Inside the Gerber Statistic with Sander Gerber of Hudson Bay Capital
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That experience, those two things combined to really create a kind of unique perspective on the world of markets, on the world of risk, and on the world of models. You know, I've used the George Box quote a million times. All models are wrong, but some are useful. And the way Gerber goes about using models is very much along the George Box lines, which is

Not only are we going to assume that models are wrong, but we want to create our own models to be able to identify when they're going to be at a great variance to what's going on in reality and then how to position ourselves to take advantage of it.

They're less directional traders than they are arbitrageurs. Hudson Bay Capital runs a dozen different strategies, and they're all quite fascinating. Everything from risk arb to private credit and real estate in the first quarter of 2025, where volatility spikes and a lot of people's expectations are dashed.

Their models do really well. I find his depth of knowledge and his technical expertise to be absolutely fascinating. I think you'll find him to be fascinating also. With no further ado, my conversation with Hudson Bay Capital's Sander Gerber.

So let's start a little bit with your background, bachelor's in humanistic philosophy and an MBA from Wharton Finance. What was the career plan? Well, actually, I was good at math. So I first entered the Wharton School undergrad. I don't have an MBA from Wharton. And then when I was at Wharton, I didn't think I was getting an education. So I

decided to transfer into the College of Arts and Sciences. So I got two degrees concurrently. I picked up a degree in philosophy, humanistic philosophy. I wanted to understand the development of thought, how we got to where we are in society. Epistemology or something more specific? It was moral philosophy, generally, starting with the ancient Greeks through the existentialists.

I think that I use my philosophy background much more than my finance background because it really gives you a different view on the world. When I was at Wharton College, Andrew Krieger came in 1987 to speak. He had majored in Sanskrit.

Eastern philosophy and then he got his MBA at Wharton and he was the leading FX trader at Bankers Trust and he spoke about how his philosophy, Eastern philosophy, helped him understand the markets that you might feel very convicted the markets should go a certain way but the markets have their own mindset and you have to accept what the markets have and

And it helped him emotionally to trade better because he realized that mother market was going to be right. And so it was from his philosophy background that he was able to reconcile that with his beliefs in terms of where markets should go. And it helped him to be a better trader. I definitely can see that. You know, the concept, I don't know if I'm stealing this from Zen Buddhism, but it's the water flows, but the rigid tree breaks in the storm. Yeah.

And it's very similar to, hey, that's an Eastern way of saying, why are you fighting the trend? Exactly. And so, you know, when I was in college, I really didn't know much about the markets. And as I told you, I still, I had entered first the Wharton School, so I was still getting my degree there, but I was really focused on the philosophy.

And, you know, people think the philosophy is not so practical. What are you going to do with it? And here the top FX trader in the world came and said, this is what you should be doing. So it was sort of, you know, ratification of what I was studying. I think you're the first person who I've ever spoken to who said, yeah, the Wharton School of Finance at University of Pennsylvania is

Not a great education. Isn't it really true that most of our education, or at least for a lot of people, you're just self-taught? Schools will give you a curriculum and here's the reading list, but it's up to you to kind of learn whatever there is to learn.

I think it's a good point. You know, the Wharton School is arguably the finest finance school, but finance is a technical discipline, and I wanted to understand the world. And I think that you can only go a certain degree using that background. And it's true that in order to, I think, upgrade yourself, you've got to be able to develop the capacity to self-learn.

to take in from the environment around you, to enable yourself to grow your skill set, to your experiences through working with others. And that's something we try to incorporate within Hudson Bay is the ability for people's careers to develop. And it is something that you have to rely on self-learning. And within college,

In certain disciplines in college, like in philosophy, a lot of it is discovery, self-discovery. In other disciplines, there is no self-discovery. So I think it is important to be humanistic background. So you come out of Wharton and University of Pennsylvania. You start your career on the floor of the American Stock Exchange as an equity options market maker.

That had to be a fascinating experience, especially 1990s and 2000s. That was a hot period in option trading. Tell us a little bit about that experience. Well, actually, when I graduated Penn, I had been – I'd clerked on the floor of the Philadelphia Options Exchange in 1987, and I liked it.

But my parents had spent all this money to send me to a fancy school. They had taken out a home equity loan to pay for my college tuition. So I thought to be a measly floor trader would be disrespectful. So I went to Bain & Company for two years. And I was in management consulting for two years. It was boring. But I did learn something from it. And then I came to

The floor of the Amex. Wait, before you jump to the Amex, aside from learning that Bain was boring, what else did you learn? I learned how people can work together in good conscience with dedication and still muck things up. Because what we would do is we would parachute into places like British Airways, Montreal Trusts,

And we were like the external strategic planning. And they would put young people like me and we'd sit next to people and interview them and figure out why projects went to muck. And I understood from that that well-meaning people can still muck things up because they don't have an appropriate guide frame or appropriate leadership or they're not...

