In this week's Stansberry Investor Hour, Dan welcomes James Weatherall to the show. Unlike most of our guests, James does not come from a finance background. However, he has found interesting ways in which physics can change investing. You can check out his book The Physics of Wall Street here.

James kicks things off by sharing his background in physics and philosophy. He's interested in mathematics and how it can be applied to the markets. He's a firm believer in using mathematical models to assist in investing but says that it's important to examine your models and check your assumptions that result from them. If one model is good for a particular use case, trying to use it in a different area or within a larger scope than it was originally intended can yield different results than expected. James discusses the models that Louis Bachelier and Edward Thorp (whom he writes about in his book The Physics of Wall Street) created that would have a major impact on investing...

Bachelier is this really fascinating, way-ahead-of-his-time figure who, right around the turn of the 20th century, writes this dissertation in mathematics in which he applies ideas that we would now think of as stochastic calculus. [These are] the kinds of things that would go into areas of physics and thermodynamics to basically options pricing... [Thorp develops] the same idea that Bachelier has, except he takes it a step further and comes up with a formula for the fair value of... something like an option.

Next, James mentions extreme events similar to Black Monday and their probability of occurring. He notes that in the long term, investors with 401(k)s would be able to survive and even recover after major crashes. However, anyone who overleverages a trade or invests heavily in the short term is at a greater risk of having their portfolios be wiped out. James also mentions the Kelly criterion, a strategy developed by mathematician John Kelly. In short, this method involves having an understanding of what could happen with stocks better than the markets and using that to your advantage to make the optimized trades possible. And when asked if he would change anything about his ideas in The Physics of Wall Street, he remains adamant that his argument still holds up...

[The Physics of Wall Street] was written at a particular time where there were particular debates happening. So it was really a response to the 2007 to 2008 crisis. And I think as a response to how people were talking about that, I wouldn't change anything. I think I stand by what I wrote there. Maybe if I went and read it again, I would not feel that way. But I do think that a lot has changed in [the] market since then.

Finally, James mentions passive trading and volatility and how, over time, the addition of new passive investors will gradually increase market volatility. He adds that there's a scalability problem in the markets. In one example, he says that private markets "worked great 20 years ago" but only "worked OK" 10 years ago. Private markets are slowly becoming less able to sustain the growth they have. And James wraps things up by sharing his personal use cases of AI and his fears with the technology...

I use [AI] in my teaching in some ways. I try to incorporate into my teaching ideas about how AI works, but also what its limitations are, what it's good for, where it can cause problems. I've experimented with using it in research, and I have not yet had great successes... There are a lot of ways in which it can put [universities] out of work. One way is that if we can't figure out how to continue to make education a meaningful experience for students. So if it becomes a situation where students are using AI to do their homework and professors are using AI to grade the homework, what's the point? Why are any of us doing this?

Click on the image below to watch the video interview with James right now. For the audio version, click "Listen" above.

(Additional past episodes are located here.)

This Week's Guest

James Weatherall is a Chancellor's Professor at the University of California, Irvine, where he focuses on physics, mathematics, and philosophy. He is also a member of the Center for Theoretical Behavioral Science, the Center for Cosmology, and the Jack W. Peltason Center for the Study of Democracy. Additionally, James has authored several books, including The Physics of Wall Street, Void: The Strange Physics of Nothing, and The Misinformation Age: How False Beliefs Spread.

James earned his PhD in philosophy from the University of California, Irvine and a Master of Fine Arts in creative writing from Fairleigh Dickinson University.

Dan Ferris:                 Get out your pens and pencils, folks. Today's guest is a really smart guy. Not a finance guy – he's a physics guy who wrote a book called The Physics of Wall Street, James Owen Weatherall. It's a fun book. It's not a hard, crazy, technical book. And we're going to talk about a lot of the ideas in it, which are very important for investors. It's important to note historically where all this stuff is coming from and all the crazy math that influences – I guarantee you – influences places that you are putting your money in your 401(k). So, pay attention and have fun with this because this stuff is pure fun to learn about. So, let's do it. Let's talk with our guest, James Owen Weatherall. Let's do it right now.

                                    James, welcome to the show. Thanks for being here man.

James Weatherall:      Thanks so much for having me, Dan.

Dan Ferris:                 We have to start with one thing that I need to tell you. I love your book, The Physics of Wall Street.

James Weatherall:      Thanks.

Dan Ferris:                 Now, I say that not having read the whole thing, but I've used this thing – I was trying to sit here and count the number of times – at least three times I've used it as a reference because you do a fantastic job of writing about some of the great characters in math history and the history of sort of math and finance together: Edward Thorp, Mandelbrot, Bachelier. I think you were one of my primary sources when I wrote my story about Bachelier. I think I had three sources and you were one of the best ones. So, thank you for that. One day –

James Weatherall:      Yeah. I'm glad I could be of help.

Dan Ferris:                 Yes, one day I hope to read the whole thing, just so you know, I'm sure there's even more good stuff in there. I'll probably go back to it for somebody – Pascal or whoever else you've got in there. So, I want to start there, if I may. Where did the book come from? Are you a Wall Street guy or are you a physics guy or both?

