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Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. We're starting with light

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Speaker 1: fair you know, yeah, but lightly fried, well probably deeply fried,

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Speaker 1: lightly breaded, deeply fried.

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Speaker 2: All right, Hello and welcome to the Money Stuff Podcast.

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Speaker 2: You're a weekly podcast where we talk about stuff related

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Speaker 2: to money. I'm Matt Levine and I write the Money

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Speaker 2: Stuff column for Bloomberg Opinion.

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Speaker 1: And I'm Katie Greifeld, a reporter for Bloomberg News and

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Speaker 1: an anchor for Bloomberg Television.

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Speaker 2: What do we got today, Katy?

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Speaker 1: We're going to talk about shrimp and lobsters, all the

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Speaker 1: fair out of the sea. We're going to talk about

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Speaker 1: fat fingers and why City Group keeps having problems with those,

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Speaker 1: and then we're going to talk about brains.

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Speaker 2: It's going to be a good one.

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Speaker 1: Yeah, Red Lobster, did they really shrimp themselves to death?

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Speaker 2: No? Maybe a little bit.

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Speaker 1: But isn't it fun to pretend?

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Speaker 2: It's more fun to pretend than almost any other thing

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Speaker 2: I've pretended about. So Red Lobster filed for bankruptcy over

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Speaker 2: the weekend. And why did they go bankrupt? Well, you know,

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Speaker 2: they like had a series of business mistakes. That's some

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Speaker 2: onerous leases that they got in connection with the leverage

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Speaker 2: via changing consumer tastes. Inflation is rough and fast, casual.

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Speaker 1: Dining, those are all boring.

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Speaker 2: Yeah. No, the real answer is that they are. Their

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Speaker 2: equity owner is a company that is also one of

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Speaker 2: their big suppliers. It's a seafood company called Tai Union,

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Speaker 2: and Red Lobster under its former CEO, embarked on an unlimited,

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Speaker 2: endless shrimp promotion where for twenty dollars you can get

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Speaker 2: all the shrimp you wanted, all the time. And in

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Speaker 2: the bankruptcy papers, the new CEO kind of like works

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Speaker 2: on behalf of the creditors sort of insinuates that there

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Speaker 2: is a scheme to pump shrimp through Red Lobster to

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Speaker 2: make money for Tie Union at the expense of Red Lobster.

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Speaker 1: To just line the pockets of Thai Union. Well, it

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Speaker 1: did turn into they were the only shrimp supplier for

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Speaker 1: Red Lobster, didn't They start out with three and then

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Speaker 1: it whittled down to just Tai Union.

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Speaker 2: It's not exactly clear, but yeah, the suggestion is that

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Speaker 2: like there were some of the regularities in the procurement

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Speaker 2: process where they got rid of some of their other

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Speaker 2: shrimp suppliers and ended up sending more and more money

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Speaker 2: directly to Taie Union for more and more shrimp, and.

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Speaker 1: Thai Union of course disputes that passionately. They say that

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Speaker 1: all of those issues concerning the company and its relationship

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Speaker 1: to Red Lobster, you know, are basically false. But it

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Speaker 1: is interesting that this started out as a limited promotion

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Speaker 1: and then I think it was May twenty twenty three,

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Speaker 1: actually it turned into a permanent promotion, this endless shrimp fiasco.

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Speaker 2: It just makes sense. Yeah, But if you own a

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Speaker 2: company that is going under, and by like February this year,

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Speaker 2: Thai Union is saying the value of their equity at zero.

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Speaker 2: You own a company that's going under, yeah, you get

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Speaker 2: as much money as you can out of it, and

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Speaker 2: you also sell that company shrimp. The goal is to

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Speaker 2: sell them as many shrimp as possible.

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Speaker 1: They did end up losing eleven million dollars on it,

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Speaker 1: which is not huge in.

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Speaker 2: The ground that gets you billion dollars.

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Speaker 1: There's a lot of money to lose on shrimp, though,

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Speaker 1: I mean, I certainly haven't come anywhere close to that.

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Speaker 2: It is a lot of money to lose on shrimp.

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Speaker 2: Red Lives the bankruptcy file has a lot of like

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Speaker 2: good stats, including they're like twenty percent of the total

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Speaker 2: market for lobster tales like in the world. So yeah,

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Speaker 2: I mean, like, if anyone is going to lose eleven

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Speaker 2: million dollars on shrimp, it's definitely red lobster. But that's

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Speaker 2: probably not enough to drive them into bankruptcy, but it

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Speaker 2: is enough to be very very funny in bankruptcy.

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Speaker 1: It made for some amazing headlines, some amazing thought pieces,

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Speaker 1: some amazing money stuff columns. Let's talk about the leases though,

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Speaker 1: because a lot of people are pointing to the leases at.

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Speaker 2: Sure sure, Like the boring answer is that, like there's

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Speaker 2: a sort of classic private equity stripping story where it's like,

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Speaker 2: you know, they were owned it on point by General Mills.

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Speaker 2: They're on a Darden restaurant company. They were sold in

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Speaker 2: a leveraged buyout where basically the buyer to finance the deal,

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Speaker 2: sold a lot of their locations and leased them back,

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Speaker 2: and so they entered into a lot of like expensive leases,

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Speaker 2: and when hard times came, they didn't own their own

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Speaker 2: real estate, and so they had a little bit less

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Speaker 2: financial flexibility because that all these lease payments, and so

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Speaker 2: that was also hard on them. I'm always like skeptical

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Speaker 2: of these explanations because it's like that would work that

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Speaker 2: fine had business kept improving, right, and it worked out

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Speaker 2: poorly because business was declining. Like it adds leverage, right,

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Speaker 2: it lowers your margin for error. But like it seems

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Speaker 2: like the explanation here is not like Red Lobster was

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Speaker 2: doing great and then like financial shenanigans destroyed it. The

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Speaker 2: story is like Red Lobster is not doing that great

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Speaker 2: and the financial maneuvers gave it less of a margin

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Speaker 2: for error. But like, ultimately, this is a change in

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Speaker 2: consumer tastes story, and it turns out the consumer taster

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Speaker 2: or not for that many shrimp. Although the funny thing

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Speaker 2: is like, in conjunction with like these stories about Red

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Speaker 2: Lobster's collapse, you also read a lot of anecdotes about

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Speaker 2: people eating one hundred and eighty shrimp at a sitting

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Speaker 2: or whatever. I mean, why wouldn't you for reasons, but

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Speaker 2: like you know that someone.

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Speaker 1: Will I mean shrimp. I don't like shrimp, but shrimp

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Speaker 1: are kind of the perfect food if you're just trying

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Speaker 1: to get a lot of protein.

