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How Investors Fall Into Bias Traps with Economists Richard Thaler & Alex Imas · Masters in Business

• 0:02
  Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. This is Masters in
• 0:11
  Speaker 1: Business with Barry Ritholts on Bloomberg Radio.
• 0:16
  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
• 11:29
  Speaker 5: was doing a cross country road trip with one of
• 11:31
  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,
• 11:51
  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
• 13:31
  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
• 15:22
  Speaker 2: called wealth effect, which is something that the economists at
• 15:25
  Speaker 2: the Federal Reserve love. The higher the market goes, the
• 15:33
  Speaker 2: wealthier people supposedly feel, and they all go out and
• 15:36
  Speaker 2: spend money. That's like just such a perfect example of
• 15:40
  Speaker 2: a model that doesn't reflect the real world. A huge
• 15:45
  Speaker 2: amount of stocks are owned by the top ten percent.
• 15:48
  Speaker 2: It's something like fifty two percent of stocks. The average
• 15:52
  Speaker 2: person doesn't really have a whole lot at stake in
• 15:55
  Speaker 2: the market. And the reality is people are spending more
• 15:59
  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
• 16:09
  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
• 16:29
  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,
• 16:42
  Speaker 4: and money you've set aside for your kid's education, and
• 16:47
  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?
• 17:12
  Speaker 4: Approximately zero? Really approximately zero. Whereas if some stock you
• 17:22
  Speaker 4: own gets bought and you get a check, you spend
• 17:27
  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.
• 17:34
  Speaker 4: So where the money sits has a big effect on
• 17:40
  Speaker 4: how much of it you spend.
• 17:42
  Speaker 2: Your response to somebody's question how are you going to
• 17:45
  Speaker 2: spend the windfall from the Nobel Prize was one of
• 17:49
  Speaker 2: my favorite answers. You said, do you recall?
• 17:54
  Speaker 4: Yeah, well I recall. I mean this was at four
• 17:57
  Speaker 4: in the morning. They call you and wake you up
• 18:02
  Speaker 4: and then say go get some coffee because you have
• 18:04
  Speaker 4: a press conference in half an hour. And I had
• 18:09
  Speaker 4: heard enough of these interviews to know that somebody was
• 18:13
  Speaker 4: likely to ask me that question, and my instinct was
• 18:18
  Speaker 4: to say, well, you know, to a real economist, this
• 18:21
  Speaker 4: is a stupid question, because how am I going to know?
• 18:26
  Speaker 3: You know, suppose I go out and buy some fancy
• 18:30
  Speaker 3: new car. Barry likes fancy cars. I don't. I like
• 18:34
  Speaker 3: fancy wine.
• 18:35
  Speaker 4: So suppose I go and buy a case of fancy wine.
• 18:39
  Speaker 4: How do I know that's the Nobel money as opposed
• 18:43
  Speaker 4: to the money I got from selling a book.
• 18:45
  Speaker 2: All dollars are fungible.
• 18:47
  Speaker 3: All dollars are fungible.
• 18:48
  Speaker 4: And you know, I've realized later that what I should
• 18:53
  Speaker 4: have done is opened up a special account, the prize,
• 18:58
  Speaker 4: Nobel Prize money, and a credit card that's linked to that,
• 19:04
  Speaker 4: and when I want to go buy something stupid, just
• 19:08
  Speaker 4: take out the Nobel card and life would be more fun.
• 19:13
  Speaker 2: But the line that you said was as irrationally as
• 19:16
  Speaker 2: I can.
• 19:17
  Speaker 3: Yeah, I said, I'll just spend it as irrationally as possible.
• 19:20
  Speaker 3: Just I knew it would be a memorable line.
• 19:23
  Speaker 2: So it's so funny because that line led to a
• 19:27
  Speaker 2: conversation with my CFO about the difference in all of
• 19:32
  Speaker 2: these you know, the Chase Sapphire card or the X
• 19:36
  Speaker 2: Platinum card where you get these points and the rational
• 19:40
  Speaker 2: CFO says, hey, I want the money back each month,
• 19:45
  Speaker 2: And my response is always it's one hundred, two hundred dollars.
• 19:49
  Speaker 2: It's lost in your bank account. You don't see it.
• 19:52
  Speaker 2: When I get the points and want to buy a
• 19:55
  Speaker 2: fancy cappuccino maker that my wife is going to yell
• 19:58
  Speaker 2: at me, Why you spending two thousand dollars on a
• 20:01
  Speaker 2: cappuccino maker, you idiot. My answer is, Oh, no, it's points,
• 20:04
  Speaker 2: it's free, and she's like, okay, go get it. It's
• 20:08
  Speaker 2: the exact same concept. If you have that silo, that
• 20:12
  Speaker 2: mental accounting, you could do as much of rationality as
• 20:15
  Speaker 2: you'd like.
• 20:15
  Speaker 4: So you know, but watch out if she listens to
• 20:19
  Speaker 4: this podcast.
• 20:20
  Speaker 2: She listens to the first five minutes, and that's this, Oh.
• 20:23
  Speaker 4: Yeah, so you're safe because so I'll tell you a
• 20:26
  Speaker 4: story about my daughter Maggie, who lives in Rhode Island,
• 20:32
  Speaker 4: and one of her neighbors grew up to be a
• 20:34
  Speaker 4: pitcher for the Mets, and the Mets were playing in
• 20:38
  Speaker 4: the playoffs the.
• 20:39
  Speaker 2: First round, so it's a long time ago, yeah, and
• 20:43
  Speaker 2: this old.
• 20:43
  Speaker 4: Story, and this guy was going to pitch. So I
• 20:48
  Speaker 4: called Maggie, Hey, would you guys want to go to
• 20:51
  Speaker 4: the game?
