From AQR Quant to Founder & CIO with Brian Hurst

Barry speaks with Brian Hurst, Founder and Chief Investment Officer of ClearAlpha. Prior to founding ClearAlpha, Brian spent 21 years at AQR Capital Management as a portfolio manager, researcher, head of trading and the first non-Founding Partner at the firm. Brian also led numerous operating and investment committees including AQR's Strategic Planning Committee and the Risk Committee. He was instrumental in the design and implementation of AQR's trading platform and his time as senior portfolio manager placed him in charge of over $15 billion in hedge fund assets. Brian also Serves as a Member of the Yale New Haven Children's Hospital Council.

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Episode Transcript

Speaker 1 (00:02):
Bloomberg Audio Studios, Podcasts, radio News. This is Masters in
Business with Barry Ritholt on Bloomberg Radio.

Speaker 2 (00:17):
This week on the podcast, yet another extra special guest.
Brian Hurst is founder, CEO, and CIO of Clear Alpha.

Speaker 3 (00:26):
They are a.

Speaker 2 (00:27):
Multi manager, multi strategy hedge fund that has put up
some pretty impressive numbers. His background is really fascinating. Cliff
Astness plucked him out of the ether to be one
of his first hires at the Quantitative Research Group at
Goldman Sachs. He was the first non founding partner at AQR,

(00:49):
the hedge fund that Asna set up, and Brian worked
there for a couple of decades before launching Clear Alpha.
He had as a fascinating perspective on where alpha comes
from as well as the entire hedge fund industry. Few
people have seen it from the unique perspective he has,

(01:11):
and I think he understands the challenges of creating alpha
where it comes from and managing the risk and looking
for ways to develop non correlated alpha that is both
sustainable and manageable from a behavioral perspective. I thought this
conversation was absolutely fascinating and I think you will also

(01:33):
with no further ado my interview with Clear Alpha's Brian Hurst.

Speaker 4 (01:38):
Thank you very appreciate it.

Speaker 3 (01:40):
Good to have you back here.

Speaker 2 (01:41):
Last time you were on a panel, we were talking
about the rise of some emerging managers, including yourself. But
let's go back to the beginning of your career Wharton
School at the University of Pennsylvania. You graduate with a
bachelor's in economics. Was quantitative finance always career plan?

Speaker 4 (02:01):
That's a great question. I think when I went to school,
I didn't even know quantity of flants was a thing,
and frankly, at that point in time, it really wasn't
much of a thing. I was taken him by my dad.
He was an accountant and CFO of a commercial real
estate company. He would take me to the office, and
I was really fascinated by business. I really wanted to
get into that. I was into computers. I learned how

(02:22):
to teach myself how to program and things like that.
But I wanted to get into business, and I said, Dad,
I want to get into real estate. And my dad
gave me some really good advice. He said, Brian, if
you think about finance as an org chart, real estate
is like one of the divisions and if you start
in real estate, it's hard to move up and go
to other divisions and try other things out. You should
really learn corporate finance and you can always switch to

(02:44):
real estate if you wanted to. And corporate fans is
kind of the top of the umbrella or the org chart.
And I said, okay, well what's corporate finance and where
are I go to learn that? He's like, well, you
should go to Wharton And then I said, well, what's Wharton?
That's how it started.

Speaker 3 (02:57):
That's hilarious.

Speaker 2 (02:58):
You finish up at Pennsylvania and you begin your career
at DLJ.

Speaker 3 (03:03):
What sort of work.

Speaker 2 (03:04):
Were you doing and what were your classmates doing? This
is the early nineties.

Speaker 3 (03:08):
You started DLJ.

Speaker 4 (03:09):
Yeah, I did DLJ. It was interesting. That was my
summer year between junior and senior at Warden, and they
kept me on throughout my senior year to finish up
an interesting project, which is basically automating the job of
the investment analyst that we're doing all the company working,
all the tank k's, tank q's, all the information. At

(03:31):
the time, there was a new company starting up and
I know on Bloomberg, but it was called fact Set
at the time. Of course, and there was a salesperson
walking around trying to get anyone to talk to them
because this is a brand new company. And I was
a summer analyst and I was like, I've got time,
I'll talk to you. And he showed me, first of
all two things. She showed me this thing called Microsoft Excel.

(03:51):
At the time, everybody was using Lettus one, two three,
and he showed me basically how you can type in
a ticker and that pulls in all of the financial
information right into this cheat for you before the internet,
but you know what was kind of the internet at
the time. I was like, wow, this is amazing. I
was like, this could save me hours and hours of work.
And so I went to the MD at the time
and I said, hey, I think I can automate most

(04:12):
of what the analysts are doing. He said, you're a
summer intern. We're not paying you much. Go at it.
And that's what I did. So I started off in that,
but I mainly learned that I didn't want to do
investment banking because it didn't hit on my course skill set,
which was like engineering back down quantitative techniques and tools.

Speaker 2 (04:29):
That sounds really interesting. It's amazing to have that sort
of experience as an intern, How did you land at
Goldman Sachs.

Speaker 4 (04:36):
Like everything in life that works out well, that's you know,
a lot of hard work but mostly luck. Because of
the DLJA experience, that was a good thing to have
on my resume. Cliff Asnas founder of AQR Capital, managing
partner there at the time. I think it was late twenties.
He was finishing up his PhD at the University of
Chicago and was working for Goldman Asset Management. He got

(04:59):
the mand to launch a new quantitative research group, and
so he wanted to hire someone who had both the
finance background and the computer science background. I had started
with a couple of friends a software business in high
school and at Penn. One of the things I did
with my roommate was we started up a hardware business,
kind of like Michael Dell, building and selling computers to

(05:20):
faculty and students on campus. So I had the computer
science background. Cliff had gone undergrad at Penn at Wharton also,
so he knew that we'd taken the same kind of courses.
We spoke the same language from that perspective, and I
had that technology background. So I was his first tire
as he was building out that new team. What my
other colleagues did back then, you had basically three choices
come out of Warden. It was accounting, investment, banking, and consulting.

(05:43):
There was really no jobs for asset management, but those
are the courses I love the most at Penn and
really wanted to pursue that. So it was a great opportunity.

Speaker 2 (05:50):
So you spend three years or so at Goldman with Cliff.
By that point he had been there for a while
and decided, hey, I think I have a little more
freedom and opportunity to find launch a fund on our own.
You were there day one, you left with him, right,
tell us a little bit about what it was like
standing up AQR with aastness.

