Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. We're starting with light
Speaker 1: fair you know, yeah, but lightly fried, well probably deeply fried,
Speaker 1: lightly breaded, deeply fried.
Speaker 2: All right, Hello and welcome to the Money Stuff Podcast.
Speaker 2: You're a weekly podcast where we talk about stuff related
Speaker 2: to money. I'm Matt Levine and I write the Money
Speaker 2: Stuff column for Bloomberg Opinion.
Speaker 1: And I'm Katie Greifeld, a reporter for Bloomberg News and
Speaker 1: an anchor for Bloomberg Television.
Speaker 2: What do we got today, Katy?
Speaker 1: We're going to talk about shrimp and lobsters, all the
Speaker 1: fair out of the sea. We're going to talk about
Speaker 1: fat fingers and why City Group keeps having problems with those,
Speaker 1: and then we're going to talk about brains.
Speaker 2: It's going to be a good one.
Speaker 1: Yeah, Red Lobster, did they really shrimp themselves to death?
Speaker 2: No? Maybe a little bit.
Speaker 1: But isn't it fun to pretend?
Speaker 2: It's more fun to pretend than almost any other thing
Speaker 2: I've pretended about. So Red Lobster filed for bankruptcy over
Speaker 2: the weekend. And why did they go bankrupt? Well, you know,
Speaker 2: they like had a series of business mistakes. That's some
Speaker 2: onerous leases that they got in connection with the leverage
Speaker 2: via changing consumer tastes. Inflation is rough and fast, casual.
Speaker 1: Dining, those are all boring.
Speaker 2: Yeah. No, the real answer is that they are. Their
Speaker 2: equity owner is a company that is also one of
Speaker 2: their big suppliers. It's a seafood company called Tai Union,
Speaker 2: and Red Lobster under its former CEO, embarked on an unlimited,
Speaker 2: endless shrimp promotion where for twenty dollars you can get
Speaker 2: all the shrimp you wanted, all the time. And in
Speaker 2: the bankruptcy papers, the new CEO kind of like works
Speaker 2: on behalf of the creditors sort of insinuates that there
Speaker 2: is a scheme to pump shrimp through Red Lobster to
Speaker 2: make money for Tie Union at the expense of Red Lobster.
Speaker 1: To just line the pockets of Thai Union. Well, it
Speaker 1: did turn into they were the only shrimp supplier for
Speaker 1: Red Lobster, didn't They start out with three and then
Speaker 1: it whittled down to just Tai Union.
Speaker 2: It's not exactly clear, but yeah, the suggestion is that
Speaker 2: like there were some of the regularities in the procurement
Speaker 2: process where they got rid of some of their other
Speaker 2: shrimp suppliers and ended up sending more and more money
Speaker 2: directly to Taie Union for more and more shrimp, and.
Speaker 1: Thai Union of course disputes that passionately. They say that
Speaker 1: all of those issues concerning the company and its relationship
Speaker 1: to Red Lobster, you know, are basically false. But it
Speaker 1: is interesting that this started out as a limited promotion
Speaker 1: and then I think it was May twenty twenty three,
Speaker 1: actually it turned into a permanent promotion, this endless shrimp fiasco.
Speaker 2: It just makes sense. Yeah, But if you own a
Speaker 2: company that is going under, and by like February this year,
Speaker 2: Thai Union is saying the value of their equity at zero.
Speaker 2: You own a company that's going under, yeah, you get
Speaker 2: as much money as you can out of it, and
Speaker 2: you also sell that company shrimp. The goal is to
Speaker 2: sell them as many shrimp as possible.
Speaker 1: They did end up losing eleven million dollars on it,
Speaker 1: which is not huge in.
Speaker 2: The ground that gets you billion dollars.
Speaker 1: There's a lot of money to lose on shrimp, though,
Speaker 1: I mean, I certainly haven't come anywhere close to that.
Speaker 2: It is a lot of money to lose on shrimp.
Speaker 2: Red Lives the bankruptcy file has a lot of like
Speaker 2: good stats, including they're like twenty percent of the total
Speaker 2: market for lobster tales like in the world. So yeah,
Speaker 2: I mean, like, if anyone is going to lose eleven
Speaker 2: million dollars on shrimp, it's definitely red lobster. But that's
Speaker 2: probably not enough to drive them into bankruptcy, but it
Speaker 2: is enough to be very very funny in bankruptcy.
Speaker 1: It made for some amazing headlines, some amazing thought pieces,
Speaker 1: some amazing money stuff columns. Let's talk about the leases though,
Speaker 1: because a lot of people are pointing to the leases at.