• 20:52
  Speaker 3: Let me see if I can get tickets, and she says, oh,
• 20:55
  Speaker 3: that'd be great. So I go online the game is
• 20:59
  Speaker 3: like tomorrow, and I find some tickets and there were
• 21:03
  Speaker 3: a bunch you know, on stub hub or something you
• 21:05
  Speaker 3: could get tickets. So I text her back and said,
• 21:09
  Speaker 3: look here, here's the website. It looks like there are
• 21:13
  Speaker 3: lots of tickets to choose from. How about the tickets
• 21:18
  Speaker 3: were about three hundred bucks.
• 21:19
  Speaker 4: I said, how about I'll text I'll send you one
• 21:23
  Speaker 4: thousand dollars buy the tickets you want, spend the rest
• 21:26
  Speaker 4: on hot talks.
• 21:27
  Speaker 2: So you're doing an experiment on your daughter to see
• 21:30
  Speaker 2: if she buys the cheap tickets or no.
• 21:31
  Speaker 5: Oh no.
• 21:32
  Speaker 4: So she texts me back and says, lol, this is
• 21:36
  Speaker 4: just like in your book. If you send me one
• 21:39
  Speaker 4: thousand dollars, I'm not going to use it on baseball tickets.
• 21:44
  Speaker 4: So I've learned my lesson. Recently, she wanted to go
• 21:50
  Speaker 4: to a concert. David Byrne is on tour and he
• 21:55
  Speaker 4: was in Providence, where she lives, and she wanted to go.
• 21:59
  Speaker 3: And she says, so, some way you could get me tickets.
• 22:02
  Speaker 3: I said, to the.
• 22:02
  Speaker 2: Tickets instead of the money, that's so funny. Let's talk
• 22:06
  Speaker 2: a little bit about the book The Winner's Curse and
• 22:09
  Speaker 2: I want to start with Alex. So this book has
• 22:12
  Speaker 2: been out since you were a young kid, and you
• 22:16
  Speaker 2: go to college, you eventually figure, let me get a
• 22:19
  Speaker 2: PhD in behavioral economics or finance and economics. How did
• 22:24
  Speaker 2: you first discover this book? What was your initial response
• 22:27
  Speaker 2: to it?
• 22:29
  Speaker 5: So I discovered it. There's not really any textbooks and
• 22:32
  Speaker 5: behavioral economics, so you kind of get here through the
• 22:35
  Speaker 5: grape vine. Oh, you should read this, you should read that.
• 22:38
  Speaker 5: You mostly read journal articles like with if you're thinking
• 22:41
  Speaker 5: about doing game theory or something like that. There's like
• 22:44
  Speaker 5: five or six textbooks that you can read with behavioral economics.
• 22:47
  Speaker 5: There's not a whole bunch. Winner's Curse was one of
• 22:50
  Speaker 5: those books that almost everybody recommends because the anomalies columns
• 22:54
  Speaker 5: are just very, very accessible, and then you read the
• 22:57
  Speaker 5: anomalies columns, they got a bunch of references. You look
• 22:59
  Speaker 5: through the reference So I had read the original Winner's
• 23:03
  Speaker 5: Curse I think second or third year at grad school,
• 23:07
  Speaker 5: and then I got my first job at Cardigi mel
• 23:10
  Speaker 5: and I had already known Richard for a while at
• 23:12
  Speaker 5: that point. We met in graduate school. His office was
• 23:15
  Speaker 5: happened to be right next to mine in San Diego,
• 23:18
  Speaker 5: and at some point I joined Booth and he called
• 23:22
  Speaker 5: me up I think like four or five months into
• 23:25
  Speaker 5: my first year, and said, hey, you know, I got
• 23:27
  Speaker 5: this opportunity. We want to the published asset to update
• 23:31
  Speaker 5: the book. I'm thinking of doing a little bit more
• 23:33
  Speaker 5: than just an update. You know the books from nineteen
• 23:36
  Speaker 5: ninety two, there's been thirty years of research. Are you
• 23:39
  Speaker 5: interested in working together on this? So, I mean I
• 23:41
  Speaker 5: jumped on the opportunity. One, you know, I get to
• 23:43
  Speaker 5: work with Richard, which is super fun. But two, I mean,
• 23:47
  Speaker 5: you know, I've been doing behavioral economics research for a while,
• 23:50
  Speaker 5: and I know how much demand there is for a
• 23:53
  Speaker 5: book that people can pick up and read and say, hey,
• 23:55
  Speaker 5: these are the original anomalies. Here's the thirty years of
• 23:57
  Speaker 5: research that has happened since. Now. I think at that
• 24:01
  Speaker 5: point we were thinking, like, you know, six months, do
• 24:03
  Speaker 5: a little update. This is twenty twenty. This conversation happened
• 24:07
  Speaker 5: in twenty twenty. The book is coming out now. We
• 24:12
  Speaker 5: you know, basically the two thirds of the book ended
• 24:15
  Speaker 5: up being brand new. We wrote, we rewrote slightly. Each
• 24:20
  Speaker 5: anomalies column is kind of the bedrock. But you know,
• 24:23
  Speaker 5: thirty years of research has happened since, and it took
• 24:26
  Speaker 5: a while to put all of that together. And essentially
• 24:29
  Speaker 5: what we showed is, look, the original anomalies, when you
• 24:33
  Speaker 5: read them, most of the experiments, most of the findings
• 24:36
  Speaker 5: are from you know, college students sometimes you know, after
• 24:39
  Speaker 5: a bad night out in the lab for you know,
• 24:42
  Speaker 5: making decisions over a dollar. And the big kind of
• 24:44
  Speaker 5: pushback from economists was, look, we don't really care about
• 24:47
  Speaker 5: these people. We care about you know, institutional investors, CEOs.
• 24:52
  Speaker 5: We care about people who are in the market with
• 24:53
  Speaker 5: money on the line, making all these big decisions. And
• 24:57
  Speaker 5: so what why has behavioral economics become a success? Honestly
• 25:01
  Speaker 5: largely because of behavioral finance, because of the fact that
• 25:04
  Speaker 5: behavioral economics behavioral economists said, look, we got access to
• 25:08
  Speaker 5: this amazing data on people making consequential decisions day in
• 25:11
  Speaker 5: and day out. They're still making mistakes.