Speaker 4 (06:13):
It was great. We started off this little background there
as a research group within g SAM so think cost
center and just putting some timeframes around this. This is nineteen
ninety four, which is one of the toughest years in
Goldman's history, even going back to the Great Depression. It
was the kind of year where trem and a partner
you get to put in money. Wow, which was was

(06:33):
it that bad a year?

Speaker 3 (06:34):
I don't remember. Ninety four is a terrible market year.

Speaker 4 (06:36):
That was the year where the Fed had the surprise
significant rate hike in feb I was actually on the floor.

Speaker 2 (06:42):
I think bonds took a whack, but equities also wobbled
a bit.

Speaker 4 (06:46):
Is that a well a bit? But yeah, it was
really a bad year for fixed income and the firm
had a lot of risk and fixing, I presume, which
led to the tough year. Yeah. So we were a
research group, cost center, and then left in people were
disappearing week by week as they were cutting down really headcount,
and so quickly we realized we've got to start gendering

(07:06):
some revenue. We want to say, alive. And Cliff went
to them and said, hey, we've been we built some
interesting models. We think we're good at picking stocks and
futures and things like that. We think we can trade
on this and make some money. He convinced the partnership
to give us some money. So it's basically a prop
trading effort. For a little while, it did very well.
They kept adding money to it, and then we opened
it up and turned it into a fund. It was

(07:28):
really Goldman's first real hedge fund coming out of g
SAM that funded very well, which really opened the door
for us to be able to leave and start up
and raise money as an independent hedge fund.

Speaker 2 (07:40):
What were the specific strategies Cliff was running at g
SAM with the partner's money.

Speaker 4 (07:46):
It was a multi strategy approach, but it was all quantitative.
And when I say quantitative, that means a lot of
things to different people. I think about every good investment
process is really a process, and whether people would label
as quantitative or or not is really how automated it is.
And so by quantity of I mean like really automated
downloading public data for the most part, pumping it through

(08:09):
some systems, and that causes you to want to buy
and sell different instruments around the world.

Speaker 2 (08:14):
But you're still creating or Cliff at the time was
creating models, and the models would give him a ranked
list of Hey, the top ten stocks on this list
of a thousand are really or whatever the number is,
are things you want to look at, either getting long
or short based on whatever that model is.

Speaker 4 (08:30):
That's right. So that you'd have many different signals, and
we're treading many different asset classes, and so it's like
you're saying all those signals you would givefferent weights different signals,
and those would add up to you like these things,
you don't like these things. We would trade global equities
in a bunch of different countries, but market neutral so
long as much as you are short, so you're not
taking a bet on is the market going to go
up or down. You're really taking a bet on this

(08:52):
group of stocks is going to perform this other group
of stocks by looking at a bunch of different characteristics.
We did that for stocks, We did it for currencies,
for commodities, you name it was. It was tradable, and
we had data we wanted to be trading it. And
that's really what the genesis of that fund was.

Speaker 2 (09:09):
How long were you guys doing that before you realized, hey,
this is really going to be a successful model, And
then how much longer was it before maybe we should
do this out from under the compliance regulations of a
broker dealer.

Speaker 4 (09:23):
We started that as a fund really in nineteen ninety five.
It had been trading prop for a little time with
Goldman's money, and we made money almost every month. Basically
it traded as a fund, and you think we left
in terms of a timing perspective, you know, they started
nineteen ninety five We left early nineteen ninety eight, so
it's only a couple of years in change that we
were trading this within g SAM before leaving to start

(09:46):
at BAQR.

Speaker 2 (09:48):
So let's talk a little bit about AQR. You there
from inception, from day one. What was that transition like
from you know, I imagine at Goldman Sachs you have access
to life, lots of support, lots of tools, lots of data,
lots of everything. What's it like starting over again from
scratch in a standalone hedge fund.

Speaker 4 (10:08):
I'll tell you a funny story. So I got into
a few different battles with the administration folks at Goldman
Sachs size management. If you remember, like in college, I
had a computer business where we'd like buy parts, build
computers and sell them, and so I knew how to
build my own computers. Goldman Sachs. At the time, the
standard computer that everybody had was what was called an
eight eighty six. This is like the first real PC

(10:32):
that IBM had out there, and you know, they were good,
but they weren't the most advanced available machines. Basically, I
went to the administration and I said, look, we need
the most advanced machines because we're trying to run a
lot of computationally intensive models and this machine we have
now is very slow at taking very long to run
our models. You can buy the latest machine at half
the price of what Goldman was paying and get twice

(10:54):
the performance. What I didn't realize at the time is
that when you're trying to run an organization that large
and complex.

Speaker 3 (10:59):
I want everything stand you.

Speaker 4 (11:00):
Can't support it unless everything's standardized. And so there was
a reason for it which I didn't understand.

Speaker 2 (11:04):
That you guys can support your own hardware.

Speaker 3 (11:06):
That's not that hard.

Speaker 4 (11:08):
Cliff eventually persuaded them to give let us get the
new machines. But one of the big changes as you
talk about leaving a place, you know you have lots
of resources and whatnot at large organizations, but you have
limited resources at every place, no matter how big you are.
There's always trade offs that you're making when you start
up as a new firm. One thing that was a
big change is that at Goldman we had to support

(11:30):
lots of other groups. We were providing research advice, investment advice,
talk to clients, help them raise money in other products.
When we launched you own hedge fund. All that matter
was making money in that hedge fund, so helping that
focus was important, and we were able to buy the
latest computers that have the cost.

Speaker 2 (11:47):
I'm going to bet that you did something a little
beefier than those IBM eight to eighty six is.

Speaker 4 (11:52):
Yeah, I was overclocking the machines. I was doing all
the pulling, all the ways to get things to go
as fast as possible.

Speaker 3 (11:57):
Huh.

Speaker 2 (11:58):
Really interesting. So at AQR you juggled a lot of responsibilities.
You were a portfolio manager, researcher, head of trading, and
apparently tech geek putting machines together. What was it like
juggling all these different responsibilities.

Speaker 4 (12:14):
There's a couple things I'll say about that. So one thing,
just from a personal perspective, my wife and I we
have five children together, and that's a lot to deal with.
My wife is amazing, and there's no way I would
be able to do all the stuff I do at
work if it weren't for her being amazing and handling
everything at home. So that's the first thing, and how
I get so many things done at work. I'm also
from a personality perspective, I get bored very quickly. I

(12:36):
like learning and doing a lot of different things. I
like being able to jump around, So to me, that's
just fun. The consequence is sleep. I don't sleep very much.

