In this episode I chat with Greg Obenshain, Partner and Director of Credit at Verdad Capital. Prior to joining Verdad, Greg worked as the high-yield portfolio manager at Apollo Global Management and Stone Tower Capital.
Despite his background as a fundamental analyst, Greg is a quant convert. His ideas are still grounded in a strong fundamental understanding of what it means to invest in credit, but in a sector where even just acquiring data may be an edge, he lets the data speak for itself.
Greg argues that within credit, excess return comes from identifying improving and declining credit conditions. And, much like quantitative equity investing, there are certain characteristics that can provide insight into how those conditions might change.
We discuss the counter-intuitive findings the data has brought to light, what Greg thinks most credit investors get wrong, and how to grapple with the dimensionality problem of fixed income.
I hope you enjoy my conversation with Greg Obenshain.
Corey Hoffstein 00:00
All right 321 Let’s do it. Hello and welcome everyone. I’m Corey Hoffstein. And this is flirting with models the podcast that pulls back the curtain to discover the human factor behind the quantitative strategy.
Corey Hoffstein Is the co founder and chief investment officer of new found research due to industry regulations he will not discuss any of new found researches funds on this podcast all opinions expressed by podcast participants are solely their own opinion and do not reflect the opinion of newfound research. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of newfound research may maintain positions in securities discussed in this podcast for more information is it think newfound.com.
Corey Hoffstein 00:50
This season is sponsored by simplify ETFs simplify seeks to help you modernize your portfolio with its innovative set of options based strategies. Full disclosure. Prior to simplify sponsoring the season, we had incorporated some of simplifies ETFs into our ETF model mandates here at New Found. If you’re interested in reading a brief case study about why and how. Visit simplified.us/flirting with models and stick around after the episode for an ongoing conversation about markets and convexity with the convexity Maven himself simplifies own Harley Bassman. In this episode, I chat with Greg open chain partner and director of credit at fair dad capital. Prior to joining for Dad, Greg worked as the high yield Portfolio Manager at Apollo Global Management and stone tower capital. Despite his background, as a fundamental analyst, Greg is a quant convert, his ideas are still grounded in a strong fundamental understanding of what it means to invest in credit. But in a sector where even just acquiring data may be an edge. He lets the data speak for itself. Greg argues that within credit excess return comes from identifying improving and declining credit conditions. And much like quantitative equity investing, there are certain characteristics that can provide insight into how those conditions might change. We discussed the counterintuitive findings the data has brought to light what Greg thinks most credit investors get wrong, and how to grapple with the dimensionality problem of fixed income. I hope you enjoy my conversation with Greg open Shane. Greg Govan, Shane, welcome to the show. Very pleased to have you here. It’s not often we get to chat with someone who’s doing quant within the fixed income space. So I’m always eager to chat with someone who’s an expert there. So thank you for joining me.
Greg Obenshain 02:42
Happy to join you and looking forward to it.
Corey Hoffstein 02:45
Now that all said, I do know that you actually started your background as a fundamental investor, not a quant you’re more of a quant convert. So let’s start there. Can you walk us through your background a bit and maybe your more traditional fundamental views that you came up with?
Greg Obenshain 03:00
Sure and converts the right word. So I did about 15 years as a fundamental analyst, both as an analyst and as a portfolio manager, reading credit agreements, going to bank meetings, reading the 10k. I mean that that is my training. And I’m still very much a fundamental analyst. So that’s how I approached the world. I think, before I taught myself all the things I needed to do to become a quant, including coding, I thought it was quant was all about the code was all about the numbers, or the math. And of course, I find myself as I’ve converted to being much more quantitative, still mostly falling back on all the things I learned. It’s a fundamental analyst and really relying on that background to create all the variables that I use. So everything I do is really still fundamental. So I still think of myself as a fundamental analyst, even though all my investing is quantitatively driven at this point. And so I think, ultimately, the code is a tool. It’s a very powerful tool. It’s something I wish I had a lot earlier in my career. The data is an incredibly powerful tool, but it’s the understanding of how the markets work. That really helps you put it all to work.
Corey Hoffstein 04:10
What was the catalyst that was the transition for you going from fundamental to quant?
Greg Obenshain 04:16
Yeah, sure. So I mean, just I should have given you a little more background. Here I am. I was formerly at a company called stone tower capital that was acquired by Apollo. I was the high yield Portfolio Manager at Apollo. I started off as an energy analyst, which I think energy debt analyst must be the greatest curse you can have specially in the last 10 years, trying to make money in an extremely volatile industry. We’re doing a asset class. It’s not supposed to be volatile. I think when you go out and look at the debt world, it’s very different than the equity world. First of all, you’re dealing with contracts, you’re dealing with bonds that they have to pay you back at the end. There’s a due date and they’ve got to give you money now like equities that can never pay you anything. There is a time when the check comes due. There’s a lot more companies that have debt than have equity. So the unit versus much bigger. And all these companies can have multiple bonds. So it’s actually a much more difficult, you have to be an expert in many more things. When you do debt, you have to understand capital structures, you have to understand what the equity is going to do understand why the debt might, what that’s going to do, you have to understand treasuries and what the Treasury market is doing, because it really does affect the debt. And that but because there are contracts, it all lends itself to really good relative value analysis, where you’re trying to look, you have a huge pool of things you can compare against. And you just can’t do that, as a fundamental analyst, I kept running into that problem, which is I wish I could somehow collect all this data and do all the comparisons that I want to do. And that was really the driving force. And as I started to try to do it, in Excel, I realized I wasn’t going to cut it. And so I bit the bullet, and taught myself R and built the databases that I needed to go off and replicate what I was doing on a fundamental basis on a quantitative basis.