Speaker 2: Sure sure, Like the boring answer is that, like there's
Speaker 2: a sort of classic private equity stripping story where it's like,
Speaker 2: you know, they were owned it on point by General Mills.
Speaker 2: They're on a Darden restaurant company. They were sold in
Speaker 2: a leveraged buyout where basically the buyer to finance the deal,
Speaker 2: sold a lot of their locations and leased them back,
Speaker 2: and so they entered into a lot of like expensive leases,
Speaker 2: and when hard times came, they didn't own their own
Speaker 2: real estate, and so they had a little bit less
Speaker 2: financial flexibility because that all these lease payments, and so
Speaker 2: that was also hard on them. I'm always like skeptical
Speaker 2: of these explanations because it's like that would work that
Speaker 2: fine had business kept improving, right, and it worked out
Speaker 2: poorly because business was declining. Like it adds leverage, right,
Speaker 2: it lowers your margin for error. But like it seems
Speaker 2: like the explanation here is not like Red Lobster was
Speaker 2: doing great and then like financial shenanigans destroyed it. The
Speaker 2: story is like Red Lobster is not doing that great
Speaker 2: and the financial maneuvers gave it less of a margin
Speaker 2: for error. But like, ultimately, this is a change in
Speaker 2: consumer tastes story, and it turns out the consumer taster
Speaker 2: or not for that many shrimp. Although the funny thing
Speaker 2: is like, in conjunction with like these stories about Red
Speaker 2: Lobster's collapse, you also read a lot of anecdotes about
Speaker 2: people eating one hundred and eighty shrimp at a sitting
Speaker 2: or whatever. I mean, why wouldn't you for reasons, but
Speaker 2: like you know that someone.
Speaker 1: Will I mean shrimp. I don't like shrimp, but shrimp
Speaker 1: are kind of the perfect food if you're just trying
Speaker 1: to get a lot of protein.
Speaker 2: It is true that if I were any one hundred
Speaker 2: and eighty of a food. It might be a shrimp. Yeah,
Speaker 2: come on, well, the Red Lobster advertised all the shrimps
Speaker 2: you could eat. That attracted some number of people who
Speaker 2: are like, oh, I can eat a lot of shrimp,
Speaker 2: Red Lobster, And it.
Speaker 1: Probably did get people in the door. But there was a.
Speaker 2: Really get negative margins.
Speaker 1: Yeah, exactly. And there was a fun Bloomberg News piece
Speaker 1: just talking about the state of the restaurant business right now.
Speaker 1: I don't think it's any surprise that inflation is still
Speaker 1: pretty high, pretty sticky. The absolute level of prices is
Speaker 1: much higher than it was. And you have all of
Speaker 1: these restaurant changs who you know, their businesses are in
Speaker 1: much better shape than Red Lobster, but they're just going
Speaker 1: for these promotions. You think about Applebee has one dollar
Speaker 1: Margaritos right now, which I actually would like to try.
Speaker 1: I can't imagine that as much alcohol. And then Chili's
Speaker 1: has recently introduced the Big Smasher. It's part of its
Speaker 1: broader campaign to have three menu items. You put them
Speaker 1: all together, they cost eleven dollars. Stay with me here anyway,
Speaker 1: And then.
Speaker 2: Maybe you say you put them all together like on
Speaker 2: a plate or like when you say, like, do they
Speaker 2: actually get a blender?
Speaker 1: You know, definitely not that one. Like you order three
Speaker 1: menu items and all together it costs like eleven dollars.
Speaker 1: But then they quote this man, he's the CEO of
Speaker 1: Aaron Allen and Associates. It's a restaurant consulting firm, and
Speaker 1: he says that these chains sabotage themselves by trading down
Speaker 1: just to get cheap hits. It's like taking grandma's jewelry
Speaker 1: to the pawn shop just to get a few quick bucks.
Speaker 1: And I guess that's kind of what Red Lobster did.
Speaker 1: Are the jewels the lobsters in this scenario, I'm torturing? Yeah,
Speaker 1: And so I don't know if I feel like it
Speaker 1: was a whole host of factors. It's probably crushing the
Speaker 1: entire restaurant industry right now is sort of the fast
Speaker 1: casual space. Those financial maneuvers that you described.
Speaker 2: Yeah, I just I love the idea that there is
Speaker 2: a shrimp conspiracy. Yeah, because, like right, there are a
Speaker 2: lot of factors that are affecting all of the competitors,
Speaker 2: but like here there's the specific factor that they are
Speaker 2: owned by their supplier, and so it's like we can
Speaker 2: just pump shrimp through the red lobster.
Speaker 1: System and no one will ever know.
Speaker 2: No one will ever know except that the CEO.