So like little things can take projects astray. So what was it that drew you to the floor of the AmEx? Well, I'd enjoyed the Philadelphia floor. And also, I always liked games. And so I had a talent, I thought, for trading. And so I went to the AmEx.

Someone gave me, it was like $1,100 a month as a stipend, and I kept roughly half the profits. And there was no training. They just threw me there. Throw you in the deep end of the pool. Whoever doesn't drown, hey, congrats, you're a trainer. That's exactly right. Exactly right. And it took me from July of 91 until December of 91, I made $500. Profit. Not for me, $500 trading profit. But you then had a split.

Which I had to split. Yes. Well, actually, because I had a draw, I didn't get anything. But then the next year, I took off. And it turned out that I did have a knack for it. I was able to understand the volatility of the markets. Usually, we're vol traders. And I did something that was two things that were novel on the floor. The first is I understood that you have to break down your volatility exposure month by month.

which back then was unusual. In other words, people had these models that would give you one volatility exposure across the entire portfolio. And I realized that July is an earnings month and August is a beach month. So you can't use those two months to offset each other. And so I was able to jerry-rig the models that were early then to be able to look at my vega exposure month by month. That was, believe it or not, unusual.

And the second thing that- That's early 90s? Yes. That was 91, 92, 93. Okay. All these things we kind of take for granted today. I know. Right. At one point in time, you wonder why it's become so increasingly difficult to beat the broad index. There was a ton of inefficiencies back then. That's right. That's right. And it was a great edge for me to come to that realization. And maybe it was because I had studied the models at the Wharton School. We had broken them down. And I understood that the models are only as good as the inputs-

And a lot of people back then were doing spreads in their head, and the other group were using these canned models that would give you one volatility exposure across, you know, the entire model. And the second thing that I realized was that you need to combine fundamentals with the technicals of the models. In other words, the models assume a normal distribution of returns. Mm-hmm.

But when you get into some kind of event, it's no longer a normal distribution of returns. It's, you know, the stock's either going to go up a lot or down a lot. That's a barbell distribution. Right. As opposed to normal distribution. Right.

And so by looking at events and when they're going to happen and breaking down the vega exposure month by month, that gave me an edge that I was able to exploit. Define vega for listeners who aren't option traders. Vega is the volatility. So an option has premium, and that premium is the extra amount you pay for the right to have limited loss and unlimited gain.

And so that premium, that value of that option to exercise or not exercise with limited loss

goes up and down in value based upon the degree of movement. So when something's moving around a lot, that has a lot more value. So premium value goes up. When things are not moving a lot, premium value goes down. And so by trading this range of volatility up and down, which is in part dependent on what's happening with the fundamentals of the stock,

you were able to grab edge. So these are really second or third level derivatives. It's not the underlying value. It's the increase in value of the option. And then within that, the range of and the variability of that increase in option value, that's what you were trading. Yes. And

You know, it's really not complicated. I mean, Wall Street tries to make things much more complicated than they are, but the simple, elegant solution is always better.

So it might sound complicated, but it's really not. Right. And that complexity is a feature, not a bug. You can sell stuff if it's complicated and hard to understand. If it's simple, well, I think I could do that. That's right. Wall Street tries to make things more complicated because it has to justify the sales commission. But things really are not so complicated. So what was your biggest takeaway from your experiences as a trader when

How did it shape how you look at the world of investing? How did it affect what you're doing at Hudson Bay today? Well, I really was grounded by that three and a half years of watching every tick on the stock, you know, and your...

You're geographically limited on the floor. You can only trade at the post that you're standing by. Like physically in space, you're tethered to that trading post. Exactly. And there are even rules that you had to do most of your trading in that geography so you couldn't move around a lot. And-

What it taught me is that, you know, like a trading post, a strategy goes in and out of favor. And if you want to be able to make money in all markets all the time, you have to develop a toolkit that can go beyond one particular strategy. So you need to have multiple strategies to develop persistent profitability. The other thing that I learned was that you can make the right decisions and still lose money. I had plenty of times where...

Looking back, it was the right decision, but the markets thought differently. And so you always have to be worried about what could go wrong. And risk is not about not losing money. Risk management is not about not losing money. Risk management is about unexpectedly losing money.

In other words, when you're evaluating a situation, you should know what is your reasonable worst case downside. Now, there's always the black swan that maybe you can't figure on, but you should. But risk management is always about

understanding what could go wrong and quantifying what could go wrong. You know, ETF volumes tend to go up in a crisis situation. You know, when the going gets tough, they get going. Why? Because they are a source of liquidity when other things are not that liquid, which is exactly why the SEC sort of sketched the ETF design out back in 1988 after the Black Monday. Hear more about the evolution of ETFs and their growing influence on portfolios.

