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Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. This is Masters in

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Speaker 1: Business with Barry Ritholts on Bloomberg Radio.

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Speaker 2: Hey, it's a bonus Masters in Business Live. One of

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Speaker 2: my favorite economists, Richard Taylor and his colleague at the

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Speaker 2: Booth School of Business, Alex Emos, We're going to give

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Speaker 2: a presentation at the New York Economic Club. And they said, Hey,

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Speaker 2: why don't we make this a live conversation and literally said, hey,

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Speaker 2: how do you feel about being our intolocutor And any

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Speaker 2: opportunity I have to spend time with Taylor, I jump

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Speaker 2: at So sure, let's do that. And so a few

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Speaker 2: weeks ago I sat down with Professor Richard Thaylor and

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Speaker 2: Professor Alex Emos, both of the Booth School of Business

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Speaker 2: at the University of Chicago, live at the New York

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Speaker 2: Economic Club, talking about their new version of the book,

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Speaker 2: The Winner's Curse. It was about thirty forty minutes of conversation.

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Speaker 2: We had some questions from the audience. It was fabulous.

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Speaker 2: In fact, it was so good. We're going to have

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Speaker 2: them back in the studio for a full Masters in Business,

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Speaker 2: which is now already recorded. It won't be out for

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Speaker 2: a couple of weeks, but in the meantime, to give

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Speaker 2: you a taste of what that full interview is like,

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Speaker 2: here is our live Masters in Business conversation at the

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Speaker 2: New York Economic Club. I'm kind of fascinated by not

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Speaker 2: only this book, but Richard's entire history, and a lot

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Speaker 2: of what I know about him really came to the

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Speaker 2: public eye through the Anomalies columns that began so long ago.

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Speaker 2: Tell us a little bit about the genesis of what

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Speaker 2: the Anomalies columns were and how that led to this book.

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Speaker 3: Sure, so, about nineteen eighty six, somebody at the American

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Speaker 3: Economics Association decided it would be a good idea to

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Speaker 3: start a new journal in which the articles would be

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Speaker 3: accessible to at least all economists and grad students and

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Speaker 3: even undergrads, because articles have become more and more specialized.

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Speaker 3: So that was the idea. I had a friend. I

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Speaker 3: have a friend named hal Varian who has been the

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Speaker 3: chief economist at Google. He was a mere professor at

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Speaker 3: the time and on the advisory board of this journal,

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Speaker 3: and they were planning some pieces that would appear in

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Speaker 3: every issue, which is once a quarter, and Hall and

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Speaker 3: I cooked up the idea of having a feature on anomalies.

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Speaker 3: So what's an anomaly in economics? An anomaly is something

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Speaker 3: that cannot easily be explained using the standard assumptions that

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Speaker 3: people are really smart, unemotional, selfish, no self control problems,

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Speaker 3: basically not like anybody you know. And so as a

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Speaker 3: result of that, there were lots of anomalies. And I

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Speaker 3: did this for almost four years. When it looked like

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Speaker 3: a pile of them looked like a book, I stapled

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Speaker 3: them together and that was the original version of The

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Speaker 3: Winner's Curse, published in nineteen ninety two.

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Speaker 2: So the book comes out in ninety two. I was curious,

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Speaker 2: how was it received by traditional economists. You're challenging core

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Speaker 2: thesis that they deeply believe in, and did the lay

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Speaker 2: public take any interest in us?

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Speaker 3: So, you know, I'm very good friends with Steve Levitt

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Speaker 3: and Stephen Dubner. They basically invented a best selling economics book.

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Speaker 3: Before Freakonomics, there was no such a thing. So this

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Speaker 3: is pre pre freeconomics. It was read, but the profession

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Speaker 3: had read the articles in the journal, and the book

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Speaker 3: was an attempt to reach out. But you know, I've

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Speaker 3: often said that, you know, I'm a professional heretic and troublemaker,

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Speaker 3: and I say, I didn't change anybody's mind over the

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Speaker 3: course of my career, and after realizing that, I decided

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Speaker 3: instead to have a strategy of corrupting the youth. And Alex, so, yeah,

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Speaker 3: I'll let Alex tell his version of the story. But

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Speaker 3: you know, the book sold better than expected and was around.

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Speaker 3: It was still in print, but for various boring reasons,

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Speaker 3: it was going to go out of print, and the

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Speaker 3: publisher asked me if I wanted to freshen it up,

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Speaker 3: and that turned into a well we first talked about

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Speaker 3: that five years ago, so you can see how long

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Speaker 3: it took. But that's that's the story of the book.

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Speaker 2: So, Alex, how did you get corrupted by doctor Thaylor?

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Speaker 4: I didn't need much much pushing, so I was.

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Speaker 5: I was a neuroscience undergrad major, so I was already

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Speaker 5: kind of interested in, you know, how human minds actually worked.

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Speaker 5: I took a bunch of abnormal psychology and things like that,

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Speaker 5: but science like STEM classes were really really hard, and

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Speaker 5: so I took economics as kind of a way to

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Speaker 5: boost my GPA. So and I thought it was fun

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Speaker 5: that the algebra was interesting, and but I didn't, you know,

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Speaker 5: I didn't think of much of it. And then at

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Speaker 5: some point I was like, I was moving and I

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Speaker 5: was a planing of medical school. By the way, I

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Speaker 5: was pre med, good immigrant kid, and I was I

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Speaker 5: was driving cross country and I was listening to the

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Speaker 5: radio and Richard was on the radio.

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Speaker 4: This was two thousand and eight. I had no idea

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Speaker 4: who Richard was. I didn't know what nudge was.

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Speaker 5: He was on on NPR talking about NUDGE and it

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Speaker 5: was about about this field called behavioral economics that I've

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Speaker 5: never heard about before. And I was like, wait, I

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Speaker 5: could take this and combine it with that, and that's amazing.

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Speaker 5: I got to Los Angeles, went online, applied to em

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Speaker 5: PhD programs right away, didn't end up going to medical school,

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Speaker 5: and I ended up going to UC San Diego for

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Speaker 5: my PhD.

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Speaker 4: And lo and behold, Richard was not on the roster.

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Speaker 5: But he spent his winters in San Diego in the

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Speaker 5: office next to me.

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Speaker 4: And you know, I would come out at first sight.

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Speaker 5: You corrupted me just way earlier, and so I was,

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Speaker 5: you know, we started chatting.

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Speaker 4: Then I got a job at Carnegie Mellon.

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Speaker 5: Afterwards, we kept in touch and then I got a

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Speaker 5: job offered at University of Chicago, and I think like

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Speaker 5: I got there in July twenty twenty.

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Speaker 4: I think within like a few months, you gave me.

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Speaker 5: A call and say, hey, my publisher is asking I

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Speaker 5: should freshen up the book, maybe to give it a

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Speaker 5: new preface, but I want to do something a little

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Speaker 5: bit more ambitious than that, and you know, we can

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Speaker 5: play around with it, add some new things. It'll take

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Speaker 5: about six months, easy, easy work.

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Speaker 4: We get to hang out, and.

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Speaker 5: I jumped at the opportunity, and then we kept talking

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Speaker 5: and then it grew and grew and grew, and that

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Speaker 5: book is about two thirds new content at this point.

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Speaker 5: So it took five years of writing and going back

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Speaker 5: and forth. And you know, there's been thirty years of

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Speaker 5: research since nineteen ninety two, and so that's kind of

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Speaker 5: the book. The original winner's courses in there a little

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Speaker 5: bit rewritten and freshened up, but the thirty years of

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Speaker 5: research are now in there too. So every single anomalies

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Speaker 5: column there were new anomalies that were added to it that.

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Speaker 4: Richard had written afterwards.

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Speaker 5: But also every single chapter comes with an update basically

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Speaker 5: reviewing everything that's happened in the last thirty years of behaviorly.

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Speaker 2: So I'm going to circle back to the book in

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Speaker 2: a moment. But for people who are UNFLI familiar with

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Speaker 2: Alex this wasn't just a random kid next door. He

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Speaker 2: wrote what could be one of the most cited finance

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Speaker 2: papers of recent years, called I'm going to get this wrong,

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Speaker 2: Selling fast and Buying Slow?

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Speaker 3: Is that right?

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Speaker 2: Selling fast and Buying Slow? It's a chapter in my

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Speaker 2: book I've written about it, and you describe how professional

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Speaker 2: fund managers are really really good buyers of stocks. What

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Speaker 2: turns out they're terrible sellers of stock. Nobody had discussed

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Speaker 2: this in this sort of detail, and that's one of

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Speaker 2: the reasons that paper has become so highly regarded. But

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Speaker 2: I just wanted the audience to be aware of who

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Speaker 2: you were. Tell us what it was like working on

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Speaker 2: the book with Richard.

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Speaker 4: It was just a lot of fun.

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Speaker 5: I mean, we would get on the phone and you know,

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Speaker 5: why don't we talk about this topic.

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Speaker 4: A lot of the research?

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Speaker 5: You know, I kind of wrote, I took lead on

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Speaker 5: the updates and we kind of went back and forth

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Speaker 5: on them, and then it was you know, just a

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Speaker 5: lot of a lot of conversations over the phone during

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Speaker 5: COVID it was a lot of zoom. Then it moved

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Speaker 5: on to you know, we were colleagues at the University

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Speaker 5: of Chicago coffees, just kind of going back and forth

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Speaker 5: on what should be included, what push, what where. A

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Speaker 5: lot of it was about framing of where the field

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Speaker 5: has come and where what we kind of found out.

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Speaker 5: I don't think this was obvious when we first started

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Speaker 5: the book, is that where the field has come is

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Speaker 5: really gone from these lab experiments that were in the

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Speaker 5: original columns in the Winner's Curse, where it was college students,

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Speaker 5: low stakes, maybe not even any stakes at all. And

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Speaker 5: the pushback from the economics profession was, look, we don't

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Speaker 5: really care about students, We care about market participants.

