May 2, 2025 • 54 mins
Barry speaks with Sander Gerber, Hudson Bay Capital CEO and CIO. Hudson Bay is a global, multi-strategy investment firm.
In 2008, Mr. Gerber developed the Gerber Statistic, which was accepted as an innovation complementary to his own work by the late Dr. Harry Markowitz, the Nobel Prize-winning economist and father of Modern Portfolio Theory (MPT). The Gerber Statistic is utilized by Hudson Bay to identify the co-movement of financial assets, enabling early detection of concentration risks and insufficient diversification.
Mr. Gerber began his investment career in 1991, as a member of the American Stock Exchange working as an equity options market maker. In 1997, he founded Gerber Asset Management to develop and engage in proprietary investment strategies. In late 2005, Mr. Gerber and Yoav Roth co-founded Hudson Bay Capital, which concentrates on generating positive returns while maintaining a focus on risk management and capital preservation.
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Speaker 1 (00:02):
Bloomberg Audio Studios, podcasts, radio news. This is Masters in
Business with Barry Ritholts on Bloomberg Radio.
Speaker 2 (00:16):
Strap yourself in for another good one. Sander Gerber, CEO
CIO of Hudson Bay Capital. What a fascinating background he has,
starting in philosophy and ending up on the floor of
the American Stock Exchange as an equity options trader. That experience,
(00:36):
those two things combined to really create a kind of
unique perspective on the world of markets, on the world
of risk, and on the world of models. You know,
I've used the George Box quote a million times. All
models are wrong, but some are useful. And the way
Gerber goes about using models is very much along the
(01:01):
George Box lines, which is, not only are we going
to assume that models are wrong, but we want to
create our own models to be able to identify when
they're going to be at a great variance to what's
going on in reality, and then how to position ourselves
to take advantage of it. They're less directional traders than
(01:21):
they are arbitreasures. Hudson Big Capital runs a dozen different
strategies and they're all quite fascinating. Everything from risk, arb
to private credit and real estate in the first quarter
of twenty twenty five, where volatility spikes and a lot
of people's expectations are dashed. Their models do really well.
(01:46):
I find his depth of knowledge and his technical expertise
to be absolutely fascinating. I think you'll find him to
be fascinating also, with no further ado, my conversation with
Hudson Bay Capitals Xander Gerbert. So let's start a little
bit with your background bachelors and humanistic philosophy and an
(02:09):
MBA from Wharton Finance. What was the career plan?
Speaker 3 (02:13):
Well, actually, I was good at math, so I first
entered the Wharton School undergrad. I don't have an MBA
from Wharton. And then when I was at Wharton, I
didn't think I was getting an education, so I decided
to transfer into the College of Arts and Sciences, so
I got two degrees. Concurrently, I picked up a degree
in philosophy. Humanistic philosophy. I wanted to understand the development
(02:37):
of thought, how we got to where we are in.
Speaker 2 (02:39):
Society, epistemology or something more specific.
Speaker 3 (02:43):
It was moral philosophy, generally, starting with the ancient Greeks
through the existentialists. I think that I used my philosophy
background much more than my finance background, because it really
gives you a different view on the world. When I
was at Wharton colleg Andrew Krieger came in nineteen eighty
seven to speak. He had majored in Sanskrit Eastern philosophy
(03:10):
and then he got his MBA at Wharton and he
was the leading FX trader at Banker's Trust. And he
spoke about how his philosophy Eastern philosophy helped him understand
the markets. That you might feel very convicted the markets
should go a certain way, but the markets have their
own mindset and you have to accept what the markets have.
(03:30):
And it helped him emotionally to trade better because he
realized that mother market was going to be right, and
so it was from his philosophy background that he was
able to reconcile that with him with his beliefs in
terms of where markets should go, and it helped him
to be a better trader.
Speaker 2 (03:47):
That I definitely can see that. You know the concept.
I don't know if I'm stealing this from Zen Buddhism,
but it's the water flows, but the rigid tree breaks
in the storm, and it's very similar to, hey, that's
an Eastern way of saying, why are you finding the
trend exactly?
Speaker 3 (04:04):
And so, you know, when I was in college, I
really didn't know much about the markets. And as I
told you, I still I had entered first the Wharton School,
so I was still getting my degree there, but I
was really focused on the philosophy. And you know, people
think the philosophy is not so practical, what are you
going to do with it? And here the top FX
trader in the world came and said, this is what
you should be doing. So it was it was sort of,
(04:26):
you know, ratification of what I was studying.
Speaker 2 (04:30):
Huh. I think you're the first person who I've ever
spoken to who said, yeah, the Wharton School of Finance
at University of Pennsylvania not a great education. Isn't it
really true that most of our education, or at least
for a lot of people, you're just self taught. Schools
will give you a curriculum and here's the reading list,
(04:51):
but it's up to you to kind of learn whatever
there is to learn.
Speaker 3 (04:56):
I think it's a good point. You know, the Wharton
School is arguable the finest finance school, but finance is
a technical discipline, and I wanted to understand the world.
And I think that you can only go a certain
degree using that background. And it's true. Then in order
to I think, upgrade yourself, you've got to be able
(05:19):
to develop the capacity to self learn, to take in
from the environment around you, to enable yourself to grow
your skill set to your experiences through working with others.
And that's something we try to incorporate within Hudson Bay
is the ability for people's careers to develop, and it
(05:40):
is something that you have to rely on self learning
and within college in certain disciplines. In college, like in philosophy,
a lot of it is you know, discovery, self discovery,
and other disciplines there is no self discovery. So I
think it is important to the humanistic background.
Speaker 2 (05:58):
So you come out of of Wharton and University of Pennsylvania,
you start your career on the floor of the American
Stock Exchange as an equity options market maker. That had
to be a fascinating experience, especially nineteen nineties and two thousands,
that was a hot period and option trading. Tell us
(06:19):
a little bit about that experience.
Speaker 3 (06:21):
Well, actually, when I graduated Penn I had been I'd
clerked on the floor of the Philadelphia Options Exchange in
nineteen eighty seven, and I liked it. But my parents
had spent all this money to send me to a
fancy school. They had taken out a home equity loan
to pay for my college tuition. So I thought to
be a muslely floor trader would be disrespectful. So I
(06:43):
went to Banning Company for two years, and I was
in management consulting for two years. It was boring, but
I did learn something from it, and then I came
to the floor of the AMEX.
Speaker 2 (06:54):
Wait before you jump to the AMEX. Aside from learning
that being was boring, what else did you learn?
Speaker 3 (07:02):
I learned how people can work together in good conscious
with dedication and still muck things up. Because what we
would do is we would parachute into places like British Airways,
Montreal Trusts, uh CIA Industries, and we were like the
external strategic planning and we would They would put young
(07:25):
people like me, and we'd sit next to people and
interview them and figure out why projects went to muck.
And I understood from that that well meaning people can
still muck things up because they don't have an appropriate
guide frame or appropriate leadership. Or they're not so like
little things can take projects astray.
Speaker 2 (07:47):
So what was it that drew you to the floor
of the well?
Speaker 3 (07:50):
I enjoyed the Philadelphia floor, and also I was I
always liked games, and so I and I had a
talent I thought for trading, and so I went to
the the AMEX. Someone gave me it was like eleven
hundred dollars a month as a stipend, and I kept
(08:12):
roughly half the profits. And there was no training. They
just threw me there very in the deep.
Speaker 2 (08:17):
End of the pool. Whoever doesn't drown. Hey, you can grab.
Speaker 3 (08:20):
Exactly right, exactly right. And it took me from July
of ninety one till December of ninety one. I made
five hundred dollars profit. Not for me, five hundred dollars.
Trading had a split which I had to split. Yes, well,
actually because I had a draw, I didn't get anything.
But then the next year I took off and it
(08:41):
turned out that I did have a knack fort I
was able to understand the volatility of the market. Is
usually we're vol traders, and I did something that was
two things that were novel on the floor. The first
is I understood that you have to break down your
volatile exposure month by month, which back then was unusual.
(09:04):
In other words, people had these models that would give
you one volatility exposure across the entire portfolio. And I
realized that julys and earnings month, and August is a
beach month, so you can't use those two months to
offset each other. And so I was able to jerry
rig the models that were early then to be able
to look at my VEGA exposure month by month. That was,
(09:24):
believe it or not unusual. And the second thing that
that's early nineties is yes, that was ninety one, ninety two.
Speaker 2 (09:31):
Okay, all these things we kind of take for granted.
I know, right at one point in time, you wonder
why it's become so increasingly difficult to beat the broad index.
It was a ton of inefficiencies, that's right, that's right.
Speaker 3 (09:43):
And it was a great edge for me to come
to that realization. And maybe it was because I had
studied the models at the Wharton School. We had broken
them down, and I understood that the models are only
as good as the inputs. And a lot of people
back then were doing spreads in their head and the
other group were using these canned models that would give
you one volatility exposure across you know, the entire model.
(10:07):
And the second thing that I realized was that you
need to combine fundamentals with the technicals of the models.
In other words, the models assumer normal distribution of returns,
but when you get into some kind of event, it's
no longer a normal distribution returns. It's you know, the
(10:29):
stock's either going to go up a lot or down
a lot. That's a barbell distribution, right as opposed to
normal distribution. And so by looking at events and when
they're going to happen and breaking down the VEGA exposure
month by month, that gave me an edge that I
was able to exploit. Do you find vega for listeners
who are Vega is the volatility So of the an
(10:53):
option has premium, and that premium is the extra amount
you pay for the right to have limited loss and
unlimited gain. And so that premium, that value of that
option to exercise or not exercise with limited loss, goes
up and down in value based upon the degree of movements.
(11:16):
So when something's moving around a lot, that has a
lot more value. So premium value goes up when things
are not moving a lot, premium value goes down, and
so by trading this range of volatility up and down,
which is in part dependent on what's happening with the
fundamentals of the stock, you were able to grab edge.
Speaker 2 (11:37):
So these are really second or third level derivatives. It's
not the underlying value. It's the increase in value of
the option and then within that the range and the
variability of that increase in option value. That's what you
were trading.
Speaker 3 (11:53):
Yes, and you know it's really not complicated. I mean
Wall Street tries to make things much more more complicated
than they are, but the simple, elegant solution is always better.
So it might sound complicated, but it's really not right.
Speaker 2 (12:09):
And that complexity is a feature, not a bug. You
can sell stuff if it's complicated and hard to understand.
If it's simple, well I think I could do that.
Speaker 3 (12:19):
That's right. Wall Street tries to make things more complicated
because it has to justify the sales commission and if
but things really are not so complicated.
Speaker 2 (12:29):
So what was your biggest takeaway from your experiences as
a trader? How did it shape how you look at
the world of investing, How did it affect what you're
doing in Hudson Bay today.
