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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: This week on the podcast, two extra special guests alex

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Speaker 2: EMUs and Richard Thaylor. Took Richard's book The Winner's Curse

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Speaker 2: and really completely rewrote it and updated it for twenty

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Speaker 2: twenty five. I've been privileged to speak with doctor Taylor

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Speaker 2: number of times over the past few years. He's been

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Speaker 2: a guest both here and live in Chicago a number

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Speaker 2: of times. Always a fascinating conversation. And alex EMUs, is

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Speaker 2: this really interesting professor who I had no idea I

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Speaker 2: have used and relied on his previous research. Selling Fast

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Speaker 2: and Buying Slow is a chapter in my book. Just

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Speaker 2: an amazing coincidence. Both fascinating people and if let This

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Speaker 2: conversation was a lot of fun, and I think you

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Speaker 2: will also with no further ado, alex Amus and Richard

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Speaker 2: Thaylor on The Winner's Curse.

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Speaker 3: Thanks Barry, great to be back.

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Speaker 2: Yes, it's so great to have you. So you started,

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Speaker 2: you wrote this book. It's thirty years ago already. We're

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Speaker 2: gonna get to this in a bit. Before we do,

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Speaker 2: I want to just talk about both of your backgrounds

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Speaker 2: and how you began collaborating. Richard, you've been called the

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Speaker 2: godfather of behavioral economics. Take us back to the beginning,

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Speaker 2: when you were a young economist. How did you become

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Speaker 2: interested in psychology and decision making? It?

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Speaker 4: So, when I was in grad school and I was

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Speaker 4: learning standard economics, I kept pausing and saying, really, because

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Speaker 4: the models that we were being taught the people, well,

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Speaker 4: there are no people, and there are agents, and there

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Speaker 4: are firms, and there are things they call consumers, but

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Speaker 4: they're not really people.

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Speaker 3: Homo economists, homo homo economicus.

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Speaker 4: And I started making a list of dumb stuff people do,

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Speaker 4: and but that was just to annoy my friends.

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Speaker 3: But then somebody introduced me to the.

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Speaker 4: Work of to Israeli psychologists Danny Kahneman and nameds. Tavski,

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Speaker 4: And when I read their papers, I had this big

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Speaker 4: Aha moment because what their research showed was not just

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Speaker 4: that people make mistakes. Of course, we all make mistakes

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Speaker 4: and I can't remember where we left our keys or

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Speaker 4: what have you. What they showed was that behavior is

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Speaker 4: predictably different from the model that economists use. And that

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Speaker 4: was an AHA moment for me because it meant I

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Speaker 4: could say, look, the model is wrong and in this direction,

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Speaker 4: and you can think about that from an investment point

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Speaker 4: of view. It's fine to say stock prices are wrong.

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Speaker 4: That's fine, but useless. If you can say which ones

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Speaker 4: are too high and which ones are too low, then

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Speaker 4: all of a sudden you're a very rich man. So

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Speaker 4: if we could say how people are different than this

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Speaker 4: artificial model, than.

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Speaker 3: We could be in business. And then.

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Speaker 4: So I was doing that for a while, managed to

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Speaker 4: get tenure at Cornell University and spent a year with

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Speaker 4: Connoman and Tiwski, and then a second sabbatic year with Connoman,

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Speaker 4: and in nineteen eighty five, the year Alex was born,

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Speaker 4: I came back from sabbatical and decided to start writing

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Speaker 4: a series of columns in a new economics journal called

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Speaker 4: the Journal of Economic Perspectives. That journal, by the way,

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Speaker 4: here's a free tip. That journal is available free to anyone,

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Speaker 4: and the articles are written to be understandable, and people

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Speaker 4: don't know about it. If you're really interested in economics,

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Speaker 4: go and read some papers.

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Speaker 2: And the the column you were writing was called anomalies,

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Speaker 2: which were all of these things that were supposed to

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Speaker 2: not be possible given traditional economic theory. You mentioned Khonomen

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Speaker 2: and Tavsky. When you think of psychologists, you don't think

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Speaker 2: of quantitative, data driven, rigorous models. But really that was

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Speaker 2: at the heart of what they were doing, wasn't it.

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Speaker 3: Well, eventually.

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Speaker 4: Their earlier work you're thinking of prospect theory, which is

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Speaker 4: nineteen seventy nine. The work they did in the seventies

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Speaker 4: leading up to that was on predictions or judgments, and

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Speaker 4: the models weren't very quantitative. They were typically a little

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Speaker 4: scenario and almost like a thought experiment. You know, there's

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Speaker 4: a famous experiment about Lynn and they give you.

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Speaker 3: A description of Linda.

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Speaker 4: She was an undergraduate active in social movements, went to

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Speaker 4: lots of demonstrations, blah blah blah. She's now And now

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Speaker 4: you get a list of occupations and you're asked to

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Speaker 4: say which is most likely. And one of the ones

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Speaker 4: is bank teller, and another one is feminist bank teller.

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Speaker 4: And people think she's more likely to be a feminist

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Speaker 4: bank teller than a bank teller. Now, obviously that cannot

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Speaker 4: be true. I shouldn't say obviously, because many people are

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Speaker 4: now listening and saying, what does he mean? Obviously, obviously

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Speaker 4: she's a feminist bank teller. She couldn't just be a

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Speaker 4: bank teller. But that's you know that number theory. They

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Speaker 4: are going to be more bank teller.

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Speaker 2: Yeah, bank.

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Speaker 4: Think of a Venn diagram, right, there's a a big

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Speaker 4: circle of bank tellers and then a small one with

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Speaker 4: feminist bank tellers. So that was the kind of things

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Speaker 4: they were doing. There was a little bit of theory.

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Speaker 4: So like they had. The idea was that life is hard,

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Speaker 4: and so people used what they called heuristics rules of thumb.

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Speaker 3: To make judgments.

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Speaker 4: One is called the availability heuristic, which is if it's

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Speaker 4: easier to think of examples of something, it's more likely.

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Speaker 4: So if you ask people what's the ratio of homicides

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Speaker 4: to suicides, people think maybe two or three to one

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Speaker 4: that homicides.

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Speaker 3: Are more likely. It's just even money, no, twice as

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Speaker 3: many suicides.

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Speaker 4: Think about this before you buy a gun, right, the

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Speaker 4: most likely person to get killed with that gun is

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Speaker 4: a family member. So but again, notice this is a

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Speaker 4: predictable mistake. And because why well, there's lots of stories

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Speaker 4: in the newspaper about homicides.

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Speaker 3: Suicides tend to be quieter.

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Speaker 2: There's a wonderful graphic from our world in data, which

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Speaker 2: was Hans Rosling's work. That shows here's how things are

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Speaker 2: reported in the media, and then here's their actual percentage

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Speaker 2: in real life. Very little reporting on cancer, heart disease,

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Speaker 2: high blood pressure, diabetes. You're fifty thousand times more likely

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Speaker 2: to suffer from that than homicide, terrorism, or shark attacks,

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Speaker 2: which they love to uh.

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Speaker 3: Write shark texts. Don't worry about so.

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Speaker 2: Chespesralia, especially in Chicago, it's probably not.

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Speaker 3: A big Yeah, they are very few.

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Speaker 2: So let's let's bring Alex in. So when Richard started

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Speaker 2: out in the field, there really wasn't any such thing

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Speaker 2: as behavioral economics. You have an advantage a few decades

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Speaker 2: later of entering the field of behavior of economics, where

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Speaker 2: behavioral economics is a thing. Tell us a little bit

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Speaker 2: about what brought you into the field and how you

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Speaker 2: found your way over to booth.

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Speaker 5: Well, so, behavioral economics was a thing out in the

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Speaker 5: economics journals, and you know there are people certainly doing

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Speaker 5: it in various departments, but it wasn't a thing as

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Speaker 5: an undergrad Like, I don't think there was a single

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Speaker 5: behavioral economics course offered an Northwestern University while and this

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Speaker 5: was two thousand, two thousand and three through two thousand

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Speaker 5: and seven. So even though you know, people were publishing

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Speaker 5: behavioral economics papers, it was all over the journals. People

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Speaker 5: generally in the field knew about it as an undergraduate,

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Speaker 5: it still had not made it into the court three

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Speaker 5: to seven.

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Speaker 2: Danny was two thousand and two on the nobelis. Yeah, right,

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Speaker 2: you would have thought someone might have picked up on that,

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Speaker 2: And yet.

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Speaker 5: No, I mean, Western is a big school. You open

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Speaker 5: up a microeconomics textbook, it's the same textbook from nineteen

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Speaker 5: seventy three.

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Speaker 2: Basically today it's still come on, Really, I would have

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Speaker 2: assumed at this point, open up a textbook Danny Kahneman,

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Speaker 2: Bob Schiller, Richard Thaylor, how many Nobels have to come

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Speaker 2: in this space.

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Speaker 5: Before starts with perfect competition? Uh huh. Then at the

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Speaker 5: end maybe you learn something about monopolies and that that's

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Speaker 5: pretty much it. So it's a I Actually I was

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Speaker 5: pre med. I I'm an immigrant kid from Oldova. So

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Speaker 5: my parents are like, well, you're going to the medical

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Speaker 5: school or you're going to fail. Basically, so I had

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Speaker 5: one option on the table. So I was pre med.

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Speaker 5: Organic chemistry was real hard, and it was eight o'clock

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Speaker 5: in the morning. So I took econ to kind of

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Speaker 5: just boost my GPA. I thought it was kind of fun, right,

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Speaker 5: and you know, I didn't. And it was interesting because

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Speaker 5: I was taking these psychiatry abnormal psychology classes learning about

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Speaker 5: human behavior. I was thinking economics, which is the study

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Speaker 5: of human behavior, and these were like two completely different worlds. Right,

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Speaker 5: Economics is these hyper rational utility maximizers had never made

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Speaker 5: any systematic mistakes, and no, I didn't learn about a

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Speaker 5: single deviation from that principle in the entire four years

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Speaker 5: I was there. And so I was thinking, this is

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Speaker 5: kind of you know, this is fun, but not something

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Speaker 5: I wanted to do. I'm interested in human beings. And

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Speaker 5: then afterwards I was applying to medical school, and I

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Speaker 5: was doing a cross country road trip with one of

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Speaker 5: my friends to Los Angeles and we were listening to

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Speaker 5: I think it was NPR, and it turned out ex Post.

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Speaker 5: I figured this out. Richard was on the radio talking

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Speaker 5: about something called behavioral economics, and I was like, what

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Speaker 5: is this? And as soon as I got to Los Angeles,

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Speaker 5: you know, I went on the internet and I was like,

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Speaker 5: I got to find out more about this field. So

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Speaker 5: I within two weeks I had, you know, talked to

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Speaker 5: my advisors at Northwest. I want to get an EYCONPHD.

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Speaker 5: If I can do something like this where I could

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Speaker 5: combine my interest in economics and bring in human behavior

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Speaker 5: into it, this is what I wanted to do.

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Speaker 2: So let's talk about that. There's something in the book

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Speaker 2: and we'll get to that shortly. Where you describe Richard.

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Speaker 2: You describe an economist developing a new model, a new

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Speaker 2: calculation for how consumers should behave in response to certain

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Speaker 2: price incentives. So the first time ever someone creates this

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Speaker 2: calculation and then immediately afterwards and therefore this is how

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Speaker 2: all consumers are or should be behaving when nobody had

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Speaker 2: thought of this previously. How do you square that circle?

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Speaker 2: How do you square the model? Driven? This is the

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Speaker 2: right way to do it. I just figured this out,

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Speaker 2: and therefore everybody should be doing it this way.

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Speaker 4: Yeah, you know, maybe just a tiny bit of history

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Speaker 4: will get us there. So economics didn't used to be

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Speaker 4: that extreme. If you go back and read Adam Smith,

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Speaker 4: he talks about self control problems and overconfidence, and people

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Speaker 4: think of him as the father of right wing economics.

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Speaker 3: That's not the guy.

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Speaker 4: He did talk about the invisible hand, but he was

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Speaker 4: a behavioral economist at heart, and economists were pretty reasonable

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Speaker 4: until about World War two. And then what happened was

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Speaker 4: people started writing math doing math, and they wanted to

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Speaker 4: write down models. And if you want to write down

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Speaker 4: a model, the easiest one to write down is a

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Speaker 4: rational model.

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Speaker 3: And that's because.

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Speaker 4: Anybody, if you've taken high school calculus, you know you

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Speaker 4: can maximize. You set the first derivet of equal to zero,

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Speaker 4: and that's the model, right, So writing down a model of.

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Speaker 3: Some ish is hard.

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Speaker 4: Then during the seventies and eighties, people started to get

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Speaker 4: ideas for even smarter behavior, and a norm kind of

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Speaker 4: developed in economics, which is, if the agents in my

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Speaker 4: model are smarter than the agents in your model, then

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Speaker 4: my models.

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Speaker 3: Better than your model. And that's kind of crazy, but.

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Speaker 4: That was the way the field was going and there

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Speaker 4: was no real stopping it.

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Speaker 3: So around the.

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Speaker 4: Time that Alex was thinking about going to grad school,

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Speaker 4: there were troublemakers like me pointing at certain body parts

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Speaker 4: of this naked emperor. But the field was rushing toward

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Speaker 4: an extreme version of homo economicus, where Homo economicus is

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Speaker 4: a genius.

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Speaker 2: So We were talking a little earlier about the so

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Speaker 2: called wealth effect, which is something that the economists at

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Speaker 2: the Federal Reserve love. The higher the market goes, the

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Speaker 2: wealthier people supposedly feel, and they all go out and

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Speaker 2: spend money. That's like just such a perfect example of

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Speaker 2: a model that doesn't reflect the real world. A huge

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Speaker 2: amount of stocks are owned by the top ten percent.

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Speaker 2: It's something like fifty two percent of stocks. The average

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Speaker 2: person doesn't really have a whole lot at stake in

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Speaker 2: the market. And the reality is people are spending more

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Speaker 2: money because the economy is doing well. They have jobs

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Speaker 2: or getting raises, which, by the way, all helps the market.

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Speaker 2: How often do we run into these correlation causation issues

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Speaker 2: in economics, Well, we.

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Speaker 3: Run into them all the time.

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Speaker 4: Look the big problem with that with the wealth effect.

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Speaker 4: There's a lot of discussion of that in this book.

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Speaker 4: One thing economists leave out is what I call mental accounting.

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Speaker 4: And if you look at an economic model of the

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Speaker 4: wealth effect, there's some big w for wealth and that's it.

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Speaker 4: And wealth will include your house and your retirement money,

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Speaker 4: and money you've set aside for your kid's education, and

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Speaker 4: then money that you intend to give to charity.

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Speaker 5: And your future expectations.

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Speaker 6: And right and all of the money that you start right,

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Speaker 6: So now the people have defed if they're saying, well,

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Speaker 6: w goes up, then people spend more. No, it turns out,

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Speaker 6: for example, if the value of your house goes up,

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Speaker 6: how much more do you spend?

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Speaker 4: Approximately zero? Really approximately zero. Whereas if some stock you

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Speaker 4: own gets bought and you get a check, you spend

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Speaker 4: a lot of that. If you win a lottery, you spend.

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Speaker 3: Like half of it and go bankrupt.

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Speaker 4: So where the money sits has a big effect on

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Speaker 4: how much of it you spend.

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Speaker 2: Your response to somebody's question how are you going to

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Speaker 2: spend the windfall from the Nobel Prize was one of

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Speaker 2: my favorite answers. You said, do you recall?

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Speaker 4: Yeah, well I recall. I mean this was at four

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Speaker 4: in the morning. They call you and wake you up

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Speaker 4: and then say go get some coffee because you have

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Speaker 4: a press conference in half an hour. And I had

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Speaker 4: heard enough of these interviews to know that somebody was

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Speaker 4: likely to ask me that question, and my instinct was

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Speaker 4: to say, well, you know, to a real economist, this

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Speaker 4: is a stupid question, because how am I going to know?