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Speaker 4: The updates are all about Look.

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Speaker 5: All of these anomalies, as you mentioned, replicate in some

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Speaker 5: of the most sophisticated economic participants out there, such as

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Speaker 5: institutional investors in the case of my paper.

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Speaker 2: So there are some ideas in the book down in

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Speaker 2: effect status quo biased Winners Curse that were or today

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Speaker 2: are everyday vocabulary, but back then people really know about it.

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Speaker 2: The whole book has aged fairly well. What do you

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Speaker 2: think about those original ideas, Richard, and how they present

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Speaker 2: in the modern world.

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Speaker 3: Well, I mean, the one of the one of the

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Speaker 3: motivations for writing the book is the so called replication crisis.

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Speaker 3: It's not really a crisis, but there's there has been

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Speaker 3: several papers and several fields where the original experiments cannot

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Speaker 3: be reproduced, and some of those papers are, let's say,

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Speaker 3: adjacent to behavioral economics, and I was worried that some

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Speaker 3: of that bad aroma would rub off on us. And

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Speaker 3: I think the reason why things replicate so well is

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Speaker 3: when I was choosing what topics to write about, I

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Speaker 3: was picking big stuff. I wasn't picking some little minor thing.

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Speaker 3: I was picking something with a very big effect size

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Speaker 3: and topics. There had already been several papers. So you know,

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Speaker 3: it's not when we started this that we realized there

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Speaker 3: really wouldn't be any problems. But we were pleasantly surprised

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Speaker 3: not to find any anything in the attic, so.

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Speaker 2: To speak, nothing aged poorly. No, not really what has

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Speaker 2: persisted or if anything, have become more widely accepted today.

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Speaker 2: That was a pleasant surprise. Amongst the chapters in the book.

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Speaker 5: WHOA the endowment effect now so you know, back then

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Speaker 5: Bick in the ninety two book, it was about mugs

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Speaker 5: and pens. So the endowment effect is this phenomenon where

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Speaker 5: you know, buyers and sellers are in a market. The

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Speaker 5: idea is that if you assign a good to a

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Speaker 5: buyer seller. There's a theorem in economics that underlies Basically,

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Speaker 5: it's one of the fundamental theorems in economics called the

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Speaker 5: coast theorem. Basically, it means that it doesn't matter who's

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Speaker 5: assigned property rights, there's going to be transactions where people

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Speaker 5: the person who wants it the most or values are

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Speaker 5: the most, that's going to end up with it. And

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Speaker 5: what Richard showed in a paper with Jack Netch and

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Speaker 5: Danny Kahneman is that basically, you know half, Let's say Barry,

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Speaker 5: you are sitting in a classroom. I'm sitting in a

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Speaker 5: class the professor gives me a mug and doesn't give

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Speaker 5: you a mug. You have some money in your pocket,

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Speaker 5: and he's asking what is the lowest amount that you

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Speaker 5: would be willing to sell the mug? And he's asking you,

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Speaker 5: is how what's the most you'd be willing to pay

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Speaker 5: for the mug? Because it doesn't matter we were randomly assigned,

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Speaker 5: we should have basically the same average valuation, but it

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Speaker 5: turns out just because I own the mug, I start

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Speaker 5: valuating it more. This is called the endowment effect, and

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Speaker 5: what Richard and Danny and Jack showed is that it's

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Speaker 5: about two and a half times more. So there's like

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Speaker 5: a breakdown in the market because of this endowment effect.

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Speaker 5: So what have we found? What has been documented thus far?

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Speaker 5: There's a paper in the American Economic Review, the top

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Speaker 5: journal in the profession, showing that there's an endowment effect

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Speaker 5: for houses, and you can actually estimate exactly the amount

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Speaker 5: of loss aversion that people have which drives them to post.

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Speaker 5: If they own the house, they post the house at

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Speaker 5: a higher price. And the number the actual quantitative number

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Speaker 5: in housing markets. This is the crazy part about it

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Speaker 5: matches the number that Richard documented in the lab. So

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Speaker 5: there's a there's there's obviously a lot of back and

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Speaker 5: forth between different experimental is, different different experiments and stuff

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Speaker 5: like that, but even most of the quantitative magnitudes have

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Speaker 5: been great.

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Speaker 2: It's robust. Let's talk about something that economic theory says

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Speaker 2: shouldn't happen. Ultimatums and cooperations let's talk about the ultimatum game.

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Speaker 2: Tell us a little bit about that.

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Speaker 3: Okay, sure, So the ultimatum game is pretty simple. Let's

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Speaker 3: say I give Barry one hundred dollars and I tell

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Speaker 3: him that he's got to share it with Alex. He

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Speaker 3: can make Alex an offer for some amount of the hundred.

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Speaker 3: You can give all.

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Speaker 2: Hundred unlikely, I'll take it.

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Speaker 3: Alex gets to say yes or no, So it's an ultimatum.

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Speaker 3: And if he says yes, deal he says no, they

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Speaker 3: both get nothing. Now that game was invented, It didn't

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Speaker 3: really surprise us. It was invented because we kind of

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Speaker 3: knew what was going to happen, which was so. But

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Speaker 3: before that, let's say, what does economic theory say and

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Speaker 3: what does game theory say? Game theory says people are

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Speaker 3: selfish and Alex, if you offer him a dollar, don't

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Speaker 3: insult him with a quarter or a penny. But if

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Speaker 3: you offer him a dollar dollars or something, and he

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Speaker 3: knows the dollars worth more than zero, so he'll take it.

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Speaker 3: And you know that he knows that, and so you

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Speaker 3: offer him a dollar and he takes it. No, no

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Speaker 3: real world person has ever done that.

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Speaker 2: Meaning in the lab experiments, what's the number under twenty dollars.

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Speaker 3: Under yeah, offers less than twenty percent of whatever the

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Speaker 3: pie is are more likely are likely to be rejected.

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Speaker 3: Allfers tend to be fifty to fifty. The profit maximizing

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Speaker 3: offer is forty percent. So there are a whole We

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Speaker 3: have two chapters that are about games sort of like this.

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Speaker 3: The big lesson is that in the world, you're dealing

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Speaker 3: with people, and if you make insulting offers, they may

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Speaker 3: get rejected. And if you want cooperation, you have to

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Speaker 3: be cooperative. Let me tell you a story that's related

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Speaker 3: to this. Years ago, my wife and I were in

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Speaker 3: Thailand and we needed to take a cab twenty minute

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Speaker 3: ride like three blocks here, but it was ten miles

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Speaker 3: and changed my so and everything is negotiated there. So

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Speaker 3: I'm negotiating with this cab driver and after wild negotiations,

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Speaker 3: we agree on some fare out four dollars and we

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Speaker 3: get to the restaurant and then there was a question

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Speaker 3: of how are we going to get back? And the

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Speaker 3: cab driver said, would you like me to take you back?

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Speaker 3: And notice there's a mutual risk of defection. He could

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Speaker 3: not be there when we get done with dinner, or

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Speaker 3: we could get done early and go back with somebody else.

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Speaker 3: He proposed a contract that no economist would ever recommend.

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Speaker 3: The contract was, he said, don't pay me anything perfect.

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Speaker 3: So when dinner's over, of course we went to look

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Speaker 3: for this cab driver. We're not going to stiff the

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Speaker 3: cab driver. And in fact he dropped us somewhere else

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Speaker 3: for some other same thing. Now, what's the lesson there?

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Speaker 3: He knew that by being trusting he would engender trust,

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Speaker 3: and that's like a big important lesson both in business

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Speaker 3: and in life and in politics.

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Speaker 2: Coming up, we continue our live conversation with doctor Richard

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Speaker 2: Taylor and doctor Alex Emos, both of the Booth School

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Speaker 2: of Business at the University of Chicago, discussing the newest

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Speaker 2: edition of their book, The Winner's Curse. I'm Barry Ridults.

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Speaker 2: You're listening to Bloomberg's Masters in Business. Let's continue our

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Speaker 2: live conversation with Richard Taylor and Alex Amos discussing the

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Speaker 2: new edition of the book The Winner's Curse. So let's

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Speaker 2: talk about some other things within the book. The title

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Speaker 2: The Winner's Curse is really a fascinating story and leads

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Speaker 2: so many places. Let's start off talking about oil leases.

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Speaker 2: Tell us about the winner's curse.

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Speaker 3: You want to go, you want me to go, you

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Speaker 3: can do it. So this is the only chapter in

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Speaker 3: the book that the research original research was not done

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Speaker 3: by psychologists or economists.

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Speaker 4: The lord one price.

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Speaker 3: Oh, you're not calling financial economists as aonymous. Oh, and

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Speaker 3: that gets back to booth. You're going to be in

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Speaker 3: a lot.

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Speaker 4: I'm in a different part of the building.

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Speaker 3: So finance is just a branch of economics, it's not

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Speaker 3: its own field. So we're both dabblers in finance. So

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Speaker 3: the research on this was done by engineers at Atlantic

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Speaker 3: Ridgefield ARCO. And here's what they found. They were bidding

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Speaker 3: for oil leases in what I still call the Gulf

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Speaker 3: of Mexico. And what they found was that for the

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Speaker 3: leases they won, there was less oil than they expected.

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Speaker 3: And they're thinking, we have great geologists, how can we

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Speaker 3: be wrong? What's going on? And then they got an insight.

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Speaker 3: The essence of the insight is that when you're in

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Speaker 3: an auction, the ones you win are not a random

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Speaker 3: sample of your bids. The auctions you win are when

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Speaker 3: you bid high. Right now, that sounds like a pretty

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Speaker 3: obvious point, but it's not because if there are a

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Speaker 3: lot of bidders. Let's suppose that the version of this

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Speaker 3: that we would run in classrooms is we'd have a

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Speaker 3: jar of coins, and we'd have people bid for the

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Speaker 3: amount of money in the jar, not the coins. And

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Speaker 3: what happens the average bid is, let's say the jar's

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Speaker 3: worth a hundred dollars, the average bid is much less

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Speaker 3: because people are risk averse, but the winning bid is

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Speaker 3: always more than a hundred, because it's the most optimistic

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Speaker 3: person or the person with the first eyesight or whatever.