• 25:14
  Speaker 2: Coming up, we continue our conversation with Richard Taylor and
• 25:17
  Speaker 2: alex iMOS discussing the book. They have recently updated, The
• 25:22
  Speaker 2: Winner's Curse Behavioral Economics Anomalies. Then and now I'm Barry
• 25:28
  Speaker 2: rid Holts. You're listening to Masters in Business on Bloomberg Radio.
• 25:43
  Speaker 2: I'm Barry rid Holts. You're listening to Masters in Business
• 25:46
  Speaker 2: on Bloomberg Radio. My extra special guests this week are
• 25:49
  Speaker 2: Richard Taylor and Alex Amos, both of the Chicago Booth
• 25:53
  Speaker 2: School Business at the University of Chicago.
• 25:57
  Speaker 5: I love what.
• 25:58
  Speaker 2: Danny Konoman wants said. That is, I suffer from all
• 26:01
  Speaker 2: the same behavioral biases that I identified. You mean to
• 26:05
  Speaker 2: tell me that we have thirty years of data, all
• 26:07
  Speaker 2: this research, a handful of books, people still make the
• 26:11
  Speaker 2: exact same behavioral mistakes they used to. Has there been
• 26:14
  Speaker 2: any change in behavior?
• 26:17
  Speaker 3: Essentially?
• 26:18
  Speaker 2: No, And.
• 26:21
  Speaker 4: That's not that surprising because the stuff we're talking about
• 26:26
  Speaker 4: has been true as long as there have been humans. Right,
• 26:31
  Speaker 4: So we talk about self control problems. It's in the Bible, right,
• 26:37
  Speaker 4: you know, Homer talks about Odysseus tying himself to the mast.
• 26:46
  Speaker 4: That's like agreeing to have money taken out of your
• 26:51
  Speaker 4: paycheck and put into a retirement plan. So human beings, yes,
• 26:58
  Speaker 4: there's evolution, but evolution and takes thousands of years, and
• 27:02
  Speaker 4: thirty years is the blink of an eye.
• 27:05
  Speaker 2: Since you mentioned retirement accounts, let's talk a little bit
• 27:08
  Speaker 2: about choice architecture. And nudge. Before I arrived here, I
• 27:16
  Speaker 2: looked up what was the impact of the default setting
• 27:21
  Speaker 2: that you helped change through choice architecture. People used to
• 27:25
  Speaker 2: get a new job, sign up for four to one K,
• 27:28
  Speaker 2: and the money would come into that account and would
• 27:30
  Speaker 2: sit there in cash. And rather than have the default
• 27:33
  Speaker 2: be cash, we through your work created a default as
• 27:39
  Speaker 2: a either a target date fund or a balance fund
• 27:42
  Speaker 2: something like that, so it's not sitting in cash. And
• 27:45
  Speaker 2: it turns out there is about four point seven trillion
• 27:49
  Speaker 2: with a t trillion dollars in those funds, of which
• 27:53
  Speaker 2: forty percent, according to recent research, was the default setting.
• 27:58
  Speaker 2: So you get credit for about two trillion dollars in
• 28:02
  Speaker 2: retirement savings that might have otherwise just been sitting around
• 28:06
  Speaker 2: in cash. How does the concept of people aren't learning
• 28:12
  Speaker 2: from their mistakes so choice architecture is so important to
• 28:16
  Speaker 2: help people make better decisions. How significant is that?
• 28:21
  Speaker 4: Well, if you think about what was going on in
• 28:25
  Speaker 4: the in the early eighties, these defined contribution plans like
• 28:30
  Speaker 4: four to one k's were real new Our parents were
• 28:37
  Speaker 4: if they If you worked at a big firm, you
• 28:40
  Speaker 4: had a define benefit plan. My father worked for Prudential insurance.
• 28:45
  Speaker 4: You know, and his pension was number of years worked
• 28:50
  Speaker 4: times some function of his final salary. No decisions to make,
• 28:55
  Speaker 4: kind of like social security and bring in these.
• 29:03
  Speaker 3: To find contribution plans.
• 29:05
  Speaker 4: You have to decide whether to join and if so,
• 29:08
  Speaker 4: how much to defer, and then how to invest it.
• 29:13
  Speaker 4: And people had no clue and a lot of people
• 29:17
  Speaker 4: just didn't even join, which is about the dumbest mistake
• 29:21
  Speaker 4: you can ever make.
• 29:22
  Speaker 2: If you have a company with a match, you're basically
• 29:24
  Speaker 2: turning down free money, right, which what economic model says
• 29:28
  Speaker 2: that's rational? No?
• 29:29
  Speaker 3: Well, right, so I would say to what ecmmists, look,
• 29:32
  Speaker 3: you would predict.
• 29:33
  Speaker 4: No one would make this mistake. But one early study,
• 29:39
  Speaker 4: half the employees at a company are not joining.
• 29:44
  Speaker 3: In the first year.
• 29:44
  Speaker 2: It's amazing.
• 29:46
  Speaker 4: So how do we fix that? Well, the simplest thing
• 29:50
  Speaker 4: was to change the default. So we say it used
• 29:56
  Speaker 4: to be you get a form to fill out a
• 29:59
  Speaker 4: piece of paper in those days, and if you want
• 30:02
  Speaker 4: to be in the plan, fill out this form and
• 30:05
  Speaker 4: say you want to join in how to invest?
• 30:10
  Speaker 3: Change that to you.