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Speaker 2: It is true that if I were any one hundred

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Speaker 2: and eighty of a food. It might be a shrimp. Yeah,

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Speaker 2: come on, well, the Red Lobster advertised all the shrimps

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Speaker 2: you could eat. That attracted some number of people who

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Speaker 2: are like, oh, I can eat a lot of shrimp,

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Speaker 2: Red Lobster, And it.

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Speaker 1: Probably did get people in the door. But there was a.

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Speaker 2: Really get negative margins.

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Speaker 1: Yeah, exactly. And there was a fun Bloomberg News piece

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Speaker 1: just talking about the state of the restaurant business right now.

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Speaker 1: I don't think it's any surprise that inflation is still

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Speaker 1: pretty high, pretty sticky. The absolute level of prices is

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Speaker 1: much higher than it was. And you have all of

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Speaker 1: these restaurant changs who you know, their businesses are in

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Speaker 1: much better shape than Red Lobster, but they're just going

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Speaker 1: for these promotions. You think about Applebee has one dollar

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Speaker 1: Margaritos right now, which I actually would like to try.

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Speaker 1: I can't imagine that as much alcohol. And then Chili's

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Speaker 1: has recently introduced the Big Smasher. It's part of its

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Speaker 1: broader campaign to have three menu items. You put them

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Speaker 1: all together, they cost eleven dollars. Stay with me here anyway,

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Speaker 1: And then.

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Speaker 2: Maybe you say you put them all together like on

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Speaker 2: a plate or like when you say, like, do they

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Speaker 2: actually get a blender?

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Speaker 1: You know, definitely not that one. Like you order three

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Speaker 1: menu items and all together it costs like eleven dollars.

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Speaker 1: But then they quote this man, he's the CEO of

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Speaker 1: Aaron Allen and Associates. It's a restaurant consulting firm, and

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Speaker 1: he says that these chains sabotage themselves by trading down

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Speaker 1: just to get cheap hits. It's like taking grandma's jewelry

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Speaker 1: to the pawn shop just to get a few quick bucks.

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Speaker 1: And I guess that's kind of what Red Lobster did.

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Speaker 1: Are the jewels the lobsters in this scenario, I'm torturing? Yeah,

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Speaker 1: And so I don't know if I feel like it

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Speaker 1: was a whole host of factors. It's probably crushing the

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Speaker 1: entire restaurant industry right now is sort of the fast

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Speaker 1: casual space. Those financial maneuvers that you described.

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Speaker 2: Yeah, I just I love the idea that there is

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Speaker 2: a shrimp conspiracy. Yeah, because, like right, there are a

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Speaker 2: lot of factors that are affecting all of the competitors,

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Speaker 2: but like here there's the specific factor that they are

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Speaker 2: owned by their supplier, and so it's like we can

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Speaker 2: just pump shrimp through the red lobster.

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Speaker 1: System and no one will ever know.

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Speaker 2: No one will ever know except that the CEO.

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Speaker 1: Know who put in place the CEO? Was it Thai Union.

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Speaker 2: Well so the old CEO Tai Union, yeah, pointed and

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Speaker 2: the allegations now that he was in the pocket of

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Speaker 2: Ti Union. But no, the new CEO is from ALPHAREZM.

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Speaker 2: Marshall is like basically part of the restructuring team. So

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Speaker 2: he like kind of works for the creditors. And you know,

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Speaker 2: I wrote in m my column in the first day

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Speaker 2: of the bankruptcy filing, he writes this statement sort of

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Speaker 2: alleging that Tai Union was doing all this stuff, the

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Speaker 2: stuff shrimp through the system. And I wrote that, you know,

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Speaker 2: in his position, quote you cast a wide net for

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Speaker 2: possible ways to claw back money for creditors, which I

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Speaker 2: didn't really intend as a lobster pun. But like several

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Speaker 2: readers put.

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Speaker 1: That claw terrible. That's just terrible.

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Speaker 2: I mean, like he works for the creditors and like

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Speaker 2: they need to find as much money as they can,

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Speaker 2: and like to the extent millions of dollars went out

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Speaker 2: the door to pay Tie Union for shrip. You have

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Speaker 2: some case to say it shouldn't have done that, and

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Speaker 2: like that was a violation of their duty to the

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Speaker 2: company and get the money back.

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Speaker 1: I mean the fact that Tai Union appointed the CEO

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Speaker 1: and then whittled it down. It's the owner of the company,

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Speaker 1: I know, but still and then whittled it down to

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Speaker 1: be the sole supplier. If they were one of several,

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Speaker 1: maybe that would be a better look.

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Speaker 2: It's definitely something that looks like a conflict of interest.

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Speaker 1: It certainly does. What I'm unclear on is whether the

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Speaker 1: promotion it's not still going on. Could you and I

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Speaker 1: go to the Red Lobster in Times Square.

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Speaker 2: I was thinking about trying to record this podcast from

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Speaker 2: a Red Lobster, but I thought they shut down a

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Speaker 2: lot of the restaurants.

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Speaker 1: But the one in Times Square is still open. I

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Speaker 1: know that because Bloomberg has actually sent some reporters there and.

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Speaker 2: They get the shrimp.

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Speaker 1: They interviewed a lot of guests, they get the shrimp

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Speaker 1: that was not in the article Red Luster.

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Speaker 2: At this late date at Red Lobster and not ordering

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Speaker 2: the end of this ship.

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Speaker 1: I know, I know it wasn't in the article. I

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Speaker 1: feel like we need a little bit more on the

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Speaker 1: ground reporting. But if the shrimp promotion is still going

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Speaker 1: on ourselves, I don't eat shrimp, but I would choke down.

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Speaker 1: Maybe maybe not one hundred and eighty, but.

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Speaker 2: No, I will say, yeah, this is less funny than

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Speaker 2: the shrimp. But people email me about this. There is

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Speaker 2: this analogy to the AI investing boom, oh.

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Speaker 1: My god, No, Okay, go ahead.

