0:00

I always used to misspell Renaissance as I was

0:02

typing it out at R-E-N and then I would

0:04

sort of like not really know what came from

0:07

there, but I learned a mnemonic to make sure

0:09

I get it right. Oh, I

0:11

thought you were going to say you've typed it so

0:13

many times now over the past month. Well, there's that

0:15

too, but you ready for this? You

0:18

can't spell Renaissance without A-I. Oh,

0:21

oh, oh. Touche,

0:24

touche. All

0:27

right, let's do it. Welcome

0:45

to Season 14, Episode 3 of

0:47

Acquired, the podcast about great companies

0:49

and the stories and playbooks behind

0:51

them. I'm Ben Gilbert. I'm David

0:53

Rosenthal. And we are your hosts.

0:56

They say, David, that as an investor,

0:58

you can't beat the market or

1:01

time the market, that you're

1:03

better off indexing and dollar cost averaging

1:05

rather than trying to be an active

1:07

stock picker. They say there's

1:09

no persistence of returns for hedge funds,

1:11

that this year's big winner can be

1:14

next year's big loser, and

1:16

that nobody gets huge outperformance without

1:18

taking huge risk. When

1:20

I was in college, I actually took an

1:22

economics class with Burton Malkiel, who of course

1:24

was involved in starting Vanguard and is a

1:27

big proponent of all that. And that is

1:29

what I learned, Ben. Well, David, it

1:31

turns out they were wrong. Today

1:34

listeners, we tell the story

1:36

of the best performing investment

1:38

firm in history, Renaissance Technologies,

1:41

or RENTEC. Their

1:43

30-year track record managing billions of

1:45

dollars has better returns than anyone

1:47

you have ever heard of, including

1:50

Berkshire Hathaway, Bridgewater, George Soros, Peter

1:52

Lynch, or anyone else. So

1:54

why haven't you heard of them? Or if

1:56

you have, why don't you know much about them?

1:59

Well, their eye popped performance is matched

2:01

only by their extreme secrecy, and

2:03

they are unusual in almost every

2:05

way. Their founder, Jim Simons,

2:07

worked for the US government in the

2:10

Cold War as a codebreaker before starting

2:12

Renaissance. None of the founders

2:14

or early employees had any investing background,

2:16

and they built the entire thing by

2:19

hiring PhD physicists, astronomers, and speech recognition

2:21

researchers. They're located in the middle of

2:23

nowhere in a tiny town on Long

2:25

Island. They don't pay attention to revenues,

2:28

profits, or even who the CEOs are

2:30

of the companies that they invest in.

2:32

And at any given time, they probably

2:34

couldn't even tell you what actual stocks

2:37

they own. Now, you may

2:39

be thinking, okay, great, I just learned about

2:41

this insane fund with unbelievable performance. And

2:44

to be specific listeners, that's 66% annual

2:47

returns before fees. And,

2:49

you know, well, I want to invest. Well,

2:51

you can't. To add to

2:53

everything else that I just said, RENTEC's

2:56

flagship medallion fund doesn't take any outside

2:58

investors. The partners of the firm have

3:00

become so wealthy from the billions that

3:02

the fund has generated that the only

3:04

investors they allow in are themselves.

3:08

Oh, we are going to talk a

3:10

lot about that towards the end of

3:12

the episode, because I think it's kind of the key to

3:14

the whole thing. Ooh, cliffhanger,

3:17

David, I'm excited. So

3:19

what exactly does Renaissance do? Why does it

3:21

work? And how did it evolve to be

3:23

the way it is today? And

3:25

while the resources are out there are

3:28

scarce, because for one, employees sign a

3:30

lifetime non-disclosure agreement, David and

3:32

I are going to take you through everything

3:34

we've learned about the firm from our research

3:36

dating all the way back before Jim Simon

3:39

started as a math professor to understand it

3:41

all. This episode was selected

3:43

by our acquired limited partners. And to be

3:45

honest, I didn't think enough people knew what

3:47

RENTEC was to pick it. But when we

3:49

put it out for a vote, the people

3:51

have spoken. So if you want to become

3:53

a limited partner and pick one episode each

3:55

season and join the quarterly zoom calls with

3:58

us, you can join at acquired.f If

4:02

you want to know every time a new episode

4:04

drops, sign up at acquired.fm slash email. These

4:06

emails also contain hints at what the next

4:09

episode will be and follow up facts from

4:11

previous episodes. For example, we

4:13

had a listener, Nicholas Cullen, email

4:15

us this time who found the

4:17

actual document with the bylaws of

4:19

Hermes's controlling family shareholder H51, which

4:21

we linked to in this most

4:25

recent email. Tom talked about

4:27

this episode with us after listening at acquired.fm slash

4:29

Slack. If you want more from David and I,

4:31

check out ACQ2. Our most

4:33

recent episode was with Lata Bier-Newton, who

4:36

led the team that created the first

4:38

GLP ones at Novo Nordisk. So awesome

4:40

follow up to the Novo episode if

4:42

you liked that one. Before

4:44

we dive in, we want to briefly share

4:47

our presenting sponsor this season is JP Morgan,

4:49

specifically their incredible payments business. Yeah,

4:51

just like how we say every company has a

4:53

story, every company's story is powered by payments. And

4:56

JP Morgan payments is a part of so many

4:58

companies that we talk about on acquired. It's not

5:00

just the Fortune 500, too. They're

5:03

also helping companies grow from seed to

5:05

IPO and beyond. So

5:07

with that, the show is not investment advice. David and

5:09

I may have investments in the companies we discuss or

5:11

perhaps wish we did. And this

5:14

show is for informational entertainment purposes

5:16

only. David, where do we start

5:18

our story today? Well,

5:20

we start in 1938 in

5:24

Newton, Massachusetts, which is

5:26

a fairly wealthy suburb just outside of Boston,

5:29

where one James Simons is

5:31

born. Both of Jim's

5:33

parents were very, very smart, especially

5:35

his mother, Marsha. His

5:37

dad was a salesman for 20th Century

5:40

Fox, the movie company. His job was

5:42

he went around to theaters in the

5:44

Northeast and sold packages of

5:46

movies to them. Super cool. And

5:49

we know all this because we have to

5:51

thank Greg Zuckerman, author of The Man Who

5:53

Solved the Market, which is the only book

5:56

out there that is solely dedicated to RENTEC

5:58

and Jim Simons. got to

6:00

talk to Greg in our research. He helped us out

6:02

a bunch. Thank you, Greg. And helped fact check a

6:04

few of our assumptions of what happened after the book

6:06

came out. So that

6:08

was Jim's parents. But really

6:11

a major influence on him growing

6:13

up was his grandfather, Marsha's

6:15

dad. There's already echoes of the Bezos

6:17

story here with the grandfather, the mother's

6:19

father, and spending a bunch of time

6:22

with him, and rubbing off on young

6:24

Jeff or young Jim in this case.

6:27

And Bezos, of course, would get his

6:29

start in his career at D.E. Shaw.

6:32

A quant fund coming up at the same

6:34

time as RENTEC. But back

6:36

to Jim here in the 1940s, his

6:39

grandfather, Peter, owned

6:41

a shoe factory that made women's

6:43

dress shoes. Jim spends a ton

6:45

of time there growing up at the factory. So

6:49

Jim's grandfather, Peter, was

6:51

quite the character. He was

6:53

a Russian immigrant. And he's

6:55

kind of like still more Russia than Boston at

6:57

this point in time. As

6:59

Greg puts it in the book, Peter

7:02

reveled in telling Jim and his cousins

7:04

stories of the motherland involving wolves, women,

7:06

caviar, and vodka. And he teaches young

7:09

Jim when he's a child here in

7:11

the factory to say Russian phrases like,

7:13

give me a cigarette and kiss my

7:16

ass. Which I think he probably would

7:18

say that thousands of times the rest of his life.

7:20

I think so. If you watch

7:22

interviews with Jim, his hands are always

7:25

twitching because he has chain smoked

7:27

his entire life probably going back to

7:29

age 10 in the factory. Three packs

7:31

of merits a day. Unbelievable.

7:33

Although I think he quit later in life,

7:35

but he definitely chain smoked the better part

7:37

of the first, call it, 75 years or

7:40

something. I mean, there's these famous stories of

7:42

the conference rooms at RENTEC and the war

7:44

rooms when the market is going through a

7:46

crazy gyration and it's just filled with cigarette

7:48

smoke. And it's all Jim. Different

7:50

time. Different time. So

7:53

back to Jim's childhood though, here in

7:55

the Boston suburbs. He

7:57

grows up certainly not uber wealthy.

8:00

are uber rich, but very, very solidly

8:02

upper middle class, and especially he's an

8:04

only child. He has all the resources

8:06

of his parents, his family, his grandfather's

8:08

this sort of well to do entrepreneur.

8:11

And Jim, you know, he gets to rub

8:13

shoulders in the Boston area with people who

8:15

are really rich. And

8:18

he says later, I observed that it's very nice

8:20

to be rich. I had no interest in business,

8:22

which is not to say I had no interest

8:24

in money. Yes, important to

8:26

tease out the difference between those two

8:29

things. Yes, very, very important. And

8:31

what he means when he says he has no interest in business,

8:34

it's because from a pretty young

8:36

age, he gets really into

8:38

math. So the legend has

8:40

it when Jim is four years old,

8:43

he stumbles into one of Zeno's famous

8:45

paradoxes from ancient Greek times. Yep,

8:47

this is great. The basic gist of

8:50

Zeno's paradoxes, if you are always taking

8:52

a quantity and dividing it by two,

8:54

you will never hit zero,

8:56

you will asymptotically approach zero, but you will

8:58

never actually touch zero, you need to do

9:01

addition or subtraction to do that division won't

9:03

cut it. And so Jim as a four

9:05

year old, when he observes they need to go

9:08

to the gas station to fill up the tank,

9:10

he throws out the idea, well, let's just use

9:12

only half the gas in the tank, because then

9:14

we'll still be able to after that only use

9:16

half the gas in the tank. And you know,

9:19

the funny thing that doesn't occur to a four

9:21

year old is, well, then we're just not going

9:23

to get very far. So

9:25

Jim's dream is to go to MIT down

9:28

the street in Cambridge and study

9:30

math. He graduates high school in

9:32

three years. And during the second

9:34

semester of Jim's freshman year there,

9:36

he enrolls in a graduate math

9:38

seminar on abstract algebra. So

9:40

pretty, you know, heady stuff. Yeah,

9:43

and Jim would go on to finish his

9:45

undergrad at MIT in three years and get

9:47

a master's in one year. Yeah,

9:49

pretty, pretty smart. But it

9:52

turns out that that freshman year grad seminar

9:54

he took actually has a big

9:56

impact on him because he doesn't do well

9:58

in the class. He can't keep up. And

10:01

Jim's pretty self-aware here. There

10:04

are other people at MIT who

10:07

never run into problems. They never

10:09

hit a limit. They never struggle

10:11

understanding any concept. And

10:13

he realizes that, oh, I'm

10:16

smart. I'm very, very smart. I'm smarter than most

10:18

other people here. But I'm not

10:20

one of those people. Right,

10:23

which is, what do you do with that information?

10:25

You realize you have to add a few of

10:27

your skills together to become the best at something.

10:29

You have to be smart and something

10:31

else. Yes. So Jim's own words on this

10:34

are, I was a good mathematician. I

10:36

wasn't the greatest in the world, but I was pretty good.

10:39

But he recognizes, like you said, Ben, that

10:41

he has a different advantage that most of

10:43

the supergeniuses lacked. And that's that as he

10:45

put it, he had good taste. So

10:48

these are his words. Taste in science is

10:50

very important. To distinguish what's a

10:52

good problem and what's a problem that no

10:54

one's going to care about the answer to

10:57

anyway, that's taste. And I think I

10:59

have good taste. By the way,

11:01

this is exactly the same thing as Jeff

11:03

Bezos. In college, realizing he

11:05

wanted to be a theoretical physicist, he

11:07

met some of the extreme brainpower

11:09

people that would go on to become the

11:11

best theoretical physicist in the world. And he

11:14

said, I'm smart, but I'm not that smart.

11:16

And so switched to computer science. I

11:18

think the analogy here is

11:20

like sports. There

11:23

are all-star players. There

11:25

are hall of famers. And then there's

11:27

LeBron and MJ. And

11:29

Jim ends up being a hall of famer

11:31

mathematician. But he's not Tom

11:33

Brady. I mean, he's got a pretty important theorem

11:35

named after him. That goes on to become a

11:38

foundation of string theory and physics, which isn't even

11:40

Jim's field. Crazy. So

11:42

this realization that Jim has about

11:44

himself, though, both that he's not

11:47

the smartest person in the room at a place like

11:49

MIT, but he can hang with them, and

11:52

that he has this taste concept,

11:55

I think becomes one of the

11:57

most important keys to the secret

11:59

sauce. that ends up getting built at RENTEC, which

12:02

is that he can relate

12:04

to everybody. He understands what's going on.

12:06

Any person off the street probably couldn't

12:09

even really have a conversation with these

12:11

folks, but he can. And

12:13

yet he also has the perspective, maybe some

12:15

of this is from his grandfather, of what

12:18

is important out there in the real world.

12:20

And as a result, all

12:22

of his friends at MIT and these super smart people, they

12:24

look up to him because you

12:27

aren't like the kid in the

12:29

corner at the high school dance.