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Speaker 3: You know, suppose I go out and buy some fancy

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Speaker 3: new car. Barry likes fancy cars. I don't. I like

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Speaker 3: fancy wine.

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Speaker 4: So suppose I go and buy a case of fancy wine.

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Speaker 4: How do I know that's the Nobel money as opposed

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Speaker 4: to the money I got from selling a book.

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Speaker 2: All dollars are fungible.

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Speaker 3: All dollars are fungible.

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Speaker 4: And you know, I've realized later that what I should

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Speaker 4: have done is opened up a special account, the prize,

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Speaker 4: Nobel Prize money, and a credit card that's linked to that,

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Speaker 4: and when I want to go buy something stupid, just

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Speaker 4: take out the Nobel card and life would be more fun.

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Speaker 2: But the line that you said was as irrationally as

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Speaker 2: I can.

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Speaker 3: Yeah, I said, I'll just spend it as irrationally as possible.

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Speaker 3: Just I knew it would be a memorable line.

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Speaker 2: So it's so funny because that line led to a

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Speaker 2: conversation with my CFO about the difference in all of

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Speaker 2: these you know, the Chase Sapphire card or the X

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Speaker 2: Platinum card where you get these points and the rational

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Speaker 2: CFO says, hey, I want the money back each month,

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Speaker 2: And my response is always it's one hundred, two hundred dollars.

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Speaker 2: It's lost in your bank account. You don't see it.

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Speaker 2: When I get the points and want to buy a

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Speaker 2: fancy cappuccino maker that my wife is going to yell

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Speaker 2: at me, Why you spending two thousand dollars on a

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Speaker 2: cappuccino maker, you idiot. My answer is, Oh, no, it's points,

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Speaker 2: it's free, and she's like, okay, go get it. It's

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Speaker 2: the exact same concept. If you have that silo, that

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Speaker 2: mental accounting, you could do as much of rationality as

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Speaker 2: you'd like.

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Speaker 4: So you know, but watch out if she listens to

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Speaker 4: this podcast.

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Speaker 2: She listens to the first five minutes, and that's this, Oh.

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Speaker 4: Yeah, so you're safe because so I'll tell you a

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Speaker 4: story about my daughter Maggie, who lives in Rhode Island,

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Speaker 4: and one of her neighbors grew up to be a

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Speaker 4: pitcher for the Mets, and the Mets were playing in

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Speaker 4: the playoffs the.

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Speaker 2: First round, so it's a long time ago, yeah, and

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Speaker 2: this old.

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Speaker 4: Story, and this guy was going to pitch. So I

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Speaker 4: called Maggie, Hey, would you guys want to go to

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Speaker 4: the game?

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Speaker 3: Let me see if I can get tickets, and she says, oh,

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Speaker 3: that'd be great. So I go online the game is

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Speaker 3: like tomorrow, and I find some tickets and there were

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Speaker 3: a bunch you know, on stub hub or something you

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Speaker 3: could get tickets. So I text her back and said,

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Speaker 3: look here, here's the website. It looks like there are

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Speaker 3: lots of tickets to choose from. How about the tickets

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Speaker 3: were about three hundred bucks.

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Speaker 4: I said, how about I'll text I'll send you one

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Speaker 4: thousand dollars buy the tickets you want, spend the rest

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Speaker 4: on hot talks.

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Speaker 2: So you're doing an experiment on your daughter to see

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Speaker 2: if she buys the cheap tickets or no.

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Speaker 5: Oh no.

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Speaker 4: So she texts me back and says, lol, this is

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Speaker 4: just like in your book. If you send me one

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Speaker 4: thousand dollars, I'm not going to use it on baseball tickets.

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Speaker 4: So I've learned my lesson. Recently, she wanted to go

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Speaker 4: to a concert. David Byrne is on tour and he

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Speaker 4: was in Providence, where she lives, and she wanted to go.

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Speaker 3: And she says, so, some way you could get me tickets.

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Speaker 3: I said, to the.

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Speaker 2: Tickets instead of the money, that's so funny. Let's talk

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Speaker 2: a little bit about the book The Winner's Curse and

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Speaker 2: I want to start with Alex. So this book has

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Speaker 2: been out since you were a young kid, and you

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Speaker 2: go to college, you eventually figure, let me get a

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Speaker 2: PhD in behavioral economics or finance and economics. How did

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Speaker 2: you first discover this book? What was your initial response

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Speaker 2: to it?

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Speaker 5: So I discovered it. There's not really any textbooks and

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Speaker 5: behavioral economics, so you kind of get here through the

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Speaker 5: grape vine. Oh, you should read this, you should read that.

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Speaker 5: You mostly read journal articles like with if you're thinking

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Speaker 5: about doing game theory or something like that. There's like

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Speaker 5: five or six textbooks that you can read with behavioral economics.

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Speaker 5: There's not a whole bunch. Winner's Curse was one of

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Speaker 5: those books that almost everybody recommends because the anomalies columns

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Speaker 5: are just very, very accessible, and then you read the

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Speaker 5: anomalies columns, they got a bunch of references. You look

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Speaker 5: through the reference So I had read the original Winner's

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Speaker 5: Curse I think second or third year at grad school,

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Speaker 5: and then I got my first job at Cardigi mel

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Speaker 5: and I had already known Richard for a while at

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Speaker 5: that point. We met in graduate school. His office was

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Speaker 5: happened to be right next to mine in San Diego,

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Speaker 5: and at some point I joined Booth and he called

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Speaker 5: me up I think like four or five months into

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Speaker 5: my first year, and said, hey, you know, I got

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Speaker 5: this opportunity. We want to the published asset to update

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Speaker 5: the book. I'm thinking of doing a little bit more

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Speaker 5: than just an update. You know the books from nineteen

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Speaker 5: ninety two, there's been thirty years of research. Are you

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Speaker 5: interested in working together on this? So, I mean I

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Speaker 5: jumped on the opportunity. One, you know, I get to

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Speaker 5: work with Richard, which is super fun. But two, I mean,

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Speaker 5: you know, I've been doing behavioral economics research for a while,

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Speaker 5: and I know how much demand there is for a

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Speaker 5: book that people can pick up and read and say, hey,

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Speaker 5: these are the original anomalies. Here's the thirty years of

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Speaker 5: research that has happened since. Now. I think at that

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Speaker 5: point we were thinking, like, you know, six months, do

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Speaker 5: a little update. This is twenty twenty. This conversation happened

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Speaker 5: in twenty twenty. The book is coming out now. We

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Speaker 5: you know, basically the two thirds of the book ended

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Speaker 5: up being brand new. We wrote, we rewrote slightly. Each

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Speaker 5: anomalies column is kind of the bedrock. But you know,

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Speaker 5: thirty years of research has happened since, and it took

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Speaker 5: a while to put all of that together. And essentially

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Speaker 5: what we showed is, look, the original anomalies, when you

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Speaker 5: read them, most of the experiments, most of the findings

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Speaker 5: are from you know, college students sometimes you know, after

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Speaker 5: a bad night out in the lab for you know,

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Speaker 5: making decisions over a dollar. And the big kind of

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Speaker 5: pushback from economists was, look, we don't really care about

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Speaker 5: these people. We care about you know, institutional investors, CEOs.

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Speaker 5: We care about people who are in the market with

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Speaker 5: money on the line, making all these big decisions. And

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Speaker 5: so what why has behavioral economics become a success? Honestly

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Speaker 5: largely because of behavioral finance, because of the fact that

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Speaker 5: behavioral economics behavioral economists said, look, we got access to

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Speaker 5: this amazing data on people making consequential decisions day in

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Speaker 5: and day out. They're still making mistakes.

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Speaker 2: Coming up, we continue our conversation with Richard Taylor and

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Speaker 2: alex iMOS discussing the book. They have recently updated, The

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Speaker 2: Winner's Curse Behavioral Economics Anomalies. Then and now I'm Barry

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Speaker 2: rid Holts. You're listening to Masters in Business on Bloomberg Radio.

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Speaker 2: I'm Barry rid Holts. You're listening to Masters in Business

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Speaker 2: on Bloomberg Radio. My extra special guests this week are

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Speaker 2: Richard Taylor and Alex Amos, both of the Chicago Booth

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Speaker 2: School Business at the University of Chicago.

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Speaker 5: I love what.

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Speaker 2: Danny Konoman wants said. That is, I suffer from all

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Speaker 2: the same behavioral biases that I identified. You mean to

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Speaker 2: tell me that we have thirty years of data, all

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Speaker 2: this research, a handful of books, people still make the

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Speaker 2: exact same behavioral mistakes they used to. Has there been

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Speaker 2: any change in behavior?

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Speaker 3: Essentially?

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Speaker 2: No, And.

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Speaker 4: That's not that surprising because the stuff we're talking about

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Speaker 4: has been true as long as there have been humans. Right,

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Speaker 4: So we talk about self control problems. It's in the Bible, right,

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Speaker 4: you know, Homer talks about Odysseus tying himself to the mast.

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Speaker 4: That's like agreeing to have money taken out of your

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Speaker 4: paycheck and put into a retirement plan. So human beings, yes,

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Speaker 4: there's evolution, but evolution and takes thousands of years, and

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Speaker 4: thirty years is the blink of an eye.

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Speaker 2: Since you mentioned retirement accounts, let's talk a little bit

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Speaker 2: about choice architecture. And nudge. Before I arrived here, I

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Speaker 2: looked up what was the impact of the default setting

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Speaker 2: that you helped change through choice architecture. People used to

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Speaker 2: get a new job, sign up for four to one K,

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Speaker 2: and the money would come into that account and would

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Speaker 2: sit there in cash. And rather than have the default

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Speaker 2: be cash, we through your work created a default as

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Speaker 2: a either a target date fund or a balance fund

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Speaker 2: something like that, so it's not sitting in cash. And

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Speaker 2: it turns out there is about four point seven trillion

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Speaker 2: with a t trillion dollars in those funds, of which

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Speaker 2: forty percent, according to recent research, was the default setting.

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Speaker 2: So you get credit for about two trillion dollars in

439
00:28:02,760 --> 00:28:06,560
Speaker 2: retirement savings that might have otherwise just been sitting around

440
00:28:06,600 --> 00:28:12,080
Speaker 2: in cash. How does the concept of people aren't learning

441
00:28:12,119 --> 00:28:16,040
Speaker 2: from their mistakes so choice architecture is so important to

442
00:28:16,800 --> 00:28:20,120
Speaker 2: help people make better decisions. How significant is that?

443
00:28:21,000 --> 00:28:24,919
Speaker 4: Well, if you think about what was going on in

444
00:28:25,000 --> 00:28:30,800
Speaker 4: the in the early eighties, these defined contribution plans like

445
00:28:30,800 --> 00:28:36,880
Speaker 4: four to one k's were real new Our parents were

446
00:28:37,280 --> 00:28:40,360
Speaker 4: if they If you worked at a big firm, you

447
00:28:40,520 --> 00:28:45,680
Speaker 4: had a define benefit plan. My father worked for Prudential insurance.

448
00:28:45,880 --> 00:28:50,000
Speaker 4: You know, and his pension was number of years worked

449
00:28:50,680 --> 00:28:55,520
Speaker 4: times some function of his final salary. No decisions to make,

450
00:28:55,880 --> 00:29:01,760
Speaker 4: kind of like social security and bring in these.

451
00:29:03,080 --> 00:29:05,000
Speaker 3: To find contribution plans.

452
00:29:05,240 --> 00:29:08,280
Speaker 4: You have to decide whether to join and if so,

453
00:29:08,760 --> 00:29:12,640
Speaker 4: how much to defer, and then how to invest it.

454
00:29:13,080 --> 00:29:16,840
Speaker 4: And people had no clue and a lot of people

455
00:29:17,040 --> 00:29:20,880
Speaker 4: just didn't even join, which is about the dumbest mistake

456
00:29:21,400 --> 00:29:22,280
Speaker 4: you can ever make.

457
00:29:22,360 --> 00:29:24,640
Speaker 2: If you have a company with a match, you're basically

458
00:29:24,680 --> 00:29:28,200
Speaker 2: turning down free money, right, which what economic model says

459
00:29:28,200 --> 00:29:29,120
Speaker 2: that's rational? No?

460
00:29:29,400 --> 00:29:31,680
Speaker 3: Well, right, so I would say to what ecmmists, look,

461
00:29:32,280 --> 00:29:33,800
Speaker 3: you would predict.

462
00:29:33,600 --> 00:29:39,520
Speaker 4: No one would make this mistake. But one early study,

463
00:29:39,880 --> 00:29:44,320
Speaker 4: half the employees at a company are not joining.

464
00:29:44,000 --> 00:29:44,760
Speaker 3: In the first year.

465
00:29:44,800 --> 00:29:45,480
Speaker 2: It's amazing.

466
00:29:46,600 --> 00:29:50,080
Speaker 4: So how do we fix that? Well, the simplest thing

467
00:29:50,520 --> 00:29:56,680
Speaker 4: was to change the default. So we say it used

468
00:29:56,680 --> 00:29:59,080
Speaker 4: to be you get a form to fill out a

469
00:29:59,120 --> 00:30:02,480
Speaker 4: piece of paper in those days, and if you want

470
00:30:02,480 --> 00:30:05,800
Speaker 4: to be in the plan, fill out this form and

471
00:30:05,880 --> 00:30:09,200
Speaker 4: say you want to join in how to invest?

472
00:30:10,200 --> 00:30:11,760
Speaker 3: Change that to you.

473
00:30:13,080 --> 00:30:20,120
Speaker 4: Welcome to Riddlets Management. We have a pension plan. We're

474
00:30:20,120 --> 00:30:26,120
Speaker 4: going to enroll you unless you opt out, and we're

475
00:30:26,160 --> 00:30:30,680
Speaker 4: going to enroll you into the default fund unless you

476
00:30:30,840 --> 00:30:37,880
Speaker 4: choose otherwise. So all of that was not possible in

477
00:30:37,920 --> 00:30:43,200
Speaker 4: the early nineties because there were companies were afraid to

478
00:30:43,280 --> 00:30:49,760
Speaker 4: do automatic enrollment because they didn't have permission, and target

479
00:30:49,800 --> 00:30:57,560
Speaker 4: DAID funds weren't legal. Ironically, in the George W. Bush administration,

480
00:30:58,720 --> 00:31:04,560
Speaker 4: one side, they were campaigning to partially privatize Social Security,

481
00:31:05,800 --> 00:31:11,880
Speaker 4: but their Labor Department was forbidding companies from investing in

482
00:31:12,000 --> 00:31:12,520
Speaker 4: anything that.

483
00:31:12,560 --> 00:31:13,280
Speaker 3: Could go down.

484
00:31:14,880 --> 00:31:18,520
Speaker 4: So there was a bill passed in two thousand and

485
00:31:18,560 --> 00:31:29,440
Speaker 4: six the said, okay, you're allowed to automatically enroll and

486
00:31:30,320 --> 00:31:35,200
Speaker 4: have automatically escalate what we used to call sayborn tomorrow,

487
00:31:35,920 --> 00:31:41,240
Speaker 4: and invest in some prudent funds.

488
00:31:42,760 --> 00:31:46,720
Speaker 3: And what was what you have to give something up

489
00:31:46,760 --> 00:31:47,200
Speaker 3: to get that.

490
00:31:48,400 --> 00:31:55,360
Speaker 4: So what I suggested to there was a Republican senator

491
00:31:55,400 --> 00:32:00,760
Speaker 4: from Utah who was the running the Relevant committee. I said,

492
00:32:01,000 --> 00:32:05,520
Speaker 4: how about if companies agree to do all three of those,

493
00:32:06,040 --> 00:32:11,760
Speaker 4: they're exempt from some burdensome paperwork of non discrimination rules.