Tune in to P. Jim's The Outthinking Investor wherever you listen.

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There's no business like small business. Hiscox Small Business Insurance. So I want to unpack what you just said because it's filled with goodness.

First, you're referring to your approach is, hey, we're really more process-focused than outcome-focused because if you have a good process, even if you get a bad outcome, it doesn't matter. Probabilities will eventually work in your favor. That's exactly right. That's number one. But then the part two, which I think a lot of investors overlook is,

And a risk management component that if the worst case happens, we still survive and live to trade another day. That's right. Exactly right. And so at Hudson Bay, I created the deal code system. Deal code system. Yes. So at the time, well, I left the floor beginning in 95 and started deploying just the money I'd earned on the floor in an off-floor trading account.

And I would develop a strategy and hire someone else to run it and develop another strategy and hire someone else to run it. And as I was having other people manage basically my trading account, I realized I had to scale my risk profile that I developed on the floor over multiple risk takers.

And I needed to do it in a manner that would produce persistent profitability. So at the time, we were trading a lot of risk arbitrage deals. So we called it a deal code. And a deal code is just a numerical moniker that we put on each trading idea.

within the book. And that enables us to focus in on how is that trade hedged? What's the risk, riskiness? How much could that trade lose in a reasonable worst case scenario? And it gives us a batting average so we can understand, is a portfolio manager winning more ideas than they lose? So to be persistently profitable, I think it's not just about winning more dollars than you lose. It's about winning more ideas than you lose.

So let's talk a little bit about Hudson Bay's strategy. You've been managing outside capital across a variety of asset classes and strategies. Tell us, talk about some of the key strategies and what has been the drivers of making those strategies successful.

Well, as I mentioned, I wanted to be able to make money in all market environments. So you need a tool set to do that. So our strategies are equity long short, converts, credit, event merger, volatility trading.

This isn't just, I'm going to buy the S&P 500 and put it away for a decade. You're active traders and you're really looking to take advantage of situations where you have a fairly good idea of what the outcome is going to look like. It's not, hey, this is open-ended. Usually you're pretty confident in here's what our range of potential outcomes look like. Well, I think that

Especially in today's world, you have to understand what your edge is versus the machine's. And a machine can calculate risk based on historical precedent. But a machine cannot calculate risk based upon some kind of uncertainty due to some kind of event, catalyst, or change that's coming up because it's new.

So the machine doesn't have the ability to calibrate for something that's new. And so generally across all our strategies, that's what we're focused on is we're focused on event catalyst change. How can we profit off of that in a way that machines cannot? So that's the fundamental criticism of models. All models assume that the world in the future is going to look like the world in the past, right?

Risk management is what happens if the world doesn't look like how it used to. Precisely. And that's why we don't use the standard risk management models. I actually created a statistic, the Gerber statistic, that helps to understand diversification between our deal codes, between our investment positions. A lot of our competitors are tied to factor-based modeling, which ultimately underneath it

is reliant on regression analysis. Regressions are straight line fits through normalized sets of data. And human relationships don't follow straight lines. And certainly market relationships don't follow straight lines. So using that as the underpinning of a risk management system is just incorrect. And so we've created a whole different structure that, as I said, we've used since 1998. And I think that's given us

the ability to weather storms and profit from it in ways that our competitors can't. So let's talk a little bit about the Gerber statistic. You had this validated by Harry Markowitz, the creator of modern portfolio theory. Tell us about that collaboration and break down the Gerber statistic a little bit. How do you guys actually use it?

So because of my distrust of models based upon my experience on the floor, and particularly the guts of the models, I never believed in the correlation statistic, that correlation is predictive. And this was, I thought, one of the underpinnings of modern portfolio theory, that you look at the expected return of the stock, the expected variance of the stock, and the covariance or correlation between the different components of a portfolio.

And at the time, you know, we used the deal code system. And on Wall Street, the banks were telling me, this is nonsense. Don't even talk about it with investors. And then in 08, when everyone lost money and we made money, I realized we were doing something different. And then I had the idea of, of course, I'd studied about Harry and modern portfolio theory. Everyone in finance has. He won the Nobel Prize. I decided, you know what? I'm going to go out to see him, to see what he thinks about the Gerber statistic.

And at the time it wasn't called the Gerber statistic, but a friend of mine said, "Gee, you really should file a patent on this before you see Harry." And so I did. And I had to name it something. So I called it the Gerber statistic and we now have, I think we just got our sixth patent on our process for diversification. So I got to see Harry in San Diego. Lovely guy. He welcomed me. And we're walking, he liked to walk along the beach. And I said, "Harry, you know, I don't think that correlation is predictive.