12:31

You're cool. He's the extroverted theoretical

12:33

mathematician. Yes. So

12:36

he was elected class president in high school.

12:39

He smokes cigarettes. He's popular with

12:41

the ladies. He kind of

12:43

looks like Humphrey Bogart. He's a popular

12:45

dude, especially at this point in time.

12:48

We're now in the late 50s when Jim's at MIT. This

12:51

is kind of James Dee and Rebel Without

12:53

a Cause era. So

12:55

after graduation, Jim leads his

12:58

buddies on a road

13:00

trip with motor scooters. You

13:02

can't make this stuff up from Boston down

13:05

to Bogota, where one of his classmates is

13:07

from. The idea is that they're going

13:09

to do something so epic that the newspapers are going

13:11

to have to write about it. So

13:13

they all load up on scooters and

13:16

drive down to Bogota. They get into all

13:18

sorts of adventures. There's knives, guns, and they

13:20

get thrown in jail. It's honestly crazy that

13:22

this group of people took this type of

13:25

risk. Totally crazy. So

13:28

after he's done at MIT and after the road

13:30

trip, Jim heads out to

13:32

Berkeley in California so that he

13:34

could do his PhD with the

13:36

professor Xing Shen Chern. And

13:38

much later in life, Jim would collaborate

13:40

with Chern for the Chern-Simons theory that

13:43

we talked about earlier that becomes one

13:45

of the foundational parts of string theory

13:47

in physics. But before Jim

13:49

leaves for the West Coast, he

13:51

meets a girl in Boston. And they

13:54

decide to get engaged in four

13:56

days. I mean, this is This

14:00

is him back there. These are the times. And

14:03

when they get to California and they

14:05

get married, Jim takes the

14:08

$5,000 wedding gift that I believe they

14:10

got from her parents, and he decides,

14:12

I want to multiply this. So

14:15

he starts driving from Berkeley into San

14:17

Francisco every morning to go hang out

14:19

at the Merrill Lynch brokerage office and

14:22

just be a rat hanging around the brokerage

14:24

and find ways to trade and turn this

14:26

money into something more. Which is

14:28

so interesting to think about because at that point in time,

14:31

there was such an advantage to just

14:33

being there. This wasn't even the trading

14:35

floor, but information is all so manual

14:37

and all so relationship-driven in the markets

14:39

that there was basically no way to

14:41

be in on the action unless you

14:43

were physically there in on the action.

14:46

Exactly. Yeah, you couldn't just

14:48

log into Yahoo Finance or something or open

14:50

the stocks app on your iPhone, which even

14:52

the information they were getting was God knows

14:54

how long delayed from New York or from

14:56

Chicago for the futures and commodities that are

14:58

being traded that Jim gets into. He's

15:01

as close to the action as he can possibly

15:03

be, but he's a long, long way from the

15:05

action. Yep. Nonetheless, when

15:08

he starts out doing this, Jim hits

15:11

a hot streak and he goes up 50% in a few

15:13

days. Trading

15:15

is easy. Trading is easy. He says,

15:17

I was hooked. It was kind of

15:19

a rush. I bet. Except

15:21

he ends up losing all of his profits

15:23

just as quickly. Important

15:26

to learn that lesson early. Yes. And

15:28

also right around this time, Barbara, his

15:30

wife, gets pregnant with their first child

15:32

and is like, you can't be

15:34

driving into San Francisco every morning at

15:37

gambling our future like this. Right,

15:39

effectively playing the ponies. Yeah, exactly.

15:42

So Jim's like, okay, okay, I'll

15:44

stop. I'll focus on academia for

15:46

now. So he finishes his

15:48

PhD in two years. They come back to

15:50

Boston and he joins MIT as

15:53

a junior professor at age 23. So

15:55

they stay one year in Boston. But

15:58

Jim, even though he's got a family... even

16:00

though he's super successful as a young academic

16:02

here, he's got kids. He's

16:04

restless. So one

16:06

of his buddies from the scooter trip to Bogota is

16:09

from Bogota and lives there, his family's there. He

16:11

has an idea to start a flooring

16:14

tile manufacturing company, because he's like, you

16:16

know, the flooring at MIT and in

16:18

Boston, it's so much nicer than at

16:20

Bogota. We should start a company and

16:22

make the same kind of flooring here.

16:24

When I read this, I couldn't believe

16:26

that this was Jim Simon's first business

16:29

venture. It's so random, but it really

16:31

is emblematic of just how much

16:33

he was thrill seeking and just looking for anything

16:36

that was unexpected, different,

16:38

exciting. He just gets bored

16:40

fast. Totally. Not

16:43

just is this the start of his

16:45

entrepreneurial career. The seeds of

16:47

this financially are what go on to

16:49

start Red Tech. It's wild. Totally

16:52

wild. So Jim takes a year

16:54

off and goes down to Bogota.

16:56

This is a guy with an

16:58

MIT undergrad and master's

17:00

and a Berkeley PhD in theoretical

17:03

math. Who's now a professor at

17:05

MIT. Who is taking a year off

17:07

to go work on a flooring company in Bogota. Yes,

17:11

accurate. So he does that for a year. They get it set

17:13

up. He gets bored again. He's like, all right, I don't want

17:15

to run this company. I've helped set it up. I have an

17:17

ownership stake in it now. He

17:19

bounces back to Boston,

17:21

this time to Harvard, as

17:23

a professor there for a year. He's really racking

17:25

him up. But he

17:28

spends a year there and he's like, ah, got

17:30

the itch again. And the junior professor's

17:33

salary isn't that much. And like

17:35

we said about him back from his childhood days,

17:37

he sees the appeal in being rich. He's like,

17:39

this is not a path to be rich. So

17:44

he's like, I'm going to go put my skills

17:46

out on the open market. He gets

17:48

a job in Princeton, New Jersey,

17:50

not at Princeton University, but

17:53

at the Institute for Defense Analyses,

17:57

which is a nonprofit organization.

18:00

that consults exclusively

18:03

for the U.S. government, specifically

18:06

the Defense Department, and

18:08

specifically the NSA. These

18:12

are the civilian codebreakers. Yes.

18:14

It was basically formed with

18:16

this idea that, one, across

18:18

various branches of our government,

18:20

we need better collaboration and

18:23

cross-funding of the same initiatives.

18:25

And two, there are going to be a

18:27

lot of people who don't work for the government that

18:30

we're going to want to hire to do some pretty

18:32

secret work. Yep. So

18:35

the IDA there in Princeton kind

18:37

of functions like the Institute

18:39

for Advanced Study, which is also

18:42

in Princeton. That's where Einstein went

18:44

when he came to America, kind

18:46

of an independent think tank research

18:48

group, except it's solely focused on

18:51

code-breaking and signal intelligence with the

18:53

Russians during the Cold War. Yeah.

18:56

And it's a pretty wild charter, and especially how

18:59

special of an organization it was. The way these

19:01

people would spend their time is part

19:04

code-breaking, but part kind of

19:06

goofing around because the creativity

19:09

of mathematicians working together on

19:11

passion projects is important to

19:13

discovering clever new algorithms. Yes.

19:16

This is so, so key. And

19:19

this culture ends up getting translated

19:21

whole cloth right into RENTEC. So the

19:23

way IDA worked, and I assume still

19:25

works to this day, is

19:28

they recruited top mathematicians

19:30

and academics to

19:32

come be code-breakers there. They would double

19:35

their salaries. And importantly, it couldn't have

19:37

been a government division if they were

19:39

going to be doing that because there's

19:41

very specific, congressionally approved budgets for payroll.

19:44

Exactly. They figured out that

19:46

they needed to attract the smartest people in the

19:49

world who weren't going to come just go work

19:51

for the Department of Defense. This was

19:53

the way to do it. So like

19:56

you said, Ben, the charter

19:58

of the group was that employees had to...

20:00

to spend 50% of their time doing code

20:02

breaking. But the other 50% of the time, they

20:06

were free to do whatever they

20:08

wanted, like research, pursue whatever they

20:10

were doing in academia, publish papers,

20:13

kind of the appeal of going there was, hey,

20:17

it's the same thing as being a professor at

20:19

MIT or Princeton or Harvard or whatever, except

20:21

you're doing code breaking instead of teaching.

20:24

And there's no bureaucracy to worry about,

20:26

there's no politics. It's just like, hey,

20:28

you do your code breaking work and

20:30

then you publish it, you can collaborate

20:32

with your colleagues there. Now,

20:36

this is pretty crazy. Very quickly

20:38

after Jim arrives at IDA,

20:41

remember he's in money-making mode at this point

20:43

in time, he recruits

20:46

a bunch of his very brilliant

20:48

colleagues to come work with him in

20:50

their 50% free time on

20:53

an idea to apply the

20:55

same work and technologies that

20:57

they're using in code breaking

20:59

and signal intelligence to

21:02

trading in the stock market. So

21:05

they come together and they publish

21:07

a paper called a probabilistic models

21:09

for and prediction of stock market

21:11

behavior. And everything

21:14

that they suggest in this paper really

21:17

is rent tech, just 20

21:20

years before rent tech. It's

21:22

crazy, 1964, this was published?

21:25

Yes, now at

21:27

this point in time, fundamental

21:29

analysis was then, as in most of

21:31

the world today still is, the

21:34

primary way of investing in things of,

21:36

hey, I know this company, I'm gonna

21:38

analyze their revenues, their price multiple, or

21:41

I'm gonna think about what's happening in

21:43

the currency markets or in the commodity

21:45

markets and why copper is

21:47

moving here or the British pound is

21:49

moving there and I'm gonna invest on

21:51

those insights. You're effectively looking at the

21:54

intrinsic value of an asset, trying to

21:56

assign it a value and make investments

21:58

based on that. Yes. fundamental investing.

22:01

There also existed in the

22:03

60s technical investing,

22:06

which kind of is

22:08

voodoo. This

22:11

is like I'm looking at

22:13

a stock chart and I've got a feeling

22:15

that it's going to go up. Like, I'm

22:17

tracing this pattern and like it's going up,

22:19

baby, or no, no, no, this pattern is

22:21

going down. Yeah, using the phrase technical might

22:23

be a little generous. But what

22:25

they're looking for, basically trying to mine

22:27

trading behavior for signal about the way

22:30

that it will trade in the future

22:32

rather than mining the intrinsic information about

22:34

an asset for what you think it

22:36

will do in the future. Right.

22:39

And what Jim and his colleagues here are

22:41

suggesting is that, but

22:44

just not really done by humans.

22:46

It's that with a lot more

22:48

data and a lot more sophisticated

22:51

signal processing. And importantly,

22:53

you might say, why is it this group

22:56

of people that came to that conclusion of

22:59

applying computational signal analysis

23:01

to investing? Well, it's effectively the

23:03

same thing as code breaking. You

23:05

are looking for signal in the

23:08

noise and trying to use computers

23:10

and algorithms to mine signal from

23:12

something that otherwise kind of looks

23:15

random. Totally. When Jim started

23:17

working on code breaking, I think

23:19

he just looked right back to his experience trading in

23:21

the markets and was like, whoa, this is the same

23:24

thing. Which is not an

23:26

insight other people had. That was the

23:28

amazing thing about his background, priming him

23:30

to realize that. Yes, there's all this

23:32

noise in this data. And it is

23:35

impossible for a human to sit here

23:37

and look at this data and say,

23:39

oh, I know what the Soviets are

23:41

saying. No, no, you have to use

23:43

mathematical models and statistical analysis to extract

23:45

the patterns. So mathematical

23:48

models, statistical analysis. We actually hear

23:50

a lot of that in the

23:52

world today because machine

23:54

learning is a thing. Yes,

23:57

what they are really doing here at IDA

24:00

and then soon in

24:02

RENTEC is early machine learning. And

24:05

Jim just had this incredibly

24:07

brilliant insight that you can

24:09

use these techniques and this

24:11

technology for making investments, which

24:14

makes this the perfect time to talk about

24:17

our presenting sponsor for this season, JP Morgan

24:19

Payments. Yes. The

24:21

finance industry has a rich history

24:23

of innovating dating all the way

24:25

back to the literal Renaissance where

24:27

double entry bookkeeping and letters of

24:30

credit revolutionize global trade and economic

24:32

development. And JP Morgan Payments really

24:34

continues that tradition in their technology

24:36

investments today. They move $10

24:38

trillion a day securely. That is

24:40

a quarter of all US dollar

24:43

flows globally. Just think about the

24:45

sheer volume of data at 5,000

24:47

transactions per second and how

24:49

important that is to the global economy. Unsurprisingly,

24:52

JP Morgan Payments has been in

24:54

the AI game for years now.

24:56

Similar to RENTEC, they were also

24:58

early to recognize the value of

25:00

AI to gather, process and analyze

25:02

those massive troves of data to

25:04

provide solutions for their customers and

25:06

mitigate risk. Like when they incorporated

25:08

AI into their cashflow forecasting tool,

25:10

which helps businesses manage liquidity and

25:12

that proved especially valuable during the

25:14

pandemic. Yep. So also unsurprisingly, JP

25:16

Morgan was ranked number one in

25:18

a recent global banking index of

25:20

AI capabilities with Fortune saying they

25:22

were quote, head and shoulders above

25:24

the others. Their customers get AI

25:27

powered payment solutions for fraud prevention,

25:29

customer insights and treasury insights, all

25:31

of which grows the bottom line.