494
00:32:12,680 --> 00:32:16,960
Speaker 4: And so that's what the Republicans got was less paperwork,

495
00:32:17,640 --> 00:32:21,000
Speaker 4: and people who cared about the workers got something.

496
00:32:21,280 --> 00:32:24,520
Speaker 2: And the workers got something, and the.

497
00:32:24,520 --> 00:32:29,400
Speaker 4: Workers got something and if they just do nothing, then

498
00:32:29,560 --> 00:32:33,880
Speaker 4: they're in and their contributions are going up, and they're

499
00:32:33,920 --> 00:32:35,480
Speaker 4: in a sensible.

500
00:32:36,640 --> 00:32:39,400
Speaker 3: Investment product.

501
00:32:40,520 --> 00:32:43,280
Speaker 2: So this is kind of not just kind of fascinating

502
00:32:43,280 --> 00:32:46,880
Speaker 2: because in the Winner's course you talk about things very

503
00:32:46,960 --> 00:32:51,480
Speaker 2: much related to what happens in investing. So there's loss,

504
00:32:51,520 --> 00:32:55,760
Speaker 2: a version, and the status quo bias and a variety

505
00:32:55,760 --> 00:33:00,400
Speaker 2: of different things. Let's talk about what are the issues

506
00:33:00,440 --> 00:33:04,520
Speaker 2: that most relate to, as Alex said, behavioral finance as

507
00:33:04,520 --> 00:33:07,600
Speaker 2: opposed to behavioral economics. What do we think are the

508
00:33:08,200 --> 00:33:16,520
Speaker 2: biggest factors that explain irrational human behavior in stock and

509
00:33:16,600 --> 00:33:17,600
Speaker 2: bond markets.

510
00:33:18,320 --> 00:33:22,560
Speaker 5: So I think there's a few things that kind of

511
00:33:22,800 --> 00:33:26,640
Speaker 5: people documented in the late nineties early two thousands that

512
00:33:26,680 --> 00:33:31,000
Speaker 5: have just replicated and just became bigger, if anything. So

513
00:33:31,040 --> 00:33:34,080
Speaker 5: the disposition effect was one of them. So the disposition effect,

514
00:33:34,120 --> 00:33:37,640
Speaker 5: this is Scheffern and Statman. They came up with a

515
00:33:37,680 --> 00:33:41,120
Speaker 5: paper nineteen eighty five documenting it. Originally, Terry O. Dean

516
00:33:41,760 --> 00:33:43,960
Speaker 5: has this giant data set that he published in nineteen

517
00:33:44,000 --> 00:33:46,440
Speaker 5: ninety nine documenting in a bit of a larger sample,

518
00:33:46,880 --> 00:33:49,760
Speaker 5: and then now it's been replicated in Finland, all over

519
00:33:49,800 --> 00:33:51,720
Speaker 5: the world and it's this tendency for people. You know,

520
00:33:52,040 --> 00:33:55,200
Speaker 5: when I buy a stock it goes up in price,

521
00:33:55,360 --> 00:33:58,080
Speaker 5: what do I do? I sell it? I want to

522
00:33:58,080 --> 00:34:02,040
Speaker 5: realize my gains. Same stock was down in price. You

523
00:34:02,040 --> 00:34:03,880
Speaker 5: know this is now a loss? What do I do?

524
00:34:04,160 --> 00:34:07,120
Speaker 5: Hold on to it? So it's this tendency to realize

525
00:34:07,120 --> 00:34:08,880
Speaker 5: your gains and hold on to your losses.

526
00:34:09,120 --> 00:34:12,520
Speaker 2: Peter Lynch, by the way, forty years ago, used to

527
00:34:12,560 --> 00:34:16,799
Speaker 2: call that cutting your flowers and watering your weeds. That

528
00:34:16,960 --> 00:34:19,279
Speaker 2: was his expression for it. So it was it was

529
00:34:19,880 --> 00:34:22,719
Speaker 2: visible to a guy running a fund at Fidelity in

530
00:34:22,760 --> 00:34:23,480
Speaker 2: the nineteen eighty.

531
00:34:23,600 --> 00:34:26,319
Speaker 5: Yes, and it's and this is just talking about like

532
00:34:26,360 --> 00:34:29,480
Speaker 5: are people learning? I mean apparently not, because it's like

533
00:34:29,520 --> 00:34:32,560
Speaker 5: it's again you I bet you you download Robinhood data

534
00:34:32,600 --> 00:34:35,560
Speaker 5: from today, you're going to see it show up. So

535
00:34:35,760 --> 00:34:38,280
Speaker 5: that's the the And this is kind of the tendency.

536
00:34:38,640 --> 00:34:41,080
Speaker 5: You know what feels good when you're when when when

537
00:34:41,120 --> 00:34:43,440
Speaker 5: you own a stock selling it at a gain, and

538
00:34:43,560 --> 00:34:45,879
Speaker 5: you know, telling your friends, hey, you know I bought

539
00:34:45,880 --> 00:34:48,360
Speaker 5: that thing for ninety it's one twenty. I just I

540
00:34:48,440 --> 00:34:50,200
Speaker 5: just made a lot of money. You know, it feels

541
00:34:50,239 --> 00:34:53,200
Speaker 5: worse telling your friends I bought it ninety and I

542
00:34:53,239 --> 00:34:55,359
Speaker 5: sold it at sixty, So you just kind of hold

543
00:34:55,400 --> 00:34:58,799
Speaker 5: on to it hoping something happens. Maybe some people even

544
00:34:58,840 --> 00:35:01,040
Speaker 5: double up buy more share is just to break even.

545
00:35:02,000 --> 00:35:04,840
Speaker 5: So the disposition effect this kind of tendency for individual

546
00:35:04,880 --> 00:35:08,880
Speaker 5: behavior to uh, you know, realize gains avoid losses. The

547
00:35:08,960 --> 00:35:11,239
Speaker 5: other thing is, in my view, this is kind of

548
00:35:11,239 --> 00:35:15,840
Speaker 5: the bigger uh, the bigger principle is limited attention. So

549
00:35:16,520 --> 00:35:19,160
Speaker 5: you know, there's a lot of stocks out there, which

550
00:35:19,200 --> 00:35:22,160
Speaker 5: ones are people buying? And this is not just retail investors,

551
00:35:22,200 --> 00:35:24,720
Speaker 5: this is this is bigger institutional investors too.

552
00:35:25,120 --> 00:35:25,440
Speaker 3: Uh.

553
00:35:25,480 --> 00:35:27,839
Speaker 5: It's the ones that are covered in the news. We're

554
00:35:27,840 --> 00:35:31,400
Speaker 5: talking about availability bias earlier. What are the things that

555
00:35:31,440 --> 00:35:34,000
Speaker 5: are coming to mind, Things that that have recently been covered,

556
00:35:34,239 --> 00:35:36,640
Speaker 5: maybe you heard an earnings announcement call or something like that.

557
00:35:36,680 --> 00:35:39,560
Speaker 5: These attention grabbing stocks that are much more likely to

558
00:35:39,680 --> 00:35:43,000
Speaker 5: go into people's portfolios. It's because people don't, you know,

559
00:35:43,080 --> 00:35:47,560
Speaker 5: aren't evaluating the entire uh, the entire universe of stocks

560
00:35:47,600 --> 00:35:49,680
Speaker 5: whenever they think they're thinking about something to buy.

561
00:35:49,760 --> 00:35:53,400
Speaker 2: So let's address that. Because the United States, happens, of

562
00:35:54,360 --> 00:35:57,840
Speaker 2: all countries, not only has such a large stock market,

563
00:35:58,280 --> 00:36:03,200
Speaker 2: but the home country buys is so acute here, and

564
00:36:03,480 --> 00:36:06,520
Speaker 2: you don't hear a lot about foreign companies all that.

565
00:36:06,640 --> 00:36:11,799
Speaker 2: Often you mostly hear about local companies, local CEOs, local products.

566
00:36:12,239 --> 00:36:16,879
Speaker 2: How significant is that sort of bias in people's portfolios

567
00:36:16,960 --> 00:36:22,399
Speaker 2: being not only overloaded with their own country, but hey,

568
00:36:22,440 --> 00:36:25,040
Speaker 2: if you're in New York, you can have more finance companies.

569
00:36:25,080 --> 00:36:27,440
Speaker 2: If you're in San Francisco, you have more tech companies.

570
00:36:27,680 --> 00:36:30,440
Speaker 2: If you're in the Midwest you can have more manufacturing companies.

571
00:36:30,520 --> 00:36:32,400
Speaker 5: It's more extreme than that. If I'm working for a

572
00:36:32,440 --> 00:36:34,759
Speaker 5: specific company, I have more of that stock.

573
00:36:34,560 --> 00:36:37,160
Speaker 2: When, if anything, you should have right.

574
00:36:38,160 --> 00:36:44,160
Speaker 3: Yeah. I think one campaign that has been moderately successful

575
00:36:44,280 --> 00:36:48,920
Speaker 3: is I think fewer companies are foisting stock of their

576
00:36:48,960 --> 00:36:52,480
Speaker 3: own company onto the workers. It used to be the

577
00:36:52,680 --> 00:36:55,840
Speaker 3: match was often paid in company stock.

578
00:36:56,800 --> 00:37:01,040
Speaker 2: Well Ge was notorious and they lost a trillion dollars

579
00:37:01,080 --> 00:37:05,120
Speaker 2: of employee investments because of their match.

580
00:37:05,719 --> 00:37:08,239
Speaker 3: Well and Uh and run and Ron.

581
00:37:09,160 --> 00:37:12,240
Speaker 5: One of my one of my friends their their their father.

582
00:37:12,400 --> 00:37:15,160
Speaker 5: He was working at Enron. He was a risk manager.

583
00:37:16,800 --> 00:37:21,120
Speaker 2: F Yi and UH just not a very good one, huge,

584
00:37:21,640 --> 00:37:25,359
Speaker 2: huge persu although it could be the greatest risk manager there.

585
00:37:25,600 --> 00:37:28,440
Speaker 2: The busses were not listening to you.

586
00:37:27,880 --> 00:37:33,520
Speaker 4: Right, But they compounded it by putting their employees money

587
00:37:33,560 --> 00:37:36,839
Speaker 4: in the four one k into and run stock. So

588
00:37:36,880 --> 00:37:43,280
Speaker 4: they get fired and their retirement money goes puff unbelievable.

589
00:37:43,400 --> 00:37:46,000
Speaker 5: But you know, looking back at what people were owning,

590
00:37:46,239 --> 00:37:48,319
Speaker 5: I mean there is that, you know, people in the

591
00:37:48,320 --> 00:37:50,880
Speaker 5: four oh one k element, but people who were working

592
00:37:50,920 --> 00:37:56,400
Speaker 5: there were freely buying and ron stock. Right, according to

593
00:37:56,480 --> 00:37:59,640
Speaker 5: economic models, you should be diversifying. You already have a

594
00:37:59,680 --> 00:38:01,320
Speaker 5: bunch of ron stock in your four one k. You

595
00:38:01,360 --> 00:38:04,279
Speaker 5: shouldn't be taking your discretionary spending and buying more and

596
00:38:04,400 --> 00:38:06,440
Speaker 5: ron stock. And that's exactly what was happening.

597
00:38:06,840 --> 00:38:12,440
Speaker 4: You know, this home bias applies all around the world,

598
00:38:12,800 --> 00:38:15,840
Speaker 4: at least the US is a big country. I wrote

599
00:38:15,840 --> 00:38:21,439
Speaker 4: a paper once about the Swedish social sort of four

600
00:38:21,440 --> 00:38:24,680
Speaker 4: to one k plan. This was a risky move because

601
00:38:24,719 --> 00:38:27,400
Speaker 4: I was making fun of it and there was some

602
00:38:27,520 --> 00:38:35,120
Speaker 4: award that might happen. But anyway, Sweden is one percent

603
00:38:35,239 --> 00:38:35,960
Speaker 4: of world.

604
00:38:35,719 --> 00:38:38,160
Speaker 2: GP tiny tiny, GDP tiny, and.

605
00:38:38,239 --> 00:38:41,040
Speaker 3: They were putting most of their money in Swedish stocks.

606
00:38:41,120 --> 00:38:44,920
Speaker 2: It's crazy they ignored the other ninety nine percent of

607
00:38:44,960 --> 00:38:48,040
Speaker 2: the world. But that just goes to show you the bias.

608
00:38:48,080 --> 00:38:52,080
Speaker 2: So the obvious question is if you two were advising

609
00:38:52,160 --> 00:38:56,400
Speaker 2: a portfolio manager, what sort of behavioral principles would you

610
00:38:56,480 --> 00:38:59,799
Speaker 2: emphasize for them to build a robust portfolio.

611
00:39:01,040 --> 00:39:07,040
Speaker 4: Well, as you know, I one hat I have is

612
00:39:07,200 --> 00:39:09,360
Speaker 4: I'm involved in a company that does.

613
00:39:09,200 --> 00:39:11,640
Speaker 2: This that has also has your name on the door.

614
00:39:11,719 --> 00:39:13,920
Speaker 4: It also has my name on the door fuller and

615
00:39:14,160 --> 00:39:19,960
Speaker 4: fail or asset management. And now let me say I

616
00:39:20,160 --> 00:39:26,160
Speaker 4: cannot name a single stock WHEELWN and no one at

617
00:39:26,160 --> 00:39:29,960
Speaker 4: the firm would think it's a good idea for me

618
00:39:30,080 --> 00:39:34,200
Speaker 4: to be making suggestions. Were buy small cap stock, so

619
00:39:35,640 --> 00:39:38,759
Speaker 4: it's you know, if we owned Apple or Tesla, I

620
00:39:38,880 --> 00:39:41,840
Speaker 4: might know it, but we don't buy any big stock.

621
00:39:41,960 --> 00:39:45,000
Speaker 3: So they're mostly companies you've never heard of, and I've

622
00:39:45,040 --> 00:39:45,879
Speaker 3: never heard of them.

623
00:39:46,000 --> 00:39:50,880
Speaker 2: But this is because you've identified a behavioral issue that

624
00:39:51,000 --> 00:39:53,920
Speaker 2: is now reflected in the model that they use to purchase.

625
00:39:54,400 --> 00:39:57,040
Speaker 3: Right, So we're not.

626
00:39:58,719 --> 00:40:01,880
Speaker 4: We're not a quatch shop, which is a little unusual

627
00:40:02,000 --> 00:40:07,000
Speaker 4: for a firm that's run by some academics. But each

628
00:40:07,120 --> 00:40:11,960
Speaker 4: strategy is based on a bias. So there's one that's

629
00:40:12,000 --> 00:40:15,719
Speaker 4: based on overreaction, there's one that's based on underreaction. So

630
00:40:15,760 --> 00:40:21,880
Speaker 4: we are we try to find stocks that we think

631
00:40:22,239 --> 00:40:25,000
Speaker 4: the rest of the market is making a mistake about,

632
00:40:26,360 --> 00:40:33,840
Speaker 4: and then we forbid the portfolio managers from forecasting earnings because.

633
00:40:34,920 --> 00:40:38,480
Speaker 3: That they're going to be You know, do we think with.

634
00:40:38,520 --> 00:40:41,600
Speaker 4: Our thirty employees that we're going to make better forecasts

635
00:40:41,680 --> 00:40:46,480
Speaker 4: than fidelity. It's crazy, but we think we have an

636
00:40:46,520 --> 00:40:49,520
Speaker 4: advantage because we're trying to predict something else. We're trying

637
00:40:49,560 --> 00:40:54,399
Speaker 4: to predict the mistakes. It's like you're a baseball fan.