Speaker 3 (12:41):
Well, I really was grounded by that three and a
half years of watching every tick on the stock. You know,
and you're you're geographically limited on the floor. You can
only trade at the post that you're standing by.
Speaker 2 (12:54):
Like physically in space, your physically heether to that trading
thing exactly.
Speaker 3 (13:00):
And there are even rules that you had to do
most of your trading in that geography, so you couldn't
move around a lot. And what it taught me is that,
you know, like a trading post, a strategy goes in
and out of favor, and if you want to be
able to make money in all markets all the time,
you have to develop a toolkit that can go beyond
(13:22):
one particular strategy. So you need to have multiple strategies
to develop persistent profitability. The other thing that I learned
was that you can make the right decisions and still
lose money. I had plenty of time where looking back,
it was the right decision, but the markets thought differently,
and so you always have to be worried about what
(13:43):
can go wrong. And risk is not about not losing money.
Risk management is not about not losing money. Risk management
is about unexpectedly losing money. In other words, when you're
evaluating a situation, you should know what is your reason
worst case downside. Now there's always the black swan that
(14:04):
maybe you can't figure on, but you should. But risk
management is always about understanding what could go wrong and
quantifying what could go wrong.
Speaker 2 (14:14):
So I want to unpack what you just said, because
it's filled with goodness. First, you're referring to your approach
is Hey, we're really more process focused than outcome focused,
because if you have a good process, even if you
get a bad outcome, it doesn't matter. Probabilities will eventually
work in your fay.
Speaker 3 (14:35):
Exactly right.
Speaker 2 (14:36):
That's number one. But then the part two, which I
think a lot of investors overlook, is and a risk
management component that if the worst case happens, we still
survive and lift to trade another.
Speaker 3 (14:50):
That's right, exactly right. And so at Hudson Bay, I
created the deal code system, uh.
Speaker 2 (14:56):
Deal code system.
Speaker 3 (14:58):
Yes, so at the time, well, I left the floor
beginning of ninety five and started deploying just the money
I'd earned on the floor in an off floor trading account.
And I would develop a strategy and hire someone else
to run it and develop another strategy and hire someone
else to run it. And as I was having other
people manage basically my trading account, I realized I had
(15:24):
to scale my risk profile that I developed on the
floor over multiple risk takers, and I needed to do
it in a manner that would produce persistent profitability. So
at the time, we were trading a lot of risk
garbitrage deals, so we called it a deal code, and
a deal code is just a numerical moniker that we
put on each trading idea within the book, and that
(15:47):
enables us to focus in on how is that trade hedged,
what's the risk riskiness? How much could that trade lose
in a reasonable worst case scenario, and it gives us
a batting average, so we can under stand is a
portfolio manager winning more ideas than they lose so to
be persistently profitable. I think it's not just about winning
(16:08):
more dollars than you lose. It's about winning more ideas
than you lose.
Speaker 2 (16:13):
So let's talk a little bit about Hudson Bay's strategy.
You've been managing outside capital across a variety of asset
classes and strategies. Tell us talk about some of the
key strategies and what has been the drivers of making
those strategies successful. Well.
Speaker 3 (16:33):
As I mentioned, I wanted to be able to make
money in all market environments, so you need a tool
set to do that. So our strategies are equity long
short converts, credit, event merger, volatility trading.
Speaker 2 (16:48):
This isn't just I'm going to buy the S and
P five hundred and put it away for a decade.
You're active traders, and you're really looking to take advantage
of situations where you have a fairly good idea of
what the outcome is going to look like. It's not hey,
this is open ended. Usually you're pretty confident in here's
(17:08):
what our range of potential outcomes are.
Speaker 3 (17:10):
I think that, especially in today's world, you have to
understand what your edge is versus the machines. And a
machine can calculate risk based on historical precedent, but a
machine cannot calculate risk based upon some kind of uncertainty
due to some kind of event, callous or change that's
coming up because it's new. So the machine doesn't have
(17:32):
the ability to calibrate for something that's new. And so
generally across all our strategies, that's what we're focused on,
is we're focused on event callous change. How can we
profit off of that in a way that machines cannot.
Speaker 2 (17:45):
So that's the fundamental criticism of models. All models assume
that the world in the future is going to look
like the world in the past. Risk management is what
happens if the world doesn't look like out at.
Speaker 3 (17:57):
Us precisely, And that's why we don't use the standard
risk management models. I actually created a statistic, the Gerber statistic,
that helps to understand diversification between our deal codes, between
our investment positions. A lot of our competitors are tied
to factor based modeling, which ultimately, underneath it is reliant
(18:19):
on regression analysis. Regressions. Our straight line fits through normalized
sets of data, and human relationships don't file straight lines,
and certainly market relationships don't file straight lines. So using
that as the underpinning of a risk management system is
just incorrect. And so we've created a whole different structure.
(18:42):
As I said, we've used since nineteen ninety eight, and
I think that's given us the ability to weather storms
and profit from it in ways that our competitors can.
Speaker 2 (18:52):
So let's talk a little bit about the Gerber statistic.
You had this validated by Harry Markowitz, the creator of
modern portfolio folio theory. Tell us about that collaboration and
break down the Garberg statistic a little bit. How do
you guys actually use it?
Speaker 3 (19:14):
So, because of my distrust of models, based upon my
experience on the floor, in particularly the guts of the models,
I never believed in the correlation statistic, that correlation is predictive,
and this was I thought one of the underpinnings of
modern portfolio theory that you look at the expected return
of the stock, the expected variants of the stock, and
(19:37):
the covariance of correlation between the different components of a portfolio.
And at the time, you know, we used the deal
code system and on Wall Street the banks were telling
me this is nonsense, but don't even talk about it
with investors. And then in eight when everyone lost money
and we made money, I realized we were doing something different.
(19:59):
And then I had the idea. Because of course I'd
studied about Harry in modern portfolio theory. Everyone in finance has.
He won the Nobel Prize. I decided, you know what,
I'm going to go out to see him to see
what he thinks about the Gerber statistic, and at the
time it wasn't called the Gerber statistic. But a friend
of mine said, gee, you really should file a patent
on this before you see Harry, and so I did,
(20:20):
and I had to name it something, so I called
it the Gerber statistic. And we now have I think
we just got our sixth patent on our process for diversification.
So I got to see Harry in San Diego. Lovely guy.
He welcomed me, and we're walking. He liked to walk
along the beach and I said, Harry, you know, I
don't think that correlation's predictive, and Harry said, you're right.
(20:43):
I said, no, no, Harry, you don't understand it. I don't
think that because it's one of the base foundational bases
for what She won the Nobel Prize in Modern portfolio theory.
Said Harry, I don't think that historical correlation has relevance
to the future. And he said, you're right. And it
turns out that in his nineteen fifty two paper that
sets forth modern portfolio theory, he said that correlation should
(21:06):
be determined by the judgment of practical men. In other words,
the stock analysts should think what will be the relationship
going forward, not to mind the past, but be forward looking.
But in the nineteen sixties, as computing power increase, people said, oh,
we can mind the statistic, this row statistic correlation, and
then we can plug it into the model as correlation.
(21:29):
He meant correlation in a semantic sense, not in a
mathematical sense in terms of using in his model. So
he actually said that the deal code system uses his system,
the modern portfolio theory system. He said that there's three
legs to his system. And so because we use limited loss,
(21:49):
because we seek to diversification through hesing on the own,
because we seek to win more than we lose in
each investment idea, he said that is accordance with his system.
But in any way, we we've written several papers together
on the Gerber statistic within modern portfolio theory and have
demonstrated that you get better performance with less risk by
(22:12):
replacing historical covariance with the Gerber statistic. And Harry and
I actually we only had really one disagreement, and the
one disagreement was on factors. There's all these you know,
factor methodologies, and Harry believed that only one factor matters
for portfolios, and I think two factors matter, so and
so that, but the other twenty three factors we both
(22:34):
agree are complete nonsets.
Speaker 2 (22:36):
So if you look at the fomb of French model,
which started out as two or three factors and then
became five fact.
Speaker 3 (22:44):
And then grow and grow. If you speak to the
research departments of bar Axioma, they'll tell you that thirty
four to forty percent of a stock price movement can
be explained by factors.
Speaker 2 (22:57):
Okay, so that's third, let's.
Speaker 3 (22:59):
Roll it a and of that third, eighty five percent
of that third can be explained by the first five factors, okay,
which means giving credit to five, which that's bar Naxioma
tells you eighty five percent of the forty percent can
be explained by five factors, which means the other twenty
factors explain the fifteen percent of forty percent of the words.
(23:22):
Six percent of a stock price movement can be explained
by twenty one factors, right, meaning which is complete. You know, nonsense,
but no, if you lever a portfolio up, you know
ten times, all of a sudden, that six percent looks
like it's sixty percent, but it's all complete nonsense. It's
numerical mumbo jumbo. It's part of the whole Wall Street
(23:44):
pizazz that is not based on reality. But you know
it sells.
Speaker 2 (23:49):
So so I want to guess the two factors. If
I had a guess, I'm going to rely on a
paper by Wes Gray of Alpha Architect and guess it's
value and momentum. But I'm curious what you feel.
Speaker 3 (24:01):
Well, actually, Harry thought it was market. I think his
market and section.
Speaker 2 (24:04):
So is market and sector. But are those really factors?
Do we really The.
Speaker 3 (24:07):
Whole idea of factors is kind of like, you know,
a little nonsense. It's like beta, you know, like market
we think of as beta. It's now been called a factor.
Speaker 2 (24:19):
So oh, I never really thought of beta as a factor.
It's just it's, hey, if you do nothing, you get
But that's market, you know, So huh, that's really it.
So you're looking at the sector it's in and the
overall market as the two driving facts.
Speaker 3 (24:34):
I think those are Now it's true that momentum, value,
these other things are relevant today because everyone else has
glommed onto it, because we have so many statistical, process
driven strategies that try to trade momentum. You know, buy cheap,
sell expensive. It pushes everything in line. And this is
what I found on the floor using models to trade options,
(24:57):
that the models would push the the values of the
options into alignment in accordance with the model because everyone's
using the same model, and so the same thing is
true in the broader market because everyone's using basically the
same factor models. It pushes things in alignment, which works
in normal market environments. But when things you know, have
(25:19):
a dislocation, it no longer works, which is why people say, oh,
our risk model broke down or whatever, because these aren't
really risk models.
Speaker 1 (25:26):
Now.
Speaker 3 (25:26):
It's one thing to use a model to trade because
the model's telling you something is some expensive or cheap.
Speaker 2 (25:34):
And relative to history.
Speaker 3 (25:36):
Right, And if something's always cheap, you just adjust the model.
So there's a validity to that. But that's different than
using the same model for risk management. Risk management, again,
is about avoiding unexpected loss.
