Ben Hunt - The Stories that Drive Markets (EP.393)

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2:31Hello, I'm Ted Seides, and this is Capital Allocators. This show is an open exploration of the people and process behind capital allocation. Through conversations with leaders in the money game, we learn how these holders of the keys to the kingdom allocate their time and their capital. You can join our mailing list and access premium content at CapitalAllocators .com. All opinions expressed by TED and podcast guests are solely their own opinions and do not reflect the opinion of capital allocators or their firms. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions.

3:11Clients of capital allocators or podcast guests may maintain positions and securities discussed on this podcast. My guest on today's show is Ben Hunt, the creator of Epsilon Theory and co -founder of Second Foundation Partners, where he writes and invests through the lens of narratives. Or in his words, if a price moves, it's because a human told themselves a story. Before turning to investing 20 years ago, Ben was a tenured political science professor and founder of two technology companies. He's been studying trends using what we now call big data ever since his first book about predicting international conflict in 1997.

3:53Our conversation covers Ben's path to finance, the power of stories, tracking and measuring narratives in markets, and applying the lens of narrative to investing. Ben's insights offer a careful consideration of what's really going on in markets. Before we get going, Brett Barroquette shared some life lessons he learned from playing hockey on our podcast. He mentioned trying hard, having teammates that rely on you, winning every battle, and realizing that every shift is a new shift. All valuable gems from the sport that apply to investing and life. My 14 -year -old son Eric has been training for boxing and recently stepped into the ring to spar.

4:38He's had some good moments and some tough ones in his early going. Here he is to discuss some of the lessons he learned from boxing. After my first time getting knocked out, I learned a very valuable lesson. No matter what the other person throws at you, never let your guard down. But if you do get knocked down, get back up. Even after hearing my nose snap and blacking out, I popped back up, ready to fight. Well, just before my coach said, what the hell are you doing? Gathering. So if you do get knocked down, get back up. But like my coach said, no one to quit. Taking my lessons to your work, when you do battle, no matter what the markets throw at you, never let your guard down.

5:25And if you do get knocked down, get back up. I mean, unless it's time to quit, then maybe you need to find another job. Along the way, you'll improve your defense and your resilience by listening to the Capital Allocators podcast. Thanks so much for spreading the word. Please enjoy my conversation with Ben Hunt. Ben, great to see you. Great to be here, Ted. Thanks for having me. Why don't you take me back to how you got on the path that eventually led to investing and investment research? As my wife likes to say, I can't keep a job. So it's been a winding path. There is a thread that goes through the path, but it started in academia, right?

6:07So I was a political science professor of all oxymorons for 10 years, got tenure. I left academia to co -found a software company. And then from there, got into venture capital. And from there, got into public markets and investing there. So it's been a winding path, but there are at least two common denominators, common threads that string through all of it. Why don't you pull them? Tell me what those common threads are. 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.

6:56It'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 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.

7:53How can we find the structure in unstructured data? That's what my professional career has been all about. So with that thread, take me back to when you're in academia. What did that look like when you were thinking about the storytelling and unstructured data in your first book? The first book, it was called Getting to War. And it was a takeoff on an old law school book called Getting to Yes. I don't know if you - A negotiations book, yeah. Exactly. We're of an age we remember these things. My field in political science was international politics, guns and bombs, security studies. My idea was that before a government would do something risky, start a war, start fighting with another country, they're going to tell efforts to tell a story to their people.

8:40So the idea was that by looking at domestic media, in particular editorials, which haven't changed in purpose or meaning for 200 years now, they are efforts to shape opinion. They're direct efforts to say, here's how you should think about a subject matter. To look at newspapers that were closely associated with a government, and I was going to do this across countries, across time, is there structure there? Can you see a pattern in the opinion -leading efforts in media, the storytelling by governments to prepare population for war? And the answer is, oh my God, absolutely. That was the first work and research I did.