638
00:40:54,840 --> 00:40:58,000
Speaker 4: If there's a picture that is a sinker ball pitcher,

639
00:40:59,400 --> 00:41:03,920
Speaker 4: so the alex this means the ball goes down as

640
00:41:03,960 --> 00:41:05,200
Speaker 4: it approaches the plate.

641
00:41:05,640 --> 00:41:06,520
Speaker 3: These foreigners.

642
00:41:06,840 --> 00:41:11,440
Speaker 4: But you know, if there's a sinker ball pitcher, you

643
00:41:11,520 --> 00:41:14,400
Speaker 4: and I can predict batters are going to hit ground

644
00:41:14,440 --> 00:41:19,040
Speaker 4: balls because they are fooled. The ball drops, and if

645
00:41:19,040 --> 00:41:22,840
Speaker 4: you hit it slightly above the center, the ball goes down.

646
00:41:23,719 --> 00:41:25,520
Speaker 4: So you don't have to be able to hit a

647
00:41:25,600 --> 00:41:30,160
Speaker 4: ball to know it's going to go down, And so

648
00:41:30,280 --> 00:41:33,080
Speaker 4: we don't have to be able to forecast earnings to

649
00:41:33,200 --> 00:41:36,680
Speaker 4: predict that other people are going to be predicting too high.

650
00:41:36,760 --> 00:41:39,760
Speaker 2: I want to bring this back to the book because

651
00:41:39,840 --> 00:41:43,239
Speaker 2: one of the concepts underlying the book was, Hey, there's

652
00:41:43,280 --> 00:41:48,680
Speaker 2: a reproducibility issue in social sciences. How well have these

653
00:41:48,719 --> 00:41:52,120
Speaker 2: anomalies and the theories you built around them, How well is

654
00:41:52,160 --> 00:41:56,920
Speaker 2: this held up? How robust and reproducible are these findings?

655
00:41:56,960 --> 00:41:59,880
Speaker 2: And it turns out very talk to us about what

656
00:42:00,000 --> 00:42:03,600
Speaker 2: but what you guys discovered when you were revisiting all

657
00:42:03,640 --> 00:42:07,280
Speaker 2: of these principles that were first written about twenty thirty

658
00:42:07,360 --> 00:42:07,719
Speaker 2: years ago.

659
00:42:08,000 --> 00:42:11,000
Speaker 5: Yeah, so, as you mentioned, there's a some might call

660
00:42:11,160 --> 00:42:14,359
Speaker 5: crisis of reproducibility and social science more broadly, so this

661
00:42:14,400 --> 00:42:19,960
Speaker 5: is psychology, some sociology, et cetera. And the worry is

662
00:42:20,000 --> 00:42:22,360
Speaker 5: that you know, these anomalies that were published in the

663
00:42:22,400 --> 00:42:24,879
Speaker 5: eighties and nineties, these are the bedrock of the entire

664
00:42:24,920 --> 00:42:29,200
Speaker 5: field of behavioral economics, and you might be worried, like, look,

665
00:42:29,239 --> 00:42:32,279
Speaker 5: maybe these things don't reproduce. And there's two ways that

666
00:42:32,320 --> 00:42:35,480
Speaker 5: they can't. They don't reproduce. One, you run the same

667
00:42:35,520 --> 00:42:38,960
Speaker 5: experiment again and doesn't work. It was p hacked, as

668
00:42:38,960 --> 00:42:42,400
Speaker 5: I said, like small sample sizes, no incentives. The second

669
00:42:42,400 --> 00:42:45,919
Speaker 5: way my not reproduce is that it literally only reproduces

670
00:42:45,960 --> 00:42:48,320
Speaker 5: in the exact conditions it was run originally with college

671
00:42:48,360 --> 00:42:50,760
Speaker 5: students at low stakes. You go out in a different

672
00:42:50,760 --> 00:42:53,520
Speaker 5: population with people who are a bit more sophisticated, know

673
00:42:53,560 --> 00:42:55,960
Speaker 5: what's going on, and you know it doesn't work. So

674
00:42:56,000 --> 00:42:58,600
Speaker 5: what we did in the book was to say, look, first,

675
00:42:58,719 --> 00:43:01,560
Speaker 5: let's take the exact same experiments and run them again.

676
00:43:01,920 --> 00:43:07,160
Speaker 5: Everybody knows about, you know, the original anomaly, So maybe

677
00:43:07,360 --> 00:43:09,600
Speaker 5: they don't work because people are like, ah, this is

678
00:43:09,640 --> 00:43:12,080
Speaker 5: a loss of version experiment. I know what's going on.

679
00:43:12,160 --> 00:43:14,760
Speaker 5: I'm not going to do this. This is the endowment effect.

680
00:43:15,080 --> 00:43:17,640
Speaker 5: I'm not going to do this. So we just replicated

681
00:43:17,640 --> 00:43:20,600
Speaker 5: them directly on a completely different platform. So we used

682
00:43:20,600 --> 00:43:27,520
Speaker 5: an online crowdsourcing platform called prolific. Basically, everything works. Everything works,

683
00:43:27,560 --> 00:43:29,200
Speaker 5: and we you know, you don't have to take our

684
00:43:29,239 --> 00:43:31,200
Speaker 5: word for it. If you go on the website of

685
00:43:31,200 --> 00:43:34,000
Speaker 5: the book, we posted all of the results of our replications,

686
00:43:34,400 --> 00:43:37,040
Speaker 5: but also instructions on how you can do it yourself.

687
00:43:37,280 --> 00:43:39,439
Speaker 5: So if somebody is like, don't I don't know about

688
00:43:39,480 --> 00:43:43,839
Speaker 5: these guys, run them yourself. And you know, people are

689
00:43:43,840 --> 00:43:46,440
Speaker 5: still loss of ours. They still have the endowment effect.

690
00:43:48,160 --> 00:43:51,320
Speaker 5: Things like the conjunction fallacy, the Linda problem that Richard

691
00:43:51,360 --> 00:43:52,680
Speaker 5: was talking about all works.

692
00:43:52,760 --> 00:43:52,960
Speaker 3: Though.

693
00:43:54,000 --> 00:43:57,960
Speaker 5: The second part is this external validity part. Does anybody

694
00:43:57,960 --> 00:44:01,120
Speaker 5: other than college students display these these effects? And that's

695
00:44:01,160 --> 00:44:03,200
Speaker 5: kind of the updates part of the book, and the

696
00:44:03,239 --> 00:44:06,080
Speaker 5: answer is yes. You know, the loss of version has

697
00:44:06,120 --> 00:44:09,759
Speaker 5: been in the myopic loss of version part that's been

698
00:44:09,840 --> 00:44:13,600
Speaker 5: used to explain the equity premium puzzle that's still reproducible.

699
00:44:13,800 --> 00:44:15,840
Speaker 5: We also do a bunch of out of sample tests

700
00:44:15,880 --> 00:44:22,040
Speaker 5: of the anomalies that didn't use experimental data, and you

701
00:44:22,040 --> 00:44:24,719
Speaker 5: know that replicates out of sample too, so people aren't

702
00:44:24,800 --> 00:44:27,200
Speaker 5: learning the psychology is the same.

703
00:44:27,400 --> 00:44:27,560
Speaker 3: You know.

704
00:44:28,040 --> 00:44:31,440
Speaker 4: One of the columns was about the equity premium puzzle.

705
00:44:31,760 --> 00:44:33,719
Speaker 4: We didn't include this in the book because it's a

706
00:44:33,719 --> 00:44:37,640
Speaker 4: little wonky, but the equity premium is just the difference

707
00:44:37,680 --> 00:44:41,800
Speaker 4: in returns between stocks and bonds. The equity premium puzzle

708
00:44:42,239 --> 00:44:46,720
Speaker 4: is how big it is, and theory says it should

709
00:44:46,760 --> 00:44:50,040
Speaker 4: be like less than one percent, and historically it was

710
00:44:50,080 --> 00:44:55,279
Speaker 4: about seven percent. And the article about that was in

711
00:44:55,320 --> 00:44:59,680
Speaker 4: the early eighties, so we've had forty years of data

712
00:44:59,760 --> 00:45:01,799
Speaker 4: since the puzzle was announced.

713
00:45:02,000 --> 00:45:03,719
Speaker 3: The equity premium.

714
00:45:03,360 --> 00:45:08,399
Speaker 4: Exactly the same, it's only one percent lower, so and

715
00:45:08,440 --> 00:45:08,919
Speaker 4: that's what.

716
00:45:08,880 --> 00:45:11,480
Speaker 3: We see basically everywhere everything's the same.

717
00:45:12,200 --> 00:45:16,600
Speaker 2: So one of the concepts that people have challenged is

718
00:45:16,640 --> 00:45:20,440
Speaker 2: not being very reproducible. Has been the concept of priming

719
00:45:20,920 --> 00:45:25,560
Speaker 2: to sometimes anchoring is similar, but that seems to be

720
00:45:25,640 --> 00:45:30,160
Speaker 2: more reproducible. But when I hear Linda the bank teller story,

721
00:45:30,719 --> 00:45:33,919
Speaker 2: that feels like the framing of that is very much

722
00:45:33,960 --> 00:45:39,000
Speaker 2: a priming, when you hear about her as politically active

723
00:45:39,200 --> 00:45:43,120
Speaker 2: and being involved in what how do you distinguish when

724
00:45:43,160 --> 00:45:48,640
Speaker 2: you have these theoretical overlapping biases that all kind of

725
00:45:48,640 --> 00:45:49,600
Speaker 2: interact with each other.

726
00:45:49,640 --> 00:45:54,080
Speaker 5: So priming is actually a huge literature and cognitive psychology.

727
00:45:54,239 --> 00:45:57,680
Speaker 5: Basic priming is very robust. So it's the idea of

728
00:45:58,040 --> 00:45:59,920
Speaker 5: you know, I say a bunch of words that start

729
00:46:00,040 --> 00:46:01,799
Speaker 5: with the K. What comes to mind a word that

730
00:46:01,840 --> 00:46:04,319
Speaker 5: starts with the K that's going to reproduce any day

731
00:46:04,320 --> 00:46:07,680
Speaker 5: of the week. There's a special subset of priming research

732
00:46:08,080 --> 00:46:10,239
Speaker 5: that was done kind of in the nineties early two

733
00:46:10,239 --> 00:46:14,000
Speaker 5: thousands that kind of took this to an extreme, which

734
00:46:14,040 --> 00:46:16,520
Speaker 5: is so here's an example. Let's say you're doing word

735
00:46:16,560 --> 00:46:20,160
Speaker 5: search and there's a bunch of words that have to

736
00:46:20,200 --> 00:46:24,880
Speaker 5: do with like oranges, palm, trees, hot weather, like vaguely

737
00:46:24,880 --> 00:46:28,600
Speaker 5: related with Florida, right, and then that's supposed to prime

738
00:46:28,680 --> 00:46:32,880
Speaker 5: in your brain old people. And the result, the dependent

739
00:46:32,960 --> 00:46:35,279
Speaker 5: variable was that those subjects who had those words, they

740
00:46:35,280 --> 00:46:37,000
Speaker 5: walked a little slower out of the lab.

741
00:46:37,880 --> 00:46:38,040
Speaker 2: Right.

742
00:46:38,120 --> 00:46:40,400
Speaker 5: I mean that's it's a little crazy, right, kind of

743
00:46:40,440 --> 00:46:44,080
Speaker 5: tough to measure. Also, yeah, so I mean it relied.

744
00:46:44,080 --> 00:46:46,480
Speaker 5: There's a lot of degrees of freedom the researcher can

745
00:46:46,520 --> 00:46:49,000
Speaker 5: be looking in a certain direction, you know, and those

746
00:46:49,040 --> 00:46:53,200
Speaker 5: tend to tend to not reproduce the sort of priming

747
00:46:53,440 --> 00:46:59,040
Speaker 5: that something like the Linda problem, for example, has. That's

748
00:46:59,080 --> 00:47:02,040
Speaker 5: more in that kind of cognitive psychology wheelhouse of like

749
00:47:02,320 --> 00:47:05,120
Speaker 5: what do you think about when I describe a person

750
00:47:05,120 --> 00:47:07,719
Speaker 5: who takes part in radical rallies? What comes to mind?

751
00:47:07,719 --> 00:47:10,440
Speaker 5: This is this is a basic concept and memory.

752
00:47:11,000 --> 00:47:11,200
Speaker 2: Right.

753
00:47:11,360 --> 00:47:14,920
Speaker 5: So that and the second part that that I wanted

754
00:47:14,960 --> 00:47:17,719
Speaker 5: to to say is that priming, as far as like

755
00:47:17,760 --> 00:47:21,280
Speaker 5: looking at the behavioral economics research, priming is a really

756
00:47:21,320 --> 00:47:23,600
Speaker 5: small part. It was actually not really featured much in

757
00:47:23,640 --> 00:47:27,879
Speaker 5: the book. But the type that is that was used

758
00:47:27,920 --> 00:47:31,919
Speaker 5: by uh, you know, uh Firsky and Konnoment. It's much

759
00:47:31,920 --> 00:47:34,719
Speaker 5: more in the Wheelhouse or just basic cognitive psychology.

760
00:47:34,360 --> 00:47:37,160
Speaker 2: More like anchoring. Does anchoring still hold up? Oh yeah,

761
00:47:37,320 --> 00:47:37,640
Speaker 2: very well?

762
00:47:37,840 --> 00:47:38,920
Speaker 5: Oh yeah, yeah yeah yeah.

763
00:47:39,040 --> 00:47:42,759
Speaker 4: And look, one of the things when when I was

764
00:47:42,800 --> 00:47:48,800
Speaker 4: writing those columns, the I could pick anything. I picked

765
00:47:48,960 --> 00:47:53,640
Speaker 4: big effect sizes and some of the problem.

766
00:47:53,880 --> 00:47:55,320
Speaker 3: You know, we talked.

767
00:47:55,080 --> 00:48:00,600
Speaker 4: Earlier about the norm and economics to make models smarter

768
00:48:00,680 --> 00:48:04,400
Speaker 4: and smarter. I think there was a norm in psychology

769
00:48:05,000 --> 00:48:07,759
Speaker 4: for results to get cleverer and cleverer.

770
00:48:08,120 --> 00:48:11,680
Speaker 2: Well, I thought Alice's paper where you randomly sell versus

771
00:48:11,680 --> 00:48:14,719
Speaker 2: what was actually sold, that was a very clever set

772
00:48:14,840 --> 00:48:16,000
Speaker 2: up for a paper.

773
00:48:16,040 --> 00:48:18,080
Speaker 3: It was clever, but it wasn't.

774
00:48:19,280 --> 00:48:23,400
Speaker 4: What I was deriding is a norm that the models

775
00:48:24,080 --> 00:48:27,719
Speaker 4: assume people are being clever as opposed to designing a

776
00:48:27,800 --> 00:48:31,759
Speaker 4: clever paper. Got we're all for clever papers, Okay, we

777
00:48:32,320 --> 00:48:36,560
Speaker 4: like clever papers. So when I was choosing what columns

778
00:48:36,600 --> 00:48:41,480
Speaker 4: to write about, I picked big stuff. And think about

779
00:48:42,480 --> 00:48:49,239
Speaker 4: there's a well known company that makes cinnamon buns and

780
00:48:49,360 --> 00:48:54,760
Speaker 4: has the strategy of pumping the smell of that out

781
00:48:54,840 --> 00:48:59,439
Speaker 4: into the airport. Now, let's say you're on a low

782
00:48:59,560 --> 00:49:05,640
Speaker 4: carb diet. Just hypothetically, you know, if you walk by

783
00:49:05,719 --> 00:49:11,200
Speaker 4: that thing that's that's priming, and that works.

