# At The Money: How to Use Narrative Information

An episode of [Masters in Business](https://invisiblenetwork.ai/podcast/730188152), 26 Nov 2025, 15 min.

Audio: https://podtrac.com/pts/redirect.mp3/traffic.omny.fm/d/clips/e73c998e-6e60-432f-8610-ae210140c5b1/4e4cd910-40a1-4619-a5f3-ae2b0012ffff/e4cad267-3ec9-490c-b7ff-b3a10177fa04/audio.mp3?utm_source=Podcast&in_playlist=5873a3cb-298f-40bc-b71f-ae2b0013000d

Do fundamentals or narratives drive market valuations? That is the question so many are wrestling with in today’s 24/7 algo-based platforms and AI-driven mediascape. Ben Hunt is founder of Perscient, a firm that studies how narratives and stories shape markets, investing, and social behavior through the lens of information theory, game theory, and unstructured data analysis. His work analyzes the language, story arcs, and viral spread of explanations in media Each week, “At the Money” discusses an important topic in money management. From portfolio construction to taxes and cutting down on fees, join Barry Ritholtz to learn the best ways to put your money to work. See omnystudio.com/listener for privacy information.

## Transcript

[0:09] Speaker 1: Investors have long been taught that fundamentals drive stock prices,
[0:15] Speaker 1: revenue growth, profits. As Benjamin Graham taught us, in the
[0:19] Speaker 1: short run, the market is a voting machine. But in
[0:22] Speaker 1: the long run it's a weighing machine. But what if
[0:26] Speaker 1: it isn't? What if narratives are driving market valuations. I'm
[0:32] Speaker 1: Barry Riddolets and on today's edition of At the Money,
[0:36] Speaker 1: we're going to discuss how to identify when market narratives
[0:40] Speaker 1: overtake fundamentals. To help us unpack all of this and
[0:44] Speaker 1: what it means for your portfolio, let's bring in Ben
[0:48] Speaker 1: Hunt of Percion. Ben's firm studies narratives and the stories
[0:53] Speaker 1: that shape markets, investing, and social behavior through the lens
[0:58] Speaker 1: of information theory. So Ben, let's start with a definition.
[1:02] Speaker 1: How do you define a narrative in the context of
[1:06] Speaker 1: markets and investing.
[1:08] Speaker 2: It's a simple definition. A narrative is an answer to
[1:13] Speaker 2: the question why why did the market go up today?
[1:18] Speaker 2: Why should you buy the stock? Why should you sell
[1:20] Speaker 2: this stock? Why should you vote for this person? Any
[1:24] Speaker 2: answer to the question why is a narrative? Well?
[1:28] Speaker 1: Sad you distinguish between something like data, which I arguably
[1:33] Speaker 1: comes with a storyline attached to it, and just a
[1:37] Speaker 1: straight up Hey, bitcoin is millennium or digital goals? Like,
[1:43] Speaker 1: how do you define the difference between this? I guess
[1:46] Speaker 1: this stock is cheap with a pe of nine? Is
[1:49] Speaker 1: a narrative?
[1:50] Speaker 2: Yeah, well why is? Why is a pe of nine cheap? Right?
[1:53] Speaker 2: Or why is a pe of three cheap or fifteen cheap?
[1:56] Speaker 2: These are all stories. Any any valuation, any multiple, any
[2:03] Speaker 2: meaning that you attached to numbers, it's a story. It's
[2:08] Speaker 2: a It's an answer to the question why why do
[2:12] Speaker 2: I call it cheap? Why do I think you should
[2:14] Speaker 2: buy it? Why is this interesting? Those are all why
[2:18] Speaker 2: questions and the answer Those are all narratives.
[2:22] Speaker 1: So how do you identify any particular narrative that may
[2:27] Speaker 1: be driving a particular stock or asset class or the
[2:32] Speaker 1: overall market? What what tools do you use to help
[2:36] Speaker 1: discern that?
[2:38] Speaker 2: Well, let me start by saying what I don't care about.
[2:40] Speaker 2: So what I don't care about is truth or accuracy.
[2:45] Speaker 2: I mean, it sounds crazy, but.