Corey Hoffstein 05:55
So I want to begin the conversation before we even dive into maybe all the quantitative aspects here with what I often hear is the biggest pushback against credit as an asset class, which is that you can ignore all the fancy details. It’s really just treasuries and equity in a combination of those two, and therefore it doesn’t actually really deserve a space and an asset allocation, because you already likely have treasuries and equity. So I’m curious as to how you’d respond to that, as someone whose whole career has been in the credit space. Yeah, sure. And
Greg Obenshain 06:26
this is a you know, something you hear a whole lot and it really comes originate with David Swensen back in the early 80s, when he had a book, his book on portfolio management actually had a whole chapter called impure fixed income, where he makes this argument that hey, credits just this hybrid asset class. And in some ways, that’s true. If you really had perfect foresight, you could recreate credit by combining an equity and treasuries. But maybe, I mean, the thing about credit that you have to realize is that the credit spread that part of bonds for which you’re really focused on when you’re doing credit, and we should talk about that, because this is actually what I do. If you think about credit, what does credit mean, it means that you get a default spread over, you get paid to take default risk in bonds, but you don’t get paid for equity upside. So you’re threading this needle, where you want to take a look at some credit spread, you want to make like a little extra return. And you don’t want to hit a default until you get something that actually has lower drawdowns, it’s safer, but you get paid a little extra. That’s the goal, the argument that equities and Treasury you can recreate a credit spread through equities and bonds. It’s just as true, you can’t do that. So why would you do credit, right? Why How was credit better than either equity or bonds. I mean, the truth is that most people don’t want to be 100% equities. But they also don’t want to be have very low returning treasures, credit threads, that needle credit gives you some return for somebody that has much lower drawdowns. And so this thematically, I think, gets into something that’s very important about credit. And I think it’s a way that a lot of people do it wrong, that go into credit. And I think I’m going to try to make a lot of returns and credit, you can do that. But it’s not the right approach. You want to use credit for what it’s best at credit. As best as protecting your portfolio against drawdowns doesn’t mean you’re not going to have drawdowns, it just means that they’re not going to be so severe that you’re going to be sitting there panic, not knowing what to do. But it’s also going to give you some return along the way. And so when I look out, and I see people say this, I think that they have it a misunderstanding of what it can do for your portfolio, you can give you a steady return in your portfolio, and dramatically take down the drawdown risk of your portfolio when you’re doing credit, right. That’s what it does. Another
Corey Hoffstein 08:39
really sort of widely held belief, or maybe common phrase that I hear when it comes to credit is that you should really only allocate to creditor or perhaps maybe high yield, in particular, when those credit spreads are really wide, that that’s when the juice is worth the squeeze. Do you think that there’s some sort of underlying fundamental truth to that rule? Or is it just overly simplistic?
Greg Obenshain 09:03
Yeah, so I’m going to answer that question. But I’m gonna take a step back first, and sort of set the stage here for how to think about credit, generally. So when we think about the credit world, if you have a triple A bond like Microsoft, you can have a triple C bond, like we work, Triple C bond, you’re getting basically equity like returns, I think we work to 10% trading at 70 right now, or you can make 2% in Microsoft, and the spectrum across that whole range is from Triple C, the worst stuff, the equity, like stuff to the stuff that’s never going to default, like Microsoft, in the middle is sort of triple B, that double B debt. Getting to your question here, the belief is that the way to make money in credit is to go down and pick the super risky stuff, the West stuff that has the juicy yields, and then that’s the way you’re actually going to make money in product. The truth is that it doesn’t work like that. So if you go and look at the height where the highest returns historically have come in credit. It actually hasn’t been from that low end. It hasn’t been from that risk. Get Stuff. It’s actually been from the middle from that triple B and double B, where you’re getting extra return. But you’re not getting all the bad things that happen. We work as an Ahmanson falling out of bed, you’re getting paid for the rescue take and actually on average, it’s getting better. So one of the keys to doing credit qualitatively is moving away beyond yield, not thinking about yield, take this concept of yield take this concept of coupon that everybody looks at everybody sells on it, every Fund says what’s their headline, you roll it out, doesn’t work. Because if you go and chase yield, you’ll also go chase losses, they’re attached to each other. And you can’t get around that the market is efficient. If you buy a 15% yield, you’ll get a 4% return because you’ll realize an 11% loss. And that’s sort of the way the math works. And so what you need to do is think about credit improvement, what’s getting better, what’s getting worse, if I own a 4% bond that trades to three and a half percent, I will do much better than owning that 8% bond that trades to 9%. And by the way, if you have a super high yield, it’s not going to trade a little bit down, it’s going to trade a lot down when it trades down. So that’s the first thing to understand now, is it true that you should now let’s get to the statement, you should only buy credit when credit spreads are wide. Okay. Is it true that the opportunity and credit is really good when credit spreads are wide? Absolutely. We wrote a piece in March, I think it came out March 17. I think we nailed the bottom called What to buy first and this is a go buy high yield. spreads are wide, these things aren’t gonna default that much. It’s a great opportunity. And that was exactly right. But actually, if you go look holistically, we’ve done a lot of work at this at for debt on our firm, if you will look and say what should I really buy when spreads are wide small cap value emerging markets, like if you’re going to swing, go swing in small cap equity in risky equity when spreads are wide? Because you’ll do really, really well if you’re going to take that risk. Take it. I would argue that’s not what credit does. I would say that and the way that we look at the world is when you’re buying credit, you’re usually buying it for preservation of capital. So I actually say the right time to buy credit when stuff is super tight, when the market is yields really low, when you don’t like what’s going on in the rest of the market. Because you’ll be able to earn a return limit your drawdowns and then you should be doing when spreads blow on this opportunity, selling some of your credit and going into the riskier stuff. That’s the way we think about it. That’s the way we think the right way to go. So I think that that is sort of a misguided that it’s true. When you look narrowly at the asset class, it’s not true when you think holistically across your portfolio. So the entire the way that we think is how can we create a product that delivers return while protecting against drawdown? And when I say product? I mean, just credit in general, how do we think about it allocating across all our portfolios,
Corey Hoffstein 12:50
the early 2000 10s saw a huge run up in Smart beta equity products, they really grew in popularity in the mid 2000 10s. And it seemed like in maybe 2016, through 2018 headlines would at least lead you to believe, hey, smart beta, fixed income is right around the corner. That’s the new frontier is quantitative fixed income. And yet it never really seemed to emerge that I know from my own conversations, my network has very few quantitative fixed income investors. And I’m curious as to where you think the barriers to entry are in the space? Why aren’t more people taking a quantitative approach to credit?