James Weatherall:      No, I'm 100% a physics guy. So, I was a grad student in physics at Stevens Institute of Technology, which is in Hoboken, New Jersey right across the river from Wall Street in 2007 to 2008. I had a lot – I was right out of college. I had a lot of friends who had been physics majors who had gone into finance. Some of them are still working in the industry now. And –

Dan Ferris:                 I'm sorry, James – I'm sorry to interrupt you, but I have to wonder, physics seems like this cool thing. What are you doing right now in the year 2026? What cool stuff are you doing? You must be doing cool stuff?

James Weatherall:      So, I'm actually – I'm not a physicist now. I'm a philosopher. So, I am a professor in Logic and Philosophy of Science. It's a niche department at University of California, Irvine in Southern California for almost 20 years now.

Dan Ferris:                 Wow.

James Weatherall:      And yeah, so, I work in philosophy of physics, sort of mathematical physics, but also the history of science, stuff like that.

Dan Ferris:                 That tracks. Good. That's cool. Very cool. So, that makes a lot more – then – now the book makes more sense to me, because what else does a mathematician or a physicist want to philosophize about except for this grand machine of Wall Street? And if you know Bachelier, then you've got the connection from the word go and away you go. Well, what do you think of? When I read your book, like I said, I just read pieces of it so I didn't get the full sweep of it. What does James Owen Weatherall think of this combination of really, from my perspective, super advanced mathematics plus Wall Street, where you really can't predict what the hell is going to happen next? What do you make of that?

James Weatherall:      Yeah, so I am interested in it from so many different perspectives. So, I'm interested in the math. I just – I like math. I like math applied to stuff. I'm super interested in the history of applied mathematics. I'm also just interested in the history of big ideas. And what could be more important in the history of the 20th century and now 21st century than this movement from these big technical ideas for mathematical physics and related areas into financial markets.

                                    But also, I'm interested in the philosophy. We're building these mathematical models to try to do something in the world. How are we supposed to think about that? Why does that work? Why does it sometimes not work? That to me is just core philosophy of science and applied in a case that really matters.

Dan Ferris:                 I'm glad you brought models up because we just – we lived through this thing, whatever, 20 years ago almost where people lost their homes and that was at the end of a chain of events that started out with risk models that didn't work. Right?

James Weatherall:      Yeah.

Dan Ferris:                 So, it's real. It's impacting – all these applications are impacting us.

James Weatherall:      Yeah, absolutely. I mean, so much happened in that that couple of years. We had a long history of applying a different kind of model, mostly in derivatives trading. What went wrong, I think in 2007 and 2008, though, was it involved derivatives trading but it also just involved how to rate risk on these [collateralized det obligations]. And I think it was a combination of overconfidence in mathematical models, but also just an ecosystem of confluences of interest between ratings agencies and big banks, and very, very difficult to sort of cut through the practice and understand how the models were working.

                                    And I think that's characteristic of the sort of widespread use of these models, which I think are ultimately sound when used correctly, but it's very, very difficult once they get integrated into the markets for ordinary traders or even people who are pretty sophisticated to see where they matter and where they're going to break and how that's going to affect things.

Dan Ferris:                 It seems like the models are great as far as they go, but people just – some people thought they went a lot farther than they really do. They're good as far as – they work. Some are good – some are useful and some are not. I forget what the quote was. Who was that, George Box, or somebody? "All models are wrong. Some are useful," I think, was the original –

James Weatherall:      Yeah. Exactly.

Dan Ferris:                 Yeah. So – but you're OK with it. You're not an anti-model guy in that –

James Weatherall:      No, no, no, not at all.

Dan Ferris:                 OK. All right.

James Weatherall:      But I am a pro "examine your assumptions." And make sure that – we have to think about these things as a kind of technology and we have to understand what the limitations of the technology are. The kinds of things that – the technology that runs my car isn't going to take us to the moon. And so, we need to understand when we're trying to adapt something beyond the scope where it's going to work.

Dan Ferris:                 Let's do this. Let's – I started out talking about how I use your book. Let's do the listener service because I can tell you've got this stuff online in your head and you – I hear your love for history coming out with every other word you say. So, let's start with Bachelier. And tell our listener in your words why he's important to – what's the connection between this 19th century mathematician and investors in the year 2026. Why do you care about him?

James Weatherall:      Yeah. So, Bachelier is this really fascinating way-ahead-of-his-time figure who right around the turn of the 20th century writes this dissertation in mathematics in which he applies ideas that we would now think of as stochastic calculus – the kinds of things that would go into areas of physics and thermodynamics – to basically options pricing. And he's the first person in a sort of long history – it goes from 1900 to 1973, basically, of people reinventing this idea of using probability theory to understand the relationship between some underlying asset and an option written on that asset.

                                    And so, he sort of comes out of nowhere applying these statistical ideas to markets and then immediately jumping to a kind of derivative trading. People sort of sleep on him for about 50 years and then his ideas start getting rediscovered and reapplied. And then, you have a number of sort of key figures that I talk about in the book leading up to [Fischer] Black and [Myron] Scholes in the early 1970s, and the Black-Scholes model is famous. That won the Nobel Prize. But there are all these people who are sort of discovering pieces of those ideas in the early part of the 20th century.