Speaker 1: Know who put in place the CEO? Was it Thai Union.
Speaker 2: Well so the old CEO Tai Union, yeah, pointed and
Speaker 2: the allegations now that he was in the pocket of
Speaker 2: Ti Union. But no, the new CEO is from ALPHAREZM.
Speaker 2: Marshall is like basically part of the restructuring team. So
Speaker 2: he like kind of works for the creditors. And you know,
Speaker 2: I wrote in m my column in the first day
Speaker 2: of the bankruptcy filing, he writes this statement sort of
Speaker 2: alleging that Tai Union was doing all this stuff, the
Speaker 2: stuff shrimp through the system. And I wrote that, you know,
Speaker 2: in his position, quote you cast a wide net for
Speaker 2: possible ways to claw back money for creditors, which I
Speaker 2: didn't really intend as a lobster pun. But like several
Speaker 2: readers put.
Speaker 1: That claw terrible. That's just terrible.
Speaker 2: I mean, like he works for the creditors and like
Speaker 2: they need to find as much money as they can,
Speaker 2: and like to the extent millions of dollars went out
Speaker 2: the door to pay Tie Union for shrip. You have
Speaker 2: some case to say it shouldn't have done that, and
Speaker 2: like that was a violation of their duty to the
Speaker 2: company and get the money back.
Speaker 1: I mean the fact that Tai Union appointed the CEO
Speaker 1: and then whittled it down. It's the owner of the company,
Speaker 1: I know, but still and then whittled it down to
Speaker 1: be the sole supplier. If they were one of several,
Speaker 1: maybe that would be a better look.
Speaker 2: It's definitely something that looks like a conflict of interest.
Speaker 1: It certainly does. What I'm unclear on is whether the
Speaker 1: promotion it's not still going on. Could you and I
Speaker 1: go to the Red Lobster in Times Square.
Speaker 2: I was thinking about trying to record this podcast from
Speaker 2: a Red Lobster, but I thought they shut down a
Speaker 2: lot of the restaurants.
Speaker 1: But the one in Times Square is still open. I
Speaker 1: know that because Bloomberg has actually sent some reporters there and.
Speaker 2: They get the shrimp.
Speaker 1: They interviewed a lot of guests, they get the shrimp
Speaker 1: that was not in the article Red Luster.
Speaker 2: At this late date at Red Lobster and not ordering
Speaker 2: the end of this ship.
Speaker 1: I know, I know it wasn't in the article. I
Speaker 1: feel like we need a little bit more on the
Speaker 1: ground reporting. But if the shrimp promotion is still going
Speaker 1: on ourselves, I don't eat shrimp, but I would choke down.
Speaker 1: Maybe maybe not one hundred and eighty, but.
Speaker 2: No, I will say, yeah, this is less funny than
Speaker 2: the shrimp. But people email me about this. There is
Speaker 2: this analogy to the AI investing boom, oh.
Speaker 1: My god, No, Okay, go ahead.
Speaker 2: There's this article from a poor Agarwalla a couple of
Speaker 2: months ago about large language model AI startups and a
Speaker 2: lot of their investors are like big cloud and chip
Speaker 2: companies like Nvidia and Google and Amazon and Microsoft. And
Speaker 2: he wrote this article being like, there is a weird
Speaker 2: tension there because these companies are investing billions of dollars
Speaker 2: in these AI startups, but a lot of that money
Speaker 2: is going right back to the cloud providers in the
Speaker 2: form of like paying for chips or paying for cloud compute,
Speaker 2: and so like when in Video or Microsoft invests in
Speaker 2: an AI startup, like it can say I'm putting in
Speaker 2: a billion dollars at a ten billion dollar valuation, but
Speaker 2: it's getting most of that money back right, It's like
Speaker 2: going to the supplier's bottom line. So he argues that's
Speaker 2: bad for venture capitalists in that space, because like the
Speaker 2: valuation of these companies is being driven up by people
Speaker 2: whose interest in the company is not just being an
Speaker 2: equity investor, but is also being a supplier. And He's
Speaker 2: like there's conflicts of interests where like if you are
Speaker 2: Microsoft or in Video and you're a big shareholder in
Speaker 2: these AI startups, you know, you're like on the board,
Speaker 2: you have control over this company. You might make decisions
Speaker 2: that are in your best interest as a supplier and
Speaker 2: not necessarily in the best interest of the company or
Speaker 2: in the other investors, because your money is not really
Speaker 2: an equity investment, it's really about finding customers for your
Speaker 2: cloud compute power.
Speaker 1: Similarly with the shrimp, Yeah, I was going to say
Speaker 1: I was skeptical, and then actually that is a really
Speaker 1: neat parallel in.