Speaker 2 (25:49):
Huh. That's really interesting. So when I started on a
trading desk, one of the things that I was always taught,
which I never contextualized as a factor, is, hey, what's
driving the stock? Well, the stock is only a tiny
part of it. The stock is twenty percent, the sector
(26:10):
is thirty percent, and half is the market. So you
could be the greatest stock in the world. If the
market's going down, it doesn't matter, and it could be
a really good stock. But if it's in a terrible sector.
You know, the metaphor was always great house in a
crappy neighborhood is a crappy house. You're really putting that
into the context of these are the broader factors that
(26:32):
are affecting that single holding.
Speaker 3 (26:34):
That's right, that's right. And you know, in our at
Hudson Bay, we seek to produce the alpha. So it's
true that the market is moving the stock, but we
try to pick stocks that outperform the market or pick
shorts that will go down more than the market. So
we seek to focus on the alpha provision.
Speaker 2 (26:54):
So let's talk about something related to this. A paper
you published, environment eats culture for lunch. It sounds like
the environment is what the market's doing with the sector is,
but give us a little detail about.
Speaker 3 (27:08):
Well, actually, I mean that that paper was related to
the human aspect, not the market. So Peter Drucker came
up with this idea that culture eats strategy for breakfast
that corporate culture is actually more important than corporate strategy
for the success of a firm. I think there's a
(27:29):
lot to that that, you know, the way people work
together in an organization. But I've always thought that this
corporate culture thing is nonsense. If you have people try
to describe their corporate culture, they cannot articulate it, right,
you know, like what's the corporate culture here at Bloomberg,
you know, like.
Speaker 2 (27:47):
Fun data driven? It's all about data, So you come up.
Speaker 3 (27:50):
On data driven. It's not a culture. Data driven is
a process. But I'm talking about what's the human aspect
of it? What's what's the human culture.
Speaker 2 (27:57):
I'm the wrong person to ask that, right.
Speaker 3 (27:59):
Because because no one can really describe corporate culture, what
you can describe as an environment. What is the environment
that people work within? And I kind of learned this
at Band and Company because Baine was described as this
like fun loving place, everyone has fun. And then when
I was there, two guys died on the locker bee
crash and Bill Bayn had milked the esop and so
(28:21):
the company almost collapsed. When I was there, they fired
half of my class, not me, They fired all the
incoming MBAs and it was the avarice of Bill Bain
that nearly collapsed the firm. We're talking back in nineteen
eighty nine to ninety.
Speaker 2 (28:38):
So the corporate culture was with pacious greed, well did
you know, and almost destroy.
Speaker 3 (28:44):
It was inauthentic. And when people try to describe culture,
they can't. And so what I wanted to do was
to describe an environment. What is the environment that you
want to work within? And you know when you speak
to when you speak to people on other firms, what's
your corporate culture? What's your value statements? Usually these things
(29:04):
go on and on and on. No one can really
remember all the value statement. If you can't remember your
value statement, it has no value.
Speaker 2 (29:12):
I'm going to imagine that twenty two twenty three when
all the big firms were saying, we want our employees
back in the office. We don't want any more remote work.
It's a matter of corporate culture. How did you think
about that? Was this a legitimate demand and is it
(29:33):
not so much corporate culture? But we want an environment
where people are in the office working together. Is that legit?
Speaker 3 (29:39):
I hate going to the office and seeing people not there.
I think that people should work together. On the other hand,
You can't force these things. You can't force independent thinking,
you can't force collaboration. You can have an environment that
engenders it, and so we try to have an environment
that engenders it. So it's my opinion that people who
(30:01):
come to the office are going to succeed more than
people who don't. Now, I understand that, you know, the
commute is a hassle and sometimes people, you know, want
to take the day off, and so you know, our
standard is two days in the office. Many teams have
a third day, but a lot of people. Usually people
are in our office three to five days a week.
(30:22):
But we don't force it. If once you force people
to be in the office, I think you're losing this
spree de corps. We want people to want to work
at Hudson Bay. If they don't want to work at
Hudson Bay, they should go elsewhere. But to force people,
I think, you know, for high performers, I don't think
that's the way to engender the right environment.
Speaker 2 (30:42):
And environment beats culture for work because the work environment
is more important than some statement that nobody remembers. Correct.
So you guys have let's talk a little bit about
independent thought. You guys have done pretty well. When the
expert's wrong. You throw five, seven, eight, and nine. You
were notably up in years where most people were down.
(31:06):
Again in Q one of twenty twenty, you guys did
really well all periods of big market turmoil. I don't
know what you were doing in two thousand and one two,
but I'm imagining the same approach held true. How do
you think about these periods? Are they truly black swans
(31:28):
or are they things that, with the right approach to
risk management, are create opportunities.
Speaker 3 (31:34):
Again, people are trying to assess risk based upon some
kind of parametric distribution with you know, standard deviation movements,
and I think that's just nonsense. The markets don't work
like that. So our system enables us to weather all
market environments through the deal code system by ignoring those
(31:58):
parametric The Gerber statistic, which is the basis for the
work with Harry, is a rank order statistic because it
recognizes the failures of parametric normal distributions. And what we
do is we set a threshold because a lot of
data is noise in the markets. If the S and
(32:19):
P moves by ten basis points, it doesn't communicate to
you how the S and P affects other things. Yet,
and all these statistical models, they're including every single data
point because if you don't include every single data point,
then in the matrix math you have a divide by
zero issue. So they're forced in all these correlation statistics,
(32:40):
these regression analyses to include every single data point. With
the Gerba statistic, we are able to create thresholds where
we ignore data below a certain degree of movement, and
so that enables us to focus on Everyone wants meaningful relationships, right,
So this is how we're able to focus on meaningful
relationships within the market.
Speaker 2 (33:01):
You know, we talked a little bit about sub prime
real estate and how the models it wasn't even that
they broke. They were so poorly constructed they were destined
to fail. You know, if you build a house really poorly,
you don't need an earthquake. Eventually, it's just going to
collapse under its own weight. But I have to ask
you some questions about real estate because Hudson Bay has
(33:22):
been increasingly invested in private credit and real estate. You've
done a number of major refinancings in and around New
York City. Six twenty Avenue of the America's is tell
Us a little bit about the work you're doing at
Hudson Bay with private credit and real estate.
Speaker 3 (33:40):
Well, we saw beginning with the the transitory higher rates,
which we thought was nonsense, right. We saw that rates
were going to be higher for longer, and we had
believed that the market had been anchored in this idea
of ultra low rates, which was really a manipulation of
(34:02):
the monetary system. So we started thinking about what's the
implications of that, and came to the notion that the
banking system would be under stress. And what's the implication
of the banking system under stress. Well, that means that
they can't extend loans in the same way, you know,
corporate as well as real estate. So we started staffing
(34:24):
up in those areas to take advantage. And now I'm
convinced that the there's now going to be a structural
shift in credit provision in the US economy, that the
banks are no longer going to be the mainstay for credit.
And that's because the government has effectively guaranteed our banking system,
which creates moral hazard. We have on the order of,
(34:48):
you know, forty three hundred banks in the United States.
It's a lot, especially when you compare it to Canada
that's got the big you know, handful, and you know
when you deposit money in the bank, that bank is
lending it out long.
Speaker 2 (35:04):
And fractionally reserving it. So it's ten to one, whatever
the precise the leverage there using.
Speaker 3 (35:10):
So I think that the whole fractional banking system notion
is challenged, particularly in the idea of the ease of
information transparency among depositors coupled with the necessity for government
guarantee and moral hazards. So private credit firms like ours
(35:31):
people invest in Hudson Bay and they know it's not
a bank account, and that gives us license to deploy
the money in ways that are appropriate, and so we
began staffing up in those areas. And now in real estate,
for instance, we have teams that work in real estate equity,
in CMBs, distress CMBs, and direct provision of real estate credit.
(35:57):
And as part of the core vet you've Hudson Bay.
These teams work together, which give us a better understanding.
It's a great advantage to have equity teams working with
credit teams, particularly all real estate's local It gives us
a much better understanding of the asset that we're looking at.
Speaker 2 (36:16):
Huh, that's really kind of interesting. You know, Ever since
the financial crisis, some of the new regulations and bank
regulations directly led to the rise of private equity, private credit.
You know, some of the forecasts are over the next decade,
this blows up to a thirteen trillion dollar asset class.
Speaker 3 (36:37):
I think we're in the third inning, early early days here, Yeah,
I think so.
Speaker 2 (36:41):
And it it feels like it's been so big because
we started with practically nothing in that space, and the
first couple of trillion dollars felt like, oh, my goodness,
is just so much capital washing over this. But this
seems to have happened in the past where woll banks
and brokers kind of move up market, they create a
(37:03):
void in the space they left, and private money rushes
in to fill that void. Is that what's going on
with private credit and real estate?
Speaker 3 (37:14):
Well, it's still early in that. I think it's a
golden age for real estate credit. The banks are not
able to they don't have the capital now to lend,
and so there's it's open season.
Speaker 2 (37:27):
Huh. Really really interesting. So how do you identify opportunities
in the real estate space. It seems like there are
so many buildings that are half empty, and yet it's
a slow motion train wreck because most of their tenants
have ten or longer year leases and they're just slowly
(37:49):
starting to recognize unless you're a super A class building,
even A buildings are having a hard time attracting renewals
and tenants. How you identify these and how far along
the repricing of commercial real estate or at least offices
do you think we are?
Speaker 3 (38:09):
Well, those are big questions. And I'm from Annaburg, Michigan,
and I saw how in Detroit, Detroit was going to
be called the museum to the desolate city because downtown
Detroit went empty when they built the Renaissance Center. Everyone
moved to the Renaissance Center and left these empty, huge
buildings in Detroit. And you see aspects of that now
(38:32):
where the A buildings, the new buildings are attracting very
high rents and buildings in other areas are you going empty?
So to understand what's going on, you really have to
understand the asset, and so that's why it's important to
have teams from different disciplines being able to understand the asset. Obviously,
(38:54):
looking through the rent rolls and understanding you know, the
weight to average lease, but also understanding the macro environment.
You know, are things growing And we have so much
uncertainty now going on, not just because of work from
home with Zoom, but also the longer term implications of
AI and what's that going to mean for the workforce
(39:16):
and even cities like New York City. It's possible that
we're not going to need the same number of junior lawyers,
junior accountants, junior bankers.
Speaker 2 (39:26):
So I've heard some people discuss AI as a tool,
and it's not that you're going to lose your job
to AI, but you're more likely to lose your job
to someone working with AI. Is that a fair assessment
or is it just still way too early to take.
Speaker 3 (39:42):
I think we still don't know. I think AI is
the greatest change in my lifetime, bigger than the Internet.
I think so, yeah, really yeah, because the ability for
natural language processing goes far beyond what I thought was possible.
You know, I studied linguistics a bit in college. The
whole idea of how we form language is a fascinating subject.