Dan Ferris:                 So, all these zero [days-to-experation ("DTE")] option traders who are speculating wildly every day is all Louis Bachelier's fault.

James Weatherall:      Ed Thorp and Fischer Black may have had –

Dan Ferris:                 And Fischer, yeah.

James Weatherall:      – something to do with it.

Dan Ferris:                 All right, so this guy basically applies this probability theory and everybody goes, "What? No," and forgets about him for 50 years. I guess the other guy I know anything about – and if there's somebody else we should talk about first – the other guy I know anything about in this – in your book is Thorp, the Beat the Dealer guy. I think – to me, he's the blackjack guy because he's the mathematician who went to Las Vegas and beat the odds and made money. And managed money as well. How does he figure into this? Why do we care about him today? And we do. Just so the listeners are clear, we do care about Ed Thorp.

James Weatherall:      Yeah, I mean, Ed Thorp is also – he's just an incredible, incredible person. In fact, he was an early faculty member here at UC Irvine. And so, he's around Southern California. Yeah, so he wrote this book, as you say, Beat the Dealer. He sort of gave a mathematical proof that card counting could work. I think lots of people in the industry are – like that kind of thing.

                                    But then, a few years later, he is trying to apply the same kind of ideas, the same kind of statistical modeling of strategic scenarios, basically, and comes up with the idea that you can model the underlying – you can treat the underlying asset of some derivative of an option as governed by some statistical processes. So, it's basically the same idea that that Bachelier has, except he takes it a step further and comes up with a formula for the fair value of a – they were detachable warrants at that point, you didn't have options market yet, but of something like an option. And he wrote a book with a guy named Sheen Kassouf, who was an economist here in Irvine, called Beat the Market.

                                    And this book comes out and no one reads it. It literally has a cheat code for options trading written into it. It tells you how to price them, how to trade them, how to manage your money. And it doesn't get any traction. And so, he decides he's just going to do it himself. He teams up with a broker-dealer and they start Princeton Newport Partners, which was, I think, the first modern quantitative hedge fund. Not exactly the fee structure of a modern hedge fund, but the sort of trading strategy. And –

Dan Ferris:                 James, I have to interrupt you for just one second because it's so funny, isn't it, for you to immediately say, "Not the fee structure," because that's what it's become. That's what a hedge fund is. It's a fee structure.

James Weatherall:      Yeah, exactly. Right. That's what a hedge fund is now.

Dan Ferris:                 Yeah, it's a riot.

James Weatherall:      Yeah, no, but back then it was a fund where they were really good at hedging.

Dan Ferris:                 Yeah, it used to be a hedged fund. Now, it's 2 and 20 or whatever. That's a funny thing. All right, I'm sorry. Just finance geek jokes here. But he started this first modern quant hedge fund.

James Weatherall:      Yeah, exactly. And they were just – they were unbelievably successful. I think they had two down quarters the entire time the fund ran. It went a little over 20 years and –

Dan Ferris:                 Wow, two down quarters in 20 years.

James Weatherall:      Yeah, no, they were incredible. And then, yeah, and he's – I think he's been very, very influential in the field since then, sort of the godfather of quants.

Dan Ferris:                 Very cool. And the other person who I mentioned earlier, who I actually – don't take it personally, James. I didn't read your whole book. I read his whole book like more than once. It was Mandelbrot. He wrote a book called Misbehavior of Markets, which is just brilliant. He's got the 10 heresies of finance in there, which I love. By your estimation, where does he figure in all of this?

James Weatherall:      Yeah, so he's another really interesting figure. I think someone who – very, very famous mathematician, very influential, but conceived of himself as and always sort of was a little bit of an outsider, a kind of a gadfly type of figure criticizing the mainstream, even though I think his ideas now are very, very widely accepted. The point that he was making was a little bit technical. It has to do with what kinds of distributions we use to think about the stochastic processes that we use to model market returns. And basically, what he observed was that when you look at actual market data, the kinds of assumptions that people like Bachelier and Thorp were making, and Black and Scholes later on, were mispricing tail risk. Basically, the probability of extreme events, according to the way that Bachelier and Thorp were doing it, is actually higher. I'm sorry, Mandelbrot was saying that the probability is actually higher than what Bachelier and Thorp were implicitly assuming.

                                    And so, he developed important probabilistic tools for thinking about probability distributions that have these fat tails, that have more probability for extreme events, a little bit less for sort of central behavior. And there's a lot that you can do. So, once you recognize that, there's a lot you can do to sort of revise your trading assumptions, revise your modeling assumptions, and improve on your pricing models. But he noticed this right around the same time that people were developing the models in the first place. But it took until the 1987 crash for people to really take this seriously. And it's really, I think, only in the early 1990s that you start seeing options models that fully account for Mandelbrot's observations.

Dan Ferris:                 Right. My favorite thing – for no apparent reason, just the quirkiness and weirdness of it about him is that – is the connection between his – he started out with cotton prices, I believe it was –

James Weatherall:      Yeah, that's right.