And Harry said, you're right. I said, no, no, Harry, you don't understand. I don't think that because this is one of the base foundational bases for which he won the Nobel Prize in modern portfolio theory. I said, Harry, I don't think that historical correlation has relevance to the future. And he said, you're right. And it turns out that in his 1952 paper that sets forth modern portfolio theory, he said that correlation should be determined by the judgment of practical men.

In other words, the stock analyst should think, what will be the relationship going forward? Not to mind the past, but be forward-looking. But in the 1960s, as computing power increased, people said, oh, we can mind the statistic, this row statistic correlation, and then we can plug it into the model as correlation. He meant correlation in a semantic sense, not in a mathematical sense.

terms of using in his model. So he actually said that the deal code system uses his system, the modern portfolio theory system. He said that there's three legs to his system. And so because we use limited loss, because we seek to diversification through hedging on the own, because we seek to win more than we lose in each investment idea, he said that is

in accordance with his system. But anyway, we've written several papers together on the Gerber statistic within modern portfolio theory and have demonstrated that you get better performance with less risk by replacing historical covariance with the Gerber statistic. And Harry and I actually, we only had really one disagreement. And the one disagreement was on factors.

There's all these factor methodologies, and Harry believes that only one factor matters for portfolios. Go on. And I think two factors matter. So you under— But the other 23 factors, we both agree, are complete nonsense. So if you look at the Fama-French model, which started out as two or three factors and then became five factors— Precisely, and then grow and grow and—

If you speak to the research departments of Barra Axioma, they'll tell you that 34% to 40% of a stock price movement can be explained by factors. Okay. So it's a third. Let's call it a third. And of that third, 85% of that third can be explained by the first five factors. Okay. So you're giving credit to five. That's Barra and Axioma. It tells you 85% of the...

40% can be explained by five factors, which means the other 20 factors explain the 15% of 40%. In other words, 6% of a stock price movement can be explained by 21 factors. Right, meaning tiny, tiny little... Which is complete nonsense. But if you lever a portfolio up 10 times, all of a sudden that 6% looks like it's 60%. But it's all complete nonsense. It's numerical mumbo-jumbo. It's part of the whole Wall Street...

pizzazz that is not based on reality, but it sells. So I want to guess the two factors. If I had to guess, I'm going to rely on a paper by Wes Gray of Alpha Architect and guess its value and momentum. But I'm curious what you think. Well, actually, Harry thought it was market. I think it's market and sector. So it's market and sector, but are those really factors? Do we really consider this? The whole idea of factors is kind of like

You know, a little nonsense. It's like beta, you know, like market we think of as beta. Right, right. But it's now been called a factor, so. Oh, I never really thought of beta as a factor. It's just, hey, if you do nothing, you get beta. Right, but that's market. Right. You know, so. Huh, that's really it. So you're looking at the sector it's in and the overall market as the two driving factors. I think those are, yeah.

Now, it's true that momentum, value, these other things are relevant today because everyone else has glommed onto it because we have so many statistical process-driven strategies that try to trade momentum, buy cheap, sell expensive. It pushes everything in line. And this is what I found on the floor using models to trade options that the models would push momentum.

the values of the options into alignment in accordance with the model because everyone's using the same model. And so the same thing is true in the broader market because everyone's using basically the same factor models. It pushes things in alignment, which works in normal market environments, but when things have a dislocation, it no longer works, which is why people say, oh, our risk model broke down or whatever because these aren't really risk models. Now, it's one thing to use a model to...

trade because the model is telling you something is some expensive or cheap and relative to history right and if something's always cheap you just adjust the model so there's a validity to that but that's different than using the same model for risk management risk management again is about avoiding unexpected loss huh that that's that's really interesting the um

So when I started on a training desk, one of the things I was always taught, which I never contextualized...

as a factor is, hey, what's driving the stock? Well, the stock is only a tiny part of it. The stock is 20%, the sector is 30%, and half is the market. So you could be the greatest stock in the world. If the market's going down, it doesn't matter. And it could be a really good stock, but if it's in a terrible sector, you know, the metaphor was always, great house in a crappy neighborhood is a crappy house. Um,

you're really putting that into the context of these are the broader factors that are affecting that single holdup. That's right. That's right. And, you know, in our...

At Hudson Bay, we seek to produce the alpha. So it's true that the market is moving the stock, but we try to pick stocks that will outperform the market or pick shorts that will go down more than the market. So we seek to focus on the alpha provision. So let's talk about something related to this, a paper you published recently.