9:25It's the same research today, but trying to understand the patterns of storytelling in markets. Nothing's changed in the math. It's the same algorithms. It's the same network math that I was using 35 years ago. What has changed is big data. So the access to all this information, all this text information. What's changed is some of the terminology. We didn't call it natural language processing back then. That's what we were doing. We didn't call it language modeling back then, but that's what we were doing. And it's made possible by big data, big compute. It's changed everything about how to apply this research that I've been doing for 35 freaking years.

10:08So when did you go back 30 years and it was, however you want to describe it, small data, where you didn't have the processing power we do today? How did you do it? I would hire graduate students and undergraduates to go read microfiche. I don't think my kids even know what microfiche is. It was just pictures of these newspapers in the bowels of Widener Library. It wasn't quite punching cards, paper cards, but it wasn't far off. Again, I'm old enough to remember digital equipment in many frames, and you would put your data in, and then the next day, you would get some results to look at. In retrospect, it was challenging but wonderful because you are learning it from the start.

10:56You're learning how to make inference work so that when these inference machines, and that's the word you hear all the time, AMD and NVIDIA will talk about inference as where that's how they classify the use cases of their generative AI work. When you learn inference from the ground up, you understand just how powerful what we have today is, and you understand its strengths and its limitations. So wouldn't have it any other way. And it is wonderful to have a career arc that really, in a sense, comes full circle with the enormous focus today on inference, on text, on natural language processing.

11:39That's very gratifying. What were the core lessons in going from software entrepreneur to venture capital and then to the public markets? 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 putting together a little group, going to start an internal long, short fund.

12:21Why 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. So what was the transition from managing money starting at that long short fund to applying this in a different way through research? 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.

13:00That'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 05. We did well in 06. We did well in 07. But everybody did well in 05 and 06 and 07. And then we did really well in 08. And that's where everything changed. So the money really started flowing in in 08 and then really started flowing in 09. So the fund got up to was close to a billion dollars. But in March of 09, you were in a seat, so you know this story pretty well, Ted, right? So in March of 09, you went to the wall and you flipped a switch on our returns and we just flatlined.

13:45We 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 it out. If you're responsible for other people's money, you say what you do and then you do what you say.

14:27Managing other people's money is not the time to figure something out. So I gave all the money back and decided, okay, I want to try to figure it out. What does matter for Alpha, for Edge? Because I was a true believer in the whole value of the catalyst story. And so I was trying to figure out why doesn't that work anymore. And so for me, that was going back to, I'll call it my roots of working with unstructured data, with stories. What is it about the communication policy forward guidance, the storytelling of the Fed that impacts markets so much? What is it about that? What is it about the way we tell stories in finance today, the growth of 24 -7 news, say, and here I'm making the air quotes here, CNBC, Bloomberg, the Journal, which is FT, which are now 24 -7 media companies, not just newspaper publishers.

15:28What is it about that and the way we tell our stories? What is it about the CEOs who come onto these shows and tell a story? What is a multiple if it's not a story? What is value investing if it's not, oh, I see something different in the story and I'm going to get paid when the rest of the world recognizes that I'm right? So that's when I started writing this blog, Epsilon Theory, where I wanted to try to figure this out in public. What is the role of storytelling? What is the structure in unstructured data? And how can we use that to be better investors and also, frankly, better citizens? Where did the name Epsilon Theory come from?

16:08It 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?

16:56But 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. So what'd you find? What I would say is that what narrative is, is it's always reactive. It's not predictive. I have predictive views. Like I think that generative AI is absolutely world economy, life changing for the good. That's my personal view. Professionally, where I've got an edge is in seeing how narratives wax and wane.

17:46Events will happen in the world and narratives will shift to explain why and to where the narrative is and what the reaction to that's going to be. How is that different from being predictive? It's signaling and predictive in the short term. It is reactive in the sense that I don't know what the real world is. I don't know what the actual inflation rate is. I don't know what the CPI is going to be tomorrow. And analysis of narrative will not tell you what information is going to come out of the world. One thing I can tell you as an investor is what the market slant on that macro news or whatever comes out is.