Dan Ferris:                 – and ends up at some point listening to submarine sonar noise or something and looking at it, looking at the data that the submarine sonar driving through Puget Sound generated. I just thought it was the weirdest connection in the world, and that's in his book, and it connects all to finance. And the connection is I – I'm not a mathematician, so I'm basically – I have you on the show to check me out and make sure I'm right about all this stuff. But I thought the point there seemed like the cotton prices behaved wilder than people said. The prices didn't – weren't all linear movements. They were herky-jerky, nonlinear movements. They didn't just glide. They leaped. That was one of his 10 heresies. Prices don't just glide, they leap.

                                    And then, when you get to the sonar data, that was about turbulence. And what we would call volatility in markets behaves like turbulence. I'm still not crystal clear. If I had to re-explain it, I'd have to almost go back and look at what I wrote because I'm on the edge of understanding what the turbulence data told us. Can you help me out with that at all? At all?

James Weatherall:      Yeah, so his big idea that kind of pervades everything that he did was the idea of self-similarity and fractals. And so, this idea that realistic processes in nature tend to have this scaling property where they look similar on different scales – turbulence is an example of a physical process that seems to have that property. It's kind of chaotic, but it's chaotic on many, many scales. You see turbulence on very small scales, but then it sort of comes together as something that looks like turbulence on larger scales and so on. Physical processes that have that character tend to be governed by these power law type scaling properties.

Dan Ferris:                 Right. What does that mean?

James Weatherall:      And – well, that basically is just the kind of function that's going to give you these fat tails. So, the sorts of distributions that he was saying govern or characterize cotton prices, also equity prices, are sort of power law distributions. They're called levy stable distributions, but they're not normal distributions. They've got this other kind of property and they have this sort of self-similar scaling behavior.

Dan Ferris:                 OK, so the other thing that I need to check with an honest-to-goodness physicist on – a real mathematician – it's my impression that these – what we call fat tail events, like an October 1987 event or something, they're still – it's still correct to think of them as unlikely.

James Weatherall:      Oh, yeah.

Dan Ferris:                 They're just far more likely than the traditional sort of bell curve-y statistical distribution that people used before Mandelbrot, let's just say, would tell you. They're still unlikely.

James Weatherall:      Yeah, that's exactly right. They're still unlikely.

Dan Ferris:                 I don't want our listeners to be too scared of this stuff.

James Weatherall:      That's right. So, they're still unlikely. It's true that they're more unlikely. And it's also true that with a normal distribution, it's not just that something that extreme is unlikely – it's that the more extreme it is, the more unlikely it is. And that's suppressed very, very fast. The probabilities go down and down and down very fast, where with these, as you sort of get into these extreme events, that territory, you start seeing more and more probability relative to a normal distribution for more and more extreme events. And so, something like Black Monday 1987 is unlikely, but it's also a much more extreme event than you would have expected from other sort of market moves in the decades leading up to it.

Dan Ferris:                 I'm an investor and I'm trying to think of all this – how to think of all this stuff. But when I listen to – when I read all the stuff that I've read over the years and all these physicists and guys like you, it suggests to me that I ought to do something about it. Now, I know you're not a professional investor, so I'm not going to ask you what to do about it. But let me ask it like maybe – you've been with a university 20 years. You've got a 401(k). Do you ever make the connection and go, "Hmm, let's see. I've got this 401(k). And I know this thing is more likely than most people, and I better than anyone, almost –" you're among the group of people who know better than most that these extreme events are more likely. Do you actually do anything with your 401(k)?

James Weatherall:      Well, I'm not day trading in my 401(k).

Dan Ferris:                 No, you're not. That's part of the point here.

James Weatherall:      So, there's – look, the thing about these sorts of extreme events is that if you're highly leveraged, if you're doing a lot of derivatives trading, or if you're doing really short-term stuff, these extreme events can kill you. If you're buying and holding for retirement, we've had them and they – the market's bounced back. Maybe it takes two minutes, like with the flash crash. Maybe it takes 10 years. But we have this long-term – this long-term pattern of equities growth that we've seen over the last century plus is robust against these extreme events.

Dan Ferris:                 The optimists have triumphed, one might say.

James Weatherall:      Yeah. Well, I can – one of the things that I'm absolutely fascinated by are the places where current practices build in assumptions that are in conflict with other sort of fundamental assumptions that go into markets. Where are the conditions today where something like the 1987 crash could happen again? And I still think that the optimists will triumph in the long run, but I think that there can be some unexpected dislocations in the meantime.

Dan Ferris:                 OK, here's what I want our listeners to know. This fascinates me. You, admittedly not a professional investor, not a guy like me writing about finance, philosophy, logic professor, and you got us to the same place where I often get us and where our finance professional guests often get us, which is to this idea of ruin. You can't set yourself up – it's true. You can't set yourself up for ruin. That's why I made fun of the zero DTE traders a moment ago., because they're setting themselves up for ruin every time they do this. And if they continue to do it, they're repeatedly setting themselves up, virtually guaranteeing it, in my opinion, even though probability may not work that way. If they keep doing this and they keep YOLOing lots of money into something super risky, they're going to blow up. And that's – that seems to be the lesson that Nassim Taleb – one of the many lessons that he's taught is about survival. And the really great traders, it's all about survival. And that's in the math that you've written about in your book.