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Speaker 3: So this insight by the engineers applies everywhere, and contractors

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Speaker 3: have either learned this or go out of business right

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Speaker 3: because when they're bidding on a job, it low bid wins,

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Speaker 3: and if you forget the roof or the hvac, you're

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Speaker 3: going to go massively over. And so one of the

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Speaker 3: interesting follow up experiments was to take a bunch of

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Speaker 3: contractors and put them into one of these experiments and

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Speaker 3: see what would happen. And the experimenters were a little

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Speaker 3: worried that the contractors were going to take them to

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Speaker 3: the cleaners, but they didn't. The reason is the contractors

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Speaker 3: never understood the math of this. Instead, they had a

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Speaker 3: fudge so they would figure out what they thought they

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Speaker 3: could build it for and then add twenty five and

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Speaker 3: that covers the stuff they forget. But they didn't have

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Speaker 3: that rule for bidding for jars, and so they were

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Speaker 3: no better than the undergrads in The Winner's Curse.

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Speaker 2: How is this held up since what does the latest

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Speaker 2: data on the Winter Scurse look like?

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Speaker 5: Well, the Winter Skurs will as we discuss in the update,

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Speaker 5: that it's held up quite well as far as you know,

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Speaker 5: the experiment replicates. First of all, you know, you run

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Speaker 5: it in Harvard, you go into econ one to one

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Speaker 5: at Harvard, you auction off of jar coins. They're going

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Speaker 5: to be overbidding for the for the coins it's just

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Speaker 5: and then you know it's hold up, held up with

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Speaker 5: the NFL teams where you're you're basically bidding for free

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Speaker 5: agents and things like that.

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Speaker 4: So it shows up in the data and the big file.

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Speaker 5: Follow up to The Winner's Curse has been the idea

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Speaker 5: the kind of abstracting from the phenomenon of the Winter's

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Speaker 5: Curse but thinking about like what leads to the Winter's

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Speaker 5: Curse in the first place, psychologically and psychologically, what leads

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Speaker 5: to the Winter's curse is you not taking into account

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Speaker 5: that you're bidding against other people. You're not just kind

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Speaker 5: of bidding alone. Oh I think this is worth this much.

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Speaker 5: That's how much I'm going to pay, shading down a

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Speaker 5: little bit. It's the fact that if I'm bidding with

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Speaker 5: a lot of people, if I end up winning, that's

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Speaker 5: real bad news for me because everybody else is just

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Speaker 5: as smart as me, and they're bidding a great they

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Speaker 5: with the similar information. And if I'm the one who's winning,

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Speaker 5: I'm making a mistake. So you kind of have to

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Speaker 5: bid down. And so the other follow up experiments have

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Speaker 5: basically explored where this sort of phenomenon shows up in

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Speaker 5: other places. So, for example, one setting is you know,

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Speaker 5: kind of thinking about there's the guessing game to be

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Speaker 5: beauty contest game, which is meant to model from from

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Speaker 5: Caines exactly, so the follow up to the idea behind

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Speaker 5: the winner score. So Kines had the model for the

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Speaker 5: stock market where he said, the stock market is a

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Speaker 5: beauty contest in the sense that it's not that the

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Speaker 5: fundamental value of a stock would determine its price, it's

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Speaker 5: what everybody else thinks the fundamental value is it's all

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Speaker 5: about the belief.

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Speaker 4: So there was this.

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Speaker 5: The reason it's called the beauty contest is the newspapers

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Speaker 5: used to run these contests where you had a whole

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Speaker 5: page of faces, and the winner of the contest was

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Speaker 5: the one who chose the face that everybody else chose

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Speaker 5: as well. And so he said, the stock markets just

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Speaker 5: like that. And so you run the beauty contest by

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Speaker 5: saying something like, all right, what is guess the number

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Speaker 5: between one and one hundred? The room guesses, everybody submits

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Speaker 5: a guess, and the winner is the one who guesses

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Speaker 5: two thirds of the average. Now again this game, think

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Speaker 5: about it for a second. This game has an e

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Speaker 5: economic solution. Then what's the solution we can we can

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Speaker 5: do right now?

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Speaker 3: It's the New York Economics Club. Somebody should know the equilibrium.

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Speaker 5: Anybody have a guess for the Nash equilibrium?

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Speaker 4: Yeah, should be Yeah, it should be one.

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Speaker 3: It should be one, would be zero or one.

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Speaker 5: If if the lowest number is one is one, it's one.

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Speaker 5: And the reason is the following. Let's say I think

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Speaker 5: i'm I'm I'm playing against a bunch of you know,

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Speaker 5: random people. They don't know what they're doing. They're guessing randomly.

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Speaker 5: If everybody guesses randomly, the average is going to be fifty.

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Speaker 5: So I should do two thirds of that, right, But

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Speaker 5: then I think, all right, if it's two thirds of that,

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Speaker 5: I guess that. Wait, well, they're probably going to think

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Speaker 5: the same thing, and so they're going to guess two

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Speaker 5: thirds of fifty, so I should do two thirds of that.

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Speaker 4: But then I think, wait, no, hold on, hold on,

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Speaker 4: I'm going to do.

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Speaker 5: Two thirds of that, and two thirds two thirds, two

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Speaker 5: thirds I get to one, right, So that's the Nash

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Speaker 5: equilibrium solution.

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Speaker 4: Would you win guessing one?

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Speaker 5: No, you would not win guessing one because other people

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Speaker 5: are not guessing one. So the way that when you

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Speaker 5: run this experiment, essentially what you see is the majority

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Speaker 5: of people guess something like fifty times two thirds times

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Speaker 5: two thirds, so they'd make it to level two and

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Speaker 5: then they stop.

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Speaker 3: I played this game once in the Financial Times with

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Speaker 3: two business class tickets from London to the US as

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Speaker 3: a prize, and the winning guess was thirteen. So there

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Speaker 3: were lots of zero ones and they didn't win. There

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Speaker 3: were a bunch of ninety nine.

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Speaker 2: And one hundred they didn't understand the game.

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Speaker 3: No, they were jerks trying to skew the numbers. Yeah,

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Speaker 3: and they were all from Oxford and they were trying

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Speaker 3: to pull it all. They weren't disguising they all had

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Speaker 3: the same dorm address. But but but it was one

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Speaker 3: guest per person. So my tas and I were the

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Speaker 3: judges and we had thirteen hundred entrance. But yeah, so again,

435
00:29:21,880 --> 00:29:24,280
Speaker 3: this is the sort of thing that we can do

436
00:29:24,360 --> 00:29:28,880
Speaker 3: in class. If you do it with NBA students, you're

437
00:29:28,920 --> 00:29:31,520
Speaker 3: going to get a number in the teams when.

438
00:29:31,400 --> 00:29:33,160
Speaker 4: Thirteen eight something like that.

439
00:29:33,200 --> 00:29:36,560
Speaker 5: The more so kind of oh I've taken economics, they'll

440
00:29:36,640 --> 00:29:40,080
Speaker 5: go like eight, Oh, I haven't taken economics.

441
00:29:39,800 --> 00:29:41,320
Speaker 4: Thirteen sixteen.

442
00:29:41,440 --> 00:29:44,080
Speaker 5: So it's it's always above one, and it just mattered.

443
00:29:44,120 --> 00:29:46,040
Speaker 5: You can see these spikes in the data. That's the

444
00:29:46,120 --> 00:29:47,200
Speaker 5: craziest part about it.

445
00:29:47,600 --> 00:29:50,600
Speaker 3: So, you know, I've written a paper with one of

446
00:29:50,600 --> 00:29:55,240
Speaker 3: my former students showing that the NFL draft is subject to.

447
00:29:55,160 --> 00:29:58,280
Speaker 2: This literally where I wanted to take this inside world.

448
00:29:58,720 --> 00:30:01,760
Speaker 3: You know, esp is one one of my one of

449
00:30:01,800 --> 00:30:06,680
Speaker 3: my skills. Arry, So, uh, well, you probably are all

450
00:30:06,680 --> 00:30:09,680
Speaker 3: familiar with the NFL draft that the end of the season,

451
00:30:09,760 --> 00:30:14,320
Speaker 3: the worst team gets the first pick of the eligible players.

452
00:30:14,840 --> 00:30:19,840
Speaker 3: What you may not know is there's a chart that

453
00:30:20,200 --> 00:30:25,080
Speaker 3: the Dallas Cowboys first created, one of the one of

454
00:30:25,080 --> 00:30:30,880
Speaker 3: the owners that plots what somebody thought was the should

455
00:30:30,920 --> 00:30:35,000
Speaker 3: be the prices to trade picks. So, for example, you

456
00:30:35,040 --> 00:30:38,000
Speaker 3: can trade the first pick for the seventh and eighth picks,

457
00:30:38,520 --> 00:30:43,080
Speaker 3: or for half a dozen second round picks. And what

458
00:30:43,120 --> 00:30:46,600
Speaker 3: we show in that paper is the early picks are

459
00:30:46,880 --> 00:30:48,400
Speaker 3: massively overvalued.

460
00:30:48,960 --> 00:30:51,920
Speaker 2: And where where does the value show up? Round twound?

461
00:30:52,000 --> 00:30:57,640
Speaker 3: Yeah, round two. Now, even bigger anomaly is you can

462
00:30:57,760 --> 00:31:03,480
Speaker 3: trade this year's pick for next year. And the rule

463
00:31:03,520 --> 00:31:08,560
Speaker 3: of thumb is if you trade a second round pick

464
00:31:08,600 --> 00:31:10,640
Speaker 3: this year, you get a first round pick next year.