Speaker 2 (12:46):
What do you mean, not very much? And you know
that only gets worse as you get older.

Speaker 4 (12:51):
We usually get to sleep around one am and you know,
six six thirty something like that.

Speaker 2 (12:56):
All right, so five hours, that's not terrible, not too terrible.
I've lived on six hours most of my life and
it's and you get older that that shrinks. I thought
you were referencing the five kids, because it's like, hey,
when you have five kids, you want how to juggle
a lot of different things at once because something is
always that's fine.

Speaker 4 (13:13):
There's always something going on, that's for sure.

Speaker 3 (13:15):
What was it like working with Cliff back in the days.

Speaker 4 (13:19):
It was fun. I think Cliff's greatd a lot of
different things, but one was he hired well. He was
able to attract really talented people and then he just
let them do what they do. So he's not a micromanager.
He just lets them run with it. And so that
was a very fortunate thing for me right place, right
time in terms of being able to get a lot
of responsibility early on, and that's how I was able

(13:41):
to not just be a researcher building models and creating
new strategies that I'd run by Cliff and he would say, Okay,
you're doing this dumb or doing that dumb, and you
got to improve this, but also doing all the trading
by myself for the firm for the first several years,
and then eventually saying, hey, Cliff, you know I need
some help here. We need to hire, you know, someone
to run technology other than me. We need to hire

(14:01):
more traders than just me so that I can actually sleep.
So that's how he ran it. And it was a
lot of fun. I mean you mentioned it earlier on.
I mean Cliff hilarious and he's.

Speaker 2 (14:10):
A funny guy. And it's rare to find someone who
is a quant who can communicate as eloquently as he
can and at the same time has such a devilish
sense of humor. Like that's an unusual trifecta right there.

Speaker 4 (14:24):
And it's part of what makes him fantastic as an individual,
but also fantastic to work with and work for. It
made the place fun even in the tough times. And
so that's a big reason why I think a lot
of people stuck through lots of the ups and downs
that any organization has.

Speaker 2 (14:41):
Let's talk a little bit about the AQR experience. The
firm seems very I almost want to say academic. They
publish a lot of white papers, they do a lot
of research, They have very specific opinions on different topics
that seem to come up in the world of finance.
How much of this intellectual firepower is part think tank

(15:06):
and how much of it is just Hey, if you're
going to have an investment perspective, you need to have
the intellectual underpinnings to justify it.

Speaker 4 (15:14):
So I think one thing that makes acar a very
powerful is its ability to attract top talent, specifically on
the academic side. The smart people want to hang out
with other smart people. That there is definitely a network
effect that happens there. And I would say part of
the compensation you're getting indirectly by being in an organization

(15:34):
like that is getting exposure to all these great minds
that you can learn from, you can bounce ideas off of.
So is it a think tank? Yeah, I think it
is a think tank from that perspective, But at the
end of the day, it's a business and they're there
to make money, make money for their investors, so I
think there is a lot of focus on that as well.
So the publications, you see a lot of white papers,

(15:57):
and sure, I would say it rhymes with a lot
of things they do. But they obviously keep a lot
of the special sauce unpublished and use that within their funds.

Speaker 2 (16:05):
But they're still writing about broad strokes. So let's talk
about a white paper that you wrote titled the Evolution
of Alpha. Tell us how has alpha evolved over the
past few decades.

Speaker 4 (16:17):
Sure, this is a white paper I wrote from my
clear Apha Cio CEO hat and it really talks about
the history of the hedge fund industry, why different models
of delivering alpha, starting with let's say single strategy hedge funds,
fund of funds, multi strategy funds, and now multi strategy

(16:40):
multi manager are multipm funds and that's the latest evolution.
And then we talk about what we think might be
the next step, part of which we think we will drive.
So that's the point of the paper. And there's reasons
why you went from different models from one to the next,
and it has to do with a variety of things.
I can Curg. You to read the paper, it's on

(17:00):
our website.

Speaker 2 (17:02):
But so let's follow that up. What were the drivers
of the shift from a single manager to multiple managers
to multi strategy to multi manager multi strategy.

Speaker 3 (17:14):
What was the key driver of that?

Speaker 4 (17:16):
Starting back this is around two thousand, let's say. Obviously
hedge funds existed before that, but that's really the point
at which at least a meaningful amount of institution investors
actually started having investments in hedge funds as like a
normal course of business. That was the year obviously that
the market sold off a lot. That was the Enron
fiasco and whatnot. A lot of Wall Street was let go,

(17:38):
so a lot of talent was being let go, and
much of that talent was investment analysts, research chants. The
covered stocks new stocks, deeply knew the management of those
companies deeply. So if you're an investment analyst at a
Wall Street bank, you go off and hang up a shingle,
start a single strategy hedge fund where you're picking stocks.
You had an argument why you'd have an edge because

(17:59):
you knew these managers these stocks deeply. And that's really
was like a Cambrian explosion of hedge funds at that
moment in time, and even to this day, I think
in terms of like sheer number count, the vast majority
of hedge funds are really stock picking hedge funds.

Speaker 2 (18:12):
Long short eleven thousand hedge funds out there too.

Speaker 4 (18:14):
Yeah, yeah, long short discretionary equity stockpicking hedge funds. That
models survived for a little while, but as investors were
investing in these individual kind of single strategy, single style
hedge funds, what they realize is that anyone single approach
is not very consistent. You know, it's going to go
through it's good periods and it's bad periods, and it's

(18:34):
hard to hang on to what I would call be
exposed to what the line item risk is. When you
have these quarterly reviews of what's going on the portfolio,
invariably the discussion is, let's talk about the things that
are down the most, and that leads to, you know,
firing managers when they're down, usually just after a environment

(18:55):
that was just bad for their approach, before it rebounds
and does well, you know, in the next year. So
that model, well it still exists today, is tough from
an investment to stick with. Then you switch to fund
of funds and soucial investors. You know, one stop shop,
buy into a fund of funds. You can get exposure
to many different strategies and styles in one vehicle. That's

(19:16):
what came out of that and was to address this inconsistency.
So fund of funds were more consistent than a single
strategy fund. But I would say the consequence and its
or the issue really is both for fund of funds
and really for portfolios of hedge funds that investors have.
It's cash inefficient. It's capital inefficient because most hedge funds

(19:39):
have a lot of cash on their balance sheet typical
hedge fund. It varies, but depending on top of style
and strategy, will have between forty and ninety percent of
the money you give them just sitting in cash.