• 30:13
  Speaker 4: Welcome to Riddlets Management. We have a pension plan. We're
• 30:20
  Speaker 4: going to enroll you unless you opt out, and we're
• 30:26
  Speaker 4: going to enroll you into the default fund unless you
• 30:30
  Speaker 4: choose otherwise. So all of that was not possible in
• 30:37
  Speaker 4: the early nineties because there were companies were afraid to
• 30:43
  Speaker 4: do automatic enrollment because they didn't have permission, and target
• 30:49
  Speaker 4: DAID funds weren't legal. Ironically, in the George W. Bush administration,
• 30:58
  Speaker 4: one side, they were campaigning to partially privatize Social Security,
• 31:05
  Speaker 4: but their Labor Department was forbidding companies from investing in
• 31:12
  Speaker 4: anything that.
• 31:12
  Speaker 3: Could go down.
• 31:14
  Speaker 4: So there was a bill passed in two thousand and
• 31:18
  Speaker 4: six the said, okay, you're allowed to automatically enroll and
• 31:30
  Speaker 4: have automatically escalate what we used to call sayborn tomorrow,
• 31:35
  Speaker 4: and invest in some prudent funds.
• 31:42
  Speaker 3: And what was what you have to give something up
• 31:46
  Speaker 3: to get that.
• 31:48
  Speaker 4: So what I suggested to there was a Republican senator
• 31:55
  Speaker 4: from Utah who was the running the Relevant committee. I said,
• 32:01
  Speaker 4: how about if companies agree to do all three of those,
• 32:06
  Speaker 4: they're exempt from some burdensome paperwork of non discrimination rules.
• 32:12
  Speaker 4: And so that's what the Republicans got was less paperwork,
• 32:17
  Speaker 4: and people who cared about the workers got something.
• 32:21
  Speaker 2: And the workers got something, and the.
• 32:24
  Speaker 4: Workers got something and if they just do nothing, then
• 32:29
  Speaker 4: they're in and their contributions are going up, and they're
• 32:33
  Speaker 4: in a sensible.
• 32:36
  Speaker 3: Investment product.
• 32:40
  Speaker 2: So this is kind of not just kind of fascinating
• 32:43
  Speaker 2: because in the Winner's course you talk about things very
• 32:46
  Speaker 2: much related to what happens in investing. So there's loss,
• 32:51
  Speaker 2: a version, and the status quo bias and a variety
• 32:55
  Speaker 2: of different things. Let's talk about what are the issues
• 33:00
  Speaker 2: that most relate to, as Alex said, behavioral finance as
• 33:04
  Speaker 2: opposed to behavioral economics. What do we think are the
• 33:08
  Speaker 2: biggest factors that explain irrational human behavior in stock and
• 33:16
  Speaker 2: bond markets.
• 33:18
  Speaker 5: So I think there's a few things that kind of
• 33:22
  Speaker 5: people documented in the late nineties early two thousands that
• 33:26
  Speaker 5: have just replicated and just became bigger, if anything. So
• 33:31
  Speaker 5: the disposition effect was one of them. So the disposition effect,
• 33:34
  Speaker 5: this is Scheffern and Statman. They came up with a
• 33:37
  Speaker 5: paper nineteen eighty five documenting it. Originally, Terry O. Dean
• 33:41
  Speaker 5: has this giant data set that he published in nineteen
• 33:44
  Speaker 5: ninety nine documenting in a bit of a larger sample,
• 33:46
  Speaker 5: and then now it's been replicated in Finland, all over
• 33:49
  Speaker 5: the world and it's this tendency for people. You know,
• 33:52
  Speaker 5: when I buy a stock it goes up in price,
• 33:55
  Speaker 5: what do I do? I sell it? I want to
• 33:58
  Speaker 5: realize my gains. Same stock was down in price. You
• 34:02
  Speaker 5: know this is now a loss? What do I do?
• 34:04
  Speaker 5: Hold on to it? So it's this tendency to realize
• 34:07
  Speaker 5: your gains and hold on to your losses.
• 34:09
  Speaker 2: Peter Lynch, by the way, forty years ago, used to
• 34:12
  Speaker 2: call that cutting your flowers and watering your weeds. That
• 34:16
  Speaker 2: was his expression for it. So it was it was
• 34:19
  Speaker 2: visible to a guy running a fund at Fidelity in
• 34:22
  Speaker 2: the nineteen eighty.
• 34:23
  Speaker 5: Yes, and it's and this is just talking about like
• 34:26
  Speaker 5: are people learning? I mean apparently not, because it's like
• 34:29
  Speaker 5: it's again you I bet you you download Robinhood data
• 34:32
  Speaker 5: from today, you're going to see it show up. So
• 34:35
  Speaker 5: that's the the And this is kind of the tendency.
• 34:38
  Speaker 5: You know what feels good when you're when when when
• 34:41
  Speaker 5: you own a stock selling it at a gain, and
• 34:43
  Speaker 5: you know, telling your friends, hey, you know I bought
• 34:45
  Speaker 5: that thing for ninety it's one twenty. I just I
• 34:48
  Speaker 5: just made a lot of money. You know, it feels
• 34:50
  Speaker 5: worse telling your friends I bought it ninety and I
• 34:53
  Speaker 5: sold it at sixty, So you just kind of hold
• 34:55
  Speaker 5: on to it hoping something happens. Maybe some people even
• 34:58
  Speaker 5: double up buy more share is just to break even.
• 35:02
  Speaker 5: So the disposition effect this kind of tendency for individual
• 35:04
  Speaker 5: behavior to uh, you know, realize gains avoid losses. The
• 35:08
  Speaker 5: other thing is, in my view, this is kind of
• 35:11
  Speaker 5: the bigger uh, the bigger principle is limited attention. So
• 35:16
  Speaker 5: you know, there's a lot of stocks out there, which
• 35:19
  Speaker 5: ones are people buying? And this is not just retail investors,
• 35:22
  Speaker 5: this is this is bigger institutional investors too.
• 35:25
  Speaker 3: Uh.
• 35:25
  Speaker 5: It's the ones that are covered in the news. We're
• 35:27
  Speaker 5: talking about availability bias earlier. What are the things that
• 35:31
  Speaker 5: are coming to mind, Things that that have recently been covered,
• 35:34
  Speaker 5: maybe you heard an earnings announcement call or something like that.