465
00:31:11,080 --> 00:31:16,040
Speaker 3: So you move up one round per year. And we

466
00:31:16,240 --> 00:31:19,640
Speaker 3: calculated that's a discount rate of one hundred and thirty

467
00:31:19,640 --> 00:31:20,360
Speaker 3: seven percent.

468
00:31:20,760 --> 00:31:22,680
Speaker 2: Really, why such a big discount, right?

469
00:31:23,000 --> 00:31:27,920
Speaker 3: Because owners want to win. Now, we have one former

470
00:31:28,480 --> 00:31:32,720
Speaker 3: franchise owner in the room, and I'm pretty sure he

471
00:31:32,880 --> 00:31:36,720
Speaker 3: didn't get his money by borrowing at one hundred and

472
00:31:36,760 --> 00:31:42,640
Speaker 3: thirty seven percent. But and NFL teams are even more

473
00:31:42,680 --> 00:31:48,200
Speaker 3: expensive than NBA teams, But that it's still they still

474
00:31:48,240 --> 00:31:53,000
Speaker 3: have that rule. And we're in the process of replicating

475
00:31:54,120 --> 00:31:56,560
Speaker 3: that study, and it's.

476
00:31:56,440 --> 00:32:03,320
Speaker 2: All exactly So this conversation reminds me of the discussion

477
00:32:03,400 --> 00:32:09,760
Speaker 2: that took place after Michael Lewis's Moneyball. So everyone's familiar

478
00:32:09,840 --> 00:32:12,200
Speaker 2: with the book about the Oakland A's and how they

479
00:32:13,160 --> 00:32:20,560
Speaker 2: brought a essentially behavioral economic approach to selecting players. What

480
00:32:20,720 --> 00:32:23,040
Speaker 2: happened after that book came out.

481
00:32:23,520 --> 00:32:27,760
Speaker 3: Well, it's interesting. I mean, one thing happened is I

482
00:32:27,800 --> 00:32:31,520
Speaker 3: got in touch with Michael Lewis, and we didn't know

483
00:32:31,560 --> 00:32:35,800
Speaker 3: each other at the time, but through his publisher or something,

484
00:32:36,440 --> 00:32:38,240
Speaker 3: I said, if you ever in Chicago, let me know

485
00:32:38,720 --> 00:32:43,080
Speaker 3: I'm there next week. And we've become very good friends

486
00:32:43,240 --> 00:32:47,760
Speaker 3: and have come to realize that sports analytics and behavioral

487
00:32:47,760 --> 00:32:53,680
Speaker 3: economics are the same field. It's trying why, well, what

488
00:32:54,760 --> 00:32:59,960
Speaker 3: was Billy Bean, who also lives in our northern California.

489
00:33:00,120 --> 00:33:02,800
Speaker 3: What was Billy trying to do? He was trying to

490
00:33:02,880 --> 00:33:10,640
Speaker 3: buy a team more cheaply than others by And it's

491
00:33:10,760 --> 00:33:13,320
Speaker 3: just like being a portfolio manager, you're trying to buy

492
00:33:13,800 --> 00:33:18,800
Speaker 3: undervalued stocks. He was trying to buy undervalued players. Michael

493
00:33:18,840 --> 00:33:22,360
Speaker 3: got so interested in this he ended up writing the

494
00:33:22,400 --> 00:33:26,000
Speaker 3: book The Undoing Project which is a great book. It's

495
00:33:26,040 --> 00:33:31,560
Speaker 3: about my mentors, Danny Kahneman and named mister Vsky. He

496
00:33:31,640 --> 00:33:35,800
Speaker 3: did stick an irrelevant chapter in the beginning about Darryl Moury.

497
00:33:35,880 --> 00:33:42,360
Speaker 2: But anyway, the line I was looking to pull from

498
00:33:42,400 --> 00:33:46,080
Speaker 2: you is not for nothing, Michael. But what you're writing

499
00:33:46,160 --> 00:33:51,680
Speaker 2: about these two Israeli psychologists have been writing about and

500
00:33:51,760 --> 00:33:57,600
Speaker 2: experimenting with for years. Moneyball directly led to your relationship

501
00:33:57,640 --> 00:33:59,680
Speaker 2: with him and that becoming a book as well.

502
00:33:59,760 --> 00:34:03,920
Speaker 3: Yeah, yeah, And I mean it's about the biases from

503
00:34:04,000 --> 00:34:08,760
Speaker 3: psychology and it's also about markets, right, So the difference

504
00:34:08,800 --> 00:34:15,719
Speaker 3: between behavioral economics and psychology is markets. So a psychologist

505
00:34:15,800 --> 00:34:22,200
Speaker 3: would be interested in the fact that teams are overconfident

506
00:34:22,360 --> 00:34:26,520
Speaker 3: in their ability to tell good players from bad. That's

507
00:34:26,600 --> 00:34:30,920
Speaker 3: pure psychology. The fact that it gets reflected in the

508
00:34:31,000 --> 00:34:33,399
Speaker 3: market for picks, that's economics.

509
00:34:34,560 --> 00:34:37,359
Speaker 2: So let me push back a little bit on this

510
00:34:37,960 --> 00:34:42,239
Speaker 2: and The Winner's Curse in that all the topics that

511
00:34:42,320 --> 00:34:46,359
Speaker 2: you write about in The Winner's Curse oil leases where

512
00:34:46,400 --> 00:34:48,520
Speaker 2: we don't know what the future oil production will be,

513
00:34:49,440 --> 00:34:51,600
Speaker 2: first round draft picks where we don't know how that

514
00:34:51,640 --> 00:34:54,640
Speaker 2: player is going to perform, and in the market's either

515
00:34:54,719 --> 00:34:59,080
Speaker 2: picking stocks, or picking fund managers to pick stocks, or

516
00:34:59,080 --> 00:35:01,560
Speaker 2: picking somebody to pick the fund managers in a fund

517
00:35:01,600 --> 00:35:05,520
Speaker 2: of fund to pick stocks. They all seem to deal with, Hey,

518
00:35:05,600 --> 00:35:09,520
Speaker 2: we really are unaware of how this is going to

519
00:35:09,600 --> 00:35:14,200
Speaker 2: play out, and we're making decisions under uncertainty. All of

520
00:35:14,239 --> 00:35:17,000
Speaker 2: this comes back to economin and Suvarski.

521
00:35:19,239 --> 00:35:23,960
Speaker 3: Well, I'm not gonna I'm not going to disagree with that.

522
00:35:24,280 --> 00:35:28,680
Speaker 3: I mean, look, I became a behavioral economist when I

523
00:35:28,840 --> 00:35:33,879
Speaker 3: discovered them. I claim that was my big discovery, was

524
00:35:34,000 --> 00:35:37,920
Speaker 3: discovering these two psychologists who were over in Israel that

525
00:35:38,040 --> 00:35:42,920
Speaker 3: economists hadn't heard of. And I went and spent a

526
00:35:43,000 --> 00:35:45,719
Speaker 3: year at Stanford where when I got winned that they

527
00:35:45,760 --> 00:35:51,440
Speaker 3: were going to be there and basically stalked them.

528
00:35:51,560 --> 00:35:54,120
Speaker 5: Well, a lot of the phenomenon they go away when

529
00:35:54,120 --> 00:35:57,919
Speaker 5: there's not a lot of uncertainty. The winner's course needs uncertainty. Right,

530
00:35:58,080 --> 00:35:59,960
Speaker 5: If we know what the value of the plot is,

531
00:36:00,560 --> 00:36:02,399
Speaker 5: there's no winner's curse. It doesn't matter how many people

532
00:36:02,400 --> 00:36:03,040
Speaker 5: are bidding.

533
00:36:03,040 --> 00:36:03,520
Speaker 4: If we know.

534
00:36:03,760 --> 00:36:07,280
Speaker 5: So, there's a famous anchoring effect, right, So if I

535
00:36:07,360 --> 00:36:10,040
Speaker 5: the anchoring effect is essentially you know, you ask a

536
00:36:10,120 --> 00:36:12,400
Speaker 5: question like how many countries are in Africa before but

537
00:36:12,440 --> 00:36:14,600
Speaker 5: before I do it, I spin a big wheel, right.

538
00:36:14,600 --> 00:36:16,280
Speaker 4: The wheel has nothing to do with the question.

539
00:36:16,920 --> 00:36:20,600
Speaker 5: And the wheel gets to like seventeen or eight or whatever,

540
00:36:20,880 --> 00:36:24,040
Speaker 5: and it turns out wherever that number gets to affects

541
00:36:24,120 --> 00:36:26,239
Speaker 5: how what number of people gets about the number of

542
00:36:26,280 --> 00:36:28,760
Speaker 5: countries in Africa. But if people know the number of countries,

543
00:36:28,760 --> 00:36:30,960
Speaker 5: they're not going to be affected by the wheel. Right,

544
00:36:31,040 --> 00:36:33,560
Speaker 5: So all of this has to do Almost every single

545
00:36:33,600 --> 00:36:37,760
Speaker 5: behavioral economic phenomenon has is bred and.

546
00:36:37,800 --> 00:36:39,920
Speaker 4: Fed through uncertainty.

547
00:36:39,960 --> 00:36:42,000
Speaker 5: And I think the way that people react to that

548
00:36:42,080 --> 00:36:45,719
Speaker 5: uncertainty and shape their preferences and beliefs, that's where all

549
00:36:45,719 --> 00:36:48,879
Speaker 5: the biases seep in in the first place. When I'm

550
00:36:48,880 --> 00:36:52,960
Speaker 5: certain when experts know what they're doing, when they're you know,

551
00:36:53,000 --> 00:36:55,080
Speaker 5: they know the value of the plot, they know what

552
00:36:55,440 --> 00:36:57,840
Speaker 5: you know, the whether the stock is going to go

553
00:36:57,960 --> 00:36:59,799
Speaker 5: up or down, there's not a lot of biases to.