[2:47] Speaker 1: Not at all because you're trying to figure out what
[2:49] Speaker 1: the crowd believes, whether it's true or not. Well, if
[2:52] Speaker 1: it affects their behavior, it matters.
[2:55] Speaker 2: It matters. Right, So I've I've given up on trying
[2:58] Speaker 2: to figure out what reality is. What I'm trying to
[3:02] Speaker 2: figure out is how is reality being presented? How is
[3:07] Speaker 2: it being presented to us? So what I'm looking for
[3:11] Speaker 2: are elements of presentation. I'm looking for word choice. I'm
[3:16] Speaker 2: looking for how loud is it being presented to you,
[3:20] Speaker 2: how frequently it's being presented to you. But most of all, Barry,
[3:25] Speaker 2: I'm looking for a concept that you talk about in
[3:29] Speaker 2: network math, and it's called density, And I'm looking for
[3:33] Speaker 2: how the language is connected to other words. In fact,
[3:37] Speaker 2: those are the measurements you use in network math. Those
[3:41] Speaker 2: are where they we talk about betweenness, we talk about
[3:45] Speaker 2: connectedness and centrality. Those are the measurements. Because I'm looking
[3:51] Speaker 2: at the presentation, not at the reality.
[3:53] Speaker 1: So how do you measure density? You hinted at some
[3:56] Speaker 1: of it, how loud it is, how repetitive it is.
[4:00] Speaker 1: How do you tell when a specific narrative is beginning
[4:04] Speaker 1: to exert influence over prices?
[4:08] Speaker 2: Yeah? So, Bob Sheller wrote a pretty good book a
[4:12] Speaker 2: couple of years back called Narrative Economics, And the takeaway
[4:16] Speaker 2: from that book is that you should think about and
[4:19] Speaker 2: understand narratives in exactly the same way you think about
[4:25] Speaker 2: and you understand disease. I mean, you use the same measurements.
[4:32] Speaker 2: You remember when we were talking about COVID, it was
[4:34] Speaker 2: are not right? How fast does it spread? How quickly
[4:38] Speaker 2: does it spread? What is the medium in which it
[4:42] Speaker 2: can spread most easily? It's exactly the same thing here, bear,
[4:47] Speaker 2: It's exactly the same thing. We're really using exactly the
[4:50] Speaker 2: same math as you would use to try to understand
[4:54] Speaker 2: epidemiology and the spread of a virus. So what we're
[4:59] Speaker 2: looking at, though, instead of the atmosphere or waste water,
[5:04] Speaker 2: we're looking at the words. We're looking at all the
[5:07] Speaker 2: words that are out there in terms of text, of
[5:12] Speaker 2: what's being written, what's being said. This is all unstructured data,
[5:18] Speaker 2: and we're looking for the presence of certain ideas, clusters
[5:21] Speaker 2: of words that spread through that medium of media in
[5:27] Speaker 2: exactly the same way that a virus spreads through the
[5:30] Speaker 2: air or through the water. If you're a financial advisor
[5:34] Speaker 2: or you're a market person of any sort and somebody
[5:38] Speaker 2: comes up to say, yeah, why the why the market
[5:41] Speaker 2: go up today? And you could say, well, there are
[5:45] Speaker 2: a hundred different reasons, but basically it was just variants.
[5:50] Speaker 2: You know, it's just it's just it's just random. It's
[5:53] Speaker 2: just a random walk. But it went up today. So
[5:56] Speaker 2: someone will look like people hate that answer because it's
[5:59] Speaker 2: a crappy story. It's just it's a it's just true.
[6:02] Speaker 1: But it's not competitive exactly.
[6:04] Speaker 2: It's not truthy, right, it doesn't it doesn't connect with you.
[6:09] Speaker 2: I said, well, that's disappointing. I want a story. So
[6:15] Speaker 2: the stories that are basically there to fill the time. Uh,
[6:19] Speaker 2: and these are these are typically examples where I call
[6:25] Speaker 2: them descriptive narratives. They're they're answering the question why, but
[6:29] Speaker 2: they're describing why something happened.
[6:35] Speaker 1: Always after the fact, never in advance.
