0:03

Bloomberg Audio Studios, Podcasts,

0:06

radio News. We're

0:08

starting with light fair you

0:12

know, yeah, but lightly

0:14

fried, well probably deeply fried, lightly

0:17

breaded, deeply fried.

0:20

All right, Hello

0:22

and welcome to the Money Stuff Podcast. You're a

0:24

weekly podcast where we talk about stuff

0:26

related to money. I'm Matt

0:29

Levine and I write the Money Stuff column for Bloomberg

0:31

Opinion.

0:32

And I'm Katie Greifeld, a reporter for Bloomberg

0:34

News and an anchor for Bloomberg Television.

0:36

What do we got today, Katy?

0:38

We're going to talk about shrimp and lobsters,

0:41

all the fair out of the sea. We're going to talk about

0:43

fat fingers and why City

0:45

Group keeps having problems with those, and

0:47

then we're going to talk about brains.

0:50

It's going to be a good one.

0:51

Yeah,

0:56

Red Lobster, did they really

0:58

shrimp themselves to death?

1:00

No? Maybe a little bit.

1:02

But isn't it fun to pretend?

1:03

It's more fun to pretend than almost any

1:05

other thing I've pretended about. So Red Lobster filed

1:08

for bankruptcy over the weekend. And why

1:11

did they go bankrupt? Well, you know, they

1:13

like had a series of

1:15

business mistakes. That's some onerous leases

1:18

that they got in connection with the leverage via

1:20

changing consumer tastes. Inflation is

1:22

rough and fast, casual.

1:24

Dining, those are all boring.

1:25

Yeah. No, the real answer is that

1:27

they are. Their

1:30

equity owner is a company

1:32

that is also one of their big suppliers.

1:34

It's a seafood company called Tai Union, and

1:37

Red Lobster under its former

1:40

CEO, embarked on an

1:42

unlimited, endless shrimp promotion

1:44

where for twenty dollars you can get all the shrimp you wanted,

1:46

all the time. And in

1:49

the bankruptcy papers, the new CEO

1:51

kind of like works on behalf of the creditors

1:54

sort of insinuates that there

1:56

is a scheme to pump

1:59

shrimp through Red Lobster to make money for Tie

2:01

Union at the expense of Red Lobster.

2:03

To just line the pockets of Thai Union.

2:06

Well, it did turn into they

2:08

were the only shrimp supplier

2:11

for Red Lobster, didn't They start out with three and

2:13

then it whittled down to just Tai Union.

2:15

It's not exactly clear, but yeah, the suggestion

2:17

is that like there were some of the regularities

2:19

in the procurement process where they got rid of some of

2:21

their other shrimp suppliers and ended up

2:23

sending more and more money directly to Taie Union

2:26

for more and more shrimp, and.

2:27

Thai Union of course disputes

2:29

that passionately. They say

2:31

that all of those issues concerning the company

2:33

and its relationship to Red Lobster,

2:36

you know, are basically false. But

2:38

it is interesting that this started out

2:40

as a limited promotion and then I think it was May

2:43

twenty twenty three, actually it turned into a permanent

2:45

promotion, this endless shrimp fiasco.

2:48

It just makes sense. Yeah, But if you

2:50

own a company that is going under,

2:53

and by like February this year, Thai Union is saying

2:55

the value of their equity at zero. You own a company

2:57

that's going under, yeah, you get as much money as you

2:59

can out of it, and you also

3:01

sell that company shrimp. The goal is

3:03

to sell them as many shrimp as possible.

3:05

They did end up losing eleven million dollars

3:07

on it, which is not huge in.

3:09

The ground that gets you billion dollars.

3:12

There's a lot of money to lose on shrimp, though, I

3:16

mean, I certainly haven't come anywhere

3:18

close to that.

3:20

It is a lot of money to lose on shrimp. Red Lives

3:22

the bankruptcy file has a lot of like good stats,

3:24

including they're like twenty percent of the total

3:26

market for lobster tales like in the world.

3:29

So yeah, I mean, like, if anyone is going to lose eleven

3:31

million dollars on shrimp, it's definitely red lobster.

3:34

But that's probably not enough to drive them into bankruptcy,

3:36

but it is enough to be very very funny in bankruptcy.

3:39

It made for some amazing headlines, some amazing

3:41

thought pieces, some amazing money stuff columns.

