Speaker 1: Investors have long been taught that fundamentals drive stock prices,
Speaker 1: revenue growth, profits. As Benjamin Graham taught us, in the
Speaker 1: short run, the market is a voting machine. But in
Speaker 1: the long run it's a weighing machine. But what if
Speaker 1: it isn't? What if narratives are driving market valuations. I'm
Speaker 1: Barry Riddolets and on today's edition of At the Money,
Speaker 1: we're going to discuss how to identify when market narratives
Speaker 1: overtake fundamentals. To help us unpack all of this and
Speaker 1: what it means for your portfolio, let's bring in Ben
Speaker 1: Hunt of Percion. Ben's firm studies narratives and the stories
Speaker 1: that shape markets, investing, and social behavior through the lens
Speaker 1: of information theory. So Ben, let's start with a definition.
Speaker 1: How do you define a narrative in the context of
Speaker 1: markets and investing.
Speaker 2: It's a simple definition. A narrative is an answer to
Speaker 2: the question why why did the market go up today?
Speaker 2: Why should you buy the stock? Why should you sell
Speaker 2: this stock? Why should you vote for this person? Any
Speaker 2: answer to the question why is a narrative? Well?
Speaker 1: Sad you distinguish between something like data, which I arguably
Speaker 1: comes with a storyline attached to it, and just a
Speaker 1: straight up Hey, bitcoin is millennium or digital goals? Like,
Speaker 1: how do you define the difference between this? I guess
Speaker 1: this stock is cheap with a pe of nine? Is
Speaker 1: a narrative?
Speaker 2: Yeah, well why is? Why is a pe of nine cheap? Right?
Speaker 2: Or why is a pe of three cheap or fifteen cheap?
Speaker 2: These are all stories. Any any valuation, any multiple, any
Speaker 2: meaning that you attached to numbers, it's a story. It's
Speaker 2: a It's an answer to the question why why do
Speaker 2: I call it cheap? Why do I think you should
Speaker 2: buy it? Why is this interesting? Those are all why
Speaker 2: questions and the answer Those are all narratives.
Speaker 1: So how do you identify any particular narrative that may
Speaker 1: be driving a particular stock or asset class or the
Speaker 1: overall market? What what tools do you use to help
Speaker 1: discern that?
Speaker 2: Well, let me start by saying what I don't care about.
Speaker 2: So what I don't care about is truth or accuracy.
Speaker 2: I mean, it sounds crazy, but.
Speaker 1: Not at all because you're trying to figure out what
Speaker 1: the crowd believes, whether it's true or not. Well, if
Speaker 1: it affects their behavior, it matters.
Speaker 2: It matters. Right, So I've I've given up on trying
Speaker 2: to figure out what reality is. What I'm trying to
Speaker 2: figure out is how is reality being presented? How is
Speaker 2: it being presented to us? So what I'm looking for
Speaker 2: are elements of presentation. I'm looking for word choice. I'm
Speaker 2: looking for how loud is it being presented to you,
Speaker 2: how frequently it's being presented to you. But most of all, Barry,
Speaker 2: I'm looking for a concept that you talk about in
Speaker 2: network math, and it's called density, And I'm looking for
Speaker 2: how the language is connected to other words. In fact,
Speaker 2: those are the measurements you use in network math. Those
Speaker 2: are where they we talk about betweenness, we talk about
Speaker 2: connectedness and centrality. Those are the measurements. Because I'm looking
Speaker 2: at the presentation, not at the reality.
Speaker 1: So how do you measure density? You hinted at some
Speaker 1: of it, how loud it is, how repetitive it is.
Speaker 1: How do you tell when a specific narrative is beginning
Speaker 1: to exert influence over prices?
Speaker 2: Yeah? So, Bob Sheller wrote a pretty good book a
Speaker 2: couple of years back called Narrative Economics, And the takeaway
Speaker 2: from that book is that you should think about and
Speaker 2: understand narratives in exactly the same way you think about
Speaker 2: and you understand disease. I mean, you use the same measurements.