18:23This all reminds me a little bit of the Keynes beauty contest. Oh, yeah. Oh, it's part and parcel of that. This is the newspaper beauty contest where a newspaper would print the pictures of 15 pretty girls. What Cain said is the first level of decision -making is you look at the pick and say, okay, I think that's the prettiest girl. And that's like, you're a discretionary portfolio manager like I was and say, oh, I think that's the prettiest stock. I'm going to short that or I'm going to go along that. Cain said, that's not it. That's not how you play the game. But it's also not how you play the game to say, oh, I talked to a lot of my friends.

18:56I get a sense of the crowd. I think the consensus is that it's this other girl over here. Cain said, that is also not how you play the game. what kane said is that you need to play it third level meaning what does the crowd think that the crowd thinks it's never about you it's never what you think and it's never what you think the consensus is always what does the crowd think and the way that gets determined and this is called the larger category here in game theory is called the common knowledge game and it's determined by what's called a missionary, that person who gets behind a microphone in front of a camera and shakes their finger at you and tells the crowd what they should think.

19:39And once you start looking for missionaries, you'll see them everywhere. And it's the stories they tell, and then the way those stories spread, that's what we're trying to analyze. How do you start thinking about analyzing stories? If you're trying to figure out what this epsilon is and there's something about storytelling and behavior in there, what do you do? Let me start by telling you what it's not. It's not sentiment. This is a path that so many examinations of unstructured data have gone down that they tried to look for mean words and nice words. That ain't story. Story is a script. Story is a story arc.

20:21Story has a beginning, a middle, and an end. Hollywood has figured this out for a lot of years. There are famously five scripts. My favorite example of this is the TV show Law and Order. I actually watch a lot of TV. People say they don't have time for TV. I say you make time for TV. But there have been 20 -something seasons of Law and Order, and I haven't seen all 600 and something episodes, but I've seen a lot of them. Every episode has exactly the same structure. is a three -act play. About halfway through Act One, you will be introduced to a tangential character, often played by a character actor who you'll recognize, a neighbor, a family member, an employee, but tangential to the crime that's being investigated.

21:10At the beginning of Act Three, that tangential character will return as the linchpin for the entire outcome of the show. Every episode has that structure because it works. What's true for Hollywood and what's true for the scripts of Hollywood, and you can type in the name of your favorite movie plus three -act structure, and you can get a chart of what's called the rising action and the declining action, the three -act structure. It's all the same. That's also true in markets. That's also true in what we read about why this analyst is bullish on a stock or why why this commentator is bearish on this sector.

21:53We tell the same stories with the same structures over and over again. There are more than five in markets, but it's a finite number. And once you start looking for those structures, once you start thinking in those terms, it really changes everything about how you see what we do for a living. What are some of the most used examples of the stories of markets? stories come about i'll say in two ways stories will be created in reaction to price so earning 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.

22:49You 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.

23:39and 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 these story arcs are growing or diminishing in density and how they're spreading out through the media body. The other kind of story is when somebody wants to change price or behavior. So this is the Fed is hawkish.

24:22The Fed is dovish. These are stories that are constructed in order to change behavior. So those are the two kinds of stories in general. stories of why to explain something that probably just happened by chance or for idiosyncratic reasons, and two, stories that are created to try to change your mind or change your behavior. 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? 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.

25:15And 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?

25:55And 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 your efforts to say, I'm sure of this.

26:44You've got to start from the top down. It has to come from, this is the way I think the world works. And then you use inference as a tool to test your hypothesis and actually find ways to apply it. It can't be the other way around. How many back tests have you seen? There's a, oh, it's a three -sharp strategy on those back tests. And then it goes live and it's, oh, that didn't work. So Ben, you started your investing career as the value of the catalyst. Yes. Assessing fundamentals. And to some extent, when you're trying to figure out 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.

27:26Stories 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? 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.

28:11the fundamentals, will out. I got to 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 got to have a healthy ego to exist in this business. You got to 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.

28:55I 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 become a conclusion that, you know what? I think I'm a pretty good discretionary manager and all that. I think I've got pretty good ideas. But there are a lot of other people just as smart as me with as smart as ideas. What is my edge? Again, that fundamental question, how do you manage money for alpha in today's world?