3:44

Let's talk about the leases though, because a

3:46

lot of people are pointing to the leases

3:49

at.

3:49

Sure sure, Like the boring answer

3:51

is that, like there's a sort of classic private equity

3:53

stripping story where it's like, you know, they

3:55

were owned it on point by General

3:57

Mills. They're on a Darden restaurant company.

4:00

They were sold in a leveraged

4:02

buyout where basically the buyer to finance

4:04

the deal, sold a lot of their locations

4:06

and leased them back, and so they

4:09

entered into a lot of like expensive leases, and

4:12

when hard times came, they

4:14

didn't own their own real estate, and so they had a little bit

4:16

less financial flexibility because that all these lease payments,

4:18

and so that was also hard on them.

4:21

I'm always like skeptical of these explanations

4:23

because it's like that

4:25

would work that fine had business

4:28

kept improving, right, and it worked out poorly

4:30

because business was declining. Like it adds leverage,

4:32

right, it lowers your margin for error. But

4:35

like it seems like

4:38

the explanation here is not like Red Lobster was

4:40

doing great and then like financial shenanigans

4:42

destroyed it. The story is like Red Lobster is not doing

4:44

that great and the financial maneuvers gave it less

4:46

of a margin for error. But like, ultimately, this

4:48

is a change in consumer tastes story,

4:51

and it turns out the consumer taster or

4:53

not for that many shrimp. Although

4:56

the funny thing is like, in conjunction with like

4:58

these stories about Red Lobster's collapse, you also

5:00

read a lot of anecdotes about people

5:02

eating one hundred and eighty shrimp at a sitting

5:04

or whatever. I mean, why wouldn't you for

5:07

reasons, but like you know that

5:09

someone.

5:10

Will I mean shrimp. I don't like

5:12

shrimp, but shrimp are kind of the perfect

5:14

food if you're just trying to get a

5:16

lot of protein.

5:17

It is true that if I were any one hundred and eighty of

5:19

a food. It might be a shrimp. Yeah, come on, well,

5:22

the Red Lobster advertised all the shrimps you could eat.

5:24

That attracted some number of people who are like,

5:26

oh, I can eat a lot of shrimp, Red Lobster,

5:29

And it.

5:29

Probably did get people in the door. But there was

5:31

a.

5:31

Really get negative margins.

5:33

Yeah, exactly. And there was a fun Bloomberg

5:35

News piece just talking about the state of the

5:37

restaurant business right now. I don't think

5:40

it's any surprise that inflation is still pretty

5:42

high, pretty sticky. The absolute level of prices

5:44

is much higher than it was. And you have all

5:46

of these restaurant changs who you

5:49

know, their businesses are in much better shape than

5:51

Red Lobster, but they're just going for

5:53

these promotions. You think about Applebee

5:56

has one dollar Margaritos right now, which

5:58

I actually would like to try. I can't imagine that as

6:00

much alcohol. And then Chili's

6:02

has recently introduced the Big Smasher.

6:05

It's part of its broader campaign to have

6:07

three menu items. You put them all together,

6:09

they cost eleven dollars. Stay with me

6:11

here anyway, And then.

6:13

Maybe you say you put them all together like

6:15

on a plate or like when

6:17

you say, like, do they actually get a blender?

6:20

You know, definitely not that

6:22

one. Like you order three menu

6:24

items and all together it costs like eleven

6:26

dollars. But then they quote this

6:28

man, he's the CEO of Aaron

6:30

Allen and Associates. It's a restaurant consulting

6:33

firm, and he says that these chains

6:36

sabotage themselves by trading down just

6:38

to get cheap hits. It's like

6:40

taking grandma's jewelry to

6:42

the pawn shop just to get a few quick

6:44

bucks. And I guess that's kind of

6:47

what Red Lobster did. Are

6:49

the jewels the lobsters in

6:52

this scenario, I'm

6:55

torturing? Yeah,

6:58

And so I don't know if I feel like it

7:01

was a whole host of

7:03

factors. It's probably crushing the entire

7:05

restaurant industry right now is sort of the fast

7:07

casual space. Those financial

7:09

maneuvers that you described.

7:11

Yeah, I just I love the idea

7:13

that there is a shrimp conspiracy. Yeah,

7:16

because, like right, there are a lot of factors that are affecting

7:18

all of the competitors, but like here

7:21

there's the specific factor that they are

7:23

owned by their supplier, and so

7:25

it's like we can just pump shrimp

7:28

through the red lobster.