Speaker 2: You remember when we were talking about COVID, it was
Speaker 2: are not right? How fast does it spread? How quickly
Speaker 2: does it spread? What is the medium in which it
Speaker 2: can spread most easily? It's exactly the same thing here, bear,
Speaker 2: It's exactly the same thing. We're really using exactly the
Speaker 2: same math as you would use to try to understand
Speaker 2: epidemiology and the spread of a virus. So what we're
Speaker 2: looking at, though, instead of the atmosphere or waste water,
Speaker 2: we're looking at the words. We're looking at all the
Speaker 2: words that are out there in terms of text, of
Speaker 2: what's being written, what's being said. This is all unstructured data,
Speaker 2: and we're looking for the presence of certain ideas, clusters
Speaker 2: of words that spread through that medium of media in
Speaker 2: exactly the same way that a virus spreads through the
Speaker 2: air or through the water. If you're a financial advisor
Speaker 2: or you're a market person of any sort and somebody
Speaker 2: comes up to say, yeah, why the why the market
Speaker 2: go up today? And you could say, well, there are
Speaker 2: a hundred different reasons, but basically it was just variants.
Speaker 2: You know, it's just it's just it's just random. It's
Speaker 2: just a random walk. But it went up today. So
Speaker 2: someone will look like people hate that answer because it's
Speaker 2: a crappy story. It's just it's a it's just true.
Speaker 1: But it's not competitive exactly.
Speaker 2: It's not truthy, right, it doesn't it doesn't connect with you.
Speaker 2: I said, well, that's disappointing. I want a story. So
Speaker 2: the stories that are basically there to fill the time. Uh,
Speaker 2: and these are these are typically examples where I call
Speaker 2: them descriptive narratives. They're they're answering the question why, but
Speaker 2: they're describing why something happened.
Speaker 1: Always after the fact, never in advance.
Speaker 2: Always after the fact. That's exactly right, Barry. So what
Speaker 2: you'll find is that I don't know, it's earning season,
Speaker 2: and who comes out first with earnings financials, right, and so, uh,
Speaker 2: you know City and Goldman, Sacks, whoever. They'll report some
Speaker 2: good earnings, the stocks will go up, and afterwards, Kramer
Speaker 2: and everyone else, it's got to tell you why. And
Speaker 2: they'll say, oh, I'm bullish on financials, and they'll give
Speaker 2: you a reason. And that the half life for that
Speaker 2: sort of narrative. It's a week or two. I mean,
Speaker 2: it's it. And what you want to do with that, honestly, Barry,
Speaker 2: is you want to fade it. You want to look
Speaker 2: for that to appear in your Bloomberg email in the
Speaker 2: morning saying, oh, market experts bullish on financials because blah
Speaker 2: blah blah. And when that drum beating gets pretty loud
Speaker 2: when it's appearing in your morning email, I want to
Speaker 2: fade it. You don't want to press it. You want
Speaker 2: to fade it because this is just the ordinary business
Speaker 2: of Wall Street. You've got to have an answer to why.
Speaker 2: It's almost usually just variants or some combination of idiosyncratic stuff,
Speaker 2: but you've got to come up with some blanket why.
Speaker 2: And if that gets a lot of play, I want
Speaker 2: to go the other way. Now there's a there's another
Speaker 2: type of narrative though, where I want to press it.
Speaker 2: And so that's what we call prescriptive narratives. It's not saying, oh,
Speaker 2: the Fed is dubvish. It's saying, you know, Mohammad l
Speaker 2: Arion will come out or I don't know, Trump will
Speaker 2: come out and say the Fed should be dubvish. You
Speaker 2: see the difference. It's not it's not describing what's already happened.
Speaker 2: It's trying to lay the framework for what should happen.
Speaker 1: So so let's delve into that. What sort of tools
Speaker 1: are you using to identify these narratives, be they descriptive
Speaker 1: or or prescriptive. How how much lead time do you
Speaker 1: get to analyze you know, this giant corpus of noise
Speaker 1: and commentary and data that is produced every single day.
Speaker 2: Well, this is what good traders, honestly and and investors
Speaker 2: have always done. They've they've they've always made these sort
Speaker 2: of assessments of the news and the stories that are happening.
Speaker 2: So this what we're trying to do is we're trying
Speaker 2: to externalize what good traders and i'll call it short
Speaker 2: term investors have always done. They've always internalized this. So
Speaker 2: anybody can do this. You start looking for the words
Speaker 2: that are trying to prescribe versus describe. That'll you you
Speaker 2: can you can pick this up in whatever you do.