• 35:36
  Speaker 5: These attention grabbing stocks that are much more likely to
• 35:39
  Speaker 5: go into people's portfolios. It's because people don't, you know,
• 35:43
  Speaker 5: aren't evaluating the entire uh, the entire universe of stocks
• 35:47
  Speaker 5: whenever they think they're thinking about something to buy.
• 35:49
  Speaker 2: So let's address that. Because the United States, happens, of
• 35:54
  Speaker 2: all countries, not only has such a large stock market,
• 35:58
  Speaker 2: but the home country buys is so acute here, and
• 36:03
  Speaker 2: you don't hear a lot about foreign companies all that.
• 36:06
  Speaker 2: Often you mostly hear about local companies, local CEOs, local products.
• 36:12
  Speaker 2: How significant is that sort of bias in people's portfolios
• 36:16
  Speaker 2: being not only overloaded with their own country, but hey,
• 36:22
  Speaker 2: if you're in New York, you can have more finance companies.
• 36:25
  Speaker 2: If you're in San Francisco, you have more tech companies.
• 36:27
  Speaker 2: If you're in the Midwest you can have more manufacturing companies.
• 36:30
  Speaker 5: It's more extreme than that. If I'm working for a
• 36:32
  Speaker 5: specific company, I have more of that stock.
• 36:34
  Speaker 2: When, if anything, you should have right.
• 36:38
  Speaker 3: Yeah. I think one campaign that has been moderately successful
• 36:44
  Speaker 3: is I think fewer companies are foisting stock of their
• 36:48
  Speaker 3: own company onto the workers. It used to be the
• 36:52
  Speaker 3: match was often paid in company stock.
• 36:56
  Speaker 2: Well Ge was notorious and they lost a trillion dollars
• 37:01
  Speaker 2: of employee investments because of their match.
• 37:05
  Speaker 3: Well and Uh and run and Ron.
• 37:09
  Speaker 5: One of my one of my friends their their their father.
• 37:12
  Speaker 5: He was working at Enron. He was a risk manager.
• 37:16
  Speaker 2: F Yi and UH just not a very good one, huge,
• 37:21
  Speaker 2: huge persu although it could be the greatest risk manager there.
• 37:25
  Speaker 2: The busses were not listening to you.
• 37:27
  Speaker 4: Right, But they compounded it by putting their employees money
• 37:33
  Speaker 4: in the four one k into and run stock. So
• 37:36
  Speaker 4: they get fired and their retirement money goes puff unbelievable.
• 37:43
  Speaker 5: But you know, looking back at what people were owning,
• 37:46
  Speaker 5: I mean there is that, you know, people in the
• 37:48
  Speaker 5: four oh one k element, but people who were working
• 37:50
  Speaker 5: there were freely buying and ron stock. Right, according to
• 37:56
  Speaker 5: economic models, you should be diversifying. You already have a
• 37:59
  Speaker 5: bunch of ron stock in your four one k. You
• 38:01
  Speaker 5: shouldn't be taking your discretionary spending and buying more and
• 38:04
  Speaker 5: ron stock. And that's exactly what was happening.
• 38:06
  Speaker 4: You know, this home bias applies all around the world,
• 38:12
  Speaker 4: at least the US is a big country. I wrote
• 38:15
  Speaker 4: a paper once about the Swedish social sort of four
• 38:21
  Speaker 4: to one k plan. This was a risky move because
• 38:24
  Speaker 4: I was making fun of it and there was some
• 38:27
  Speaker 4: award that might happen. But anyway, Sweden is one percent
• 38:35
  Speaker 4: of world.
• 38:35
  Speaker 2: GP tiny tiny, GDP tiny, and.
• 38:38
  Speaker 3: They were putting most of their money in Swedish stocks.
• 38:41
  Speaker 2: It's crazy they ignored the other ninety nine percent of
• 38:44
  Speaker 2: the world. But that just goes to show you the bias.
• 38:48
  Speaker 2: So the obvious question is if you two were advising
• 38:52
  Speaker 2: a portfolio manager, what sort of behavioral principles would you
• 38:56
  Speaker 2: emphasize for them to build a robust portfolio.
• 39:01
  Speaker 4: Well, as you know, I one hat I have is
• 39:07
  Speaker 4: I'm involved in a company that does.
• 39:09
  Speaker 2: This that has also has your name on the door.
• 39:11
  Speaker 4: It also has my name on the door fuller and
• 39:14
  Speaker 4: fail or asset management. And now let me say I
• 39:20
  Speaker 4: cannot name a single stock WHEELWN and no one at
• 39:26
  Speaker 4: the firm would think it's a good idea for me
• 39:30
  Speaker 4: to be making suggestions. Were buy small cap stock, so
• 39:35
  Speaker 4: it's you know, if we owned Apple or Tesla, I
• 39:38
  Speaker 4: might know it, but we don't buy any big stock.
• 39:41
  Speaker 3: So they're mostly companies you've never heard of, and I've
• 39:45
  Speaker 3: never heard of them.
• 39:46
  Speaker 2: But this is because you've identified a behavioral issue that
• 39:51
  Speaker 2: is now reflected in the model that they use to purchase.
• 39:54
  Speaker 3: Right, So we're not.
• 39:58
  Speaker 4: We're not a quatch shop, which is a little unusual
• 40:02
  Speaker 4: for a firm that's run by some academics. But each
• 40:07
  Speaker 4: strategy is based on a bias. So there's one that's
• 40:12
  Speaker 4: based on overreaction, there's one that's based on underreaction. So
• 40:15
  Speaker 4: we are we try to find stocks that we think
• 40:22
  Speaker 4: the rest of the market is making a mistake about,
• 40:26
  Speaker 4: and then we forbid the portfolio managers from forecasting earnings because.
• 40:34
  Speaker 3: That they're going to be You know, do we think with.