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Speaker 2: There's this article from a poor Agarwalla a couple of

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Speaker 2: months ago about large language model AI startups and a

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Speaker 2: lot of their investors are like big cloud and chip

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Speaker 2: companies like Nvidia and Google and Amazon and Microsoft. And

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Speaker 2: he wrote this article being like, there is a weird

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Speaker 2: tension there because these companies are investing billions of dollars

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Speaker 2: in these AI startups, but a lot of that money

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Speaker 2: is going right back to the cloud providers in the

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Speaker 2: form of like paying for chips or paying for cloud compute,

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Speaker 2: and so like when in Video or Microsoft invests in

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Speaker 2: an AI startup, like it can say I'm putting in

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Speaker 2: a billion dollars at a ten billion dollar valuation, but

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Speaker 2: it's getting most of that money back right, It's like

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Speaker 2: going to the supplier's bottom line. So he argues that's

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Speaker 2: bad for venture capitalists in that space, because like the

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Speaker 2: valuation of these companies is being driven up by people

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Speaker 2: whose interest in the company is not just being an

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Speaker 2: equity investor, but is also being a supplier. And He's

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Speaker 2: like there's conflicts of interests where like if you are

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Speaker 2: Microsoft or in Video and you're a big shareholder in

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Speaker 2: these AI startups, you know, you're like on the board,

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Speaker 2: you have control over this company. You might make decisions

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Speaker 2: that are in your best interest as a supplier and

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Speaker 2: not necessarily in the best interest of the company or

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Speaker 2: in the other investors, because your money is not really

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Speaker 2: an equity investment, it's really about finding customers for your

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Speaker 2: cloud compute power.

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Speaker 1: Similarly with the shrimp, Yeah, I was going to say

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Speaker 1: I was skeptical, and then actually that is a really

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Speaker 1: neat parallel in.

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Speaker 2: Most of these cases, like these companies are not the

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Speaker 2: controlling shawl, they're appointing the sea. Yeah. Right, Usually there's

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Speaker 2: like some independent you know, like the startup has its

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Speaker 2: own business model. But right, I mean, like there are

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Speaker 2: conflicts of interest there where if you're like a big supplier,

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Speaker 2: a big exclusive supplier, a lot of the equity investment

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Speaker 2: is going directly to you, Like your interest is maybe

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Speaker 2: less as an equity investor more as a supplier.

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Speaker 1: I will say, like imagining, you know, an executive with

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Speaker 1: semiconductor chips falling out of his pockets and his briefcase.

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Speaker 1: It's definitely not as much fun as shrimp. But that

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Speaker 1: does make a lot of sense.

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Speaker 2: Right, Like you hope that like this is like in

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Speaker 2: the earlier stage where it's like not as fun. You're

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Speaker 2: not like, oh, they're like stuffing computing power into these

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Speaker 2: AI startups, right because like they're all trying to make money.

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Speaker 2: They're all, you know, optimistic and hopeful. It's not like

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Speaker 2: Red Lovester with like sort of the end of the run.

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Speaker 2: But uh, I don't know, food.

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Speaker 1: For thoughts, some shrimp for thought. From shrimp to fat fingers.

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Speaker 1: This is a sad story because that's a great story.

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Speaker 1: I mean, it is about human error and who among

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Speaker 1: us hasn't accidentally tried to sell four hundred and forty

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Speaker 1: four billion dollars worth of equities.

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Speaker 2: It is the most natural mistake it can possibly make.

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Speaker 1: Why don't you walk us through the worst fourteen minutes

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Speaker 1: of this trader's career.

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Speaker 2: Okay, So there's a trader at city in London, apparently working.

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Speaker 1: From home on a Monday holiday.

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Speaker 2: On a Monday Bank holiday.

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Speaker 1: Talk about a bad Monday.

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Speaker 2: So this trader works on the Delta one desk and

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Speaker 2: city to hedge an index features order has to sell

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Speaker 2: a big basket of stocks on thirteen different European stock exchanges.

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Speaker 2: Pulls up the order management system. There's like a box

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Speaker 2: where you enter the quantity you want to sell. Actually

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Speaker 2: there's two boxes. You can enter the quantity in terms

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Speaker 2: of like units basically like shares of the index, or

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Speaker 2: you can enter the quantity in dollars. This person wants

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Speaker 2: to sell fifty eight million dollars, enters fifty eight million

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Speaker 2: in the shares field, and therefore sells fifty eight million units,

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Speaker 2: which is you know, four hundred and forty four billion

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Speaker 2: dollars worth of stocks. So fills out the form and

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Speaker 2: then this is the amazing part to me, the order

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Speaker 2: management system displays you know, fifty eight million units, but

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Speaker 2: then also displays a dollar amount. But instead of displaying

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Speaker 2: four hundred and forty four billion, which is the actual

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Speaker 2: dollar amount, it displays negative fifty eight million. Because in

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Speaker 2: that field, it like pulls in from an external pricing

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Speaker 2: source and the pricing source is like turned off that

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Speaker 2: morning because I don't know if because the market's not opener,

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Speaker 2: because the bank holiday or whatever. The pricing source is

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Speaker 2: turned off. And so City's system says, the pricing sources

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Speaker 2: isn't available, so we're going to default to negative one

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Speaker 2: dollar as a price for each share, right, So the

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Speaker 2: trader types in fifty eight million shares, and the thing

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Speaker 2: pulls in a price of negative fifty eight million. So

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Speaker 2: the trader looks at that and says, yes, right, I

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Speaker 2: wanted fifty eight million dollars. It's showing me fifty eight

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Speaker 2: million dollars. Now there's a minussignment. You know, everything is

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Speaker 2: like no one knows what the sign is supposed to

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Speaker 2: be on any of these systems, And so the trader's like, yes, okay,

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Speaker 2: fifty eight million dollars just like I thought. Clicks okay.

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Speaker 2: Then the next thing the system does pops up seven

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Speaker 2: hundred and eleven warning error messages right saying, are you

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Speaker 2: sure you want to sell you know, a billion dollars

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Speaker 2: worth of stock in Sweden? Are you sure you want

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Speaker 2: to sell a billion dollars worth of that? Suck? And

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Speaker 2: the trader, first of all, only sees eighteen of these

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Speaker 2: messages because you have to scroll down to see the

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Speaker 2: other six hundred nine and why would you and telling yeah,

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Speaker 2: it's a big order whatever, I'm clicking yes, so trader

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00:14:51,960 --> 00:14:55,840
Speaker 2: clicks yes. Then it shows like a final confirmation, like

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Speaker 2: do you really want to sell? And at this point

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Speaker 2: it has crossed off half of the orders. It's like

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Speaker 2: half of these orders. Even cities somewhat chanky system knows

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Speaker 2: that it should not sell, you know, more than two

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Speaker 2: billion dollars worth of any stock, so like all the

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Speaker 2: stocks that s to some more than two billion dollars worth,

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Speaker 2: it cuts out, but still it says, okay, fine, do

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Speaker 2: you really want to sell one hundred and ninety six

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Speaker 2: billion dollars worth of stock? And at this point the

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Speaker 2: trader says sure and clicks yes, and off City goes

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Speaker 2: to sell one hundred ninety six billion dollars worth of stock,

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Speaker 2: which causes a flash crash, and like a bunch of

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Speaker 2: different European stock markets.