Speaker 2 (19:51):
Really, that's a giant number. Half is a giant number.
I thought you were going to go in a different direction.
I have a friend who's an allocator at a big foundation,
and he calls the fund of funds fund of fees
because you're paying layers on top of layers of fees,
and it definitely acts as as a long term drag.

(20:11):
But I never would have guessed that fifty plus percent
of assets handed to hedge funds are in cash at
any one time. I always assumed it was the opposite
that all right there, you know, like the one thirty
thirty funds or whichever variation you're looking at. I always
assume that they're leveraged up and even if they're long short,
all that money is put to work. You're saying that's

(20:33):
not the case.

Speaker 4 (20:34):
Well, technically all the you know, they will put the
money to work in the sense of it's not pure
cash hitting there. But really there's a lot of barring power.
You love assets that you're holding, there's a tremendous amount
of barring power you can borrow against those assets that
you hold to then create a more efficient portfolio. And
that's where kind of multi strategy funds evolved. So multi
strategy funds gave you the benefit of many different strategies

(20:55):
and styles, yet put into the same vehicle all these
positions held in the same vehicle to get much more
cash efficiency, cap efficiency, higher return on capital, plus the consistency.

Speaker 2 (21:06):
So I'm assuming if you're using a multi manager, multi
strategy approach, anyone strategy at any given time is either
going to be doing well or poorly, but the overall
performance of a multi strat will offset that. So it's
not like, hey, this guy has a bad quarter because

(21:26):
what they do is out of favor and the clients
pull out their cash just before the recovery. Is there
a tendency to leave money with a multi strat multi
manager approach for longer and so you don't have those
sort of bad quarter, bad month, whatever it is, because
this just isn't working now, but it'll start working eventually.

(21:48):
Is that the underlying thinking.

Speaker 4 (21:50):
That's really the approach? In fact, a lot of successful
single manager businesses evolve to the multi strategy approach because
they recognize that lack of consistency for a single approach,
a single investing style was a threat to their own business,
and so expanding into other strategies and styles is how

(22:10):
a lot of these more successful single strategy funds evolved.

Speaker 2 (22:14):
So it sounds like, if you're running either a multi
manager or a multi strategy or both, everything needs to
be very non correlated. You don't want everything down at
the same time. How do you approach picking various strategies
that are not correlated.

Speaker 4 (22:31):
That's a great question. I think it's helpful. I don't
like the gambling angle, but I think it's a helpful
analogy because most people are used to the analogy. If
you think about the casino, people go to the casino
knowing that if they play the games long enough, they're
going to lose their money. I think most people think
that the multi strategy hedge fund is really like the

(22:54):
house where each table or each game in the casino
in their house has slight edge, and if they make
sure that there's not going to be massive losses at
different tables on the same night, same weekend, same month,
over time, they will just just stashysically accrue profits in

(23:15):
a more consistent manner. So that is a big focus.
And if you think about what risk manners would do
at a casino, it's the same thing. They're going to
make sure that these tables, these games are not going
to be making or losing money at the same time.

Speaker 2 (23:27):
So let's talk about some of these diversified, non correlated strategies.
I'm assuming some include momentum, long, short, any other sort
of approaches that people would really readily understand.

Speaker 4 (23:42):
Sure, when I think about most hedge fund strategies, the
ones that people know about, the ones that there are.
If you look at hedge fund indicies, there's a category
for it, you know. So it could be long short
stock picking, it could be merger arbitrage, it could be
index rebound arbitra, or basis trading. There's a variety, and

(24:02):
there's like dozens of these kind of well known, well
understand activists exactly. These are all out there, they're they're
well known. When you look at each one of those,
you can break it down between kind of cheap passive beta.
So let's take an example long short, discretionary stockpicking. Most
of these hedge funds, the way they're implemented is the
manager's net long the stock market, and so some portion

(24:26):
of their returns, it's actually a pretty sniffing portion is
just being going to be driven by whether the stock
markets separate down there just pure data, pure beta, and
that's that's a I think about the scarce resources your
risk budget, and how do you want to allocate that
risk budget. If you're allocking a lot of your risk
budget to just pure beta, that might work for the manager,
but for an investor that doesn't make a lot of

(24:46):
sense because I can go and get pure beta. I
can buy an index fund for you know, single digit
basis points. At this point, it's effectively free these multi
strategy funds in order to reduce the correlation across their managers.
They don't want to have all these manager's long pure beta.
That's a common risk that will cause them to make
and lose money at the same time. And so when
you're running a multi strategy fund, it's really about looking

(25:08):
at these common risks. BITA is the simplest example. It
could be sector exposure, it could be factor exposure like
momentum you mentioned earlier, and there's a lot of other
less well known but known in the industry risks that
take place. You know, people talk about crowding. There's reasons
why crowding happens. So being able to be aware of
those and look for signs of that and trying to

(25:29):
mitigate those common allies across your different strategies is a
really key component to managing risk for these multi strategy funds.

Speaker 2 (25:36):
Huh, there's so many different ways to go with this.
So you're implying with these crowded funds that there's a
way to identify when when you're in a crowded fund,
I recall the quant quake a couple of years back,
where all these big quant shops post GFC really seem
like they were having the same sort of exposure in

(25:59):
the same sort of problem problems. How can you identify
an event like that before it takes your fun down
ten twenty percent.

Speaker 4 (26:07):
That's a great question, And I would say a more
recent example might be COVID March of twenty twenty when
they're so I talked about a couple of different common risks.
One is beta one. Another one might be factors, a simple.
Other one is just there's a well known strategy. Let's
say merge arbatrage. You know there are plenty of funds

(26:27):
that are running merge arbatrage is one of their strategies
within the fund. Okay, simply because a lot of people
are doing something that in a sense, when there is
some other exogenous event that causes people to de risk,
it actually makes it bad to be in well known,
well understood trading strategies, so that you know ahead of

(26:47):
time that this is something that is crowded. You know
that there are other players that are doing the same
kind of trades as you going in.

Speaker 2 (26:54):
Huh, that's really interesting, And just to put some meat
on the bones. Multi strategy, multi manager, multi model funds
have really gained prominence lately, names like Citadel, Point seventy two, Millennium,
lots of other larger funds have very much adopted this approach.

Speaker 3 (27:15):
Fair statement, that's very fair.