25:33

They can even analyze transaction data

25:35

to predict and mitigate fraud patterns

25:37

in real time with their validation

25:40

services, helping stop millions of dollars

25:42

for customers in attempted fraud. Yep.

25:45

We were doing some research to prep

25:47

for this segment and we came across

25:49

something pretty wild. The United States Treasury

25:51

Department has started using AI to detect

25:53

suspected check fraud and recovered over $375

25:56

million in 2023. utilizing

26:00

the new tools. The U.S. Treasury

26:03

Department disperses trillions of dollars annually. So

26:05

if they continue to employ new technologies

26:07

like this, it could really add up

26:09

to the tune of billions. So

26:11

how does this fit in? Well, the

26:13

Treasury Department recently selected J.P.

26:15

Morgan to provide account validation

26:18

services for federal agencies. Obviously,

26:20

payment integrity and this issue of improper

26:22

payments is top of mind for them

26:25

and at enormous scale. So whether you

26:27

are one of the largest institutions in

26:29

the world or a small business like

26:31

us here at Acquired, J.P. Morgan offers

26:33

you peace of mind and protection. Yeah.

26:37

One more playbook theme in common between

26:39

Rentec and J.P. Morgan Payments. They both

26:41

analyze data to uncover patterns and insights

26:43

you may never think to look for.

26:45

One of their clients, a furniture store,

26:48

discovered a correlation with customers who also

26:50

shop at pet stores where shoppers spent

26:52

76 percent more than the average customer

26:54

when this was the case. So

26:57

the furniture store launched a line of

26:59

pet friendly furnishings for that audience. These

27:02

are the sorts of insights that drive growth with J.P.

27:04

Morgan Payments as your partner. To

27:20

learn more, check out jpmorgan.com/acquired and

27:22

fun fact listeners, it is fraud

27:24

prevention month. So listeners can learn

27:26

even more by following JP Morgan

27:28

on LinkedIn. Okay,

27:30

David, so this paper is

27:32

published. They're gonna trade and make

27:35

a whole bunch of money in the stock

27:37

market by applying this code breaking signal

27:40

processing data analysis approach

27:42

to investing. Yep, so

27:45

then the natural question is, okay, what

27:47

is the model here? How are they gonna do

27:49

this? And it turns out that

27:51

one of the employees of IDA at this time and one of

27:53

the members of this sort of rebel group,

27:55

shall we say, within the organization is a

27:58

guy named Lenny Baum. And Lenny

28:00

just happens to be the world

28:03

expert in a mathematical

28:05

concept called a Markov model. Specifically,

28:08

a version of the Markov

28:10

model called a hidden Markov model. Now

28:14

a Markov model is a

28:16

statistical concept that's used to

28:18

model pseudo-random or

28:20

chaotic situations. Basically, it

28:23

says, let's abandon any

28:25

attempt to actually understand what is going

28:27

on in all of this data that

28:29

we have, and instead

28:31

just focus on what are the

28:33

observable states that we can see

28:36

of the situation. Can we identify

28:38

different states that the situation is

28:40

in? And if we just

28:42

do that, can we predict

28:45

future states based on

28:47

what we've observed about the patterns of

28:49

past states? And the answer to that

28:51

is usually yes, even

28:53

if you don't know anything about fundamentally how

28:55

the system operates. So the great

28:58

example that Greg Zuckerman gives in the book

29:00

is... Yes, a baseball game. There's

29:02

three balls and two strikes. That

29:05

state has a narrow

29:07

set of states after it. It's

29:09

going to be a strikeout, they're going to get

29:11

on base, it's going to be a walk, or

29:13

maybe they foul it off and it keeps going.

29:15

There's only really a narrow set of things that

29:17

could happen after that. Whereas when it's zero balls

29:19

and zero strikes, there's a lot that

29:22

could happen. They could just keep pitching. And

29:24

if you don't know the rules, you're like,

29:26

why do they just keep pitching? And so

29:28

it's this sort of great way to explain

29:30

this idea of the black box that if

29:33

nobody tells you the rules to the game

29:35

by observing the outputs enough and observing, okay,

29:37

in this state, these outputs are possible, you

29:40

actually can kind of get pretty good

29:43

at least, if

29:45

not predicting, understanding the probability distribution

29:47

of the outcomes for any given

29:49

state in the game. So

29:52

we brought up machine learning and AI a

29:54

minute ago. This is

29:56

a foundational concept to modern

29:58

day AI. If you think

30:00

about large language models and predicting what

30:03

comes next, it's not like these large

30:05

language models necessarily understand

30:07

English. They're just

30:09

really, really good at predicting states and

30:12

the next state, i.e., characters and the

30:14

next character, or pixels and the next

30:16

set of pixels or frame, etc. And

30:19

obviously, they're much fancier than that, but that is

30:22

kind of the underpinning of it all. I

30:24

mean, I remember in my sophomore year of

30:26

college computer science class, I had a Markov

30:29

chain assignment, and it was basically write a

30:31

Java program to ingest this public domain book,

30:33

and then I would give it a seed

30:35

word, the first word of each sentence, and

30:37

press return, return, return, return, return. And it

30:39

would scan through the probability tree and give

30:41

me the most probable word based on the

30:43

corpus of the book that it just read

30:45

to create some sentence. And it feels like

30:47

magic. And of course, in these early rudimentary

30:50

Markov chain things like the one I did

30:52

in college, it kind of spits out nonsense.

30:54

But that would evolve to be the LLMs that

30:57

we know of today. Yes,

30:59

totally. And that is what they

31:01

were using at IDA to do code breaking. And

31:03

that's what they propose in this paper that

31:06

they could use in the stock market. Exactly.

31:09

And the way that this applies to investing

31:12

is just like you might

31:14

not know the rules of baseball. But if

31:17

you've watched enough baseball, you can kind of

31:19

guess at what the probabilities of the next

31:21

thing to happen are based on the state.

31:24

That's kind of the same thing, or at

31:27

least the stock market movements are where you

31:29

don't know the future, you don't know what's

31:31

going to happen. You don't know if stock

31:33

X affects stock Y in some way, because

31:36

you don't know in what way those companies

31:38

do business together or who holds both stocks.

31:40

Are they overlapping investors? You don't know the

31:42

relationship between those companies. So you can't

31:45

forecast with 100% certainty what is

31:47

going to happen. However, if you

31:49

suck in enough data about what has

31:51

happened in the past and the probability

31:53

distribution from every given state in the

31:55

past, you probably could make some educated

31:58

guesses or at least understand. and

32:00

the probability of any individual outcome based

32:02

on a state today of what could

32:05

happen next. Yes, exactly.

32:08

So Jim and Lenny and

32:10

this whole little crew, they're

32:13

pretty fired up. They're like, oh,

32:15

great. Let's go

32:17

raise a fund and invest

32:20

in the markets using this strategy. Certainly

32:22

we're gonna be successful at raising that

32:24

fund and certainly we're gonna be very

32:26

profitable because we've got this great idea. Totally,

32:28

what could go wrong? Well,

32:31

in the mid sixties, the

32:34

idea that some wonky academics at

32:36

some random secretive agency in

32:38

Princeton, New Jersey could

32:41

go raise money was

32:43

non-viable. I mean, it was hard

32:45

enough for Warren Buffett to raise money at this

32:47

point in time for his fund. And

32:50

he was Benjamin Graham's anointed,

32:52

appointed disciple. And here

32:54

are these academics who are working

32:56

at some random unknown nonprofit saying,

32:59

give us money. We don't

33:01

know anything about these companies that we're gonna

33:03

invest in. We don't know anything about fundamentals,

33:05

but we've got a really good algorithm. People

33:07

are probably like, what is an algorithm?

33:10

So they just have no access to capital. Right,

33:12

this was decades before it became high

33:15

pedigree to come from a technical computer

33:17

science background in the world of investing.

33:20

Yes. So a bunch

33:22

of kind of Keystone Cops style fundraising

33:24

happens here. They're going around in secret.

33:27

They're trying to keep the IDA bosses

33:29

from knowing what they're doing. One

33:32

of the group ends up leaving a

33:34

copy of the investment prospectus on the

33:36

copy machine one

33:39

night and the boss discovers it and calls them

33:41

all into his office and is like, guys, what

33:43

are you doing here? Right, it's a little bit

33:45

of a clown show on the operational side, even

33:47

if the idea is good. Yes.

33:50

So they end up abandoning the

33:52

effort both because they can't raise money

33:54

and because IDA has found out about this and they're

33:57

not too pleased. Shortly after all

33:59

of this though, Jim. ends up moving on

34:01

anyway, because the Vietnam

34:03

War starts, and he, as

34:06

you can imagine from his background, is not

34:08

a supporter of the Vietnam War at this point in time.

34:11

Jim writes an op-ed in

34:13

the New York Times denouncing the

34:15

Vietnam War and saying, like, yeah, he's, you

34:17

know, sort of part of the Defense Department,

34:19

but like, not everybody in the Defense Department

34:21

is for the war. Which is

34:23

so naive, thinking you can write an op-ed

34:26

in the New York freaking Times, and

34:29

it's not going to create issues for you in

34:31

your job. Even more than that, amazingly,

34:33

nobody really paid attention to it except

34:35

a reporter at Newsweek who then comes

34:38

to interview Jim and ask him some

34:40

more questions, and he just doubles down

34:42

on this. And when the Newsweek piece

34:44

comes out, that's when the Department of Defense

34:46

is like, all right, you got to fire this guy. So

34:51

Jim gets fired in

34:53

1967, even though he's a

34:56

star code breaker, he made supposedly huge

34:58

contributions to the group, which are still

35:00

classified. But at age 30,

35:02

with a wife and three kids, he's

35:05

out on the street. And

35:07

even though he's super smart, his

35:09

colleagues love him clearly, he's now

35:11

bounced out of MIT, he's bounced out

35:13

of Harvard, he's gone

35:15

to this seemingly final

35:17

home for him, a great place at IDA, he

35:20

gets bounced out of there too. His

35:23

job prospects are not great. Yeah.

35:26

So he takes pretty much

35:28

the only halfway decent paying job that

35:31

he could get, which is

35:33

to be the chair of the newly

35:35

established or maybe reestablished math

35:37

department at the State University

35:40

of New York, Stony Brook,

35:42

which is the Long Island campus

35:45

of the State University of New

35:47

York. This is

35:49

not Harvard. This is not MIT.

35:52

No, it is not. But

35:54

it did have one very important thing going

35:57

for it, which is why Jim ended up

35:59

there. And that is that Nelson

36:01

Rockefeller, who was then the governor of New York,

36:04

had launched a campaign, a

36:06

hundred million dollar campaign to

36:08

try and turn this Long

36:11

Island campus of the State University of New

36:13

York into a mathematical

36:16

powerhouse to become the Berkeley

36:18

of the East. I

36:20

sort of thought MIT was the Berkeley of

36:22

the East already, but Rockefeller is waging a

36:24

campaign that he wants Stony

36:27

Brook to become a math

36:30

and sciences powerhouse.

36:32

And Jim is the key. He

36:35

wouldn't be able to recruit somebody like Jim otherwise,

36:37

but because he's now kind of tarnished

36:39

his career, here's a

36:42

like very talented mathematician that they can convince

36:44

to come be chair of the department. Yep.

36:47

So they basically give Jim an

36:49

unlimited budget and leeway to

36:51

go try and poach math professors

36:54

from departments all over the country and the

36:56

world and bring them there to Long Island.

36:59

And part of how Jim goes and

37:01

recruits folks is money, like the old, hey, I'll

37:03

double your salary line. But

37:05

the other part of it too is

37:07

he's given such leeway and

37:10

Stony Brook is so different from the politics

37:13

of an MIT or a Harvard or a

37:15

Princeton. He says, hey, come here, I'll pay

37:17

you more. But even more

37:19

importantly, you can just focus on

37:21

your research. You're not gonna have

37:23

to deal with committees. You're not gonna have to

37:26

do all this stuff. There is none of this

37:28

stuff here. You might have to teach a little

37:30

bit, but that's not even the point. Rockefeller doesn't

37:32

want this necessarily to become a great

37:34

teaching institution. He just wants to assemble talent

37:36

there. Yep. And

37:39

amazingly, it works. Jim starts

37:41

getting a bunch of great talent, including James

37:43

Axe, who is a superstar in algebra and

37:45

number theory from Cornell. And

37:47

he ends up at Stony

37:49

Brook recruiting and building one

37:52

of the best math departments in the world. Amazing.

37:56

Totally amazing. But in true

37:58

Jim fashion after a couple of years of this. this, and

38:01

also his marriage with Barbara falling apart,

38:04

he starts getting restless again. He decides

38:06

that he wants to go on a sabbatical

38:08

and go back to Berkeley and reunite with

38:10

his old advisor there and go spend some

38:12

time out on the coast in California. And

38:15

this is where Chern and Simons end

38:17

up collaborating and developing the Chern-Simons theory

38:19

that ends up winning the highest award

38:21

in geometry from the American Mathematical Society,

38:24

and really kind of is Jim's

38:26

personal mark on mathematics.