Speaker 2: Most of these cases, like these companies are not the
Speaker 2: controlling shawl, they're appointing the sea. Yeah. Right, Usually there's
Speaker 2: like some independent you know, like the startup has its
Speaker 2: own business model. But right, I mean, like there are
Speaker 2: conflicts of interest there where if you're like a big supplier,
Speaker 2: a big exclusive supplier, a lot of the equity investment
Speaker 2: is going directly to you, Like your interest is maybe
Speaker 2: less as an equity investor more as a supplier.
Speaker 1: I will say, like imagining, you know, an executive with
Speaker 1: semiconductor chips falling out of his pockets and his briefcase.
Speaker 1: It's definitely not as much fun as shrimp. But that
Speaker 1: does make a lot of sense.
Speaker 2: Right, Like you hope that like this is like in
Speaker 2: the earlier stage where it's like not as fun. You're
Speaker 2: not like, oh, they're like stuffing computing power into these
Speaker 2: AI startups, right because like they're all trying to make money.
Speaker 2: They're all, you know, optimistic and hopeful. It's not like
Speaker 2: Red Lovester with like sort of the end of the run.
Speaker 2: But uh, I don't know, food.
Speaker 1: For thoughts, some shrimp for thought. From shrimp to fat fingers.
Speaker 1: This is a sad story because that's a great story.
Speaker 1: I mean, it is about human error and who among
Speaker 1: us hasn't accidentally tried to sell four hundred and forty
Speaker 1: four billion dollars worth of equities.
Speaker 2: It is the most natural mistake it can possibly make.
Speaker 1: Why don't you walk us through the worst fourteen minutes
Speaker 1: of this trader's career.
Speaker 2: Okay, So there's a trader at city in London, apparently working.
Speaker 1: From home on a Monday holiday.
Speaker 2: On a Monday Bank holiday.
Speaker 1: Talk about a bad Monday.
Speaker 2: So this trader works on the Delta one desk and
Speaker 2: city to hedge an index features order has to sell
Speaker 2: a big basket of stocks on thirteen different European stock exchanges.
Speaker 2: Pulls up the order management system. There's like a box
Speaker 2: where you enter the quantity you want to sell. Actually
Speaker 2: there's two boxes. You can enter the quantity in terms
Speaker 2: of like units basically like shares of the index, or
Speaker 2: you can enter the quantity in dollars. This person wants
Speaker 2: to sell fifty eight million dollars, enters fifty eight million
Speaker 2: in the shares field, and therefore sells fifty eight million units,
Speaker 2: which is you know, four hundred and forty four billion
Speaker 2: dollars worth of stocks. So fills out the form and
Speaker 2: then this is the amazing part to me, the order
Speaker 2: management system displays you know, fifty eight million units, but
Speaker 2: then also displays a dollar amount. But instead of displaying
Speaker 2: four hundred and forty four billion, which is the actual
Speaker 2: dollar amount, it displays negative fifty eight million. Because in
Speaker 2: that field, it like pulls in from an external pricing
Speaker 2: source and the pricing source is like turned off that
Speaker 2: morning because I don't know if because the market's not opener,
Speaker 2: because the bank holiday or whatever. The pricing source is
Speaker 2: turned off. And so City's system says, the pricing sources
Speaker 2: isn't available, so we're going to default to negative one
Speaker 2: dollar as a price for each share, right, So the
Speaker 2: trader types in fifty eight million shares, and the thing
Speaker 2: pulls in a price of negative fifty eight million. So
Speaker 2: the trader looks at that and says, yes, right, I
Speaker 2: wanted fifty eight million dollars. It's showing me fifty eight
Speaker 2: million dollars. Now there's a minussignment. You know, everything is
Speaker 2: like no one knows what the sign is supposed to
Speaker 2: be on any of these systems, And so the trader's like, yes, okay,
Speaker 2: fifty eight million dollars just like I thought. Clicks okay.
Speaker 2: Then the next thing the system does pops up seven
Speaker 2: hundred and eleven warning error messages right saying, are you
Speaker 2: sure you want to sell you know, a billion dollars
Speaker 2: worth of stock in Sweden? Are you sure you want
Speaker 2: to sell a billion dollars worth of that? Suck? And
Speaker 2: the trader, first of all, only sees eighteen of these
Speaker 2: messages because you have to scroll down to see the
Speaker 2: other six hundred nine and why would you and telling yeah,
Speaker 2: it's a big order whatever, I'm clicking yes, so trader
Speaker 2: clicks yes. Then it shows like a final confirmation, like
Speaker 2: do you really want to sell? And at this point
Speaker 2: it has crossed off half of the orders. It's like
Speaker 2: half of these orders. Even cities somewhat chanky system knows
Speaker 2: that it should not sell, you know, more than two
Speaker 2: billion dollars worth of any stock, so like all the
Speaker 2: stocks that s to some more than two billion dollars worth,
Speaker 2: it cuts out, but still it says, okay, fine, do
Speaker 2: you really want to sell one hundred and ninety six
Speaker 2: billion dollars worth of stock? And at this point the
Speaker 2: trader says sure and clicks yes, and off City goes
Speaker 2: to sell one hundred ninety six billion dollars worth of stock,
Speaker 2: which causes a flash crash, and like a bunch of
Speaker 2: different European stock markets.