7:29

System and no one will ever know.

7:31

No one will ever know except that the CEO.

7:33

Know who put in place the CEO? Was

7:35

it Thai Union.

7:37

Well so the old CEO Tai Union, yeah,

7:39

pointed and the allegations

7:41

now that he was in the pocket of Ti Union. But no, the

7:43

new CEO is from ALPHAREZM. Marshall is like basically

7:45

part of the restructuring team. So he like kind

7:47

of works for the creditors. And you know, I wrote

7:49

in m my column in the first day of the bankruptcy

7:52

filing, he writes this statement sort of alleging

7:55

that Tai Union was doing all this stuff, the stuff

7:57

shrimp through the system. And I wrote

7:59

that, you know, in his position, quote

8:02

you cast a wide net for possible ways

8:04

to claw back money for creditors, which

8:06

I didn't really intend as a lobster pun.

8:08

But like several readers put.

8:09

That claw terrible. That's just

8:11

terrible.

8:12

I mean, like he works for the creditors and like they need

8:14

to find as much money as they can, and like to

8:17

the extent millions of dollars went out the

8:19

door to pay Tie Union for shrip. You have some

8:21

case to say it shouldn't have done that, and like that

8:23

was a violation of their duty to the company and

8:25

get the money back.

8:26

I mean the fact that Tai Union appointed the CEO

8:29

and then whittled it down. It's the owner of the company,

8:31

I know, but still and then whittled it down

8:33

to be the sole supplier. If they were one of several,

8:36

maybe that would be a better look.

8:38

It's definitely something that looks

8:40

like a conflict of interest.

8:41

It certainly does. What I'm unclear

8:44

on is whether the promotion

8:46

it's not still going on. Could you and I go

8:48

to the Red Lobster in Times Square.

8:50

I was thinking about trying to record this podcast

8:52

from a Red Lobster, but I thought they shut

8:55

down a lot of the restaurants.

8:56

But the one in Times Square is still

8:58

open. I know that because Bloomberg has

9:00

actually sent some reporters there and.

9:01

They get the shrimp.

9:02

They interviewed a lot of guests, they

9:05

get the shrimp that was not in the article

9:08

Red Luster.

9:10

At this late date at Red

9:12

Lobster and not ordering the end of this ship.

9:14

I know, I know it wasn't in the article. I feel

9:16

like we need a little bit more on the ground reporting.

9:18

But if the shrimp promotion is still going

9:21

on ourselves, I don't

9:23

eat shrimp, but I would choke down. Maybe

9:26

maybe not one hundred and eighty, but.

9:27

No, I will say, yeah,

9:30

this is less funny than the shrimp. But people

9:33

email me about this. There is this analogy

9:35

to the AI investing

9:38

boom, oh.

9:39

My god, No, Okay, go

9:41

ahead.

9:42

There's this article from a poor Agarwalla a

9:44

couple of months ago about large language

9:46

model AI startups and a

9:48

lot of their investors

9:51

are like big cloud and chip

9:53

companies like Nvidia and Google and Amazon

9:55

and Microsoft. And he wrote

9:57

this article being like, there is a

10:00

weird tension there because these companies are investing

10:02

billions of dollars in these AI startups, but

10:05

a lot of that money is going right back to the cloud

10:07

providers in the form of like paying for chips or paying

10:09

for cloud compute, and so like when

10:12

in Video or Microsoft invests in an AI startup,

10:14

like it can say I'm putting in a billion dollars at

10:16

a ten billion dollar valuation, but it's getting most of that money

10:19

back right, It's like going to the supplier's

10:21

bottom line. So he

10:23

argues that's bad for venture

10:25

capitalists in that space, because like the valuation

10:27

of these companies is being driven up by people

10:29

whose interest in the company is not just being

10:31

an equity investor, but is also being a supplier.

10:34

And He's like there's conflicts of interests where like if

10:36

you are Microsoft or in Video and you're

10:38

a big shareholder in these AI startups, you know, you're

10:40

like on the board, you have control over this company. You

10:43

might make decisions that are in your best interest as a

10:45

supplier and not necessarily in the best interest

10:47

of the company or in the other investors, because

10:50

your money is not really an equity

10:52

investment, it's really about finding

10:55

customers for your cloud compute power.