Greg Obenshain 13:30
Yeah, so I think barriers entry is just the right way to ask the question. And so if you think about it, let’s say I want to go start a credit fund. First of all, I need to have credit expertise, personal, there’s just not as many people like me who have credit expertise. Most people have equity expertise, they don’t have credit expertise. Again, credit takes is a specialized skill to go get the data, where can I get the data snapshot really hard to get? In turns out that well, every data provider will give you the whole you there’s 1000 ways you can go get historical equity data with Financials, it’s very, very difficult to do in debt. In fact, for us, we had to go build our own custom database, which was a massive undertaking, line by line, matching bonds to equities. And so that kind of database which is very, very hard to access. So there’s a barrier to entry issue. And then by the way, you get into fixed income. And they tell you this thing about being equip women, I am not allowed to trade half the bonds unless I am a qualified institutional buyer. What that’s crazy. And so the barriers to entry to this space are massive. But also I think there’s a problem. I think that most people that try to break into this space, try to do it through the distress side. And why do they do that? Because the yields are higher, because the potential returns are higher because they have these periods of time when they get great returns. And guess what? That’s a smaller market. There’s not as much room for people to come in. And so you start thinking about building quantitative models around there. First of all, they don’t work as well because there’s a lot more idiosyncratic risk in the district. to market to, you can’t get the liquidity you want Is there enough names out there, and three, you’re probably going to blow yourself up relatively early. So the opportunity for quality credit is actually huge. But it’s at a higher quality level, where the returns are higher where the opportunities are there. It takes expertise, it takes data, it’s not something that anybody can just jump into. And I can tell you from experience having beaten my way into it, it took a ton of work.
Corey Hoffstein 15:29
So people might look at the credit space and look at some sort of high level concept like maybe a default adjusted, spread, or maybe yield as a main driver of total return and credit. I’m curious as to what you think the main driver of cross sectional excess return and credit is?
Greg Obenshain 15:50
Yeah, exactly. So it goes back to this whole idea of what’s getting better, what’s getting worse? How do you think about that, and moving away from yield to this idea of it. And the way that we express is spreading those spread to treasuries, which is really similar kind of compensation. But moving away from this idea that you need to make money through the coupon through that part of it, and toward the idea that what you want to do is provide capital to companies that are getting better, right, because they think there were good things are happening. And you can do that on a fundamental basis. But on a quantitative basis and credit, it turns out that it actually works pretty well. Because what’s beautiful about quant and credit is when you go to figure out what’s gonna get better, what’s gonna get worse, you already have this concept of rating agencies who already have this concept of upgrades and downgrades. It’s already built into the spectrum. In equities. They don’t, you don’t have a list of bonds that are rated our equities that are triple A down to Triple C, it doesn’t exist. Now say what you want about the rating agencies. They’re basically they’re sort of the early quants, right? They made an effort to force rank the entire universe, if you use that you don’t have to accept their ratings. But you can use those ratings as data and use that concept that okay, there’s, I know in fixed income, and it gives us the way it works that have I have a bond trading like it’s a double B bond, and it gets better. It’ll trade like it’s a triple B bond eventually. And I can tell you exactly what that’s worth. I know what that’s worth, I know how many points that’s worth, I know what a percent return to what that is. So for me when I look at bonds, and when we think about it is okay, I can make money through the carry through the coupon. But the way I really make money is by understanding those transitions, those upgrades, those downgrades, that’s how we build those models, our models, what’s getting better, what’s getting worse, where are they moving within the universe?
Corey Hoffstein 17:36
So if the important thing to get right in terms of finding excess returns is sort of figuring out where the upgrades and downgrades are gonna be? What do you think the actual important characteristics are for sort of identifying those features?