• 40:38
  Speaker 4: Our thirty employees that we're going to make better forecasts
• 40:41
  Speaker 4: than fidelity. It's crazy, but we think we have an
• 40:46
  Speaker 4: advantage because we're trying to predict something else. We're trying
• 40:49
  Speaker 4: to predict the mistakes. It's like you're a baseball fan.
• 40:54
  Speaker 4: If there's a picture that is a sinker ball pitcher,
• 40:59
  Speaker 4: so the alex this means the ball goes down as
• 41:03
  Speaker 4: it approaches the plate.
• 41:05
  Speaker 3: These foreigners.
• 41:06
  Speaker 4: But you know, if there's a sinker ball pitcher, you
• 41:11
  Speaker 4: and I can predict batters are going to hit ground
• 41:14
  Speaker 4: balls because they are fooled. The ball drops, and if
• 41:19
  Speaker 4: you hit it slightly above the center, the ball goes down.
• 41:23
  Speaker 4: So you don't have to be able to hit a
• 41:25
  Speaker 4: ball to know it's going to go down, And so
• 41:30
  Speaker 4: we don't have to be able to forecast earnings to
• 41:33
  Speaker 4: predict that other people are going to be predicting too high.
• 41:36
  Speaker 2: I want to bring this back to the book because
• 41:39
  Speaker 2: one of the concepts underlying the book was, Hey, there's
• 41:43
  Speaker 2: a reproducibility issue in social sciences. How well have these
• 41:48
  Speaker 2: anomalies and the theories you built around them, How well is
• 41:52
  Speaker 2: this held up? How robust and reproducible are these findings?
• 41:56
  Speaker 2: And it turns out very talk to us about what
• 42:00
  Speaker 2: but what you guys discovered when you were revisiting all
• 42:03
  Speaker 2: of these principles that were first written about twenty thirty
• 42:07
  Speaker 2: years ago.
• 42:08
  Speaker 5: Yeah, so, as you mentioned, there's a some might call
• 42:11
  Speaker 5: crisis of reproducibility and social science more broadly, so this
• 42:14
  Speaker 5: is psychology, some sociology, et cetera. And the worry is
• 42:20
  Speaker 5: that you know, these anomalies that were published in the
• 42:22
  Speaker 5: eighties and nineties, these are the bedrock of the entire
• 42:24
  Speaker 5: field of behavioral economics, and you might be worried, like, look,
• 42:29
  Speaker 5: maybe these things don't reproduce. And there's two ways that
• 42:32
  Speaker 5: they can't. They don't reproduce. One, you run the same
• 42:35
  Speaker 5: experiment again and doesn't work. It was p hacked, as
• 42:38
  Speaker 5: I said, like small sample sizes, no incentives. The second
• 42:42
  Speaker 5: way my not reproduce is that it literally only reproduces
• 42:45
  Speaker 5: in the exact conditions it was run originally with college
• 42:48
  Speaker 5: students at low stakes. You go out in a different
• 42:50
  Speaker 5: population with people who are a bit more sophisticated, know
• 42:53
  Speaker 5: what's going on, and you know it doesn't work. So
• 42:56
  Speaker 5: what we did in the book was to say, look, first,
• 42:58
  Speaker 5: let's take the exact same experiments and run them again.
• 43:01
  Speaker 5: Everybody knows about, you know, the original anomaly, So maybe
• 43:07
  Speaker 5: they don't work because people are like, ah, this is
• 43:09
  Speaker 5: a loss of version experiment. I know what's going on.
• 43:12
  Speaker 5: I'm not going to do this. This is the endowment effect.
• 43:15
  Speaker 5: I'm not going to do this. So we just replicated
• 43:17
  Speaker 5: them directly on a completely different platform. So we used
• 43:20
  Speaker 5: an online crowdsourcing platform called prolific. Basically, everything works. Everything works,
• 43:27
  Speaker 5: and we you know, you don't have to take our
• 43:29
  Speaker 5: word for it. If you go on the website of
• 43:31
  Speaker 5: the book, we posted all of the results of our replications,
• 43:34
  Speaker 5: but also instructions on how you can do it yourself.
• 43:37
  Speaker 5: So if somebody is like, don't I don't know about
• 43:39
  Speaker 5: these guys, run them yourself. And you know, people are
• 43:43
  Speaker 5: still loss of ours. They still have the endowment effect.
• 43:48
  Speaker 5: Things like the conjunction fallacy, the Linda problem that Richard
• 43:51
  Speaker 5: was talking about all works.
• 43:52
  Speaker 3: Though.
• 43:54
  Speaker 5: The second part is this external validity part. Does anybody
• 43:57
  Speaker 5: other than college students display these these effects? And that's
• 44:01
  Speaker 5: kind of the updates part of the book, and the
• 44:03
  Speaker 5: answer is yes. You know, the loss of version has
• 44:06
  Speaker 5: been in the myopic loss of version part that's been
• 44:09
  Speaker 5: used to explain the equity premium puzzle that's still reproducible.
• 44:13
  Speaker 5: We also do a bunch of out of sample tests
• 44:15
  Speaker 5: of the anomalies that didn't use experimental data, and you
• 44:22
  Speaker 5: know that replicates out of sample too, so people aren't
• 44:24
  Speaker 5: learning the psychology is the same.
• 44:27
  Speaker 3: You know.
• 44:28
  Speaker 4: One of the columns was about the equity premium puzzle.
• 44:31
  Speaker 4: We didn't include this in the book because it's a
• 44:33
  Speaker 4: little wonky, but the equity premium is just the difference
• 44:37
  Speaker 4: in returns between stocks and bonds. The equity premium puzzle
• 44:42
  Speaker 4: is how big it is, and theory says it should
• 44:46
  Speaker 4: be like less than one percent, and historically it was
• 44:50
  Speaker 4: about seven percent. And the article about that was in
• 44:55
  Speaker 4: the early eighties, so we've had forty years of data
• 44:59
  Speaker 4: since the puzzle was announced.