10:57

Similarly with the shrimp, Yeah, I

11:00

was going to say I was skeptical, and then actually

11:03

that is a really neat parallel

11:06

in.

11:06

Most of these cases, like these companies are not the controlling

11:08

shawl, they're appointing the sea. Yeah. Right, Usually there's

11:10

like some independent you know, like the startup has

11:12

its own business model. But right, I mean, like there

11:14

are conflicts of interest there where if you're like a big supplier,

11:17

a big exclusive supplier, a lot of the equity

11:19

investment is going directly to you, Like your

11:21

interest is maybe less as an equity investor

11:24

more as a supplier.

11:25

I will say, like imagining, you know, an

11:27

executive with semiconductor chips falling

11:29

out of his pockets and his briefcase. It's definitely not

11:32

as much fun as shrimp. But that

11:34

does make a lot of sense.

11:35

Right, Like you hope that like

11:38

this is like in the earlier stage where it's like not as fun.

11:41

You're not like, oh, they're like stuffing computing power

11:43

into these AI startups, right because like they're all trying

11:45

to make money. They're all, you know, optimistic and hopeful.

11:47

It's not like Red Lovester with like sort of the end of the run.

11:49

But uh, I don't know, food.

11:51

For thoughts, some shrimp for

11:53

thought.

12:09

From shrimp to fat fingers.

12:12

This is a sad story because that's a great

12:14

story. I mean, it is about human

12:17

error and who among us hasn't accidentally

12:19

tried to sell four hundred and forty four billion

12:21

dollars worth of equities.

12:24

It is the most natural

12:26

mistake it can possibly make.

12:28

Why don't you walk us through the worst

12:30

fourteen minutes of this trader's

12:33

career.

12:33

Okay, So there's a trader at city in

12:36

London, apparently working.

12:38

From home on a Monday holiday.

12:40

On a Monday Bank holiday.

12:42

Talk about a bad Monday.

12:46

So this trader works

12:48

on the Delta one desk and city to hedge an

12:50

index features order has to sell a

12:52

big basket of stocks on thirteen different European

12:55

stock exchanges. Pulls

12:57

up the order management system. There's like a

12:59

box where you enter the quantity you want to sell.

13:02

Actually there's two boxes. You can enter the quantity

13:04

in terms of like units basically like shares of the

13:06

index, or you can enter the quantity in dollars.

13:08

This person wants to sell fifty

13:10

eight million dollars, enters fifty eight million

13:13

in the shares field, and

13:15

therefore sells fifty

13:18

eight million units, which is you know,

13:21

four hundred and forty four billion dollars

13:23

worth of stocks. So

13:26

fills out the form and then this

13:28

is the amazing part to me, the

13:31

order management system displays you know, fifty

13:33

eight million units, but then also displays a

13:35

dollar amount. But instead of displaying

13:37

four hundred and forty four billion, which is the actual dollar

13:40

amount, it displays negative fifty

13:42

eight million. Because

13:45

in that field, it like pulls in from an external

13:47

pricing source and the pricing source is like turned

13:50

off that morning because I don't know if because the market's

13:52

not opener, because the bank holiday or whatever. The

13:54

pricing source is turned off. And so City's system

13:56

says, the pricing sources isn't available, so we're

13:58

going to default to negative one dollar

14:01

as a price for each share, right,

14:04

So the trader types in fifty eight million shares,

14:06

and the thing pulls in a price of

14:08

negative fifty eight million. So the trader looks at

14:10

that and says, yes, right, I wanted

14:12

fifty eight million dollars. It's showing me fifty

14:14

eight million dollars. Now there's a minussignment. You

14:16

know, everything is like no one knows what the sign is supposed

14:19

to be on any of these systems, And so the

14:21

trader's like, yes, okay, fifty eight million dollars

14:23

just like I thought. Clicks okay.

14:26

Then the next thing the system does

14:29

pops up seven hundred and eleven

14:31

warning error messages right saying, are you sure

14:33

you want to sell you know, a billion dollars worth of stock

14:35

in Sweden? Are you sure you want to sell a billion dollars worth of that?