784
00:49:11,239 --> 00:49:15,759
Speaker 3: And it's not clever, it's just right. It's a big

785
00:49:15,800 --> 00:49:16,799
Speaker 3: effect size.

786
00:49:17,520 --> 00:49:22,960
Speaker 4: So there are and so everything I wrote about was big,

787
00:49:23,520 --> 00:49:27,400
Speaker 4: and it's because I wanted to pick things that I

788
00:49:27,440 --> 00:49:34,319
Speaker 4: thought were well established and so, you know, I think

789
00:49:34,360 --> 00:49:39,160
Speaker 4: if I had looked for cute little things, then some

790
00:49:39,280 --> 00:49:40,960
Speaker 4: of them would have failed to replicate.

791
00:49:41,120 --> 00:49:44,319
Speaker 5: You also pick things that people were actively attacking and

792
00:49:44,800 --> 00:49:47,520
Speaker 5: adversarially trying to replicate at the time that you were

793
00:49:47,520 --> 00:49:48,040
Speaker 5: writing it.

794
00:49:49,080 --> 00:49:52,400
Speaker 3: Yeah, I mean, look, take take the ultimatum.

795
00:49:52,480 --> 00:49:57,520
Speaker 4: Yeah, that's one of the original columns and one that

796
00:49:57,560 --> 00:50:00,520
Speaker 4: we include in the book. The game is very, very simple.

797
00:50:01,560 --> 00:50:05,400
Speaker 4: I give Barry one hundred dollars. I say, share it

798
00:50:05,440 --> 00:50:10,280
Speaker 4: with Alex. You can give whatever proportion of the hundred

799
00:50:10,320 --> 00:50:14,799
Speaker 4: you want to Alex. He says yes or no. If

800
00:50:15,360 --> 00:50:19,560
Speaker 4: if he says yes, he gets whatever you offered.

801
00:50:19,239 --> 00:50:21,520
Speaker 3: And you get the rest. If he says no, you

802
00:50:21,600 --> 00:50:22,360
Speaker 3: both get nothing.

803
00:50:23,600 --> 00:50:29,239
Speaker 4: Now, the standard economic model at the time predicts that

804
00:50:30,400 --> 00:50:34,880
Speaker 4: Alex will accept anything, because something is better than nothing.

805
00:50:35,800 --> 00:50:41,200
Speaker 4: Barry knows that Alex will accept anything, and so he

806
00:50:41,280 --> 00:50:47,120
Speaker 4: offers him a dollar and Alex accepts. Now, real people

807
00:50:48,560 --> 00:50:52,360
Speaker 4: only an economist would think that that's a really good prediction.

808
00:50:53,600 --> 00:50:56,400
Speaker 3: Anybody who's not an economist is going to say, what

809
00:50:56,440 --> 00:50:57,040
Speaker 3: are you kidding.

810
00:50:57,520 --> 00:50:59,480
Speaker 4: I'm not going to take a dollar and give you

811
00:50:59,560 --> 00:51:04,120
Speaker 4: ninety You didn't do anything to deserve that ninety nine.

812
00:51:04,920 --> 00:51:10,440
Speaker 4: So if you run that experiment, if you offered less

813
00:51:10,480 --> 00:51:15,520
Speaker 4: than twenty percent, you're going to get rejected. And the

814
00:51:15,560 --> 00:51:23,640
Speaker 4: profit maximizing offer is about and most people offer half. Okay,

815
00:51:24,520 --> 00:51:30,800
Speaker 4: Now there were big fights. There was a professor from

816
00:51:32,800 --> 00:51:43,279
Speaker 4: britt who was saying this was challenging game theory, and no,

817
00:51:43,360 --> 00:51:47,880
Speaker 4: it wasn't challenging game theory. It was challenging the idea

818
00:51:48,160 --> 00:51:54,520
Speaker 4: that the agents only care about money and don't care

819
00:51:54,600 --> 00:51:56,200
Speaker 4: about being treated fairly.

820
00:51:57,160 --> 00:52:04,239
Speaker 2: So let's address that. Because of the evolutionary biology of this,

821
00:52:05,080 --> 00:52:10,600
Speaker 2: humans were cooperative social primates. We have neither fangs nor claws,

822
00:52:10,600 --> 00:52:12,600
Speaker 2: so we had to come up with some way to

823
00:52:12,680 --> 00:52:15,640
Speaker 2: stay alive, and it turns out cooperating in a tribe

824
00:52:16,040 --> 00:52:21,840
Speaker 2: is very useful survival tactic. It seems that an inherent

825
00:52:21,920 --> 00:52:25,520
Speaker 2: sense of fairness is somewhat built into all of us,

826
00:52:25,560 --> 00:52:31,120
Speaker 2: as well as social status seeking. So how much of

827
00:52:31,160 --> 00:52:37,479
Speaker 2: this issue in economics derives from not understanding a little

828
00:52:37,480 --> 00:52:39,040
Speaker 2: bit of evolutionary history.

829
00:52:40,160 --> 00:52:45,080
Speaker 4: You know, it's a tricky thing. Obviously, we have evolved

830
00:52:45,480 --> 00:52:49,279
Speaker 4: to be who we are. There are some people who

831
00:52:49,320 --> 00:52:53,000
Speaker 4: then say, well, that means whatever we do is optimal.

832
00:52:54,000 --> 00:52:57,920
Speaker 3: Well maybe maybe not. No, that's stupid. I mean we

833
00:52:58,080 --> 00:53:00,480
Speaker 3: evolved on the savannah.

834
00:53:00,239 --> 00:53:05,280
Speaker 2: Right, right, nothing about picking muni bonds from a larger site.

835
00:53:05,600 --> 00:53:05,879
Speaker 3: You know.

836
00:53:06,600 --> 00:53:11,359
Speaker 4: Amos Tversky was famous for one liners, and he had

837
00:53:11,400 --> 00:53:16,000
Speaker 4: a one liner about loss of version, which was there

838
00:53:16,200 --> 00:53:21,200
Speaker 4: may have been species that did not exhibit loss of version,

839
00:53:21,520 --> 00:53:26,200
Speaker 4: and they're now extinct. Right, So if you're at subsistence,

840
00:53:27,320 --> 00:53:30,239
Speaker 4: it's really smart to be worried about.

841
00:53:29,960 --> 00:53:31,680
Speaker 2: Losing It's an existential threat.

842
00:53:31,920 --> 00:53:32,200
Speaker 3: Right.

843
00:53:32,800 --> 00:53:37,680
Speaker 4: But you know, none, the three of us, we could

844
00:53:37,760 --> 00:53:43,720
Speaker 4: go several days without eating, some more more things than others, right, right,

845
00:53:44,080 --> 00:53:50,279
Speaker 4: so we're not at subsistence, and yeah, we have Managing

846
00:53:50,320 --> 00:53:54,799
Speaker 4: our own portfolios is something people have been doing for

847
00:53:54,960 --> 00:53:59,719
Speaker 4: thirty years, right, Yeah, the rich people, but they had

848
00:53:59,719 --> 00:54:04,720
Speaker 4: their oker do it, right, So there's no evolutionary history

849
00:54:05,239 --> 00:54:11,440
Speaker 4: of how to manage a portfolio, and even saving for retirement.

850
00:54:13,719 --> 00:54:17,000
Speaker 4: People didn't live long enough to worry about that. And

851
00:54:17,239 --> 00:54:22,320
Speaker 4: if you were unlucky enough to reach my age. Then

852
00:54:22,880 --> 00:54:25,799
Speaker 4: you hope your kids would take care of you, and

853
00:54:25,840 --> 00:54:32,680
Speaker 4: they lived nearby. You know, then people started scattering and penicillin,

854
00:54:33,000 --> 00:54:38,759
Speaker 4: and you know, so now we live long and our

855
00:54:38,880 --> 00:54:42,440
Speaker 4: kids are scattered and they have no interest in having

856
00:54:42,520 --> 00:54:45,640
Speaker 4: us move in with them. So people had to learn

857
00:54:46,000 --> 00:54:49,920
Speaker 4: a very new thing and they needed some help.

858
00:54:51,480 --> 00:54:56,000
Speaker 2: Really really fascinating, So we didn't talk about the where

859
00:54:56,040 --> 00:54:59,640
Speaker 2: that from? Whence the title of the Winner's Curse comes from?

860
00:55:00,160 --> 00:55:01,719
Speaker 5: Before we get to the future.

861
00:55:01,360 --> 00:55:04,840
Speaker 2: Of behavioral finance, let's talk about the Winner's Curse.

862
00:55:05,400 --> 00:55:07,320
Speaker 5: Tell us where the name comes.

863
00:55:07,040 --> 00:55:11,000
Speaker 4: From the concept. I should say the title of the

864
00:55:11,040 --> 00:55:14,279
Speaker 4: book comes from the title of one of the chapters,

865
00:55:15,000 --> 00:55:18,680
Speaker 4: and I picked it as the title back then because

866
00:55:18,719 --> 00:55:22,360
Speaker 4: it's sort of a fun phrase and a bit intriguing,

867
00:55:22,719 --> 00:55:27,200
Speaker 4: and the concept itself is interesting and important. The idea

868
00:55:27,440 --> 00:55:30,840
Speaker 4: is this, suppose you have a lot of people bidding

869
00:55:31,400 --> 00:55:36,480
Speaker 4: for some object that's worth the same to everybody, and

870
00:55:36,680 --> 00:55:40,719
Speaker 4: it could be. When you do this as a demonstration

871
00:55:40,880 --> 00:55:43,960
Speaker 4: in the class, you fill up a jar of jelly

872
00:55:43,960 --> 00:55:48,919
Speaker 4: beans or coins and say it's ten cents for each

873
00:55:49,000 --> 00:55:53,520
Speaker 4: jelly bean, and it's one hundred dollars in the jar.

874
00:55:54,080 --> 00:55:57,000
Speaker 4: Now we're going to auction it off. High bid or

875
00:55:57,040 --> 00:55:59,799
Speaker 4: wins the one hundred dollars. But they don't know what

876
00:56:00,320 --> 00:56:02,400
Speaker 4: they don't know it's worth one hundred dollars. All they

877
00:56:02,400 --> 00:56:07,880
Speaker 4: see is a lot of coins or jellybeans. What happens, well,

878
00:56:08,200 --> 00:56:11,680
Speaker 4: the average bid is less than one hundred dollars because

879
00:56:11,719 --> 00:56:16,560
Speaker 4: people are risk averse. But the winning bid is always

880
00:56:16,600 --> 00:56:21,200
Speaker 4: above one hundred dollars if you have enough people, because

881
00:56:22,239 --> 00:56:27,759
Speaker 4: the most optimistic forecast is likely the highest bid and

882
00:56:27,840 --> 00:56:33,240
Speaker 4: it's too high. Now, this was not discovered by psychologists

883
00:56:33,239 --> 00:56:38,279
Speaker 4: in the lab. It was discovered by engineers at Atlantic

884
00:56:38,400 --> 00:56:44,399
Speaker 4: Richfield Arco, the energy company. The energy company who discovered

885
00:56:44,840 --> 00:56:50,600
Speaker 4: they were bidding for oil leases in what I continue

886
00:56:50,640 --> 00:56:55,000
Speaker 4: to insist on calling the Gulf of Mexico. And they

887
00:56:55,360 --> 00:57:02,799
Speaker 4: realized that the leases they won had less oil than

888
00:57:02,880 --> 00:57:03,759
Speaker 4: they expected.

889
00:57:04,920 --> 00:57:08,040
Speaker 3: And they said, we thought we had world class geologists.

890
00:57:08,800 --> 00:57:12,800
Speaker 3: What's going on? Are they dummies? And then they realized

891
00:57:13,760 --> 00:57:19,120
Speaker 3: that it's quite subtle that the the what you're trying

892
00:57:19,160 --> 00:57:22,880
Speaker 3: to do is make a bid that will make you

893
00:57:23,040 --> 00:57:28,840
Speaker 3: money if you win, and that if you win part

894
00:57:30,000 --> 00:57:34,120
Speaker 3: if there's one hundred people bidding, gee, do I really

895
00:57:34,200 --> 00:57:39,760
Speaker 3: want to win because maybe I misunderstood something right. So

896
00:57:39,880 --> 00:57:42,640
Speaker 3: that's the winner's curse, and.

897
00:57:44,520 --> 00:57:49,760
Speaker 4: It was found and replicated on bidding for oil leases.

898
00:57:51,320 --> 00:57:53,520
Speaker 3: It's relevant in book publishing.

899
00:57:54,240 --> 00:57:57,640
Speaker 2: When they're bidding contests for books for yes, yes, So

900
00:57:57,920 --> 00:58:02,120
Speaker 2: let me see if I can clarify the way you're

901
00:58:02,120 --> 00:58:04,640
Speaker 2: describing the winner's carse. So we're bidding for oil leases,

902
00:58:05,080 --> 00:58:07,280
Speaker 2: we don't know exactly how much oil is going to

903
00:58:07,360 --> 00:58:10,880
Speaker 2: come out of this hole for or area for the

904
00:58:10,960 --> 00:58:14,720
Speaker 2: next ten to twenty years, and when there's many people bidding,

905
00:58:15,000 --> 00:58:18,480
Speaker 2: all of which are advised by geologists. If you make

906
00:58:18,520 --> 00:58:21,160
Speaker 2: a conservative bet, the odds are you're going to lose.

907
00:58:21,880 --> 00:58:24,280
Speaker 2: But if you make a bet that's high enough that

908
00:58:24,400 --> 00:58:27,120
Speaker 2: you're gonna win, the odds are it's not going to

909
00:58:27,160 --> 00:58:27,960
Speaker 2: be a moneymaker.

910
00:58:28,320 --> 00:58:28,480
Speaker 3: Right.

911
00:58:28,800 --> 00:58:32,480
Speaker 2: Coming up, we continue our conversation with Richard Taylor and

912
00:58:32,600 --> 00:58:37,200
Speaker 2: alex EMUs discussing the book. They have recently updated the

913
00:58:37,240 --> 00:58:43,720
Speaker 2: Winner's Curse behavioral economics anomalies. Then and now, I'm Barry Ritholtz.

914
00:58:43,840 --> 00:59:06,840
Speaker 2: You're listening to Masters in Business on Bloomberg Radio. I'm

915
00:59:06,840 --> 00:59:10,440
Speaker 2: Barry ridolts You're listening to Masters in Business on Bloomberg Radio.

916
00:59:10,640 --> 00:59:13,880
Speaker 2: My extra special guests this week are Richard Taylor and

917
00:59:14,000 --> 00:59:17,560
Speaker 2: Alex Emos, both of the Chicago Booth School Business at

918
00:59:17,600 --> 00:59:22,560
Speaker 2: the University of Chicago. I shared with you an article

919
00:59:22,600 --> 00:59:26,560
Speaker 2: I saw recently. Some real estate group did a study

920
00:59:26,640 --> 00:59:30,760
Speaker 2: of tens of thousands of home transactions where there was

921
00:59:30,760 --> 00:59:34,720
Speaker 2: a bidding war, and they found something very similar. The

922
00:59:34,760 --> 00:59:38,320
Speaker 2: winners of the bidding war ended up paying much more

923
00:59:38,360 --> 00:59:42,520
Speaker 2: than the subsequent home value was determined by looking at

924
00:59:42,520 --> 00:59:47,720
Speaker 2: comparable homes in the neighborhood. So is the purpose of

925
00:59:47,760 --> 00:59:52,160
Speaker 2: an auction to identify something at a fair value where

926
00:59:52,160 --> 00:59:54,640
Speaker 2: it's profitable for you, or is the purpose of an

927
00:59:54,640 --> 00:59:57,080
Speaker 2: auction to win at any cost?

928
00:59:57,800 --> 01:00:04,120
Speaker 4: Well, two interesting aspects of that. One is suppose you're

929
01:00:04,240 --> 01:00:07,520
Speaker 4: these engineers and you've discovered this, what should you do?