Greg Obenshain 17:51
Yeah. So let’s talk about the landscape of debt for a second again, so you have super high quality companies or investment grade, you have high yield, but they’re all doing the same thing. What’s the business though? It’s in the business of spending dollar and making dollars. And I think when you really step away and look at why it is that investment grade companies generally don’t default, and high yield companies do? What’s going on? Like, what what are the drivers are getting it. And we’re all taught that when you think about the high yield and the investment grade market, that triple A is the highest, and there’s a triple B and there’s a double B, and there’s a single being a triple A. And that’s not useful for anybody to think about because well, how did you arrive at those levels? Well, people think, Oh, I one has more debt one doesn’t, I think there’s a simpler way to think about it. And it’s this, if I’m borrowing money at 7%, and my return on investment is 4%. I am liquidating the company, that is a single via credit. That is what it is. If I make 20% On my return, not even 20 minutes, it makes 7% Return on assets are 10% return on invested capital, and I’m paying 3% on my debt. By the way, I don’t even need debt because I produce so much cash so that I just got some debt because I was doing something maybe I wanted cash overseas. I don’t know I got a little bit. That’s investment grade. That’s the difference. Okay. And so the number of times you go and in an investment grade guys, you don’t have to think about this. You’re thinking about growth, earnings in high yield, you think about survival. And what is the best indicator of survival, making money? What’s the best indicator that you’re gonna die? Slow liquidation by taking on cost of capital that’s larger than our cost of capital that’s higher than your return on invested capital? That’s the rule. Can you do it that easily? No. But there’s great proxies for that total assets is when they it’s a sign of success to get a lot of assets, you have to have done something, right. That’s actually a really good indicator. But other things like revenue, decompose profits, right. One of the terms that comes out of the DuPont analysis is revenue to total assets. Seems like a bizarre metric. Well, why would that be useful? Actually, it’s kind of a return on spending metric. How many dollars Am I getting into the company for the money I’ve spent historically, that’s really useful and you know, What that’s better than debt to EBIT, da, I’m going to oil and gas company debt to EBIT, da means nothing. Because my EBIT, da, cut my EBIT, da on average private or my profit negative from there, because I have so much depreciation and amortization. But if I have a good sense of how much I generally spend, and then what my revenue is on top of that, and what my return is, I can get a very good idea of return, how profitable Am I versus the cost of my capital. And so I see this all the time, when people go into debt, the thing that you’ve got to realize it, that yield comes with a real cost, that’s a cost to the company. If you have a lot of interest expense, you cannot reinvest, you can’t help, you can’t try to grow your way out of a problem. If you can’t reinvest, you are constantly having to cut things you probably shouldn’t. And so leverage kills, not because of the debt level itself, it’s because it distracts from your ability to reinvest in the business. Also, you can’t make mistakes. If you have a ton of debt, you have this sorting where you can’t mess up. And so once you mess up once become more dangerous, your debt cost goes up, you have lower returns, your cost goes up, and you get into this world of trouble. So those are the kinds of things that really drive it, building metrics that really reflect that, that capture that is what drives the upgrade downgrade. And you can see it by the way. So if metrics as simple as revenue to total assets are improving, that’s good. If they’re getting worse, that’s bad. Here’s another one, if you’re adding debt, not good, general, you need more money, and you’re increasing interest expense, you don’t want to be adding, if you already have a lot of debt, you don’t want to be adding more, that’s not a good sign, you are going in the wrong direction, you at least want to be paying it down and not paying it down by saying our adjusted EBIT, da was higher. So our leverage went down now really dollars to the debt. Debt pay down is one of the most wonderful return strategies a company can have. If your company has a lot of debt, and they’re paying you dividends, you should be angry, you should be very, because what they should be doing is paying down their debt, because that still goes to you that dollar debt payment went directly to you, your equity value went up. But now they have a lot more flexibility. Now they have an ability to reinvest, and then they can pay the dividend, but pay the debt down first. So I think there’s just a lot of mistakes made in the market, not thinking about profitability, not thinking about capital allocation. So those things all work. And when you build that models, and you can make money off.
Corey Hoffstein 22:24
So as the catalysts for the excess return, the actual agency rating agency upgrades and downgrades or does the market sort of forecast that price that in and you see price move into an expected upgrade or downgrade?
Greg Obenshain 22:40
Yeah, so I think most people would say, Oh, the rating agencies don’t know anything. And of course, the market knows that the market always prices at first and December. So if that’s true, right, because the rating agencies that are not in the business of willy nilly upgrading and downgrading people, right, they’re going to be very slow to upgrade or downgrade somebody by the time they do it. You knew it was coming. So the market does price it first. Interestingly, because they actually use consistent methodologies. They get a lot of individual credits wrong. But on the whole, they generally it’s a forced ranking exercise, they’ve generally do pretty well at force ranking. So when we’ve done we’ve done I’ve broken this down and said okay, where’s the market rating? It was the agency’s rating it what happens, we wrote a piece on this and what happens when there’s a disagreement there? Well, it turns out that a lot of the time, the markets, right, and the rating issues come towards the market, but also about half the time, the market actually comes a little bit towards the rating agencies, especially when stuff has sold off relative to the race. And so when you actually dig into the data, you know the markets right most of the time, but you really can’t ignore what the agencies are saying, or at least you should do so at your peril. Like where we see this most is where the rating agencies say listen, that is a B three bond. I mean, that’s one step above triple C, and you’ll see people buying it left and right, because it has a lot of yield. And sure enough, those are the worst things you can possibly buy. The downgrade rates on those are horrific. But actually, even worse, the actual returns on those are horrific. And so people reach down to take that extra yield and the agencies and the whole time. And I’ll give credit to Moody’s here on oil and gas, which they screamed. I mean, if you go look at their ratings on oil and gas, all up until the oil and gas crisis. They were love. They were telling you whole time these guys can’t support the capital structure. They had it right and the market edit dead wrong. So I think I have a lot of respect for the process the agencies go through, even if I disagree on individual credits, and of course I will there’s 1000s of artists that reading so
Corey Hoffstein 24:30
So in theory, the rating agencies, there’s a qualitative process, but there’s a big quantitative process to what they do as well. And they’re fairly transparent in their methodologies. They’ve written quite a bit about how they’re incorporating different variables over time. I’m interested in knowing as you evaluate at least what’s public about their process, and what you’ve sort of come to understand is their process and you built your own sort of private process for understanding where upgrades and downgrades will occur, what sort of characteristics do you think that they over emphasize in their analysis? And what do you think they under emphasize?