• 45:02
  Speaker 3: The equity premium.
• 45:03
  Speaker 4: Exactly the same, it's only one percent lower, so and
• 45:08
  Speaker 4: that's what.
• 45:08
  Speaker 3: We see basically everywhere everything's the same.
• 45:12
  Speaker 2: So one of the concepts that people have challenged is
• 45:16
  Speaker 2: not being very reproducible. Has been the concept of priming
• 45:20
  Speaker 2: to sometimes anchoring is similar, but that seems to be
• 45:25
  Speaker 2: more reproducible. But when I hear Linda the bank teller story,
• 45:30
  Speaker 2: that feels like the framing of that is very much
• 45:33
  Speaker 2: a priming, when you hear about her as politically active
• 45:39
  Speaker 2: and being involved in what how do you distinguish when
• 45:43
  Speaker 2: you have these theoretical overlapping biases that all kind of
• 45:48
  Speaker 2: interact with each other.
• 45:49
  Speaker 5: So priming is actually a huge literature and cognitive psychology.
• 45:54
  Speaker 5: Basic priming is very robust. So it's the idea of
• 45:58
  Speaker 5: you know, I say a bunch of words that start
• 46:00
  Speaker 5: with the K. What comes to mind a word that
• 46:01
  Speaker 5: starts with the K that's going to reproduce any day
• 46:04
  Speaker 5: of the week. There's a special subset of priming research
• 46:08
  Speaker 5: that was done kind of in the nineties early two
• 46:10
  Speaker 5: thousands that kind of took this to an extreme, which
• 46:14
  Speaker 5: is so here's an example. Let's say you're doing word
• 46:16
  Speaker 5: search and there's a bunch of words that have to
• 46:20
  Speaker 5: do with like oranges, palm, trees, hot weather, like vaguely
• 46:24
  Speaker 5: related with Florida, right, and then that's supposed to prime
• 46:28
  Speaker 5: in your brain old people. And the result, the dependent
• 46:32
  Speaker 5: variable was that those subjects who had those words, they
• 46:35
  Speaker 5: walked a little slower out of the lab.
• 46:37
  Speaker 2: Right.
• 46:38
  Speaker 5: I mean that's it's a little crazy, right, kind of
• 46:40
  Speaker 5: tough to measure. Also, yeah, so I mean it relied.
• 46:44
  Speaker 5: There's a lot of degrees of freedom the researcher can
• 46:46
  Speaker 5: be looking in a certain direction, you know, and those
• 46:49
  Speaker 5: tend to tend to not reproduce the sort of priming
• 46:53
  Speaker 5: that something like the Linda problem, for example, has. That's
• 46:59
  Speaker 5: more in that kind of cognitive psychology wheelhouse of like
• 47:02
  Speaker 5: what do you think about when I describe a person
• 47:05
  Speaker 5: who takes part in radical rallies? What comes to mind?
• 47:07
  Speaker 5: This is this is a basic concept and memory.
• 47:11
  Speaker 2: Right.
• 47:11
  Speaker 5: So that and the second part that that I wanted
• 47:14
  Speaker 5: to to say is that priming, as far as like
• 47:17
  Speaker 5: looking at the behavioral economics research, priming is a really
• 47:21
  Speaker 5: small part. It was actually not really featured much in
• 47:23
  Speaker 5: the book. But the type that is that was used
• 47:27
  Speaker 5: by uh, you know, uh Firsky and Konnoment. It's much
• 47:31
  Speaker 5: more in the Wheelhouse or just basic cognitive psychology.
• 47:34
  Speaker 2: More like anchoring. Does anchoring still hold up? Oh yeah,
• 47:37
  Speaker 2: very well?
• 47:37
  Speaker 5: Oh yeah, yeah yeah yeah.
• 47:39
  Speaker 4: And look, one of the things when when I was
• 47:42
  Speaker 4: writing those columns, the I could pick anything. I picked
• 47:48
  Speaker 4: big effect sizes and some of the problem.
• 47:53
  Speaker 3: You know, we talked.
• 47:55
  Speaker 4: Earlier about the norm and economics to make models smarter
• 48:00
  Speaker 4: and smarter. I think there was a norm in psychology
• 48:05
  Speaker 4: for results to get cleverer and cleverer.
• 48:08
  Speaker 2: Well, I thought Alice's paper where you randomly sell versus
• 48:11
  Speaker 2: what was actually sold, that was a very clever set
• 48:14
  Speaker 2: up for a paper.
• 48:16
  Speaker 3: It was clever, but it wasn't.
• 48:19
  Speaker 4: What I was deriding is a norm that the models
• 48:24
  Speaker 4: assume people are being clever as opposed to designing a
• 48:27
  Speaker 4: clever paper. Got we're all for clever papers, Okay, we
• 48:32
  Speaker 4: like clever papers. So when I was choosing what columns
• 48:36
  Speaker 4: to write about, I picked big stuff. And think about
• 48:42
  Speaker 4: there's a well known company that makes cinnamon buns and
• 48:49
  Speaker 4: has the strategy of pumping the smell of that out
• 48:54
  Speaker 4: into the airport. Now, let's say you're on a low
• 48:59
  Speaker 4: carb diet. Just hypothetically, you know, if you walk by
• 49:05
  Speaker 4: that thing that's that's priming, and that works.
• 49:11
  Speaker 3: And it's not clever, it's just right. It's a big
• 49:15
  Speaker 3: effect size.
• 49:17
  Speaker 4: So there are and so everything I wrote about was big,
• 49:23
  Speaker 4: and it's because I wanted to pick things that I
• 49:27
  Speaker 4: thought were well established and so, you know, I think
• 49:34
  Speaker 4: if I had looked for cute little things, then some
• 49:39
  Speaker 4: of them would have failed to replicate.
