, they're going to look at your credit history up until this point, and they're going to look at your current financial information. That is not to say that credit analysts don't care at all about the future. They certainly do. The company, if it's heading for bankruptcy, is certainly an interesting thing to keep in mind. But they don't necessarily benefit from the growth of the company, so they don't look out that far into the future. For equity investors, they care about the valuation of the business. It's imperative to their investment decision. You certainly would want to buy a company that's cheaply valued that they think is undervalued and then sell a company that's overvalued. Credit investors, the valuation really doesn't matter. You're not investing in the company or the growth of the company, so that stuff is not as relevant. This is a big point too. The balance sheet analysis for equity investors can be an afterthought. It's not a new construct. We care deeply about The balance sheet as well as the income statement and the cash flow statement. But for a lot of people, equity investors, the balance sheet can be a secondary afterthought in their analysis. Credit investors, the balance sheet is imperative. It's a very important thing for them to look at. They can't have it be an afterthought. And then also lastly, equity investors are less risk-averse. That's apparent. They're willing to go down with the ship. If you buy a stock, you're willing to accept that that Stock might go to zero. Credit investors do have some downside, but it's much more limited because they have seniority in the case of liquidation. So they're more risk-averse. They don't want to take as much risk. That, those are the key distinctions between equity and credit investors and something that we have to keep in mind when we're developing the credit rating system.

Hey, Hunter?

Yes, Tammy.

I have a very noobish question. When I'm an equity investor, I'm buying stock. What am I buying when I'm a credit investor?

You're really just buying incremental cash flows. So you're investing in the business in a way, but you're only investing in them, say, paying interest coupons every semi-annually or annually. So you are investing in the business, but not in the growth of the business. You're more or less investing in the stability of the business and the cash flows of the business.

Another way to answer that question is that they sell They sell debt securities. So you can buy, just like you can buy Treasury securities, you can buy a 10-year bond, 10-year Treasury, or 5-year, 2-year, whatever. You can buy bonds from any company. They will issue that publicly. You can buy it like a stock. It's just a debt instrument.

When we were thinking about what particular metrics we wanted to focus on for the credit rating, we wanted to have metrics that really captured the entire business. So we wanted to look at the balance sheet. We wanted to look at the cash flow statement. We wanted to look at the income statement. We came up with these 5 particular metrics that we think accomplishes that. So if you think about something like debt-to-capital, we're looking at the total leverage of the company. How much debt do they have relative to the value of the equity? And then when you think about something like liquidity, we're looking at EBITDA, earnings before interest, taxes, depreciation, amortization to debt, as well as free cash flow to debt. And then lastly, coverage. We're looking at cash-to-debt and interest coverage ratio. So when you look at these, debt-to-capital and cash-to-debt are really more balance sheet heavy type analysis. Whereas EBITDA-to-debt and interest coverage ratio look at the income statement. They're concerned with operating profits and how they relate to the total debt as well as to the interest payments. And then free cash flow-to-debt is the cash flow type analysis. And all of these are relevant to the overall credit rating in their own individual way. We believe that they synchronize to give you the best picture of the total, the creditworthiness of the company as a whole, which was really what we were trying to capture. And that's why we chose these 3 categories and these 5 metrics. So there's 2 versions of each of those metrics that I just showed you. The first is the traditional version that is just using the balance sheet, income statement, and cash flow statement. So very basic. This is what we would compare to the data that you would get from traditional data providers, and what we presume is the data that most of the traditional legacy credit rating companies are using. And that goes back to what David was saying about how they weren't even encompassing off-balance sheet debt for a long time. So we're pretty confident that they aren't putting as much focus on getting additional information from the notes. The traditional version of each of those metrics is really the basic version. The adjusted takes the traditional and it adds all of our new constructs, value add. So everything that we get from the notes, converting it to more of the core operations of the business. And really the adjusted is the data that was proven to be better by Harvard and MIT. So the adjusted is what we use to dictate the actual rating. And I'll go into more detail about that on the next slide here. So this is an actual example from the website. I believe it's Apple. That's why everything's extremely attractive. But when you look at the credit rating on the website, you'll see that there's the 5 different metrics, and then you'll see bands for each of the underlying ratings. So looking at debt-to-capital, if they have a debt-to-capital that's greater than 0.75, they're going to receive a very unattractive individual component rating, and so on and so forth. Those bands are what we call thresholds, and those thresholds are determined using the traditional metrics. And I'll go into more detail about that in the next slide. But the actual values that you see at the very bottom there, those are adjusted metrics. So those have all the new constructs adjustments that make them improved upon versions of the traditional values. But the adjusted actual values are compared to those thresholds, and that's how you get the individual ratings for each one of these 5 metrics. But what I mentioned before there, so each one of those individual ratings gets a number, numerical number between 1 through 5, and that's related to the rating scale that you see on the left there. So it starts with very unattractive. Those get a value of 5, and then it goes, scales all the way up to very attractive, and those get a value of 1. That ultimately gets summed up and we do a simple average, and that simple average gets rounded to the nearest integer. And that is the value of the final rating that you see. So the example there on the right highlights that where if you had all of those individual components, They would sum up to the neutral rating. So what's a little bit easier is if you look at this slide, this is an actual example as of a few days ago. So hopefully GM, this correlates to what you would see today. But if you look at each one of their component ratings, so debt-to-capital is unattractive, the numerical rank would be 4. EBITDA-to-debt is neutral, so it'd get a 3. Free cash flow-to-debt is unattractive, so it'd get a 4, and so on and so forth. Cash-to-debt, 2. Interest coverage ratio, 2. If you add all of those up and you divide it by 5, you'd get an overall neutral rating of 3. That's just an idea of how to read this on the website and how we get the overall credit rating. One thing to note there is that each one gets an equal weight in the total rating. We don't value one of these 5 metrics greater than the other. They all play an equal role in terms of what the actual final credit rating for the company is.