Greg Obenshain 25:05
So they’re very good about publishing their methodologies, which is, Listen, if you’re an analyst somewhere and you’re looking to get an industry? Well, I think one thing that people should do, and they don’t do is just go read those methodologies. They tell you how to think about an industry, it’s pretty good listening, they do a good job, they’ve been doing this for years. Some of these redundancies are 100 years old, they’ve been doing this for a while they’ve got some good ideas. You know, I think any time you need to publish and have a formal methodology, you tend to over emphasize things like quality, like stability of the business, you’re talking to the company, so they’re selling you, they’re pitching you on their growth prospects, they’re pitching on the quality of the management team. One thing I’ve never been able to figure out is how you judge the quality of the management team, I still don’t know other than looking at the numbers, which you’ve already done. So I think they are anybody who has to do fundamental analysis and write about it and make a story about it, which is what they’re doing is going to be misled a little bit by that story, they’re never going to pitch the company that actually quantitatively looks pretty good, but scary. And they’re always going to rate it a little too low. Conversely, they’re always going to rate the company that’s been around for a long time has a very deep management team, excellent ESG practices very, very highly. And that’s probably not worth it. So that’s generally where the mistakes are made. But they’re actually the same mistakes that are made on the fundamentals. I don’t like to criticize the agencies, because I think they get more than their fair share of criticism for doing what actually is a very good service. Because you can go online and for free, see where they rated things and how they do their methodology. It’s pretty good. I’m not going to complain,
Corey Hoffstein 26:45
now that you’ve accumulated all this data, and you’ve got the data set to work with, I’m curious as to what maybe is the most counter intuitive finding that the data is brought to light for you, maybe especially in light, given your background as a fundamental investor and some preconceived biases you might have had coming into it.
Greg Obenshain 27:04
Yeah. So I mean, I think I think there’s some really simple ones, right? There’s things like debt to EBIT, da right? Every debt investor uses debt to EBIT da doesn’t actually test that well, compared to other metrics, equity, market valuations equity markets, right a lot. So actually, equity market value over debt is actually far better thing to use or enterprise value to debt. And then there’s things that were totally stupid at work. And you sort of can’t believe it, but you, the data tells you one of the dumbest things you see is total assets. If you have two companies that are trading at roughly the same spread, or the same yield, pick the one that’s bigger, it’s much less likely to go to fault. And it’s got a lot more options if bad things happen. And returns are generally higher. Total Assets is a very strong value variable rate in of many variables, not something I would have ever as a fundamental Elena’s really hung my hat on. I mean, I’m really digging into the profitability of the country. I know we’ve company I know what’s going on. Now. How big is it? How long has it been around? Listen, as total assets are a sign of past success, something went right, they’ve got a lot of hidden value somewhere. Not a bad thing to use. I mean, that that I think that was surprising. You know, there’s some things that I think are counterintuitive. You know, there’s been a lot of focus on private debt, and private lending. I think there’s a thought that that outperforms public debt, my opinion. And my data says it actually doesn’t, it’s just a form of taking extra risk for extra yield. And we’ve written a lot about that. But I think right now, where I see signs of just private debt was a category that didn’t really exist before. And it’s got a good marketing story behind it. But ultimately, it’s lending to highly levered private equity firms in the single big category. And that’s what a lot of it is, not all of it, but a mash of share. And there’s no way that the private debt markets could have grown as quickly as they have without the private equity market, feeding them most of the deals. So I think that’s something that was, I think, counterintuitive to most people. And I think that also tying into that there’s this, like, when you’re outside the debt world, people will tell you, Oh, I’m buying floating rate, senior secured debt, because that’s the safest debt out there. Right. It’s floating rate on a great risk senior secured. So how can it get any better? Yeah, that’s actually the worst debt you can buy. And here’s the reason if I’m a really strong credit, I issue fixed rate as far out as I can. So the markets not gonna give me fixed rate, long dated debt, unless I’m a good issuer. If you are a weak issuer, and I’m concerned about you, I’m going to give you floating rate secured debt with covenants is not a signal that the debt is good, it’s a single debt is weak. And so that’s something I see all the time when people talk about debt is a misunderstanding of who can actually issue what kind of structure
Corey Hoffstein 29:42
there’s a lot of characteristics that we use in the quant equity world that ties back to the underlying health are sort of quality characteristics of the business itself. And I would presume that a lot of those characteristics crossover into having meaning and impact act in the world of credit. And so I’m curious whether you’ve looked into some of these factors. I mean, you mentioned total assets, I think in the quant equity world, we might just look at something like market cap. And that’s the size factor. So I’m curious if there’s others. And even if something like equity price momentum can be an indicator in something like credit.
Greg Obenshain 30:18
Yeah, so a lot of the stuff that works in equity works in debt. In fact, very first thing I did when I was engaging in this process was to go read all the equity literature and test all the equity variables that worked. So if you sort of broadly look at FACTOR research in equities, its value, its momentum, both of those work, both of those are core to my process. There’s quality as an overlay, right as a way to prevent and when you say you’re doing something you’re doing value, there’s sort of a quality overlay to make sure that you’re not buying things but to go imminently bankrupt. That works in debt to that a lot of factors. I think, very simple things like the atrocity score is a paper by trusting I think the round 2000 that laid out a whole bunch of composite factors that work in equity works in debt to works exactly the same. So there’s a huge amount of crossover. The problem, then the challenge with debt is how do you normalize for everything? How do you define value a debt is very different than valuing equity debt, it’s really coming up with an idea of what’s the probability default, or the relative ranking of that bond to others, which we can go into more detail on the core ideas of equity have exactly the same.
Corey Hoffstein 31:30
One of the issues that I always sort of imagine when dealing with a big credit database is just the sheer dimensionality of the problem. When I work in the equity space, and I talk about buying Coca Cola stock and you buy Coca Cola stock, generally, it’s the same stock with rare exception. If I buy a bond from Coca Cola, it precludes you from buying that bond. And you may choose to buy a bond of a totally different maturity and coupon and has totally different characteristics to it. I’m curious as to how you think about tackling this dimensionality problem.