554
00:36:59,800 --> 00:37:06,520
Speaker 3: Talk, you know. One aspect of this that surprised me

555
00:37:08,520 --> 00:37:14,760
Speaker 3: is in the sports world, teams have learned but really slowly.

556
00:37:15,400 --> 00:37:22,560
Speaker 3: I mean it's like shockingly slowly. When the three point

557
00:37:22,600 --> 00:37:27,359
Speaker 3: shot was introduced in basketball, Darryl Moorey, I always tease him.

558
00:37:27,400 --> 00:37:30,960
Speaker 3: He's the general manager of the Philadelphia seventy six ers.

559
00:37:31,160 --> 00:37:33,680
Speaker 3: I always tease him that he was the first guy

560
00:37:33,719 --> 00:37:37,120
Speaker 3: who figured out that three is one point five times two,

561
00:37:37,840 --> 00:37:41,840
Speaker 3: So you should take three point shots because they have

562
00:37:41,880 --> 00:37:44,680
Speaker 3: a higher expected value. Every team has somebody who can

563
00:37:44,760 --> 00:37:48,560
Speaker 3: make forty of their three point shots, and they make

564
00:37:48,600 --> 00:37:51,800
Speaker 3: about half their two point shots. But if you look,

565
00:37:52,120 --> 00:37:54,920
Speaker 3: Larry Bird was really good at making three point shots,

566
00:37:54,920 --> 00:37:58,880
Speaker 3: and he took two or three a game. Steph takes twenty.

567
00:37:59,040 --> 00:38:03,279
Speaker 3: He's a little better than Larry Bird. But now if

568
00:38:03,320 --> 00:38:09,919
Speaker 3: you look at that trend, it's really shallow, and it's

569
00:38:10,000 --> 00:38:14,839
Speaker 3: the same as football. Teams have learned to punt.

570
00:38:15,000 --> 00:38:18,600
Speaker 2: Less but still sill on fourth down.

571
00:38:18,600 --> 00:38:21,879
Speaker 3: They should go for it on fourth down. They get

572
00:38:21,880 --> 00:38:27,080
Speaker 3: it right now half the time they and the closer

573
00:38:27,120 --> 00:38:31,200
Speaker 3: they get to if they get over the fifty yard line,

574
00:38:31,239 --> 00:38:33,680
Speaker 3: they're more likely to get it right. But they're still terrible.

575
00:38:34,080 --> 00:38:38,560
Speaker 3: So the learning is slow, and the reason is people

576
00:38:38,600 --> 00:38:40,600
Speaker 3: don't like to look like fools.

577
00:38:40,920 --> 00:38:43,719
Speaker 2: There was a Wall Street Journal article about I don't

578
00:38:43,760 --> 00:38:46,360
Speaker 2: remember it was a high school or a college coach

579
00:38:46,680 --> 00:38:48,520
Speaker 2: who always went for an on fourth down.

580
00:38:48,719 --> 00:38:50,800
Speaker 3: Yeah, and I love that guy.

581
00:38:50,440 --> 00:38:51,680
Speaker 2: Ten years ago.

582
00:38:51,719 --> 00:38:55,720
Speaker 3: Right, yeah, twenty years ago. Probably he didn't have a kicker.

583
00:38:56,280 --> 00:39:01,160
Speaker 3: He didn't have any Yeah, even he he didn't have

584
00:39:01,200 --> 00:39:05,640
Speaker 3: a field goal kicker or a punter. Now it's this

585
00:39:05,719 --> 00:39:10,960
Speaker 3: is obviously the case for high school because think about

586
00:39:11,560 --> 00:39:14,480
Speaker 3: you gotta hike it back to some kid. He has

587
00:39:14,560 --> 00:39:17,120
Speaker 3: to catch it, he has to put it down, and

588
00:39:17,160 --> 00:39:19,839
Speaker 3: then the other kid has to kick it, and the

589
00:39:19,840 --> 00:39:25,960
Speaker 3: weather's lousy. So he just had no kickers and he

590
00:39:26,680 --> 00:39:30,440
Speaker 3: was winning the state championship in Arkansas, I mean, not

591
00:39:30,640 --> 00:39:36,560
Speaker 3: some place with lousy football. So teams will get better.

592
00:39:36,680 --> 00:39:40,600
Speaker 3: People do learn, But you know, people are always asking

593
00:39:40,640 --> 00:39:43,480
Speaker 3: me what surprised me. What has surprised me is how

594
00:39:43,600 --> 00:39:48,799
Speaker 3: slowly the learning has taken place in Look, how long

595
00:39:48,840 --> 00:39:51,399
Speaker 3: did it take it's the World Series? How long did

596
00:39:51,400 --> 00:39:55,200
Speaker 3: it take them to add a pitch clock? Baseball had

597
00:39:55,200 --> 00:39:59,200
Speaker 3: become unwatchable. They add a pitchclock, It cuts half hour

598
00:39:59,239 --> 00:40:02,320
Speaker 3: off the game. Well, you know that's not a genius.

599
00:40:02,360 --> 00:40:07,120
Speaker 3: They had twenty four second clock in basketball for forty years,

600
00:40:07,520 --> 00:40:09,160
Speaker 3: so they could have figured this out.

601
00:40:10,040 --> 00:40:12,360
Speaker 4: But it's not just sports.

602
00:40:12,400 --> 00:40:15,120
Speaker 5: I think, like the thing that economists push back with

603
00:40:15,160 --> 00:40:18,319
Speaker 5: behavioral economics, it's oh, people just don't know, have opportunities

604
00:40:18,360 --> 00:40:22,439
Speaker 5: to learn right in the endowment effect. If they trade enough,

605
00:40:22,480 --> 00:40:24,680
Speaker 5: if people know what they're doing for long, even for

606
00:40:24,719 --> 00:40:26,640
Speaker 5: a short period of time, they'll figure it out. These

607
00:40:26,640 --> 00:40:29,279
Speaker 5: biases are going to go away. That's the whole Like, look,

608
00:40:29,320 --> 00:40:31,480
Speaker 5: these are just confused subjects. They go to the market,

609
00:40:31,480 --> 00:40:34,319
Speaker 5: they'll get some feedback, everything will be fine. But I

610
00:40:34,360 --> 00:40:37,080
Speaker 5: think that's the biggest I think with Richard being surprised,

611
00:40:37,120 --> 00:40:40,320
Speaker 5: I think not just in sports everywhere that these anomalies

612
00:40:40,360 --> 00:40:42,480
Speaker 5: have held up, and the fact that these anomalies are

613
00:40:42,520 --> 00:40:46,239
Speaker 5: holding up in very, very experienced people. So in the

614
00:40:46,239 --> 00:40:49,319
Speaker 5: paper with traders, you know there, we find that they

615
00:40:49,440 --> 00:40:52,040
Speaker 5: trade really well when they're buying, but on the selling

616
00:40:52,080 --> 00:40:53,920
Speaker 5: they have to do a lot of selling. They sell

617
00:40:53,960 --> 00:40:56,160
Speaker 5: all the time, they have to sell it order to buy.

618
00:40:56,400 --> 00:40:58,920
Speaker 5: They sell all the time. Some of them have years,

619
00:40:59,040 --> 00:41:02,160
Speaker 5: decades of experience, and they're doing worse than random and

620
00:41:02,200 --> 00:41:03,080
Speaker 5: they're selling well.

621
00:41:03,200 --> 00:41:05,720
Speaker 2: Explain that, because that was what was so brilliant about

622
00:41:05,719 --> 00:41:10,760
Speaker 2: that paper. In order to figure out how well they sold,

623
00:41:11,560 --> 00:41:14,360
Speaker 2: you guys came up with the solution of let's randomly

624
00:41:14,440 --> 00:41:18,759
Speaker 2: sell anything else from that manager's holdings and compare it

625
00:41:18,800 --> 00:41:21,800
Speaker 2: to what they actually sold, right, so what are the results?

626
00:41:21,880 --> 00:41:24,640
Speaker 5: The results are, so we basically said, look, we want

627
00:41:24,680 --> 00:41:27,520
Speaker 5: to give these guys a large benefit of the doubt.

628
00:41:27,640 --> 00:41:29,880
Speaker 5: We don't know what their conditions are, so we're just

629
00:41:29,880 --> 00:41:33,439
Speaker 5: going to compare them to a very very easy counterfactual.

630
00:41:33,840 --> 00:41:35,400
Speaker 5: I don't know what they're facing. I'm just going to

631
00:41:35,440 --> 00:41:38,120
Speaker 5: throw a dart in their portfolio and sell that instead.

632
00:41:38,400 --> 00:41:42,520
Speaker 5: And they did worse than random. And basically what we

633
00:41:42,560 --> 00:41:45,359
Speaker 5: found is that they were they didn't have they weren't

634
00:41:45,360 --> 00:41:48,239
Speaker 5: spending a lot of time on the selling decisions.

635
00:41:48,320 --> 00:41:49,960
Speaker 4: We interviewed them and we said, like, what are you

636
00:41:49,960 --> 00:41:52,480
Speaker 4: guys doing. It's like, ah, selling is not really that important.

637
00:41:52,520 --> 00:41:53,960
Speaker 4: It's not really an investment decision.

638
00:41:54,160 --> 00:41:58,600
Speaker 5: And I'm like, you know, all right, I told my

639
00:41:59,040 --> 00:42:01,719
Speaker 5: friends at the University Chicago finance department. They were very

640
00:42:01,760 --> 00:42:07,440
Speaker 5: surprised that selling wasn't an investment decision, and so they

641
00:42:07,440 --> 00:42:09,239
Speaker 5: were just not really paying attention to it. So they

642
00:42:09,239 --> 00:42:11,719
Speaker 5: were just selling the things that were very very salient

643
00:42:11,800 --> 00:42:14,279
Speaker 5: on their screens and the things that they were least

644
00:42:14,320 --> 00:42:17,359
Speaker 5: attached to. So going back to the endowment effect, they

645
00:42:17,360 --> 00:42:19,680
Speaker 5: were selling the things that they had recently bought, but

646
00:42:19,719 --> 00:42:22,560
Speaker 5: if you're good at buying, that's not what you should

647
00:42:22,600 --> 00:42:23,040
Speaker 5: be selling.