930
01:00:09,000 --> 01:00:13,080
Speaker 4: And if you're losing money every time you win an auction,

931
01:00:14,160 --> 01:00:16,880
Speaker 4: you could not bid, But then you don't have any

932
01:00:16,880 --> 01:00:20,240
Speaker 4: places to drill. And what did they decide to do.

933
01:00:20,320 --> 01:00:23,480
Speaker 4: It's really clever. They decided to write a paper.

934
01:00:24,600 --> 01:00:26,680
Speaker 2: In order to get people to stop overbidding.

935
01:00:26,840 --> 01:00:32,080
Speaker 3: Yeah, now that's different than what the owners of Major

936
01:00:32,160 --> 01:00:33,240
Speaker 3: League Baseball did.

937
01:00:33,360 --> 01:00:36,360
Speaker 2: I was going to say, why didn't they just moneyball? Itt,

938
01:00:36,360 --> 01:00:40,000
Speaker 2: Why didn't they just start looking at ugly but productive.

939
01:00:40,760 --> 01:00:47,840
Speaker 4: Well, what the Major League Baseball owners did is they colluded, right,

940
01:00:47,920 --> 01:00:50,040
Speaker 4: and they said, look, let's not bid anymore.

941
01:00:50,360 --> 01:00:52,240
Speaker 2: Well, the salary caps that are in all.

942
01:00:52,080 --> 01:00:56,600
Speaker 3: These no, no, there was just outright collusion when baseball

943
01:00:56,640 --> 01:01:01,120
Speaker 3: players first became free agents. Uh huh, or said hey, we're.

944
01:01:00,880 --> 01:01:04,680
Speaker 4: Losing money on these crazy auctions, let's just not bid,

945
01:01:05,000 --> 01:01:10,919
Speaker 4: and then they got slapped down, right, So what can

946
01:01:11,000 --> 01:01:11,600
Speaker 4: you do?

947
01:01:14,360 --> 01:01:15,840
Speaker 3: You can tried.

948
01:01:16,600 --> 01:01:21,800
Speaker 4: A good strategy is to bid very low on every site,

949
01:01:23,040 --> 01:01:26,960
Speaker 4: and in the data there were lots of sites that

950
01:01:27,000 --> 01:01:28,600
Speaker 4: you could have gotten for a dollar.

951
01:01:29,600 --> 01:01:35,280
Speaker 2: Really, and because because the consensus was is no, they're

952
01:01:35,320 --> 01:01:38,400
Speaker 2: there and were any of them productive? And any of

953
01:01:38,440 --> 01:01:42,320
Speaker 2: these really? So now let's bring this back to sports,

954
01:01:42,720 --> 01:01:45,960
Speaker 2: because you've written papers on NFL drafts.

955
01:01:48,320 --> 01:01:52,880
Speaker 3: So the NFL draft. Every year there's.

956
01:01:54,720 --> 01:01:58,560
Speaker 4: They have a draft for new players. The first pick

957
01:01:58,680 --> 01:02:00,840
Speaker 4: is given to the team with the worst record the

958
01:02:00,880 --> 01:02:04,960
Speaker 4: previous year, with the idea that that's going to be

959
01:02:05,200 --> 01:02:08,400
Speaker 4: a big advantage to them and will help them improve.

960
01:02:09,360 --> 01:02:12,880
Speaker 4: Cade Massey, one of my former students. He and I

961
01:02:12,920 --> 01:02:19,320
Speaker 4: wrote a paper showing that the first pick is actually

962
01:02:20,000 --> 01:02:25,160
Speaker 4: not the most valuable because the league has a salary,

963
01:02:26,160 --> 01:02:29,800
Speaker 4: the first player gets paid the most. And you can

964
01:02:29,960 --> 01:02:34,440
Speaker 4: trade the first pick for the seventh and eighth picks

965
01:02:34,960 --> 01:02:39,200
Speaker 4: or for five second round picks. And what we showed

966
01:02:39,320 --> 01:02:44,120
Speaker 4: is if you trade the first pick for lower picks,

967
01:02:44,920 --> 01:02:46,439
Speaker 4: you get more.

968
01:02:47,680 --> 01:02:48,080
Speaker 3: Value.

969
01:02:48,480 --> 01:02:52,000
Speaker 2: So now this is known like the Arco engineers publishing

970
01:02:52,040 --> 01:02:55,320
Speaker 2: by paper, and yet there still seems to be this

971
01:02:55,480 --> 01:03:00,560
Speaker 2: frenetic war for top top one, top ten, three, top

972
01:03:00,640 --> 01:03:05,880
Speaker 2: five picks. Has the NFL learned any lessons from the research?

973
01:03:07,160 --> 01:03:08,160
Speaker 3: Almost nothing.

974
01:03:09,320 --> 01:03:13,880
Speaker 4: They so they have learned that you should they only

975
01:03:14,080 --> 01:03:18,560
Speaker 4: trade up to get the first pick to pick a quarterback.

976
01:03:19,920 --> 01:03:26,040
Speaker 4: So that's smart because the quarterbacks are more valuable than

977
01:03:26,080 --> 01:03:26,840
Speaker 4: any other player.

978
01:03:26,960 --> 01:03:28,760
Speaker 2: Well, of course you want that first pick, so you

979
01:03:28,760 --> 01:03:29,880
Speaker 2: could get a Tom Brady.

980
01:03:30,160 --> 01:03:33,520
Speaker 4: Yeah, except Tom Brady was taken with one hundred and

981
01:03:33,680 --> 01:03:38,600
Speaker 4: ninety ninth pick. And all the listeners who are football

982
01:03:38,600 --> 01:03:43,160
Speaker 4: fans can have their list of people who were taken

983
01:03:43,200 --> 01:03:46,200
Speaker 4: with the first pick and turned out to be busts.

984
01:03:46,640 --> 01:03:50,640
Speaker 4: The Chicago Bears seemed to specialize in that. Although let's

985
01:03:50,640 --> 01:03:52,440
Speaker 4: hope this current guy.

986
01:03:54,040 --> 01:03:56,880
Speaker 2: Is so new one, So how much of this like

987
01:03:57,360 --> 01:04:00,320
Speaker 2: I'm seeing this through the lens of my book, which

988
01:04:00,320 --> 01:04:02,440
Speaker 2: I don't want to talk about. But how much of

989
01:04:02,480 --> 01:04:07,480
Speaker 2: this is just how difficult it is to predict the future,

990
01:04:07,560 --> 01:04:13,600
Speaker 2: to to have truly expert judgment about these very complex,

991
01:04:13,840 --> 01:04:22,320
Speaker 2: very variable so selecting quarterbacks, identifying oil leases like it

992
01:04:22,480 --> 01:04:27,680
Speaker 2: seems that supposed expert advice ain't all that expert. How

993
01:04:27,760 --> 01:04:30,000
Speaker 2: much of this is? Aren't we better off just being

994
01:04:30,040 --> 01:04:33,240
Speaker 2: a little more humble about our Let's give up the

995
01:04:33,280 --> 01:04:36,000
Speaker 2: top pick and have five second round. Somebody in that

996
01:04:36,080 --> 01:04:37,760
Speaker 2: five is likely to be half decent? Yeah?

997
01:04:37,800 --> 01:04:41,720
Speaker 4: Right, So let me stick to the sports for one second,

998
01:04:42,320 --> 01:04:48,520
Speaker 4: because there's one statistic from our paper, and we've just coincidentally,

999
01:04:48,600 --> 01:04:51,680
Speaker 4: we've been in a process of replicating.

1000
01:04:50,920 --> 01:04:53,240
Speaker 3: That study, so I have the new data. But here's

1001
01:04:53,280 --> 01:04:54,040
Speaker 3: the statistic.

1002
01:04:54,480 --> 01:04:58,240
Speaker 4: Take all the players at a given position, say quarterback

1003
01:04:58,640 --> 01:05:02,160
Speaker 4: or cornerback or running back, rank them in the order

1004
01:05:02,200 --> 01:05:08,320
Speaker 4: in which they're picked, and now ask the fourth guy,

1005
01:05:08,600 --> 01:05:11,440
Speaker 4: what's the chance he's better than the fifth guy? So

1006
01:05:12,000 --> 01:05:15,120
Speaker 4: for the for the whole thing, so what's the chance

1007
01:05:15,640 --> 01:05:18,880
Speaker 4: the earlier player is better than the next one?

1008
01:05:19,720 --> 01:05:22,360
Speaker 2: One over two, ten, over eleven, five, over six?

1009
01:05:22,800 --> 01:05:26,760
Speaker 4: Now, if they're perfect it'll be one hundred percent. If

1010
01:05:26,760 --> 01:05:31,840
Speaker 4: they're coin flipping, it'll be fifty percent. What do you

1011
01:05:31,880 --> 01:05:32,360
Speaker 4: think it is.

1012
01:05:32,440 --> 01:05:33,880
Speaker 2: I think it's less than fifty percent.

1013
01:05:33,920 --> 01:05:37,920
Speaker 3: I think negative. They they know less than nothing, right.

1014
01:05:37,800 --> 01:05:40,120
Speaker 2: That's right. I think it's in the thirties or forties.

1015
01:05:40,600 --> 01:05:43,760
Speaker 4: Well, they're not that bad, right, I mean, because if

1016
01:05:43,800 --> 01:05:47,919
Speaker 4: they were, then you could just you know, you no, no,

1017
01:05:48,280 --> 01:05:51,960
Speaker 4: you'd want to what the George Costanza. You wanted to

1018
01:05:51,960 --> 01:05:58,280
Speaker 4: be opposite, right, So, so no, they don't have negative knowledge.

1019
01:05:58,640 --> 01:06:04,800
Speaker 4: They have a tiny little bay. So but that's your point, really,

1020
01:06:05,120 --> 01:06:09,720
Speaker 4: which is they think they know this guy is the

1021
01:06:09,760 --> 01:06:14,040
Speaker 4: next Tom Brady, and there's only a fifty three percent

1022
01:06:14,280 --> 01:06:19,760
Speaker 4: chance that he's better than the next one. And you know,

1023
01:06:20,800 --> 01:06:24,520
Speaker 4: Patrick Mahomes, Josh Allen think none of these were first.

1024
01:06:24,560 --> 01:06:27,880
Speaker 2: Right, right, So Mahomes kid is gonna be pretty good one.

1025
01:06:27,960 --> 01:06:29,320
Speaker 3: I think he might make it.

1026
01:06:29,440 --> 01:06:31,200
Speaker 2: He's got some potential, right.

1027
01:06:31,080 --> 01:06:35,920
Speaker 4: So, And I think that is so getting off of sports,

1028
01:06:36,280 --> 01:06:39,440
Speaker 4: I think that your general point is exactly right that

1029
01:06:40,600 --> 01:06:45,040
Speaker 4: people are look overconfidence. Danny Codeman used to say that's

1030
01:06:45,120 --> 01:06:51,760
Speaker 4: the mother of all biases. And we fall into these

1031
01:06:51,960 --> 01:06:56,360
Speaker 4: traps because we think we know more than we do,

1032
01:06:57,920 --> 01:07:03,840
Speaker 4: and if we add some humility, maybe if we listen

1033
01:07:03,960 --> 01:07:08,600
Speaker 4: to our spouses more often, because at least in my house,

1034
01:07:10,960 --> 01:07:15,320
Speaker 4: my wife doesn't think that I know anything, so she's

1035
01:07:15,440 --> 01:07:19,960
Speaker 4: always bringing me back to fifty percent right, and she's

1036
01:07:20,080 --> 01:07:20,760
Speaker 4: usually right.

1037
01:07:21,160 --> 01:07:24,000
Speaker 2: My wife is from the same cut, from the same cloth.

1038
01:07:24,360 --> 01:07:27,680
Speaker 2: So I want to bring Alex back into this. So

1039
01:07:27,760 --> 01:07:31,520
Speaker 2: when we're thinking about the future of behavioral economics and

1040
01:07:33,000 --> 01:07:39,640
Speaker 2: what this means for investors or regular people making financial

1041
01:07:39,680 --> 01:07:44,320
Speaker 2: decisions or in significant decisions, what direction are we moving in.

1042
01:07:44,360 --> 01:07:49,080
Speaker 2: Are are we learning from all of this knowledge that's

1043
01:07:49,080 --> 01:07:52,280
Speaker 2: been accumulated, or are we just destined to make the

1044
01:07:52,280 --> 01:07:54,000
Speaker 2: same mistakes over and over again.

1045
01:07:55,400 --> 01:07:58,280
Speaker 5: I don't think we're destined to do anything. I think

1046
01:07:58,680 --> 01:08:03,440
Speaker 5: it's a choice to take, you know, read papers and

1047
01:08:03,800 --> 01:08:07,160
Speaker 5: look at papers on kind of published and financial journals

1048
01:08:07,200 --> 01:08:10,600
Speaker 5: where people are making mistakes, and then to choose to say, like, look,

1049
01:08:10,640 --> 01:08:13,440
Speaker 5: I actually can correct this by having a particular decision

1050
01:08:13,440 --> 01:08:17,879
Speaker 5: aid or asking my spouse what to do, or something

1051
01:08:17,920 --> 01:08:20,960
Speaker 5: like that. So you know what a paper you mentioned earlier,

1052
01:08:21,040 --> 01:08:25,320
Speaker 5: we published this actually just last year, called Selling fast

1053
01:08:25,360 --> 01:08:28,840
Speaker 5: and Buying Slow. And in that paper, basically we look

1054
01:08:28,840 --> 01:08:33,439
Speaker 5: at institutional investors, So thinking about who in the economy

1055
01:08:33,439 --> 01:08:36,080
Speaker 5: are least likely to be exhibiting beha role of biases.

1056
01:08:36,120 --> 01:08:38,759
Speaker 5: You know, maybe retail traders they're like, you know, drinking

1057
01:08:38,800 --> 01:08:41,439
Speaker 5: beer in their basement while trading stocks on robin Hood.

1058
01:08:41,600 --> 01:08:43,920
Speaker 5: Maybe this is not the sophisticated people we want to

1059
01:08:43,960 --> 01:08:47,280
Speaker 5: be looking at. But you know, institutional investors, the average

1060
01:08:47,280 --> 01:08:49,479
Speaker 5: portfolio on the data set was like six hundred million,

1061
01:08:49,560 --> 01:08:53,040
Speaker 5: seven hundred million dollars or something like that. We had

1062
01:08:53,080 --> 01:08:55,800
Speaker 5: a data set where we actually saw every single they

1063
01:08:56,200 --> 01:08:58,599
Speaker 5: think they did over something like a twelve or thirteen

1064
01:08:58,680 --> 01:09:01,719
Speaker 5: year period far as what they're buying or what they're selling.

1065
01:09:02,240 --> 01:09:05,240
Speaker 5: And what we found is because the data is so rich,

1066
01:09:05,280 --> 01:09:08,760
Speaker 5: we can actually construct these counterfactual portfolios. We can say, look,

1067
01:09:08,800 --> 01:09:11,360
Speaker 5: I see what you're buying. What if you bought something else,

1068
01:09:11,640 --> 01:09:13,760
Speaker 5: So it could be something else from your portfolio, you

1069
01:09:13,800 --> 01:09:15,960
Speaker 5: can top something up, or you can buy something new

1070
01:09:15,960 --> 01:09:18,640
Speaker 5: from the from the universe. On the other hand, we

1071
01:09:18,640 --> 01:09:21,800
Speaker 5: could say, look the same thing for selling. I saw

1072
01:09:21,880 --> 01:09:25,160
Speaker 5: you sold Apple, I saw you sold Samsung. Let me

1073
01:09:25,720 --> 01:09:28,919
Speaker 5: sell something else instead. How how would that perform relative

1074
01:09:28,960 --> 01:09:31,600
Speaker 5: to what you actually did. And what we found is

1075
01:09:31,600 --> 01:09:34,840
Speaker 5: that on the buying side. People actually did really well.