14:38

Suck? And the trader, first

14:40

of all, only sees eighteen of these messages because

14:43

you have to scroll down to see the other six hundred nine

14:46

and why would you and telling yeah,

14:49

it's a big order whatever, I'm clicking yes, so

14:51

trader clicks yes. Then it

14:54

shows like a final confirmation, like do you really want

14:56

to sell? And at this point it has crossed off half

14:58

of the orders. It's like half of these orders. Even

15:00

cities somewhat chanky system knows

15:03

that it should not sell, you know, more than

15:05

two billion dollars worth of any stock, so like all

15:07

the stocks that s to some more than two billion dollars worth,

15:09

it cuts out, but still it

15:11

says, okay, fine, do you really want to sell

15:13

one hundred and ninety six billion dollars worth of stock?

15:15

And at this point the trader says sure and

15:18

clicks yes, and off City goes

15:21

to sell one hundred ninety six billion dollars

15:23

worth of stock, which causes a

15:25

flash crash, and like a bunch of different

15:27

European stock markets.

15:29

Yeah, I mean, it's

15:31

just amazing. Hearing you describe it makes

15:34

my blood run cold. I do like to think

15:36

that somewhere along that line I would

15:38

have stopped myself, but one never

15:40

knows.

15:41

I got a lot of emails from people being like this

15:44

person should have stopped themselves. I

15:47

tell you I know at

15:49

this point from my own computer use. I

15:51

know enough about myself to know I would not.

15:53

Have lived there messages.

15:55

You know, because you're an experienced

15:57

trader, right, you've done this before? You

15:59

fill the form. You see the

16:01

form show you fifty eight million dollars. You're

16:03

like, yes, click yes, and

16:06

then you get like seven more click buttons

16:08

that to you are just a waste. They're

16:10

like, okay, I've already checked it. It's already

16:12

yes, yes, yes, yes, yes, yes, yes yes, and you just click.

16:15

You don't look, I think, is what happened here. I'm

16:17

not responsible for trading tens or

16:19

hundreds of billions of dollars of stocks

16:22

across Europe, but like I

16:24

click yes all the.

16:25

Time, man, I mean, I think about like my

16:27

own stupid mistakes. I don't have anything of this magnitude,

16:29

but like sending an instant Bloomberg

16:32

message to the wrong person, making typos,

16:34

et cetera, it happens. What is amazing.

16:37

You described it as city somewhat janky system.

16:40

It wouldn't have happened in New York necessarily,

16:42

but it did happen in London.

16:44

They had hard limits on how much you could sell at

16:46

one time in New York.

16:49

Yeah, And the natural question

16:51

is why, But I don't know if you have that

16:53

answer.

16:54

I don't know the answer. But you know, everything is like silent

16:56

and hierarchical, and some people put

16:58

it in place and some people don't. You know, I

17:00

don't have a good answer to why. But one loss

17:02

put it in one didn't.

17:04

This in and of itself is amazing,

17:07

but it's even more amazing when you think about City's

17:09

history, which you write about, and you think about

17:11

what happened with Revlon, for

17:13

example, because it hits a lot of the same notes.

17:15

Yeah, I mean the Revlon thing. City

17:18

was like the administrativevision on this big loan to Revlon,

17:21

and like there was a sort of fight over whether Reverend

17:23

was in default. And as part of that

17:25

fight, Revlin meant to pay like seven million dollars

17:27

an interest to a couple of hedge funds, and instead

17:30

City paid nine hundred million dollars paid

17:32

back the loan in full. And then City was

17:34

like, oops, can we have the money back? Like half

17:36

of the lenders were like sure, here's the money back, and

17:38

half of them were like in this fight with Revlin and we're like,

17:40

no, we're keeping the money and going to court. They kind

17:43

of went in court and eventually lost, but in the court

17:45

decision in loving detailed

17:47

describes how city messed this up, and

17:50

it's just incredible, like they

17:52

had this system where like the

17:54

only way for them to make this interest payment was

17:57

for some reason, to pay off the entire

17:59

principle of the loan, and the City's

18:01

like, well, I was going want to do that. So no, it's okay,

18:03

you can pay off the entire principle to like a fake memo

18:06

account. So the system thinks it's paying off the

18:08

entire principle, but it's not really, no money is going

18:10

out the door. And so he's like, sure, that sounds great, let's do

18:12

that, right, which is first of all, like a terrifying thing to

18:14

agree to. But then secondly, in order to have

18:16

it only be paid to

18:18

a fake account and not actually go out the door, you

18:20

have to click like three boxes, and

18:23

one is like pay the principle to the fake account. You're like,

18:25

okay, click the principal box, and then the other two

18:27

are like just bizarre buzzwords,

18:29

and so like the three people, three people

18:31

had to do this, the three people signing off

18:33

on the city thing, like yep, the right boxes

18:35

checked, but then they didn't check the other

18:37

two boxes that were more complicated, and so

18:40

they sent out nine hundred million dollars by

18:42

accident. And then when they saw

18:44

that it had gone out, they called tech support and we

18:46

like, the system isn't working. And ultimately

18:48

turned out the system was working, but like you know, the

18:50

system was terribly.