38:30

Now also, right around

38:32

the same time, remember the

38:34

Colombian flooring company, it

38:37

gets acquired. And Jim

38:40

and his buddies who are partners in it come

38:42

into a good amount of money. And

38:44

Jim is newly divorced,

38:46

he's restless in academia, he

38:49

has these ideas back from

38:51

when he was an IDA about what

38:53

you could do in the markets if

38:56

you had capital, he

38:58

starts trading again, and he gets

39:00

more and more into it. Meanwhile,

39:02

like we said, he's becoming disillusioned

39:04

again and restless at academia. And

39:07

in 1978, he leaves

39:09

to focus full time on trading, which

39:12

is a huge shock to the academic community.

39:14

Remember he's assembled this superstar team there at

39:16

Stony Brook. There's a quote in

39:18

Greg's book from another mathematician at Cornell.

39:21

We looked down on him when he did this,

39:23

like he had been corrupted and had sold his

39:25

soul to the devil. Yeah, I

39:27

mean, it was really viewed in the math

39:29

community as anyone who's going to do investing

39:31

is throwing away their talent. And it wasn't

39:33

even that it was common the way that

39:35

it sort of is today. Right, Jim was

39:37

the first one, but the idea that you

39:39

would leave to do anything commercial, you're

39:42

doing a disservice to humanity.

39:44

Yes, exactly. And leaving to

39:46

do anything, sure, but leaving to

39:48

do investing was almost just seen as dirty.

39:50

Like it's this rich person's game that

39:53

provides no value to society. Right.

39:55

Yeah, I don't think it was that the

39:57

rest of the math world was skeptical that

39:59

it could work, they probably were like, oh yeah, this

40:01

could work. But they were like, ew.

40:05

Academics tend to be much more motivated by prestige

40:07

than money. So I could totally see this other

40:09

people being like, oh, I could do that if

40:11

I wanted, but I have this higher calling and

40:13

everyone respects me for this higher calling and my

40:15

currency is the papers I publish and the awards

40:17

that I win and that's what I want. Yep.

40:20

Now, Stony Brook, we should say too, like it's

40:22

a very nice place. Yes. But it's

40:24

in the middle of Long Island on the North

40:26

Shore. This is not the Hamptons. It's

40:29

like the Long Island suburbs. Yep.

40:32

The wooded Long Island suburbs. Yes, the

40:34

wooded Long Island suburbs. Here's

40:36

Jim in a strip mall next to a

40:38

pizza joint, setting up his trading operation that

40:40

he decides very cleverly to

40:43

call monometrics, a

40:45

combination of money and

40:48

metrics or econometrics. And

40:51

he recruits his old

40:53

IDA buddy, original

40:55

partnering crime on the

40:58

trading idea, Lenny Baum to

41:00

come and join him. And

41:02

this time though, they have

41:04

some capital from the sale of the flooring company. And

41:07

how much did he make on that flooring sale? I

41:10

think together with Jim,

41:12

his partners and whatever money Lenny

41:14

put in, they had a little

41:16

less than $4 million in

41:18

this initial capital. In 1978. Yep.

41:22

Now, Jim also has another advantage at this point

41:25

in time, which is he's right

41:27

down the street from Stony Brook and

41:29

he's just recruited all of these

41:32

superstar mathematicians. The

41:34

table has been set. Yes. And those

41:36

folks are more loyal to Jim than they are

41:38

to Stony Brook. But they're more

41:40

loyal right now to academia than they

41:43

are to finance. This is not a

41:45

paved pathway until Jim paves this pathway.

41:48

Yes, in general, but some of them

41:50

and in particular, the superstar James

41:52

Axe, Jim convinces

41:55

to come join him in his

41:57

trading operations. So having Baum

41:59

and Axe and

42:01

Simon's, it's like suddenly this

42:03

extremely credible team in the

42:05

math world. Yes. Beyond

42:08

credible. Right. All the theorems that

42:11

a lot of mathematicians are using every day are

42:13

all named after these three guys who are now at

42:15

the same firm trading. Yes. And it's

42:17

led by Jim, who's somebody

42:19

that they respect as an academic, but even

42:22

more important is somebody they want to work

42:24

for and they look up to and they

42:26

think is cool. And he's out

42:28

there being like, hey, I think we can make

42:30

money. Right. Now,

42:33

at this point, they're primarily

42:35

trading currencies, not

42:38

stocks. And currencies are

42:40

obviously large markets, but they

42:42

aren't impacted by as many signals

42:44

and as many factors as

42:47

stocks are, or really even slightly more

42:49

complex commodities like, I don't know, soybeans

42:51

or whatever. And it seemed

42:53

to me like a lot of the trading

42:56

of currencies they were doing was basically based

42:58

on feelings that they had around how a

43:00

central bank was acting. Like if the head

43:02

of state of a certain country was going

43:04

to do something or not, it's basically like

43:06

betting on how one single

43:09

actor who was in control of

43:11

currencies at governments would act. So

43:14

to your point about very few signals

43:16

impacting price, it's knowing what one

43:18

person is going to do. Yes. And

43:20

this is super important. At

43:22

the end of the day, they built some models

43:24

there, they're getting the early

43:27

versions and infrastructure and scaffolding

43:30

of this quantitative approach set

43:32

up. But in terms

43:34

of the actual trades they're putting on, they're still doing

43:36

all of it by hand. And

43:38

they're still all really going

43:40

on a fundamental type

43:43

analysis. They'll take some signals from

43:45

the model, they'll see what's interesting, what they spit

43:47

out, but they're not going to act on anything

43:49

unless they can be like, oh yeah,

43:51

I see what is going

43:53

on here. I have a hypothesis. Right. The

43:56

computers are by no means running loose at this

43:58

point. By no means. at all. Yeah,

44:01

they're just suggesting patterns and ideas.

44:03

And Jim and Lenny and James, they have to then decide,

44:05

hey, are we going to do this or not?

44:07

Or are we going to do something just totally different than we

44:09

think is what's going to happen? Yeah.

44:12

And this actually does make

44:14

sense, really for two reasons.

44:17

One, computers and computing

44:19

power just wasn't

44:21

sophisticated enough yet

44:24

to really build

44:26

AI in a way that's powerful enough

44:28

that it could work well enough, you could

44:30

really trust it. That's one part. The

44:33

other part is these folks

44:35

are mathematicians. They're not

44:37

computer scientists. Right. And

44:40

they're really, really good at

44:42

building models, decoding signals, obviously,

44:45

but they're much more from this

44:48

realm of theory. And I actually

44:50

spoke with Howard Morgan, who's going to come up here in

44:52

a second. And he made this point to me. He's like,

44:55

in math, there's this concept of

44:57

traceability that's a really, really important

44:59

cultural tenet. It's like proving a

45:01

proof or proving a theorem or

45:03

something like that. You

45:06

really need to understand why to

45:08

get ahead in the field. It's not like you

45:10

can just say, oh, hey, the data suggests this.

45:12

It's like, no, no, no, you need proof. And

45:15

that's the world that these guys are coming

45:17

from. They're like, oh, we can use data

45:19

to sort of help us here. But ultimately,

45:21

we want to have a rock solid theory

45:23

of what is fundamentally happening here. Fascinating,

45:26

which is very different than we'll cram a huge

45:28

amount of data in and then whatever the data

45:30

suggests, we know it's true because the data suggests

45:32

it, which is sort of where

45:34

they would end up many years later

45:36

once they had both the hardware you're

45:38

referring to sophisticated computers, the clean data

45:41

that would be required to make all

45:43

of those incredibly numerous and

45:45

fast calculations, and also the

45:47

real computer engineering architecture to build

45:50

these scale systems to actually

45:52

act on large amounts of signals and understand

45:54

them all to come up with results. They

45:56

just didn't have any of that at the

45:58

time. So it was is hunches and

46:01

chalkboards. Yes. And so

46:03

much so that even Jim is

46:05

ringleader here. He's far from

46:07

convinced that he should put all of his wealth

46:09

into this thing. He's like, oh yeah, this is

46:11

interesting. We're building, we're experimenting, like great. But

46:14

I also wanna put my money

46:16

somewhere else too for some diversification. So

46:19

this is where Howard Morgan comes in. You

46:22

know, we used to talk about this on old acquired

46:24

episodes that in the early days of Silicon Valley, there

46:26

were only 10 people out here and they all knew

46:28

each other and they were all doing the same thing.

46:32

This was also the case in East

46:34

Coast finance and technology and early VC

46:36

in these days. Howard Morgan

46:38

would go on to be one of the co-founders of

46:41

first round capital. Which was essentially

46:44

spun out of Renaissance. Like it was kind

46:46

of the venture capital work that they were

46:48

doing at Renaissance that didn't fit with the

46:50

rest of Renaissance. Yes. So

46:52

here's how it all went down. And this is

46:55

so poorly understood out there. Yes. Howard

46:58

was a computer science and business

47:00

school professor at the University of

47:02

Pennsylvania. So he taught CS at

47:04

Penn and business at

47:06

Wharton. And he

47:08

had been involved in bringing ARPANET to

47:10

Penn and was kind

47:13

of like early, early internet pioneer.

47:16

And so as a result, he was super

47:18

plugged into tech and

47:21

early startups and really early, early

47:23

proto internet stuff. And

47:26

Jim gets excited about investing

47:28

together with Howard. So they say

47:30

like, hey, maybe we should partner

47:33

together. And in 1982,

47:36

Jim actually winds down monometrics

47:39

and he and Howard co-found a

47:41

new firm together that's gonna

47:43

reflect both of their backgrounds and be

47:46

a great diversification. Jim and

47:48

his group are gonna bring in

47:50

the quantitative trading thing. And

47:53

again, trading on currencies

47:55

and commodities at this point. And

47:57

Howard's gonna bring in private

47:59

companies. company technology investing,

48:02

and they pick a name for

48:04

a firm that is going to reflect this

48:07

Renaissance Technologies. It's crazy. And that

48:10

is why RENTEC is called RENTEC.

48:13

I could not, when we figured this out

48:15

in the research, I could not believe that

48:17

this is not a more widely understood story,

48:19

that this is the origins of what

48:22

is today a fantastic venture capital

48:24

firm, first round capital, but you

48:26

could not name two more different

48:29

strategies in investing. I mean,

48:31

a long-term illiquid thing

48:33

like venture capital, highly

48:35

speculative versus, you know, we're

48:37

going to trade whether we think the French

48:40

franc is going to go up or down

48:42

tomorrow based on the whim of some government

48:44

leader. It's unbelievable these were under the same

48:46

roof. Totally. But

48:48

when you know the whole background in history, it kind

48:50

of makes sense because this is their personal money.

48:53

This is Jim and his buddies, and

48:55

Lenny and James and Howard. There's

48:58

not institutional capital here. They're not

49:00

out pitching LPs of like, oh,

49:02

you should invest in my diversified

49:04

strategy of currency trading and private

49:06

technology startups. Yeah, when they say

49:08

multi-strategy, this is really multi-strategy. Yeah.

49:12

We'll get into what multi-strategy today means later.

49:15

But in these early days of

49:17

RENTEC, 50% of the portfolio was venture

49:19

capital and 50% was

49:21

currency trading. And in fact,

49:24

a couple of years after they get started, the currency

49:26

trading side of the firm almost

49:29

blows up when Lenny goes

49:32

super long on government bonds

49:35

and the market goes against him and the whole portfolio

49:37

drops 40%, which is wild. That

49:42

ends up triggering a clause in Lenny's

49:44

agreement with Jim and they

49:46

sell off Lenny's entire portfolio and he

49:48

leaves the firm. This

49:50

is crazy. Blow-up risk

49:52

is always an issue in the markets,

49:54

but this happened to RENTEC. And

49:57

because we quickly got to this point in the story, it would

49:59

be easy to say. Well, that's a clause that has a lot

50:01

of teeth. There were many sort of

50:03

rumbles of something like this potentially happening. Simon's

50:05

going to Lenny and saying, hey, maybe we

50:07

should cut some of our losses and it's

50:10

okay to trade out of these positions. And

50:12

Lenny was just very dug in on I'm

50:14

a true believer. And that's how you can

50:16

get into a situation where you'd trigger a

50:18

covenant like this. Totally. And again,

50:21

also shows they weren't doing

50:23

model based quantitative trading really at this

50:25

point in time. Now, so much gut.

50:29

So as a result of that, for

50:31

a while, RenTech is truly almost entirely

50:34

a venture capital firm. At

50:36

one point on the

50:38

venture side, just one investment, Franklin dictionaries.

50:41

Do you remember Ben Franklin electronic dictionaries?

50:43

Yeah, that was one of their biggest

50:45

investments. That one investment

50:47

is half of Jim's net worth.

50:50

What? At this low point for the trading side.

50:53

Yes. I had no idea.

50:55

That's crazy. Yeah. In

50:57

the book, Greg talks about, oh, Jim was

50:59

focused on venture capital. And that's kind of

51:01

the story out there. It's like, well,

51:03

he was focused on venture capital because that was the

51:05

only thing working and making money. Well, I

51:07

mean, it's the only thing where they actually had

51:10

an edge from Howard's access to deal flow because

51:12

they certainly didn't have an edge in the global

51:14

currency markets. So I think perhaps

51:17

in part because of the trading losses, James

51:20

Axe starts to get a little dissolution

51:22

too. And he tells Jim

51:24

that he wants to move out to California

51:26

with Sandor Strauss, who started working with them

51:28

at this point. Sandor was another Stony Brook

51:31

alum that joined them. And

51:33

the two of them want to move out to California and

51:35

do trading out there. Jim

51:38

says, sir, fine. I'm here with

51:40

Howard. I'm doing venture capital stuff.