James Weatherall:      I mean, I think of this actually as Ed Thorp's big contribution. I mean, so the options trading stuff is one big contribution, but the other thing that he brought into trading was a money management strategy based on this work by a mathematician at Bell Labs called John Kelly. He introduces this idea of the Kelly betting criterion, which basically is you assess what your edge is. So, you have some idea that you understand what's going to happen better than the market. You try to assess what your edge is and then you use that plus your current – well, basically you use that to manage your bank role. There's a theorem that says what the optimal way of doing that is.

                                    This is something that Thorp came up with in his blackjack days for figuring out how to survive at the blackjack table. So, he could count cards. Counting cards give you a big advantage when you get to the end of the deck. Early on in the deck, you don't have that big advantage, and so you need to figure out how to manage your bets [and] manage your money so you're still there when the advantage comes. But the fact that you have an edge also doesn't mean you win every time. It just means that you have a higher chance of winning. And so, again, it's all about survival. It's all about just managing your money at the table until you get to the place where you do have the edge and then sort of optimizing.

Dan Ferris:                 Right. That's where all the traders take us. It doesn't matter – it matters how much you win on your winners and how little your losses are on your losers. That's the key thing that you're watching all the time. People think that they're going to get certainty. It's a typical novice investor who thinks they're going to figure out a way to just snap their fingers and double their money. And then, they find out that that is not the way it is. And then, they learn all this stuff we're talking about, about managing money, managing position size, etc., etc. It's really cool.

                                    Let me ask you something. The book came out in, what, 2014? The Physics of Wall Street?

James Weatherall:      I think it was January 2013.

Dan Ferris:                 2013. OK, so 13 years on, how would you characterize – if you could sum up the book, the 2013 version that exists, and then say, "What I would do –" is there anything you'd say or do differently now, do you think?

James Weatherall:      Yeah, well, look, that book was written at a particular time where there were particular debates happening. So, it was really a response to the response to the 2007 to 2008 crisis. And I think as a response to how people were talking about that, I wouldn't change anything. I think I stand by what I wrote there. Maybe if I went and read it again, I would not feel that way. But I do think that a lot has changed in markets since then.

Dan Ferris:                 And just – so, for our listeners, James, have we covered the main conclusion, which is "Models screwed this up, but models are good if they're used responsibly"? What more is there to it than that for you?

James Weatherall:      Yeah, so, no, I think of that as the main message.

Dan Ferris:                 OK, great.

James Weatherall:      So, let's understand where these models came from, what people were trying to do. Let's look at the way in which the people who introduced these models talked about them. We've talked about Mandelbrot and Thorp and Bachelier. Fischer Black is another person who I think is really fascinating. He wrote an article in the 1980s called "The Holes in Black-Scholes," where – it could have been written by Mandelbrot. He just goes through and gives all of these assumptions that are unrealistic that go into the Black-Scholes equation and just lists them. And it's like "Look, these are the ways in which this thing doesn't track reality. Here are the places where it's going to fail. Here are the problems with it. And here's why we think you can use it anyway."

                                    So, I think when you look at how the people who were originally developing ideas thought about them, you see a kind of humility about what the models can do and a kind of sensitivity to figuring out when they're going to fail. And I think that that's just a lesson that very easily gets lost. So, I think most of the people who work with this kind of modeling stuff nowadays, many of them are coming out of financial engineering backgrounds. They're coming out of sort of specialized training in applying these models. I think it's hard to get the full perspective that the originators had. But I think that that's something that's important to focus on. And so, that's what I see as the big lesson.

Dan Ferris:                 Hey, you don't have to know a guy named Emanuel Derman, do you?

James Weatherall:      Yeah. Yeah, sure.

Dan Ferris:                 You know him personally or you know of him?

James Weatherall:      I'm trying to remember if I – no, no, I've met him personally. Yeah, we've corresponded over the years, but I went and visited him at Columbia when I was writing the book.

Dan Ferris:                 I like his book Models.Behaving.Badly. I think it's wonderful. And I think –

James Weatherall:      He's got an awesome book on the volatility smile. Don't get me started on that, but he has another book that he wrote on the volatility smile.

Dan Ferris:                 I'm happy to get you started. I'm happy to pull the rip cord on the volatility smile. I don't even know what that term means.

James Weatherall:      Ah. OK, so the volatility smile – so, this is like a little history and philosophy of Black-Scholes here. When the Black-Scholes model was first introduced – I mean, this is the same as when Ed Thorp was doing his stuff – options markets were very inefficient. And so, you could use the model plus your knowledge about the sort of historical statistics of the underlying asset to come up with a fair market price, and you can be pretty confident that when the option expired its value was going to be very close to what you predicted the option price – the fair price was going to be. And so, you can look for mispriced assets in options markets.

                                    By the early '80s you couldn't do this anymore because everybody was using Black-Scholes. OK, so at that point Black-Scholes sort of has a different kind of role. It's more like just the thing that we use to set prices amongst ourselves, but you can start using it in an inverse way as well where you can infer backwards from the market price of something to what the implied volatility of the underlying asset is. What does the market think these statistical properties of this thing are going to be?