(40:05):
And now the computer is able to be coachent in
their responses, We've you know, kind of approaching hard AI
in a way that I did not think was possible,
and it's only going to get better.
Speaker 2 (40:19):
Let me push back a little bit. And I'm not
necessarily saying I believe this, but so I've had this
conversation over and over again with a number of different people.
How are you using AI in your daily work? What
are you finding? And someone who hosts a different podcast said,
(40:40):
they created this really interesting set of prompts with AI
to get an answer to how to do certain things,
and the first time they got the answer, they were
really impressed. Oh my god, this is a genius insight,
and look how smart this is and how it it
figured out exactly what I needed. And then they asked
(41:03):
a different question with a different subject kind of got
the same answer, and it was like, oh, this is
a party trick. This isn't really intelligence. It just looks
like intelligence, and even though it's getting better, it's still
kind of dumb relative to it impresses us. But once
(41:26):
you peer behind the curtain and see the wizard is
just a man, you figure out this is less what
it purports to be in more like a very useful,
clever trick.
Speaker 3 (41:38):
I was thinking of a Wizard of Oz also while
you were while you were saying that, But I don't
think there's a guy behind the curtain that's giving the answers.
That's why I think that it helps with the junior
analysts that you have to check anyway, and it certainly
speeds up the research process in ways that were not
possible before, for sure, and it's only going to get better.
(41:59):
And it may makes mistakes, but the junior analyst makes
mistakes also. I mean, I've used it for things my
lawyers probably will hate me, but sometimes when I've had
a discussion with the lawyers on how to express something
in a document, to all ask AI the question. It
will give me a range of possibilities and enables me
then to be more on a level playing field with
(42:20):
my lawyers who have had a lot more experience than
I have. But it has enabled me to bring to
the discussion insights that we might not have thought of.
Speaker 2 (42:28):
I'm glad you brought up the attorneys, because a judge
just sanctions a lawyer for using AI and in certain
of his answers, and this unfortunate tendency to hallucinate. I
don't think the problem was that he used AI to
help him in research. He didn't double check it, and
(42:49):
he failed to disclose that AI was plathiness.
Speaker 3 (42:52):
You know, it's just plain laziness. The the AI is
good for the junior person, and I think as implications
for the workforce, you know, what is the workforce going
to look like? Given that, maybe we don't need the
same failans of junior accountants, junior lawyers, junior bankers.
Speaker 2 (43:12):
How do you become a senior account lawyer, banker if
you're never a junior It's a tough question. So let
me give you an opportunity to update your twenty twenty
one piece in investing. Don't short human judgment? Do you?
Are you still holding that for you?
Speaker 3 (43:29):
Absolutely? I mean we are in the human judgment business. Really,
we are trying to beat the machines. We do that,
as I said, through understanding uncertainty, events, catalysts, and change,
and I think ultimately human judgment is superior in the machines.
(43:49):
I hope we won't go into a Hell two thousand
type situation that human judgment will always be superior. You
wouldn't want to have a machine be the a in
the United States. How could a machine possibly make those decisions,
you know. So obviously human judgment will always be there,
and I don't think that we're at a terminator type,
(44:11):
you know, situation, but there are certain experts that say
that ultimately that's where we'll go. I mean, I do
know that in the military, you know, the idea of
robots creating robots is a real idea, and it very
might well change battlefield dynamics. But I believe that certainly,
(44:32):
at this point in time, the human capacity to ingest
a mosaic of information and to make the right decision
is superior. If you take a chessboard, the machine can
beat the master, but if you put an extra bishop
on the board, the machine can't deal with it, right,
(44:53):
And I think that's the paradigm. And life does not
mimic a chessboard, you know. Life mimics the chessboard with
extra pieces being put on randomly, and it's that randomness
that I don't think the machines will be superior than
human judgment. Now, it might appear at times that the
machine can beat the human, but I think ultimately the
human judgment is superior, and so our business is based
(45:16):
on human judgment.
Speaker 2 (45:18):
You mentioned the wartime usage of AI. There was a
pretty big article I don't remember. I want to say
the Times, not the journal, that figured out that in
the Ukraine Russian War, which started out as a conventional
bombardment between tanks and mortars and anti tank weapons, over
(45:38):
the past six twelve months, seventy percent of the casualties
have been drone AI warfare driven, and it's very much
a brave new world. It's not like the old world
of warfare. What it sounds like you're suggesting with AI
is that they're both code developed, that you'll still have
(46:01):
humans driving the process, but AIS become an increasingly large
part of it, regardless of whether we're talking about warfare,
business or investing. I don't want to put words into
your mouth, but is that a fair way to assess that.
Speaker 3 (46:15):
I think so. I mean, I think that the humans
always have to be on top of the machines. Machines
have a lot of latitude, both to produce themselves as
as well as to target. You know, the markets are
different because the markets follow a behavioral dynamic. The evaluation
of risk versus reward is something that I think a
(46:36):
machine cannot do in the same way the human can.
Speaker 2 (46:39):
So given some of the volatility we've been seeing in
the first quarter of twenty twenty five. Has that changed
how you're looking at your models, how you're viewing your
approach or is it, Hey, this is just another one
of those things that comes along and we have to
be able to trade through.
Speaker 3 (47:00):
We actually like the dislocation because the dislocation proves the
models are wrong.
Speaker 2 (47:05):
Well, I know you guys don't release public performance numbers,
but I know you're doing much better than your benchmark
this quarter. Volatility is your friend. Is that what you're saying?
Because volatility disrupts traditional models and you're a non traditional model. Correct.
So I know you've worked with Harry Markowitz. What other
(47:26):
academics and what other institutions have you worked with?
Speaker 3 (47:29):
Well, at Imperial College London, there's further work being done
on the Gerber statistic and incorporating it. The idea of
thresholding data and ways to do it to For instance,
if you want to understand the significance of a stock
price movement, maybe you should exclude days where there's very
(47:50):
low volume and only include days when there's high volume.
There's a variety of ways to incorporate it.
Speaker 2 (47:57):
I know, I only have you for a limited amount
of time. Let me jump some of my favorite questions.
I ask all of our guests, what are you watching
or listening to? With? What's keeping you entertained?
Speaker 3 (48:07):
Recently I streamed Eastern Gate?
Speaker 2 (48:10):
Oh really?
Speaker 3 (48:11):
Which is I saw in the New York Times. It
was this spy thriller series on the conflict between Poland
and Belarus, and I wanted to understand the dynamic between it.
So I thought I'd get a little entertainment and understand
something I couldn't pick up here. And it's a little slapstick,
but I think it's worth it.
Speaker 2 (48:30):
Eastern Gate. Yes, did you happen to watch any of
Fouda when that was just the most heart wrenching stuff
to watch? It's so stressful.
Speaker 3 (48:40):
Yeah, and pretty realistic.
Speaker 2 (48:42):
Very realistic. Let's talk about mentors who helped shape your career.
Speaker 3 (48:47):
I gotta give a lot of credit to Dave Patrice.
Speaker 2 (48:50):
Who I know that name, who really.
Speaker 3 (48:53):
Helped me get into shape. And he was on my
case every day, the diet, the working out. We're workout partners,
and I was thirty five forty pounds heavier, uh huh,
and he got me to recognize they needed to get
in shape. I thought I was in shape, but I
(49:13):
wasn't in shape. I think I think a lot of
people think they're doing okay when they could do a
lot better. And he taught me I could do a
lot better. And I think it's affected me overall, my
mental acuity, my mood, my stamina. I really give him
a lot of credit.
Speaker 2 (49:30):
You mentioned books earlier. What are some of your favorites?
What are you reading right now?
Speaker 3 (49:34):
One book that I really enjoyed, which was long, was
Walter Isaacson's book on Elon Musk, which I read before
the election, and it made a big impact on me
because I believe in questioning the experts, but must takes
it to a different level. He's questioning metallurgical properties that
were well grounded in science and engineering, and he's saying,
(49:54):
why does that have to be? And oftentimes he was
right that the established can census regarding properties of medals
was wrong.
Speaker 2 (50:04):
M really really interesting. Any of the books you want
to mention?
Speaker 3 (50:09):
I read The Melting Point by Frank Mackenzie recently. He
was the head of Scentcom and he talked about what
it was like to lead Sentcom and he also had
a MA He measured in English, and he thought that
his English background to be a commanding general was very
helpful because I helped him to articulate better and to
(50:33):
form consensus, you know, among his colleagues.
Speaker 2 (50:36):
Really really interesting. Our final two questions what sort of
advice would you give to a recent grad interested in
a career in either filling the blank, investing options trading,
multi strategy management. What advice would you give to them?
Speaker 3 (50:54):
I think it's, you know, across all certainly service occupations,
is you got to be will beat the machines, and
to do that, you need to be independent thinker. You
need to go against the grain, question the experts. You
need to be able to do that. You need ab
to work with other people, to learn from them, to
(51:16):
expand your horizons, to expand the mosaic that you can
bring to your independent thinking. And you got to be
able to respect your colleague. So I think that those
three things are a real guideposts for people.
Speaker 2 (51:28):
This goes back to your corporate culture, which your environment,
corporate environment, my bad, your corporate environment, think independently, collaborate
and respect the individual. Correct huh? And our final question,
what do you know about the world of investing in finance?
Today would have been useful when you were first getting
started in the early nineties.
Speaker 3 (51:51):
I think that you know everything you learn in business
school or economics, you can just throw out the window
economics and of science. People try to portray economics as
a science, and it simply is not. And so all
the notions that we brought up regarding money supply, you know,
Milton Freem would be turning over in his grave. Even
(52:13):
though these principles might have some grounding, It's not scientific,
you know. This is this is not a natural science.
It's a behavioral science, and it's based upon how people
interact with each other. And I think that that appreciation
leads to the notion that oftentimes the academy or the
experts try to profer things that everyone everyone seems to
(52:40):
believe one way, and you think, how could I be right?
Because everyone believes one way because this is what they
studied in school, and if the authorities say it's that
one way. And I think that as you go through
life and you age, you realize that the Ivory Tower
isn't always correct. In fact, a lot of times the
Ivory Tower doesn't have the real life experience, and so
(53:01):
they're flat out wrong.
Speaker 2 (53:03):
I'm trying to remember where I'm stealing this quote from
Science Advance's One Funeral at a Time. The same is
true with other things that Dick Thaylor said. Rather than
wait for the rest of economics to catch up with
behavioral finance, I'm just going to teach it to the
(53:23):
younger generation and it'll infiltrate much more quickly than waiting
for all of my peers to accept it. Really really fascinating, Sander.
Thank you for being so generous with your time. We
have been speaking with Sandra Gerber. He is CEO and
CIO of Hudson Bay Capital. If you enjoy this conversation, well,
(53:46):
be sure and check out any of the previous five
hundred and fifty we've done over the past eleven years.
