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Speaker 1: Bloomberg Audio Studios, podcasts, radio news. This is Masters in

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Speaker 1: Business with Barry Ritholts on Bloomberg Radio.

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Speaker 2: Strap yourself in for another good one. Sander Gerber, CEO

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Speaker 2: CIO of Hudson Bay Capital. What a fascinating background he has,

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Speaker 2: starting in philosophy and ending up on the floor of

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Speaker 2: the American Stock Exchange as an equity options trader. That experience,

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Speaker 2: those two things combined to really create a kind of

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Speaker 2: unique perspective on the world of markets, on the world

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Speaker 2: of risk, and on the world of models. You know,

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Speaker 2: I've used the George Box quote a million times. All

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Speaker 2: models are wrong, but some are useful. And the way

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Speaker 2: Gerber goes about using models is very much along the

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Speaker 2: George Box lines, which is, not only are we going

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Speaker 2: to assume that models are wrong, but we want to

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Speaker 2: create our own models to be able to identify when

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Speaker 2: they're going to be at a great variance to what's

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Speaker 2: going on in reality, and then how to position ourselves

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Speaker 2: to take advantage of it. They're less directional traders than

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Speaker 2: they are arbitreasures. Hudson Big Capital runs a dozen different

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Speaker 2: strategies and they're all quite fascinating. Everything from risk, arb

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Speaker 2: to private credit and real estate in the first quarter

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Speaker 2: of twenty twenty five, where volatility spikes and a lot

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Speaker 2: of people's expectations are dashed. Their models do really well.

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Speaker 2: I find his depth of knowledge and his technical expertise

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Speaker 2: to be absolutely fascinating. I think you'll find him to

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Speaker 2: be fascinating also, with no further ado, my conversation with

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Speaker 2: Hudson Bay Capitals Xander Gerbert. So let's start a little

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Speaker 2: bit with your background bachelors and humanistic philosophy and an

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Speaker 2: MBA from Wharton Finance. What was the career plan?

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Speaker 3: Well, actually, I was good at math, so I first

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Speaker 3: entered the Wharton School undergrad. I don't have an MBA

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Speaker 3: from Wharton. And then when I was at Wharton, I

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Speaker 3: didn't think I was getting an education, so I decided

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Speaker 3: to transfer into the College of Arts and Sciences, so

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Speaker 3: I got two degrees. Concurrently, I picked up a degree

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Speaker 3: in philosophy. Humanistic philosophy. I wanted to understand the development

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Speaker 3: of thought, how we got to where we are in.

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Speaker 2: Society, epistemology or something more specific.

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Speaker 3: It was moral philosophy, generally, starting with the ancient Greeks

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Speaker 3: through the existentialists. I think that I used my philosophy

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Speaker 3: background much more than my finance background, because it really

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Speaker 3: gives you a different view on the world. When I

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Speaker 3: was at Wharton colleg Andrew Krieger came in nineteen eighty

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Speaker 3: seven to speak. He had majored in Sanskrit Eastern philosophy

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Speaker 3: and then he got his MBA at Wharton and he

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Speaker 3: was the leading FX trader at Banker's Trust. And he

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Speaker 3: spoke about how his philosophy Eastern philosophy helped him understand

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Speaker 3: the markets. That you might feel very convicted the markets

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Speaker 3: should go a certain way, but the markets have their

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Speaker 3: own mindset and you have to accept what the markets have.

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Speaker 3: And it helped him emotionally to trade better because he

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Speaker 3: realized that mother market was going to be right, and

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Speaker 3: so it was from his philosophy background that he was

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Speaker 3: able to reconcile that with him with his beliefs in

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Speaker 3: terms of where markets should go, and it helped him

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Speaker 3: to be a better trader.

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Speaker 2: That I definitely can see that. You know the concept.

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Speaker 2: I don't know if I'm stealing this from Zen Buddhism,

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Speaker 2: but it's the water flows, but the rigid tree breaks

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Speaker 2: in the storm, and it's very similar to, hey, that's

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Speaker 2: an Eastern way of saying, why are you finding the

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Speaker 2: trend exactly?

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Speaker 3: And so, you know, when I was in college, I

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Speaker 3: really didn't know much about the markets. And as I

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Speaker 3: told you, I still I had entered first the Wharton School,

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Speaker 3: so I was still getting my degree there, but I

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Speaker 3: was really focused on the philosophy. And you know, people

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Speaker 3: think the philosophy is not so practical, what are you

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Speaker 3: going to do with it? And here the top FX

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Speaker 3: trader in the world came and said, this is what

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Speaker 3: you should be doing. So it was it was sort of,

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Speaker 3: you know, ratification of what I was studying.

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Speaker 2: Huh. I think you're the first person who I've ever

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Speaker 2: spoken to who said, yeah, the Wharton School of Finance

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Speaker 2: at University of Pennsylvania not a great education. Isn't it

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Speaker 2: really true that most of our education, or at least

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Speaker 2: for a lot of people, you're just self taught. Schools

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Speaker 2: will give you a curriculum and here's the reading list,

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Speaker 2: but it's up to you to kind of learn whatever

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Speaker 2: there is to learn.

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Speaker 3: I think it's a good point. You know, the Wharton

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Speaker 3: School is arguable the finest finance school, but finance is

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Speaker 3: a technical discipline, and I wanted to understand the world.

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Speaker 3: And I think that you can only go a certain

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Speaker 3: degree using that background. And it's true. Then in order

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Speaker 3: to I think, upgrade yourself, you've got to be able

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Speaker 3: to develop the capacity to self learn, to take in

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Speaker 3: from the environment around you, to enable yourself to grow

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Speaker 3: your skill set to your experiences through working with others.

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Speaker 3: And that's something we try to incorporate within Hudson Bay

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Speaker 3: is the ability for people's careers to develop, and it

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Speaker 3: is something that you have to rely on self learning

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Speaker 3: and within college in certain disciplines. In college, like in philosophy,

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Speaker 3: a lot of it is you know, discovery, self discovery,

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Speaker 3: and other disciplines there is no self discovery. So I

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Speaker 3: think it is important to the humanistic background.

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Speaker 2: So you come out of of Wharton and University of Pennsylvania,

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Speaker 2: you start your career on the floor of the American

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Speaker 2: Stock Exchange as an equity options market maker. That had

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Speaker 2: to be a fascinating experience, especially nineteen nineties and two thousands,

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Speaker 2: that was a hot period and option trading. Tell us

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Speaker 2: a little bit about that experience.

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Speaker 3: Well, actually, when I graduated Penn I had been I'd

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Speaker 3: clerked on the floor of the Philadelphia Options Exchange in

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Speaker 3: nineteen eighty seven, and I liked it. But my parents

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Speaker 3: had spent all this money to send me to a

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Speaker 3: fancy school. They had taken out a home equity loan

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Speaker 3: to pay for my college tuition. So I thought to

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Speaker 3: be a muslely floor trader would be disrespectful. So I

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Speaker 3: went to Banning Company for two years, and I was

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Speaker 3: in management consulting for two years. It was boring, but

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Speaker 3: I did learn something from it, and then I came

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Speaker 3: to the floor of the AMEX.

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Speaker 2: Wait before you jump to the AMEX. Aside from learning

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Speaker 2: that being was boring, what else did you learn?

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Speaker 3: I learned how people can work together in good conscious

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Speaker 3: with dedication and still muck things up. Because what we

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Speaker 3: would do is we would parachute into places like British Airways,

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Speaker 3: Montreal Trusts, uh CIA Industries, and we were like the

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Speaker 3: external strategic planning and we would They would put young

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Speaker 3: people like me, and we'd sit next to people and

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Speaker 3: interview them and figure out why projects went to muck.

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Speaker 3: And I understood from that that well meaning people can

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Speaker 3: still muck things up because they don't have an appropriate

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Speaker 3: guide frame or appropriate leadership. Or they're not so like

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Speaker 3: little things can take projects astray.

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Speaker 2: So what was it that drew you to the floor

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Speaker 2: of the well?

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Speaker 3: I enjoyed the Philadelphia floor, and also I was I

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Speaker 3: always liked games, and so I and I had a

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Speaker 3: talent I thought for trading, and so I went to

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Speaker 3: the the AMEX. Someone gave me it was like eleven

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Speaker 3: hundred dollars a month as a stipend, and I kept

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Speaker 3: roughly half the profits. And there was no training. They

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Speaker 3: just threw me there very in the deep.

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Speaker 2: End of the pool. Whoever doesn't drown. Hey, you can grab.

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Speaker 3: Exactly right, exactly right. And it took me from July

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Speaker 3: of ninety one till December of ninety one. I made

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Speaker 3: five hundred dollars profit. Not for me, five hundred dollars.

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Speaker 3: Trading had a split which I had to split. Yes, well,

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Speaker 3: actually because I had a draw, I didn't get anything.

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Speaker 3: But then the next year I took off and it

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Speaker 3: turned out that I did have a knack fort I

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Speaker 3: was able to understand the volatility of the market. Is

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Speaker 3: usually we're vol traders, and I did something that was

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Speaker 3: two things that were novel on the floor. The first

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Speaker 3: is I understood that you have to break down your

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Speaker 3: volatile exposure month by month, which back then was unusual.

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Speaker 3: In other words, people had these models that would give

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Speaker 3: you one volatility exposure across the entire portfolio. And I

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Speaker 3: realized that julys and earnings month, and August is a

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Speaker 3: beach month, so you can't use those two months to

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Speaker 3: offset each other. And so I was able to jerry

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Speaker 3: rig the models that were early then to be able

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Speaker 3: to look at my VEGA exposure month by month. That was,

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Speaker 3: believe it or not unusual. And the second thing that

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Speaker 3: that's early nineties is yes, that was ninety one, ninety two.

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Speaker 2: Okay, all these things we kind of take for granted.

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Speaker 2: I know, right at one point in time, you wonder

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Speaker 2: why it's become so increasingly difficult to beat the broad index.

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Speaker 2: It was a ton of inefficiencies, that's right, that's right.