Environment eats culture for lunch. It sounds like the environment is what the market's doing, what the sector is, but give us a little detail about that piece. Well, actually, I mean, that paper was related to the human aspect, not the market. So Peter Drucker came up with this idea that culture eats strategy for breakfast.

that corporate culture is actually more important than corporate strategy for the success of a firm. I think there's a lot to that, that, you know, the way people work together in an organization. But I've always thought that this corporate culture thing is nonsense. If you have people try to describe their corporate culture, they cannot articulate it. You know, like, what's the corporate culture here at Bloomberg? You know, like...

Fun loving. Data driven. It's all about data. So you come up on the system. Data driven is not a culture. Data driven is a process. But I'm talking about what's the human aspect of it. What's the human culture? I'm the wrong person to ask that because I'm a mutant. Right, because no one can really describe corporate culture. What you can describe is an environment. What is the environment that people work within?

And I kind of learned this at Bain & Company because Bain was described as this, like, fun-loving place. Everyone has fun. And then when I was there, two guys died in the Lockerbie crash, and Bill Bain had milked the Aesop, and so the company almost collapsed. When I was there, they fired half of my class, not me. They fired all the incoming MBAs and students.

It was the avarice of Bill Bain that nearly collapsed the firm. We're talking back in 1989, 90. So the corporate culture was rapacious greed and it almost destroyed them. It was inauthentic. And when people try to describe culture, they can't. And so what I wanted to do was to describe an environment. What is the environment that you want to work within? And when you speak to...

When you speak to people in other firms, what's your corporate culture? What's your value statements? Usually these things go on and on and on. No one can really remember all the value statement. If you can't remember your value statement, it has no value. I'm going to imagine that 22, 23, when all the big firms were saying, we want our employees back in the office. We don't want any more remote work. It's a matter of corporate culture.

How did you think about that? Was this a legitimate demand? And is it not so much corporate culture, but we want an environment where people are in the office working together? Is that legit? Well, I hate going in the office and seeing people not there. I think that people should work together. On the other hand, you can't force these things. You can't force independent thinking.

You can't force collaboration. You can have an environment that engenders it. And so we try to have an environment that engenders it. So it's my opinion that people who come to the office are going to succeed more than people who don't. Now, I understand that, you know, the commute is a hassle. And sometimes people, you know, want to take the day off. And so...

Our standard is two days in the office. Many teams have a third day. But a lot of people, usually people are in our office three to five days a week, but we don't force it. Once you force people to be in the office, I think you're losing the esprit de corps. We want people to want to work at Hudson Bay. If they don't want to work at Hudson Bay, they should go elsewhere. But to force people, I think for high performers—

I don't think that's the way to engender the right environment. And environment beats culture for work because the work environment is more important than some statement that nobody remembers. So you guys have—let's talk a little bit about independent thought. You guys have done pretty well when the experts were wrong. You thrived in 07, 08, and 09. You were notably up in years where most people were down. Yeah.

again in Q1 of 2020. You guys did really well. All periods of big market turmoil. I don't know what you were doing in 2001, too, but I'm imagining the same approach held true.

How do you think about these periods? Are they truly black swans, or are they things that, with the right approach to risk management, create opportunities? Again, people are trying to assess risk based upon some kind of parametric distribution with standard deviation movements. And I think that's just nonsense. The markets don't work like that. So

Our system enables us to weather all market environments through the deal code system by ignoring those parametric. The Gerber statistic, which is the basis for the work with Harry, is a rank order statistic because it recognizes the failures of parametric normal distributions. And what we do is we set a threshold.

Because a lot of data is noise in the markets. If the S&P moves by 10 basis points, it doesn't communicate to you how the S&P affects other things. Yet in all these statistical models, they're including every single data point. Because if you don't include every single data point, then in the matrix math, you have a divide by zero issue. So they're forced to...

in all these correlation statistics, these regression analyses to include every single data point. With the Gerber statistic, we are able to create thresholds where we ignore data

below a certain degree of movement. And so that enables us to focus on meaning. Everyone wants meaningful relationships, right? So this is how we're able to focus on meaningful relationships within the market. You know, ETF volumes tend to go up in a crisis situation. You know, when the going gets tough, they get going. Why? Because they are a source of liquidity when other things are not that liquid, which is exactly why the SEC sort of sketched the ETF design out back in 1988 after the Black Monday.

Hear more about the evolution of ETFs and their growing influence on portfolios. Tune in to PGM's The Outthinking Investor wherever you listen.

Hiscox Small Business Insurance knows there is no business like your business. Across America, over 600,000 small businesses, from accountants and architects to photographers and yoga instructors, look to Hiscox Insurance for protection. Find flexible coverage that adapts to the needs of your small business with a fast, easy online quote at Hiscox.com. That's H-I-S-C-O-X dot com.

There's no business like small business. Hiscox Small Business Insurance. You know, we talked a little bit about subprime real estate and how the models change.