You can find those at iTunes, Spotify, YouTube, Bloomberg, wherever
you find your favorite podcast. And be sure and check
out my new book How Not to Invest The Ideas,
numbers and behavior that Destroys Wealth out today wherever you
(54:10):
find your favorite books. I would be remiss if I
do not thank the correct team that helps put these
conversations together each week. John Washerman is my audio engineer.
Ana Luke is my producer Sean Russo is my researcher.
I'm Barry Ritholtz. You've been listening to Masters in Business
on Bloomberg Radio
Bloomberg Audio Studios, podcasts, radio news. This is Masters in
Business with Barry Ritholts on Bloomberg Radio.
Speaker 2 (00:16):
Strap yourself in for another good one. Sander Gerber, CEO
CIO of Hudson Bay Capital. What a fascinating background he has,
starting in philosophy and ending up on the floor of
the American Stock Exchange as an equity options trader. That experience,
(00:36):
those two things combined to really create a kind of
unique perspective on the world of markets, on the world
of risk, and on the world of models. You know,
I've used the George Box quote a million times. All
models are wrong, but some are useful. And the way
Gerber goes about using models is very much along the
(01:01):
George Box lines, which is, not only are we going
to assume that models are wrong, but we want to
create our own models to be able to identify when
they're going to be at a great variance to what's
going on in reality, and then how to position ourselves
to take advantage of it. They're less directional traders than
(01:21):
they are arbitreasures. Hudson Big Capital runs a dozen different
strategies and they're all quite fascinating. Everything from risk, arb
to private credit and real estate in the first quarter
of twenty twenty five, where volatility spikes and a lot
of people's expectations are dashed. Their models do really well.
(01:46):
I find his depth of knowledge and his technical expertise
to be absolutely fascinating. I think you'll find him to
be fascinating also, with no further ado, my conversation with
Hudson Bay Capitals Xander Gerbert. So let's start a little
bit with your background bachelors and humanistic philosophy and an
(02:09):
MBA from Wharton Finance. What was the career plan?
Speaker 3 (02:13):
Well, actually, I was good at math, so I first
entered the Wharton School undergrad. I don't have an MBA
from Wharton. And then when I was at Wharton, I
didn't think I was getting an education, so I decided
to transfer into the College of Arts and Sciences, so
I got two degrees. Concurrently, I picked up a degree
in philosophy. Humanistic philosophy. I wanted to understand the development
(02:37):
of thought, how we got to where we are in.
Speaker 2 (02:39):
Society, epistemology or something more specific.
Speaker 3 (02:43):
It was moral philosophy, generally, starting with the ancient Greeks
through the existentialists. I think that I used my philosophy
background much more than my finance background, because it really
gives you a different view on the world. When I
was at Wharton colleg Andrew Krieger came in nineteen eighty
seven to speak. He had majored in Sanskrit Eastern philosophy
(03:10):
and then he got his MBA at Wharton and he
was the leading FX trader at Banker's Trust. And he
spoke about how his philosophy Eastern philosophy helped him understand
the markets. That you might feel very convicted the markets
should go a certain way, but the markets have their
own mindset and you have to accept what the markets have.
(03:30):
And it helped him emotionally to trade better because he
realized that mother market was going to be right, and
so it was from his philosophy background that he was
able to reconcile that with him with his beliefs in
terms of where markets should go, and it helped him
to be a better trader.
Speaker 2 (03:47):
That I definitely can see that. You know the concept.
I don't know if I'm stealing this from Zen Buddhism,
but it's the water flows, but the rigid tree breaks
in the storm, and it's very similar to, hey, that's
an Eastern way of saying, why are you finding the
trend exactly?
Speaker 3 (04:04):
And so, you know, when I was in college, I
really didn't know much about the markets. And as I
told you, I still I had entered first the Wharton School,
so I was still getting my degree there, but I
was really focused on the philosophy. And you know, people
think the philosophy is not so practical, what are you
going to do with it? And here the top FX
trader in the world came and said, this is what
you should be doing. So it was it was sort of,
(04:26):
you know, ratification of what I was studying.
Speaker 2 (04:30):
Huh. I think you're the first person who I've ever
spoken to who said, yeah, the Wharton School of Finance
at University of Pennsylvania not a great education. Isn't it
really true that most of our education, or at least
for a lot of people, you're just self taught. Schools
will give you a curriculum and here's the reading list,
(04:51):
but it's up to you to kind of learn whatever
there is to learn.
Speaker 3 (04:56):
I think it's a good point. You know, the Wharton
School is arguable the finest finance school, but finance is
a technical discipline, and I wanted to understand the world.
And I think that you can only go a certain
degree using that background. And it's true. Then in order
to I think, upgrade yourself, you've got to be able
(05:19):
to develop the capacity to self learn, to take in
from the environment around you, to enable yourself to grow
your skill set to your experiences through working with others.
And that's something we try to incorporate within Hudson Bay
is the ability for people's careers to develop, and it
(05:40):
is something that you have to rely on self learning
and within college in certain disciplines. In college, like in philosophy,
a lot of it is you know, discovery, self discovery,
and other disciplines there is no self discovery. So I
think it is important to the humanistic background.
Speaker 2 (05:58):
So you come out of of Wharton and University of Pennsylvania,
you start your career on the floor of the American
Stock Exchange as an equity options market maker. That had
to be a fascinating experience, especially nineteen nineties and two thousands,
that was a hot period and option trading. Tell us
(06:19):
a little bit about that experience.
Speaker 3 (06:21):
Well, actually, when I graduated Penn I had been I'd
clerked on the floor of the Philadelphia Options Exchange in
nineteen eighty seven, and I liked it. But my parents
had spent all this money to send me to a
fancy school. They had taken out a home equity loan
to pay for my college tuition. So I thought to
be a muslely floor trader would be disrespectful. So I
(06:43):
went to Banning Company for two years, and I was
in management consulting for two years. It was boring, but
I did learn something from it, and then I came
to the floor of the AMEX.
Speaker 2 (06:54):
Wait before you jump to the AMEX. Aside from learning
that being was boring, what else did you learn?
Speaker 3 (07:02):
I learned how people can work together in good conscious
with dedication and still muck things up. Because what we
would do is we would parachute into places like British Airways,
Montreal Trusts, uh CIA Industries, and we were like the
external strategic planning and we would They would put young
(07:25):
people like me, and we'd sit next to people and
interview them and figure out why projects went to muck.
And I understood from that that well meaning people can
still muck things up because they don't have an appropriate
guide frame or appropriate leadership. Or they're not so like
little things can take projects astray.
Speaker 2 (07:47):
So what was it that drew you to the floor
of the well?
Speaker 3 (07:50):
I enjoyed the Philadelphia floor, and also I was I
always liked games, and so I and I had a
talent I thought for trading, and so I went to
the the AMEX. Someone gave me it was like eleven
hundred dollars a month as a stipend, and I kept
(08:12):
roughly half the profits. And there was no training. They
just threw me there very in the deep.
Speaker 2 (08:17):
End of the pool. Whoever doesn't drown. Hey, you can grab.
Speaker 3 (08:20):
Exactly right, exactly right. And it took me from July
of ninety one till December of ninety one. I made
five hundred dollars profit. Not for me, five hundred dollars.
Trading had a split which I had to split. Yes, well,
actually because I had a draw, I didn't get anything.
But then the next year I took off and it
(08:41):
turned out that I did have a knack fort I
was able to understand the volatility of the market. Is
usually we're vol traders, and I did something that was
two things that were novel on the floor. The first
is I understood that you have to break down your
volatile exposure month by month, which back then was unusual.
(09:04):
In other words, people had these models that would give
you one volatility exposure across the entire portfolio. And I
realized that julys and earnings month, and August is a
beach month, so you can't use those two months to
offset each other. And so I was able to jerry
rig the models that were early then to be able
to look at my VEGA exposure month by month. That was,
(09:24):
believe it or not unusual. And the second thing that
that's early nineties is yes, that was ninety one, ninety two.
Speaker 2 (09:31):
Okay, all these things we kind of take for granted.
I know, right at one point in time, you wonder
why it's become so increasingly difficult to beat the broad index.
It was a ton of inefficiencies, that's right, that's right.
Speaker 3 (09:43):
And it was a great edge for me to come
to that realization. And maybe it was because I had
studied the models at the Wharton School. We had broken
them down, and I understood that the models are only
as good as the inputs. And a lot of people
back then were doing spreads in their head and the
other group were using these canned models that would give
you one volatility exposure across you know, the entire model.
(10:07):
And the second thing that I realized was that you
need to combine fundamentals with the technicals of the models.
In other words, the models assumer normal distribution of returns,
but when you get into some kind of event, it's
no longer a normal distribution returns. It's you know, the
(10:29):
stock's either going to go up a lot or down
a lot. That's a barbell distribution, right as opposed to
normal distribution. And so by looking at events and when
they're going to happen and breaking down the VEGA exposure
month by month, that gave me an edge that I
was able to exploit. Do you find vega for listeners
who are Vega is the volatility So of the an
(10:53):
option has premium, and that premium is the extra amount
you pay for the right to have limited loss and
unlimited gain. And so that premium, that value of that
option to exercise or not exercise with limited loss, goes
up and down in value based upon the degree of movements.
(11:16):
So when something's moving around a lot, that has a
lot more value. So premium value goes up when things
are not moving a lot, premium value goes down, and
so by trading this range of volatility up and down,
which is in part dependent on what's happening with the
fundamentals of the stock, you were able to grab edge.
Speaker 2 (11:37):
So these are really second or third level derivatives. It's
not the underlying value. It's the increase in value of
the option and then within that the range and the
variability of that increase in option value. That's what you
were trading.
Speaker 3 (11:53):
Yes, and you know it's really not complicated. I mean
Wall Street tries to make things much more more complicated
than they are, but the simple, elegant solution is always better.
So it might sound complicated, but it's really not right.
Speaker 2 (12:09):
And that complexity is a feature, not a bug. You
can sell stuff if it's complicated and hard to understand.
If it's simple, well I think I could do that.
Speaker 3 (12:19):
That's right. Wall Street tries to make things more complicated
because it has to justify the sales commission and if
but things really are not so complicated.
Speaker 2 (12:29):
So what was your biggest takeaway from your experiences as
a trader? How did it shape how you look at
the world of investing, How did it affect what you're
doing in Hudson Bay today.
Speaker 3 (12:41):
Well, I really was grounded by that three and a
half years of watching every tick on the stock. You know,
and you're you're geographically limited on the floor. You can
only trade at the post that you're standing by.
Speaker 2 (12:54):
Like physically in space, your physically heether to that trading
thing exactly.