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Speaker 3: And it was a great edge for me to come

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Speaker 3: to that realization. And maybe it was because I had

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Speaker 3: studied the models at the Wharton School. We had broken

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Speaker 3: them down, and I understood that the models are only

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Speaker 3: as good as the inputs. And a lot of people

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Speaker 3: back then were doing spreads in their head and the

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Speaker 3: other group were using these canned models that would give

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Speaker 3: you one volatility exposure across you know, the entire model.

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Speaker 3: And the second thing that I realized was that you

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Speaker 3: need to combine fundamentals with the technicals of the models.

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Speaker 3: In other words, the models assumer normal distribution of returns,

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Speaker 3: but when you get into some kind of event, it's

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Speaker 3: no longer a normal distribution returns. It's you know, the

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Speaker 3: stock's either going to go up a lot or down

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Speaker 3: a lot. That's a barbell distribution, right as opposed to

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Speaker 3: normal distribution. And so by looking at events and when

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Speaker 3: they're going to happen and breaking down the VEGA exposure

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Speaker 3: month by month, that gave me an edge that I

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Speaker 3: was able to exploit. Do you find vega for listeners

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Speaker 3: who are Vega is the volatility So of the an

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Speaker 3: option has premium, and that premium is the extra amount

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Speaker 3: you pay for the right to have limited loss and

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Speaker 3: unlimited gain. And so that premium, that value of that

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Speaker 3: option to exercise or not exercise with limited loss, goes

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Speaker 3: up and down in value based upon the degree of movements.

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Speaker 3: So when something's moving around a lot, that has a

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Speaker 3: lot more value. So premium value goes up when things

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Speaker 3: are not moving a lot, premium value goes down, and

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Speaker 3: so by trading this range of volatility up and down,

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Speaker 3: which is in part dependent on what's happening with the

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Speaker 3: fundamentals of the stock, you were able to grab edge.

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Speaker 2: So these are really second or third level derivatives. It's

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Speaker 2: not the underlying value. It's the increase in value of

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Speaker 2: the option and then within that the range and the

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Speaker 2: variability of that increase in option value. That's what you

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Speaker 2: were trading.

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Speaker 3: Yes, and you know it's really not complicated. I mean

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Speaker 3: Wall Street tries to make things much more more complicated

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Speaker 3: than they are, but the simple, elegant solution is always better.

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Speaker 3: So it might sound complicated, but it's really not right.

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Speaker 2: And that complexity is a feature, not a bug. You

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Speaker 2: can sell stuff if it's complicated and hard to understand.

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Speaker 2: If it's simple, well I think I could do that.

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Speaker 3: That's right. Wall Street tries to make things more complicated

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Speaker 3: because it has to justify the sales commission and if

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Speaker 3: but things really are not so complicated.

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Speaker 2: So what was your biggest takeaway from your experiences as

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Speaker 2: a trader? How did it shape how you look at

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Speaker 2: the world of investing, How did it affect what you're

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Speaker 2: doing in Hudson Bay today.

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Speaker 3: Well, I really was grounded by that three and a

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Speaker 3: half years of watching every tick on the stock. You know,

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Speaker 3: and you're you're geographically limited on the floor. You can

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Speaker 3: only trade at the post that you're standing by.

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Speaker 2: Like physically in space, your physically heether to that trading

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Speaker 2: thing exactly.

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Speaker 3: And there are even rules that you had to do

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Speaker 3: most of your trading in that geography, so you couldn't

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Speaker 3: move around a lot. And what it taught me is that,

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Speaker 3: you know, like a trading post, a strategy goes in

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Speaker 3: and out of favor, and if you want to be

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Speaker 3: able to make money in all markets all the time,

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Speaker 3: you have to develop a toolkit that can go beyond

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Speaker 3: one particular strategy. So you need to have multiple strategies

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Speaker 3: to develop persistent profitability. The other thing that I learned

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Speaker 3: was that you can make the right decisions and still

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Speaker 3: lose money. I had plenty of time where looking back,

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Speaker 3: it was the right decision, but the markets thought differently,

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Speaker 3: and so you always have to be worried about what

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Speaker 3: can go wrong. And risk is not about not losing money.

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Speaker 3: Risk management is not about not losing money. Risk management

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Speaker 3: is about unexpectedly losing money. In other words, when you're

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Speaker 3: evaluating a situation, you should know what is your reason

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Speaker 3: worst case downside. Now there's always the black swan that

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Speaker 3: maybe you can't figure on, but you should. But risk

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Speaker 3: management is always about understanding what could go wrong and

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Speaker 3: quantifying what could go wrong.

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Speaker 2: So I want to unpack what you just said, because

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Speaker 2: it's filled with goodness. First, you're referring to your approach

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Speaker 2: is Hey, we're really more process focused than outcome focused,

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Speaker 2: because if you have a good process, even if you

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Speaker 2: get a bad outcome, it doesn't matter. Probabilities will eventually

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Speaker 2: work in your fay.

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Speaker 3: Exactly right.

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Speaker 2: That's number one. But then the part two, which I

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Speaker 2: think a lot of investors overlook, is and a risk

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Speaker 2: management component that if the worst case happens, we still

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Speaker 2: survive and lift to trade another.

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Speaker 3: That's right, exactly right. And so at Hudson Bay, I

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Speaker 3: created the deal code system, uh.

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Speaker 2: Deal code system.

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Speaker 3: Yes, so at the time, well, I left the floor

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Speaker 3: beginning of ninety five and started deploying just the money

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Speaker 3: I'd earned on the floor in an off floor trading account.

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Speaker 3: And I would develop a strategy and hire someone else

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Speaker 3: to run it and develop another strategy and hire someone

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Speaker 3: else to run it. And as I was having other

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Speaker 3: people manage basically my trading account, I realized I had

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Speaker 3: to scale my risk profile that I developed on the

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Speaker 3: floor over multiple risk takers, and I needed to do

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Speaker 3: it in a manner that would produce persistent profitability. So

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Speaker 3: at the time, we were trading a lot of risk

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Speaker 3: garbitrage deals, so we called it a deal code, and

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Speaker 3: a deal code is just a numerical moniker that we

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Speaker 3: put on each trading idea within the book, and that

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Speaker 3: enables us to focus in on how is that trade hedged,

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Speaker 3: what's the risk riskiness? How much could that trade lose

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Speaker 3: in a reasonable worst case scenario, and it gives us

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Speaker 3: a batting average, so we can under stand is a

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Speaker 3: portfolio manager winning more ideas than they lose so to

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Speaker 3: be persistently profitable. I think it's not just about winning

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Speaker 3: more dollars than you lose. It's about winning more ideas

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Speaker 3: than you lose.

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Speaker 2: So let's talk a little bit about Hudson Bay's strategy.

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Speaker 2: You've been managing outside capital across a variety of asset

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Speaker 2: classes and strategies. Tell us talk about some of the

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Speaker 2: key strategies and what has been the drivers of making

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Speaker 2: those strategies successful. Well.

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Speaker 3: As I mentioned, I wanted to be able to make

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Speaker 3: money in all market environments, so you need a tool

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Speaker 3: set to do that. So our strategies are equity long

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Speaker 3: short converts, credit, event merger, volatility trading.

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Speaker 2: This isn't just I'm going to buy the S and

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Speaker 2: P five hundred and put it away for a decade.

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Speaker 2: You're active traders, and you're really looking to take advantage

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Speaker 2: of situations where you have a fairly good idea of

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Speaker 2: what the outcome is going to look like. It's not hey,

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Speaker 2: this is open ended. Usually you're pretty confident in here's

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Speaker 2: what our range of potential outcomes are.

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Speaker 3: I think that, especially in today's world, you have to

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Speaker 3: understand what your edge is versus the machines. And a

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Speaker 3: machine can calculate risk based on historical precedent, but a

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Speaker 3: machine cannot calculate risk based upon some kind of uncertainty

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Speaker 3: due to some kind of event, callous or change that's

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Speaker 3: coming up because it's new. So the machine doesn't have

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Speaker 3: the ability to calibrate for something that's new. And so

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Speaker 3: generally across all our strategies, that's what we're focused on,

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Speaker 3: is we're focused on event callous change. How can we

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Speaker 3: profit off of that in a way that machines cannot.

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Speaker 2: So that's the fundamental criticism of models. All models assume

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Speaker 2: that the world in the future is going to look

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Speaker 2: like the world in the past. Risk management is what

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Speaker 2: happens if the world doesn't look like out at.

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Speaker 3: Us precisely, And that's why we don't use the standard

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Speaker 3: risk management models. I actually created a statistic, the Gerber statistic,

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Speaker 3: that helps to understand diversification between our deal codes, between

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Speaker 3: our investment positions. A lot of our competitors are tied

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Speaker 3: to factor based modeling, which ultimately, underneath it is reliant

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Speaker 3: on regression analysis. Regressions. Our straight line fits through normalized

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Speaker 3: sets of data, and human relationships don't file straight lines,

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Speaker 3: and certainly market relationships don't file straight lines. So using

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Speaker 3: that as the underpinning of a risk management system is

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Speaker 3: just incorrect. And so we've created a whole different structure.

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Speaker 3: As I said, we've used since nineteen ninety eight, and

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Speaker 3: I think that's given us the ability to weather storms

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Speaker 3: and profit from it in ways that our competitors can.

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Speaker 2: So let's talk a little bit about the Gerber statistic.

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Speaker 2: You had this validated by Harry Markowitz, the creator of

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Speaker 2: modern portfolio folio theory. Tell us about that collaboration and

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Speaker 2: break down the Garberg statistic a little bit. How do

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Speaker 2: you guys actually use it?

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Speaker 3: So, because of my distrust of models, based upon my

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Speaker 3: experience on the floor, in particularly the guts of the models,

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Speaker 3: I never believed in the correlation statistic, that correlation is predictive,

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Speaker 3: and this was I thought one of the underpinnings of

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Speaker 3: modern portfolio theory that you look at the expected return

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Speaker 3: of the stock, the expected variants of the stock, and

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Speaker 3: the covariance of correlation between the different components of a portfolio.