Speaker 4 (27:17):
I do think it's the best way to deliver alpha.

Speaker 2 (27:20):
So you're reducing correlation, you're reducing risk, you're increasing the
odds of about performance. At how broad are firms like
I don't know, Citadel or Millennium that they don't run
into that crowded trade risk? You would think given their
size and they're tens of billions of dollars, a crowded
trade becomes increasingly more likely, right.

Speaker 4 (27:42):
Right, And there's a reason for why that's the case.
There are literally thousands of different types of ways to
make money in the markets, thousands, but there's only dozens
of ways of making money in the markets that have
lots of capacity. And you can put a lot of
dollars in general, a lot of dollars to scale up,
to scale up, and if you're going to be a
very large fund, you by definition have to put more

(28:04):
and more of your money into the well known large
trading strategies and so they have to be particularly attuned
to the fact that they are large and their competitors
are also large, and then the same kind of trades.
So it is at risk. And when these things, you know,
when one of these shops sells out or reduces risks
and one of these common strategies, it's going to affect
the other ones. It's hard to avoid that. But they

(28:25):
are fairly well diversified across many different types of strategies,
so that's why you see still very consistent returns. But
there is this exogenous risk element of having being big
in the credit. The way you avoid that is by
being smaller, focusing on smaller strategies. They're a little bit different.

Speaker 3 (28:40):
Huh.

Speaker 2 (28:40):
Really interesting. So you mentioned earlier early days of hedge funds,
the fund of funds were popular. It feels like they're
kind of going away. You certainly hear much less about
them these days. Is that a fair assessment. Just because
you don't hear about stuff doesn't mean it's disappeared. But
I certainly only read much less about fund of funds

(29:02):
that they are in the news much less. Have multi manager,
multi strat multi model broad funds replaced the concept of
fund of funds.

Speaker 4 (29:12):
I think as an evolution, it doesn't mean that the
fund of funds model is going away entirely. There's certain
managers out there who have commingled vehicles that only you know.
They won't run an SMA for you, they won't trade
their strategy into your account. Fund of funds can access that,
So there's a reason for that. And you know they're
nice one stop shops and they can maybe a little
more transparent. But there are You talked about this earlier,

(29:34):
the fees being an issue, and it's really about the
fee is a percentage of the dollars of P and
L being earned. There's an academic paper recently published that
did a really interesting study over ten years of looking
at institutional hedge fund portfolios. What it showed is that
for every dollar of P and L being generated by
these hedge fund strategies, at the end of the day,

(29:56):
the institutional investor took home about thirty seven cents, really,
which is I think a shocking number from right right.

Speaker 2 (30:03):
So you're saying almost two thirds of the money never
either it's fees or costs or some other factor, but
only let a little more than a third ends up
with the actual investor.

Speaker 4 (30:16):
That's right, and it's actually it's really interesting breaks down
the sources of all these things. Part of it is
fees and double layers of fees and things like that.
A big part of it is the behavioral nature, which
I think is driven by governance of investing organizations.

Speaker 3 (30:31):
Where filled with humans.

Speaker 4 (30:33):
Yes, strategy is down. What's been down, Let's get out
of that. Let's get into the thing that's been up
recently that costs about a third of your offha.

Speaker 2 (30:41):
That doesn't surprise me at all, even though you expect
big endowments and foundations and hedge funds to be smarter
than that. Fillm with people and you're going to get
those behavioral problems, aren't you.

Speaker 4 (30:52):
Yeah, Well, there's agency issues in between, and I think
investors are well aware of these, so that causes part
of it too. But a big thing and the thing
that kind of the multi manager, multi strategy approach tackles
that a fund of funds can't is you get a
lot of netting benefits both from you know, one manager's
long apple another manager's short apple. Right in a fund

(31:13):
to fund approach where you're investing in two different funds, well,
a they don't know that. And b the managers who
long Apple, they're paying a financing spread to go leverage
long Apple, and the managers's shortest paying financing spread to
go short Apples. You're paying a lot of extra cost
there just to be net flat, just to be net flat.
So if those two managers instead traded those positions into
the same vehicle, you're getting that efficiency and that's worth

(31:37):
you know, in the order of like two to three
percent per year just that alone. The enhanced risk management
you can get by having daily position transparency and all
the trades, if all the different pans are doing, being
able to hedge out all these beta risk factor, risk
sector risks, things like that allows you to be much
more efficient with how you deploy that capital. And so

(31:57):
you see that these multi manager funds tend to be
a little more invested than a hedge fund portfolio typically
could be, and that creates a lot of efficiencies. And
so when you look at the returns that they're generating,
you know, it's closer to like fifty to fifty, where
like for every dollar that's generative P and L, fifty
cents is going to the investor. So it's a much
more efficient delivery mechanism of alpha.

Speaker 2 (32:18):
So we were talking earlier and I mentioned off air
that the funny element of individual investors tending to underperform
their own investments. I know you've done some research on that.
Tell us a little bit about what you say.

Speaker 4 (32:34):
Yeah, this is really something that's very important to me
when I think about the industry and like, what are
the big problems that are facing the industry. What's really
causing investors not to get as much money in their
retirem accounts as we possibly could get there. One of
them is this behavioral issue, which I think also ties
to like incentives and governance and agency issues within investing organizations.

(32:58):
Morning Star does a study that they call Mine the Gap,
and they do it on a regular basis. Some of
your relitiers might have heard this, and it's definitely worth reading.
I'll quote some numbers off the top of my head.
I might be remembering incorrectly, but what it does is
it's measuring the time weighted returns of funds, which is
the returns that funds report. These are the returns that

(33:19):
if you invested a dollar at the beginning and you
held it all the way through, the returns you would
have gotten if you never went to or went out
of that fund. Then they compare that to the asset
weighted returns, right, and that is going to be the
asset weight returns are counting for the fact that you know,
the fund does well, everybody gets excited, money comes in
larger assets, and then it maybe does not as well

(33:41):
after that, and so the larger assets earn less return.
And so the asset way to return minus the time
way to return is a really good way to measuring
what's the actual impact of this behavioral element of investing,
which is a really critical part of investing.

Speaker 2 (33:55):
And the gap refers to the behavior gap, which is
the difference between what the fund generates and what the
actual investors are getting.

Speaker 3 (34:03):
Yeap, please continue.

Speaker 4 (34:04):
And so what you find is that for like sixty
to forty balanced funds, which typically are in retirement accounts
where people maybe aren't looking at them every single day,
they get statements once a quarter that are delayed.