It wasn't even that they broke. They were so poorly constructed, they were destined to fail. If you build a house really poorly, you don't need an earthquake. Eventually, it's just going to collapse under its own weight. But I have to ask you some questions about real estate because Hudson Bay has been increasingly invested in private credit and real estate. You've done a number of major refinancings in and around New York City. 620 Avenue of the Americas is a

Tell us a little bit about the work you're doing at Hudson Bay with private credit and real estate. Well, we saw, beginning with the higher, the transitory higher rates, which we thought was nonsense. Right. We saw that rates were going to be higher for longer. And we had believed that the market had been anchored in this idea of ultra-low rates, which was really a manipulation of the monetary system. Right.

So we started thinking about what's the implications of that and came to the notion that the banking system would be under stress. And what's the implication of the banking system under stress? Well, that means that they can't extend loans in the same way, you know, corporate as well as real estate.

So we started staffing up in those areas to take advantage. And now I'm convinced that there's now going to be a structural shift in credit provision in the U.S. economy, that the banks are no longer going to be the mainstay for credit. And that's because the government has effectively guaranteed our banking system, which creates moral hazard. We have on the order of 4,300 banks in the United States. It's a lot.

especially when you compare it to Canada that's got the big handful. And when you deposit money in the bank, that bank is lending it out long. And fractionally reserving it so it's 10 to 1, 20 to 1, whatever the leverage they're using. So I think that the whole fractional banking system notion is challenged, particularly in the idea of the ease of information transparency. Yeah.

among depositors, coupled with the necessity for government guarantee and moral hazard. So private credit, firms like ours, people invest in Hudson Bay, and they know it's not a bank account. And that gives us license to deploy the money in ways that are appropriate. And so we began staffing up in those areas. And now in real estate, for instance, we have teams that work in real estate equity in CMBS.

Distressed CMBS and direct provision of real estate credit.

And as part of the core value of Hudson Bay, these teams work together, which give us a better understanding. It's a great advantage to have equity teams working with credit teams, particularly all real estate's local. It gives us a much better understanding of the asset that we're looking at. Huh. That's really kind of interesting. You know, ever since the financial crisis, some of the new regulations and bank regulations—

directly led to the rise of private equity, private credit. Some of the forecasts are over the next decade, this blows up to a $13 trillion asset class. I think we're in the third inning now. Early days here. Yeah, I think so. And it feels like it's been so big because we started with practically nothing in that space. And the first couple of trillion dollars felt like, oh my goodness, there's just so much capital washing over this. But

This seems to have happened in the past where Wall Street banks and brokers kind of move up market. They create a void in the space they left and private money rushes in to fill that void. Is that what's going on with private credit and real estate? Well, it's still early in that. I think it's a golden age for real estate credit. The banks are not able to – they don't have the capital now to lend.

And so it's open season. Really interesting. So how do you identify opportunities in the real estate space? It seems like there are so many buildings that are half empty, and yet it's a slow motion train wreck because most of their tenants have 10 or longer year contracts.

leases, and they're just slowly starting to recognize, unless you're a super A class building, even A buildings are having a hard time attracting renewals and tenants. How do you identify these? And how far along the repricing of commercial real estate, or at least offices, do you think we are?

Well, those are big questions. And I'm from Ann Arbor, Michigan. And I saw how in Detroit, Detroit was going to be called the museum to the...

I don't know, desolate city because downtown Detroit went empty. When they built the Renaissance Center, everyone moved to the Renaissance Center and left these empty, huge buildings in Detroit. And you see aspects of that now where the A buildings, the new buildings, are attracting very high rents and buildings in other areas are going empty.

So to understand what's going on, you really have to understand the asset. And so that's why it's important to have teams from different disciplines being able to understand the asset, obviously looking through the rent rolls and understanding, you know, the weighted average lease requirements.

But also understanding the macro environment. Are things growing? And we have so much uncertainty now going on, not just because of work from home with Zoom, but also the longer-term implications of AI and what's that going to mean for the workforce. And even cities like New York City, it's possible that we're not going to need the same number of junior lawyers, junior accountants, junior bankers.

So I've heard some people discuss AI as a tool, and it's not that you're going to lose your job to AI, but you're more likely to lose your job to someone working with AI. Is that a fair assessment, or is it just still way too early to tell? I think we still don't know. I think AI is the greatest change in my lifetime. Bigger than the internet? I think so, yeah. Really? Yeah, because...

The ability for natural language processing goes far beyond what I thought was possible. You know, I studied linguistics a bit in college, and the whole idea of how we form language is a fascinating subject. And now the computer is able to be cogent in their processing.

responses, we've kind of approaching hard AI in a way that I did not think was possible. And it's only going to get better. Let me push back a little bit. And I'm not necessarily saying I believe this, but so I've had this conversation over and over again with a number of different people. How are you using AI in your daily work? What are you finding?