                                    OK, now fast-forward to 1987 after the market crash. That whole crash is driven by failures of Black-Scholes and the portfolio insurance based on it. People start thinking, "What's going on with this equation." They start trusting it less and trading differently. And all of a sudden now, if you do this inverse calculation – by 1989 you start seeing this very clearly – you start doing this inverse calculation to what the implied volatility is, it's no longer a number. It's a curve. And it's a curve that depends on how far out of the money you are, basically how unlikely it is that an option is going to be valuable. And one way of interpreting this is that traders kind of got wise to Mandelbrot's ideas. They figured out that actually these extreme events are more likely than Black-Scholes was assuming.

Dan Ferris:                 OK, I was going to ask you to make it a little more concrete for me, but that's it right there. That's it.

James Weatherall:      And so, nowadays the volatility smile is the main thing people use the Black-Scholes equation for. It's to figure out sort of what the whole – it'll become a volatility surface or a multidimensional analysis where you can see how other traders in the market are thinking about the likelihood of extreme events. And so, yeah, so that's a common quant technique now and Derman has a whole book just on that.

Dan Ferris:                 That's cool. Yeah, I'm looking at – I was clicking around while you were talking; that's why I was looking at my computer here – at the book. And it's – the blurb sounds great if you're into this stuff and if you know Derman's writing, which I don't know if the writing in this is as good as the other one, but I really enjoyed the other book. So, who knows, maybe I'll pick this one up, too. Is there – let me ask you, do you know the book well? Am I going to go in and find a million equations I'm not going to understand?

James Weatherall:      No. There are equations in the book. It's written for a sophisticated professional audience. It's not like a textbook but it is a book for quants. And he's really interested in the history and he's really interested in how to think about the volatility smile because it's something that – the way I think about it is the volatility smile is kind of a contradiction within the Black-Scholes model. If the Black-Scholes model is correct, you don't get a volatility smile. We get a volatility smile, so the Black-Scholes is incorrect. But you only get a volatility smile – you only can see what it is by applying the Black-Scholes model. And so, it's like – as a philosopher, it's like a great example of a circular, paradoxical reasoning where it works and it seems to provide a lot of information, but you're contradicting yourself somewhere in there.

Dan Ferris:                 Right. And that smile demonstrates the contradiction for you.

James Weatherall:      Yeah.

Dan Ferris:                 All right. Interesting. All right. Well, I'm going to add that one to my cart. We'll see. I'm not pulling the – sometimes I'll just click right as you're talking on the show because I like to read and I just like good recommendations, but this one seems a little heady even for me. So, let's move on. And I just want to actually conclude this. So, you wouldn't change anything that you said about models. You wouldn't change the point of the book from 2013. We said that.

James Weatherall:      Yeah.

Dan Ferris:                 But if you were writing it today, it sounds like with that event farther in the past maybe you'd have something else to say. I don't know.

James Weatherall:      Well, I think I would say more about algorithmic trading. I think I would say more about AI. There's sort of other changes that have happened in markets.

Dan Ferris:                 AI, of course, is the one – everybody listening to this just heard you say that. "Oh, tell me about that."

James Weatherall:      Yeah, the other thing that I think is really important is the rise of passive trading. So ETFs are huge. Passive ETFs are huge. And I think this is an example of a place where [you can say,] "Look, here's how markets are supposed to work." There's price discovery. You've got people with different amounts of information who are trading on that information and the market price reflects some kind of clearing of all of this information. And if everyone's trading passively, that doesn't happen. Everything is sort of in lockstep by capitalization and most of the money that's flowing in isn't information – doesn't have information at all. And so, at some point markets break under that kind of scenario.

Dan Ferris:                 Right. And there's – I guess we've had on the show before Mike Green, and another guest we've had on the show, Hari Krishnan, who recently put out some work suggesting – the basic suggestion, as I understand it, is that passive trading could become so large a part of the market, it could generate so much, and that would generate so much more volatility that there's a nonzero chance the market could actually go to zero, and that we're probably getting to that level of concentration within five years.

                                    And Mike, I think it was in a video or something I saw where he said, "Five years until the end of the world." But others have suggested there's something there. There must be something wrong there because they don't see the market going to zero. They don't see that high of a probability of the market going to zero.

James Weatherall:      Yeah. So, I agree that the market going to zero is a pretty extreme possibility.

Dan Ferris:                 Well, it's extreme. Yeah.

James Weatherall:      But I do think that we can see really significant dislocations. And this is connected to all sorts of other related trends, like the underperformance of value over the last two decades compared to the five decades before that and wild price-to-earnings ratios. This is just normal – this isn't like – you don't need physics to talk about this stuff.

Dan Ferris:                 No, but understanding extreme events is kind of key here, though, right?

James Weatherall:      Yeah, that's right. And I also think that this this general theme of saying "Look, our – once models, including portfolio theory, get built into products and into standard practices, we stop seeing the assumptions." And so, once portfolio insurance was a product that you would sell people as an – you conceived of it as a kind of insurance in the '80s, all of a sudden the assumptions that go into Black-Scholes are invisible to you. Similarly, when you talk about options trading in terms of the Greeks, the Greeks are all based on a Black-Scholes model. And if the Black-Scholes model's not right, then those parameters aren't the right ones to look at in all scenarios.

                                    And I think that the ETF, the passive trading is another example where this idea from portfolio theory that you should just diversify and buy the market is a great idea when everyone else is trying to trade on information, but it doesn't make as much sense once that's what everyone's doing. And yeah, I mean, it's a place where an assumption about how markets work kind of goes out of touch with what markets are actually doing.