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Speaker 1: Yeah, I mean, it's just amazing. Hearing you describe it

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Speaker 1: makes my blood run cold. I do like to think

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Speaker 1: that somewhere along that line I would have stopped myself,

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Speaker 1: but one never knows.

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Speaker 2: I got a lot of emails from people being like

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Speaker 2: this person should have stopped themselves. I tell you I

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Speaker 2: know at this point from my own computer use. I

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Speaker 2: know enough about myself to know I would not.

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Speaker 1: Have lived there messages.

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Speaker 2: You know, because you're an experienced trader, right, you've done

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Speaker 2: this before? You fill the form. You see the form

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Speaker 2: show you fifty eight million dollars. You're like, yes, click yes,

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Speaker 2: and then you get like seven more click buttons that

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Speaker 2: to you are just a waste. They're like, okay, I've

330
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Speaker 2: already checked it. It's already yes, yes, yes, yes, yes, yes,

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Speaker 2: yes yes, and you just click. You don't look, I think,

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Speaker 2: is what happened here. I'm not responsible for trading tens

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Speaker 2: or hundreds of billions of dollars of stocks across Europe,

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Speaker 2: but like I click yes all the.

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Speaker 1: Time, man, I mean, I think about like my own

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Speaker 1: stupid mistakes. I don't have anything of this magnitude, but

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Speaker 1: like sending an instant Bloomberg message to the wrong person,

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Speaker 1: making typos, et cetera, it happens. What is amazing. You

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Speaker 1: described it as city somewhat janky system. It wouldn't have

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Speaker 1: happened in New York necessarily, but it did happen in London.

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Speaker 2: They had hard limits on how much you could sell

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Speaker 2: at one time in New York.

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Speaker 1: Yeah, And the natural question is why, But I don't

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Speaker 1: know if you have that answer.

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Speaker 2: I don't know the answer. But you know, everything is

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00:16:55,840 --> 00:16:58,840
Speaker 2: like silent and hierarchical, and some people put it in

347
00:16:58,840 --> 00:17:01,000
Speaker 2: place and some people don't. You know, I don't have

348
00:17:01,040 --> 00:17:03,120
Speaker 2: a good answer to why. But one loss put it

349
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Speaker 2: in one didn't.

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Speaker 1: This in and of itself is amazing, but it's even

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Speaker 1: more amazing when you think about City's history, which you

352
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Speaker 1: write about, and you think about what happened with Revlon,

353
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Speaker 1: for example, because it hits a lot of the same notes.

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Speaker 2: Yeah, I mean the Revlon thing. City was like the

355
00:17:18,640 --> 00:17:21,399
Speaker 2: administrativevision on this big loan to Revlon, and like there

356
00:17:21,440 --> 00:17:23,880
Speaker 2: was a sort of fight over whether Reverend was in default.

357
00:17:23,960 --> 00:17:26,480
Speaker 2: And as part of that fight, Revlin meant to pay

358
00:17:26,520 --> 00:17:28,479
Speaker 2: like seven million dollars an interest to a couple of

359
00:17:28,760 --> 00:17:31,919
Speaker 2: hedge funds, and instead City paid nine hundred million dollars

360
00:17:32,200 --> 00:17:34,919
Speaker 2: paid back the loan in full. And then City was like, oops,

361
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Speaker 2: can we have the money back? Like half of the

362
00:17:36,920 --> 00:17:39,040
Speaker 2: lenders were like sure, here's the money back, and half

363
00:17:39,080 --> 00:17:40,639
Speaker 2: of them were like in this fight with Revlin and

364
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Speaker 2: we're like, no, we're keeping the money and going to court.

365
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Speaker 2: They kind of went in court and eventually lost, but

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Speaker 2: in the court decision in loving detailed describes how city

367
00:17:48,800 --> 00:17:52,720
Speaker 2: messed this up, and it's just incredible, like they had

368
00:17:52,720 --> 00:17:55,720
Speaker 2: this system where like the only way for them to

369
00:17:55,760 --> 00:17:59,120
Speaker 2: make this interest payment was for some reason, to pay

370
00:17:59,119 --> 00:18:01,800
Speaker 2: off the entire principle of the loan, and the City's like, well,

371
00:18:01,800 --> 00:18:03,720
Speaker 2: I was going want to do that. So no, it's okay,

372
00:18:03,840 --> 00:18:05,520
Speaker 2: you can pay off the entire principle to like a

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Speaker 2: fake memo account. So the system thinks it's paying off

374
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Speaker 2: the entire principle, but it's not really, no money is

375
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Speaker 2: going out the door. And so he's like, sure, that

376
00:18:11,720 --> 00:18:13,560
Speaker 2: sounds great, let's do that, right, which is first of all,

377
00:18:13,640 --> 00:18:15,840
Speaker 2: like a terrifying thing to agree to. But then secondly,

378
00:18:15,960 --> 00:18:18,879
Speaker 2: in order to have it only be paid to a

379
00:18:18,880 --> 00:18:20,919
Speaker 2: fake account and not actually go out the door, you

380
00:18:20,920 --> 00:18:23,560
Speaker 2: have to click like three boxes, and one is like

381
00:18:23,560 --> 00:18:25,399
Speaker 2: pay the principle to the fake account. You're like, okay,

382
00:18:25,400 --> 00:18:27,280
Speaker 2: click the principal box, and then the other two are

383
00:18:27,320 --> 00:18:30,840
Speaker 2: like just bizarre buzzwords, and so like the three people,

384
00:18:31,240 --> 00:18:33,520
Speaker 2: three people had to do this, the three people signing

385
00:18:33,560 --> 00:18:36,200
Speaker 2: off on the city thing, like yep, the right boxes checked,

386
00:18:36,760 --> 00:18:38,560
Speaker 2: but then they didn't check the other two boxes that

387
00:18:38,600 --> 00:18:41,080
Speaker 2: were more complicated, and so they sent out nine hundred

388
00:18:41,080 --> 00:18:44,359
Speaker 2: million dollars by accident. And then when they saw that

389
00:18:44,400 --> 00:18:46,440
Speaker 2: it had gone out, they called tech support and we like,

390
00:18:46,480 --> 00:18:49,160
Speaker 2: the system isn't working. And ultimately turned out the system

391
00:18:49,240 --> 00:18:51,639
Speaker 2: was working, but like you know, the system was terribly.