And someone who hosts a different podcast said they created this really interesting...

set of prompts with AI to get an answer to how to do certain things. And the first time they got the answer, they were really impressed. Oh my God, this is a genius insight and look how smart this is and how it, it figured out exactly what I needed. And then they asked a different question with a different subject and

kind of got the same answer. And it was like, oh, this is a party trick. This isn't really intelligence. It just looks like intelligence. And even though it's getting better, it's still kind of dumb relative to it impresses us that

But once you peer behind the curtain and see the wizard is just a man, you figure out this is less what it purports to be and more like a very useful, clever trick. I was thinking of the Wizard of Oz also while you were saying that, but I don't think there's a guy behind the curtain that's giving the answers. That's why I think that it helps with the junior analysts that you have to check anyway. Yeah.

And it certainly speeds up the research process in ways that were not possible before. For sure. And it's only going to get better. And it makes mistakes. But the junior analyst makes mistakes.

Also, I mean, I've used it for things my lawyers probably will hate me, but sometimes when I've had a discussion with the lawyers on how to express something in a document, I'll ask AI the question. It'll give me a range of possibilities and enables me then to be more on a level playing field with my lawyers who've had a lot more experience than I have, but it has enabled me to bring to the discussion insights that we might not have thought of.

I'm glad you brought up the attorneys because a judge just sanctioned a lawyer for using AI in certain of its answers and this unfortunate tendency to hallucinate. I don't think the problem was that he used AI to help him in research. He didn't double check it and he failed to disclose that AI was part of the process. It's just plain laziness. Yeah.

The AI is good for the junior person. And I think that has implications for the workforce. What is the workforce going to look like given that maybe we don't need the same phalanx of junior accountants, junior lawyers, junior bankers? How do you become a senior accountant, lawyer, banker if you're never a junior? It's a tough question. So let me give you an opportunity to update your 2021 piece

In investing, don't short human judgment. Right. Are you still holding that view? Absolutely. I mean, we are in the human judgment business. Really? We are trying to beat the machines. We do that, as I said, through understanding uncertainty, events, catalysts, and change.

And I think ultimately human judgment is superior than machines. I hope we won't go into a HAL 2000 type situation, that human judgment will always be superior. You wouldn't want to have a machine be the President of the United States. How could a machine possibly make those decisions?

So, obviously, human judgment will always be there, and I don't think that we're at a Terminator-type situation, but there are certain experts that say that ultimately that's where we'll go. I mean, I do know that in the military, the idea of robots creating robots is a real idea, and it very might well change battlefield dynamics, but...

I believe that certainly at this point in time, the human capacity to ingest a mosaic of information and to make the right decision is superior. If you take a chessboard, the machine can beat the master. But if you put an extra bishop on the board, the machine can't deal with it. Right.

And I think that's the paradigm, and life does not mimic a chessboard. You know, life mimics the chessboard with extra pieces being put on randomly, and it's that randomness that I don't think the machines will be superior than human judgment. Now, it might appear at times that the machine can beat the human, but I think ultimately the human judgment is superior. And so our business is based on human judgment. You mentioned the wartime usage of AI. Yes.

There was a pretty big article, I don't remember, I want to say the Times, not the Journal, that figured out that in the Ukraine-Russian war, which started out as a conventional bombardment between tanks and mortars and anti-tank weapons, over the past 6-12 months...

70% of the casualties have been drone AI warfare driven, and it's very much a brave new world. It's not like the old world of warfare. What it sounds like you're suggesting with AI is that they're both going to co-develop, that you'll still have humans driving the process, but AI has become an increasingly large process.

Part of it, regardless of whether we're talking about warfare, business, or investing, I don't want to put words into your mouth, but is that a fair way to assess that? I think so. I mean, I think that the humans always have to be on top of the machines. Machines have a lot of latitude both to produce themselves as well as to target people.

The markets are different because the markets follow a behavioral dynamic. The evaluation of risk versus reward is something that I think a machine cannot do in the same way that a human can. So given some of the volatility we've been seeing in the first quarter of 2025, what

Has that changed how you're looking at your models, how you're viewing your approach? Or is it, hey, this is just another one of those things that comes along and we have to be able to trade through it? We actually like the dislocation because the dislocation proves the models are wrong. I know you guys don't release public performance numbers, but I know you're doing much better than your benchmark this quarter in

volatility is your friend? Is that what you're saying? Because volatility disrupts traditional models and you're a non-traditional model. Correct. So I know you've worked with Harry Markowitz. What other academics and what other institutions have you worked with? Well, at Imperial College London, there's further work being done on the Gerber statistic and incorporating it. The idea of thresholding data

and ways to do it to, for instance, if you want to understand the significance of a stock price movement, maybe you should exclude days where there's very low volume and only include days when there's high volume. There's a variety of ways to incorporate it. I know I only have you for a limited amount of time. Let me jump some of my...