[6:37] Speaker 2: Always after the fact. That's exactly right, Barry. So what
[6:41] Speaker 2: you'll find is that I don't know, it's earning season,
[6:44] Speaker 2: and who comes out first with earnings financials, right, and so, uh,
[6:50] Speaker 2: you know City and Goldman, Sacks, whoever. They'll report some
[6:54] Speaker 2: good earnings, the stocks will go up, and afterwards, Kramer
[6:59] Speaker 2: and everyone else, it's got to tell you why. And
[7:02] Speaker 2: they'll say, oh, I'm bullish on financials, and they'll give
[7:05] Speaker 2: you a reason. And that the half life for that
[7:09] Speaker 2: sort of narrative. It's a week or two. I mean,
[7:14] Speaker 2: it's it. And what you want to do with that, honestly, Barry,
[7:19] Speaker 2: is you want to fade it. You want to look
[7:22] Speaker 2: for that to appear in your Bloomberg email in the
[7:25] Speaker 2: morning saying, oh, market experts bullish on financials because blah
[7:32] Speaker 2: blah blah. And when that drum beating gets pretty loud
[7:36] Speaker 2: when it's appearing in your morning email, I want to
[7:40] Speaker 2: fade it. You don't want to press it. You want
[7:42] Speaker 2: to fade it because this is just the ordinary business
[7:46] Speaker 2: of Wall Street. You've got to have an answer to why.
[7:49] Speaker 2: It's almost usually just variants or some combination of idiosyncratic stuff,
[7:55] Speaker 2: but you've got to come up with some blanket why.
[7:57] Speaker 2: And if that gets a lot of play, I want
[7:59] Speaker 2: to go the other way. Now there's a there's another
[8:02] Speaker 2: type of narrative though, where I want to press it.
[8:07] Speaker 2: And so that's what we call prescriptive narratives. It's not saying, oh,
[8:13] Speaker 2: the Fed is dubvish. It's saying, you know, Mohammad l
[8:17] Speaker 2: Arion will come out or I don't know, Trump will
[8:21] Speaker 2: come out and say the Fed should be dubvish. You
[8:26] Speaker 2: see the difference. It's not it's not describing what's already happened.
[8:31] Speaker 2: It's trying to lay the framework for what should happen.
[8:36] Speaker 1: So so let's delve into that. What sort of tools
[8:38] Speaker 1: are you using to identify these narratives, be they descriptive
[8:45] Speaker 1: or or prescriptive. How how much lead time do you
[8:49] Speaker 1: get to analyze you know, this giant corpus of noise
[8:55] Speaker 1: and commentary and data that is produced every single day.
[9:00] Speaker 2: Well, this is what good traders, honestly and and investors
[9:06] Speaker 2: have always done. They've they've they've always made these sort
[9:11] Speaker 2: of assessments of the news and the stories that are happening.
[9:17] Speaker 2: So this what we're trying to do is we're trying
[9:19] Speaker 2: to externalize what good traders and i'll call it short
[9:22] Speaker 2: term investors have always done. They've always internalized this. So
[9:29] Speaker 2: anybody can do this. You start looking for the words
[9:32] Speaker 2: that are trying to prescribe versus describe. That'll you you
[9:38] Speaker 2: can you can pick this up in whatever you do.
[9:41] Speaker 2: What we're able to do though today is because there's
[9:44] Speaker 2: now big data, as because there's big compute, is we
[9:48] Speaker 2: can read everything. Humans we're all limited in what we
[9:51] Speaker 2: can read, what we can pay attention to. In our
[9:53] Speaker 2: little corner of the world. What's possible today is to
[9:58] Speaker 2: read everything into animal lies exactly, this sort of word choice.
[10:04] Speaker 1: So what sort of output do you get from your
[10:07] Speaker 1: big compute that's scraping everything, Yeah, sifting through all the language, like,
[10:14] Speaker 1: does it identify specific words? Does it identify themes? I'm
[10:19] Speaker 1: assuming artificial intelligence and big data analytics is a key
[10:23] Speaker 1: part of this. What does the output look like to you?