18:51

Dissigned, right, and that happened in twenty

18:53

twenty one. Yeah, this trade happened in twenty

18:55

twenty two, so maybe maybe maybe

18:58

it's all fix, it's all fun. I thought it

19:00

was an interesting size and scope. So ultimately they

19:02

were fined seventy eight million dollars.

19:04

And apparently when regulators were thinking about

19:07

how big the fine should actually

19:09

be, they were thinking about how

19:11

much it cost them in part. So basically,

19:16

the Banks Delta one division had

19:18

generated roughly six hundred and twelve million dollars

19:21

in the nine years leading up to this trade,

19:23

in average about sixty eight million dollars a

19:25

year, So you layer on the fines and

19:27

the trading losses from that day, and

19:30

that trade costs those desks nearly

19:32

two years of revenue, which is pretty

19:34

painful. That's two

19:37

years, two years and fourteen minutes.

19:39

That's so sad.

19:40

It is so sad.

19:41

It's so sad everything you work for and

19:43

you put like one number in the wrong box

19:46

and it's like it's all all goes

19:48

up in flames.

19:49

I always think about that when you read stories

19:51

like this about someone who just makes

19:53

a human accident, some unintended

19:56

pilot hour, no malicious intentions

19:59

and just a all over. And I always think about like

20:01

the months in the weeks leading up them

20:04

going about their lives, not knowing

20:06

that this cataclysmic event was

20:08

going to happen. And here we are, and

20:10

Bloomberg News has reported that this trader

20:12

doesn't work at City anymore. I'm not that.

20:15

Surprised, but I will say this fine

20:17

and even this loss are not about the trader putting

20:19

the number in the wrong box. Like this is a system design

20:21

issue, right, Like yeah, the problem here is like, yes,

20:23

putting the number in the wrong box, but like having

20:26

a system that, like the

20:28

computer also got confused between the number

20:30

of shares on the dollar in a matter right the computer

20:32

was like, oh, yeah, they're all worth one dollar a share,

20:35

right, like wrongly, but then also like the

20:37

after trade checks were just not effective.

20:40

Right in New York, they would have blocked this

20:42

trade automatically, even in London,

20:44

Like if you're popping

20:47

up seven hundred and eleven error messages, it's

20:49

like maybe make a bigger run.

20:51

But all that being said,

20:53

do you think that any of the other banks who

20:55

could potentially hire this trader

20:57

are thinking that way? Or are they?

21:00

I would hire this trader really, Yeah, that's

21:02

not like on the list of like.

21:03

Well, they're probably never going to do it again.

21:05

Right, One, they're never going to do it again,

21:08

and two like, look, obviously

21:10

people care a lot about attention to detail,

21:12

but like there are other skills and like probably

21:14

everyone would mess this up once. It's just that if they

21:16

have better software to catch it, they won't actually

21:19

cost the bank one hundred million dollars.

21:21

Yeah, make better software.

21:23

I think that's the answer that this port trader. Now

21:26

I feel really bad.

21:27

Yeah, this was kind of a bummer.

21:29

I do think. The other thing that's amazing in this

21:31

case is that the risk managers

21:33

who are there directly responsible for oversight

21:36

of this, like their job was to catch this. They

21:38

went on vacation eight minutes before the trade

21:40

happened.

21:41

That is wild. I mean just

21:43

the timeline of this whole thing, but the eight minutes

21:46

that seems like almost unbelievable.

21:48

I'm exaggerating when I say they went on vacation. Like

21:50

what happened is that, like the handoff from like the right team

21:53

to the wrong team happened eight minutes before this

21:55

trade, which I think must mean that the right team in like

21:57

Asia was handing it off to the wrong team in London

21:59

because the damon London was out for the bank

22:01

holiday.