648
00:42:23,840 --> 00:42:26,000
Speaker 4: You should be holding onto that for longer to get

649
00:42:26,040 --> 00:42:26,880
Speaker 4: that alpha out.

650
00:42:27,400 --> 00:42:30,240
Speaker 5: And we actually we have a graph in the paper

651
00:42:30,280 --> 00:42:32,399
Speaker 5: called like the alpha decay graph and it was about

652
00:42:32,480 --> 00:42:34,600
Speaker 5: nine months and they were selling it around six.

653
00:42:35,239 --> 00:42:40,200
Speaker 3: You know, we all have blind spots. And you just

654
00:42:40,280 --> 00:42:44,360
Speaker 3: wrote a book recently, you have a chapter on selling

655
00:42:44,600 --> 00:42:48,640
Speaker 3: because of Alex. If I hadn't written that paper, you

656
00:42:48,680 --> 00:42:50,320
Speaker 3: wouldn't have had a chapter on selling.

657
00:42:50,440 --> 00:42:52,080
Speaker 2: I would have had a different chapter. It wouldn't have

658
00:42:52,080 --> 00:42:52,600
Speaker 2: been as good.

659
00:42:53,200 --> 00:42:58,320
Speaker 3: Or there are no articles about selling, basically very few,

660
00:42:58,680 --> 00:43:01,120
Speaker 3: very few. So if you look at the Journal of

661
00:43:01,200 --> 00:43:06,759
Speaker 3: Finance or one of the other top journals, there'll be

662
00:43:06,800 --> 00:43:11,360
Speaker 3: one hundred articles of the form. Use the following three

663
00:43:11,360 --> 00:43:15,279
Speaker 3: criteria a former portfolio, hold for one year, then sell.

664
00:43:16,560 --> 00:43:21,440
Speaker 3: And these guys said, oh, maybe selling could be interesting

665
00:43:22,160 --> 00:43:26,960
Speaker 3: and they could maybe double their alpha if they're selling

666
00:43:27,000 --> 00:43:29,719
Speaker 3: decisions or as good as they're buying decisions.

667
00:43:30,360 --> 00:43:35,200
Speaker 2: Coming up, we continue our live conversation with doctor Richard

668
00:43:35,239 --> 00:43:38,880
Speaker 2: Taylor and doctor Alex Emos, both of the Booth School

669
00:43:38,920 --> 00:43:43,560
Speaker 2: of Business at the University of Chicago, discussing the newest

670
00:43:43,880 --> 00:44:02,760
Speaker 2: edition of their book The Winner's Curse. I'm Barry Ridults.

671
00:44:02,760 --> 00:44:06,759
Speaker 2: You're listening to Bloomberg's Masters in Business. Let's continue our

672
00:44:06,880 --> 00:44:11,960
Speaker 2: live conversation with Richard Taylor and Alex EMUs discussing the

673
00:44:12,120 --> 00:44:16,799
Speaker 2: new edition of the book The Winner's Curse. Well, we

674
00:44:17,000 --> 00:44:20,880
Speaker 2: talked about this last time we discussed this issue. The

675
00:44:21,000 --> 00:44:27,040
Speaker 2: buys are very quantitative and rigorous. The cells are just squishy,

676
00:44:27,160 --> 00:44:31,040
Speaker 2: and every emotional bias that comes in, Oh, this is

677
00:44:31,080 --> 00:44:34,440
Speaker 2: starting to falter. It's not doing what I expected. Something

678
00:44:34,440 --> 00:44:37,879
Speaker 2: else shiny comes along and catches their attention. They need

679
00:44:37,920 --> 00:44:43,200
Speaker 2: to make room in the portfolio. You mentioned blind spots.

680
00:44:44,000 --> 00:44:47,279
Speaker 2: Your friend Danny Koneman used to talk about. I used

681
00:44:47,280 --> 00:44:50,600
Speaker 2: to ask him on occasion, how do you avoid all

682
00:44:50,640 --> 00:44:54,000
Speaker 2: of these biases we all succumb to? And he's like,

683
00:44:54,160 --> 00:44:56,359
Speaker 2: I'm subject to every one of them. We all have

684
00:44:56,960 --> 00:45:01,120
Speaker 2: a bias, blind spot. There's no getting a way from it.

685
00:45:00,880 --> 00:45:02,120
Speaker 2: Is there hope for us?

686
00:45:02,680 --> 00:45:05,960
Speaker 3: Well, the only way you can learn anything is feedback,

687
00:45:06,920 --> 00:45:12,760
Speaker 3: and most of us don't bother, so we don't get

688
00:45:12,800 --> 00:45:21,760
Speaker 3: the feedback. And you know when when my friend Caide,

689
00:45:21,920 --> 00:45:24,359
Speaker 3: my former student that I did the football paper with,

690
00:45:24,880 --> 00:45:28,600
Speaker 3: we were hired for a while by one of the

691
00:45:28,680 --> 00:45:34,319
Speaker 3: NFL teams, and we're they're showing us around their facility

692
00:45:35,280 --> 00:45:39,440
Speaker 3: and we go and there's some room about the size

693
00:45:39,440 --> 00:45:43,080
Speaker 3: of this, full of file cabinets, and we say, what's

694
00:45:43,120 --> 00:45:49,600
Speaker 3: in there? Oh, old scouting reports. Our eyes are getting back,

695
00:45:49,880 --> 00:45:56,719
Speaker 3: you know, like, well, have you ever studied those? No? Right,

696
00:45:56,920 --> 00:46:02,280
Speaker 3: so you know that they have. They have probably twenty

697
00:46:02,360 --> 00:46:09,040
Speaker 3: scouts going around watching games. They've built a two billion

698
00:46:09,160 --> 00:46:14,120
Speaker 3: dollar taj Mahele Stadium. But do they invest a little

699
00:46:14,120 --> 00:46:19,480
Speaker 3: research in improving the process of picking players? No, And

700
00:46:19,760 --> 00:46:23,800
Speaker 3: I must say most firms are not that much better.

701
00:46:24,920 --> 00:46:31,200
Speaker 3: Really well, firms still do interviews, interviews.

702
00:46:31,320 --> 00:46:33,960
Speaker 2: Symphonies are doing blind auditions.

703
00:46:33,520 --> 00:46:35,600
Speaker 3: Yeah, they but they still listen.

704
00:46:38,200 --> 00:46:40,440
Speaker 2: Yes, so arguably.

705
00:46:41,560 --> 00:46:46,040
Speaker 3: Interview look, and I think a great musician can here

706
00:46:48,640 --> 00:46:54,280
Speaker 3: whether you're playing well or poorly, learning anything useful about

707
00:46:54,440 --> 00:46:57,000
Speaker 3: how somebody is going to do on the job from

708
00:46:57,600 --> 00:47:01,719
Speaker 3: the usual job. Interview is very.

709
00:47:01,920 --> 00:47:02,800
Speaker 4: One of our postdocs.

710
00:47:02,800 --> 00:47:06,479
Speaker 5: Actually, as a paper Brian Dreberian on So he worked

711
00:47:06,480 --> 00:47:10,080
Speaker 5: with a company in the Philippines. They replaced all first

712
00:47:10,160 --> 00:47:16,880
Speaker 5: round interviews with artificial intelligence, and retention ended up increasing. Basically,

713
00:47:16,920 --> 00:47:18,879
Speaker 5: they were able to extract the signals that they needed

714
00:47:18,920 --> 00:47:21,600
Speaker 5: to extract at that stage, and the humans were missing.

715
00:47:21,719 --> 00:47:24,359
Speaker 2: So let's talk about this before we're going to open

716
00:47:24,440 --> 00:47:26,520
Speaker 2: up this up for questions in a minute. But let's

717
00:47:26,560 --> 00:47:31,239
Speaker 2: talk about that sort of choice architecture, and I just

718
00:47:31,280 --> 00:47:35,200
Speaker 2: have to share some numbers with people as to how

719
00:47:35,239 --> 00:47:42,640
Speaker 2: significant this could be. Richard's book Nudge described a variety

720
00:47:42,680 --> 00:47:48,160
Speaker 2: of different ways to affect decision making. Perhaps the most

721
00:47:48,200 --> 00:47:50,840
Speaker 2: significant was when you open a four oh one K,

722
00:47:51,840 --> 00:47:55,000
Speaker 2: there's no obligation for you to participate in the company.

723
00:47:55,400 --> 00:47:57,879
Speaker 2: When money goes into it, there's no obligation to put

724
00:47:57,920 --> 00:48:02,520
Speaker 2: that money to work. Sure convinced the SEC and the

725
00:48:02,560 --> 00:48:06,160
Speaker 2: government to change that so that the default is that

726
00:48:06,400 --> 00:48:10,080
Speaker 2: you're assumed to participate and the money goes into some

727
00:48:11,000 --> 00:48:14,600
Speaker 2: qualified fund, either a balance fund or a target date fund.

728
00:48:15,200 --> 00:48:18,520
Speaker 2: And to just put some flesh on how significant that is,

729
00:48:19,600 --> 00:48:23,720
Speaker 2: the US's four oh one K, not counting today's trading action,

730
00:48:24,320 --> 00:48:29,560
Speaker 2: is four point seven trillion dollars. Historically, forty percent of

731
00:48:29,560 --> 00:48:34,040
Speaker 2: that was defaulted to cash, which means there's two trillion

732
00:48:34,120 --> 00:48:37,680
Speaker 2: dollars being invested today that otherwise would have been sitting

733
00:48:37,680 --> 00:48:40,680
Speaker 2: around in cash for god knows how many years. So,

734
00:48:40,960 --> 00:48:45,200
Speaker 2: given what we know about choice architecture, how should we

735
00:48:45,320 --> 00:48:49,840
Speaker 2: be addressing choices and options, whether it's in hiring or

736
00:48:49,880 --> 00:48:53,320
Speaker 2: putting money to work or bidding in auctions? How should

737
00:48:53,360 --> 00:48:55,000
Speaker 2: what should we be doing well better?