392
00:18:51,359 --> 00:18:54,360
Speaker 1: Dissigned, right, and that happened in twenty twenty one. Yeah,

393
00:18:54,640 --> 00:18:57,720
Speaker 1: this trade happened in twenty twenty two, so maybe maybe

394
00:18:58,200 --> 00:19:00,560
Speaker 1: maybe it's all fix, it's all fun. I thought it

395
00:19:00,600 --> 00:19:02,840
Speaker 1: was an interesting size and scope. So ultimately they were

396
00:19:02,880 --> 00:19:06,520
Speaker 1: fined seventy eight million dollars. And apparently when regulators were

397
00:19:06,560 --> 00:19:10,080
Speaker 1: thinking about how big the fine should actually be, they

398
00:19:10,080 --> 00:19:13,480
Speaker 1: were thinking about how much it cost them in part.

399
00:19:13,760 --> 00:19:19,680
Speaker 1: So basically, the Banks Delta one division had generated roughly

400
00:19:19,680 --> 00:19:21,920
Speaker 1: six hundred and twelve million dollars in the nine years

401
00:19:22,000 --> 00:19:24,960
Speaker 1: leading up to this trade, in average about sixty eight

402
00:19:24,960 --> 00:19:27,640
Speaker 1: million dollars a year, So you layer on the fines

403
00:19:27,680 --> 00:19:30,919
Speaker 1: and the trading losses from that day, and that trade

404
00:19:31,000 --> 00:19:34,359
Speaker 1: costs those desks nearly two years of revenue, which is

405
00:19:34,560 --> 00:19:39,439
Speaker 1: pretty painful. That's two years, two years and fourteen minutes.

406
00:19:39,640 --> 00:19:40,520
Speaker 2: That's so sad.

407
00:19:40,600 --> 00:19:41,440
Speaker 1: It is so sad.

408
00:19:41,560 --> 00:19:44,399
Speaker 2: It's so sad everything you work for and you put

409
00:19:44,600 --> 00:19:46,800
Speaker 2: like one number in the wrong box and it's like

410
00:19:46,840 --> 00:19:49,119
Speaker 2: it's all all goes up in flames.

411
00:19:49,119 --> 00:19:51,520
Speaker 1: I always think about that when you read stories like

412
00:19:51,560 --> 00:19:55,240
Speaker 1: this about someone who just makes a human accident, some

413
00:19:56,080 --> 00:20:00,480
Speaker 1: unintended pilot hour, no malicious intentions and just a all over.

414
00:20:00,520 --> 00:20:02,480
Speaker 1: And I always think about like the months in the

415
00:20:02,680 --> 00:20:06,120
Speaker 1: weeks leading up them going about their lives, not knowing

416
00:20:06,160 --> 00:20:09,800
Speaker 1: that this cataclysmic event was going to happen. And here

417
00:20:09,840 --> 00:20:12,400
Speaker 1: we are, and Bloomberg News has reported that this trader

418
00:20:12,920 --> 00:20:15,000
Speaker 1: doesn't work at City anymore. I'm not that.

419
00:20:15,040 --> 00:20:17,600
Speaker 2: Surprised, but I will say this fine and even this

420
00:20:17,720 --> 00:20:19,720
Speaker 2: loss are not about the trader putting the number in

421
00:20:19,720 --> 00:20:22,360
Speaker 2: the wrong box. Like this is a system design issue, right,

422
00:20:22,400 --> 00:20:24,240
Speaker 2: Like yeah, the problem here is like, yes, putting the

423
00:20:24,280 --> 00:20:27,000
Speaker 2: number in the wrong box, but like having a system that,

424
00:20:27,119 --> 00:20:30,800
Speaker 2: like the computer also got confused between the number of

425
00:20:30,920 --> 00:20:32,719
Speaker 2: shares on the dollar in a matter right the computer

426
00:20:32,880 --> 00:20:35,240
Speaker 2: was like, oh, yeah, they're all worth one dollar a share, right,

427
00:20:35,280 --> 00:20:38,919
Speaker 2: like wrongly, but then also like the after trade checks

428
00:20:38,920 --> 00:20:42,280
Speaker 2: were just not effective. Right in New York, they would

429
00:20:42,280 --> 00:20:46,520
Speaker 2: have blocked this trade automatically, even in London, Like if

430
00:20:46,520 --> 00:20:49,080
Speaker 2: you're popping up seven hundred and eleven error messages, it's

431
00:20:49,119 --> 00:20:51,160
Speaker 2: like maybe make a bigger run.

432
00:20:51,440 --> 00:20:54,440
Speaker 1: But all that being said, do you think that any

433
00:20:54,480 --> 00:20:57,760
Speaker 1: of the other banks who could potentially hire this trader

434
00:20:57,880 --> 00:20:59,280
Speaker 1: are thinking that way? Or are they?

435
00:21:00,000 --> 00:21:02,520
Speaker 2: I would hire this trader really, Yeah, that's not like

436
00:21:02,600 --> 00:21:03,680
Speaker 2: on the list of like.

437
00:21:03,960 --> 00:21:06,000
Speaker 1: Well, they're probably never going to do it again.

438
00:21:05,800 --> 00:21:08,560
Speaker 2: Right, One, they're never going to do it again, and

439
00:21:08,640 --> 00:21:12,080
Speaker 2: two like, look, obviously people care a lot about attention

440
00:21:12,119 --> 00:21:14,439
Speaker 2: to detail, but like there are other skills and like

441
00:21:14,520 --> 00:21:16,560
Speaker 2: probably everyone would mess this up once. It's just that

442
00:21:16,680 --> 00:21:18,840
Speaker 2: if they have better software to catch it, they won't

443
00:21:19,000 --> 00:21:21,320
Speaker 2: actually cost the bank one hundred million dollars.

444
00:21:21,560 --> 00:21:23,040
Speaker 1: Yeah, make better software.

445
00:21:23,960 --> 00:21:26,040
Speaker 2: I think that's the answer that this port trader. Now

446
00:21:26,080 --> 00:21:26,840
Speaker 2: I feel really bad.

447
00:21:27,400 --> 00:21:29,000
Speaker 1: Yeah, this was kind of a bummer.

448
00:21:29,280 --> 00:21:31,320
Speaker 2: I do think. The other thing that's amazing in this

449
00:21:31,400 --> 00:21:35,240
Speaker 2: case is that the risk managers who are there directly

450
00:21:35,280 --> 00:21:37,359
Speaker 2: responsible for oversight of this, like their job was to

451
00:21:37,400 --> 00:21:40,320
Speaker 2: catch this. They went on vacation eight minutes before the

452
00:21:40,359 --> 00:21:41,120
Speaker 2: trade happened.

453
00:21:41,400 --> 00:21:44,199
Speaker 1: That is wild. I mean just the timeline of this

454
00:21:44,240 --> 00:21:48,520
Speaker 1: whole thing, but the eight minutes that seems like almost unbelievable.