Speaker 1: Yeah, I mean, it's just amazing. Hearing you describe it
Speaker 1: makes my blood run cold. I do like to think
Speaker 1: that somewhere along that line I would have stopped myself,
Speaker 1: but one never knows.
Speaker 2: I got a lot of emails from people being like
Speaker 2: this person should have stopped themselves. I tell you I
Speaker 2: know at this point from my own computer use. I
Speaker 2: know enough about myself to know I would not.
Speaker 1: Have lived there messages.
Speaker 2: You know, because you're an experienced trader, right, you've done
Speaker 2: this before? You fill the form. You see the form
Speaker 2: show you fifty eight million dollars. You're like, yes, click yes,
Speaker 2: and then you get like seven more click buttons that
Speaker 2: to you are just a waste. They're like, okay, I've
Speaker 2: already checked it. It's already yes, yes, yes, yes, yes, yes,
Speaker 2: yes yes, and you just click. You don't look, I think,
Speaker 2: is what happened here. I'm not responsible for trading tens
Speaker 2: or hundreds of billions of dollars of stocks across Europe,
Speaker 2: but like I click yes all the.
Speaker 1: Time, man, I mean, I think about like my own
Speaker 1: stupid mistakes. I don't have anything of this magnitude, but
Speaker 1: like sending an instant Bloomberg message to the wrong person,
Speaker 1: making typos, et cetera, it happens. What is amazing. You
Speaker 1: described it as city somewhat janky system. It wouldn't have
Speaker 1: happened in New York necessarily, but it did happen in London.
Speaker 2: They had hard limits on how much you could sell
Speaker 2: at one time in New York.
Speaker 1: Yeah, And the natural question is why, But I don't
Speaker 1: know if you have that answer.
Speaker 2: I don't know the answer. But you know, everything is
Speaker 2: like silent and hierarchical, and some people put it in
Speaker 2: place and some people don't. You know, I don't have
Speaker 2: a good answer to why. But one loss put it
Speaker 2: in one didn't.
Speaker 1: This in and of itself is amazing, but it's even
Speaker 1: more amazing when you think about City's history, which you
Speaker 1: write about, and you think about what happened with Revlon,
Speaker 1: for example, because it hits a lot of the same notes.
Speaker 2: Yeah, I mean the Revlon thing. City was like the
Speaker 2: administrativevision on this big loan to Revlon, and like there
Speaker 2: was a sort of fight over whether Reverend was in default.
Speaker 2: And as part of that fight, Revlin meant to pay
Speaker 2: like seven million dollars an interest to a couple of
Speaker 2: hedge funds, and instead City paid nine hundred million dollars
Speaker 2: paid back the loan in full. And then City was like, oops,
Speaker 2: can we have the money back? Like half of the
Speaker 2: lenders were like sure, here's the money back, and half
Speaker 2: of them were like in this fight with Revlin and
Speaker 2: we're like, no, we're keeping the money and going to court.
Speaker 2: They kind of went in court and eventually lost, but
Speaker 2: in the court decision in loving detailed describes how city
Speaker 2: messed this up, and it's just incredible, like they had
Speaker 2: this system where like the only way for them to
Speaker 2: make this interest payment was for some reason, to pay
Speaker 2: off the entire principle of the loan, and the City's like, well,
Speaker 2: I was going want to do that. So no, it's okay,
Speaker 2: you can pay off the entire principle to like a
Speaker 2: fake memo account. So the system thinks it's paying off
Speaker 2: the entire principle, but it's not really, no money is
Speaker 2: going out the door. And so he's like, sure, that
Speaker 2: sounds great, let's do that, right, which is first of all,
Speaker 2: like a terrifying thing to agree to. But then secondly,
Speaker 2: in order to have it only be paid to a
Speaker 2: fake account and not actually go out the door, you
Speaker 2: have to click like three boxes, and one is like
Speaker 2: pay the principle to the fake account. You're like, okay,
Speaker 2: click the principal box, and then the other two are
Speaker 2: like just bizarre buzzwords, and so like the three people,
Speaker 2: three people had to do this, the three people signing
Speaker 2: off on the city thing, like yep, the right boxes checked,
Speaker 2: but then they didn't check the other two boxes that
Speaker 2: were more complicated, and so they sent out nine hundred
Speaker 2: million dollars by accident. And then when they saw that
Speaker 2: it had gone out, they called tech support and we like,
Speaker 2: the system isn't working. And ultimately turned out the system
Speaker 2: was working, but like you know, the system was terribly.