Greg Obenshain 32:03
And this is not a small problem. This is not challenging dealing with that is if you’re an investment, and you have 15 different issues in high yield is going up two or three. And they have different maturity dates, they trade in different spreads, they actually could have different security levels, one can be secured, one can be unsecured. So that dimensionality actually turns into a bit of an advantage when you start to think about so the very first thing you need to do is you can simplify, where you can get the average spread for all the bonds, and just use that as your price and look at I guess, create a composite bond and the composite equity and use those and that works. But then you can start to think about some really interesting things. I think Deray I think different tenors on bonds is one of the most interesting things in equities, you don’t have this choice of tenor, you’re basically buying the longest dated stream, you can and you’re gonna get paid last and furthest out. But in bonds, I can get paid tomorrow, I can get paid in six months, I can get paid 10 years from now I have my choice. In that concept of duration becomes really an advantage, when you start to think about risk. If I think a company is going to get upgraded, and I want to be very exposed to it, I can buy a very long bond, because they will move a lot more than the shorter bond. If I liked the company, but I’m concerned about various things, I could buy the shorter bond, and I can reduce my exposure to that company without actually changing my dollar amount. So my exposure is actually varying by the duration of the bond. When you start to think tactically about that, that gives you a lot more options. And it’s something that we definitely use very actively. So the dimensionality there becomes an advantage. Same thing with security. If I think a bond is going to get upgraded, I want the lowest rated the most subordinated, longest dated bond in that capital structure. And I might be doing it in a very safe company. So I’m not taking a lot of risk. But I’m increasing my exposure to the upside that I think is there. Conversely, if I really like a bond, but it’s got a lot of leverage, I’ve owned bonds were completely covered by the cash on the balance sheet, they just haven’t been taken out yet. And trading at a high yield because people don’t like the credit but completely covered by revolver or cash, take your choice. So some of that you can model and there’s still a lot of that you can’t a lot of that you just have to have had experience and credit and think about capital structures and decide where you want to be
Corey Hoffstein 34:25
talking about modeling. One of the things that whenever I think about a high dimensionality problem, it seems to me like it’s often an area that might be well suited for machine learning where you can have these different statistical techniques that can help you pick up on interaction and conditional effects among all these different characteristics and variables. I know you’ve started to incorporate machine learning and you’ve published a couple pieces on machine learning. I was hoping you might be able to recap some of the ways in which you have incorporated it and it’s been successful for you.
Greg Obenshain 34:56
Yeah, so I think incredibly especially machine learning is really important because there’s a lot, there’s a lot more nonlinear. And not even just duration is actually a pretty linear concept. Longer the bond, the more risk you have, it’s actually that’s actually pretty linear, you can put a model that in linear model, when you start to get into different rating categories. And by rating categories in shorthand for quality, so shorthand for bonds that might go default, and bonds that probably won’t go to fall, bonds that might go default, acts completely differently than bonds. In fact, some of the variables momentum variables in particular, are actually opposite. So if you have a super risky bond and starts to trade off, it usually keeps going the wrong way. Whereas an investment grade bond trades often mean reverts very quickly. It’s a contract that has to pay off at par, it’s not going bankrupt. The mean reversion is very quickly. So you actually have an opposite sign there, then that’s bizarre, it’s really hard to handle in a linear model. And So machine learning is very well suited to fixed income. And so what we did was we actually went out and took all the data built, we had about 90, I think we use about 98 variables, a lot of them are custom variables that we built, based on experience from general linear models, actually, and built an upgrade downgrade model to look at how we could better predict upgrades and downgrades and credit and buy upgrades and downgrades and credit. I’m talking about price upgrades and downgrades Is it getting better or worse. And we use the shorthand of ratings to describe that. But we’re really we’re doing it’s price improvement or present price decline. We did this with Brian Toronto, who’s our quant. And I’ve heard on and we worked on this together. And this was I think highly successful, it became very clear to us that if you looked at the top, actually where it was really successful was on the downward side. So if we said that a bond when it said a bond was going to get downgraded on average across history, the total returns for bonds are about eight 9%. Since 2000, in return for our bottom downgrade was close to zero just above zero, so I think it was maybe three or 4%. And he was highly effective at identifying bonds that were going to get worse, irrespective of they were rated Triple C or if they were rated triple B across the rating spectrum, it did a great job of figuring out what was going to go down in quality and much better job than the linear models. So it was it’s a very good thing to use for credit.
Corey Hoffstein 37:22
So speaking of non linearity is I think one of the very well documented popular nonlinear trades that shows up in credit is the fallen angels trade, which is this example of a structural edge that emerges in downgrades due to institutional constraints within the credit space. I’m curious that when you survey the data and sort of review your experience as a fundamental manager, do you see evidence of other similar types of trades within credit? Yeah,
Greg Obenshain 37:53
this fallen angel trade is a great trade, it’s been around for a while. It’s actually what got me interested originally and trying to figure out why it work is broader than just fallen angels. By the way. Basically, the category of debt just below investment grade tends to be the highest absent highest returns, you can get in credit, as I mentioned before higher than triple C’s higher than the junk, because it actually has a better base rate of upgrade to investment grade. And when you go back up where you recover from having dropped down, you do really well. They also happen to have a little bit longer duration, higher quality, all the things I was talking about that work works there works brilliantly, I would say that’s the best opportunity and credit, the worst opportunity and credit is the polar opposite of that range. Where if you’re in a high yield manager, usually the Triple C basket means you can’t buy more than x triple C’s hit 20%. So what are you doing, you’re trying to get yield and try to load up on all the stuff that’s trading like a triple C, but not really rated like a triple C. And so I see this all the time, when you actually do the historical returns on what’s really, really bad. It’s that stuff. It’s that stuff that has super high yield right below the Triple C basket is that is where everybody’s reaching for yield. And the markets are mostly efficient. But greed still is a big factor. And the greed factor really drives some pretty, pretty horrible returns. I think that’s sort of the flip side of the Fallen Angel Tree.