Speaker 3 (34:17):
Set and forget just it's kind of a set of Yeah.

Speaker 4 (34:20):
That gap is on the order of sixty basis points,
relatively small, relatively small, but it costs still. It costs
sixty basis points year for the average investor. This beaver
for those simple funds. Now for alternative funds, when they
look at those, that gap is one hundred and seventy
basis points a year.

Speaker 3 (34:35):
Okay, that's starting it up.

Speaker 4 (34:36):
That really I mean, if you think about that compounding
over a decade, that was a massive hit to wealth.
Why is there such a big gap for alternatives and
not as much of a gap for the sixty forty
I think it has a lot to do with investor
understanding of what those products are and therefore the confidence
people invest in alternatives. They don't necessarily understand them, and

(34:57):
so you're setting yourself up for FI. You're a little
bit there, because when it has bad performance you don't
understand what it does, you're more likely to redeem.

Speaker 3 (35:06):
That makes a lot of sense.

Speaker 4 (35:07):
So to me, investor education, really understanding what they're investing
is is a critical component to being a successful investor.

Speaker 2 (35:13):
Really really interesting. So you talk a lot about idea meritocracy.
It's on your site, you've written about it. Explain a
little bit what is idea meritocracy?

Speaker 4 (35:25):
This is a really important part and it's part of
our culture at clear Alpha. The idea is to get
all ideas surfaced so that the organization can make the
best decisions. How do you know what prevents good ideas
from surfacing. One is that people may not know that
you know, a questions even being asked. So many organizations

(35:45):
are run fairly siloed different groups, and a lot of
that happens associally large large organizations. It's hard for everybody
to be constantly communicate with one another, so just not
even knowing a question exists. So the way we address
that is that we use Microsoft Teams at the office
and most people are in various channels and we're seeing

(36:06):
questions going on all the time. I really discourage people
from asking me a one on one question. I will
usually redirect a question someone asked me to here's the
broad company, here's the question that was asked, here's the answer.
So then immediately the entire company learns you know what
this topic was, and very often that says, oh, someone else,
I have another idea about that that I want to

(36:28):
now share. So getting accessibility for people to deliver. But
the most important about idea meritocracy is really from a
leadership standpoint. People have to feel safe bringing up ideas
that they're not going to get you know, yelled at.
You know, there's no bad questions, there's only people not
asking questions. That's not bad. And the only way that

(36:49):
that for people to feel safe about that is that
they need to see me as the leader and my
other partners as the leaders, to be willing to take
in feedback, be challenged even publicly and say, you know what,
that's a really good idea, let's go with that. And
so just having them feel that safe environment so that
people can always ask and bring questions up.

Speaker 2 (37:10):
Huh, that's really interesting. Also, you've discussed generating less common
ideas earlier, we were talking about crowded trades. How do
you generate less common ideas? How do you find non
correlated sources of return when you're you know, in a
hyper competitive marketplace.

Speaker 4 (37:29):
Great question. So I'll use an example here. There's a
common strategy that people might be familiar with. It's called
merge arbitrage. And basically, company A is going to buy
company B, whether it's for cash consideration or stock for
stock type transaction. And you know, merge arbitragers look at
that and they might go, you know, long the company's
being acquired, short, the company's doing the acquire, and then

(37:51):
make money if that deal ultimately closes. That's a that's
a very common, well known strategy. That would be the
common version of implementing the strategy. A less common version
implemented is you try to find one that you like
more than others. So you might think they all are
like the vast majority are going to close, but some
you might like better than others, and so you could
go long half of them and short half of them,

(38:14):
so you're not exposed to this common element of merger
arbitrage deals closing. You're neutral to those. So if a
large pod shop, you know, one of these large multi managers,
if they decided to get out of merger arbitrage and
they're selling all these positions down, half your portfolio will

(38:34):
get helped and half your portfolio will get hurt, but
you're less exposed to that crowding risk, and that common
what I would say, a risk factor that these other
common strategies have. So that's a niche version of how
we might implement that kind of a strategy.

Speaker 3 (38:46):
You mentioned niche.

Speaker 2 (38:47):
I never heard the phrase prior to reading something you
had written called niche alpha. Tell us a little bit
what niche alpha is.

Speaker 4 (38:56):
That's a great question. The simple answer is you're unlikely
to have any or much of it in your hedge
footy portfolio. That's how I would describe it. And so
it's looking for people that are either implementing common strategies
in a very different way that makes them less susceptible
or more immune to people getting out of that strategy,

(39:17):
or people have a completely different idea of how to
make money that I haven't heard of before. And I've
interviewed hundreds, if not thousands of portfolio managers and worked
with developed many strateges of my own. So it's trying
to find things that people aren't doing.

Speaker 2 (39:30):
Huh is there given what we know about the efficient
market hypothesis? And Gene Fama was Cliff Astness's doctoral advisor,
is that what or mba Cliff was Gensta Yes? So,
given how mostly efficient the market is, is are there
really Nietzsches left that have not been discovered? How many

(39:53):
more opportunities are out there that we don't know about?

Speaker 4 (39:57):
That taps into something we talked about earlier, which is
there are thousands of ways to make money in the markets.
There's only dozens of ways to make money and big
dollar size at scale at scale.

Speaker 2 (40:09):
So these smaller ideas is that where the mostly kind
of eventually efficient market hasn't quite reached yet.

Speaker 4 (40:18):
Well, it's what I think about is the amount of
dollars you can make. This is the race. I think
about the amount of dollars you can make, divided by
the complexity or how much brain damage you have to
inflict upon yourself to actually implement the strategy.

Speaker 3 (40:30):
Uh huh.

Speaker 4 (40:30):
A lot of these small stranges, they're complex and difficult
to do. That might require, you know, some kind of
new technique that is difficult, are rare to implement, And
the actual P and L that you can generate profit
less you can generate is small villid for that effort.

Speaker 2 (40:47):
Small in terms of percentage returns or small in terms
of dollars. Hey, there's only one hundred million to arbitrage
away with this, and once that is mined, that's it.

Speaker 3 (40:56):
It's done.

Speaker 4 (40:57):
It's about dollars of P and L you can extract
from the markets. Percentage returns can be very high for
these strategies, but I'll give you a sense. You know,
most other large shops they're going to look for strategies
that can generate at least one hundred million dollars at
P and L to make it worth their while to invest.
We're looking at strategies that are generating ten, twenty thirty
forty million dollars per year.