Speaker 1: Dissigned, right, and that happened in twenty twenty one. Yeah,
Speaker 1: this trade happened in twenty twenty two, so maybe maybe
Speaker 1: maybe it's all fix, it's all fun. I thought it
Speaker 1: was an interesting size and scope. So ultimately they were
Speaker 1: fined seventy eight million dollars. And apparently when regulators were
Speaker 1: thinking about how big the fine should actually be, they
Speaker 1: were thinking about how much it cost them in part.
Speaker 1: So basically, the Banks Delta one division had generated roughly
Speaker 1: six hundred and twelve million dollars in the nine years
Speaker 1: leading up to this trade, in average about sixty eight
Speaker 1: million dollars a year, So you layer on the fines
Speaker 1: and the trading losses from that day, and that trade
Speaker 1: costs those desks nearly two years of revenue, which is
Speaker 1: pretty painful. That's two years, two years and fourteen minutes.
Speaker 2: That's so sad.
Speaker 1: It is so sad.
Speaker 2: It's so sad everything you work for and you put
Speaker 2: like one number in the wrong box and it's like
Speaker 2: it's all all goes up in flames.
Speaker 1: I always think about that when you read stories like
Speaker 1: this about someone who just makes a human accident, some
Speaker 1: unintended pilot hour, no malicious intentions and just a all over.
Speaker 1: And I always think about like the months in the
Speaker 1: weeks leading up them going about their lives, not knowing
Speaker 1: that this cataclysmic event was going to happen. And here
Speaker 1: we are, and Bloomberg News has reported that this trader
Speaker 1: doesn't work at City anymore. I'm not that.
Speaker 2: Surprised, but I will say this fine and even this
Speaker 2: loss are not about the trader putting the number in
Speaker 2: the wrong box. Like this is a system design issue, right,
Speaker 2: Like yeah, the problem here is like, yes, putting the
Speaker 2: number in the wrong box, but like having a system that,
Speaker 2: like the computer also got confused between the number of
Speaker 2: shares on the dollar in a matter right the computer
Speaker 2: was like, oh, yeah, they're all worth one dollar a share, right,
Speaker 2: like wrongly, but then also like the after trade checks
Speaker 2: were just not effective. Right in New York, they would
Speaker 2: have blocked this trade automatically, even in London, Like if
Speaker 2: you're popping up seven hundred and eleven error messages, it's
Speaker 2: like maybe make a bigger run.
Speaker 1: But all that being said, do you think that any
Speaker 1: of the other banks who could potentially hire this trader
Speaker 1: are thinking that way? Or are they?
Speaker 2: I would hire this trader really, Yeah, that's not like
Speaker 2: on the list of like.
Speaker 1: Well, they're probably never going to do it again.
Speaker 2: Right, One, they're never going to do it again, and
Speaker 2: two like, look, obviously people care a lot about attention
Speaker 2: to detail, but like there are other skills and like
Speaker 2: probably everyone would mess this up once. It's just that
Speaker 2: if they have better software to catch it, they won't
Speaker 2: actually cost the bank one hundred million dollars.
Speaker 1: Yeah, make better software.
Speaker 2: I think that's the answer that this port trader. Now
Speaker 2: I feel really bad.
Speaker 1: Yeah, this was kind of a bummer.
Speaker 2: I do think. The other thing that's amazing in this
Speaker 2: case is that the risk managers who are there directly
Speaker 2: responsible for oversight of this, like their job was to
Speaker 2: catch this. They went on vacation eight minutes before the
Speaker 2: trade happened.
Speaker 1: That is wild. I mean just the timeline of this
Speaker 1: whole thing, but the eight minutes that seems like almost unbelievable.
Speaker 2: I'm exaggerating when I say they went on vacation. Like
Speaker 2: what happened is that, like the handoff from like the
Speaker 2: right team to the wrong team happened eight minutes before
Speaker 2: this trade, which I think must mean that the right
Speaker 2: team in like Asia was handing it off to the
Speaker 2: wrong team in London because the damon London was out
Speaker 2: for the bank holiday.