Corey Hoffstein 39:12
I’m curious as to what your take on private credit in the B spaces. I know this is an area you’ve had done a lot of research on, obviously private credit has grown as an asset class. What does the data tell you? As far as you can tell?
Greg Obenshain 39:28
Yeah, so private credit is really interesting, right? Because it sounds so good. You know, all these companies who can’t get capital, and we’re gonna go out and the banks aren’t lending to them anymore. So there’s this real opportunity to go make some great returns providing capital to private credit. And you ask yourself, well, I you know, I’ve been doing this a long time. I don’t remember a whole lot of people being starved for loans hasn’t really seemed to be the problem in the market, that there’s just not enough loans being made. And why are the banks getting out of this? This is bizarre or if this is such a good opportunity, why are banks not doing these loans? They’re the ones who are in the best position to do it. They don’t have to go source their customers, the customers are there. I mean, private credit shop has to go source the customers, that’s crazy expensive. How could this be good? What’s really happening? Private credit, large part is a euphemism for private equity lending. And it’s no different than what’s been going on in the leveraged loan market. Now, some of those structures are great, don’t get me wrong. Some of these private equity deals are wonderful deals to do. I’m not saying they’re bad. But for the most part, you’re dealing with a Counterparty who is pushing leverage as far as it’s going to go. They’ve got much better lawyers than you, the banks are working for them, not you. The issuer is not a repeat issuer, but the private equity company is and they make all their fees issue. So you know, what’s coming your way is going to be over levered and not well documented, or at least not the credit and recycling on your favorite. So I think, and it falls into that part of credit that I just spoke about that single B area where you’re not triple C, but you’re getting a you can sort of reach for yield. So I think the setup is really bad in primary credit. And I don’t really need to argue this too strongly, because the public face of private credit, or BDCs, that deliver negative 40% returns in March. So when things go bad, the asymmetry on these are horrible. This is where debt gets a bad name. If you’re getting paid a personal loan, and you go down negative 40%. That’s just not a good trade. So I think there’s a lot of reaching for a yield there. There’s a lot of storytelling that’s happening. But I’m challenged to see that private credit is truly a solution to a problem that really existed, and not something that’s really just being sold.
Corey Hoffstein 41:43
So one of the things that I think a lot of quants that I talked to are struggling with right now is sort of emergent phenomena, that a depth of data is just not going to help you with and when I look at the credit space naively, I would presume that the expanding role of monetary and fiscal policy is something that’s still somewhat new, but something that’s going to have a really big impact. So I’m curious, how is it affecting your research? How are you thinking about incorporating these new emergent features?
Greg Obenshain 42:14
Well, first of all, credit is no less affected by the Fed than the equity market. accommodative monetary policy reduces default risk. And so names that should have I would have thought when would go it takes a long time to go bankrupt now, a very long time. And you can get there always seems to be a refinancing way for companies that should have been dead years ago get refinanced, because the market is just it’s very accommodative. So I think that’s hard. That is hard. And I do, I don’t buy into the Fed bailing out the high yield market, what they did last March was actually really about the fallen angel market, it was really about the investment grade market, they were trying to make sure that the mutual funds and they were forced to sell investment grade bonds, because they became fallen angels could do it and not destroyed the liquidity of the market. That’s what that was really about. Companies can still default, you still have to get paid your spread, because you’ll get hurt very badly, very quickly, if you try to buy something that should go default. And does I mean, it’s just it’s probably the best evidence of this is that we have data on the credit spread for the last at least 100 years. And really, if you go back, you could probably get it for the railroad bonds in the 1800s. Right. So we know what credit spreads have generally been on higher quality debt, but it’s useful. And they’re actually a little white right now, relative to the very long histories. So there’s good evidence that while the Fed can affect rates, they can affect the short rate, in particular, have a very hard time sustainably suppressing the credit spread, because the credit spread reflects something real, which is losses, losses that investors will take. So I go back and forth on how much impact it has. The models all still work very well, I haven’t seen a whole lot of change. When I look at my spreads between my deciles. Over time, it’s been very stable. And the opportunities haven’t really gone away in the same way that has gone away in the equity markets when they were reversed. Actually, for a little while. That hasn’t happened in the credit markets, the credit markets still seem to be very rational, mostly because I think the threat of default is always very real. So
Corey Hoffstein 44:26
I hate to timestamp a podcast because I like these to be evergreen. But you mentioned the fact that you thought spreads aren’t that tight? And yet, if I look at a really naive measure of spreads like the Bank of America, high yield OAS, I think it’s now just reached points that it hasn’t seen since 2007. So I’m curious as to how you would reconcile those two ideas that it’s the tightest it’s been in almost 15 years. And yet you would sort of say no, maybe it’s fair or even potentially a little wide.