51:43

Why don't you go move out

51:45

to California? You can start your

51:47

own firm, which they do. It's

51:49

called Axecom, A-X-C-O-M. And

51:52

we'll contract with Axecom to run

51:54

what's left of the trading operations

51:56

here for RENTEC. arm's

52:00

length thing where Jim

52:02

strikes a deal where he's gonna own a part

52:04

of Axecom in exchange for

52:06

this very favorable contractual relationship where

52:08

they're gonna hire them to be

52:10

the manager for this pot of

52:13

money that Renaissance has raised. But

52:15

you know, it's technically not Renaissance, it's

52:17

Axecom. Right, it's another company that

52:19

is now doing the quantitative trading. Yep,

52:22

and I think Jim owned a quarter of it,

52:24

is that right? Yes, that's right. And

52:27

importantly, I don't think anyone had

52:29

any idea what Axecom would become

52:31

or how unbelievably profitable

52:34

it would be. No,

52:38

nobody would have done what they did had

52:40

they known what was coming. Yes, wouldn't have

52:42

spun it out. No, so

52:45

once Axe and Strauss get

52:47

out to California, Strauss,

52:50

he's kind of on the computing data

52:52

infrastructure side, that's what he was doing

52:54

at Stony Brook, and that's what he

52:56

came into Renaissance to build. He

52:59

starts getting really into data, and

53:02

he starts collecting intraday

53:04

pricing movements on securities.

53:06

At this point in time, I think

53:09

really the best data you could

53:11

get from providers out there was

53:14

maybe open and close data on

53:16

securities pricing. Strauss finds

53:18

a way to get tick

53:20

data, like every 20 minute

53:23

data on the securities

53:26

throughout the day. Not only that,

53:28

he's getting historical data that predates what

53:30

your traditional data providers would give you,

53:32

and then ingesting it into computers and

53:34

cleaning the data to get it into

53:36

the same format as the tick data.

53:38

So he's getting early 1900s, even 1800s

53:40

stuff to

53:43

try to just say, at some

53:45

point, hopefully we'll be able to make use

53:47

of this, and I wanna have this just

53:49

really, really clean data set about the way

53:51

that these markets interact. Yeah, I

53:53

mean, he's doing ETL on the data.

53:55

Yes. Before anybody knew what ETL

53:58

was. Again, no one told him to do. That

54:00

was just a self-motivated, almost like obsession of like,

54:02

well, if we're going to have data, it should

54:04

be well formatted and well understood and labeled and

54:07

all that. So that's one thing that

54:09

happens. The other thing is

54:11

Jim says, oh, you're going out to California. Let

54:13

me hook you up with my buddy

54:16

who's a Berkeley professor out there, Elwyn

54:18

Burlacamp. And Burlacamp

54:22

had studied with folks like

54:24

John Nash and Claude Shannon

54:26

at MIT. I love that

54:28

Claude Shannon is coming in again. I know. We

54:31

talked about it a lot on the

54:33

Qualcomm episode, Father of Information Theory, really

54:35

the center of gravity for attracting tons

54:37

of talent to MIT and kind of

54:39

paving the way for what would become

54:42

phone technology and telecommunications broadly in

54:44

the future. But the fact that

54:46

Burlacamp is crossing paths at MIT

54:49

with Claude Shannon, so cool. So

54:51

cool. And most importantly

54:54

for this specific case, Burlacamp

54:56

had worked with John Kelly,

54:58

who developed the Kelly criterion on

55:00

bet sizing, which poker players will

55:03

likely be well familiar with. So

55:06

with this combination now of much, much,

55:08

much better and deeper data from Strauss

55:11

and Burlacamp coming in and working with Axe

55:13

on the models and saying, hey,

55:16

we should be smart about the bet sizing that we're

55:18

doing in the trades that are coming out of these

55:20

models. Versus I don't know what they were doing

55:22

before. Maybe it was naive of like

55:25

every trade was the same or just

55:27

like we should actually be systematic about this. The

55:30

models start really working. Yep.

55:32

This is the turning point. Yeah. In

55:36

these kind of mid 80s years,

55:38

Axecom is generating IRRs of like

55:40

20 plus percent on the

55:42

trading side. You know, not necessarily

55:45

going to beat venture capital IRRs, but

55:47

liquid. Yes. Reliable.

55:50

Well, that's the thing. They don't know how reliable

55:52

yet. They know they've done it kind of a

55:54

few years in a row here. But the

55:56

question is how uncorrelated to the stock market over

55:58

a long period of time. and

56:00

how predictable are these returns? Or

56:02

is it just super high variance?

56:05

Yes, but the early results are

56:08

really good. And Jim and

56:10

Berlekamp especially are very

56:12

encouraged by this. So

56:14

in 1988, Jim and

56:17

Howard Morgan decided to spin out

56:19

the venture investments and Howard goes

56:21

to manage those with basically

56:23

their own money. Fun coda

56:25

on this, when Howard

56:27

starts first round a number of years later

56:30

with Josh Koppelman, Jim of course is

56:32

a large LP. And

56:35

Howard of course remains

56:37

an investor in RENTEC. The

56:41

first institutional fund that

56:44

first round ended up raising was

56:47

a 50X on $125 million fund. It

56:50

had Roblox, Uber and Square.

56:53

So I believe this is

56:56

right. I think Jim made as much

56:58

money from his investments in first round

57:00

as Howard did from his LP stake

57:03

in RENTEC. That's wild.

57:06

Isn't that amazing? Wow, that

57:08

is a untold story about Jim

57:10

Simons. I think I read basically

57:12

every primary source thing on Jim

57:15

or Renaissance on the whole internet. But

57:17

I assume you got that from Howard. Yeah, it

57:19

was super fun talking to Howard about this. And

57:21

just the history of how first round started early

57:24

Super Angel Investing and everything that

57:26

became. I also didn't realize that

57:28

first rounds fund one was a 50X on $125

57:31

million fund. First

57:34

institutional fund, which

57:36

I believe they called fund two. I mean,

57:39

wild, wild stuff. Totally

57:41

wild. So when Howard

57:44

spins out the venture activities, Jim

57:47

then decides to set up

57:49

a new fund as

57:51

a joint venture between RENTEC and Axcom. And

57:54

they decide to name it after

57:56

all of the collective mathematical

57:59

award. that Jim

58:01

and James and Burlacamp and all these

58:03

prestigious mathematicians have won in

58:06

their careers. They name it the

58:09

Medallion Fund. Bada-da! And

58:12

listeners, we've arrived. This is the part

58:15

of the story that matters. The Medallion

58:17

Fund is the crown jewel, or you

58:19

might even say actually the only interesting

58:21

thing about Renaissance. And it

58:24

is born out of this observation

58:26

that, oh my god, what they're doing over there at AXCOM

58:28

is really interesting. Maybe they

58:30

shouldn't be doing it all the way over there. Maybe

58:33

that should be a deeper part of the

58:35

fold here at RENTEC, and we shouldn't have

58:37

let that get away, or frankly given up

58:39

on the quantitative trading strategies too early. And

58:42

again, still just currencies, still just

58:44

commodities futures, not playing the stock

58:46

market at all, but

58:49

the seeds and the ideas, the

58:51

huge amount of clean data, the

58:53

robust engineering infrastructure to process all

58:56

that data, the mining

58:58

of signals from data to

59:00

figure out what trading strategies to execute.

59:02

That is really starting to form here

59:05

in this new joint venture, this Medallion

59:07

Fund. Those ideas had

59:10

all existed before. This is the first

59:12

time that it's all brought together and

59:15

actually working and operationalized. And

59:17

frankly, that computers got good enough to actually do it

59:20

too. That's another big piece of this. Yeah,

59:22

I don't know that Strauss could

59:24

have done his data engineering

59:27

too much earlier in time. But

59:30

before we get into the just

59:33

absolutely insane run that this Medallion

59:35

Fund is about to go on,

59:38

that continues right through to this day, now

59:40

is the perfect time for another story

59:42

about ServiceNow. ServiceNow is one

59:45

of our big partners here in Season

59:47

14 and is just an incredible company.

59:49

Yep, ServiceNow digitally transforms your enterprise, helping

59:51

automate processes, improve service delivery, and increase

59:54

operational efficiency all in one intelligent platform.

59:56

Over 85% of the users are in

59:58

the industry. of the Fortune 500 runs

1:00:01

on them, and they have quickly joined the

1:00:03

Microsofts and the NVIDIAs as one of the

1:00:05

most important enterprise software companies in the world

1:00:08

today. So we talked

1:00:10

on our Novo Nordisk episode about

1:00:12

how ServiceNow founder Fred Luddy discovered

1:00:14

this core insight that software can

1:00:16

transform and eliminate manual tasks. And

1:00:19

on Hermes, we told the story of

1:00:21

how current CEO Bill McDermott came in

1:00:23

and turbocharged that into an absolute monster

1:00:25

$150 billion market

1:00:27

cap global behemoth. The

1:00:30

key thread that connects those two eras

1:00:32

is that from day one, Fred knew

1:00:34

the ServiceNow platform could be used across

1:00:36

the whole enterprise. But at the same

1:00:38

time, he also knew from his decades

1:00:40

of prior software experience that launching a

1:00:42

broad horizontal offering right out of the

1:00:44

gate as a startup was a recipe

1:00:46

for failure. You need to

1:00:48

start with a specific vertical use case. And

1:00:50

in this case, he chose IT service management.

1:00:53

Yeah, and that's been true for us here

1:00:55

on Acquired too, David, if we didn't name

1:00:57

it Acquired and cover technology acquisitions that actually

1:00:59

went well, we never could have broadened and

1:01:02

become the podcast that tells the stories of great

1:01:04

companies. You can't just start as that. Totally.

1:01:07

Well, this is what's so cool and where I think the

1:01:09

playbook lesson really is for listeners. Because you

1:01:11

can't just pick any use case, you have

1:01:13

to be strategic about it. And IT was

1:01:15

the perfect vertical because every other department has

1:01:18

to interface with them from the CEO on

1:01:20

down. So they're going to notice when IT

1:01:23

service management rapidly improves, all

1:01:25

of those support tickets that used to

1:01:27

take forever are now just magically resolved.

1:01:29

And that greases the wheels for the

1:01:32

other departments to say, hey, maybe we

1:01:34

should adopt ServiceNow to turbo charge and

1:01:36

digitally transform our service levels too. Yep.

1:01:39

Once those other departments do pull the

1:01:41

trigger on joining the ServiceNow platform, who

1:01:44

is in charge of rolling it out for them? Of

1:01:46

course, it's IT who

1:01:48

are already true ServiceNow believers. I'm

1:01:50

honestly not sure that there's a

1:01:52

better enterprise software playbook in history

1:01:55

than ServiceNow's. So once

1:01:57

they established the beachhead in IT, they then took

1:01:59

the same. platform to HR with employee

1:02:01

experience. They took it to CSM with

1:02:04

customer service requests. They took it to

1:02:06

finance with regulatory reporting, audit and expense

1:02:08

approvals. And now they're adding AI, which

1:02:10

will take everything to the next level.

1:02:13

Yup. So if you want to

1:02:15

learn more about the ServiceNow platform and playbook,

1:02:17

and hear how it can transform your business,

1:02:19

head on over to servicenow.com/acquired. And when you

1:02:22

get in touch, just tell them that Ben

1:02:24

and David sent you. So

1:02:27

they've got this grand new plan and

1:02:29

vision with the Medallion Fund. Unfortunately,

1:02:32

right out

1:02:34

of the gate, the fund stumbles

1:02:36

a bit. And

1:02:38

Axe ends up getting burned out.

1:02:41

Berlacamp though is like, no, no, no,

1:02:43

no, no. This is an anomaly. Like we're going

1:02:45

to fix this. I really, really believe that

1:02:48

what we're doing with these models is going

1:02:50

to be extremely profitable. So

1:02:52

he buys out most of Axe's stake

1:02:55

in the summer of 1989. And he

1:02:57

moves the

1:03:00

offices up to Berkeley. And

1:03:02

there he comes up with the idea

1:03:05

that, hey, we should trade

1:03:08

more frequently, a lot

1:03:10

more frequently. Because if what we're

1:03:12

trying to do is understand the state of the market

1:03:14

from the data we have, and then predict the future

1:03:16

state of the market, and then

1:03:18

combine that with figuring out the right bet

1:03:20

sizing to make, we actually want

1:03:22

to make a lot more trades to get a

1:03:25

lot more data points and learn a lot more

1:03:27

about the bets we're making so that we can

1:03:29

then size them up or size them down. It's

1:03:31

that and it's two other things. One

1:03:34

is the further into the future you

1:03:36

look, the less certain you can be about

1:03:38

it. If you know something is worth $10

1:03:40

right now, what you know

1:03:43

five minutes from now is it's probably gonna be worth

1:03:45

about $10. The most likely

1:03:47

situation is it's within 5% of

1:03:49

that. If you ask me three years from

1:03:51

now, I have almost no intuition about that.

1:03:53

And a state machine is the same way.