Dan Ferris:                 And this idea that everybody doing what seems like the right thing and then more and more and more people do it, it's that old thing: "The fool does at the end what the wise man does in the beginning." And even more to the point for investors, there's – you could pick the greatest strategy anyone's ever thought of, and if too many people are doing it, it's just going to work less and less well and create more and more risk, is really the point.

James Weatherall:      Yeah.

Dan Ferris:                 The increase in risk. Yeah.

James Weatherall:      That's right.

Dan Ferris:                 Markets – you can't sleep on markets. They're – it's always changing.

James Weatherall:      Well, there's also – there's just a general scalability problem. So – and I think you can see that in a few different places. One is the rise of private markets. I won't go into – I won't go in at length about all my views on private markets, but it's a strategy that worked great 20 years ago.

Dan Ferris:                 Gangbusters.

James Weatherall:      Oh, OK, 10 years ago. It's not clear that it's sustainable under arbitrary growth. But of course, the more the more equities are traded privately, the more private debt you have, all of that, the less diverse the public markets are and the harder it is to get out of the S&P 500 blue-chip-type stocks.

                                    And then, another place is quant strategies. I think they're really interesting examples of strategies that worked really, really well when you were in the hundreds of millions, a few billion. When you try to scale them up to hundreds of billions or trillions, they just – the opportunities aren't there. And so, there – I think there are some examples of very, very successful firms that have tried to offer products that they hoped could scale differently and just haven't. It hasn't worked.

Dan Ferris:                 It's interesting. Everything changes with size. Size is so important. Just – the guy named Vaclav Smil, who you've probably heard of, has a whole book called Size and it's just about scale in nature and in life and stuff. It's fascinating to me because we – investors see it all the time. Any investor who's been to the Berkshire Hathaway annual meetings hears it – has heard it every year for decades. "Well, we're bigger than we used to be, so we're not going to make as much money." And [Warren] Buffett has said this every single year for 20-some years now, at least that I've been going. I've gone a couple times in the past 20 years.

                                    So, it's super-duper important, the issue of size. What size company are you buying? What size pool of capital are you – are your assets in? And frankly, I have to say it's not – it's one of those things you discover. It's nothing you ever would have thought would have been important, but it really becomes very important. I don't know.

James Weatherall:      Yeah, I completely agree.

Dan Ferris:                 All right. A minute ago – I don't know if you want to go here, but a minute ago, you said, "I won't tell you all my views on private markets." It sounded like there was some stuff there. I don't know if you want to get into it.

James Weatherall:      Yeah, I think I'd better not get into my views on private markets.

Dan Ferris:                 OK. All right. Maybe it gets a little political or something. And that's cool. I respect that. And we thank you. We try to keep it real and not get into areas that like that. So, I appreciate it. But maybe we could talk a bit more – I mentioned AI and you had something that you wanted to talk about. But –

James Weatherall:      Well, I was just going to ask, do you want to – you had said at one point that you wanted to talk about [John] von Neumann. Should we talk about von Neumann?

Dan Ferris:                 Yes, if only because, wow, what a genius. Unbelievable character there.

James Weatherall:      Yeah. Yeah. So, I've been writing a biography of von Neumann for the last – almost a decade now.

Dan Ferris:                 Cool. Very cool.

James Weatherall:      And yeah, so incredible guy. I think part of what I find so compelling about him is how he thought about applied mathematics. These same sorts of themes that I think have become so important to how we think about financial modeling are themes that really originate with him. Applied math wasn't the kind of thing that it is today before – really before World War II. And so much of what we now think of as sort of places where you can apply math – game theory, mathematical economics, but also computer science, all of this is stuff that von Neumann played a central role in developing, including AI. His last book, which was published right after he died, was called The Computer in the Brain, where he sort of sketches the idea of an artificial neural network and revisit some assumptions that he had made earlier on in his career about the relationship between electronic computers and brains and sort of began to lay the foundations for modern AI.

Dan Ferris:                 Yeah, people who saw this coming fascinate me because I sure as hell didn't. It was all sci-fi until it – practically until it arrived. And then 2022 came along and ChatGPT came out and I was as blown away as the average person. I've never been a technical guy. But I – let me ask you this. I use Claude every single day. I use Claude every – and not only that, I use it on the weekends. It helped me clear out my Amazon "save for later" cart, which had over a hundred books in it. And you see behind me – this is not half of it. So, I've got to chill out with the books. And I use it for lots of stuff – taxes, all kinds of things. And I use it to help me do research, financial research. What do you do with it? Do you use AI?

James Weatherall:      Yeah, so, I use it for a couple of things. I use it in my teaching in some ways. I try to incorporate into my teaching ideas about what – how AI works, but also what its limitations are, what it's good for, where it can cause problems. I've experimented with using it in in research and I have not yet had great successes. I have colleagues who swear by it, including mathematicians who –

Dan Ferris:                 I was going to say hard sciences swear by it.

James Weatherall:      Yeah. And so, some people claim that that it can help them do new math. The kinds of examples that I've seen that I'm most convinced by, it's really effective as a deep literature review tool, finding things that are already out there that you wouldn't know how to look for.