Speaker 3 (13:00):
And there are even rules that you had to do
most of your trading in that geography, so you couldn't
move around a lot. And what it taught me is that,
you know, like a trading post, a strategy goes in
and out of favor, and if you want to be
able to make money in all markets all the time,
you have to develop a toolkit that can go beyond
(13:22):
one particular strategy. So you need to have multiple strategies
to develop persistent profitability. The other thing that I learned
was that you can make the right decisions and still
lose money. I had plenty of time where looking back,
it was the right decision, but the markets thought differently,
and so you always have to be worried about what
(13:43):
can go wrong. And risk is not about not losing money.
Risk management is not about not losing money. Risk management
is about unexpectedly losing money. In other words, when you're
evaluating a situation, you should know what is your reason
worst case downside. Now there's always the black swan that
(14:04):
maybe you can't figure on, but you should. But risk
management is always about understanding what could go wrong and
quantifying what could go wrong.
Speaker 2 (14:14):
So I want to unpack what you just said, because
it's filled with goodness. First, you're referring to your approach
is Hey, we're really more process focused than outcome focused,
because if you have a good process, even if you
get a bad outcome, it doesn't matter. Probabilities will eventually
work in your fay.
Speaker 3 (14:35):
Exactly right.
Speaker 2 (14:36):
That's number one. But then the part two, which I
think a lot of investors overlook, is and a risk
management component that if the worst case happens, we still
survive and lift to trade another.
Speaker 3 (14:50):
That's right, exactly right. And so at Hudson Bay, I
created the deal code system, uh.
Speaker 2 (14:56):
Deal code system.
Speaker 3 (14:58):
Yes, so at the time, well, I left the floor
beginning of ninety five and started deploying just the money
I'd earned on the floor in an off floor trading account.
And I would develop a strategy and hire someone else
to run it and develop another strategy and hire someone
else to run it. And as I was having other
people manage basically my trading account, I realized I had
(15:24):
to scale my risk profile that I developed on the
floor over multiple risk takers, and I needed to do
it in a manner that would produce persistent profitability. So
at the time, we were trading a lot of risk
garbitrage deals, so we called it a deal code, and
a deal code is just a numerical moniker that we
put on each trading idea within the book, and that
(15:47):
enables us to focus in on how is that trade hedged,
what's the risk riskiness? How much could that trade lose
in a reasonable worst case scenario, and it gives us
a batting average, so we can under stand is a
portfolio manager winning more ideas than they lose so to
be persistently profitable. I think it's not just about winning
(16:08):
more dollars than you lose. It's about winning more ideas
than you lose.
Speaker 2 (16:13):
So let's talk a little bit about Hudson Bay's strategy.
You've been managing outside capital across a variety of asset
classes and strategies. Tell us talk about some of the
key strategies and what has been the drivers of making
those strategies successful. Well.
Speaker 3 (16:33):
As I mentioned, I wanted to be able to make
money in all market environments, so you need a tool
set to do that. So our strategies are equity long
short converts, credit, event merger, volatility trading.
Speaker 2 (16:48):
This isn't just I'm going to buy the S and
P five hundred and put it away for a decade.
You're active traders, and you're really looking to take advantage
of situations where you have a fairly good idea of
what the outcome is going to look like. It's not hey,
this is open ended. Usually you're pretty confident in here's
(17:08):
what our range of potential outcomes are.
Speaker 3 (17:10):
I think that, especially in today's world, you have to
understand what your edge is versus the machines. And a
machine can calculate risk based on historical precedent, but a
machine cannot calculate risk based upon some kind of uncertainty
due to some kind of event, callous or change that's
coming up because it's new. So the machine doesn't have
(17:32):
the ability to calibrate for something that's new. And so
generally across all our strategies, that's what we're focused on,
is we're focused on event callous change. How can we
profit off of that in a way that machines cannot.
Speaker 2 (17:45):
So that's the fundamental criticism of models. All models assume
that the world in the future is going to look
like the world in the past. Risk management is what
happens if the world doesn't look like out at.
Speaker 3 (17:57):
Us precisely, And that's why we don't use the standard
risk management models. I actually created a statistic, the Gerber statistic,
that helps to understand diversification between our deal codes, between
our investment positions. A lot of our competitors are tied
to factor based modeling, which ultimately, underneath it is reliant
(18:19):
on regression analysis. Regressions. Our straight line fits through normalized
sets of data, and human relationships don't file straight lines,
and certainly market relationships don't file straight lines. So using
that as the underpinning of a risk management system is
just incorrect. And so we've created a whole different structure.
(18:42):
As I said, we've used since nineteen ninety eight, and
I think that's given us the ability to weather storms
and profit from it in ways that our competitors can.
Speaker 2 (18:52):
So let's talk a little bit about the Gerber statistic.
You had this validated by Harry Markowitz, the creator of
modern portfolio folio theory. Tell us about that collaboration and
break down the Garberg statistic a little bit. How do
you guys actually use it?
Speaker 3 (19:14):
So, because of my distrust of models, based upon my
experience on the floor, in particularly the guts of the models,
I never believed in the correlation statistic, that correlation is predictive,
and this was I thought one of the underpinnings of
modern portfolio theory that you look at the expected return
of the stock, the expected variants of the stock, and
(19:37):
the covariance of correlation between the different components of a portfolio.
And at the time, you know, we used the deal
code system and on Wall Street the banks were telling
me this is nonsense, but don't even talk about it
with investors. And then in eight when everyone lost money
and we made money, I realized we were doing something different.
(19:59):
And then I had the idea. Because of course I'd
studied about Harry in modern portfolio theory. Everyone in finance has.
He won the Nobel Prize. I decided, you know what,
I'm going to go out to see him to see
what he thinks about the Gerber statistic, and at the
time it wasn't called the Gerber statistic. But a friend
of mine said, gee, you really should file a patent
on this before you see Harry, and so I did,
(20:20):
and I had to name it something, so I called
it the Gerber statistic. And we now have I think
we just got our sixth patent on our process for diversification.
So I got to see Harry in San Diego. Lovely guy.
He welcomed me, and we're walking. He liked to walk
along the beach and I said, Harry, you know, I
don't think that correlation's predictive, and Harry said, you're right.
(20:43):
I said, no, no, Harry, you don't understand it. I don't
think that because it's one of the base foundational bases
for what She won the Nobel Prize in Modern portfolio theory.
Said Harry, I don't think that historical correlation has relevance
to the future. And he said, you're right. And it
turns out that in his nineteen fifty two paper that
sets forth modern portfolio theory, he said that correlation should
(21:06):
be determined by the judgment of practical men. In other words,
the stock analysts should think what will be the relationship
going forward, not to mind the past, but be forward looking.
But in the nineteen sixties, as computing power increase, people said, oh,
we can mind the statistic, this row statistic correlation, and
then we can plug it into the model as correlation.
(21:29):
He meant correlation in a semantic sense, not in a
mathematical sense in terms of using in his model. So
he actually said that the deal code system uses his system,
the modern portfolio theory system. He said that there's three
legs to his system. And so because we use limited loss,
(21:49):
because we seek to diversification through hesing on the own,
because we seek to win more than we lose in
each investment idea, he said that is accordance with his system.
But in any way, we we've written several papers together
on the Gerber statistic within modern portfolio theory and have
demonstrated that you get better performance with less risk by
(22:12):
replacing historical covariance with the Gerber statistic. And Harry and
I actually we only had really one disagreement, and the
one disagreement was on factors. There's all these you know,
factor methodologies, and Harry believed that only one factor matters
for portfolios, and I think two factors matter, so and
so that, but the other twenty three factors we both
(22:34):
agree are complete nonsets.
Speaker 2 (22:36):
So if you look at the fomb of French model,
which started out as two or three factors and then
became five fact.
Speaker 3 (22:44):
And then grow and grow. If you speak to the
research departments of bar Axioma, they'll tell you that thirty
four to forty percent of a stock price movement can
be explained by factors.
Speaker 2 (22:57):
Okay, so that's third, let's.
Speaker 3 (22:59):
Roll it a and of that third, eighty five percent
of that third can be explained by the first five factors, okay,
which means giving credit to five, which that's bar Naxioma
tells you eighty five percent of the forty percent can
be explained by five factors, which means the other twenty
factors explain the fifteen percent of forty percent of the words.
(23:22):
Six percent of a stock price movement can be explained
by twenty one factors, right, meaning which is complete. You know, nonsense,
but no, if you lever a portfolio up, you know
ten times, all of a sudden, that six percent looks
like it's sixty percent, but it's all complete nonsense. It's
numerical mumbo jumbo. It's part of the whole Wall Street
(23:44):
pizazz that is not based on reality. But you know
it sells.
Speaker 2 (23:49):
So so I want to guess the two factors. If
I had a guess, I'm going to rely on a
paper by Wes Gray of Alpha Architect and guess it's
value and momentum. But I'm curious what you feel.
Speaker 3 (24:01):
Well, actually, Harry thought it was market. I think his
market and section.
Speaker 2 (24:04):
So is market and sector. But are those really factors?
Do we really The.
Speaker 3 (24:07):
Whole idea of factors is kind of like, you know,
a little nonsense. It's like beta, you know, like market
we think of as beta. It's now been called a factor.
Speaker 2 (24:19):
So oh, I never really thought of beta as a factor.
It's just it's, hey, if you do nothing, you get
But that's market, you know, So huh, that's really it.
So you're looking at the sector it's in and the
overall market as the two driving facts.
Speaker 3 (24:34):
I think those are Now it's true that momentum, value,
these other things are relevant today because everyone else has
glommed onto it, because we have so many statistical, process
driven strategies that try to trade momentum. You know, buy cheap,
sell expensive. It pushes everything in line. And this is
what I found on the floor using models to trade options,
(24:57):
that the models would push the the values of the
options into alignment in accordance with the model because everyone's
using the same model, and so the same thing is
true in the broader market because everyone's using basically the
same factor models. It pushes things in alignment, which works
in normal market environments. But when things you know, have
(25:19):
a dislocation, it no longer works, which is why people say, oh,
our risk model broke down or whatever, because these aren't
really risk models.
Speaker 1 (25:26):
Now.
Speaker 3 (25:26):
It's one thing to use a model to trade because
the model's telling you something is some expensive or cheap.
Speaker 2 (25:34):
And relative to history.
Speaker 3 (25:36):
Right, And if something's always cheap, you just adjust the model.
So there's a validity to that. But that's different than
using the same model for risk management. Risk management, again,
is about avoiding unexpected loss.
Speaker 2 (25:49):
Huh. That's really interesting. So when I started on a
trading desk, one of the things that I was always taught,
which I never contextualized as a factor, is, hey, what's
driving the stock? Well, the stock is only a tiny
part of it. The stock is twenty percent, the sector
(26:10):
is thirty percent, and half is the market. So you
could be the greatest stock in the world. If the
market's going down, it doesn't matter, and it could be
a really good stock. But if it's in a terrible sector.
You know, the metaphor was always great house in a
crappy neighborhood is a crappy house. You're really putting that
into the context of these are the broader factors that
(26:32):
are affecting that single holding.