1076
01:09:35,120 --> 01:09:37,280
Speaker 5: I mean, these guys are they.

1077
01:09:37,240 --> 01:09:39,719
Speaker 3: Happen really well? But better better than.

1078
01:09:39,680 --> 01:09:44,000
Speaker 2: Random Fund managers create some value in their stock selection

1079
01:09:44,080 --> 01:09:46,840
Speaker 2: when they're making purchases, yes, but the flip side of

1080
01:09:46,880 --> 01:09:48,160
Speaker 2: that not so much.

1081
01:09:48,240 --> 01:09:50,960
Speaker 5: No, not so much. We had we really wanted to

1082
01:09:50,960 --> 01:09:54,160
Speaker 5: be conservative. We didn't want to, you know, say, compare

1083
01:09:54,160 --> 01:09:56,280
Speaker 5: them to the benchmark or something like that. We said,

1084
01:09:56,360 --> 01:09:58,840
Speaker 5: let's throw a dart at your portfolio and sell that

1085
01:09:59,000 --> 01:09:59,880
Speaker 5: instead of what you act.

1086
01:10:00,120 --> 01:10:03,080
Speaker 2: So instead of selling what the manager wants to sell,

1087
01:10:03,479 --> 01:10:06,240
Speaker 2: you would sell something else randomly from the rest of the.

1088
01:10:06,200 --> 01:10:07,719
Speaker 5: Portfolio, a random selling.

1089
01:10:07,920 --> 01:10:10,640
Speaker 2: And the performance difference was how significant?

1090
01:10:11,080 --> 01:10:15,680
Speaker 5: Basically same difference but in the opposite direction, meaning they

1091
01:10:15,720 --> 01:10:17,200
Speaker 5: were losing a ton of money.

1092
01:10:17,439 --> 01:10:20,080
Speaker 2: So one hundred two hundred basis points on a random

1093
01:10:20,120 --> 01:10:24,120
Speaker 2: cell better, Yes, performance exactly. And you know the way

1094
01:10:24,160 --> 01:10:26,960
Speaker 2: when I read that paper and wrote about it, the

1095
01:10:27,040 --> 01:10:31,000
Speaker 2: way I rationalized it, or tried to conceptualize it was

1096
01:10:32,040 --> 01:10:35,759
Speaker 2: they're bringing a very objective, quantitative approach to the stock

1097
01:10:35,880 --> 01:10:40,320
Speaker 2: selection issue, but it seems that their cells are filled

1098
01:10:40,320 --> 01:10:43,240
Speaker 2: with biases and squishy decision making.

1099
01:10:43,360 --> 01:10:47,240
Speaker 5: Is that a fair description? Yeah, exactly. So we found

1100
01:10:47,280 --> 01:10:49,720
Speaker 5: no evidence for heuristics on their buying decision, Like, we

1101
01:10:49,760 --> 01:10:52,720
Speaker 5: couldn't find anything, Like they just seemed to be very

1102
01:10:52,760 --> 01:10:56,840
Speaker 5: disciplined and principled about what they're buying. But on the

1103
01:10:56,840 --> 01:11:00,160
Speaker 5: selling side we found literally the same biases that we

1104
01:11:00,200 --> 01:11:00,960
Speaker 5: found in the lab.

1105
01:11:01,320 --> 01:11:05,959
Speaker 2: Has there been any evolution or improvement in this recently?

1106
01:11:06,280 --> 01:11:09,320
Speaker 2: That's the question that I keep coming back to. It

1107
01:11:09,439 --> 01:11:13,439
Speaker 2: seems that Richard, you've figured a lot of these things

1108
01:11:13,479 --> 01:11:17,719
Speaker 2: out twenty five thirty five years ago. Are we any

1109
01:11:17,840 --> 01:11:21,839
Speaker 2: better at making unbiased decisions? Or are we still subject

1110
01:11:21,840 --> 01:11:23,520
Speaker 2: to the same voibles.

1111
01:11:23,760 --> 01:11:25,960
Speaker 5: I think you need something extra right. You can't just

1112
01:11:25,960 --> 01:11:27,960
Speaker 5: say I'm not going to do this and I have

1113
01:11:28,080 --> 01:11:30,920
Speaker 5: decided not to listen to my psychology. That's what it

1114
01:11:30,920 --> 01:11:31,519
Speaker 5: would look.

1115
01:11:31,320 --> 01:11:35,760
Speaker 2: Like to be choice architecture of building, exam guardrails and

1116
01:11:36,000 --> 01:11:36,880
Speaker 2: but this is a fault.

1117
01:11:37,320 --> 01:11:39,720
Speaker 5: This is where overconfidence comes in. When you read a

1118
01:11:39,760 --> 01:11:43,120
Speaker 5: paper about somebody doing something silly, your first reaction is

1119
01:11:43,439 --> 01:11:47,599
Speaker 5: not me right, that's the blind spot. This is called

1120
01:11:47,600 --> 01:11:50,160
Speaker 5: the bias blind spot exactly. So this is a well

1121
01:11:50,200 --> 01:11:53,040
Speaker 5: replicated finding. When you ask people to what extent do

1122
01:11:53,080 --> 01:11:56,880
Speaker 5: you exhibit a bias? I don't obviously I'm a smart person,

1123
01:11:57,280 --> 01:12:00,400
Speaker 5: but to what extent to other people. Of course people

1124
01:12:00,439 --> 01:12:02,720
Speaker 5: are bad at selling. I'm really good. But in order

1125
01:12:02,760 --> 01:12:06,400
Speaker 5: to adopt choice architecture to help you out when making decisions,

1126
01:12:06,400 --> 01:12:08,960
Speaker 5: you actually have to be have some You have to

1127
01:12:09,000 --> 01:12:13,200
Speaker 5: have a lot of humility to say, look these institutional investors,

1128
01:12:13,240 --> 01:12:16,519
Speaker 5: to say, look, looks like I'm not really doing so

1129
01:12:16,560 --> 01:12:20,640
Speaker 5: well on selling. I'm going to adopt some choice architectures

1130
01:12:20,680 --> 01:12:22,840
Speaker 5: so I don't suffer from these biases. Maybe I'll hire

1131
01:12:22,840 --> 01:12:26,559
Speaker 5: somebody else to help me out. Maybe I'll think longer,

1132
01:12:26,760 --> 01:12:29,519
Speaker 5: or use the same sort of research technology for my

1133
01:12:29,600 --> 01:12:32,599
Speaker 5: selling as I'm doing for my buying. And because that

1134
01:12:32,640 --> 01:12:35,479
Speaker 5: requires humility, which most people don't have a lot of,

1135
01:12:36,360 --> 01:12:38,320
Speaker 5: that's really hard to do. So I think that's why

1136
01:12:38,360 --> 01:12:41,800
Speaker 5: we're seeing a lot of these biases just be perpetuated

1137
01:12:42,760 --> 01:12:46,240
Speaker 5: forward to the point where we're running the same analysis

1138
01:12:46,240 --> 01:12:47,880
Speaker 5: now as we did thirty years ago and finding the

1139
01:12:47,920 --> 01:12:48,639
Speaker 5: exact same thing.

1140
01:12:49,000 --> 01:12:52,439
Speaker 2: So the question that since we were talking about sports

1141
01:12:52,479 --> 01:12:57,320
Speaker 2: and lack of knowledge, and then you mentioned Robin Hood,

1142
01:12:57,920 --> 01:13:01,800
Speaker 2: one of the things that's a little concerning is how

1143
01:13:02,120 --> 01:13:06,280
Speaker 2: some companies are putting our knowledge of biases and bad

1144
01:13:06,360 --> 01:13:09,920
Speaker 2: behavior to work for their own profit. So when we

1145
01:13:09,960 --> 01:13:14,519
Speaker 2: see the gamification of investing with Robinhood or just the

1146
01:13:14,560 --> 01:13:18,240
Speaker 2: incredible rise not just of sports books and gambling, but

1147
01:13:18,320 --> 01:13:20,760
Speaker 2: you could bet on every play, it's reached a point

1148
01:13:20,760 --> 01:13:24,720
Speaker 2: where it's ridiculous, and there is a robust evidence that,

1149
01:13:25,240 --> 01:13:29,120
Speaker 2: especially young men, are having all sorts of how can

1150
01:13:29,160 --> 01:13:32,040
Speaker 2: we how can we deal with what seems to be

1151
01:13:33,000 --> 01:13:36,760
Speaker 2: not a good use of choice architecture, but a bad

1152
01:13:36,880 --> 01:13:39,960
Speaker 2: use of choice choice architecture, at least as far as

1153
01:13:40,040 --> 01:13:41,599
Speaker 2: the public is concerned.

1154
01:13:41,840 --> 01:13:44,600
Speaker 4: Yeah, it's a very good question, Barry, and one to

1155
01:13:44,680 --> 01:13:48,160
Speaker 4: which I don't have a pad answer. I mean, it's

1156
01:13:48,240 --> 01:13:55,040
Speaker 4: tempting to say, look, all these sports betting apps and

1157
01:13:55,120 --> 01:14:00,240
Speaker 4: the gamification of investing are bad for people. On the

1158
01:14:00,320 --> 01:14:07,200
Speaker 4: other hand, people like doing it. They're mostly adults, and

1159
01:14:08,000 --> 01:14:14,160
Speaker 4: you know, prohibition basically didn't work. So I think some

1160
01:14:15,080 --> 01:14:24,200
Speaker 4: disclosure would help. It's difficult to find out what the odds.

1161
01:14:23,800 --> 01:14:28,880
Speaker 3: Are and a lot of these things. But it's a

1162
01:14:28,920 --> 01:14:29,639
Speaker 3: tough question.

1163
01:14:30,200 --> 01:14:35,799
Speaker 4: I had a conversation on this book when Nate Silver

1164
01:14:35,880 --> 01:14:38,200
Speaker 4: a couple of weeks ago, and we talked a lot

1165
01:14:38,240 --> 01:14:44,400
Speaker 4: about sports gambling. He's a professional gambler and he spent

1166
01:14:44,479 --> 01:14:47,920
Speaker 4: a year betting on NBA games and basically broke even.

1167
01:14:48,960 --> 01:14:52,360
Speaker 4: So you know, if Nate can't make money doing this,

1168
01:14:53,320 --> 01:14:58,280
Speaker 4: chances are you can't. And you know, my advice would be, look,

1169
01:14:58,320 --> 01:15:03,800
Speaker 4: if you really think you doing this, do it on

1170
01:15:03,840 --> 01:15:04,839
Speaker 4: a small scale.

1171
01:15:05,880 --> 01:15:08,759
Speaker 3: You know, don't bet the house list, you don't write.

1172
01:15:09,000 --> 01:15:12,840
Speaker 3: And the same with weekly options or daily.

1173
01:15:12,600 --> 01:15:14,559
Speaker 5: That's one of the most popular. One of our colleagues

1174
01:15:14,560 --> 01:15:17,280
Speaker 5: at University of Chicago she did an analysis of what

1175
01:15:17,479 --> 01:15:20,320
Speaker 5: retail traders are actually doing on Robinhood and one of

1176
01:15:20,320 --> 01:15:24,440
Speaker 5: the most popular products, which because it's pushed by Robinhood,

1177
01:15:24,880 --> 01:15:26,040
Speaker 5: is weekly options.

1178
01:15:26,520 --> 01:15:30,040
Speaker 2: And there's now end of day options where it expires

1179
01:15:30,760 --> 01:15:33,479
Speaker 2: you have till four o'clock to either make money or not.

1180
01:15:34,040 --> 01:15:37,200
Speaker 3: So you know, if you want to risk one month's

1181
01:15:37,240 --> 01:15:38,360
Speaker 3: pay on.

1182
01:15:38,360 --> 01:15:42,000
Speaker 2: That, fine, not just not every month.

1183
01:15:42,000 --> 01:15:47,040
Speaker 3: No, yes, yeah, that's in your lifetime budget and when

1184
01:15:47,080 --> 01:15:50,519
Speaker 3: it goes to zero, switch to something else.

1185
01:15:51,479 --> 01:15:54,240
Speaker 2: I'm a big fan of the cowboy account, where you

1186
01:15:54,280 --> 01:15:57,200
Speaker 2: take three or four percent of your portfolio and if

1187
01:15:57,240 --> 01:15:59,840
Speaker 2: you want to fool around with options, whatever, knock your

1188
01:16:00,280 --> 01:16:03,599
Speaker 2: out and if it makes money, great, But like we've

1189
01:16:03,640 --> 01:16:06,600
Speaker 2: seen you mentioned Apple, if it was your whole portfolio.

1190
01:16:06,640 --> 01:16:09,200
Speaker 2: You would never have been able to ride it to

1191
01:16:09,280 --> 01:16:11,880
Speaker 2: be a five extra tenants. You would have taken I'm

1192
01:16:11,960 --> 01:16:13,960
Speaker 2: up twenty bucks. I'm taking the money off the table.

1193
01:16:14,080 --> 01:16:14,519
Speaker 3: Yeah yeah.

1194
01:16:14,640 --> 01:16:20,720
Speaker 4: So you know, people long ago would adopt the strategy

1195
01:16:20,760 --> 01:16:23,960
Speaker 4: of bringing a certain amount of money to the casino, right,

1196
01:16:24,479 --> 01:16:28,479
Speaker 4: and then of course the casinos put ATM on the floor.

1197
01:16:28,800 --> 01:16:31,880
Speaker 3: So it's a battle. But mental accounting.

1198
01:16:32,720 --> 01:16:37,200
Speaker 4: You have a gambling account, but that's it.

1199
01:16:37,520 --> 01:16:40,400
Speaker 3: Yeah. I mean it would be better if it were zero, right,

1200
01:16:40,520 --> 01:16:43,000
Speaker 3: but otherwise set it up.

1201
01:16:43,200 --> 01:16:46,280
Speaker 4: That's something you can afford to lose. And when you've

1202
01:16:46,360 --> 01:16:47,520
Speaker 4: lost it all.

1203
01:16:47,479 --> 01:16:49,479
Speaker 5: Your stump, don't go to the ATM.

1204
01:16:49,560 --> 01:16:50,120
Speaker 3: That's right.

1205
01:16:50,200 --> 01:16:52,280
Speaker 2: So I only have you for a few more minutes.

1206
01:16:52,320 --> 01:16:55,439
Speaker 2: I want to ask two of my favorite questions that

1207
01:16:55,600 --> 01:16:57,200
Speaker 2: I want to ask each of you, that I ask

1208
01:16:57,400 --> 01:17:01,559
Speaker 2: all of my guests, starting with what sort of advice

1209
01:17:01,600 --> 01:17:04,360
Speaker 2: would you give a recent college grad interest in the

1210
01:17:04,439 --> 01:17:08,240
Speaker 2: career and either behavioral finance or economics. Alse you're the

1211
01:17:08,320 --> 01:17:11,479
Speaker 2: more recent grad, what would you what would your advice be?

1212
01:17:12,040 --> 01:17:16,040
Speaker 5: Get tacked up? Really get tacked up. I think that's

1213
01:17:16,080 --> 01:17:18,680
Speaker 5: the biggest kind of difference between even when I was

1214
01:17:18,720 --> 01:17:21,720
Speaker 5: in graduate school and when I'm seeing hiring preducts and

1215
01:17:21,960 --> 01:17:24,719
Speaker 5: ras the sort of work that you're doing in modern

1216
01:17:24,760 --> 01:17:28,200
Speaker 5: behavioral economics, modern finance, it just requires a different level

1217
01:17:28,240 --> 01:17:28,880
Speaker 5: of analysis.