Speaker 1: Yeah, but in any case, right, they probably sent that email,
Speaker 1: slammed the Laptop show, and then they literally were on vacation.
Speaker 2: So yeah, I wonder if they got controlled.
Speaker 1: This is the section of this podcast that I have
Speaker 1: the lowest expectations for.
Speaker 2: Your nucleus accumbents is not lighting up with this one.
Speaker 1: I was hoping they section of your.
Speaker 2: Brain that anticipates rewards.
Speaker 1: I did google that and the definition was something along
Speaker 1: those lines, and then it said it was an incomplete definition.
Speaker 1: To just think of it as like the reward center
Speaker 1: of your brain. But I don't know what.
Speaker 2: No, I have a much more nuanced understanding of the
Speaker 2: nucleus accumbents, I will not share with you.
Speaker 1: Very good, very well. So we're in this situation again
Speaker 1: where we're recording this podcast before your or newsletter on
Speaker 1: this topic has come out. I read the actual paper
Speaker 1: and I got to say a lot of it was
Speaker 1: beyond me. But why don't you tell us about this paper?
Speaker 2: This is an incredible favor by basically business school professors
Speaker 2: as opposed to medical school professors. But it's called brain
Speaker 2: activity of professional investors signals future stock performance. So what
Speaker 2: they did is they took a bunch of like Dutch
Speaker 2: professional investors, like people who work at mutual funds, and
Speaker 2: they popped them in an MRI machine and they showed
Speaker 2: them slides of investment presentations about forty five stocks, and
Speaker 2: they're all like historical. So they would show these like
Speaker 2: sort of masked investment cases for a bunch of stocks
Speaker 2: at like different times of the past ten years, and
Speaker 2: they were like, what do you think would you buy
Speaker 2: the stock right? Do you think the stock will up
Speaker 2: perform its sector? The investors in the MRI machine either
Speaker 2: said yes or no. And they also ran the MRIO
Speaker 2: while they're doing this, and it turns out that the
Speaker 2: investor's answers were worthless, Like they did not accurately predict
Speaker 2: no better than chance's ability to predict whether the stocks
Speaker 2: would go up or down. But their brains, their brains
Speaker 2: did accurately predict whether the stocks would cover down, which
Speaker 2: is to say that if you looked at like ridging,
Speaker 2: their brain called the nucleus siccumbents, which is like sort
Speaker 2: of the thing that anticipates rewards, right, it lit up
Speaker 2: when they saw presentations about stocks that were going to
Speaker 2: go up, so subconsciously they knew which stocks would go up,
Speaker 2: even though consciously they did not know what stocks would
Speaker 2: go up.
Speaker 1: I do love that. It's amazing, but the how do
Speaker 1: you apply it?
Speaker 2: How do you possibly you put your portfolio manager in
Speaker 2: an MRI all day?
Speaker 1: That's it? Yes, I mean I think that that would
Speaker 1: be a terrible a terrible existence.
Speaker 2: But like if you made a lot of money.
Speaker 1: Yeah, true, I mean.
Speaker 2: If this really worked, by the way, I'm not you know,
Speaker 2: I'm not so sure how well this really worked, but
Speaker 2: it worked in some experimental design. But it's fascinating too
Speaker 2: because like how could this work? Right? Like, how could
Speaker 2: it be that you can subconsciously.
Speaker 1: Just know which stocks secatifitely know, but you can't translate
Speaker 1: that into an actionable decision.
Speaker 2: Yeah. But so here's what I think the explanation is.
Speaker 2: To the extent this is real in the introduction to paper,
Speaker 2: they're like the other experiments like this, and one is
Speaker 2: like a sort of famous. One is like you play
Speaker 2: songs to people in an MRI, and the songs that
Speaker 2: light up their brains in certain ways go on to
Speaker 2: become hit songs. It sounds like people's brains instinctively know
Speaker 2: it's going to be hit songs. Well that makes a
Speaker 2: lot of sense, right, right, Like something about that song
Speaker 2: instinctively like makes everyone like it, right, Like it lights
Speaker 2: up a section of your brain, Like, of course that's
Speaker 2: going to go on to be a hit. Right. Maybe
Speaker 2: it's the same with stocks, right, It's like a meme
Speaker 2: stock phenomenon, where like something about a stock makes these
Speaker 2: investors like it, that's going to make other investors like it,
Speaker 2: and so the stock will go up. Right. So that's
Speaker 2: how it works. It has nothing to do with fundamental
Speaker 2: financial analysis. It just has to do with something in
Speaker 2: the shape of the stock makes people like it, and
Speaker 2: stocks that people like go up because they buy them right,
Speaker 2: And if you look at the paper, like the way
Speaker 2: they present these investment cases to like the portfolio managers
Speaker 2: in the MRI is like they show them the company description,
Speaker 2: and they show them a price chart, and they show
Speaker 2: them like a fundamentals page that's like a bunch of
Speaker 2: like ratios and earnings information, and they show them news
Speaker 2: they show them like summaries of It's like some news
Speaker 2: items about the stocks. What actually lights up their brains
Speaker 2: is just the description and the price chart. So like
Speaker 2: the fundamental page worthless for the price chart. They see
Speaker 2: the price chart and their brain lights up, like that's
Speaker 2: a good sign.