Favorite questions I ask all of our guests. What are you watching or listening to? What's keeping you entertained? Recently, I streamed Eastern Gate. Oh, really? Which is, I saw in the New York Times, it was this spy thriller series on...

the conflict between Poland and Belarus. And I wanted to understand the dynamic between it. So I thought I'd get a little entertainment and understand something I couldn't pick up here. And it's a little slapstick, but I think it's worth it. Eastern Gate. Yes. Did you happen to watch any of Fauda when that was? Yeah, I watched all of Fauda. Just Fauda?

Most heart-wrenching stuff to watch. It's so stressful. Yeah, and pretty realistic. Very realistic. Let's talk about mentors who helped shape your career. I got to give a lot of credit to Dave Petraeus, who... I know that name. Who really helped me get into shape. And he was on my case every day. The diet, the working out, we were workout partners. Yeah.

And I was 35, 40 pounds heavier. And he got me to recognize that I needed to get in shape. I thought I was in shape, but I wasn't in shape. I think a lot of people think they're doing okay when they could do a lot better. And he taught me I could do a lot better. And I think it's affected me overall, my mental acuity, my mood, my stamina.

I really give him a lot of credit. You mentioned books earlier. What are some of your favorites? What are you reading right now? One book that I really enjoyed, which was long, was Walter Isaacson's book on Elon Musk, which I read before the election. And it made a big impact on me because I believe in questioning the experts, but Musk takes it to a different level. He's questioning metallurgical properties that were well-grounded in science and engineering. And he's saying, why does that have to be?

And oftentimes he was right that the established consensus regarding properties of metals was wrong. Really, really interesting. Any other books you want to mention? I read The Melting Point by Frank McKenzie recently. He was the head of CENTCOM and he talked about what it was like to lead CENTCOM and he

He also had a – he majored in English, and he thought that his English background to be a commanding general was very helpful because it helped him to articulate better and to form consensus among his colleagues. Really interesting. Our final two questions –

What sort of advice would you give to a recent grad interested in a career in either fill in the blank, investing, options trading, multi-strategy, management? What advice would you give to them? I think it's across all certainly service occupations is you got to be able to beat the machines. And to do that, you need to be independent thinker. You need to go against the grain, question the experts.

You need to be able to do that. You need to be able to work with other people to learn from them, to expand your horizons, to expand the mosaic that you can bring to your independent thinking.

And you got to be able to respect your colleague. So I think that those three things are real guideposts for people. This goes back to your corporate culture, which is- Corporate environment. Corporate environment, my bad. Your corporate environment, think independently, collaborate, and respect the individual. Correct. And our final question, what do you know about the world of investing in finance today would have been useful when you were first getting started in the early 90s?

I think that, you know, everything you learn in business school or economics, you can just throw out the window. Economics is not a science.

People try to portray economics as a science, and it simply is not. And so all the notions that we brought up regarding money supply, Milton Friedman would be turning over in his grave, even though these principles might have some grounding, it's not scientific. This is not a natural science. It's a behavioral science, and it's based upon how people interact with each other. And I think that that appreciation leads to the notion that

Oftentimes the academy or the experts try to proffer things that everyone seems to believe one way, and you think, how could I be right? Because everyone believes one way because this is what they studied in school, and the authorities say it's that one way. And I think that as you –

go through life and you age, you realize that the ivory tower isn't always correct. In fact, a lot of times the ivory tower doesn't have the real life experience. Um, and so they're flat out wrong. Um, I'm trying to remember, uh, where, where I'm stealing this quote from science advances one funeral at a time. The same is true with, uh,

Other things that Dick Thaler said, rather than wait for the rest of economics to catch up with behavioral finance, I'm just going to teach it to the younger generation and it'll infiltrate much more quickly than waiting for all of my peers to accept it. Really fascinating.

Uh, Sander, thank you for being so generous with your time. We have been speaking with Sander Gerber. He is CEO and CIO of Hudson Bay Capital. If you enjoy this conversation, well, be sure and check out any of the previous 550 we've done over the past 11 years. You can find those at iTunes, Spotify, YouTube, Bloomberg, wherever you find your favorite podcast. Uh,

And be sure and check out my new book, How Not to Invest, The Ideas, Numbers, and Behavior That Destroys Wealth. Out today wherever you find your favorite books. I would be remiss if I did not thank the crack team that helps put these conversations together each week. John Wasserman is my audio engineer. Anna Luke is my producer. Sean Russo is my researcher.

I'm Barry Ritholtz. You've been listening to Masters in Business on Bloomberg Radio.

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