455
00:21:48,720 --> 00:21:50,720
Speaker 2: I'm exaggerating when I say they went on vacation. Like

456
00:21:50,880 --> 00:21:52,480
Speaker 2: what happened is that, like the handoff from like the

457
00:21:52,560 --> 00:21:55,040
Speaker 2: right team to the wrong team happened eight minutes before

458
00:21:55,080 --> 00:21:56,920
Speaker 2: this trade, which I think must mean that the right

459
00:21:56,920 --> 00:21:58,399
Speaker 2: team in like Asia was handing it off to the

460
00:21:58,440 --> 00:22:01,160
Speaker 2: wrong team in London because the damon London was out

461
00:22:01,160 --> 00:22:02,040
Speaker 2: for the bank holiday.

462
00:22:02,240 --> 00:22:05,280
Speaker 1: Yeah, but in any case, right, they probably sent that email,

463
00:22:05,960 --> 00:22:09,440
Speaker 1: slammed the Laptop show, and then they literally were on vacation.

464
00:22:09,280 --> 00:22:11,719
Speaker 2: So yeah, I wonder if they got controlled.

465
00:22:26,680 --> 00:22:29,480
Speaker 1: This is the section of this podcast that I have

466
00:22:29,560 --> 00:22:30,960
Speaker 1: the lowest expectations for.

467
00:22:31,440 --> 00:22:33,800
Speaker 2: Your nucleus accumbents is not lighting up with this one.

468
00:22:33,920 --> 00:22:35,560
Speaker 1: I was hoping they section of your.

469
00:22:35,440 --> 00:22:37,440
Speaker 2: Brain that anticipates rewards.

470
00:22:37,880 --> 00:22:41,440
Speaker 1: I did google that and the definition was something along

471
00:22:41,480 --> 00:22:43,879
Speaker 1: those lines, and then it said it was an incomplete definition.

472
00:22:43,960 --> 00:22:45,640
Speaker 1: To just think of it as like the reward center

473
00:22:45,680 --> 00:22:47,000
Speaker 1: of your brain. But I don't know what.

474
00:22:47,359 --> 00:22:49,840
Speaker 2: No, I have a much more nuanced understanding of the

475
00:22:49,920 --> 00:22:52,080
Speaker 2: nucleus accumbents, I will not share with you.

476
00:22:52,560 --> 00:22:57,480
Speaker 1: Very good, very well. So we're in this situation again

477
00:22:57,640 --> 00:23:01,800
Speaker 1: where we're recording this podcast before your or newsletter on

478
00:23:01,840 --> 00:23:05,000
Speaker 1: this topic has come out. I read the actual paper

479
00:23:05,520 --> 00:23:08,199
Speaker 1: and I got to say a lot of it was

480
00:23:08,880 --> 00:23:11,560
Speaker 1: beyond me. But why don't you tell us about this paper?

481
00:23:11,800 --> 00:23:15,360
Speaker 2: This is an incredible favor by basically business school professors

482
00:23:16,160 --> 00:23:19,400
Speaker 2: as opposed to medical school professors. But it's called brain

483
00:23:19,440 --> 00:23:22,840
Speaker 2: activity of professional investors signals future stock performance. So what

484
00:23:22,880 --> 00:23:24,600
Speaker 2: they did is they took a bunch of like Dutch

485
00:23:24,680 --> 00:23:27,520
Speaker 2: professional investors, like people who work at mutual funds, and

486
00:23:28,320 --> 00:23:32,960
Speaker 2: they popped them in an MRI machine and they showed

487
00:23:32,960 --> 00:23:36,760
Speaker 2: them slides of investment presentations about forty five stocks, and

488
00:23:36,760 --> 00:23:39,639
Speaker 2: they're all like historical. So they would show these like

489
00:23:40,400 --> 00:23:43,520
Speaker 2: sort of masked investment cases for a bunch of stocks

490
00:23:44,000 --> 00:23:46,880
Speaker 2: at like different times of the past ten years, and

491
00:23:47,240 --> 00:23:48,840
Speaker 2: they were like, what do you think would you buy

492
00:23:48,880 --> 00:23:50,800
Speaker 2: the stock right? Do you think the stock will up

493
00:23:50,840 --> 00:23:54,080
Speaker 2: perform its sector? The investors in the MRI machine either

494
00:23:54,119 --> 00:23:56,679
Speaker 2: said yes or no. And they also ran the MRIO

495
00:23:56,680 --> 00:23:58,760
Speaker 2: while they're doing this, and it turns out that the

496
00:23:58,840 --> 00:24:03,960
Speaker 2: investor's answers were worthless, Like they did not accurately predict

497
00:24:04,200 --> 00:24:06,960
Speaker 2: no better than chance's ability to predict whether the stocks

498
00:24:06,960 --> 00:24:11,320
Speaker 2: would go up or down. But their brains, their brains

499
00:24:12,000 --> 00:24:14,440
Speaker 2: did accurately predict whether the stocks would cover down, which

500
00:24:14,520 --> 00:24:16,840
Speaker 2: is to say that if you looked at like ridging,

501
00:24:16,880 --> 00:24:20,440
Speaker 2: their brain called the nucleus siccumbents, which is like sort

502
00:24:20,480 --> 00:24:24,040
Speaker 2: of the thing that anticipates rewards, right, it lit up

503
00:24:24,320 --> 00:24:27,879
Speaker 2: when they saw presentations about stocks that were going to

504
00:24:27,920 --> 00:24:32,120
Speaker 2: go up, so subconsciously they knew which stocks would go up,

505
00:24:32,359 --> 00:24:34,480
Speaker 2: even though consciously they did not know what stocks would

506
00:24:34,480 --> 00:24:34,800
Speaker 2: go up.

507
00:24:35,160 --> 00:24:39,439
Speaker 1: I do love that. It's amazing, but the how do

508
00:24:39,480 --> 00:24:40,119
Speaker 1: you apply it?

509
00:24:40,160 --> 00:24:42,800
Speaker 2: How do you possibly you put your portfolio manager in

510
00:24:42,840 --> 00:24:43,760
Speaker 2: an MRI all day?

511
00:24:43,880 --> 00:24:47,200
Speaker 1: That's it? Yes, I mean I think that that would

512
00:24:47,240 --> 00:24:51,280
Speaker 1: be a terrible a terrible existence.

513
00:24:51,320 --> 00:24:53,560
Speaker 2: But like if you made a lot of money.

514
00:24:53,600 --> 00:24:54,840
Speaker 1: Yeah, true, I mean.