1:03:55

If you flash forward a whole bunch of

1:03:57

states, you sort of lose predictability you

1:04:00

sort of continue down that chain. The

1:04:02

second thing is, if your models are showing that

1:04:04

you're going to be right, call it something like

1:04:06

50.25% of the time, then the amount of money

1:04:11

you can make is gated by the

1:04:13

number of bets you can make at

1:04:15

a quarter percent edge. If I walk

1:04:17

up to the casino, and I think

1:04:19

I'm right about this particular roulette wheel,

1:04:21

which of course you're not, 50.25% of

1:04:25

the time, and I decide to play once or

1:04:27

play twice or play five times, there's

1:04:29

a chance I could lose all my money, or if

1:04:31

I have tiny little bet sizes, then I'm just not

1:04:33

going to make that much money. But if I walk

1:04:35

up to said game with a little bit of edge,

1:04:37

and I use small bet sizes, and I played 10,000

1:04:39

times, I'm going to walk out with a

1:04:41

lot of money. There is

1:04:43

a great Bob Mercer quote about this

1:04:46

later. He says, we're right 50.75%

1:04:48

of the time. And I do think he's

1:04:51

making up that number. I think it's illustrative.

1:04:53

Right. But we're 100%

1:04:55

right. 50.75% of the time, you can make billions

1:05:01

that way. It's so true. When

1:05:04

you have that little edge, it's about making sure

1:05:06

that you're not betting so much that a few

1:05:08

bets that don't break your way can take you

1:05:10

down to zero. And to make

1:05:12

sure you can just play the game a

1:05:14

lot, a lot. Yes. And then

1:05:17

back to the Kelly criterion, adjust your bet

1:05:19

sizes over time as you're making those bets.

1:05:22

Now, of course, this is all great in

1:05:24

the abstract. If it's that you're literally sitting

1:05:26

at a casino when you're somehow perfectly making

1:05:28

these bets, and you're just sitting right there

1:05:30

at the table, and then you can walk

1:05:32

over to the cashier, it gets a little

1:05:34

bit different in the market. For example, there

1:05:37

are real transaction costs, especially at this point

1:05:39

in history before some of these more innovative

1:05:41

trading business models with pay for order flow

1:05:43

and zero transaction fees and all this stuff.

1:05:45

There's real transaction costs to putting on these

1:05:47

trades. And of course, you're going to move

1:05:49

the market when you put on these trades.

1:05:51

Yes, this is slippage. There's all sorts

1:05:53

of practical consideration. You could get

1:05:55

front run by other people. It's

1:05:58

not just a computer program that gets executed. executed,

1:06:00

you actually have to meet the constraints of the

1:06:02

real world when you're deciding instead of a few

1:06:04

big bets, we're going to have 100,000 tiny bets.

1:06:08

Yes. And as time goes on,

1:06:10

and the whole quant industry emerges and becomes

1:06:12

much more sophisticated, I think it's

1:06:14

particularly the slippage there that becomes the governor

1:06:16

on how high velocity you can actually be

1:06:19

on this. And the slippage is that

1:06:22

once you are at a certain scale, you are going to

1:06:24

move the market with your trades. So the

1:06:26

deeper you get into the order book, like, let's

1:06:28

say you want to buy $5 million of something,

1:06:30

maybe your first $100,000, you're pretty sure you can

1:06:33

get the quoted price. But

1:06:35

by your last $100,000 of that $5

1:06:37

million buy, the price might have gotten

1:06:39

pretty different already. Yeah,

1:06:41

we're going to come back to this in just a

1:06:43

minute. But this certainly for early rent heck, and

1:06:46

then even now still for all of

1:06:48

quantitative finance is a really, really, really

1:06:50

important thing. Yeah, and

1:06:53

David, in a very crude way, calls back

1:06:55

to last episode on Hermes, the

1:06:57

idea that the price would be highest for

1:06:59

the family member that is willing to sell

1:07:02

now and sort of goes down over time.

1:07:05

If the family was going to sell to Bernard

1:07:07

Arnaud, it would behoove you to be first in

1:07:09

the order book, not last in the order book.

1:07:11

Yes. I feel like there's this meta

1:07:14

lesson that I've been learning through acquired and my

1:07:17

own personal investing over the past couple of years.

1:07:20

Every market is dependent on supply and

1:07:22

demand. You can see

1:07:24

quoted valuations and quoted price streams,

1:07:27

but oftentimes that's like the mistake of just

1:07:29

looking at averages. Exactly. Yes, looking

1:07:31

at the quoted price of an asset

1:07:34

is wrong. You actually should be looking at what

1:07:36

is the volume that is willing to buy and

1:07:38

what is the volume that is willing to sell.

1:07:40

And for all of those buyers and all of

1:07:42

those sellers, what are the price at which they

1:07:44

are willing to transact? And the

1:07:46

way that tends to manifest on a stock chart

1:07:48

is here's the price of the share right now,

1:07:51

but that's not actually what's going

1:07:53

on under the surface. It's a whole bunch of

1:07:55

buyers and sellers who have different willingness to pay

1:07:57

and have different amounts that they're trying to buy.

1:08:00

yourself. Yes. Now,

1:08:02

at this point in time when the Medallion Fund is

1:08:04

first starting to work in, say, late 1989,

1:08:06

early 1990, it's small

1:08:09

enough that this isn't a big consideration

1:08:11

yet. Yeah, right. Medallion

1:08:13

was about $27 million under management

1:08:15

when Burley Camp bought out ACK.

1:08:19

In 1990, the first full year after that, the

1:08:22

fund gains 77.8% gross, which after fees and carry

1:08:29

was 55% net. Now

1:08:33

what were the fees and carry? I mean,

1:08:35

either one of those numbers is shooting

1:08:37

the freaking lights out. Assuming that

1:08:39

this is not a crazy

1:08:41

high risk strategy that they executed and it'll

1:08:43

completely fall apart under different market conditions, like

1:08:46

if this is an actual

1:08:48

repeatable strategy that produces the numbers you

1:08:50

just said, unbelievable,

1:08:52

world changing. Hell

1:08:54

yeah, let's go. And

1:08:57

indeed, it was a hell yeah,

1:08:59

let's go situation. So the

1:09:02

numbers you quoted me, the gross and the net sounded quite

1:09:04

different. Talk to me about the fees and carry. So

1:09:06

carry, I've seen different sources of whether it was 20% or

1:09:08

25% in the early days, but the management

1:09:12

fee on the fund was 5%, which is crazy. The

1:09:16

top venture capital firms in the world charge a

1:09:18

3% management fee, and

1:09:20

even that is like everybody holds their nose and

1:09:22

is like, this is ridiculous. How

1:09:24

on earth were these nobodies charging

1:09:27

a 5% management

1:09:29

fee out the gate to

1:09:31

their investors? Well, a

1:09:34

couple things. One their

1:09:36

investors were not sophisticated. It was mostly their

1:09:38

own money and their buddy's money. So

1:09:40

they set that precedent. They set that precedent. But

1:09:43

two though, they actually needed

1:09:45

the money because Strauss's

1:09:48

infrastructure costs were about $800,000 a year.

1:09:52

So they just backed into the management fee based on like, hey,

1:09:54

we need $800,000 a year to

1:09:57

run the infrastructure. Plus, we need some money to... pay

1:10:00

folks and whatnot like great 5% management fee

1:10:02

and so the pitch they're making the investor

1:10:04

base is like if you believe that we

1:10:07

should be able to massively outperform the market

1:10:09

doing quantitative trading. What we're gonna need

1:10:11

a lot of fees to do that and so the

1:10:13

investors basically took the deal if they thought about it

1:10:16

enough. Okay so that's the

1:10:18

fees on the performance that twenty or

1:10:20

twenty five percent it's just not actually

1:10:22

that far above market if it's above

1:10:24

market at all what you're seeing is

1:10:26

a high fee normal ish performance fee

1:10:28

fund at this point in time. Yes

1:10:30

high management fee normal ish carrier

1:10:33

performance element. Yeah so

1:10:36

at the end of nineteen ninety simons

1:10:38

is so jazzed about what's

1:10:40

going on that he

1:10:42

tells brilacamp hey you

1:10:44

should move here to long island

1:10:47

let's re-centralize everything here. I

1:10:49

want to go all in on this I

1:10:52

think with some tweaks we can be up

1:10:54

eighty percent after fees next year. Really

1:10:57

camp is a little more

1:10:59

circumspect a he wants to stay

1:11:01

in berkeley he doesn't have any desire to move to long

1:11:03

island. And be I couldn't tell

1:11:05

how much of this is just he's a

1:11:07

little more conservative than jim or

1:11:09

how much of this actually might be his whole

1:11:12

poker bet sizing thing. He

1:11:14

turns to jim and he says well if

1:11:16

you're so optimistic why don't you

1:11:19

buy me out so jim

1:11:21

does at six

1:11:23

x the basis that berlacamp

1:11:25

had paid a year earlier.

1:11:28

On the one hand making a six x in

1:11:31

one year sounds great on the other hand this

1:11:33

is the equivalent of when

1:11:36

don valentine sold sequoias apple

1:11:38

steak before the IPO to

1:11:40

lock in a great game.

1:11:43

But miss out on all the upside

1:11:45

to come. David I think

1:11:47

we should throw this out so people understand the

1:11:49

volume of this they've generated on

1:11:52

the order of sixty billion dollars

1:11:54

of performance fees for

1:11:57

the owners of the fund over their

1:11:59

entire life. time. So on

1:12:01

the one hand, 6x in a year

1:12:03

ain't bad. On the other hand, you

1:12:05

owned a giant part of something that

1:12:07

has dividended $60 billion in

1:12:09

cash out to its owners. Yeah,

1:12:13

that's just on the carry side. I mean, the

1:12:15

owners are the principles. So

1:12:17

just like dollars out of the firm,

1:12:19

it's probably twice that. I would

1:12:22

estimate probably $150 to $200 billion

1:12:25

that have come out of Medallion over

1:12:27

the last 35 years. So

1:12:31

Jim buys out Brillicamp.

1:12:34

He rolls everything

1:12:36

in the Medallion fund back into

1:12:38

RENTEC itself, moves everything back

1:12:40

to Stony Brook. Strauss moves to Stony

1:12:42

Brook. So it's now the

1:12:44

Jim Simon show in New York with Strauss

1:12:46

building the engineering systems and Axe, I think,

1:12:49

still had a small stake. Yes,

1:12:51

that's right. And Strauss had a stake as well. So

1:12:54

once Jim takes control and moves

1:12:56

everything back, he

1:12:58

basically decides that

1:13:01

he's going to turn RENTEC

1:13:04

into an even

1:13:07

better, even more

1:13:09

idealized version of

1:13:12

IDA and the math department

1:13:14

at Stony Brook. He's going to make

1:13:16

this an academic's paradise,

1:13:20

where if you are one

1:13:23

of the absolute smartest

1:13:25

mathematicians or systems engineers

1:13:28

in the world, this is

1:13:30

where you want to be. So

1:13:33

of course, he starts reading the

1:13:36

Stony Brook department itself again. And

1:13:38

this is when Henry Laufer joins

1:13:41

full time. Laufer had

1:13:43

been consulting with Medallion in the

1:13:45

early days and working with Brillicamp

1:13:47

as they're doing bet sizing, as

1:13:49

they're making more frequent trades. But

1:13:51

now, once the whole operation has

1:13:54

moved back to Long Island, Laufer is like,

1:13:56

Oh, okay, great. I'll come full time. I'm here at

1:13:58

Stony Brook anyway. This is way more fun. And

1:14:01

listeners, I imagine this is probably the point where you're

1:14:03

starting to get confused and saying there are so many

1:14:06

people in this story. I think we're on eight or

1:14:08

nine. We just keep introducing more people. And

1:14:10

that is the story of

1:14:12

Renaissance. It is not this singular,

1:14:15

clean narrative. It is a very

1:14:19

complex reality of

1:14:21

a whole bunch of different

1:14:23

people that came in and out

1:14:26

at different eras where the firm

1:14:28

was trying different things and eventually

1:14:30

became phenomenally successful with a very

1:14:32

particular approach. But while they were

1:14:34

figuring it out along the way, it took a lot of people.

1:14:36

A lot of people. And just a lot of

1:14:38

time, too. This is 25 years. This

1:14:41

is a quarter century from the

1:14:44

time that Bauman-Simons

1:14:47

write the paper at

1:14:49

IDA until Medallion really

1:14:51

starts to work. It

1:14:53

takes a long time. We haven't

1:14:55

even introduced the two people who

1:14:57

would become the co-CEOs of this

1:14:59

company for 20 years. Yes.

1:15:03

Well, let's get to that. So

1:15:06

Jim Moves Everything Back to Long Island sets it

1:15:09

up as this academic paradise who's recruiting the

1:15:11

smartest people in the world. In

1:15:13

1991, the next year, the firm does 54.3% gross returns and 39.4% net returns

1:15:15

after fees. So

1:15:25

not Jim's bogey of 80%, but

1:15:27

still pretty freaking great. And

1:15:29

we should say the years of modest

1:15:31

performance are behind them. From every single year

1:15:34

forward, they shoot the lights out. From 1990

1:15:36

onward, they never lose money. And

1:15:41

on a gross basis, they never even do less

1:15:43

than 30%. It's

1:15:46

working. It's going. The whole rest

1:15:48

of the story is about hold

1:15:50

on, keep the machine working, and

1:15:52

we're on the train. The

1:15:55

historic run has begun, let's just

1:15:57

say. Yep. So... Nineteen

1:16:00

Eighty Two, Grace Returns Or Forty

1:16:02

Seven percent. Ninety. Three Their

1:16:04

fifty four percent. At.