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Speaker 3: And at the time, you know, we used the deal

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Speaker 3: code system and on Wall Street the banks were telling

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Speaker 3: me this is nonsense, but don't even talk about it

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Speaker 3: with investors. And then in eight when everyone lost money

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Speaker 3: and we made money, I realized we were doing something different.

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Speaker 3: And then I had the idea. Because of course I'd

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Speaker 3: studied about Harry in modern portfolio theory. Everyone in finance has.

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Speaker 3: He won the Nobel Prize. I decided, you know what,

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Speaker 3: I'm going to go out to see him to see

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Speaker 3: what he thinks about the Gerber statistic, and at the

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Speaker 3: time it wasn't called the Gerber statistic. But a friend

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Speaker 3: of mine said, gee, you really should file a patent

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Speaker 3: on this before you see Harry, and so I did,

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Speaker 3: and I had to name it something, so I called

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Speaker 3: it the Gerber statistic. And we now have I think

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Speaker 3: we just got our sixth patent on our process for diversification.

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Speaker 3: So I got to see Harry in San Diego. Lovely guy.

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Speaker 3: He welcomed me, and we're walking. He liked to walk

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Speaker 3: along the beach and I said, Harry, you know, I

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Speaker 3: don't think that correlation's predictive, and Harry said, you're right.

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Speaker 3: I said, no, no, Harry, you don't understand it. I don't

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Speaker 3: think that because it's one of the base foundational bases

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Speaker 3: for what She won the Nobel Prize in Modern portfolio theory.

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Speaker 3: Said Harry, I don't think that historical correlation has relevance

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Speaker 3: to the future. And he said, you're right. And it

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Speaker 3: turns out that in his nineteen fifty two paper that

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Speaker 3: sets forth modern portfolio theory, he said that correlation should

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Speaker 3: be determined by the judgment of practical men. In other words,

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Speaker 3: the stock analysts should think what will be the relationship

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Speaker 3: going forward, not to mind the past, but be forward looking.

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Speaker 3: But in the nineteen sixties, as computing power increase, people said, oh,

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Speaker 3: we can mind the statistic, this row statistic correlation, and

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Speaker 3: then we can plug it into the model as correlation.

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Speaker 3: He meant correlation in a semantic sense, not in a

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Speaker 3: mathematical sense in terms of using in his model. So

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Speaker 3: he actually said that the deal code system uses his system,

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Speaker 3: the modern portfolio theory system. He said that there's three

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Speaker 3: legs to his system. And so because we use limited loss,

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Speaker 3: because we seek to diversification through hesing on the own,

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Speaker 3: because we seek to win more than we lose in

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Speaker 3: each investment idea, he said that is accordance with his system.

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Speaker 3: But in any way, we we've written several papers together

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Speaker 3: on the Gerber statistic within modern portfolio theory and have

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Speaker 3: demonstrated that you get better performance with less risk by

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Speaker 3: replacing historical covariance with the Gerber statistic. And Harry and

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Speaker 3: I actually we only had really one disagreement, and the

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Speaker 3: one disagreement was on factors. There's all these you know,

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Speaker 3: factor methodologies, and Harry believed that only one factor matters

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Speaker 3: for portfolios, and I think two factors matter, so and

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Speaker 3: so that, but the other twenty three factors we both

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Speaker 3: agree are complete nonsets.

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Speaker 2: So if you look at the fomb of French model,

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Speaker 2: which started out as two or three factors and then

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Speaker 2: became five fact.

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Speaker 3: And then grow and grow. If you speak to the

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Speaker 3: research departments of bar Axioma, they'll tell you that thirty

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Speaker 3: four to forty percent of a stock price movement can

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Speaker 3: be explained by factors.

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Speaker 2: Okay, so that's third, let's.

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Speaker 3: Roll it a and of that third, eighty five percent

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Speaker 3: of that third can be explained by the first five factors, okay,

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Speaker 3: which means giving credit to five, which that's bar Naxioma

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Speaker 3: tells you eighty five percent of the forty percent can

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Speaker 3: be explained by five factors, which means the other twenty

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Speaker 3: factors explain the fifteen percent of forty percent of the words.

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Speaker 3: Six percent of a stock price movement can be explained

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Speaker 3: by twenty one factors, right, meaning which is complete. You know, nonsense,

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Speaker 3: but no, if you lever a portfolio up, you know

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Speaker 3: ten times, all of a sudden, that six percent looks

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Speaker 3: like it's sixty percent, but it's all complete nonsense. It's

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Speaker 3: numerical mumbo jumbo. It's part of the whole Wall Street

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Speaker 3: pizazz that is not based on reality. But you know

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Speaker 3: it sells.

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Speaker 2: So so I want to guess the two factors. If

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Speaker 2: I had a guess, I'm going to rely on a

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Speaker 2: paper by Wes Gray of Alpha Architect and guess it's

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Speaker 2: value and momentum. But I'm curious what you feel.

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Speaker 3: Well, actually, Harry thought it was market. I think his

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Speaker 3: market and section.

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Speaker 2: So is market and sector. But are those really factors?

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Speaker 2: Do we really The.

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Speaker 3: Whole idea of factors is kind of like, you know,

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Speaker 3: a little nonsense. It's like beta, you know, like market

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Speaker 3: we think of as beta. It's now been called a factor.

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Speaker 2: So oh, I never really thought of beta as a factor.

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Speaker 2: It's just it's, hey, if you do nothing, you get

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Speaker 2: But that's market, you know, So huh, that's really it.

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Speaker 2: So you're looking at the sector it's in and the

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Speaker 2: overall market as the two driving facts.

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Speaker 3: I think those are Now it's true that momentum, value,

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Speaker 3: these other things are relevant today because everyone else has

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Speaker 3: glommed onto it, because we have so many statistical, process

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Speaker 3: driven strategies that try to trade momentum. You know, buy cheap,

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Speaker 3: sell expensive. It pushes everything in line. And this is

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Speaker 3: what I found on the floor using models to trade options,

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Speaker 3: that the models would push the the values of the

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Speaker 3: options into alignment in accordance with the model because everyone's

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Speaker 3: using the same model, and so the same thing is

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Speaker 3: true in the broader market because everyone's using basically the

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Speaker 3: same factor models. It pushes things in alignment, which works

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Speaker 3: in normal market environments. But when things you know, have

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Speaker 3: a dislocation, it no longer works, which is why people say, oh,

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Speaker 3: our risk model broke down or whatever, because these aren't

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Speaker 3: really risk models.

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Speaker 1: Now.

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Speaker 3: It's one thing to use a model to trade because

436
00:25:31,240 --> 00:25:34,400
Speaker 3: the model's telling you something is some expensive or cheap.

437
00:25:34,359 --> 00:25:36,600
Speaker 2: And relative to history.

438
00:25:36,400 --> 00:25:39,119
Speaker 3: Right, And if something's always cheap, you just adjust the model.

439
00:25:39,960 --> 00:25:42,600
Speaker 3: So there's a validity to that. But that's different than

440
00:25:42,720 --> 00:25:46,120
Speaker 3: using the same model for risk management. Risk management, again,

441
00:25:46,240 --> 00:25:48,680
Speaker 3: is about avoiding unexpected loss.

442
00:25:49,040 --> 00:25:53,560
Speaker 2: Huh. That's really interesting. So when I started on a

443
00:25:53,640 --> 00:25:56,440
Speaker 2: trading desk, one of the things that I was always taught,

444
00:25:56,640 --> 00:26:02,880
Speaker 2: which I never contextualized as a factor, is, hey, what's

445
00:26:02,960 --> 00:26:06,000
Speaker 2: driving the stock? Well, the stock is only a tiny

446
00:26:06,000 --> 00:26:09,920
Speaker 2: part of it. The stock is twenty percent, the sector

447
00:26:10,240 --> 00:26:13,680
Speaker 2: is thirty percent, and half is the market. So you

448
00:26:13,720 --> 00:26:15,679
Speaker 2: could be the greatest stock in the world. If the

449
00:26:15,720 --> 00:26:18,359
Speaker 2: market's going down, it doesn't matter, and it could be

450
00:26:18,359 --> 00:26:21,440
Speaker 2: a really good stock. But if it's in a terrible sector.

451
00:26:21,960 --> 00:26:25,359
Speaker 2: You know, the metaphor was always great house in a

452
00:26:25,359 --> 00:26:29,840
Speaker 2: crappy neighborhood is a crappy house. You're really putting that

453
00:26:29,920 --> 00:26:32,679
Speaker 2: into the context of these are the broader factors that

454
00:26:32,720 --> 00:26:34,200
Speaker 2: are affecting that single holding.

455
00:26:34,280 --> 00:26:37,159
Speaker 3: That's right, that's right. And you know, in our at

456
00:26:37,240 --> 00:26:41,600
Speaker 3: Hudson Bay, we seek to produce the alpha. So it's

457
00:26:41,720 --> 00:26:44,639
Speaker 3: true that the market is moving the stock, but we

458
00:26:44,720 --> 00:26:48,160
Speaker 3: try to pick stocks that outperform the market or pick

459
00:26:48,240 --> 00:26:50,560
Speaker 3: shorts that will go down more than the market. So

460
00:26:51,200 --> 00:26:54,240
Speaker 3: we seek to focus on the alpha provision.

461
00:26:54,480 --> 00:26:58,800
Speaker 2: So let's talk about something related to this. A paper

462
00:26:58,840 --> 00:27:02,760
Speaker 2: you published, environment eats culture for lunch. It sounds like

463
00:27:02,800 --> 00:27:06,520
Speaker 2: the environment is what the market's doing with the sector is,

464
00:27:06,600 --> 00:27:08,720
Speaker 2: but give us a little detail about.

