The Exchanges

Q So the private credit story, you hear that those types of loans are better deployed in the hands of asset managers because of the funding model for them. What creates the risk that you're attuned to that reminds you of a seven or eight going into it?

A The main difference in the funding models that I see is that, obviously, they're not relying on deposits for a source of funding, but they're increasingly reliant on insurance premiums, right, through captive or affiliated insurance and reinsurance entities. Apollo with Athene really got that ball rolling, but now they've all got their insurance affiliate In their reinsurance affiliates. So Blackstone's got theirs. Aries has got theirs. KKR's got theirs. Blue Owl's got theirs, right? They've all got this insurance reinsurance edifice that's been created, and I get it, right? You want the float. You want the essentially permanent capital that exists there, but it reminds me for all the world, like Citigroup and The SIVs, as you recall back in, oh five, oh six, oh seven. You can't destroy risk. It's either created or destroyed. You just move it somewhere else. And my strong belief is that we've shifted around a lot of risk into the insurance, particularly the offshore reinsurance world, where leverage ratio is incredibly high, increasingly invested with affiliates. And we all know what that means, but it's, the reason I say it reminds me of Citigroup and the SIVs is an effort to take risk off balance sheet, but when the shit hits the fan, that risk comes back on balance sheet. And understanding the regulatory arbitrage that's going on here now is where I'm really focused, because…

AI assessment note: “an effort to take risk off balance sheet, but when the shit hits the fan”

Q Where did the name Epsilon Theory come from?

A It comes from the core econometric model. All familiar with it, right? So what are your returns? It's alpha, your idiosyncratic return profile in your portfolio. It's beta, how much you go along with the broad market. And then there's another Greek letter in there, and it's there in every econometric formula you'll come across, plus epsilon. So epsilon in econometrics, that's the error term. Epsilon for error. And I named it epsilon theory, what I was writing about, because the fact is, epsilon is just what's not in your econometric formula. It's not error. It's what you don't know. Yes, there is error and stochastic stuff in there, right? But there's also human interaction. Strategic interaction. There's also how humans systematically respond to story and the structures that exist in stories. So I named it Epsilon Theory because that's the place to explore. I'm convinced to figure new stuff out, and that's what I was trying to do.

AI assessment note: “It comes from the core econometric model. All familiar with it, right?”

Q As you're trying to map out what's happening in markets through these stories, do you start with a hypothesis and then map the hypothesis? The alternative is, do you let the hypothesis come to you through the data?

A It's always the first. So there are a couple of lessons that have served me well my entire life from being there at the beginning of understanding inference and large data set analysis. And the first one was a real mentor of mine. His name's Gary King. So he runs the whole social science quantitative research up at Harvard and phenomenal guy wrote a book Called inference. I remember that first grad course on econometrics, and he was saying, look, all of what we're going to do, both in this course and in all the courses you're going to take here, it's lots of ways just to try to answer two questions. What's your best guess, and how certain are you? And he said, what you'll find is that in the real world, 99% of what people focus on is that first question, what's your best guess? And very little effort goes into, how sure are you about it? That second question, how sure are you? That is at least as important as what's your best guess. The reason this comes back to your question specifically about, do you start with a, I'll call it a deductive approach where I'd say, nope, I'm the human here. I'm going to say, this is how I think the world works. And then I'm going to test it. Or do you start by, oh, let's let the data tell me what the patterns are. That latter approach, let's let the data tell me what the patterns are, that is at the key to inference, but is also how you fail in …

AI assessment note: “It's always the first.”

Q Assessing fundamentals, and to some extent, when you're trying to figure out, say, the intrinsic value of a business, you're trying to get at some base truth of what something's worth, independent of what the markets are thinking. Stories seem like it's the opposite. What are people saying about what's happening? How do you think about the difference between the storytelling that you're seeing and what's true?

A This has been a real evolution, Ted. I would say that I started off as a weak form narrativist, meaning that I thought, okay, narrativism story, that can drive price for a little while, but in pretty short, the truth, i.e. the fundamentals, will out. And over time, I became, I'll call a semi-strong form narrativist, meaning, oh, wow, stories can, Dominate for a lot longer than I thought, but I'm sure that ultimately the truth, i.e. the fundamentals will out. I gotta tell you, Ted, I am today a strong form narrativist. I think it's all stories all the way down, and that includes the stories we tell ourselves about value, about growth. The challenge with that is I always want to be right. I have opinions. There's no shortage of healthy ego in our business. You gotta have a healthy ego to exist in this business. You gotta say, I think X. The most difficult thing personally, but frankly, it's been the most important thing, I think, in learning how to manage other people's money in this world that we exist in, you must be profoundly agnostic. I don't know. And that's the hardest thing for a portfolio manager to say, I don't know. But that's the key to all of this. I don't know. I honestly, in the investment strategies we try to develop, I don't care about the truth. And that sounds terrible. I do very much personally. But for managing other people's money, I've had to come to the co…

AI assessment note: “I am today a strong form narrativist. I think it's all stories all the way down”

Q Why don't you pull them? Tell me what those comments are.