515
00:24:54,880 --> 00:24:57,280
Speaker 2: If this really worked, by the way, I'm not you know,

516
00:24:58,000 --> 00:25:00,479
Speaker 2: I'm not so sure how well this really worked, but

517
00:25:00,600 --> 00:25:03,479
Speaker 2: it worked in some experimental design. But it's fascinating too

518
00:25:03,520 --> 00:25:06,280
Speaker 2: because like how could this work? Right? Like, how could

519
00:25:06,320 --> 00:25:09,440
Speaker 2: it be that you can subconsciously.

520
00:25:08,920 --> 00:25:12,160
Speaker 1: Just know which stocks secatifitely know, but you can't translate

521
00:25:12,200 --> 00:25:14,080
Speaker 1: that into an actionable decision.

522
00:25:14,400 --> 00:25:16,600
Speaker 2: Yeah. But so here's what I think the explanation is.

523
00:25:16,640 --> 00:25:19,160
Speaker 2: To the extent this is real in the introduction to paper,

524
00:25:19,200 --> 00:25:21,119
Speaker 2: they're like the other experiments like this, and one is

525
00:25:21,119 --> 00:25:23,639
Speaker 2: like a sort of famous. One is like you play

526
00:25:24,480 --> 00:25:29,000
Speaker 2: songs to people in an MRI, and the songs that

527
00:25:29,080 --> 00:25:32,560
Speaker 2: light up their brains in certain ways go on to

528
00:25:32,560 --> 00:25:35,679
Speaker 2: become hit songs. It sounds like people's brains instinctively know

529
00:25:35,720 --> 00:25:36,960
Speaker 2: it's going to be hit songs. Well that makes a

530
00:25:36,960 --> 00:25:39,440
Speaker 2: lot of sense, right, right, Like something about that song

531
00:25:39,880 --> 00:25:42,680
Speaker 2: instinctively like makes everyone like it, right, Like it lights

532
00:25:42,720 --> 00:25:44,320
Speaker 2: up a section of your brain, Like, of course that's

533
00:25:44,320 --> 00:25:46,160
Speaker 2: going to go on to be a hit. Right. Maybe

534
00:25:46,200 --> 00:25:48,960
Speaker 2: it's the same with stocks, right, It's like a meme

535
00:25:49,000 --> 00:25:52,800
Speaker 2: stock phenomenon, where like something about a stock makes these

536
00:25:52,840 --> 00:25:56,239
Speaker 2: investors like it, that's going to make other investors like it,

537
00:25:56,280 --> 00:25:58,440
Speaker 2: and so the stock will go up. Right. So that's

538
00:25:58,440 --> 00:26:00,919
Speaker 2: how it works. It has nothing to do with fundamental

539
00:26:01,000 --> 00:26:03,520
Speaker 2: financial analysis. It just has to do with something in

540
00:26:03,560 --> 00:26:06,240
Speaker 2: the shape of the stock makes people like it, and

541
00:26:06,280 --> 00:26:08,880
Speaker 2: stocks that people like go up because they buy them right,

542
00:26:09,560 --> 00:26:11,760
Speaker 2: And if you look at the paper, like the way

543
00:26:11,760 --> 00:26:14,600
Speaker 2: they present these investment cases to like the portfolio managers

544
00:26:14,600 --> 00:26:18,040
Speaker 2: in the MRI is like they show them the company description,

545
00:26:18,400 --> 00:26:21,400
Speaker 2: and they show them a price chart, and they show

546
00:26:21,440 --> 00:26:23,760
Speaker 2: them like a fundamentals page that's like a bunch of

547
00:26:23,800 --> 00:26:26,840
Speaker 2: like ratios and earnings information, and they show them news

548
00:26:26,880 --> 00:26:28,400
Speaker 2: they show them like summaries of It's like some news

549
00:26:28,400 --> 00:26:31,960
Speaker 2: items about the stocks. What actually lights up their brains

550
00:26:32,680 --> 00:26:36,359
Speaker 2: is just the description and the price chart. So like

551
00:26:36,960 --> 00:26:40,640
Speaker 2: the fundamental page worthless for the price chart. They see

552
00:26:40,640 --> 00:26:42,720
Speaker 2: the price chart and their brain lights up, like that's

553
00:26:42,760 --> 00:26:43,320
Speaker 2: a good sign.

554
00:26:43,520 --> 00:26:45,400
Speaker 1: People like lines, yeah, but.

555
00:26:45,400 --> 00:26:49,040
Speaker 2: Only some lines, right. The lines that make their brains

556
00:26:49,080 --> 00:26:51,359
Speaker 2: light up are the lines that will go up in

557
00:26:51,400 --> 00:26:53,919
Speaker 2: the future, right, Like the good looking lines are the

558
00:26:53,920 --> 00:26:56,639
Speaker 2: good stocks to buy. It's like technical analysis. If you

559
00:26:56,680 --> 00:26:58,439
Speaker 2: look at a chart and the chart makes you happy,

560
00:26:58,640 --> 00:27:00,240
Speaker 2: then it'll probably make other people happy, and so you

561
00:27:00,240 --> 00:27:01,000
Speaker 2: should buy that stock.

562
00:27:01,160 --> 00:27:04,359
Speaker 1: That is the best reason why technical analysis might be

563
00:27:04,440 --> 00:27:07,880
Speaker 1: real that I've heard. The only reason it's just good lines.

564
00:27:08,160 --> 00:27:12,879
Speaker 2: Technical analysis is like organized mass psychology. Right, It's like, oh,

565
00:27:12,920 --> 00:27:15,480
Speaker 2: this line shows that people like the stock, right, It's

566
00:27:15,480 --> 00:27:17,359
Speaker 2: not that weird to think that you could grasp that

567
00:27:17,400 --> 00:27:19,600
Speaker 2: at a pre conscious level, right where you would see

568
00:27:19,640 --> 00:27:22,680
Speaker 2: the lines and you don't know what the lines mean,

569
00:27:22,720 --> 00:27:24,720
Speaker 2: but like somewhere deep in your animal brain you're like,

570
00:27:24,720 --> 00:27:25,480
Speaker 2: oh that's a good stock.

571
00:27:25,640 --> 00:27:29,560
Speaker 1: You're here. Lizard brain activates, and that's basically the foundation

572
00:27:29,640 --> 00:27:34,240
Speaker 1: of technical analysis. I mean, again, who knows if any

573
00:27:34,280 --> 00:27:36,520
Speaker 1: of this is actually real? I feel like the only

574
00:27:36,520 --> 00:27:38,720
Speaker 1: way to really find out is if this was applied

575
00:27:38,760 --> 00:27:39,840
Speaker 1: at scale and we gave this.

576
00:27:39,960 --> 00:27:43,200
Speaker 2: Yeah, all you know academic finance paper is it's like okay,

577
00:27:43,280 --> 00:27:45,240
Speaker 2: sure that's an interesting result, but like what hedge funds

578
00:27:45,240 --> 00:27:45,960
Speaker 2: are implementing it?