Speaker 3 (26:34):
That's right, that's right. And you know, in our at
Hudson Bay, we seek to produce the alpha. So it's
true that the market is moving the stock, but we
try to pick stocks that outperform the market or pick
shorts that will go down more than the market. So
we seek to focus on the alpha provision.
Speaker 2 (26:54):
So let's talk about something related to this. A paper
you published, environment eats culture for lunch. It sounds like
the environment is what the market's doing with the sector is,
but give us a little detail about.
Speaker 3 (27:08):
Well, actually, I mean that that paper was related to
the human aspect, not the market. So Peter Drucker came
up with this idea that culture eats strategy for breakfast
that corporate culture is actually more important than corporate strategy
for the success of a firm. I think there's a
(27:29):
lot to that that, you know, the way people work
together in an organization. But I've always thought that this
corporate culture thing is nonsense. If you have people try
to describe their corporate culture, they cannot articulate it, right,
you know, like what's the corporate culture here at Bloomberg,
you know, like.
Speaker 2 (27:47):
Fun data driven? It's all about data, So you come up.
Speaker 3 (27:50):
On data driven. It's not a culture. Data driven is
a process. But I'm talking about what's the human aspect
of it? What's what's the human culture.
Speaker 2 (27:57):
I'm the wrong person to ask that, right.
Speaker 3 (27:59):
Because because no one can really describe corporate culture, what
you can describe as an environment. What is the environment
that people work within? And I kind of learned this
at Band and Company because Baine was described as this
like fun loving place, everyone has fun. And then when
I was there, two guys died on the locker bee
crash and Bill Bayn had milked the esop and so
(28:21):
the company almost collapsed. When I was there, they fired
half of my class, not me, They fired all the
incoming MBAs and it was the avarice of Bill Bain
that nearly collapsed the firm. We're talking back in nineteen
eighty nine to ninety.
Speaker 2 (28:38):
So the corporate culture was with pacious greed, well did
you know, and almost destroy.
Speaker 3 (28:44):
It was inauthentic. And when people try to describe culture,
they can't. And so what I wanted to do was
to describe an environment. What is the environment that you
want to work within? And you know when you speak
to when you speak to people on other firms, what's
your corporate culture? What's your value statements? Usually these things
(29:04):
go on and on and on. No one can really
remember all the value statement. If you can't remember your
value statement, it has no value.
Speaker 2 (29:12):
I'm going to imagine that twenty two twenty three when
all the big firms were saying, we want our employees
back in the office. We don't want any more remote work.
It's a matter of corporate culture. How did you think
about that? Was this a legitimate demand and is it
(29:33):
not so much corporate culture? But we want an environment
where people are in the office working together. Is that legit?
Speaker 3 (29:39):
I hate going to the office and seeing people not there.
I think that people should work together. On the other hand,
You can't force these things. You can't force independent thinking,
you can't force collaboration. You can have an environment that
engenders it, and so we try to have an environment
that engenders it. So it's my opinion that people who
(30:01):
come to the office are going to succeed more than
people who don't. Now, I understand that, you know, the
commute is a hassle and sometimes people, you know, want
to take the day off, and so you know, our
standard is two days in the office. Many teams have
a third day, but a lot of people. Usually people
are in our office three to five days a week.
(30:22):
But we don't force it. If once you force people
to be in the office, I think you're losing this
spree de corps. We want people to want to work
at Hudson Bay. If they don't want to work at
Hudson Bay, they should go elsewhere. But to force people,
I think, you know, for high performers, I don't think
that's the way to engender the right environment.
Speaker 2 (30:42):
And environment beats culture for work because the work environment
is more important than some statement that nobody remembers. Correct.
So you guys have let's talk a little bit about
independent thought. You guys have done pretty well. When the
expert's wrong. You throw five, seven, eight, and nine. You
were notably up in years where most people were down.
(31:06):
Again in Q one of twenty twenty, you guys did
really well all periods of big market turmoil. I don't
know what you were doing in two thousand and one two,
but I'm imagining the same approach held true. How do
you think about these periods? Are they truly black swans
(31:28):
or are they things that, with the right approach to
risk management, are create opportunities.
Speaker 3 (31:34):
Again, people are trying to assess risk based upon some
kind of parametric distribution with you know, standard deviation movements,
and I think that's just nonsense. The markets don't work
like that. So our system enables us to weather all
market environments through the deal code system by ignoring those
(31:58):
parametric The Gerber statistic, which is the basis for the
work with Harry, is a rank order statistic because it
recognizes the failures of parametric normal distributions. And what we
do is we set a threshold because a lot of
data is noise in the markets. If the S and
(32:19):
P moves by ten basis points, it doesn't communicate to
you how the S and P affects other things. Yet,
and all these statistical models, they're including every single data
point because if you don't include every single data point,
then in the matrix math you have a divide by
zero issue. So they're forced in all these correlation statistics,
(32:40):
these regression analyses to include every single data point. With
the Gerba statistic, we are able to create thresholds where
we ignore data below a certain degree of movement, and
so that enables us to focus on Everyone wants meaningful relationships, right,
So this is how we're able to focus on meaningful
relationships within the market.
Speaker 2 (33:01):
You know, we talked a little bit about sub prime
real estate and how the models it wasn't even that
they broke. They were so poorly constructed they were destined
to fail. You know, if you build a house really poorly,
you don't need an earthquake. Eventually, it's just going to
collapse under its own weight. But I have to ask
you some questions about real estate because Hudson Bay has
(33:22):
been increasingly invested in private credit and real estate. You've
done a number of major refinancings in and around New
York City. Six twenty Avenue of the America's is tell
Us a little bit about the work you're doing at
Hudson Bay with private credit and real estate.
Speaker 3 (33:40):
Well, we saw beginning with the the transitory higher rates,
which we thought was nonsense, right. We saw that rates
were going to be higher for longer, and we had
believed that the market had been anchored in this idea
of ultra low rates, which was really a manipulation of
(34:02):
the monetary system. So we started thinking about what's the
implications of that, and came to the notion that the
banking system would be under stress. And what's the implication
of the banking system under stress. Well, that means that
they can't extend loans in the same way, you know,
corporate as well as real estate. So we started staffing
(34:24):
up in those areas to take advantage. And now I'm
convinced that the there's now going to be a structural
shift in credit provision in the US economy, that the
banks are no longer going to be the mainstay for credit.
And that's because the government has effectively guaranteed our banking system,
which creates moral hazard. We have on the order of,
(34:48):
you know, forty three hundred banks in the United States.
It's a lot, especially when you compare it to Canada
that's got the big you know, handful, and you know
when you deposit money in the bank, that bank is
lending it out long.
Speaker 2 (35:04):
And fractionally reserving it. So it's ten to one, whatever
the precise the leverage there using.
Speaker 3 (35:10):
So I think that the whole fractional banking system notion
is challenged, particularly in the idea of the ease of
information transparency among depositors coupled with the necessity for government
guarantee and moral hazards. So private credit firms like ours
(35:31):
people invest in Hudson Bay and they know it's not
a bank account, and that gives us license to deploy
the money in ways that are appropriate, and so we
began staffing up in those areas. And now in real estate,
for instance, we have teams that work in real estate equity,
in CMBs, distress CMBs, and direct provision of real estate credit.
(35:57):
And as part of the core vet you've Hudson Bay.
These teams work together, which give us a better understanding.
It's a great advantage to have equity teams working with
credit teams, particularly all real estate's local It gives us
a much better understanding of the asset that we're looking at.
Speaker 2 (36:16):
Huh, that's really kind of interesting. You know, Ever since
the financial crisis, some of the new regulations and bank
regulations directly led to the rise of private equity, private credit.
You know, some of the forecasts are over the next decade,
this blows up to a thirteen trillion dollar asset class.
Speaker 3 (36:37):
I think we're in the third inning, early early days here, Yeah,
I think so.
Speaker 2 (36:41):
And it it feels like it's been so big because
we started with practically nothing in that space, and the
first couple of trillion dollars felt like, oh, my goodness,
is just so much capital washing over this. But this
seems to have happened in the past where woll banks
and brokers kind of move up market, they create a
(37:03):
void in the space they left, and private money rushes
in to fill that void. Is that what's going on
with private credit and real estate?
Speaker 3 (37:14):
Well, it's still early in that. I think it's a
golden age for real estate credit. The banks are not
able to they don't have the capital now to lend,
and so there's it's open season.
Speaker 2 (37:27):
Huh. Really really interesting. So how do you identify opportunities
in the real estate space. It seems like there are
so many buildings that are half empty, and yet it's
a slow motion train wreck because most of their tenants
have ten or longer year leases and they're just slowly
(37:49):
starting to recognize unless you're a super A class building,
even A buildings are having a hard time attracting renewals
and tenants. How you identify these and how far along
the repricing of commercial real estate or at least offices
do you think we are?
Speaker 3 (38:09):
Well, those are big questions. And I'm from Annaburg, Michigan,
and I saw how in Detroit, Detroit was going to
be called the museum to the desolate city because downtown
Detroit went empty when they built the Renaissance Center. Everyone
moved to the Renaissance Center and left these empty, huge
buildings in Detroit. And you see aspects of that now
(38:32):
where the A buildings, the new buildings are attracting very
high rents and buildings in other areas are you going empty?
So to understand what's going on, you really have to
understand the asset, and so that's why it's important to
have teams from different disciplines being able to understand the asset. Obviously,
(38:54):
looking through the rent rolls and understanding you know, the
weight to average lease, but also understanding the macro environment.
You know, are things growing And we have so much
uncertainty now going on, not just because of work from
home with Zoom, but also the longer term implications of
AI and what's that going to mean for the workforce
(39:16):
and even cities like New York City. It's possible that
we're not going to need the same number of junior lawyers,
junior accountants, junior bankers.
Speaker 2 (39:26):
So I've heard some people discuss AI as a tool,
and it's not that you're going to lose your job
to AI, but you're more likely to lose your job
to someone working with AI. Is that a fair assessment
or is it just still way too early to take.
Speaker 3 (39:42):
I think we still don't know. I think AI is
the greatest change in my lifetime, bigger than the Internet.
I think so, yeah, really yeah, because the ability for
natural language processing goes far beyond what I thought was possible.
You know, I studied linguistics a bit in college. The
whole idea of how we form language is a fascinating subject.
(40:05):
And now the computer is able to be coachent in
their responses, We've you know, kind of approaching hard AI
in a way that I did not think was possible,
and it's only going to get better.
Speaker 2 (40:19):
Let me push back a little bit. And I'm not
necessarily saying I believe this, but so I've had this
conversation over and over again with a number of different people.