Greg Obenshain 44:57
Yeah, so the BWI spread is what I was talking about. So this is The investment grade spreads just over a very long prison. It’s very tight right now, don’t get me wrong, actually within high yield. So we’ll talk specifics right now. So it’s early April 2021, right? Triple C’s spreads very tight right now. And that’s actually what’s driving the overall high yield spread, tighter, the double B spread, which is the high end of high yield, is it actually, by the way, investment grade spreads are also pretty tight. The high end of high yield is actually tight, but not much tighter than it’s been. It’s not at extremes. And that’s actually half the market. So that’s really being driven by the Triple C spread, which ends up when you take an average being really important, because it’s a smaller part of the market, maybe 20% of the market. But because the spreads are so wide, that disproportionately affect the average spread. So there is a composition element to this. But let’s go back to what I this idea of when you buy credit, why do you buy credit? And how do you make money in credit, when I say now is a great time to drive Triple C bonds with spreads right now, because that tends to be very, when the market cycles when this gets slammed into your upside downside of that makes no sense. But if you’re trying to invest in something that’s going to be able to at least minimize your drawdowns while giving you some return, you probably want to invest in relatively safe credit or higher quality, credit, and exactly times when spreads are tight. And I know that’s counterintuitive. But tight spreads isn’t necessarily the best indicator of credit returns. Credit returns are driven by the business cycle. So when you have a business cycle, you have a business cycle, when the yield curve is inverted, you have a business cycle, when actually spreads are started not going doing what they’re doing right now, which is going down, right? They’re going up and going up quickly in triple digit going to triple C’s going tighter means that safer quality, you’re further away from a credit event, you typically see, actually the Fed starting to lower rates even more because they’re concerned and they’ve raised rates and they’re lowering them because now they’re concerned about the economy. And they’re seeing data in there. And none of those things are happening right now. So it’s actually fairly benign environment. There’s good argument you should be long risk right now, if you were actually following that methodology. But also, if you don’t want to be 100% equities, you want to make some return in something that’s going to give you lower drawdowns, right, it’s good. So it’s counterintuitive thing that actually the maybe the right time to buy credit is when spreads are kind of tight. Just don’t go buy the stuff that should never have tight spreads. Right. That’s an end. And that’s what I’d say about right now. Now, I prefer it spreads right 100 But we don’t have that opportunity right
Corey Hoffstein 47:31
now. Greg, last question for you. The world seems to be healing a little bit from the COVID crisis. The more and more people I talked to every day are on their first or even second round vaccine. Fingers crossed the world is open up by the time this podcast goes live. What do you most looking forward to in the future?
Greg Obenshain 47:50
I’m excited to travel again. And go places I think excited to have barbecues excited to have dinner parties, and do all the things that we haven’t been able to do. I know you’re you’ve already travelled you’re already in an exotic place, but I think it’s I’ve been in the Northeast. I’m ready to get out go someplace for
Corey Hoffstein 48:08
especially coming out of a dark cold winter of the Northeast that I know all too well. There’s nothing you want more after that than a nice spring barbecue. Well, Greg, I can’t thank you enough for joining me highly educational really enjoyed it unique perspective. Hopefully we’ll get a chance to talk again soon. All right, thanks. If you’re enjoying the season, please consider heading over to your favorite podcast platform and leaving us a rating or review and sharing us with friends or on social media. It helps new people find us and helps us grow. Finally, if you’d like to learn more about newfound research, our investment mandates mutual funds or associated ETFs please visit think newfound.com. And now welcome back to my ongoing conversation with Harley Bassman. Some people might look at a portfolio that just buys the s&p 500. And either an out of the money put or an out of the money call or maybe a combination of both as being a little bit simplistic. But I think that probably that apparent simplicity belies how deceivingly difficult it can be to manage the path dependency of convex instruments. So hoping you could spend a little time expanding on some of these complications, and maybe some ways in which they need to be navigated.
Harley Bassman 49:26
I’ve written a few times about the dangers of passive investing and by danger, I don’t mean to the investor per se. I mean that as money has gone from active management to passive management, that creates a negatively convex profile is a long reason why that’s the case. But it adds negative convexity to the market by the dynamic of how this money flows in and out. Let’s take it off the table for a second and go back to the more simple idea icon To think that if you’re a professional investor, or you spend a great deal of time in the topic, you’re welcome to go pick single names. And you can be very clever about doing that you do your homework, you size it properly, you source out interesting things Be my guest. I think for a lot of people, the idea really is having the right horizon and allocating accordingly. And being consistent about it. I think a lot of The Simple Strategies of laddering of dollar averaging in, they’re just not wrong. And that’s okay. I mean, everybody wants to go to the cocktail party and brag about how they bought Amazon at $10, which is a good story. But truth be told, I’m sure they bought pets.com. Also, way south. So I think nothing wrong with simplicity, what’s more important is that you’re not too conservative or too aggressive, you invest in a manner that is consistent. I left PIMCO a couple of years ago, where that actually, and I hadn’t my 401k with them, and I had a 60 65%, in one of the equity type funds, and 30 preps and one of the bond type funds, they were enhanced, they were just playing up Vanguard style. And I will say that that’s basically been my best investment over time, because I don’t look at it, and it just grinds away quietly. And it’s done very well, because I haven’t had to do anything to it. Admittedly, the Fed helped a lot by pumping an extra trillion dollars. But what’s wrong with that? Now, what helps isn’t having optionality on top of that. And so when you go and you add some wings to it, that work in your favor, you will catch a few homeruns. I mean, going back to the bigger concept of equity versus debt versus other instruments, what is a stock, and we call an asset, but what the stock really is, it’s a call option on a company where the strike price is the value of their bonds. That’s why when you see a billionaire, most of them tend to have started companies, and they own the stock in their company, not the bonds in their company. And so owning equities over a very long horizon. I mean, there are guys out there right now, the stocks are crazy rich, and they go down by half, maybe they’re right. But over the fullness of time, equities are a call option, and to the extent that you own equities, and with simplify, you go and add optionality to that owning up from an options is not the worst idea in the world over the course of time. And so I kind of liked that I think people play make a mistake of being too conservative, sometimes with their money, because if you think about it, the bond trade in this description over here is actually short the put, whereas the equity and stock is long the call. So being long bonds. I mean, I don’t fully have them, but you got to keep that construct in mind that you’re short the option when you own a bond, it’s not Treasury.