465
00:27:08,600 --> 00:27:12,480
Speaker 3: Well, actually, I mean that that paper was related to

466
00:27:12,520 --> 00:27:17,240
Speaker 3: the human aspect, not the market. So Peter Drucker came

467
00:27:17,280 --> 00:27:20,160
Speaker 3: up with this idea that culture eats strategy for breakfast

468
00:27:20,880 --> 00:27:26,800
Speaker 3: that corporate culture is actually more important than corporate strategy

469
00:27:26,880 --> 00:27:29,000
Speaker 3: for the success of a firm. I think there's a

470
00:27:29,000 --> 00:27:33,359
Speaker 3: lot to that that, you know, the way people work

471
00:27:33,480 --> 00:27:36,639
Speaker 3: together in an organization. But I've always thought that this

472
00:27:36,720 --> 00:27:39,440
Speaker 3: corporate culture thing is nonsense. If you have people try

473
00:27:39,480 --> 00:27:42,520
Speaker 3: to describe their corporate culture, they cannot articulate it, right,

474
00:27:42,920 --> 00:27:45,440
Speaker 3: you know, like what's the corporate culture here at Bloomberg,

475
00:27:45,880 --> 00:27:46,360
Speaker 3: you know, like.

476
00:27:47,000 --> 00:27:50,359
Speaker 2: Fun data driven? It's all about data, So you come up.

477
00:27:50,240 --> 00:27:52,800
Speaker 3: On data driven. It's not a culture. Data driven is

478
00:27:52,840 --> 00:27:55,480
Speaker 3: a process. But I'm talking about what's the human aspect

479
00:27:55,480 --> 00:27:57,520
Speaker 3: of it? What's what's the human culture.

480
00:27:57,920 --> 00:27:59,760
Speaker 2: I'm the wrong person to ask that, right.

481
00:27:59,640 --> 00:28:02,960
Speaker 3: Because because no one can really describe corporate culture, what

482
00:28:03,040 --> 00:28:05,840
Speaker 3: you can describe as an environment. What is the environment

483
00:28:06,000 --> 00:28:09,679
Speaker 3: that people work within? And I kind of learned this

484
00:28:09,720 --> 00:28:13,040
Speaker 3: at Band and Company because Baine was described as this

485
00:28:13,200 --> 00:28:16,560
Speaker 3: like fun loving place, everyone has fun. And then when

486
00:28:16,560 --> 00:28:19,080
Speaker 3: I was there, two guys died on the locker bee

487
00:28:19,119 --> 00:28:21,800
Speaker 3: crash and Bill Bayn had milked the esop and so

488
00:28:21,920 --> 00:28:24,879
Speaker 3: the company almost collapsed. When I was there, they fired

489
00:28:24,920 --> 00:28:27,639
Speaker 3: half of my class, not me, They fired all the

490
00:28:27,680 --> 00:28:32,200
Speaker 3: incoming MBAs and it was the avarice of Bill Bain

491
00:28:32,400 --> 00:28:36,280
Speaker 3: that nearly collapsed the firm. We're talking back in nineteen

492
00:28:37,160 --> 00:28:38,440
Speaker 3: eighty nine to ninety.

493
00:28:38,280 --> 00:28:42,080
Speaker 2: So the corporate culture was with pacious greed, well did

494
00:28:42,760 --> 00:28:44,080
Speaker 2: you know, and almost destroy.

495
00:28:44,160 --> 00:28:48,360
Speaker 3: It was inauthentic. And when people try to describe culture,

496
00:28:48,560 --> 00:28:50,640
Speaker 3: they can't. And so what I wanted to do was

497
00:28:50,680 --> 00:28:53,680
Speaker 3: to describe an environment. What is the environment that you

498
00:28:53,720 --> 00:28:56,880
Speaker 3: want to work within? And you know when you speak

499
00:28:56,920 --> 00:29:01,120
Speaker 3: to when you speak to people on other firms, what's

500
00:29:01,160 --> 00:29:04,560
Speaker 3: your corporate culture? What's your value statements? Usually these things

501
00:29:04,600 --> 00:29:06,360
Speaker 3: go on and on and on. No one can really

502
00:29:06,480 --> 00:29:09,719
Speaker 3: remember all the value statement. If you can't remember your

503
00:29:09,800 --> 00:29:11,640
Speaker 3: value statement, it has no value.

504
00:29:12,440 --> 00:29:16,400
Speaker 2: I'm going to imagine that twenty two twenty three when

505
00:29:16,400 --> 00:29:19,520
Speaker 2: all the big firms were saying, we want our employees

506
00:29:19,560 --> 00:29:22,840
Speaker 2: back in the office. We don't want any more remote work.

507
00:29:23,160 --> 00:29:27,480
Speaker 2: It's a matter of corporate culture. How did you think

508
00:29:27,520 --> 00:29:33,040
Speaker 2: about that? Was this a legitimate demand and is it

509
00:29:33,200 --> 00:29:35,920
Speaker 2: not so much corporate culture? But we want an environment

510
00:29:35,920 --> 00:29:38,960
Speaker 2: where people are in the office working together. Is that legit?

511
00:29:39,400 --> 00:29:43,440
Speaker 3: I hate going to the office and seeing people not there.

512
00:29:43,560 --> 00:29:46,080
Speaker 3: I think that people should work together. On the other hand,

513
00:29:46,400 --> 00:29:50,600
Speaker 3: You can't force these things. You can't force independent thinking,

514
00:29:51,280 --> 00:29:54,800
Speaker 3: you can't force collaboration. You can have an environment that

515
00:29:54,840 --> 00:29:57,440
Speaker 3: engenders it, and so we try to have an environment

516
00:29:57,440 --> 00:30:01,240
Speaker 3: that engenders it. So it's my opinion that people who

517
00:30:01,280 --> 00:30:03,760
Speaker 3: come to the office are going to succeed more than

518
00:30:03,800 --> 00:30:06,720
Speaker 3: people who don't. Now, I understand that, you know, the

519
00:30:06,800 --> 00:30:11,280
Speaker 3: commute is a hassle and sometimes people, you know, want

520
00:30:11,320 --> 00:30:13,840
Speaker 3: to take the day off, and so you know, our

521
00:30:13,880 --> 00:30:17,360
Speaker 3: standard is two days in the office. Many teams have

522
00:30:17,440 --> 00:30:20,480
Speaker 3: a third day, but a lot of people. Usually people

523
00:30:20,520 --> 00:30:22,360
Speaker 3: are in our office three to five days a week.

524
00:30:22,400 --> 00:30:24,600
Speaker 3: But we don't force it. If once you force people

525
00:30:24,600 --> 00:30:26,680
Speaker 3: to be in the office, I think you're losing this

526
00:30:26,800 --> 00:30:29,440
Speaker 3: spree de corps. We want people to want to work

527
00:30:29,480 --> 00:30:31,360
Speaker 3: at Hudson Bay. If they don't want to work at

528
00:30:31,400 --> 00:30:34,320
Speaker 3: Hudson Bay, they should go elsewhere. But to force people,

529
00:30:34,800 --> 00:30:39,240
Speaker 3: I think, you know, for high performers, I don't think

530
00:30:39,360 --> 00:30:42,200
Speaker 3: that's the way to engender the right environment.

531
00:30:42,560 --> 00:30:46,560
Speaker 2: And environment beats culture for work because the work environment

532
00:30:47,280 --> 00:30:50,840
Speaker 2: is more important than some statement that nobody remembers. Correct.

533
00:30:51,200 --> 00:30:53,760
Speaker 2: So you guys have let's talk a little bit about

534
00:30:53,800 --> 00:30:57,840
Speaker 2: independent thought. You guys have done pretty well. When the

535
00:30:57,920 --> 00:31:01,920
Speaker 2: expert's wrong. You throw five, seven, eight, and nine. You

536
00:31:01,960 --> 00:31:05,880
Speaker 2: were notably up in years where most people were down.

537
00:31:06,520 --> 00:31:09,400
Speaker 2: Again in Q one of twenty twenty, you guys did

538
00:31:09,440 --> 00:31:13,880
Speaker 2: really well all periods of big market turmoil. I don't

539
00:31:13,920 --> 00:31:16,480
Speaker 2: know what you were doing in two thousand and one two,

540
00:31:16,680 --> 00:31:22,560
Speaker 2: but I'm imagining the same approach held true. How do

541
00:31:22,640 --> 00:31:27,920
Speaker 2: you think about these periods? Are they truly black swans

542
00:31:28,000 --> 00:31:30,960
Speaker 2: or are they things that, with the right approach to

543
00:31:31,040 --> 00:31:33,600
Speaker 2: risk management, are create opportunities.

544
00:31:34,560 --> 00:31:39,720
Speaker 3: Again, people are trying to assess risk based upon some

545
00:31:39,840 --> 00:31:45,760
Speaker 3: kind of parametric distribution with you know, standard deviation movements,

546
00:31:45,840 --> 00:31:47,920
Speaker 3: and I think that's just nonsense. The markets don't work

547
00:31:48,000 --> 00:31:52,800
Speaker 3: like that. So our system enables us to weather all

548
00:31:52,960 --> 00:31:58,160
Speaker 3: market environments through the deal code system by ignoring those

549
00:31:58,200 --> 00:32:02,360
Speaker 3: parametric The Gerber statistic, which is the basis for the

550
00:32:02,440 --> 00:32:08,560
Speaker 3: work with Harry, is a rank order statistic because it

551
00:32:08,600 --> 00:32:14,840
Speaker 3: recognizes the failures of parametric normal distributions. And what we

552
00:32:14,880 --> 00:32:17,160
Speaker 3: do is we set a threshold because a lot of

553
00:32:17,240 --> 00:32:19,800
Speaker 3: data is noise in the markets. If the S and

554
00:32:19,840 --> 00:32:22,560
Speaker 3: P moves by ten basis points, it doesn't communicate to

555
00:32:22,640 --> 00:32:25,560
Speaker 3: you how the S and P affects other things. Yet,

556
00:32:25,560 --> 00:32:28,400
Speaker 3: and all these statistical models, they're including every single data

557
00:32:28,440 --> 00:32:32,320
Speaker 3: point because if you don't include every single data point,

558
00:32:32,360 --> 00:32:34,440
Speaker 3: then in the matrix math you have a divide by

559
00:32:34,560 --> 00:32:40,240
Speaker 3: zero issue. So they're forced in all these correlation statistics,

560
00:32:40,280 --> 00:32:44,400
Speaker 3: these regression analyses to include every single data point. With

561
00:32:44,440 --> 00:32:47,920
Speaker 3: the Gerba statistic, we are able to create thresholds where

562
00:32:47,920 --> 00:32:51,840
Speaker 3: we ignore data below a certain degree of movement, and

563
00:32:51,880 --> 00:32:55,800
Speaker 3: so that enables us to focus on Everyone wants meaningful relationships, right,

564
00:32:55,840 --> 00:32:58,719
Speaker 3: So this is how we're able to focus on meaningful

565
00:32:58,840 --> 00:33:00,000
Speaker 3: relationships within the market.