Dan Ferris:                 That characterizes my use actually rather well. I use something called NotebookLM that you can just dump a bunch of 10-Ks and other SEC filings and investor presentations from a company website – you can just put it all in one place and basically tell NotebookLM, "Only look at these sources." And it's a fantastic way to run through a whole – say you have 10 or 15 10-Ks and you want to know historical stuff. Fantastic tool for doing that.

                                    But the thing that really gets me is how Claude has a voice. It's like the latest – and I think – I forget what that latest model is. Is it called Opus or something? And it's – it makes connections, or helps me make connections that I might not have made otherwise. Partially, I realized just by the sheer volume of ideas that it can put in front of your face and it'll connect something and it'll say, "Oh, this connects to your earlier idea." And I'll say, "Whoa," just because I'm a human and I can't make – it can make 30 connections in a split second or something and I can make 30 in 30 years. You know what I'm saying? It's just – it's a little bit faster than Dan at the age of 64 is all I'm saying, I guess. But it's useful.

James Weatherall:      Yeah.

Dan Ferris:                 Of course, that's the problem with it, isn't it, that it's so human and so useful. And I'm trying to convince my stepdaughter. "It's a terrible therapist. Don't tell it your personal problems." She keeps telling it her personal problems. "ChatGPT said I should do this." So, there's genuine concern out there. And are you – where do you come down on the scale of people saying it's going to kill us all and become Skynet and kill us all in the future or put us all out of work? Do you have those fears?

James Weatherall:      Put us all out of work – yeah, I work at a university. There are a lot of ways in which it can put us out of work. One way is that we can't figure out how to continue to make education a meaningful experience for students. So, if it becomes a situation where students are using AI to do their homework and professors are using AI to grade the homework, what's the point? Why are any of us doing this? And once that goes away –

Dan Ferris:                 At a time, I might add, James, when the university, it's not the sort of hallowed institution that – before, we didn't question it. "Everybody should go to college." Now, that is not as popular an idea at all, is it?

James Weatherall:      Yeah, that's right. Well, and I'll tell you, it's the computer science majors who engineered themselves out of jobs. And so, we had this, I think, very, very strange labor market situation where AI is booming. Everyone says, "I want to get involved in the AI boom, and so I'm going to learn what I need to learn to go and get these jobs." But then AI got good enough even before they had graduated that you just don't need programmers anymore. This is the place in my research where I use AI the most. It's just integrated into to sort of programming software.

Dan Ferris:                 Generating code? Generating code? Is that –?

James Weatherall:      Generating code. Yeah. Exactly. Yeah, and it's incredible at that. It can read your mind. You type the name of a function and then it produces the function. It's like, "How – where in my code – I believe I'm the first one writing this program. Where in my code did you figure this out from?" But yeah, it can do it. It's incredible.

Dan Ferris:                 Wow. OK. So, maybe we'll – I don't know, maybe one day we'll get you back and we'll just talk about AI. But it's been great talking with you. It's time for our final question, which is the same for every guest, no matter what the topic, even nonfinancial professional guests like yourself. Same identical question, no matter what. But it's simply this. It's for our listeners' sake. If you could give them one takeaway, one thought today, what would you like to leave them with? It can be anything.

James Weatherall:      Yeah, the – my one thought is think meta. Not – that's not a stock tip. OK? Think meta about the ways in which the financial products and the financial services that we're participating in are built on now these mathematical models that assume stuff about what the world is like. And it's very, very hard to see those assumptions, but everything that we do can break when those assumptions break. The more integrated they are into our practices, the harder they are to see and the more dangerous they become.

Dan Ferris:                 All right. Wise words. Thanks for being here, James. And I look forward to having you back on the show sometime.

James Weatherall:      Yeah, thanks a lot, Dan. This was fun.

Dan Ferris:                 That was incredible. For me, at least. I hope it was for you, too. It was very highly educational. I recommend James' book The Physics of Wall Street. It's actually very well written and it's not full of technical mumbo-jumbo and equations. It's full of really good stories about important things that people mostly don't know a whole lot about, which is fun. It's fun to learn about these historical figures and why they're important and what they said that nobody was saying at the time. And it'll teach you something about kind of how markets work in a way that you're just probably not going to read a lot of other places.

                                    I mentioned the book by Mandelbrot, which is Misbehavior of Markets. We mentioned Edward Thorp's book Beat the Market. We talked about John von Neumann and some other people. So, there's other books by the innovators themselves, the thinkers themselves. But you usually – you don't often get a smart guy like James who wants to make it so that everybody can understand it so that he can explain it to you. So, that's why I like The Physics of Wall Street. That's why we wanted to have James on the show. I was like "A guy who can tell stories about math and finance so that a guy like me can understand it and really enjoy it, that's got some value." And I think – I hope it has value for you too. So, I recommend the book and I recommend you learn about all these characters: Bachelier and Thorp and Mandelbrot and all the rest of them.

                                    So, that was another great interview and another episode of the Stansberry Investor Hour. I hope you enjoyed it as much as we did. And remember, hit like, hit subscribe and by all means sign up for our free daily email.

Announcer:                 Opinions expressed on this program are solely those of the contributor and do not necessarily reflect the opinions of Stansberry Research, its parent company, or affiliates.

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