738
00:48:55,440 --> 00:48:59,960
Speaker 3: I mean there's so you know. Let on the retire

739
00:49:00,160 --> 00:49:05,200
Speaker 3: and saving thing. We did three things. One was changed

740
00:49:05,239 --> 00:49:11,040
Speaker 3: the default, and we had to get congressional approval for

741
00:49:11,160 --> 00:49:16,120
Speaker 3: all of this because companies said, oh, if we enroll

742
00:49:16,239 --> 00:49:20,000
Speaker 3: somebody without their permission, some lawyer is going to sue

743
00:49:20,080 --> 00:49:25,279
Speaker 3: us as soon as the market goes down. So we

744
00:49:25,280 --> 00:49:27,320
Speaker 3: were able to get a bill passed in two thousand

745
00:49:27,360 --> 00:49:31,640
Speaker 3: and six that said it was okay to automatically enroll,

746
00:49:32,120 --> 00:49:35,800
Speaker 3: it was okay to invest in something I could target

747
00:49:35,880 --> 00:49:38,759
Speaker 3: day fund even though it could go down, and it

748
00:49:38,880 --> 00:49:44,000
Speaker 3: was okay to slowly ramp up their contributions what I

749
00:49:44,040 --> 00:49:47,520
Speaker 3: call save more tomorrow because we all have more self

750
00:49:47,560 --> 00:49:59,080
Speaker 3: control next week. So that you know, those three ingredients

751
00:49:59,640 --> 00:50:05,800
Speaker 3: were important. Maybe that for trillion is twice what it

752
00:50:05,840 --> 00:50:10,360
Speaker 3: would have been without it. It's it's not easy to

753
00:50:10,680 --> 00:50:15,120
Speaker 3: just say, you know, what, what can you do to

754
00:50:16,920 --> 00:50:28,520
Speaker 3: solve obesity or some other problem. My mantra is make

755
00:50:28,600 --> 00:50:33,719
Speaker 3: it easy. If you want people to do something, make

756
00:50:33,800 --> 00:50:38,000
Speaker 3: it easy. I always say, what I would like is

757
00:50:38,080 --> 00:50:42,840
Speaker 3: a world that's like GPS. I have a terrible sense

758
00:50:42,840 --> 00:50:49,040
Speaker 3: of direction, and now I don't get lost hardly ever. Right,

759
00:50:49,200 --> 00:50:52,040
Speaker 3: And notice the GPS doesn't tell you where to go.

760
00:50:53,600 --> 00:50:57,520
Speaker 3: You had the wrong address coming tonight. So but but

761
00:50:57,960 --> 00:51:02,200
Speaker 3: I plugged in the right address. You know, well I

762
00:51:02,320 --> 00:51:04,799
Speaker 3: just had to walk down Fifth Avenue. But even I

763
00:51:04,840 --> 00:51:10,520
Speaker 3: could do that. But right, So for complicated things like

764
00:51:11,880 --> 00:51:18,560
Speaker 3: improving your diet or exercising more, or whatever problem you're

765
00:51:18,600 --> 00:51:24,600
Speaker 3: trying to solve, you have to figure out what's preventing

766
00:51:24,800 --> 00:51:29,600
Speaker 3: people from getting it right and then eliminate that. And

767
00:51:30,640 --> 00:51:35,760
Speaker 3: you know, when when David Cameron was elected Prime Minister

768
00:51:35,920 --> 00:51:41,200
Speaker 3: in the UK, he had get this. They have They

769
00:51:41,239 --> 00:51:47,440
Speaker 3: don't have platforms, they have manifestos. And they had told

770
00:51:47,520 --> 00:51:50,919
Speaker 3: me that they were going to put in their manifesto

771
00:51:51,320 --> 00:51:53,520
Speaker 3: that if they got elected they were going to create

772
00:51:54,000 --> 00:51:57,879
Speaker 3: a nudge unit. And I said, yeah, yeah, because I'm

773
00:51:58,000 --> 00:52:02,759
Speaker 3: used to the US. So but they did it, and

774
00:52:02,800 --> 00:52:05,359
Speaker 3: they called me up and said, hey, we're starting this thing.

775
00:52:05,400 --> 00:52:08,040
Speaker 3: You better come over and figure out how to do this.

776
00:52:08,960 --> 00:52:14,600
Speaker 3: And the main lesson I learned was we'd go to

777
00:52:14,680 --> 00:52:18,799
Speaker 3: the branch. One of the things we did was we

778
00:52:19,880 --> 00:52:23,120
Speaker 3: changed the way they dealt with people who owe money

779
00:52:23,120 --> 00:52:27,640
Speaker 3: on their taxes. And what do you do, Well, we

780
00:52:27,719 --> 00:52:30,560
Speaker 3: send a letter. What does the letter say? Oh, well,

781
00:52:30,600 --> 00:52:32,719
Speaker 3: they showed us the letter. Oh I think we can

782
00:52:32,760 --> 00:52:37,160
Speaker 3: improve the letter. So and we told them truthfully, ninety

783
00:52:37,160 --> 00:52:41,800
Speaker 3: percent of people paid their taxes on time. That increased

784
00:52:41,920 --> 00:52:47,520
Speaker 3: the speed, so brought in money very fast. Costs nothing, Right,

785
00:52:47,600 --> 00:52:50,640
Speaker 3: they're sending a letter. It doesn't cost anymore to write

786
00:52:50,640 --> 00:52:57,480
Speaker 3: a good letter. But you have to talk to the

787
00:52:57,600 --> 00:53:03,680
Speaker 3: experts and understand what it is that's preventing them from

788
00:53:03,960 --> 00:53:06,120
Speaker 3: doing the right thing, and then make it easy for

789
00:53:06,200 --> 00:53:07,759
Speaker 3: them to do that.

790
00:53:07,960 --> 00:53:10,480
Speaker 2: Remove the obstacles, make it easy. All right, So we

791
00:53:10,560 --> 00:53:13,319
Speaker 2: have a few more minutes. Let's get some questions. Can

792
00:53:13,360 --> 00:53:16,279
Speaker 2: I see some hands? Wait for the mic. Let's start

793
00:53:16,360 --> 00:53:18,399
Speaker 2: right up front and work our way back.

794
00:53:20,320 --> 00:53:24,560
Speaker 6: Thank you so much for super interesting discussion. I'm curious

795
00:53:24,680 --> 00:53:28,840
Speaker 6: Robert Oman has a theory that says that reasonable people

796
00:53:29,120 --> 00:53:32,720
Speaker 6: can't disagree, and I'm curious, like how you would relate

797
00:53:32,760 --> 00:53:34,960
Speaker 6: that to the winner's person if you've ever had a

798
00:53:35,000 --> 00:53:35,520
Speaker 6: discussion with.

799
00:53:35,560 --> 00:53:39,120
Speaker 3: Him about that. Reasonable people can't disagree.

800
00:53:39,360 --> 00:53:40,440
Speaker 2: He's never been online.

801
00:53:40,480 --> 00:53:46,640
Speaker 3: I mean, you know, Alex and I are both extremely reasonable.

802
00:53:47,000 --> 00:53:49,680
Speaker 3: We disagree, we disagree on all kinds of things.

803
00:53:49,840 --> 00:53:52,359
Speaker 5: Well, his theorem is basically, if you have the same

804
00:53:52,400 --> 00:53:55,840
Speaker 5: information set, you have to arrive in the same belief

805
00:53:56,880 --> 00:54:00,479
Speaker 5: the same information you need, common knowledge, you need.

806
00:54:00,560 --> 00:54:03,120
Speaker 2: You need com each other's rationality.

807
00:54:03,239 --> 00:54:05,000
Speaker 3: You don't need the same confition.

808
00:54:05,560 --> 00:54:10,080
Speaker 5: But and then you arrive at the exact same posterior belief.

809
00:54:10,360 --> 00:54:10,560
Speaker 3: Right.

810
00:54:10,680 --> 00:54:14,239
Speaker 5: But the problem is that people have confirmation bias in

811
00:54:14,280 --> 00:54:18,799
Speaker 5: the real world, right, so they seek out information that

812
00:54:18,880 --> 00:54:22,000
Speaker 5: confirms their beliefs. So they end up not only uh,

813
00:54:22,239 --> 00:54:24,799
Speaker 5: they basically end up in completely different worlds thinking that

814
00:54:24,880 --> 00:54:27,560
Speaker 5: they're rational and somebody else who's not agreeing with them

815
00:54:27,880 --> 00:54:32,560
Speaker 5: is irrational. And that if you go back to Robert Auman,

816
00:54:33,080 --> 00:54:37,760
Speaker 5: that breaks that that condition for being able to agree.

817
00:54:37,800 --> 00:54:39,799
Speaker 5: So one of the things about the Internet age and

818
00:54:39,840 --> 00:54:43,279
Speaker 5: digital the digital transformation is that look, if you read

819
00:54:43,520 --> 00:54:46,120
Speaker 5: like the texts of what technologists we're writing, we're going

820
00:54:46,200 --> 00:54:49,840
Speaker 5: to be in a utopia, millions of library of Alexandria's

821
00:54:49,880 --> 00:54:52,759
Speaker 5: at our fingertip. Everybody will have all information at the

822
00:54:52,800 --> 00:54:57,399
Speaker 5: same time. We should all agree, right, but we're not

823
00:54:57,440 --> 00:55:00,800
Speaker 5: there yet, we have all the information, we are there yet.

824
00:55:00,920 --> 00:55:03,360
Speaker 3: But what ended up happening agreeing?