566
00:33:01,080 --> 00:33:03,520
Speaker 2: You know, we talked a little bit about sub prime

567
00:33:03,560 --> 00:33:06,960
Speaker 2: real estate and how the models it wasn't even that

568
00:33:07,000 --> 00:33:10,200
Speaker 2: they broke. They were so poorly constructed they were destined

569
00:33:10,240 --> 00:33:13,120
Speaker 2: to fail. You know, if you build a house really poorly,

570
00:33:13,200 --> 00:33:15,920
Speaker 2: you don't need an earthquake. Eventually, it's just going to

571
00:33:15,960 --> 00:33:18,720
Speaker 2: collapse under its own weight. But I have to ask

572
00:33:18,760 --> 00:33:22,600
Speaker 2: you some questions about real estate because Hudson Bay has

573
00:33:22,640 --> 00:33:27,080
Speaker 2: been increasingly invested in private credit and real estate. You've

574
00:33:27,080 --> 00:33:30,440
Speaker 2: done a number of major refinancings in and around New

575
00:33:30,480 --> 00:33:35,360
Speaker 2: York City. Six twenty Avenue of the America's is tell

576
00:33:35,440 --> 00:33:37,000
Speaker 2: Us a little bit about the work you're doing at

577
00:33:37,040 --> 00:33:39,760
Speaker 2: Hudson Bay with private credit and real estate.

578
00:33:40,080 --> 00:33:47,479
Speaker 3: Well, we saw beginning with the the transitory higher rates,

579
00:33:47,720 --> 00:33:50,360
Speaker 3: which we thought was nonsense, right. We saw that rates

580
00:33:50,360 --> 00:33:55,160
Speaker 3: were going to be higher for longer, and we had

581
00:33:55,200 --> 00:33:57,800
Speaker 3: believed that the market had been anchored in this idea

582
00:33:57,840 --> 00:34:01,960
Speaker 3: of ultra low rates, which was really a manipulation of

583
00:34:02,000 --> 00:34:05,480
Speaker 3: the monetary system. So we started thinking about what's the

584
00:34:05,480 --> 00:34:09,880
Speaker 3: implications of that, and came to the notion that the

585
00:34:09,920 --> 00:34:13,880
Speaker 3: banking system would be under stress. And what's the implication

586
00:34:13,920 --> 00:34:16,359
Speaker 3: of the banking system under stress. Well, that means that

587
00:34:16,480 --> 00:34:20,440
Speaker 3: they can't extend loans in the same way, you know,

588
00:34:20,600 --> 00:34:24,480
Speaker 3: corporate as well as real estate. So we started staffing

589
00:34:24,560 --> 00:34:27,919
Speaker 3: up in those areas to take advantage. And now I'm

590
00:34:27,960 --> 00:34:31,319
Speaker 3: convinced that the there's now going to be a structural

591
00:34:31,360 --> 00:34:34,080
Speaker 3: shift in credit provision in the US economy, that the

592
00:34:34,120 --> 00:34:38,600
Speaker 3: banks are no longer going to be the mainstay for credit.

593
00:34:38,840 --> 00:34:44,040
Speaker 3: And that's because the government has effectively guaranteed our banking system,

594
00:34:44,360 --> 00:34:48,000
Speaker 3: which creates moral hazard. We have on the order of,

595
00:34:48,120 --> 00:34:50,320
Speaker 3: you know, forty three hundred banks in the United States.

596
00:34:51,800 --> 00:34:54,520
Speaker 3: It's a lot, especially when you compare it to Canada

597
00:34:54,560 --> 00:34:58,680
Speaker 3: that's got the big you know, handful, and you know

598
00:34:58,680 --> 00:35:02,120
Speaker 3: when you deposit money in the bank, that bank is

599
00:35:02,200 --> 00:35:03,200
Speaker 3: lending it out long.

600
00:35:04,200 --> 00:35:08,600
Speaker 2: And fractionally reserving it. So it's ten to one, whatever

601
00:35:08,640 --> 00:35:10,239
Speaker 2: the precise the leverage there using.

602
00:35:10,560 --> 00:35:14,960
Speaker 3: So I think that the whole fractional banking system notion

603
00:35:15,480 --> 00:35:19,319
Speaker 3: is challenged, particularly in the idea of the ease of

604
00:35:20,120 --> 00:35:26,360
Speaker 3: information transparency among depositors coupled with the necessity for government

605
00:35:26,400 --> 00:35:30,560
Speaker 3: guarantee and moral hazards. So private credit firms like ours

606
00:35:31,040 --> 00:35:34,400
Speaker 3: people invest in Hudson Bay and they know it's not

607
00:35:34,440 --> 00:35:38,320
Speaker 3: a bank account, and that gives us license to deploy

608
00:35:38,360 --> 00:35:42,480
Speaker 3: the money in ways that are appropriate, and so we

609
00:35:42,600 --> 00:35:45,359
Speaker 3: began staffing up in those areas. And now in real estate,

610
00:35:45,400 --> 00:35:49,040
Speaker 3: for instance, we have teams that work in real estate equity,

611
00:35:49,400 --> 00:35:56,280
Speaker 3: in CMBs, distress CMBs, and direct provision of real estate credit.

612
00:35:57,000 --> 00:36:00,799
Speaker 3: And as part of the core vet you've Hudson Bay.

613
00:36:00,880 --> 00:36:03,960
Speaker 3: These teams work together, which give us a better understanding.

614
00:36:04,400 --> 00:36:07,000
Speaker 3: It's a great advantage to have equity teams working with

615
00:36:07,080 --> 00:36:11,279
Speaker 3: credit teams, particularly all real estate's local It gives us

616
00:36:11,320 --> 00:36:16,399
Speaker 3: a much better understanding of the asset that we're looking at.

617
00:36:16,680 --> 00:36:19,399
Speaker 2: Huh, that's really kind of interesting. You know, Ever since

618
00:36:19,480 --> 00:36:23,600
Speaker 2: the financial crisis, some of the new regulations and bank

619
00:36:23,640 --> 00:36:30,520
Speaker 2: regulations directly led to the rise of private equity, private credit.

620
00:36:31,000 --> 00:36:33,680
Speaker 2: You know, some of the forecasts are over the next decade,

621
00:36:33,760 --> 00:36:37,720
Speaker 2: this blows up to a thirteen trillion dollar asset class.

622
00:36:37,719 --> 00:36:40,759
Speaker 3: I think we're in the third inning, early early days here, Yeah,

623
00:36:40,760 --> 00:36:41,120
Speaker 3: I think so.

624
00:36:41,280 --> 00:36:44,880
Speaker 2: And it it feels like it's been so big because

625
00:36:45,239 --> 00:36:48,440
Speaker 2: we started with practically nothing in that space, and the

626
00:36:48,480 --> 00:36:51,200
Speaker 2: first couple of trillion dollars felt like, oh, my goodness,

627
00:36:51,360 --> 00:36:55,719
Speaker 2: is just so much capital washing over this. But this

628
00:36:55,760 --> 00:37:00,600
Speaker 2: seems to have happened in the past where woll banks

629
00:37:00,640 --> 00:37:02,920
Speaker 2: and brokers kind of move up market, they create a

630
00:37:03,000 --> 00:37:08,240
Speaker 2: void in the space they left, and private money rushes

631
00:37:08,280 --> 00:37:11,000
Speaker 2: in to fill that void. Is that what's going on

632
00:37:11,080 --> 00:37:12,720
Speaker 2: with private credit and real estate?

633
00:37:14,360 --> 00:37:16,759
Speaker 3: Well, it's still early in that. I think it's a

634
00:37:16,800 --> 00:37:19,880
Speaker 3: golden age for real estate credit. The banks are not

635
00:37:20,040 --> 00:37:23,080
Speaker 3: able to they don't have the capital now to lend,

636
00:37:24,920 --> 00:37:27,480
Speaker 3: and so there's it's open season.

637
00:37:27,800 --> 00:37:31,399
Speaker 2: Huh. Really really interesting. So how do you identify opportunities

638
00:37:31,440 --> 00:37:34,400
Speaker 2: in the real estate space. It seems like there are

639
00:37:34,440 --> 00:37:39,319
Speaker 2: so many buildings that are half empty, and yet it's

640
00:37:39,360 --> 00:37:43,400
Speaker 2: a slow motion train wreck because most of their tenants

641
00:37:43,440 --> 00:37:49,640
Speaker 2: have ten or longer year leases and they're just slowly

642
00:37:49,800 --> 00:37:54,440
Speaker 2: starting to recognize unless you're a super A class building,

643
00:37:54,840 --> 00:37:59,040
Speaker 2: even A buildings are having a hard time attracting renewals

644
00:37:59,040 --> 00:38:02,560
Speaker 2: and tenants. How you identify these and how far along

645
00:38:02,719 --> 00:38:07,040
Speaker 2: the repricing of commercial real estate or at least offices

646
00:38:08,239 --> 00:38:09,040
Speaker 2: do you think we are?