22:02

Yeah, but in any case, right, they probably

22:04

sent that email, slammed the Laptop

22:07

show, and then they literally were on vacation.

22:09

So yeah, I wonder if they got

22:11

controlled.

22:26

This is the section of this podcast

22:29

that I have the lowest expectations for.

22:31

Your nucleus accumbents is not lighting up with this

22:33

one.

22:33

I was hoping they section of your.

22:35

Brain that anticipates rewards.

22:37

I did google that and the definition

22:40

was something along those lines, and then it said it was an

22:42

incomplete definition. To just think of it as like

22:44

the reward center of your brain. But I don't know

22:46

what.

22:47

No, I have a much more nuanced understanding

22:49

of the nucleus accumbents, I will not share

22:51

with you.

22:52

Very good, very well. So

22:56

we're in this situation again where we're recording

22:58

this podcast before your or newsletter

23:01

on this topic has come out. I

23:03

read the actual paper and I

23:05

got to say a lot of it was

23:08

beyond me. But why don't you tell us about

23:11

this paper?

23:11

This is an incredible favor by basically

23:14

business school professors as

23:16

opposed to medical school professors. But

23:18

it's called brain activity of professional investors

23:21

signals future stock performance. So what they did

23:23

is they took a bunch of like Dutch professional investors,

23:25

like people who work at mutual funds, and they

23:29

popped them in an MRI machine and

23:31

they showed them slides of

23:34

investment presentations about forty five stocks,

23:36

and they're all like historical. So they

23:38

would show these like sort of masked

23:41

investment cases for a bunch of stocks at

23:44

like different times of the past ten years, and

23:47

they were like, what do you think would you buy the stock

23:49

right? Do you think the stock will up perform its sector?

23:52

The investors in the MRI machine either

23:54

said yes or no. And they also ran

23:56

the MRIO while they're doing this, and it turns out

23:58

that the investor's answers were worthless,

24:01

Like they did not accurately predict

24:04

no better than chance's ability to predict whether

24:06

the stocks would go up or down. But their

24:09

brains, their brains

24:12

did accurately predict whether the stocks would cover down,

24:14

which is to say that if you looked at like ridging,

24:16

their brain called the nucleus siccumbents, which is like sort

24:20

of the thing that anticipates rewards,

24:22

right, it lit up when they

24:24

saw presentations about stocks

24:27

that were going to go up, so

24:29

subconsciously they knew which stocks would

24:31

go up, even though consciously they did not know

24:33

what stocks would go up.

24:35

I do love that. It's

24:37

amazing, but the how do you apply

24:40

it?

24:40

How do you possibly you put your portfolio

24:42

manager in an MRI all day?

24:43

That's it? Yes, I mean

24:46

I think that that would be a

24:48

terrible a terrible existence.

24:51

But like if you made a lot of money.

24:53

Yeah, true, I mean.

24:54

If this really worked, by the way, I'm not

24:57

you know, I'm not so sure how well

24:59

this really worked, but it worked in some

25:01

experimental design. But it's fascinating

25:03

too because like how could this work?

25:05

Right? Like, how could it be that you can

25:08

subconsciously.

25:08

Just know which stocks secatifitely know, but

25:11

you can't translate that into an actionable

25:13

decision.

25:14

Yeah. But so here's what I think the explanation

25:16

is. To the extent this is real in the introduction

25:18

to paper, they're like the other experiments like this, and one

25:21

is like a sort of famous. One is like you

25:23

play songs to

25:25

people in an MRI, and the

25:28

songs that light up their brains in certain

25:30

ways go on to become

25:32

hit songs. It sounds like people's brains

25:35

instinctively know it's going to be hit songs. Well that makes a lot

25:37

of sense, right, right, Like something about that song

25:39

instinctively like makes everyone like it,

25:42

right, Like it lights up a section of your brain, Like, of course

25:44

that's going to go on to be a hit. Right. Maybe

25:46

it's the same with stocks, right, It's

25:48

like a meme stock phenomenon, where like something

25:50

about a stock makes these

25:52

investors like it, that's

25:54

going to make other investors like it, and so the stock will

25:57

go up. Right. So that's how it works. It

25:59

has nothing to do with fundamental financial

26:01

analysis. It just has to do with something in

26:03

the shape of the stock makes people

26:05

like it, and stocks that people like go up

26:07

because they buy them right, And if

26:09

you look at the paper, like the way they present

26:12

these investment cases to like the portfolio managers

26:14

in the MRI is like they show them

26:17

the company description, and they show them

26:19

a price chart, and they show them like a

26:22

fundamentals page that's like a bunch of like ratios

26:24

and earnings information, and they show them

26:26

news they show them like summaries of It's like some news items

26:28

about the stocks. What actually

26:30

lights up their brains is just

26:34

the description and the price chart. So

26:36

like the fundamental page worthless

26:39

for the price chart. They see the price chart

26:41

and their brain lights up, like that's a good sign.