A Well, the first is that I've got the entrepreneurial bug. It's not a feature. It's a bug. I can't help myself, but to be thinking about connections and a way to implement it. That's why I think at heart, I didn't stay in academia, in the church, as I like to call it, our modern day church. It's not a place that lends itself to entrepreneurial thinking at all. And so to engage in that, I had to leave. And what I found in venture capital and then in public markets, which is really where I've been since 2005, it's that the ability to be entrepreneurial exists in this field, even if you're within a pretty large organization. So it scratches that itch for me. The other common thread, and this is really more applicable to what I do. Is that I've always been consumed by 10 dollar word or phrase unstructured data storytelling. So whether that's the stories we tell ourselves in politics, whether that's the stories that we tell ourselves in markets, that's always been the subject matter that just fascinates me. How can we find the structure of And unstructured data. That's what my professional career has been all about.

AI assessment note: “Well, the first is that I've got the entrepreneurial bug.”

Q Let's take that down a level to how you do that. In theory, there's all these stories out there. How do you use all this big data into something that turns into an investment strategy?

A There are two aspects to, I think, understanding story, and then applying it to investing. The first is, what's the structure of the story? Meaning, what's the beginning, the middle, the end? What's the story arc? What's the language that one uses to say that you're bullish about financials, or that the Fed should be hawkish, or that In this business cycle, we're moving from late stage growth to recession. So the first step is to figure out what is the language model for those types of stories, and these are stories we tell ourselves over and over again, and so we have tools and work to try to build what's the language model for the story. The second part of this is, what's the model for how human beings respond to these stories, to the news? In both of those, it starts from an experienced human Saying, okay, these are all the stories that I'm familiar with in, I don't know, my commodity trading around the stories that I hear all the time. And then the second part of that is I might talk to an allocator like you, Ted. Tell me, what are the stories that you hear from commodity traders over and over again? So you build language models around both. You track all the news. Ultimately, what you're building is Sometimes it's called a digital twin. You're trying to simulate the behavior of a trader or a PM or an allocator. You're trying to say, okay, I'm going to build an aggregate ve…

AI assessment note: “You're trying to simulate the behavior of a trader or a PM or an allocator.”

Q What were the core lessons in going from software entrepreneur to venture capital and then to the public markets?

A The core lesson for me is that it's all game playing. The game is different, but it's all playing a game. It's both the immediate game of the problem you're trying to solve, and then there's the meta game of how does the business work. And in fact, the way I got into public markets was a friend of mine was the head of research at a long-only shop, and we were always talking about companies that I was looking at or different technologies and the like, and he said, yeah, we're Put together a little group, going to start an internal long, short fund. Why don't you come join me over here? I said, I don't know. Why do I know about it? And the answer was, I knew nothing. I knew nothing about public equity investing. But my friend said was, this is the biggest game in the world, and that's what sold me. So that was in 2005, and that's what I've been doing ever since.

AI assessment note: “The core lesson for me is that it's all game playing.”

Q What are some of the most used examples of the stories of markets?

A Stories come about, I'll say, in two ways. Stories will be created in reaction to price. So, earnings season, I don't know, who's first up with financials? JP Morgan. For whatever idiosyncratic reasons, they have a good report, and the stock jumps three, four percent that day. The job of Our news media is to tell you why. You can't just say, oh, yeah, I guess it was a good quarter, or what often happens when stocks move up and down, you can't just say, that was variance, right? Variance doesn't get eyeballs. Variance isn't a good story. You have to tell your reader, your listener, you have to tell them why. You have to tell a story. So, That evening, Kramer will come on, and he'll say, yeah, I'm bullish on the banks because X, Y, Z, and he'll tell a story. That's not a knock on Kramer or anyone else who writes a story for the why. That's the job. So these stories of why we're now bullish on financials, or why we now think that financials are cheap, they have words and phrases and an arc to them. And you can track the density of that language and the way it spreads virus-like. It's the same math that you use to track viruses. It's the same math that you'll use to track the words and their density across all the words that are being published and spoken in financial media. All the words every night. And you can then, with infinite computing processing power, you can track how the…

AI assessment note: “So this is the Fed is hawkish. The Fed is dovish.”