Speaker 2: What we're able to do though today is because there's
Speaker 2: now big data, as because there's big compute, is we
Speaker 2: can read everything. Humans we're all limited in what we
Speaker 2: can read, what we can pay attention to. In our
Speaker 2: little corner of the world. What's possible today is to
Speaker 2: read everything into animal lies exactly, this sort of word choice.
Speaker 1: So what sort of output do you get from your
Speaker 1: big compute that's scraping everything, Yeah, sifting through all the language, like,
Speaker 1: does it identify specific words? Does it identify themes? I'm
Speaker 1: assuming artificial intelligence and big data analytics is a key
Speaker 1: part of this. What does the output look like to you?
Speaker 2: Well, let me start with where it starts, because it's
Speaker 2: not that the output comes from what you put into
Speaker 2: it and what you want to put into it, and
Speaker 2: this has to be human generated. Are well, what are
Speaker 2: the ideas? What are the narratives that I care about?
Speaker 2: Here's why that's important, Barry, because let's say you're a
Speaker 2: value investor. You've got a vision, you know, you've got
Speaker 2: a view on a stock, and like all value investors,
Speaker 2: you think the market has not recognized the story about
Speaker 2: this stuff that I think is really important. So what
Speaker 2: you're looking for is not how loud is that story
Speaker 2: right now? That story is dormant. You're looking for that
Speaker 2: story to start being told. So you can't start this
Speaker 2: from asking AI, Hey, AI, what are the prominent stories
Speaker 2: right now, because it can only tell you what's being
Speaker 2: spread right then and there. What I find the way
Speaker 2: to really make money with narratives is to say, what
Speaker 2: narrative is dormant today? I want to see when it
Speaker 2: starts to get discovered. Is that discovery phase when the
Speaker 2: market quote unquote wakes up to a story that you've
Speaker 2: been looking for the market to wake up to. That's
Speaker 2: how I find you can most reliably make money from
Speaker 2: narrative investments.
Speaker 1: So I get the sense we're still in the early
Speaker 1: days of narrative investing as a key strategy, at least
Speaker 1: in terms of using AI and big data. What do
Speaker 1: you see developing in this space? What's the narrative about
Speaker 1: narrative investing?
Speaker 2: Barry? This is, like I say, this is an old idea.
Speaker 2: So if you if you go back and kind of
Speaker 2: look at your Wall Street history, or if you talk
Speaker 2: to kind of traders today, what they're always looking at
Speaker 2: is the news that comes along, and they're trying to say, hey,
Speaker 2: do I fade that you know? Is that story worn out?
Speaker 2: Is it is that? Is it topping over? Or does
Speaker 2: this story have legs? Is this the start of its spread?
Speaker 2: Like a virus. So it's an old idea. What's possible
Speaker 2: today is to quantify it. What's possible today is to
Speaker 2: external life what good traders have internalized in the past.
Speaker 2: So it's not that it's you know, inventing cold fusion
Speaker 2: or really doing anything that's new in the world. What
Speaker 2: it is able to do, though, is to make that
Speaker 2: sort of analysis, to look at not what reality is,
Speaker 2: but how reality is being presented, and make it available
Speaker 2: over a much wider scope, not over just that little
Speaker 2: area that the good trader really knows a lot about,
Speaker 2: and make it available over a lot more publications and
Speaker 2: things that are being written because humans can't read everything. Really,
Speaker 2: I think it's a way of I hate to call
Speaker 2: it democratizing investing. I really hate that idea, but it
Speaker 2: really is a notion of creating a new instrument so
Speaker 2: every investor can understand, well, what are the stories that
Speaker 2: are being told about the world today.
Speaker 1: Wrap up technology has enabled us to read much more
Speaker 1: than we were capable of reading as individuals. In fact,
Speaker 1: the machines can read everything, and we can use those
Speaker 1: machines to identify dormant narratives that might lead to increases
Speaker 1: in either a particular stock or asset class or market
Speaker 1: fair statement.
Speaker 2: Very fair. What you're looking for is what's new, what's changed,
Speaker 2: because when the narrative changes, that changes behavior.
Speaker 1: Fascinating stuff. I'm Barry Riddtolts. This is Bloomberg's at the Money,