1218
01:17:29,080 --> 01:17:31,400
Speaker 2: So is this learning to code or is this becoming

1219
01:17:31,439 --> 01:17:33,160
Speaker 2: a prompt engineer for AI?

1220
01:17:33,360 --> 01:17:35,320
Speaker 5: What? I think you still got to learn to code.

1221
01:17:35,640 --> 01:17:39,719
Speaker 5: I think you know where I work in the Applied

1222
01:17:39,760 --> 01:17:42,360
Speaker 5: A I group at booth, you know, you still got

1223
01:17:42,400 --> 01:17:44,120
Speaker 5: to learn to code. And a lot of this sort

1224
01:17:44,160 --> 01:17:47,160
Speaker 5: of modern analyzes that people are doing, particularly as behavioral

1225
01:17:47,200 --> 01:17:50,160
Speaker 5: finance behavioral ekon has moved out of the lab into

1226
01:17:50,200 --> 01:17:54,959
Speaker 5: the field. These data sets are huge machine learning AI tools.

1227
01:17:55,600 --> 01:17:57,960
Speaker 5: The type of people who are getting hired are doing

1228
01:17:58,000 --> 01:18:00,160
Speaker 5: sophisticated analys So it's still.

1229
01:18:00,080 --> 01:18:04,719
Speaker 2: Basically STEM groups science, technology, engineering, and math for people

1230
01:18:05,280 --> 01:18:09,559
Speaker 2: who don't know the acronym, but applied specifically to the Feeah.

1231
01:18:09,640 --> 01:18:11,759
Speaker 5: Yeah, And like you know, you take your economics courses,

1232
01:18:11,800 --> 01:18:14,400
Speaker 5: you take your finance courses, take some CS courses on

1233
01:18:14,439 --> 01:18:18,080
Speaker 5: the side. Those are this is what I wish I

1234
01:18:18,120 --> 01:18:20,200
Speaker 5: would have done. Right when I was getting my PhD.

1235
01:18:20,280 --> 01:18:22,160
Speaker 5: It wasn't on my radar to take, you know, a

1236
01:18:22,200 --> 01:18:25,000
Speaker 5: coding class in the CS department. People coming out now

1237
01:18:25,040 --> 01:18:27,320
Speaker 5: the ones who are really successful, you have to have

1238
01:18:27,360 --> 01:18:32,439
Speaker 5: good ideas. That's a necessary condition. It's not a sufficient condition.

1239
01:18:33,280 --> 01:18:36,000
Speaker 5: You still you got you need to be TechEd up

1240
01:18:36,160 --> 01:18:38,400
Speaker 5: to a level that I don't think we were seeing

1241
01:18:38,880 --> 01:18:39,719
Speaker 5: when I was graduating.

1242
01:18:39,720 --> 01:18:40,360
Speaker 3: Really interesting.

1243
01:18:40,840 --> 01:18:44,639
Speaker 4: I'll reinforce that with the following. I think you need

1244
01:18:44,680 --> 01:18:48,400
Speaker 4: some practical experience. Yeah, because the part that you don't

1245
01:18:48,439 --> 01:18:53,720
Speaker 4: learn in a textbook is you you get this gigantic

1246
01:18:53,840 --> 01:18:58,519
Speaker 4: data set and it's noisy, and there are errors and

1247
01:18:59,800 --> 01:19:03,760
Speaker 4: so so learning how to clean up a data set.

1248
01:19:06,240 --> 01:19:08,200
Speaker 3: You got to learn that through experience.

1249
01:19:08,960 --> 01:19:11,960
Speaker 2: Really interesting. And our final question, what do you know

1250
01:19:12,000 --> 01:19:15,559
Speaker 2: about the world of behavioral economics today, would have been

1251
01:19:15,640 --> 01:19:19,280
Speaker 2: useful back in the nineteen seventies and eighties when you

1252
01:19:19,320 --> 01:19:22,720
Speaker 2: were getting started, and in the two thousands when you

1253
01:19:22,760 --> 01:19:23,559
Speaker 2: were getting started.

1254
01:19:24,360 --> 01:19:26,439
Speaker 3: So I think if we go back.

1255
01:19:26,520 --> 01:19:31,680
Speaker 4: We talked about the changes that I helped make in

1256
01:19:31,760 --> 01:19:37,280
Speaker 4: the retirement plans, and what I wish I had been

1257
01:19:37,280 --> 01:19:43,880
Speaker 4: able to accomplish more of is making retirement saving at

1258
01:19:43,880 --> 01:19:48,839
Speaker 4: the workplace available to the possibly forty percent of American

1259
01:19:48,880 --> 01:19:54,320
Speaker 4: workers whose firms don't offer that option. And there were

1260
01:19:55,200 --> 01:19:59,920
Speaker 4: plans around and they didn't get passed.

1261
01:20:01,240 --> 01:20:01,920
Speaker 3: I think.

1262
01:20:03,439 --> 01:20:06,879
Speaker 4: The system they have in the UK is a reasonable model,

1263
01:20:07,400 --> 01:20:12,080
Speaker 4: which is there's a requirement that any firm with more

1264
01:20:12,240 --> 01:20:17,280
Speaker 4: than I'm not show the number, say twenty employees, has

1265
01:20:17,360 --> 01:20:24,559
Speaker 4: to offer a plan and automatically enroll people into the plan,

1266
01:20:25,000 --> 01:20:29,240
Speaker 4: and the government has like a generic plan they can use,

1267
01:20:29,920 --> 01:20:35,080
Speaker 4: like the government thrift program. So and this is useful

1268
01:20:35,120 --> 01:20:39,840
Speaker 4: because big firms like Fidelity and Vanguard don't really want

1269
01:20:39,920 --> 01:20:46,639
Speaker 4: tiny accounts, so make it easy for an employer. They

1270
01:20:46,720 --> 01:20:49,800
Speaker 4: don't have to do anything. They just have to let

1271
01:20:49,880 --> 01:20:53,840
Speaker 4: their employees enroll in this and then when they change

1272
01:20:53,960 --> 01:20:58,080
Speaker 4: jobs they can keep it there. Because the real problem

1273
01:20:58,160 --> 01:21:02,400
Speaker 4: is they go to war for a while at this

1274
01:21:02,520 --> 01:21:05,800
Speaker 4: firm and they worked there for a year and they've

1275
01:21:05,800 --> 01:21:08,920
Speaker 4: saved six hundred dollars and then they leave and.

1276
01:21:08,880 --> 01:21:10,240
Speaker 3: They take the cash out.

1277
01:21:10,479 --> 01:21:16,320
Speaker 4: So we need automatic roll over. So that's the piece

1278
01:21:16,360 --> 01:21:20,120
Speaker 4: of the puzzle that I don't know whether I could

1279
01:21:20,160 --> 01:21:23,200
Speaker 4: have done anything about, but it's what I wish we

1280
01:21:23,240 --> 01:21:24,120
Speaker 4: could work on now.

1281
01:21:24,120 --> 01:21:26,120
Speaker 2: So related to that, what do you think of these

1282
01:21:26,160 --> 01:21:30,280
Speaker 2: new baby accounts? Every newborn in America next year gets

1283
01:21:30,320 --> 01:21:34,760
Speaker 2: one thousand dollars has to be invested domestically, which you know,

1284
01:21:34,880 --> 01:21:36,960
Speaker 2: we can have an argument. We were talking about home

1285
01:21:37,000 --> 01:21:41,160
Speaker 2: country bias, but still you're starting every infant off with

1286
01:21:41,320 --> 01:21:44,080
Speaker 2: a portfolio. What are your thoughts on that.

1287
01:21:45,400 --> 01:21:47,760
Speaker 3: I don't know the details of how that's going to work.

1288
01:21:47,840 --> 01:21:48,520
Speaker 3: You could.

1289
01:21:50,880 --> 01:21:55,240
Speaker 4: My friend and sometimes colleague when I'm in Berkeley, Ulrica

1290
01:21:55,360 --> 01:21:59,760
Speaker 4: Melmendie has made a similar proposal in Germany, where she's

1291
01:21:59,800 --> 01:22:02,880
Speaker 4: on the German Council of Economic Advisors.

1292
01:22:03,400 --> 01:22:09,200
Speaker 3: I think the idea is to give kids.

1293
01:22:08,439 --> 01:22:13,640
Speaker 4: Some experience with the stock market, and I think that

1294
01:22:13,680 --> 01:22:19,320
Speaker 4: could be useful. I don't know how this is going

1295
01:22:19,400 --> 01:22:23,760
Speaker 4: to wash out and all of these things end up

1296
01:22:23,920 --> 01:22:28,280
Speaker 4: being tilted toward the rich. I mean, there were big

1297
01:22:28,400 --> 01:22:33,760
Speaker 4: reforms made recently to the retirement plans. One of the

1298
01:22:33,840 --> 01:22:39,160
Speaker 4: reforms was to let you wait longer to start making withdrawals.

1299
01:22:39,680 --> 01:22:41,920
Speaker 3: Who do you think that helps people who.

1300
01:22:41,760 --> 01:22:44,759
Speaker 2: Are wealthy, healthy and going to have a longer lifespan.

1301
01:22:44,960 --> 01:22:49,960
Speaker 4: Right, So most people start taking the money out at

1302
01:22:50,000 --> 01:22:54,280
Speaker 4: fifty nine and a half. Raising the data at which

1303
01:22:54,320 --> 01:22:59,040
Speaker 4: you have to start from seventy to seventy two doesn't

1304
01:22:59,120 --> 01:23:01,200
Speaker 4: help anybody that's in any trouble.

1305
01:23:01,280 --> 01:23:03,960
Speaker 2: Yeah, that makes a lot of sense, Alex, What do

1306
01:23:04,000 --> 01:23:07,040
Speaker 2: you know today about behavioral finance that you wish you

1307
01:23:07,120 --> 01:23:08,879
Speaker 2: knew when you were getting started.

1308
01:23:10,280 --> 01:23:13,640
Speaker 5: I think what I when I was getting started, I

1309
01:23:13,680 --> 01:23:16,800
Speaker 5: wasn't really thinking about kind of target being able to

1310
01:23:16,840 --> 01:23:21,360
Speaker 5: target these big institutional investors and thinking about getting data

1311
01:23:21,400 --> 01:23:26,400
Speaker 5: sets on smart money in the economy. So I think

1312
01:23:26,400 --> 01:23:28,320
Speaker 5: when I was starting out, I was really focused on

1313
01:23:28,360 --> 01:23:30,479
Speaker 5: lab experiments. I was really focused on kind of the

1314
01:23:30,560 --> 01:23:34,599
Speaker 5: data that's that was available. And if I was starting

1315
01:23:34,600 --> 01:23:37,200
Speaker 5: out now, I would I would start my PhD trying

1316
01:23:37,200 --> 01:23:39,519
Speaker 5: to get trying to get data sets that I eventually

1317
01:23:39,640 --> 01:23:42,280
Speaker 5: was able to get because the types of as far

1318
01:23:42,360 --> 01:23:44,200
Speaker 5: as like looking at my research, what has had the

1319
01:23:44,320 --> 01:23:48,519
Speaker 5: largest impact, what has had you know, people in finance

1320
01:23:48,600 --> 01:23:51,120
Speaker 5: in the professional world calling me up and saying, hey,

1321
01:23:51,600 --> 01:23:53,880
Speaker 5: what do you think about this? Or that it's been

1322
01:23:54,400 --> 01:23:57,640
Speaker 5: looking at the population that people are actually interested in,

1323
01:23:57,680 --> 01:23:59,719
Speaker 5: which are you know, the smart money in the economy.

1324
01:23:59,760 --> 01:24:04,320
Speaker 5: So I think and this is I think, you know,

1325
01:24:04,439 --> 01:24:08,120
Speaker 5: you have this analogy of looking under the spot under

1326
01:24:08,160 --> 01:24:11,080
Speaker 5: the street light, where is a lot of behavioral finance

1327
01:24:11,120 --> 01:24:14,599
Speaker 5: for the missing exactly? You know where are you're looking? Oh,

1328
01:24:14,600 --> 01:24:17,040
Speaker 5: it's where the data sets already are. So you know,

1329
01:24:17,120 --> 01:24:20,400
Speaker 5: Terry o'dean had this genius idea in ninety eight when

1330
01:24:20,439 --> 01:24:22,920
Speaker 5: he published his paper. What did he do? He made

1331
01:24:22,920 --> 01:24:26,240
Speaker 5: that big data set available. Now it's easy to just oh,

1332
01:24:26,280 --> 01:24:28,080
Speaker 5: I got an idea, Why don't I look at Terry's

1333
01:24:28,160 --> 01:24:30,760
Speaker 5: data set. Terry's data set is great, but it's three

1334
01:24:30,800 --> 01:24:33,000
Speaker 5: years in the nineties with you know, a couple hundred

1335
01:24:33,040 --> 01:24:37,599
Speaker 5: retail traders, and that tells you about that's specific population.

1336
01:24:37,680 --> 01:24:40,920
Speaker 5: But you can't have a field evolve looking at three

1337
01:24:41,000 --> 01:24:44,320
Speaker 5: years of retail traders with ten thousand dollars portfolios. So

1338
01:24:44,360 --> 01:24:46,200
Speaker 5: I think if I was going to going into the

1339
01:24:46,200 --> 01:24:50,479
Speaker 5: field now and thinking about, you know, what have I learned?

1340
01:24:50,520 --> 01:24:54,760
Speaker 5: It's the power of getting data sets and running analyzes

1341
01:24:54,840 --> 01:24:59,080
Speaker 5: on on populations that are important for the economy and

1342
01:24:59,160 --> 01:24:59,759
Speaker 5: for finance.

1343
01:25:00,120 --> 01:25:03,920
Speaker 2: Really really fascinating. Gentlemen, Thank you so much for doing this.

1344
01:25:03,960 --> 01:25:07,400
Speaker 2: We have been speaking with Richard Taylor and Alex Emos,

1345
01:25:07,680 --> 01:25:10,879
Speaker 2: both of the Booth School of Business, about their updated

1346
01:25:11,000 --> 01:25:15,280
Speaker 2: version of The Winner's Curse. Strong recommendation if you enjoy

1347
01:25:15,360 --> 01:25:18,400
Speaker 2: this conversation, well check out any of the five hundred

1348
01:25:18,439 --> 01:25:22,080
Speaker 2: and ninety two we've done over the past twelve years.

1349
01:25:22,120 --> 01:25:27,360
Speaker 2: You can find those at iTunes, Spotify, Bloomberg YouTube, wherever

1350
01:25:27,400 --> 01:25:30,080
Speaker 2: you get your favorite podcasts, and be sure to check

1351
01:25:30,080 --> 01:25:33,719
Speaker 2: out my new book, How Not to Invest The ideas, numbers,

1352
01:25:33,760 --> 01:25:37,280
Speaker 2: and behavior that destroys wealth and how to avoid them.

1353
01:25:37,800 --> 01:25:38,760
Speaker 5: I would be remiss if.

1354
01:25:38,720 --> 01:25:41,440
Speaker 2: I thank the Crack team that helps put these conversations

1355
01:25:41,439 --> 01:25:46,639
Speaker 2: together each week. Alexis Noriega is my video producer. Sean

1356
01:25:46,760 --> 01:25:50,599
Speaker 2: Russo is my researcher. Anna Luke is my producer. Safe

1357
01:25:50,680 --> 01:25:54,200
Speaker 2: Bauman is the head of podcasts here at Bloomberg. I'm

1358
01:25:54,240 --> 01:25:57,880
Speaker 2: Barry Ritolts. You've been listening to Masters in Business on

1359
01:25:58,000 --> 01:25:59,000
Speaker 2: Bloomberg Radio.

1360
01:26:00,080 --> 01:26:03,599
Speaker 5: Brast transud to reco