825
00:55:03,760 --> 00:55:06,839
Speaker 5: But it's because people are seeking information that confirms their

826
00:55:06,880 --> 00:55:09,759
Speaker 5: priors and confirms the priors of the people that they're

827
00:55:09,920 --> 00:55:12,960
Speaker 5: the and and because you can't think that your prior

828
00:55:13,080 --> 00:55:15,440
Speaker 5: is wrong if you could, if you meet somebody with

829
00:55:15,480 --> 00:55:18,120
Speaker 5: a different prior or a different belief, you assume that

830
00:55:18,160 --> 00:55:21,400
Speaker 5: they are irrational. And then we're not going to agree.

831
00:55:21,400 --> 00:55:22,880
Speaker 5: I'm going to hold out to my belief. You're going

832
00:55:22,920 --> 00:55:25,520
Speaker 5: to hold onto your belief. And the more this kind

833
00:55:25,520 --> 00:55:29,560
Speaker 5: of ramps up, the ability to anddogenously gather information and

834
00:55:29,880 --> 00:55:34,560
Speaker 5: the feeding of that process from the information providers gets

835
00:55:34,600 --> 00:55:35,240
Speaker 5: worse and worse.

836
00:55:36,480 --> 00:55:40,600
Speaker 2: The fascinating part about the stock market is all the

837
00:55:40,640 --> 00:55:43,560
Speaker 2: economic data is out there, all the market analysis is

838
00:55:43,560 --> 00:55:47,240
Speaker 2: out there. Bulls and bears go out and they find

839
00:55:47,320 --> 00:55:53,239
Speaker 2: what supports their view. They rarely seek disconfirming advice. And hey,

840
00:55:53,400 --> 00:55:57,160
Speaker 2: trade is where there's a disagreement on value, button agreement

841
00:55:57,160 --> 00:56:00,279
Speaker 2: on price, and it's all the same information people check

842
00:56:00,320 --> 00:56:01,200
Speaker 2: pick And.

843
00:56:00,960 --> 00:56:04,600
Speaker 3: Perhaps the biggest anomaly in financial markets is the volume

844
00:56:04,640 --> 00:56:05,480
Speaker 3: of trade.

845
00:56:05,520 --> 00:56:07,400
Speaker 4: That people are trading from place.

846
00:56:07,400 --> 00:56:10,799
Speaker 3: That that's the counter example, how can we have a

847
00:56:10,800 --> 00:56:20,880
Speaker 3: trillion shares traded if everybody agrees? Yeah, right, the mic right.

848
00:56:21,680 --> 00:56:23,360
Speaker 2: So this is actually a related question.

849
00:56:24,080 --> 00:56:26,520
Speaker 7: You've heard a paper in nineteen ninety seven on the

850
00:56:26,560 --> 00:56:29,759
Speaker 7: equity premium puzzle. You finished it by saying, you have

851
00:56:29,800 --> 00:56:32,279
Speaker 7: any issues with this, call me in twenty seventeen. So

852
00:56:32,400 --> 00:56:35,600
Speaker 7: in twenty seventeen, two fourth years at the University of

853
00:56:35,640 --> 00:56:38,200
Speaker 7: Chicago studied economics. Email do you try to call you

854
00:56:38,239 --> 00:56:41,920
Speaker 7: on your bluff? You met with them, which me and

855
00:56:41,920 --> 00:56:46,160
Speaker 7: my now husband do appreciate, and we both work in

856
00:56:46,200 --> 00:56:49,799
Speaker 7: finance now and I am curious, especially you mentioned, like

857
00:56:49,920 --> 00:56:53,680
Speaker 7: the volume of trading Democratization of trading Robin Hood Traders.

858
00:56:54,160 --> 00:56:57,200
Speaker 7: As you're refreshing this book more broadly, how is technology

859
00:56:57,760 --> 00:57:00,920
Speaker 7: changed or magnified some of the behavioral confects that you

860
00:57:00,960 --> 00:57:03,359
Speaker 7: were writing about in nineteen ninety two.

861
00:57:04,120 --> 00:57:10,520
Speaker 3: So, of course I vividly remember this. And there's a

862
00:57:10,560 --> 00:57:16,920
Speaker 3: bridge over in here, Brooklyn, you know the So you're right,

863
00:57:16,920 --> 00:57:20,520
Speaker 3: there was a chapter. It wasn't a chapter in the

864
00:57:20,520 --> 00:57:23,280
Speaker 3: original book, but I did write a column on the

865
00:57:23,280 --> 00:57:26,840
Speaker 3: equity premium puzzle and then a paper about it, and

866
00:57:26,880 --> 00:57:30,920
Speaker 3: we talked about whether it included. Instead, we just have

867
00:57:31,680 --> 00:57:36,479
Speaker 3: a couple pages saying the equity premium is within one

868
00:57:36,560 --> 00:57:40,920
Speaker 3: percent of what it was when the first paper was published.

869
00:57:41,280 --> 00:57:44,360
Speaker 3: It's going from seven down to six or something like that.

870
00:57:44,800 --> 00:57:50,560
Speaker 3: So it didn't seem worth a chapter. But I'll let Alex,

871
00:57:50,840 --> 00:57:55,680
Speaker 3: who's the now part talk about the technology.

872
00:57:56,440 --> 00:57:59,240
Speaker 5: Yeah, So I think the democracy you mentioned the democratization

873
00:57:59,280 --> 00:58:02,000
Speaker 5: of finance, and I think there was this hope that again,

874
00:58:02,200 --> 00:58:07,400
Speaker 5: you know, people have access to basically there's no transaction fees,

875
00:58:07,440 --> 00:58:09,560
Speaker 5: you log onto your phone, you have access to these

876
00:58:09,640 --> 00:58:12,760
Speaker 5: equities and financial products that you didn't have access before.

877
00:58:13,040 --> 00:58:16,400
Speaker 5: So now everyday people have you know, can can get

878
00:58:16,400 --> 00:58:18,040
Speaker 5: a piece of the pie. They can get a piece

879
00:58:18,040 --> 00:58:20,440
Speaker 5: of the real growth of the economy. So some of

880
00:58:20,440 --> 00:58:23,560
Speaker 5: our colleagues wrote a paper at the University of Chicago recently.

881
00:58:23,680 --> 00:58:24,680
Speaker 4: I think he came out.

882
00:58:24,480 --> 00:58:28,520
Speaker 5: This year doing an audit of what people are trading

883
00:58:28,560 --> 00:58:33,680
Speaker 5: on platforms like Robinhood. They're trading weekly options and they're

884
00:58:33,720 --> 00:58:37,240
Speaker 5: losing billions of dollars. I think within the last two

885
00:58:37,280 --> 00:58:42,320
Speaker 5: years they lost six billion dollars, right, and so, and

886
00:58:42,480 --> 00:58:45,560
Speaker 5: why is that? Well, the same sort of biases that

887
00:58:45,600 --> 00:58:48,160
Speaker 5: you have that you're documenting them lap that you document

888
00:58:48,240 --> 00:58:51,840
Speaker 5: in the field, they're on steroids in digital spaces. Right.

889
00:58:51,880 --> 00:58:54,600
Speaker 4: What is robinhood doing? Where is it making its money?

890
00:58:54,600 --> 00:58:58,840
Speaker 5: Because the Robinhood does make money, it's making it on spreads, right,

891
00:58:59,200 --> 00:59:04,080
Speaker 5: and the itis spreads are crazy instruments like options that

892
00:59:04,480 --> 00:59:07,720
Speaker 5: generate these lottery like returns that we know human people

893
00:59:07,720 --> 00:59:10,840
Speaker 5: are really attracted to. And so it's making money off

894
00:59:10,840 --> 00:59:13,560
Speaker 5: of behavioral biases and people are losing a lot of

895
00:59:13,560 --> 00:59:14,120
Speaker 5: money on it.

896
00:59:14,240 --> 00:59:17,320
Speaker 2: So it's not even the weekly options, it's the single

897
00:59:17,400 --> 00:59:19,720
Speaker 2: day options now have become the biggest volume.

898
00:59:20,920 --> 00:59:27,000
Speaker 3: Right. And of course pure gambling sports gambling is even

899
00:59:27,040 --> 00:59:28,320
Speaker 3: worse odds.

900
00:59:29,400 --> 00:59:32,440
Speaker 2: Where you can bet on every play, not even the

901
00:59:32,440 --> 00:59:33,400
Speaker 2: outcome of the well.

902
00:59:33,520 --> 00:59:37,640
Speaker 3: And now they're learning that these bets on what a

903
00:59:37,720 --> 00:59:41,600
Speaker 3: specific player does, they've got to get rid of those

904
00:59:41,880 --> 00:59:44,800
Speaker 3: because there's just going to be too many scandals.

905
00:59:45,600 --> 00:59:48,959
Speaker 2: That was my live conversation with Richard Taylor and Aleximus.

906
00:59:49,680 --> 00:59:52,040
Speaker 2: Be sure and check out the full version of this

907
00:59:52,160 --> 00:59:55,680
Speaker 2: coming sometime in the coming months. I would be remiss

908
00:59:55,720 --> 00:59:57,880
Speaker 2: if I didn't thank the Cracked team that helps put

909
00:59:57,920 --> 01:00:02,400
Speaker 2: these conversations together. A special thanks goes to the Economic

910
01:00:02,440 --> 01:00:06,240
Speaker 2: Club of New York for hosting this event. My audio

911
01:00:06,280 --> 01:00:11,080
Speaker 2: engineer is Justin Milner. Anna Luke is my producer. Sean

912
01:00:11,160 --> 01:00:15,880
Speaker 2: Russo is my researcher. I'm Barry Renaults. You've been listening

913
01:00:15,920 --> 01:00:20,480
Speaker 2: to a bonus live edition of Masters in Business on

914
01:00:20,600 --> 01:00:21,560
Speaker 2: Bloomberg Radio.