How are you using AI in your daily work? What
are you finding? And someone who hosts a different podcast said,
(40:40):
they created this really interesting set of prompts with AI
to get an answer to how to do certain things,
and the first time they got the answer, they were
really impressed. Oh my god, this is a genius insight,
and look how smart this is and how it it
figured out exactly what I needed. And then they asked
(41:03):
a different question with a different subject kind of got
the same answer, and it was like, oh, this is
a party trick. This isn't really intelligence. It just looks
like intelligence, and even though it's getting better, it's still
kind of dumb relative to it impresses us. But once
(41:26):
you peer behind the curtain and see the wizard is
just a man, you figure out this is less what
it purports to be in more like a very useful,
clever trick.
Speaker 3 (41:38):
I was thinking of a Wizard of Oz also while
you were while you were saying that, But I don't
think there's a guy behind the curtain that's giving the answers.
That's why I think that it helps with the junior
analysts that you have to check anyway, and it certainly
speeds up the research process in ways that were not
possible before, for sure, and it's only going to get better.
(41:59):
And it may makes mistakes, but the junior analyst makes
mistakes also. I mean, I've used it for things my
lawyers probably will hate me, but sometimes when I've had
a discussion with the lawyers on how to express something
in a document, to all ask AI the question. It
will give me a range of possibilities and enables me
then to be more on a level playing field with
(42:20):
my lawyers who have had a lot more experience than
I have. But it has enabled me to bring to
the discussion insights that we might not have thought of.
Speaker 2 (42:28):
I'm glad you brought up the attorneys, because a judge
just sanctions a lawyer for using AI and in certain
of his answers, and this unfortunate tendency to hallucinate. I
don't think the problem was that he used AI to
help him in research. He didn't double check it, and
(42:49):
he failed to disclose that AI was plathiness.
Speaker 3 (42:52):
You know, it's just plain laziness. The the AI is
good for the junior person, and I think as implications
for the workforce, you know, what is the workforce going
to look like? Given that, maybe we don't need the
same failans of junior accountants, junior lawyers, junior bankers.
Speaker 2 (43:12):
How do you become a senior account lawyer, banker if
you're never a junior It's a tough question. So let
me give you an opportunity to update your twenty twenty
one piece in investing. Don't short human judgment? Do you?
Are you still holding that for you?
Speaker 3 (43:29):
Absolutely? I mean we are in the human judgment business. Really,
we are trying to beat the machines. We do that,
as I said, through understanding uncertainty, events, catalysts, and change,
and I think ultimately human judgment is superior in the machines.
(43:49):
I hope we won't go into a Hell two thousand
type situation that human judgment will always be superior. You
wouldn't want to have a machine be the a in
the United States. How could a machine possibly make those decisions,
you know. So obviously human judgment will always be there,
and I don't think that we're at a terminator type,
(44:11):
you know, situation, but there are certain experts that say
that ultimately that's where we'll go. I mean, I do
know that in the military, you know, the idea of
robots creating robots is a real idea, and it very
might well change battlefield dynamics. But I believe that certainly,
(44:32):
at this point in time, the human capacity to ingest
a mosaic of information and to make the right decision
is superior. If you take a chessboard, the machine can
beat the master, but if you put an extra bishop
on the board, the machine can't deal with it, right,
(44:53):
And I think that's the paradigm. And life does not
mimic a chessboard, you know. Life mimics the chessboard with
extra pieces being put on randomly, and it's that randomness
that I don't think the machines will be superior than
human judgment. Now, it might appear at times that the
machine can beat the human, but I think ultimately the
human judgment is superior, and so our business is based
(45:16):
on human judgment.
Speaker 2 (45:18):
You mentioned the wartime usage of AI. There was a
pretty big article I don't remember. I want to say
the Times, not the journal, that figured out that in
the Ukraine Russian War, which started out as a conventional
bombardment between tanks and mortars and anti tank weapons, over
(45:38):
the past six twelve months, seventy percent of the casualties
have been drone AI warfare driven, and it's very much
a brave new world. It's not like the old world
of warfare. What it sounds like you're suggesting with AI
is that they're both code developed, that you'll still have
(46:01):
humans driving the process, but AIS become an increasingly large
part of it, regardless of whether we're talking about warfare,
business or investing. I don't want to put words into
your mouth, but is that a fair way to assess that.
Speaker 3 (46:15):
I think so. I mean, I think that the humans
always have to be on top of the machines. Machines
have a lot of latitude, both to produce themselves as
as well as to target. You know, the markets are
different because the markets follow a behavioral dynamic. The evaluation
of risk versus reward is something that I think a
(46:36):
machine cannot do in the same way the human can.
Speaker 2 (46:39):
So given some of the volatility we've been seeing in
the first quarter of twenty twenty five. Has that changed
how you're looking at your models, how you're viewing your
approach or is it, Hey, this is just another one
of those things that comes along and we have to
be able to trade through.
Speaker 3 (47:00):
We actually like the dislocation because the dislocation proves the
models are wrong.
Speaker 2 (47:05):
Well, I know you guys don't release public performance numbers,
but I know you're doing much better than your benchmark
this quarter. Volatility is your friend. Is that what you're saying?
Because volatility disrupts traditional models and you're a non traditional model. Correct.
So I know you've worked with Harry Markowitz. What other
(47:26):
academics and what other institutions have you worked with?
Speaker 3 (47:29):
Well, at Imperial College London, there's further work being done
on the Gerber statistic and incorporating it. The idea of
thresholding data and ways to do it to For instance,
if you want to understand the significance of a stock
price movement, maybe you should exclude days where there's very
(47:50):
low volume and only include days when there's high volume.
There's a variety of ways to incorporate it.
Speaker 2 (47:57):
I know, I only have you for a limited amount
of time. Let me jump some of my favorite questions.
I ask all of our guests, what are you watching
or listening to? With? What's keeping you entertained?
Speaker 3 (48:07):
Recently I streamed Eastern Gate?
Speaker 2 (48:10):
Oh really?
Speaker 3 (48:11):
Which is I saw in the New York Times. It
was this spy thriller series on the conflict between Poland
and Belarus, and I wanted to understand the dynamic between it.
So I thought I'd get a little entertainment and understand
something I couldn't pick up here. And it's a little slapstick,
but I think it's worth it.
Speaker 2 (48:30):
Eastern Gate. Yes, did you happen to watch any of
Fouda when that was just the most heart wrenching stuff
to watch? It's so stressful.
Speaker 3 (48:40):
Yeah, and pretty realistic.
Speaker 2 (48:42):
Very realistic. Let's talk about mentors who helped shape your career.
Speaker 3 (48:47):
I gotta give a lot of credit to Dave Patrice.
Speaker 2 (48:50):
Who I know that name, who really.
Speaker 3 (48:53):
Helped me get into shape. And he was on my
case every day, the diet, the working out. We're workout partners,
and I was thirty five forty pounds heavier, uh huh,
and he got me to recognize they needed to get
in shape. I thought I was in shape, but I
(49:13):
wasn't in shape. I think I think a lot of
people think they're doing okay when they could do a
lot better. And he taught me I could do a
lot better. And I think it's affected me overall, my
mental acuity, my mood, my stamina. I really give him
a lot of credit.
Speaker 2 (49:30):
You mentioned books earlier. What are some of your favorites?
What are you reading right now?
Speaker 3 (49:34):
One book that I really enjoyed, which was long, was
Walter Isaacson's book on Elon Musk, which I read before
the election, and it made a big impact on me
because I believe in questioning the experts, but must takes
it to a different level. He's questioning metallurgical properties that
were well grounded in science and engineering, and he's saying,
(49:54):
why does that have to be? And oftentimes he was
right that the established can census regarding properties of medals
was wrong.
Speaker 2 (50:04):
M really really interesting. Any of the books you want
to mention?
Speaker 3 (50:09):
I read The Melting Point by Frank Mackenzie recently. He
was the head of Scentcom and he talked about what
it was like to lead Sentcom and he also had
a MA He measured in English, and he thought that
his English background to be a commanding general was very
helpful because I helped him to articulate better and to
(50:33):
form consensus, you know, among his colleagues.
Speaker 2 (50:36):
Really really interesting. Our final two questions what sort of
advice would you give to a recent grad interested in
a career in either filling the blank, investing options trading,
multi strategy management. What advice would you give to them?
Speaker 3 (50:54):
I think it's, you know, across all certainly service occupations,
is you got to be will beat the machines, and
to do that, you need to be independent thinker. You
need to go against the grain, question the experts. You
need to be able to do that. You need ab
to work with other people, to learn from them, to
(51:16):
expand your horizons, to expand the mosaic that you can
bring to your independent thinking. And you got to be
able to respect your colleague. So I think that those
three things are a real guideposts for people.
Speaker 2 (51:28):
This goes back to your corporate culture, which your environment,
corporate environment, my bad, your corporate environment, think independently, collaborate
and respect the individual. Correct huh? And our final question,
what do you know about the world of investing in finance?
Today would have been useful when you were first getting
started in the early nineties.
Speaker 3 (51:51):
I think that you know everything you learn in business
school or economics, you can just throw out the window
economics and of science. People try to portray economics as
a science, and it simply is not. And so all
the notions that we brought up regarding money supply, you know,
Milton Freem would be turning over in his grave. Even
(52:13):
though these principles might have some grounding, It's not scientific,
you know. This is this is not a natural science.
It's a behavioral science, and it's based upon how people
interact with each other. And I think that that appreciation
leads to the notion that oftentimes the academy or the
experts try to profer things that everyone everyone seems to
(52:40):
believe one way, and you think, how could I be right?
Because everyone believes one way because this is what they
studied in school, and if the authorities say it's that
one way. And I think that as you go through
life and you age, you realize that the Ivory Tower
isn't always correct. In fact, a lot of times the
Ivory Tower doesn't have the real life experience, and so
(53:01):
they're flat out wrong.
Speaker 2 (53:03):
I'm trying to remember where I'm stealing this quote from
Science Advance's One Funeral at a Time. The same is
true with other things that Dick Thaylor said. Rather than
wait for the rest of economics to catch up with
behavioral finance, I'm just going to teach it to the
(53:23):
younger generation and it'll infiltrate much more quickly than waiting
for all of my peers to accept it. Really really fascinating, Sander.
Thank you for being so generous with your time. We
have been speaking with Sandra Gerber. He is CEO and
CIO of Hudson Bay Capital. If you enjoy this conversation, well,
(53:46):
be sure and check out any of the previous five
hundred and fifty we've done over the past eleven years.
You can find those at iTunes, Spotify, YouTube, Bloomberg, wherever
you find your favorite podcast. And be sure and check
out my new book How Not to Invest The Ideas,
numbers and behavior that Destroys Wealth out today wherever you
(54:10):
find your favorite books. I would be remiss if I
do not thank the correct team that helps put these
conversations together each week. John Washerman is my audio engineer.
Ana Luke is my producer Sean Russo is my researcher.
I'm Barry Ritholtz. You've been listening to Masters in Business
on Bloomberg Radio
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