Q What are you trying to understand about the stories to the regulator?

A There should not be regulation, essentially. This is at the core, I think, of so much that's happening in the commercial banking world. You have incredible regulation. That creates entities that cannot fail in the form of JP Morgan, Citi, B of A, and Wells. Those are the four. You've got your own issues around everyone who's outside of that protection with, God knows we saw that last March with the regional banks and First Republic and all like that. The regulatory requirements on capital are what Drives, I think, everything in the banking world today. What's our capital requirement on doing X, on making this loan, or it's too much, so we don't want to be in that business. So you get a less regulated entity, the asset managers, drinking that milkshake. And because they're less regulated, and let's be honest, they're smarter than the regulators in setting up the affiliate structure between the Insurance platform and the reinsurer, and then, oh, let's offload this. Let's create some insurance-linked security. That all started with cat bonds, and now it, you can do it with anything. You've created this enormous edifice that, again, looks all the world to me, like RMBS, Alt-A, all the CLOs becoming the new CDOs, and it's the whole cast of characters. It's a story that is rhyming.

AI assessment note: “The regulatory requirements on capital are what Drives, I think, everything in the banking world”

Q What are some of the narratives you're most focused on today?

A There are, I'll call them the bread and butter narratives, meaning the, oh, we're bullish on financials, or we're bearish on consumer, whatever it is. That's like, it's the Muzak of Wall Street. It's always playing. Personally, my faith is still value with a catalyst, and I ran a big short book, and that's how I see the world, as a short seller. I'm always looking at those sort of long vols, I'll call them trades. I'm fine with that. What are the systemic risks that are out there, and how can a technology and a practice of being focused on stories help us identify if and when those are real risks? I think there are those risks today, and I haven't felt this way in 14 years. My spidey sense is Really tingling right now, and so really focused on, away from the bread and butter of trades, I'm really looking at two geopolitical trades, or concerns, right? China and Taiwan, and then what I like to call the phony war between Iran and Israel. And the third is around the rise of private credit, and how that's A real shifting of risk. I don't think it's a creation of new risk. Remember the movie There Will Be Blood? It's a great film. Tad dark. But there's this great scene towards the end where Daniel Day-Lewis is talking about sucking out all the oil from a neighboring field. And he uses that, I have a straw, and I'm going to drink your milkshake, boy. I drink your milkshake. And Hones…

AI assessment note: “China and Taiwan, and then what I like to call the phony war between Iran and Israel”

Q So what was the transition from managing money starting at that long short fund to applying this in a different way through research?

A When you come in, your investment approach is, what does your shop do? And this is a value shop, value with a catalyst. So that was my faith or my religion. That's how I was trained, and I was just learning how the game was played. And we did well, this little internal money, long, short fund. We did well in O-Five. We did well in O-Five. We did well in O-Five. We did well in O-Five, in O-Five, in O-Five, in O-Five. And then we did really well in oh eight. And that's where everything changed. So the money really started flowing in, in oh eight, and then really started flowing in oh nine. So the fund got up to was close to a billion dollars. But in March of oh nine, you were in a seat. So you know this story pretty well, Ted, right? So in March of oh nine, you went to the wall and you flipped a switch on our returns and we just flatlined. We never lost money for our clients, and it's something that I'm really proud of to this day, but what was increasingly clear to me that what we did, value with a catalyst, it just didn't work. So in 2012, and this was, I'm pretty sure my wife still hasn't forgiven me for this, but we gave all the money back to our clients. It's a lot of money. In retrospect, like I say, it was the smartest business game decision I ever made. Because I didn't lose anybody any money, but it was hard at the time. We gave the money back because I wanted to figure …

AI assessment note: “We gave the money back because I wanted to figure it out.”

Q What's one fact that most people don't know about you?

A My grandfather was a dairy farmer, and he would laugh for me to call myself a farmer. He would have a great belly laugh about that, because I'm just a dilettante farmer, where if I have a bad day, it's the artisanal mezcal and the amuse-bouche at the local farm-to-table restaurant. So I feel silly calling myself a farmer, but that dilettante farmer has been this kind of Stock comedic character since Roman days, and I feel comfortable with that. It has been magic to live with animals and to raise a family around that. It's been absolute magic. I write about that a lot, so maybe people do know that about me. That's given me a grounding and a sense of what is important away from this ersatz narrative world that you and I live in. Anyway, that's been the best.

AI assessment note: “I'm just a dilettante farmer... It has been magic to live with animals”