1:16:07

The End of Ninety Ninety Three. Simon's.

1:16:09

Decides to close the find and not

1:16:11

allowed new Lps. and so if you're

1:16:14

an existing Lp, you can stay and

1:16:16

but they're no longer open for new

1:16:18

inflows the Us. So much confidence in

1:16:21

what they're doing. That. He thinks

1:16:23

they're all going to make more money. Without.

1:16:25

Accepting new capital by just keeping it to

1:16:27

the existing investor base. Maybe. Ninety

1:16:29

Four Gross returns are

1:16:32

Ninety Three Freaking percent.

1:16:35

Medallion. At this

1:16:37

point is stacking up cast it

1:16:39

is a. Meeting for

1:16:41

fun. It's about two hundred and

1:16:43

fifty million dollars total at this

1:16:45

point in time, which is small,

1:16:47

but we're talking about Nineteen Ninety

1:16:50

Four with a bunch of outsiders

1:16:52

and academics that have managed to

1:16:54

amass a quarter billion dollars here.

1:16:56

People. States pay attention. And.

1:16:58

The performances on this are. Seven.

1:17:01

Million Dollars. Thirteen Million Dollars. Fifty

1:17:03

Two Million Dollars. The free cash

1:17:06

flow flowing to partners here is

1:17:08

certainly becoming real to. Just.

1:17:11

But. As they get into that. Call.

1:17:13

It on the order of magnitude of a

1:17:15

billion dollars scale. They

1:17:17

start bumping into the Moving Markets

1:17:20

problem and the Slip. It's. That.

1:17:22

we were talking about earlier yup, and that sort of

1:17:25

in the mid nineties. Yup, As they're

1:17:27

hitting this two hundred fifty million half a

1:17:29

billion dollar scale right, the computer model spits

1:17:31

out we should go by this huge amount

1:17:33

of something at this price they go to

1:17:35

do with they can only buy ten twenty

1:17:37

thirty percent of the amount they want at

1:17:39

that price And and suddenly the price is

1:17:41

very different. Yeah up. To

1:17:43

this point. The. Vast majority of

1:17:45

what. Medallion. Is doing. Is.

1:17:48

Trading currencies and commodities.

1:17:51

Not. Equities. Because.

1:17:53

He might be thinking. Okay, yeah, I

1:17:55

hear you. The nineties was a different

1:17:57

era, but. Half. a billion dollars fund

1:18:00

doesn't sound that big. How are they moving

1:18:02

markets with half a billion dollars? It's

1:18:04

not the equity markets. It's because

1:18:06

they're in these thinner markets. It's

1:18:08

not that commodities and futures are

1:18:10

small markets. They're large, but they're

1:18:12

thin compared to equities. There's just not

1:18:15

that much volume and you just can't trade

1:18:17

that much without slipage becoming a huge issue.

1:18:19

And Medallion is now hitting that limit. So,

1:18:23

Simon's decides the

1:18:25

only thing we can do here to expand,

1:18:27

which I'm such a believer in what we're

1:18:29

doing, we need to expand, is

1:18:32

we need to move into equities. Equities

1:18:34

are the holy grail. If we can make

1:18:36

this work there, the depth

1:18:38

in those markets will let us scale

1:18:41

way, way, way bigger than we are now. And

1:18:44

there's so much more data about

1:18:47

equities pricing that we can feed into

1:18:49

our models and the signal processing that

1:18:51

we can do and the signals that

1:18:53

we can find are going to be even better. Right.

1:18:56

There's so many buyers and sellers every

1:18:58

day showing up to trade so many

1:19:00

different companies at such high velocity. It's

1:19:03

almost this honeypot for

1:19:05

Renaissance's systems. This is sort

1:19:07

of their moment. This is what they were built

1:19:09

for. And it's kind of funny that they've just

1:19:11

been in kid glove land the whole time with

1:19:13

these thinly traded markets with minimal data. Yes.

1:19:16

And this brings us to Peter Brown

1:19:19

and Bob Mercer. And in

1:19:21

1993, one of the mathematicians that

1:19:25

Jim had recruited to RENTEC, a guy

1:19:27

named Nick Patterson, gets

1:19:29

especially passionate about going out and recruiting new

1:19:32

talent along with Jim. And this is, I

1:19:34

think, one of the keys to RENTEC and

1:19:36

the culture there. People want

1:19:38

other smart people to come be

1:19:40

there too. Nick's sitting there like,

1:19:43

this is a joy. I want to go

1:19:45

find other best people in the world to

1:19:47

hang out with. And he had

1:19:49

read in the newspaper that IBM was

1:19:51

going through cost cutting and was about

1:19:53

to do layoffs. And

1:19:55

he also knew that the speech recognition

1:19:58

group at IBM had some them absolutely

1:20:01

fantastic mathematical talent.

1:20:05

Really, what they were doing was

1:20:07

again another vector

1:20:09

in the early AI machine

1:20:11

learning research. Specifically,

1:20:14

IBM's Deep Blue Chess

1:20:16

Project of the time had come

1:20:18

out of this group. Peter

1:20:21

Brown there was the one that actually

1:20:23

spearheaded the project. Yeah, and

1:20:26

it's interesting that you talk about

1:20:28

speech recognition as the perfect

1:20:31

fit for what they were doing. And you might say,

1:20:33

why is that? Well, the actual

1:20:35

work that goes into speech

1:20:38

recognition, natural language processing is

1:20:40

kind of the same signal processing

1:20:42

that Renaissance is doing to analyze

1:20:44

the market. It's not just kind

1:20:46

of, it's exactly the same signal

1:20:48

processing. Right, speech recognition is a

1:20:50

hidden Markov process, where the computer

1:20:53

that's listening to the sounds to

1:20:55

try to turn it into language

1:20:57

doesn't actually know English, right, obviously.

1:20:59

But what it does know is

1:21:01

when I hear this set of

1:21:03

frequencies and tonalities and sounds, there's

1:21:05

a limited set of likely things

1:21:07

that could come after it. And

1:21:09

in Greg's book, he greatly points out

1:21:11

this perfect example. When I say Apple,

1:21:13

you might say Pi. The

1:21:16

probability that Pi is going to be

1:21:18

the next word following Apple is significantly

1:21:20

higher. And so these people who have

1:21:22

spent their careers not only doing the

1:21:24

math and the theoretical computer science behind

1:21:26

speech recognition to help figure out and

1:21:28

predict the next words that you have

1:21:30

a narrow set of likely words to

1:21:32

choose from. So when you're listening to

1:21:34

those frequencies, you can say, it's

1:21:37

probably going to be one of these three rather than

1:21:39

search the entire dictionary for any word that it could

1:21:41

be to narrow the processing power. It's

1:21:44

not only the theoretical side, but

1:21:46

it's also people who have built

1:21:48

those systems at IBM, like a

1:21:50

real operational computer company. Yes, at

1:21:53

operational scale. And this is what's so

1:21:55

important and why the two of them

1:21:57

become Probably the most critical.

1:22:00

The hires. In. Rent

1:22:02

Accessory even including all the great academics

1:22:04

the game before them. Because.

1:22:06

They're good on the mass sides. But. They

1:22:09

have this large systems experience.

1:22:12

And. Gym and Nick know that

1:22:14

if they're going to move into equities. Because.

1:22:16

Of the volume of data and because of how

1:22:18

much more complex that market is. They. Need

1:22:21

more complex systems?

1:22:24

And. The current talent at rents are coming from

1:22:26

academia is just never experienced that are built anything

1:22:28

like it. And the world that

1:22:30

they're entering, his just exploding

1:22:33

in complexity and dimensionality. And.

1:22:35

When I say that, here's what I mean. The. Data

1:22:37

that they are mining that there

1:22:39

are for is this intraday tic

1:22:42

data between every stock trading. So.

1:22:45

They're in this sort of trying to map the

1:22:47

relationship between one stock and every other stock. Not

1:22:49

just about Mon and time, but every time before

1:22:51

it, in, every time after it. They.

1:22:53

Are also once they do identify patterns

1:22:56

which this is Keith the algorithms identify

1:22:58

the patterns is not a human with

1:23:00

a hunch saying I think when. Oil

1:23:03

prices go up to the airline. Prices are

1:23:05

going to get his. It's computers doing machine

1:23:08

learning to discover the patterns in the data.

1:23:10

Then. There's the second piece of wool. What

1:23:12

trades do you actually put on? To.

1:23:16

Be profitable from. The

1:23:18

probabilities that you just discovered all

1:23:20

these weights of relationships between all

1:23:22

of these different companies? Yeah, just

1:23:24

putting on one trade. You're putting

1:23:26

on ten. A hundred thousands of

1:23:28

simultaneous trades. Both two heads to

1:23:31

be able to isolate some particular

1:23:33

variable that you're looking for. Again,

1:23:35

not you been a computer is

1:23:37

looking for and you also need

1:23:39

to do it in such specific

1:23:41

bite sizes so that you don't

1:23:43

move the market. So you're looking

1:23:45

for a super multi, very yet

1:23:47

multi dimensional problem. both on the data

1:23:49

in just inside he and on the

1:23:51

how do i actually react to it

1:23:53

side and all of this computation can't

1:23:55

take a long time because you must

1:23:57

act you know not in milliseconds it's

1:23:59

not a high frequency trading that's front running

1:24:02

the market. That's not actually what they do. A

1:24:04

lot of people think it is, but we'll get

1:24:06

to that later. But they do need to act

1:24:08

with reasonable quickness, probably on the order of minutes.

1:24:11

So these need to be really efficient computer

1:24:13

systems too. Yeah. And

1:24:15

the universe of equities is so much

1:24:19

more multidimensional and interrelated. There

1:24:21

are only so many currencies in the

1:24:23

world, and there are especially only so

1:24:25

many currencies that are large enough trading

1:24:27

markets that you can operate. There's

1:24:30

not infinite, but thousands and thousands of

1:24:32

equities in the world that are deep

1:24:35

enough markets that you can operate in.

1:24:37

And to some degree, they're all correlated

1:24:39

with one another. And just

1:24:41

keep adding layers of complexity here. Keep

1:24:43

adding new things to

1:24:45

multiply by. Many of these are traded

1:24:48

on multiple exchanges. So you might also

1:24:50

be looking for pricing disparities on the

1:24:52

same equity on different markets at different

1:24:54

points in time. So there's just dimensions

1:24:57

upon dimensions of things to analyze, correlate,

1:24:59

and act upon. So

1:25:02

Patterson and Simons go raid

1:25:04

IBM. They're like Steve

1:25:06

Jobs, raiding Xerox, Burke. They

1:25:08

bring Peter and

1:25:10

Bob and one of their programming

1:25:13

colleagues, David Magerman, over from

1:25:15

IBM into RENTEC. And

1:25:17

they get started on building the equities

1:25:19

model. But it turns out, A,

1:25:23

they're obviously very successful at that,

1:25:26

but the impact that they have and what

1:25:28

they build is even bigger because

1:25:31

Bob and Peter realize

1:25:34

that pay actually, we

1:25:37

should just have one model for everything

1:25:39

here, for currencies,

1:25:42

for commodities, for equities.

1:25:45

Everything is correlated. Everything is a

1:25:47

signal. It's not like the equities

1:25:49

market is wholly independent

1:25:51

and separate from what's

1:25:53

happening in currencies or what's happening

1:25:55

in commodities. There are relationships everywhere.

1:25:59

We really. want just one model. This

1:26:02

is like a fantastical undertaking, especially

1:26:04

in the early to mid-90s. Right.

1:26:07

But if you can nail it, it means

1:26:09

that you can do interesting things like, hey,

1:26:12

we don't have a lot of data on this

1:26:15

particular market, but it looks

1:26:17

a lot like something we do have data on.

1:26:19

So if it's all part of the same model,

1:26:22

we can kind of just apply all the learnings

1:26:24

from this other thing onto this brand

1:26:26

new thing that we're looking at with little data

1:26:28

for the first time. And because we're putting it

1:26:30

all in one model and no one else in

1:26:32

the world is, we can discover patterns that no

1:26:34

one else knows about. It turns

1:26:37

out that this was actually the second

1:26:39

most important innovation that

1:26:41

Bob and Peter bring to RENTEC, the

1:26:43

actual product and performance of having one

1:26:46

model. The most important

1:26:48

thing is that if you

1:26:50

have only one model, one

1:26:52

infrastructure, everybody in

1:26:55

the firm is working on that

1:26:57

same model. You can

1:26:59

all collaborate all together,

1:27:02

which is especially important when you have the

1:27:05

smartest people in the entire world, all

1:27:07

in one building. Before this,

1:27:09

there were separate models within RENTEC.

1:27:12

So insights and innovations

1:27:14

and work that one team was doing

1:27:16

on one model wouldn't

1:27:18

get applied or translate over to

1:27:21

work that was happening by another team on another

1:27:23

model. They did have the cultural element where it

1:27:25

was encouraged that you share your learnings, but someone

1:27:28

would have to take the time during their lunch

1:27:30

break and go learn from you about those and

1:27:32

then implement it in their version. There's a lag

1:27:34

and it may actually not get implemented. Yeah,

1:27:37

this is wholly

1:27:40

unique and revolutionary. No

1:27:43

other at scale

1:27:46

investment firm, period,

1:27:48

and especially QuantFirm, operates this way

1:27:50

today with just one model. Their

1:27:53

portfolio managers and teams and

1:27:55

multi-strategy, people are culturally

1:27:57

competitive with one another, but even if they're not,

1:28:00

work that you're doing on this side of Citadel

1:28:02

is not impacting the work that you're doing on

1:28:04

that side of Citadel. What Bob

1:28:06

and Peter do is they unify everything

1:28:08

at RENTEC. So all the wood is

1:28:11

going behind one arrow. And

1:28:14

before we talk about the impact of

1:28:16

that, we want to thank

1:28:18

our longtime friend of the show,

1:28:21

Vanta, the leading trust management platform.