[10:27] Speaker 2: Well, let me start with where it starts, because it's
[10:30] Speaker 2: not that the output comes from what you put into
[10:34] Speaker 2: it and what you want to put into it, and
[10:36] Speaker 2: this has to be human generated. Are well, what are
[10:41] Speaker 2: the ideas? What are the narratives that I care about?
[10:44] Speaker 2: Here's why that's important, Barry, because let's say you're a
[10:47] Speaker 2: value investor. You've got a vision, you know, you've got
[10:50] Speaker 2: a view on a stock, and like all value investors,
[10:54] Speaker 2: you think the market has not recognized the story about
[10:59] Speaker 2: this stuff that I think is really important. So what
[11:04] Speaker 2: you're looking for is not how loud is that story
[11:09] Speaker 2: right now? That story is dormant. You're looking for that
[11:13] Speaker 2: story to start being told. So you can't start this
[11:17] Speaker 2: from asking AI, Hey, AI, what are the prominent stories
[11:21] Speaker 2: right now, because it can only tell you what's being
[11:26] Speaker 2: spread right then and there. What I find the way
[11:31] Speaker 2: to really make money with narratives is to say, what
[11:34] Speaker 2: narrative is dormant today? I want to see when it
[11:39] Speaker 2: starts to get discovered. Is that discovery phase when the
[11:43] Speaker 2: market quote unquote wakes up to a story that you've
[11:48] Speaker 2: been looking for the market to wake up to. That's
[11:52] Speaker 2: how I find you can most reliably make money from
[11:59] Speaker 2: narrative investments.
[12:00] Speaker 1: So I get the sense we're still in the early
[12:03] Speaker 1: days of narrative investing as a key strategy, at least
[12:08] Speaker 1: in terms of using AI and big data. What do
[12:13] Speaker 1: you see developing in this space? What's the narrative about
[12:17] Speaker 1: narrative investing?
[12:21] Speaker 2: Barry? This is, like I say, this is an old idea.
[12:25] Speaker 2: So if you if you go back and kind of
[12:27] Speaker 2: look at your Wall Street history, or if you talk
[12:29] Speaker 2: to kind of traders today, what they're always looking at
[12:33] Speaker 2: is the news that comes along, and they're trying to say, hey,
[12:37] Speaker 2: do I fade that you know? Is that story worn out?
[12:41] Speaker 2: Is it is that? Is it topping over? Or does
[12:45] Speaker 2: this story have legs? Is this the start of its spread?
[12:50] Speaker 2: Like a virus. So it's an old idea. What's possible
[12:55] Speaker 2: today is to quantify it. What's possible today is to
[12:59] Speaker 2: external life what good traders have internalized in the past.
[13:04] Speaker 2: So it's not that it's you know, inventing cold fusion
[13:09] Speaker 2: or really doing anything that's new in the world. What
[13:13] Speaker 2: it is able to do, though, is to make that
[13:16] Speaker 2: sort of analysis, to look at not what reality is,
[13:20] Speaker 2: but how reality is being presented, and make it available
[13:25] Speaker 2: over a much wider scope, not over just that little
[13:29] Speaker 2: area that the good trader really knows a lot about,
[13:32] Speaker 2: and make it available over a lot more publications and
[13:36] Speaker 2: things that are being written because humans can't read everything. Really,
[13:41] Speaker 2: I think it's a way of I hate to call
[13:44] Speaker 2: it democratizing investing. I really hate that idea, but it
[13:48] Speaker 2: really is a notion of creating a new instrument so
[13:51] Speaker 2: every investor can understand, well, what are the stories that
[13:57] Speaker 2: are being told about the world today.
[14:00] Speaker 1: Wrap up technology has enabled us to read much more
[14:05] Speaker 1: than we were capable of reading as individuals. In fact,
[14:09] Speaker 1: the machines can read everything, and we can use those
[14:12] Speaker 1: machines to identify dormant narratives that might lead to increases
[14:19] Speaker 1: in either a particular stock or asset class or market
[14:25] Speaker 1: fair statement.
[14:27] Speaker 2: Very fair. What you're looking for is what's new, what's changed,
[14:32] Speaker 2: because when the narrative changes, that changes behavior.
[14:37] Speaker 1: Fascinating stuff. I'm Barry Riddtolts. This is Bloomberg's at the Money,

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