579
00:27:46,080 --> 00:27:46,639
Speaker 1: Right? Yeah?

580
00:27:46,680 --> 00:27:49,840
Speaker 2: And like I don't know, like you could see Steve

581
00:27:49,960 --> 00:27:52,520
Speaker 2: copy like hmm right, Like that's like a thing.

582
00:27:52,400 --> 00:27:54,200
Speaker 1: That someone should send him this paper.

583
00:27:55,119 --> 00:27:55,960
Speaker 2: It's in money Stock.

584
00:27:56,040 --> 00:27:57,159
Speaker 1: Well I hope he read it.

585
00:27:57,600 --> 00:27:59,800
Speaker 2: I mean you probably have to pay a premium. But

586
00:27:59,840 --> 00:28:03,320
Speaker 2: like if there's some marginal investor who like can't quite

587
00:28:03,400 --> 00:28:06,280
Speaker 2: get a job as a portfolio manager at a top

588
00:28:06,320 --> 00:28:09,040
Speaker 2: multi strategy fund, but if you were in an MRI machine,

589
00:28:09,080 --> 00:28:10,960
Speaker 2: he could oh yeah, yeah.

590
00:28:10,760 --> 00:28:13,680
Speaker 1: I mean it's all in there. It was bright exactly.

591
00:28:13,680 --> 00:28:16,680
Speaker 1: He just he can't translate it. I will say it's

592
00:28:16,680 --> 00:28:18,840
Speaker 1: been a while since I read a paper like this,

593
00:28:18,960 --> 00:28:21,840
Speaker 1: and I always love methodology. And there were a few

594
00:28:21,920 --> 00:28:25,879
Speaker 1: charming details in here about the actual participants, Like you said,

595
00:28:25,920 --> 00:28:30,399
Speaker 1: they're from leading Dutch investment companies, thirty four participants. Only

596
00:28:30,480 --> 00:28:34,200
Speaker 1: one was a one. I noticed that immediately the mean

597
00:28:34,280 --> 00:28:35,440
Speaker 1: age was forty seven.

598
00:28:35,520 --> 00:28:37,719
Speaker 2: You want to do that experiment better, right, But like

599
00:28:37,960 --> 00:28:40,080
Speaker 2: I would be interested in doing this experiment because these

600
00:28:40,080 --> 00:28:42,800
Speaker 2: are all like Dutch professional money maners. Yeah, I'm interested

601
00:28:42,840 --> 00:28:45,600
Speaker 2: in doing it on day traders right. Oh yeah, because

602
00:28:45,600 --> 00:28:47,800
Speaker 2: the thing that is happening here is not like deep

603
00:28:47,840 --> 00:28:50,240
Speaker 2: fundamental analysis, right. Something that's happening here is like, oh

604
00:28:50,320 --> 00:28:52,520
Speaker 2: that line looks good. Maybe like a random amateur would

605
00:28:52,520 --> 00:28:53,160
Speaker 2: be just as good.

606
00:28:53,360 --> 00:28:55,640
Speaker 1: Yeah, that's true. We should try it on all different

607
00:28:55,680 --> 00:28:59,520
Speaker 1: members of the population. I also thought this was cute.

608
00:28:59,600 --> 00:29:03,880
Speaker 1: So participants received no compensation, but whoever was the most

609
00:29:03,920 --> 00:29:06,680
Speaker 1: accurate in predicting stock outcomes would be awarded a prize

610
00:29:06,680 --> 00:29:09,520
Speaker 1: of five hundred euros. There were two winners, so that

611
00:29:09,560 --> 00:29:11,560
Speaker 1: they had to split the prize. They each got two

612
00:29:11,680 --> 00:29:15,800
Speaker 1: hundred and fifty euros for all told, this lasted eighty

613
00:29:15,800 --> 00:29:19,040
Speaker 1: five minutes, so that's a pretty good return on your time.

614
00:29:19,840 --> 00:29:22,840
Speaker 2: I mean I don't know. I mean probably you're like

615
00:29:23,120 --> 00:29:25,600
Speaker 2: a one in like thirty chance that two hundred and

616
00:29:25,600 --> 00:29:28,160
Speaker 2: fifty euros for an hour and a half at an MRI, Like.

617
00:29:28,080 --> 00:29:30,000
Speaker 1: I don't know, I would do it. Tell it's a.

618
00:29:29,920 --> 00:29:32,520
Speaker 2: Professional money manager's like their day job is picking slacks

619
00:29:32,520 --> 00:29:34,000
Speaker 2: that will go up and they get paid more than that.

620
00:29:33,960 --> 00:29:35,960
Speaker 1: For Yeah, but it sounds like they're not too good

621
00:29:35,960 --> 00:29:36,200
Speaker 1: at that.

622
00:29:46,080 --> 00:29:47,520
Speaker 2: And that was The Money Stuff Podcast.

623
00:29:47,680 --> 00:29:49,680
Speaker 1: I'm Matt Levian and I'm Katie Greifeld.

624
00:29:50,240 --> 00:29:52,280
Speaker 2: You can find my work by subscribing to the money

625
00:29:52,320 --> 00:29:54,200
Speaker 2: Stuff newsletter on Bloomberg.

626
00:29:53,760 --> 00:29:56,120
Speaker 1: Dot com, and you can find me on Bloomberg TV

627
00:29:56,240 --> 00:29:58,600
Speaker 1: every day between ten to eleven am Eastern.

628
00:29:59,200 --> 00:30:01,360
Speaker 2: We'd love to hear You can send an email to

629
00:30:01,560 --> 00:30:04,600
Speaker 2: Moneypot at Bloomberg dot net, ask us a question and

630
00:30:04,600 --> 00:30:05,640
Speaker 2: we might answer it on air.

631
00:30:06,560 --> 00:30:08,760
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632
00:30:08,800 --> 00:30:10,840
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633
00:30:10,840 --> 00:30:11,720
Speaker 1: people find the show.

634
00:30:12,760 --> 00:30:15,560
Speaker 2: The Money Stuff Podcast is produced by Anna Maserakus and

635
00:30:15,600 --> 00:30:16,719
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636
00:30:16,760 --> 00:30:18,400
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637
00:30:18,280 --> 00:30:21,840
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638
00:30:21,800 --> 00:30:23,960
Speaker 1: And Stage Bauman is Bloomberg's head of podcasts.

639
00:30:24,480 --> 00:30:26,800
Speaker 2: Thanks for listening to the Money Stuff Podcast. We'll be

640
00:30:26,880 --> 00:30:28,400
Speaker 2: back next week with more stuff