647
00:38:09,760 --> 00:38:12,880
Speaker 3: Well, those are big questions. And I'm from Annaburg, Michigan,

648
00:38:13,120 --> 00:38:15,799
Speaker 3: and I saw how in Detroit, Detroit was going to

649
00:38:15,800 --> 00:38:21,920
Speaker 3: be called the museum to the desolate city because downtown

650
00:38:22,000 --> 00:38:25,720
Speaker 3: Detroit went empty when they built the Renaissance Center. Everyone

651
00:38:25,760 --> 00:38:29,120
Speaker 3: moved to the Renaissance Center and left these empty, huge

652
00:38:29,120 --> 00:38:32,799
Speaker 3: buildings in Detroit. And you see aspects of that now

653
00:38:32,920 --> 00:38:36,440
Speaker 3: where the A buildings, the new buildings are attracting very

654
00:38:36,480 --> 00:38:42,080
Speaker 3: high rents and buildings in other areas are you going empty?

655
00:38:43,200 --> 00:38:46,040
Speaker 3: So to understand what's going on, you really have to

656
00:38:46,120 --> 00:38:48,400
Speaker 3: understand the asset, and so that's why it's important to

657
00:38:48,400 --> 00:38:54,480
Speaker 3: have teams from different disciplines being able to understand the asset. Obviously,

658
00:38:54,560 --> 00:38:58,239
Speaker 3: looking through the rent rolls and understanding you know, the

659
00:38:58,280 --> 00:39:03,960
Speaker 3: weight to average lease, but also understanding the macro environment.

660
00:39:04,040 --> 00:39:05,719
Speaker 3: You know, are things growing And we have so much

661
00:39:05,800 --> 00:39:09,920
Speaker 3: uncertainty now going on, not just because of work from

662
00:39:09,920 --> 00:39:14,200
Speaker 3: home with Zoom, but also the longer term implications of

663
00:39:14,239 --> 00:39:16,880
Speaker 3: AI and what's that going to mean for the workforce

664
00:39:16,920 --> 00:39:20,799
Speaker 3: and even cities like New York City. It's possible that

665
00:39:20,800 --> 00:39:23,680
Speaker 3: we're not going to need the same number of junior lawyers,

666
00:39:23,760 --> 00:39:25,719
Speaker 3: junior accountants, junior bankers.

667
00:39:26,440 --> 00:39:30,359
Speaker 2: So I've heard some people discuss AI as a tool,

668
00:39:30,640 --> 00:39:32,720
Speaker 2: and it's not that you're going to lose your job

669
00:39:32,840 --> 00:39:36,560
Speaker 2: to AI, but you're more likely to lose your job

670
00:39:36,680 --> 00:39:40,880
Speaker 2: to someone working with AI. Is that a fair assessment

671
00:39:41,040 --> 00:39:42,799
Speaker 2: or is it just still way too early to take.

672
00:39:42,880 --> 00:39:45,040
Speaker 3: I think we still don't know. I think AI is

673
00:39:45,040 --> 00:39:48,840
Speaker 3: the greatest change in my lifetime, bigger than the Internet.

674
00:39:48,960 --> 00:39:53,279
Speaker 3: I think so, yeah, really yeah, because the ability for

675
00:39:53,400 --> 00:39:57,640
Speaker 3: natural language processing goes far beyond what I thought was possible.

676
00:39:57,840 --> 00:40:00,400
Speaker 3: You know, I studied linguistics a bit in college. The

677
00:40:00,440 --> 00:40:05,400
Speaker 3: whole idea of how we form language is a fascinating subject.

678
00:40:05,480 --> 00:40:07,840
Speaker 3: And now the computer is able to be coachent in

679
00:40:07,920 --> 00:40:14,280
Speaker 3: their responses, We've you know, kind of approaching hard AI

680
00:40:14,400 --> 00:40:17,280
Speaker 3: in a way that I did not think was possible,

681
00:40:17,280 --> 00:40:18,520
Speaker 3: and it's only going to get better.

682
00:40:19,040 --> 00:40:21,440
Speaker 2: Let me push back a little bit. And I'm not

683
00:40:21,480 --> 00:40:28,319
Speaker 2: necessarily saying I believe this, but so I've had this

684
00:40:28,400 --> 00:40:31,360
Speaker 2: conversation over and over again with a number of different people.

685
00:40:31,440 --> 00:40:34,279
Speaker 2: How are you using AI in your daily work? What

686
00:40:34,680 --> 00:40:40,839
Speaker 2: are you finding? And someone who hosts a different podcast said,

687
00:40:40,840 --> 00:40:47,080
Speaker 2: they created this really interesting set of prompts with AI

688
00:40:47,800 --> 00:40:50,440
Speaker 2: to get an answer to how to do certain things,

689
00:40:50,880 --> 00:40:54,080
Speaker 2: and the first time they got the answer, they were

690
00:40:54,120 --> 00:40:56,960
Speaker 2: really impressed. Oh my god, this is a genius insight,

691
00:40:57,440 --> 00:41:00,239
Speaker 2: and look how smart this is and how it it

692
00:41:00,280 --> 00:41:03,720
Speaker 2: figured out exactly what I needed. And then they asked

693
00:41:03,760 --> 00:41:07,680
Speaker 2: a different question with a different subject kind of got

694
00:41:07,719 --> 00:41:10,600
Speaker 2: the same answer, and it was like, oh, this is

695
00:41:10,680 --> 00:41:16,720
Speaker 2: a party trick. This isn't really intelligence. It just looks

696
00:41:16,800 --> 00:41:20,880
Speaker 2: like intelligence, and even though it's getting better, it's still

697
00:41:21,000 --> 00:41:26,080
Speaker 2: kind of dumb relative to it impresses us. But once

698
00:41:26,080 --> 00:41:28,680
Speaker 2: you peer behind the curtain and see the wizard is

699
00:41:29,320 --> 00:41:32,920
Speaker 2: just a man, you figure out this is less what

700
00:41:33,000 --> 00:41:37,000
Speaker 2: it purports to be in more like a very useful,

701
00:41:37,320 --> 00:41:38,160
Speaker 2: clever trick.

702
00:41:38,400 --> 00:41:40,319
Speaker 3: I was thinking of a Wizard of Oz also while

703
00:41:40,320 --> 00:41:42,080
Speaker 3: you were while you were saying that, But I don't

704
00:41:42,120 --> 00:41:44,520
Speaker 3: think there's a guy behind the curtain that's giving the answers.

705
00:41:44,520 --> 00:41:48,000
Speaker 3: That's why I think that it helps with the junior

706
00:41:48,000 --> 00:41:52,280
Speaker 3: analysts that you have to check anyway, and it certainly

707
00:41:52,440 --> 00:41:56,279
Speaker 3: speeds up the research process in ways that were not

708
00:41:56,480 --> 00:41:59,160
Speaker 3: possible before, for sure, and it's only going to get better.

709
00:41:59,440 --> 00:42:02,319
Speaker 3: And it may makes mistakes, but the junior analyst makes

710
00:42:02,360 --> 00:42:05,960
Speaker 3: mistakes also. I mean, I've used it for things my

711
00:42:06,320 --> 00:42:08,399
Speaker 3: lawyers probably will hate me, but sometimes when I've had

712
00:42:08,440 --> 00:42:12,320
Speaker 3: a discussion with the lawyers on how to express something

713
00:42:12,360 --> 00:42:15,320
Speaker 3: in a document, to all ask AI the question. It

714
00:42:15,320 --> 00:42:17,719
Speaker 3: will give me a range of possibilities and enables me

715
00:42:17,800 --> 00:42:20,480
Speaker 3: then to be more on a level playing field with

716
00:42:20,520 --> 00:42:22,400
Speaker 3: my lawyers who have had a lot more experience than

717
00:42:22,440 --> 00:42:25,480
Speaker 3: I have. But it has enabled me to bring to

718
00:42:25,560 --> 00:42:28,440
Speaker 3: the discussion insights that we might not have thought of.

719
00:42:28,600 --> 00:42:32,320
Speaker 2: I'm glad you brought up the attorneys, because a judge

720
00:42:32,360 --> 00:42:37,320
Speaker 2: just sanctions a lawyer for using AI and in certain

721
00:42:37,360 --> 00:42:45,440
Speaker 2: of his answers, and this unfortunate tendency to hallucinate. I

722
00:42:45,440 --> 00:42:47,400
Speaker 2: don't think the problem was that he used AI to

723
00:42:47,440 --> 00:42:49,920
Speaker 2: help him in research. He didn't double check it, and

724
00:42:49,960 --> 00:42:52,480
Speaker 2: he failed to disclose that AI was plathiness.

725
00:42:52,640 --> 00:42:57,440
Speaker 3: You know, it's just plain laziness. The the AI is

726
00:42:57,480 --> 00:43:00,799
Speaker 3: good for the junior person, and I think as implications

727
00:43:00,800 --> 00:43:02,839
Speaker 3: for the workforce, you know, what is the workforce going

728
00:43:02,880 --> 00:43:07,600
Speaker 3: to look like? Given that, maybe we don't need the

729
00:43:07,640 --> 00:43:12,920
Speaker 3: same failans of junior accountants, junior lawyers, junior bankers.

730
00:43:12,960 --> 00:43:15,640
Speaker 2: How do you become a senior account lawyer, banker if

731
00:43:15,640 --> 00:43:18,239
Speaker 2: you're never a junior It's a tough question. So let

732
00:43:18,320 --> 00:43:23,600
Speaker 2: me give you an opportunity to update your twenty twenty

733
00:43:23,640 --> 00:43:28,440
Speaker 2: one piece in investing. Don't short human judgment? Do you?

734
00:43:28,640 --> 00:43:29,759
Speaker 2: Are you still holding that for you?

735
00:43:29,880 --> 00:43:33,520
Speaker 3: Absolutely? I mean we are in the human judgment business. Really,

736
00:43:34,280 --> 00:43:40,000
Speaker 3: we are trying to beat the machines. We do that,

737
00:43:40,160 --> 00:43:45,680
Speaker 3: as I said, through understanding uncertainty, events, catalysts, and change,

738
00:43:45,840 --> 00:43:49,880
Speaker 3: and I think ultimately human judgment is superior in the machines.