Speaker 1: People like lines, yeah, but.
Speaker 2: Only some lines, right. The lines that make their brains
Speaker 2: light up are the lines that will go up in
Speaker 2: the future, right, Like the good looking lines are the
Speaker 2: good stocks to buy. It's like technical analysis. If you
Speaker 2: look at a chart and the chart makes you happy,
Speaker 2: then it'll probably make other people happy, and so you
Speaker 2: should buy that stock.
Speaker 1: That is the best reason why technical analysis might be
Speaker 1: real that I've heard. The only reason it's just good lines.
Speaker 2: Technical analysis is like organized mass psychology. Right, It's like, oh,
Speaker 2: this line shows that people like the stock, right, It's
Speaker 2: not that weird to think that you could grasp that
Speaker 2: at a pre conscious level, right where you would see
Speaker 2: the lines and you don't know what the lines mean,
Speaker 2: but like somewhere deep in your animal brain you're like,
Speaker 2: oh that's a good stock.
Speaker 1: You're here. Lizard brain activates, and that's basically the foundation
Speaker 1: of technical analysis. I mean, again, who knows if any
Speaker 1: of this is actually real? I feel like the only
Speaker 1: way to really find out is if this was applied
Speaker 1: at scale and we gave this.
Speaker 2: Yeah, all you know academic finance paper is it's like okay,
Speaker 2: sure that's an interesting result, but like what hedge funds
Speaker 2: are implementing it?
Speaker 1: Right? Yeah?
Speaker 2: And like I don't know, like you could see Steve
Speaker 2: copy like hmm right, Like that's like a thing.
Speaker 1: That someone should send him this paper.
Speaker 2: It's in money Stock.
Speaker 1: Well I hope he read it.
Speaker 2: I mean you probably have to pay a premium. But
Speaker 2: like if there's some marginal investor who like can't quite
Speaker 2: get a job as a portfolio manager at a top
Speaker 2: multi strategy fund, but if you were in an MRI machine,
Speaker 2: he could oh yeah, yeah.
Speaker 1: I mean it's all in there. It was bright exactly.
Speaker 1: He just he can't translate it. I will say it's
Speaker 1: been a while since I read a paper like this,
Speaker 1: and I always love methodology. And there were a few
Speaker 1: charming details in here about the actual participants, Like you said,
Speaker 1: they're from leading Dutch investment companies, thirty four participants. Only
Speaker 1: one was a one. I noticed that immediately the mean
Speaker 1: age was forty seven.
Speaker 2: You want to do that experiment better, right, But like
Speaker 2: I would be interested in doing this experiment because these
Speaker 2: are all like Dutch professional money maners. Yeah, I'm interested
Speaker 2: in doing it on day traders right. Oh yeah, because
Speaker 2: the thing that is happening here is not like deep
Speaker 2: fundamental analysis, right. Something that's happening here is like, oh
Speaker 2: that line looks good. Maybe like a random amateur would
Speaker 2: be just as good.
Speaker 1: Yeah, that's true. We should try it on all different
Speaker 1: members of the population. I also thought this was cute.
Speaker 1: So participants received no compensation, but whoever was the most
Speaker 1: accurate in predicting stock outcomes would be awarded a prize
Speaker 1: of five hundred euros. There were two winners, so that
Speaker 1: they had to split the prize. They each got two
Speaker 1: hundred and fifty euros for all told, this lasted eighty
Speaker 1: five minutes, so that's a pretty good return on your time.
Speaker 2: I mean I don't know. I mean probably you're like
Speaker 2: a one in like thirty chance that two hundred and
Speaker 2: fifty euros for an hour and a half at an MRI, Like.
Speaker 1: I don't know, I would do it. Tell it's a.
Speaker 2: Professional money manager's like their day job is picking slacks
Speaker 2: that will go up and they get paid more than that.
Speaker 1: For Yeah, but it sounds like they're not too good
Speaker 1: at that.
Speaker 2: And that was The Money Stuff Podcast.
Speaker 1: I'm Matt Levian and I'm Katie Greifeld.
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