Speaker 3 (41:16):
Huh.

Speaker 2 (41:16):
That's really kind of intriguing. So what sort of demand
is there for lower capacity strategies? I mean, so you
guys are less than half a billion dollars, You're not
an enormous funds. Are there more hedge funds looking to
swim in these ponds or is this something that hey,

(41:36):
once you cross a certain size, you just have to
leave behind and stay with those larger capacity, scalable strategies.

Speaker 4 (41:44):
Yeah. I think this is a general thing for all investors,
not just other hedge funds. Everybody wants to be in
the interesting things. They want to be in the lower
capacity things. They know that they're less crowded, the difficulty
and really what I think are kind of our business
model is is you're paying for us to go out
and search the world and source them because it's expensive.
It's expensive exercise to do. People might not have the
expertise or the background to underwrite these types of strategies.

(42:07):
It takes a lot of work, and at the end
of the day, alpha is either about being smarter or
working harder. The being smarter can work in the short term,
but eventually that does get our way. Eventually someone smart
enough comes by. The working harder, to me is the
thing that actually stays.

Speaker 2 (42:23):
Huh, that's really interesting. You would think if the incentive
was there enough, people would just eventually grind away in
that space.

Speaker 4 (42:30):
I mean, the incentive is there, it's just not enough
to be worth the time. And so if you are
a very large investor of organization, you do have to
prioritize you still have limited resources in time to look
for things, so you're going to have you know, thresholds.
I'm not going to invest at least, you know, at
this amount of dollars, and that's where we step in
is kind of fill that gap.

Speaker 2 (42:51):
So you're very much a student of what's going on
in the hedge fund world. What are you seeing in
terms of strategies driving cost down and the question of
where fees are. They've certainly pulled back from the days
of two and twenty. What's happening in terms of efficiency
and cost.

Speaker 4 (43:10):
There's a bunch of things to talk about there. So
The first thing I would say is the higher capacity
strategies that have become well known. I think that those
costs are going down because there's a lot of people
who can implement those strategies and so that you think
just simple supply and demand, lots of portfolio managers you
can do them, and so then it's just a competition
of who's going to be able to do it most efficiently.
Then there's unique alpha. I think that's harder, and actually

(43:32):
the cost of that has gone up over time. It's
not gone down. The cost it takes to compete in
the space has increased over time. So there's a bifurcation
that's been going on. We think that there's still a
lot of efficiencies you can carve out of the system
that exists now that we're attacking lot through technology, a
lot of three ways of working that can just make

(43:54):
the organization more efficient and deliver more net returns to investors.

Speaker 2 (43:57):
So we've seen some motion towards fees for alpha and
not betos. Some people call it pivot fees. There's like
a lot of different names for this. I haven't heard
much about that recently. What are your thoughts on where
hedge fund fees are going in the future.

Speaker 4 (44:13):
I'll answer that with a different story that we'll draw
on analogy here with the rise of indexing, which has
been happening for decades now, and thank god for indexing.
It's a fantastic invention that has helped a lot of investors.
The original thought was, well, as the market goes more
and more indexing, and I don't know what the number is,

(44:33):
it's probably seventy percent is indexed of the invested dollars,
then it makes the markets, you know, it's easier to
make money because there's less people trying to compete for that.
But that's not what actually happens. What actually happens is
it's become more and more difficult to make money because
the talent pool is of higher quality now than it

(44:54):
used to be. That's searching for that alpha. And just
like sport, it's when there's a zero sum game right right,
and it's just it's very small differences between you know,
the number one person and the number five person. What
you see is the rewards and the compensation tends to

(45:16):
be a power law, meaning that the very few get
get paid a lot. And I see for pure alpha,
where there's real competition that the investment talent will actually
get paid more and more over time, it will get
more and more difficult to be that person. Whereas for
the common stuff, the well known things that have higher capacity,
I think you're gonna see fees keep going down.

Speaker 2 (45:36):
On that side, Michael Mobison calls that the paradox of
skill that the more skillful the players are, whether it's sports,
investing business, the more of a role luck plays, which
is really really kind of kind of fascinating. You've also
written about portable alpha. Discuss discuss portable alpha. What is

(45:57):
that and how can we get some.

Speaker 4 (46:00):
Think portal alfa is a great way for investors to
get exposure to alternative return streams. What portal alfa is
is mixing a beta like s and P five hundred
exposure with an alpha stream and really just pop in
that offa stream on top of the SMP five hundred returns.
So it lets investors get exposure to SMP, which most
investors already have, but now exposure to a different type

(46:22):
of return stream. Usually people historically at least have tried
to beat the SMP by picking a manager who's trying
to pick stocks, overweighting stocks that they like versus the
index and underwaitting stocks that they don't like. But that
comes with a lot of constraints. One is the manager
can only overweight underweight stocks in the index. They can't
trade other asset classes, they can't utilize any kind of

(46:45):
sophisticated investment techniques to try to beat that benchmark. Portal
alpha get rid of all of those constraints, and so
what you typically see is portal alpha programs are much
better and consistently beating traditional active programs.

Speaker 2 (47:01):
I like the phrase Corey Hofstein uses for that return stacking.
Is that same concept for portable alpha? That's right, Yeah,
really really interesting. Before I get to my favorite questions
that I ask, well, my guests, I just have to
throw you a a little bit of a curveball. So
you're a member of the Yale New Haven Children's Hospital Council,

(47:24):
tell us a little bit about what you do with that.

Speaker 4 (47:26):
Sure. So just how we got involved, My wife and
I we with the five kids, three of which had
severe peenaut allergies, and we were very concerned about that.
You know, that's become a rising epidemic within a society
over time, and we wanted to see if we could
solve that invest in basically research, try to solve this problem.

(47:47):
So we work with both Yale and our local hospital too.
Can we fund a research effort and a clinical effort
to basically collect data because a lot of the research
really needs data, So we work with them. That's how
we got originally with yellows an organization, and then they
have this council that's focused on children's health issues and

(48:08):
what it is. It's a collection of individuals who are
interested in this topic. We meet typically quarterly. They'll have,
you know, some of their top researchers from Yale come
in and talk about whatever research they're working on and
their clinical experiences with you know, children as patients, and
that usually generates ideas, Okay, how can we make this
more effective? How can we get more funds directed towards

(48:29):
this activity?