739
00:43:49,920 --> 00:43:52,160
Speaker 3: I hope we won't go into a Hell two thousand

740
00:43:52,239 --> 00:43:56,440
Speaker 3: type situation that human judgment will always be superior. You

741
00:43:56,440 --> 00:44:00,239
Speaker 3: wouldn't want to have a machine be the a in

742
00:44:00,239 --> 00:44:04,360
Speaker 3: the United States. How could a machine possibly make those decisions,

743
00:44:05,040 --> 00:44:08,279
Speaker 3: you know. So obviously human judgment will always be there,

744
00:44:08,480 --> 00:44:11,560
Speaker 3: and I don't think that we're at a terminator type,

745
00:44:11,719 --> 00:44:14,919
Speaker 3: you know, situation, but there are certain experts that say

746
00:44:14,920 --> 00:44:17,400
Speaker 3: that ultimately that's where we'll go. I mean, I do

747
00:44:17,520 --> 00:44:20,160
Speaker 3: know that in the military, you know, the idea of

748
00:44:20,280 --> 00:44:24,279
Speaker 3: robots creating robots is a real idea, and it very

749
00:44:24,360 --> 00:44:31,520
Speaker 3: might well change battlefield dynamics. But I believe that certainly,

750
00:44:32,360 --> 00:44:36,680
Speaker 3: at this point in time, the human capacity to ingest

751
00:44:36,800 --> 00:44:41,840
Speaker 3: a mosaic of information and to make the right decision

752
00:44:42,080 --> 00:44:46,640
Speaker 3: is superior. If you take a chessboard, the machine can

753
00:44:46,680 --> 00:44:49,280
Speaker 3: beat the master, but if you put an extra bishop

754
00:44:49,320 --> 00:44:52,799
Speaker 3: on the board, the machine can't deal with it, right,

755
00:44:53,360 --> 00:44:55,759
Speaker 3: And I think that's the paradigm. And life does not

756
00:44:56,360 --> 00:45:00,160
Speaker 3: mimic a chessboard, you know. Life mimics the chessboard with

757
00:45:00,200 --> 00:45:04,320
Speaker 3: extra pieces being put on randomly, and it's that randomness

758
00:45:04,360 --> 00:45:06,520
Speaker 3: that I don't think the machines will be superior than

759
00:45:06,600 --> 00:45:09,839
Speaker 3: human judgment. Now, it might appear at times that the

760
00:45:09,840 --> 00:45:12,520
Speaker 3: machine can beat the human, but I think ultimately the

761
00:45:12,600 --> 00:45:16,160
Speaker 3: human judgment is superior, and so our business is based

762
00:45:16,239 --> 00:45:17,920
Speaker 3: on human judgment.

763
00:45:18,440 --> 00:45:21,920
Speaker 2: You mentioned the wartime usage of AI. There was a

764
00:45:22,000 --> 00:45:24,399
Speaker 2: pretty big article I don't remember. I want to say

765
00:45:24,440 --> 00:45:28,120
Speaker 2: the Times, not the journal, that figured out that in

766
00:45:28,160 --> 00:45:33,240
Speaker 2: the Ukraine Russian War, which started out as a conventional

767
00:45:33,400 --> 00:45:38,759
Speaker 2: bombardment between tanks and mortars and anti tank weapons, over

768
00:45:38,800 --> 00:45:43,359
Speaker 2: the past six twelve months, seventy percent of the casualties

769
00:45:43,880 --> 00:45:49,600
Speaker 2: have been drone AI warfare driven, and it's very much

770
00:45:49,719 --> 00:45:52,640
Speaker 2: a brave new world. It's not like the old world

771
00:45:52,920 --> 00:45:58,080
Speaker 2: of warfare. What it sounds like you're suggesting with AI

772
00:45:58,680 --> 00:46:01,640
Speaker 2: is that they're both code developed, that you'll still have

773
00:46:01,800 --> 00:46:06,680
Speaker 2: humans driving the process, but AIS become an increasingly large

774
00:46:07,160 --> 00:46:09,680
Speaker 2: part of it, regardless of whether we're talking about warfare,

775
00:46:10,320 --> 00:46:12,879
Speaker 2: business or investing. I don't want to put words into

776
00:46:12,920 --> 00:46:15,319
Speaker 2: your mouth, but is that a fair way to assess that.

777
00:46:15,719 --> 00:46:17,880
Speaker 3: I think so. I mean, I think that the humans

778
00:46:18,080 --> 00:46:20,640
Speaker 3: always have to be on top of the machines. Machines

779
00:46:20,680 --> 00:46:24,040
Speaker 3: have a lot of latitude, both to produce themselves as

780
00:46:24,120 --> 00:46:28,040
Speaker 3: as well as to target. You know, the markets are

781
00:46:28,120 --> 00:46:32,600
Speaker 3: different because the markets follow a behavioral dynamic. The evaluation

782
00:46:32,680 --> 00:46:36,759
Speaker 3: of risk versus reward is something that I think a

783
00:46:36,760 --> 00:46:38,919
Speaker 3: machine cannot do in the same way the human can.

784
00:46:39,360 --> 00:46:43,640
Speaker 2: So given some of the volatility we've been seeing in

785
00:46:43,680 --> 00:46:48,680
Speaker 2: the first quarter of twenty twenty five. Has that changed

786
00:46:48,719 --> 00:46:52,640
Speaker 2: how you're looking at your models, how you're viewing your

787
00:46:52,680 --> 00:46:55,839
Speaker 2: approach or is it, Hey, this is just another one

788
00:46:55,880 --> 00:46:57,640
Speaker 2: of those things that comes along and we have to

789
00:46:57,640 --> 00:47:00,000
Speaker 2: be able to trade through.

790
00:47:00,560 --> 00:47:04,680
Speaker 3: We actually like the dislocation because the dislocation proves the

791
00:47:04,719 --> 00:47:05,359
Speaker 3: models are wrong.

792
00:47:05,960 --> 00:47:09,360
Speaker 2: Well, I know you guys don't release public performance numbers,

793
00:47:09,360 --> 00:47:13,440
Speaker 2: but I know you're doing much better than your benchmark

794
00:47:13,520 --> 00:47:17,280
Speaker 2: this quarter. Volatility is your friend. Is that what you're saying?

795
00:47:17,560 --> 00:47:21,840
Speaker 2: Because volatility disrupts traditional models and you're a non traditional model. Correct.

796
00:47:22,239 --> 00:47:25,879
Speaker 2: So I know you've worked with Harry Markowitz. What other

797
00:47:26,160 --> 00:47:29,600
Speaker 2: academics and what other institutions have you worked with?

798
00:47:29,840 --> 00:47:33,240
Speaker 3: Well, at Imperial College London, there's further work being done

799
00:47:33,520 --> 00:47:37,440
Speaker 3: on the Gerber statistic and incorporating it. The idea of

800
00:47:37,520 --> 00:47:43,320
Speaker 3: thresholding data and ways to do it to For instance,

801
00:47:43,800 --> 00:47:46,440
Speaker 3: if you want to understand the significance of a stock

802
00:47:46,480 --> 00:47:50,000
Speaker 3: price movement, maybe you should exclude days where there's very

803
00:47:50,040 --> 00:47:53,000
Speaker 3: low volume and only include days when there's high volume.

804
00:47:53,440 --> 00:47:56,279
Speaker 3: There's a variety of ways to incorporate it.

805
00:47:57,040 --> 00:47:59,000
Speaker 2: I know, I only have you for a limited amount

806
00:47:59,040 --> 00:48:02,360
Speaker 2: of time. Let me jump some of my favorite questions.

807
00:48:02,400 --> 00:48:04,800
Speaker 2: I ask all of our guests, what are you watching

808
00:48:04,880 --> 00:48:07,359
Speaker 2: or listening to? With? What's keeping you entertained?

809
00:48:07,800 --> 00:48:10,160
Speaker 3: Recently I streamed Eastern Gate?

810
00:48:10,760 --> 00:48:11,320
Speaker 2: Oh really?

811
00:48:11,400 --> 00:48:13,440
Speaker 3: Which is I saw in the New York Times. It

812
00:48:13,600 --> 00:48:18,640
Speaker 3: was this spy thriller series on the conflict between Poland

813
00:48:18,680 --> 00:48:22,279
Speaker 3: and Belarus, and I wanted to understand the dynamic between it.

814
00:48:22,320 --> 00:48:25,239
Speaker 3: So I thought I'd get a little entertainment and understand

815
00:48:25,360 --> 00:48:29,160
Speaker 3: something I couldn't pick up here. And it's a little slapstick,

816
00:48:29,200 --> 00:48:29,960
Speaker 3: but I think it's worth it.

817
00:48:30,239 --> 00:48:33,480
Speaker 2: Eastern Gate. Yes, did you happen to watch any of

818
00:48:33,600 --> 00:48:38,320
Speaker 2: Fouda when that was just the most heart wrenching stuff

819
00:48:38,360 --> 00:48:39,839
Speaker 2: to watch? It's so stressful.

820
00:48:40,200 --> 00:48:42,480
Speaker 3: Yeah, and pretty realistic.

821
00:48:42,360 --> 00:48:46,440
Speaker 2: Very realistic. Let's talk about mentors who helped shape your career.

822
00:48:47,080 --> 00:48:49,880
Speaker 3: I gotta give a lot of credit to Dave Patrice.

823
00:48:50,440 --> 00:48:53,800
Speaker 2: Who I know that name, who really.