I think it's also important to point out Just a second here, how this rating is set up is to be very similar to our equity rating. That's on purpose. We're not trying to throw another design and product at you to make our products harder to use. We want to make it simple because the stock rating system is great and the credit rating system is equally excellent. And it's set up to be simple like that. A couple questions in the Q&A, Hunter. Have we done any work to determine if equities with a higher or better credit rating have better performance?

We've not. That's certainly something that I'm personally interested in. I mean, there's definitely a lot of analysis you could do with that if you were sending it to somebody to run a backtest. But no, we have not done an analysis like that. That's something that's very interesting though.

Our clients have. Some of our fixed income clients have found that then when the equity cushion is really, really strong, that tends to be a good indicator for the debt as well. And then so vice versa, if you've got a good equity cushion, an equity cushion would be defined as return on invested capital minus the cost of capital. That's going to be a good— that's going to result in a good equity rating and a good credit rating. So we've seen some of that from there, but have we done a big study on that? No, we don't have anything to publish on that. The other thing to keep in mind is that The credit rating is really another way to look at the fundamentals. To get an attractive stock rating, you've got to have good fundamentals and a cheap valuation. You can have good fundamentals and a bad valuation, in which case those 2 things will not align. It's not always apples to apples because the fixed income people just care more about cash flow. They don't care about equity valuation.

Absolutely. Is there another question that you wanted to ask?

Your assumption is each of the parameters is equally weighted. Is that really a valid assumption? It's not an assumption, it's our calculation, and we think it's valid. You could make an argument that some of these elements are more important than others. I don't know if I could win that argument. It'd be very difficult. It's pretty subjective. So we feel like it just makes better sense to equally weight them. And then I do got a question here. Are numerical ranks based on traditional or new construct scrubbed data? I want to be really clear because this is complicated. When we set these thresholds the way we did, we divided up the number of companies that would fall into each category in a way that was consistent with the way the legacy rating providers divided up companies as well as a percentage of debt. Here we go. This is a great slide, Hunter. So the answer to the question is that, that, that colorful chart on the right, the debt as a percentage of total and how that was divided up for legacy providers. We aim to divide up the universe of companies in the same way based on their score On the unscrubbed or the legacy traditional criteria or data. So we said, okay, we're going to divide it up, make it look like the way the other guys look if we're just looking with unscrubbed data, and then we're going to actually score them based on the adjusted data. So the difference between our rating and the legacy ratings is going to be the superior data. That was the idea. That's, I think that's cool. I think that's the way it ought to be. At the end of the day, if our value add is a superior analytical dataset, then let's make sure we emphasize that in what we do. And Hunter put this together in a brilliant way to do just that.

I'll just echo what you said. 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 ratings. I'm sorry, the visual's a little blurry, but. I wanted to talk about some of the key findings here that we saw. What you could 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 can 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 the 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. 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, you know, 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. 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. 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 or 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 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. 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 that 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 and very attractive. 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 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 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 We, 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. One other comment I want to make in the Q&A from one of our clients, our fixed income clients, they actually did a study several years ago and found that companies with a positive equity cushion, companies almost never default. So. They did actually a study, and that's probably part of why they're a client, because they realized, hey, if we got the best data in the world to measure the equity cushion, then we can be really sure that we are not going to have risk of default. That wraps us up here, Tam. Are we going to give Kyle a second to brag about some of our stock picks or do we close it out?

Oh, I would love to give Kyle as short a time as humanly possible. Let's see how fast Kyle can do this.

The first one we've got here is on the short side. Compass, the zombie stock we wrote on in February of this year, down 7% in the past 5 days, 17% over the past month, and actually 24% since we named it a zombie. It's actually an interesting one because we wrote on it pre-IPO warning people. It's down 80+% since then, but we technically closed the position a few months later 'cause it was down so big at the time.

But had we kept it open as a danger zone pick the whole time, it'd be down even worse.

So this has been a good short slash danger zone call since day one. Basically, it came out IPO during the 2021 IPO hysteria. And then on the long side, might be a boring company, but Skechers up 3%, up 25% year to date, and up 73% since our long idea in May 2018. They're just consistently growing profits and selling shoes and apparel to those that are interested. They might not be high fashion, but they have a steady and consistent business.

Thanks, Kyle. And thank you, Hunter. We really appreciate you taking the time and putting all this information together. I know the audience really enjoyed it and I learned a ton. We'll see you guys soon and hope that you have a wonderful weekend.