26:43

People like lines, yeah, but.

26:45

Only some lines, right. The lines that

26:47

make their brains light up

26:49

are the lines that will go up in the future, right,

26:52

Like the good looking lines are the good

26:54

stocks to buy. It's like technical analysis.

26:56

If you look at a chart and the chart makes you happy,

26:58

then it'll probably make other people happy, and so you should buy

27:00

that stock.

27:01

That is the best reason why technical

27:03

analysis might be real that I've heard.

27:05

The only reason it's just good lines.

27:08

Technical analysis is like organized

27:11

mass psychology. Right, It's like, oh, this line

27:13

shows that people like the stock, right, It's

27:15

not that weird to think that you could grasp that at

27:17

a pre conscious level, right where you would see

27:19

the lines and you don't know

27:21

what the lines mean, but like somewhere deep in your

27:23

animal brain you're like, oh that's a good stock.

27:25

You're here. Lizard brain activates, and that's

27:28

basically the foundation of technical analysis.

27:31

I mean, again, who

27:33

knows if any of this is actually real? I

27:35

feel like the only way to really find out is if

27:38

this was applied at scale and we gave this.

27:39

Yeah, all you know academic finance

27:42

paper is it's like okay, sure that's an interesting result,

27:44

but like what hedge funds are implementing it?

27:46

Right? Yeah?

27:46

And like I don't know, like

27:48

you could see Steve copy like hmm right,

27:51

Like that's like a thing.

27:52

That someone should send him this paper.

27:55

It's in money Stock.

27:56

Well I hope he read it.

27:57

I mean you probably have to pay a premium. But

27:59

like if there's some marginal investor who

28:02

like can't quite get a job as

28:04

a portfolio manager at a top multi

28:06

strategy fund, but if you were in an MRI machine,

28:09

he could oh yeah, yeah.

28:10

I mean it's all in there. It was bright

28:13

exactly. He just he can't translate

28:15

it. I will say it's been a while since I

28:17

read a paper like this, and I always

28:19

love methodology. And there were a

28:21

few charming details in here about the actual

28:24

participants, Like you said, they're

28:26

from leading Dutch investment companies, thirty

28:28

four participants. Only

28:30

one was a one. I noticed

28:32

that immediately the mean age was

28:34

forty seven.

28:35

You want to do that experiment better, right, But like

28:37

I would be interested in doing this experiment because

28:39

these are all like Dutch professional money maners.

28:42

Yeah, I'm interested in doing it on day

28:44

traders right. Oh yeah, because the thing that is happening

28:46

here is not like deep fundamental analysis,

28:48

right. Something that's happening here is like, oh that line looks good.

28:51

Maybe like a random amateur would be just as good.

28:53

Yeah, that's true. We should try it on all different

28:55

members of the population. I also

28:57

thought this was cute. So

29:00

participants received no compensation,

29:02

but whoever was the most accurate in predicting

29:04

stock outcomes would be awarded a prize of five

29:07

hundred euros. There were two winners,

29:09

so that they had to split the prize. They each got

29:11

two hundred and fifty euros for

29:14

all told, this lasted eighty five

29:16

minutes, so that's a pretty good return

29:18

on your time.

29:19

I mean I don't know. I mean probably

29:22

you're like a one in like thirty

29:24

chance that two hundred and fifty euros for an hour

29:26

and a half at an MRI, Like.

29:28

I don't know, I would do it. Tell it's a.

29:29

Professional money manager's like their day job is

29:32

picking slacks that will go up and they get paid more than that.

29:33

For Yeah, but it sounds like they're not too good

29:35

at that.

29:46

And that was The Money Stuff Podcast.

29:47

I'm Matt Levian and I'm Katie Greifeld.

29:50

You can find my work by subscribing to the money

29:52

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29:59

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30:24

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