1:28:24

Vanta, of course, automates your security

1:28:26

reviews and compliance efforts. So frameworks like

1:28:28

SOC 2, ISO 27001, GDPR, and HIPAA

1:28:30

compliance and monitoring, which is quite topical

1:28:33

if you are in the heavily regulated

1:28:35

finance industry and you need a

1:28:37

lot of security and compliance. Vanta takes

1:28:39

care of these otherwise incredibly time and

1:28:42

resource draining efforts for your organization and

1:28:44

makes them fast and simple. Yep,

1:28:46

Vanta is the perfect example of the quote

1:28:49

that we talk about all the time here

1:28:51

on Acquired. Jeff Bezos, his idea that a

1:28:53

company should only focus on what actually makes

1:28:56

your beer taste better, i.e. spend your time

1:28:58

and resources only on what's actually going to

1:29:00

move the needle for your product and your

1:29:02

customers and outsource everything else that doesn't. In

1:29:05

RENTEC's case, this would be the model.

1:29:08

Every company needs compliance and trust with their

1:29:10

vendors and customers. It plays a major role

1:29:13

in enabling revenue because customers and partners demand

1:29:15

it, but yet it adds zero flavor to

1:29:17

your actual product. Vanta takes care

1:29:19

of all of it for you. No

1:29:21

more spreadsheets, no fragmented tools, no manual

1:29:23

reviews to cobble together your security and

1:29:26

compliance requirements. It is one single software

1:29:28

pane of glass, just like one model,

1:29:30

that connects to all of your services

1:29:32

via APIs and eliminates countless hours of

1:29:34

work for your organization. There are now

1:29:36

AI capabilities to make this even more

1:29:38

powerful and they even integrate with over

1:29:40

300 external tools, plus they

1:29:42

let customers build private integrations with

1:29:44

their internal systems. And perhaps

1:29:46

most importantly, your security reviews are now

1:29:49

real-time instead of static, so you can monitor

1:29:51

and share with your customers and partners to

1:29:53

give them added confidence. So whether you're a

1:29:55

startup or a large enterprise and your company

1:29:57

is ready to automate compliance and streamline security

1:30:00

reviews like Vanta's 7,000 customers around

1:30:02

the globe and go back

1:30:04

to making your beer taste better, head on

1:30:06

over to vanta.com/acquired and just tell them that

1:30:08

Ben and David sent you. And

1:30:10

thanks to friend of the show, Christina,

1:30:12

Vanta CEO, all acquired listeners get $1,000

1:30:15

of free credit,

1:30:18

vanta.com/acquired. So

1:30:20

David, the equities machine. Yes.

1:30:23

And indeed, a machine it is.

1:30:26

So Peter and Bob come in in 1993, and 1994, 1995, they're building

1:30:31

this, RenTech is getting into equities. And

1:30:34

yet just imagine the computers that you

1:30:36

were using during 1994 and 1995. It

1:30:38

is astonishing the

1:30:41

level of computational complexity and

1:30:43

coordination and results that they

1:30:45

are pulling off, again,

1:30:48

in real time analyzing these markets with

1:30:50

the technology that was available during those

1:30:52

years. Yes. And here's

1:30:54

what's amazing returns

1:30:56

go down maybe slightly, certainly a bit

1:30:58

from the blowout year that 1994 was,

1:31:00

but they're still above 30% every single

1:31:03

year, most years above 40%.

1:31:09

This is unbelievable that they're

1:31:11

maintaining this performance as

1:31:13

they're going into this hugely more

1:31:15

complex market, and they're scaling assets

1:31:18

under management. So by the

1:31:20

end of the 1990s, Medallion has almost

1:31:22

$2 billion in assets under management, while

1:31:25

maintaining roughly the

1:31:27

same performance by getting into equities.

1:31:30

This is huge. Yep.

1:31:33

And David, if you just kind of look at this and do the math,

1:31:35

okay, so 94, their AUM was 276 million,

1:31:41

and they grew 93%. And then their AUM the next year

1:31:43

was 462 million, and then they grew

1:31:48

52%. And their AUM the

1:31:50

next year was 637 million, you kind of

1:31:52

quickly get where I'm going here, which is, Oh,

1:31:54

they're scaling AUM Not by bringing in

1:31:56

new investors, right? It's close to new

1:31:58

investors. It's all just. Compounding. This.

1:32:01

Is the same capital that they had

1:32:03

in Nineteen Ninety Three that has gone

1:32:05

from a hundred and twenty two million

1:32:07

at the beginning of that year, to

1:32:09

Ninety Ninety Nine being one point five

1:32:11

billion. Just. And. Then.

1:32:14

In the year two thousand. They.

1:32:16

Just totally blow the doors off.

1:32:19

A hundred and twenty eight

1:32:21

percent. Gross returns.

1:32:24

Net. Returns after fees.

1:32:27

Of. Ninety Eight Point Five Percent

1:32:29

This. but man, they grow

1:32:31

the fund from one point

1:32:33

nine billion to three point

1:32:35

eight billion of assets under

1:32:37

management against purely by investing

1:32:40

gains, not by getting any

1:32:42

new investors. The. Year

1:32:44

the tech bubble burst. Just.

1:32:46

A while the whole rest of the market. Is.

1:32:49

Down big time. Medallion.

1:32:52

Is up a hundred and twenty eight

1:32:54

percent girls on the air and this

1:32:56

becomes a team. High volatility is when

1:32:58

medallion of really science and here you

1:33:00

go. On. Correlate, they have

1:33:03

their final stamp of approval right

1:33:05

here of not only are we

1:33:07

have money printing machine, we are

1:33:09

a money printing machine in all

1:33:11

environments regardless of the state of

1:33:13

the broad market. And save it.

1:33:15

As he said, volatility actually makes

1:33:17

their algorithms work even better because

1:33:19

what are they doing? There are

1:33:21

for scenarios where the market's gonna

1:33:23

act a radically and they can

1:33:25

take advantage of people making decisions

1:33:27

that they send and time any

1:33:29

investors are under pressure. There's. A little

1:33:32

bit of edge that can accrue to a medallion

1:33:34

that saying okay your fear selling right now? Well

1:33:36

I can determine if you should be for you're

1:33:38

selling or not and if I determine that you

1:33:40

shouldn't be dumping net asset and buy it from

1:33:43

you. So there's a really fun

1:33:45

story around this that. Really? Oh

1:33:47

streets. Gyms. Genius

1:33:49

in managing the from and

1:33:51

the people. And.

1:33:54

How. This. Year was when they

1:33:56

really figured this out. So. the

1:33:58

first couple days of

1:34:00

the tech bubble bursting, Medallion

1:34:03

actually takes a bunch

1:34:05

of large losses. And part of

1:34:07

it might be that the model wasn't tuned

1:34:09

right yet because nobody at RENTEC had seen

1:34:11

this type of behavior in the market before.

1:34:14

Part of it might also be too that it didn't

1:34:16

perform well for those couple of days. It's

1:34:18

a really stressful time for everybody.

1:34:21

You know, everybody's in Jim's office, Jim's

1:34:23

smoking his cigarettes, it's a cloud of

1:34:25

smoke, and they're debating what

1:34:27

to do. And Jim makes the call to take some

1:34:30

risk off. He's worried about blowing

1:34:32

up. We're not very far removed at this

1:34:34

point from long-term capital management. The

1:34:36

model may be saying we should stay long here,

1:34:38

but let's not blow up the firm. Yep.

1:34:42

After this goes down, Peter

1:34:44

Brown comes to Jim and offers to

1:34:46

resign, given the losses that they

1:34:49

incurred over these couple of days. And

1:34:51

Jim says, what are you talking about? Of

1:34:54

course you shouldn't resign. You are way more

1:34:56

valuable to the firm now that

1:34:58

you've lived through this, and you now know not

1:35:01

to 100% trust the model in

1:35:03

all situations. Fascinating. It's such

1:35:05

a good insight. That illustrates Jim

1:35:07

as a leader right there. It

1:35:10

totally does. There's a parallel

1:35:12

story when Jim ultimately does

1:35:14

retire in 2009, and

1:35:17

Peter and Bob take over as co-CEOs, where

1:35:20

a year or so before the quote unquote

1:35:22

quant quake had happened, where similar to

1:35:24

the tech bubble bursting, there was all

1:35:26

of a sudden very large

1:35:28

drawdowns about all quantitative firms in the

1:35:31

market and rent tech gets hit. And

1:35:34

during that period, Peter

1:35:37

argued very strenuously that we should trust

1:35:39

the model, stay risk on. This is

1:35:41

gonna be an incredibly profitable time for

1:35:43

us. And Jim pumped

1:35:46

the brakes and stepped in, intervened and took

1:35:48

risk off. And Peter

1:35:50

goes to Jim again around the

1:35:52

CEO transition and says, hey Jim,

1:35:55

Aren't you worried that with me running the place now,

1:35:57

I'm gonna be too aggressive and blow it up. one

1:35:59

of the. The Is. And. Gym says

1:36:01

no, I'm not worried at all. I.

1:36:04

Know you were only so aggressive in that

1:36:06

moment because I was there pushing back on

1:36:08

you. and when you're in the seat. You're.

1:36:11

Gonna be less aggressive resist such a master

1:36:13

it insight into human behavior. It is so

1:36:15

true though I don't find this about myself

1:36:17

that I was naturally take the position of

1:36:19

the foil for the person across from heat.

1:36:21

So if somebody is being pushy and some

1:36:24

way I'll find myself taking a position where

1:36:26

if I read, pause and reflect and like.

1:36:28

I don't think I expected to take this

1:36:31

position coming into this conversation, but in a

1:36:33

you naturally want to sort of play the

1:36:35

other side to balance out the person sitting

1:36:37

across from you. So

1:36:40

backs. Of year Two thousand and this

1:36:42

incredible performance. And to what you

1:36:44

were saying earlier about uncorrelated returns, Not only

1:36:46

do they suit the lights out that year,

1:36:48

they're doing it when the market is down.

1:36:51

We. Got to introduce this concept of a surprise

1:36:53

you have now with for all of you listeners.

1:36:56

Better in the finance world you'll notice but for

1:36:58

everybody else this is a really important concept. And.

1:37:00

I think people grasp at intuitively.

1:37:02

We've mentioned this concept a couple

1:37:04

times this episode where. Okay,

1:37:06

great. It's amazing to have a fund

1:37:09

that twenty five Axis or a year

1:37:11

where you have a hundred percent investment

1:37:13

returns. or I bought Bitcoin yesterday and

1:37:15

it doubled over night. Does that make

1:37:17

you one of the best investors in

1:37:20

the world? We all intuitively. No, No,

1:37:22

it doesn't because. Maybe. That

1:37:24

was a fluke. Maybe you're taking on an

1:37:26

extreme amount of risk and then the question

1:37:28

is always. Adjusting. For the

1:37:31

risk that you're taking, can you produce

1:37:33

a superior return? Taking. The

1:37:35

risk into that accounts and so you

1:37:37

basically can provide value to investors. As

1:37:39

a fund manager in two ways: you

1:37:41

can outperform the market, or you can

1:37:43

be entirely on correlated with the market

1:37:45

and get market returns. Or. What

1:37:47

you can do as Rent Tech

1:37:49

is both. You can be uncorrelated,

1:37:51

he and massively outperform, which is

1:37:53

effectively the holy grail of money

1:37:55

management. Just. And so the surprise

1:37:57

you is a measurement. combining

1:38:00

these two concepts. Exactly. So it's named after

1:38:02

the economist William F. Sharp, it was pioneered

1:38:04

in 1966. It is effectively

1:38:07

the measure of a fund's performance

1:38:09

relative to the risk free rate.

1:38:11

So if you performed at 15%

1:38:13

that year, and the

1:38:16

risk free rate was 3%, then you

1:38:18

know, your numerator is going to be

1:38:20

12%. And it is compared against the

1:38:23

volatility or the standard deviation is technically

1:38:25

what it is. But effectively, how

1:38:28

volatile have you been the last x

1:38:30

years and typically it's looked at as a three year sharp

1:38:32

or a five year sharp or a 10 year sharp. The

1:38:35

sharp ratio represents the additional amount

1:38:37

of return that an investor receives

1:38:39

per unit of an increase in

1:38:41

risk. And so David, you're starting

1:38:43

to throw out numbers, low

1:38:46

sharp ratios are bad, negative sharp ratios are

1:38:48

worth because that means you're underperforming the risk

1:38:50

free rate, high sharp ratios are good because

1:38:52

it means that you're producing lots of returns,

1:38:55

and your variance or your standard deviation or

1:38:57

your sort of risk is low. So

1:38:59

in 1990,

1:39:01

they had a sharp of 2.0, which

1:39:03

was twice that of the S&P 500

1:39:06

benchmark. Awesome. Yep. Good. 1995 to 2000 sharp ratio

1:39:10