824
00:48:53,560 --> 00:48:57,840
Speaker 3: Helped me get into shape. And he was on my

825
00:48:57,920 --> 00:49:03,920
Speaker 3: case every day, the diet, the working out. We're workout partners,

826
00:49:04,840 --> 00:49:08,400
Speaker 3: and I was thirty five forty pounds heavier, uh huh,

827
00:49:08,400 --> 00:49:12,279
Speaker 3: and he got me to recognize they needed to get

828
00:49:12,320 --> 00:49:13,880
Speaker 3: in shape. I thought I was in shape, but I

829
00:49:13,920 --> 00:49:15,680
Speaker 3: wasn't in shape. I think I think a lot of

830
00:49:15,719 --> 00:49:17,960
Speaker 3: people think they're doing okay when they could do a

831
00:49:17,960 --> 00:49:20,360
Speaker 3: lot better. And he taught me I could do a

832
00:49:20,400 --> 00:49:22,880
Speaker 3: lot better. And I think it's affected me overall, my

833
00:49:22,960 --> 00:49:29,359
Speaker 3: mental acuity, my mood, my stamina. I really give him

834
00:49:29,400 --> 00:49:29,920
Speaker 3: a lot of credit.

835
00:49:30,239 --> 00:49:32,640
Speaker 2: You mentioned books earlier. What are some of your favorites?

836
00:49:32,680 --> 00:49:33,760
Speaker 2: What are you reading right now?

837
00:49:34,040 --> 00:49:37,480
Speaker 3: One book that I really enjoyed, which was long, was

838
00:49:37,520 --> 00:49:40,520
Speaker 3: Walter Isaacson's book on Elon Musk, which I read before

839
00:49:40,680 --> 00:49:43,040
Speaker 3: the election, and it made a big impact on me

840
00:49:43,160 --> 00:49:46,319
Speaker 3: because I believe in questioning the experts, but must takes

841
00:49:46,320 --> 00:49:50,440
Speaker 3: it to a different level. He's questioning metallurgical properties that

842
00:49:50,520 --> 00:49:54,120
Speaker 3: were well grounded in science and engineering, and he's saying,

843
00:49:54,800 --> 00:49:57,440
Speaker 3: why does that have to be? And oftentimes he was

844
00:49:57,520 --> 00:50:02,760
Speaker 3: right that the established can census regarding properties of medals

845
00:50:03,160 --> 00:50:03,560
Speaker 3: was wrong.

846
00:50:04,080 --> 00:50:07,040
Speaker 2: M really really interesting. Any of the books you want

847
00:50:07,080 --> 00:50:07,359
Speaker 2: to mention?

848
00:50:09,040 --> 00:50:12,839
Speaker 3: I read The Melting Point by Frank Mackenzie recently. He

849
00:50:13,000 --> 00:50:16,560
Speaker 3: was the head of Scentcom and he talked about what

850
00:50:16,600 --> 00:50:22,520
Speaker 3: it was like to lead Sentcom and he also had

851
00:50:22,520 --> 00:50:25,040
Speaker 3: a MA He measured in English, and he thought that

852
00:50:25,120 --> 00:50:28,040
Speaker 3: his English background to be a commanding general was very

853
00:50:28,040 --> 00:50:32,440
Speaker 3: helpful because I helped him to articulate better and to

854
00:50:33,600 --> 00:50:36,280
Speaker 3: form consensus, you know, among his colleagues.

855
00:50:36,880 --> 00:50:41,279
Speaker 2: Really really interesting. Our final two questions what sort of

856
00:50:41,320 --> 00:50:44,560
Speaker 2: advice would you give to a recent grad interested in

857
00:50:44,600 --> 00:50:49,920
Speaker 2: a career in either filling the blank, investing options trading,

858
00:50:50,440 --> 00:50:54,239
Speaker 2: multi strategy management. What advice would you give to them?

859
00:50:54,440 --> 00:50:59,320
Speaker 3: I think it's, you know, across all certainly service occupations,

860
00:50:59,440 --> 00:51:02,560
Speaker 3: is you got to be will beat the machines, and

861
00:51:02,920 --> 00:51:06,920
Speaker 3: to do that, you need to be independent thinker. You

862
00:51:07,000 --> 00:51:10,080
Speaker 3: need to go against the grain, question the experts. You

863
00:51:10,160 --> 00:51:12,719
Speaker 3: need to be able to do that. You need ab

864
00:51:12,719 --> 00:51:16,880
Speaker 3: to work with other people, to learn from them, to

865
00:51:16,920 --> 00:51:19,920
Speaker 3: expand your horizons, to expand the mosaic that you can

866
00:51:19,920 --> 00:51:22,600
Speaker 3: bring to your independent thinking. And you got to be

867
00:51:22,640 --> 00:51:26,080
Speaker 3: able to respect your colleague. So I think that those

868
00:51:26,080 --> 00:51:28,760
Speaker 3: three things are a real guideposts for people.

869
00:51:28,840 --> 00:51:32,280
Speaker 2: This goes back to your corporate culture, which your environment,

870
00:51:32,400 --> 00:51:38,200
Speaker 2: corporate environment, my bad, your corporate environment, think independently, collaborate

871
00:51:38,320 --> 00:51:41,840
Speaker 2: and respect the individual. Correct huh? And our final question,

872
00:51:42,400 --> 00:51:45,200
Speaker 2: what do you know about the world of investing in finance?

873
00:51:45,239 --> 00:51:47,960
Speaker 2: Today would have been useful when you were first getting

874
00:51:48,400 --> 00:51:50,440
Speaker 2: started in the early nineties.

875
00:51:51,760 --> 00:51:54,280
Speaker 3: I think that you know everything you learn in business

876
00:51:54,320 --> 00:51:58,040
Speaker 3: school or economics, you can just throw out the window

877
00:51:59,280 --> 00:52:03,359
Speaker 3: economics and of science. People try to portray economics as

878
00:52:03,360 --> 00:52:06,960
Speaker 3: a science, and it simply is not. And so all

879
00:52:06,960 --> 00:52:10,960
Speaker 3: the notions that we brought up regarding money supply, you know,

880
00:52:11,000 --> 00:52:13,080
Speaker 3: Milton Freem would be turning over in his grave. Even

881
00:52:13,080 --> 00:52:17,480
Speaker 3: though these principles might have some grounding, It's not scientific,

882
00:52:17,920 --> 00:52:20,920
Speaker 3: you know. This is this is not a natural science.

883
00:52:21,040 --> 00:52:25,200
Speaker 3: It's a behavioral science, and it's based upon how people

884
00:52:25,480 --> 00:52:28,680
Speaker 3: interact with each other. And I think that that appreciation

885
00:52:29,120 --> 00:52:32,799
Speaker 3: leads to the notion that oftentimes the academy or the

886
00:52:32,840 --> 00:52:40,040
Speaker 3: experts try to profer things that everyone everyone seems to

887
00:52:40,040 --> 00:52:42,200
Speaker 3: believe one way, and you think, how could I be right?

888
00:52:42,239 --> 00:52:44,880
Speaker 3: Because everyone believes one way because this is what they

889
00:52:44,920 --> 00:52:47,600
Speaker 3: studied in school, and if the authorities say it's that

890
00:52:47,640 --> 00:52:51,719
Speaker 3: one way. And I think that as you go through

891
00:52:51,760 --> 00:52:54,920
Speaker 3: life and you age, you realize that the Ivory Tower

892
00:52:55,000 --> 00:52:57,400
Speaker 3: isn't always correct. In fact, a lot of times the

893
00:52:57,440 --> 00:53:01,560
Speaker 3: Ivory Tower doesn't have the real life experience, and so

894
00:53:01,600 --> 00:53:02,520
Speaker 3: they're flat out wrong.

895
00:53:03,480 --> 00:53:07,480
Speaker 2: I'm trying to remember where I'm stealing this quote from

896
00:53:07,800 --> 00:53:11,160
Speaker 2: Science Advance's One Funeral at a Time. The same is

897
00:53:11,200 --> 00:53:16,839
Speaker 2: true with other things that Dick Thaylor said. Rather than

898
00:53:16,880 --> 00:53:20,560
Speaker 2: wait for the rest of economics to catch up with

899
00:53:20,640 --> 00:53:23,600
Speaker 2: behavioral finance, I'm just going to teach it to the

900
00:53:23,920 --> 00:53:29,160
Speaker 2: younger generation and it'll infiltrate much more quickly than waiting

901
00:53:29,200 --> 00:53:34,440
Speaker 2: for all of my peers to accept it. Really really fascinating, Sander.

902
00:53:34,480 --> 00:53:37,200
Speaker 2: Thank you for being so generous with your time. We

903
00:53:37,440 --> 00:53:41,160
Speaker 2: have been speaking with Sandra Gerber. He is CEO and

904
00:53:41,440 --> 00:53:46,399
Speaker 2: CIO of Hudson Bay Capital. If you enjoy this conversation, well,

905
00:53:46,520 --> 00:53:48,399
Speaker 2: be sure and check out any of the previous five

906
00:53:48,480 --> 00:53:51,960
Speaker 2: hundred and fifty we've done over the past eleven years.

907
00:53:52,480 --> 00:53:57,320
Speaker 2: You can find those at iTunes, Spotify, YouTube, Bloomberg, wherever

908
00:53:57,400 --> 00:54:01,359
Speaker 2: you find your favorite podcast. And be sure and check

909
00:54:01,360 --> 00:54:05,439
Speaker 2: out my new book How Not to Invest The Ideas,

910
00:54:05,520 --> 00:54:10,520
Speaker 2: numbers and behavior that Destroys Wealth out today wherever you

911
00:54:10,640 --> 00:54:13,799
Speaker 2: find your favorite books. I would be remiss if I

912
00:54:13,840 --> 00:54:15,759
Speaker 2: do not thank the correct team that helps put these

913
00:54:15,800 --> 00:54:20,120
Speaker 2: conversations together each week. John Washerman is my audio engineer.

914
00:54:20,239 --> 00:54:24,440
Speaker 2: Ana Luke is my producer Sean Russo is my researcher.

915
00:54:25,200 --> 00:54:29,000
Speaker 2: I'm Barry Ritholtz. You've been listening to Masters in Business

916
00:54:29,600 --> 00:54:35,440
Speaker 2: on Bloomberg Radio