Speaker 2 (48:30):
All right, we only have you for a couple of minutes.
Let's jump to my favorite questions that we ask all
of our guests, starting with what are you streaming these days?
What's keeping you entertained? Either Netflix, podcast, Amazon, whatever.

Speaker 4 (48:45):
My wife and I, after going through the litany of
all the kids and their issues each day, it's usually
very late and so we don't get to watch as
much TV as probably would like. There's a lot of
great content out there. Lately, we're watching Lionis on Paramount,
which is.

Speaker 2 (48:58):
I just finished season one of few weeks ago and
taking a break before season two.

Speaker 3 (49:03):
But it's fantastic.

Speaker 4 (49:03):
It's fantastic. Yeah, we've really enjoyed it so far.

Speaker 3 (49:06):
But I would say, are you up to season two yet?

Speaker 4 (49:09):
No, we're three or four episodes in. Oh, this season one?

Speaker 3 (49:13):
Brace yourself, you have quite right?

Speaker 4 (49:14):
Okay, great, But in terms of like favorite shows, one
of my favorites was the remake of Battlestar Galactica, which
was a show when I was growing up as a kid.

Speaker 2 (49:24):
With a terrible special effects in the old one, yes,
and the new one is great.

Speaker 4 (49:28):
Right, that's right. And there's there's a scene that's actually
relevant to our conversation a little bit today. The leader
of the sidelines, which is like the robots, is talking
with Human is one of the fighter pilots, and they're
watching a video of one of the battles and the
humans win this battle. But then the sieline says, this

(49:49):
is how we're going to beat you, and Human's like,
what do you mean? Because they just watched, like one
of the humans kill one of the robot fighter pilots,
and she says, well, every time that we make a
mistake and we lose a battle, every single other Cylon
learns from that, and so inevitably we will learn every

(50:12):
way that we can avoid dying and we will take
you over. And that has a lot to do with
how we approach the business on the investing side, always
learn from mistakes, get the communication out there, and constantly improve.
If you improve by a few percent a year, that
really compounds over time.

Speaker 2 (50:32):
Well, what does it matter if the AI silon is
eventually going to kill all of us, It won't make
any difference. Alpha is only here until the cylons beat
us in a space battle.

Speaker 3 (50:44):
We view it that's way off in the distance.

Speaker 4 (50:47):
We like intelligence augmentation versus artificial intelligence IA instead of
AI using these tools to be more effective.

Speaker 2 (50:56):
That makes a lot of sense. Let's talk about your
mentor who helped to shape your career.

Speaker 4 (51:02):
Well, I would say of all the ones I could
think of, Cliff would be the top mentor. And Cliff
wasn't the kind of guy who would you know put
your brand? Is his arm around you say hey, you
know this, you do X, Y and Z, and you
should do this differently. He did have a good several
conversations with me like that. Most of his mentorship was
through his actions. Clip's extremely principled, very ethical, and it's

(51:26):
it's a very fortunate thing to be able to be
in business with someone like that, where you can be
successful at business but do it in a very ethical,
principled way that's always doing right by the client, and
that's something some of the biggest things I've taken away
from working with him.

Speaker 2 (51:40):
Let's talk about books. What are some of your favorites
and what are you reading right now?

Speaker 4 (51:44):
I like history, specifically financial history. The one I'm reading
right now is called The World for Sale. It's actually
written by a couple of journalists that cover the commodity industry,
and it's really about the physical commodity traders and the
whole history of that, which is which is kind of interesting.
I love biographies. One of particularly liked was the Michael
Dell one Played Nice but When where it's kind of

(52:07):
chronologically it's this whole story. I really connected with the
building computers in his dorm and selling them. Obviously, he
was much more successful at that than I was really interesting.

Speaker 2 (52:16):
Any chance you read McCullough's Wright Brothers, I have not
really fascinating. I like, it's unusual to read something that
you think, oh, I know that history, and then it's like, no,
you have no idea what's going on in the history.
And he's just a great writer, really really really interesting.
Our final two questions, what sort of advice would you

(52:36):
give to a recent college grad interested in a career
in either quantitative or investment finance.

Speaker 4 (52:45):
I don't know if the advice would be specific to
those things. But talk less and listen more, that is
what I would say. There's a curve. I forget the
name of the curve, but it's you know, you start
thinking you know a lot, especially Unny Kruger. Yeah, Dunning Kriger,
that's what it is. Yeah, that is such a true effect.

(53:06):
I thought I knew everything, and if I just listened
to those around me who knew a lot more people
are trying to help you more than you realize as
a young person, and I should have just listened to
more advice. I would have been more successful, much more
earlier if I had.

Speaker 2 (53:22):
So here's the funny thing about the Dunning Kruger curve,
and this comes straight from David Dunning. They did not
create the Dunning Kruger curve. It kind of came from
just pop psychology and social media. And then when they
went back and tested it, I think the paper was
like ninety nine or two thousand and four, something like that.

(53:43):
When they went back and tested it, it turned out
that the Dunning Kruger curve turned out to be a realistic,
measurable effect. And it's mount stupid. The valley of Despair
and the slope of enlightenment are just sort of the
the pop terms of it, but it's really really funny.

(54:03):
And our final question, what do you know about the
world of investing today? You wish you knew back in
the early nineties that would have been helpful to you
over those decades.

Speaker 4 (54:15):
There's a lot of smart people out there, as smart
as you might be. There's a lot to learn from
everybody else. Everybody has some insight, some perspective that you
don't have. Don't presume how you know what people are thinking.
So ask questions and listen.

Speaker 2 (54:36):
Sounds like good advice for everybody. We have been speaking
with Brian Hurst. He's the founder and CIO of Clear
Alpha If you enjoy this conversation, well, be sure and
check out any of the five hundred and thirty we've
done over the past ten years. You can find those
at iTunes, Spotify, YouTube, Bloomberg, wherever you find your favorite podcasts,

(54:58):
be sure and check out my latest podcast, At the Money,
short ten minute conversations with experts about topics that affect
your money, spending it, earning it, and most importantly, investing it.
At the Money. Wherever you find your favorite podcasts, I
would be remiss if it and not thank the crack
team that helps us put these conversations together each week.

(55:19):
Sarah Livesey is my audio engineer. Sage Bauman is the
head of podcasts. Sean Russo is my researcher. Anna Luca
is my producer.

Speaker 3 (55:30):
I'm Barry Retolts.

Speaker 2 (55:31):
You've been listening to Masters in Business on Bloomberg Radio.