• 49:41
  Speaker 5: You also pick things that people were actively attacking and
• 49:44
  Speaker 5: adversarially trying to replicate at the time that you were
• 49:47
  Speaker 5: writing it.
• 49:49
  Speaker 3: Yeah, I mean, look, take take the ultimatum.
• 49:52
  Speaker 4: Yeah, that's one of the original columns and one that
• 49:57
  Speaker 4: we include in the book. The game is very, very simple.
• 50:01
  Speaker 4: I give Barry one hundred dollars. I say, share it
• 50:05
  Speaker 4: with Alex. You can give whatever proportion of the hundred
• 50:10
  Speaker 4: you want to Alex. He says yes or no. If
• 50:15
  Speaker 4: if he says yes, he gets whatever you offered.
• 50:19
  Speaker 3: And you get the rest. If he says no, you
• 50:21
  Speaker 3: both get nothing.
• 50:23
  Speaker 4: Now, the standard economic model at the time predicts that
• 50:30
  Speaker 4: Alex will accept anything, because something is better than nothing.
• 50:35
  Speaker 4: Barry knows that Alex will accept anything, and so he
• 50:41
  Speaker 4: offers him a dollar and Alex accepts. Now, real people
• 50:48
  Speaker 4: only an economist would think that that's a really good prediction.
• 50:53
  Speaker 3: Anybody who's not an economist is going to say, what
• 50:56
  Speaker 3: are you kidding.
• 50:57
  Speaker 4: I'm not going to take a dollar and give you
• 50:59
  Speaker 4: ninety You didn't do anything to deserve that ninety nine.
• 51:04
  Speaker 4: So if you run that experiment, if you offered less
• 51:10
  Speaker 4: than twenty percent, you're going to get rejected. And the
• 51:15
  Speaker 4: profit maximizing offer is about and most people offer half. Okay,
• 51:24
  Speaker 4: Now there were big fights. There was a professor from
• 51:32
  Speaker 4: britt who was saying this was challenging game theory, and no,
• 51:43
  Speaker 4: it wasn't challenging game theory. It was challenging the idea
• 51:48
  Speaker 4: that the agents only care about money and don't care
• 51:54
  Speaker 4: about being treated fairly.
• 51:57
  Speaker 2: So let's address that. Because of the evolutionary biology of this,
• 52:05
  Speaker 2: humans were cooperative social primates. We have neither fangs nor claws,
• 52:10
  Speaker 2: so we had to come up with some way to
• 52:12
  Speaker 2: stay alive, and it turns out cooperating in a tribe
• 52:16
  Speaker 2: is very useful survival tactic. It seems that an inherent
• 52:21
  Speaker 2: sense of fairness is somewhat built into all of us,
• 52:25
  Speaker 2: as well as social status seeking. So how much of
• 52:31
  Speaker 2: this issue in economics derives from not understanding a little
• 52:37
  Speaker 2: bit of evolutionary history.
• 52:40
  Speaker 4: You know, it's a tricky thing. Obviously, we have evolved
• 52:45
  Speaker 4: to be who we are. There are some people who
• 52:49
  Speaker 4: then say, well, that means whatever we do is optimal.
• 52:54
  Speaker 3: Well maybe maybe not. No, that's stupid. I mean we
• 52:58
  Speaker 3: evolved on the savannah.
• 53:00
  Speaker 2: Right, right, nothing about picking muni bonds from a larger site.
• 53:05
  Speaker 3: You know.
• 53:06
  Speaker 4: Amos Tversky was famous for one liners, and he had
• 53:11
  Speaker 4: a one liner about loss of version, which was there
• 53:16
  Speaker 4: may have been species that did not exhibit loss of version,
• 53:21
  Speaker 4: and they're now extinct. Right, So if you're at subsistence,
• 53:27
  Speaker 4: it's really smart to be worried about.
• 53:29
  Speaker 2: Losing It's an existential threat.
• 53:31
  Speaker 3: Right.
• 53:32
  Speaker 4: But you know, none, the three of us, we could
• 53:37
  Speaker 4: go several days without eating, some more more things than others, right, right,
• 53:44
  Speaker 4: so we're not at subsistence, and yeah, we have Managing
• 53:50
  Speaker 4: our own portfolios is something people have been doing for
• 53:54
  Speaker 4: thirty years, right, Yeah, the rich people, but they had
• 53:59
  Speaker 4: their oker do it, right, So there's no evolutionary history
• 54:05
  Speaker 4: of how to manage a portfolio, and even saving for retirement.
• 54:13
  Speaker 4: People didn't live long enough to worry about that. And
• 54:17
  Speaker 4: if you were unlucky enough to reach my age. Then
• 54:22
  Speaker 4: you hope your kids would take care of you, and
• 54:25
  Speaker 4: they lived nearby. You know, then people started scattering and penicillin,
• 54:33
  Speaker 4: and you know, so now we live long and our
• 54:38
  Speaker 4: kids are scattered and they have no interest in having
• 54:42
  Speaker 4: us move in with them. So people had to learn
• 54:46
  Speaker 4: a very new thing and they needed some help.
• 54:51
  Speaker 2: Really really fascinating, So we didn't talk about the where
• 54:56
  Speaker 2: that from? Whence the title of the Winner's Curse comes from?
• 55:00
  Speaker 5: Before we get to the future.
• 55:01
  Speaker 2: Of behavioral finance, let's talk about the Winner's Curse.
• 55:05
  Speaker 5: Tell us where the name comes.
• 55:07
  Speaker 4: From the concept. I should say the title of the
• 55:11
  Speaker 4: book comes from the title of one of the chapters,
• 55:15
  Speaker 4: and I picked it as the title back then because
• 55:18
  Speaker 4: it's sort of a fun phrase and a bit intriguing,
• 55:22
  Speaker 4: and the concept itself is interesting and important. The idea
• 55:27
  Speaker 4: is this, suppose you have a lot of people bidding
• 55:31
  Speaker 4: for some object that's worth the same to everybody, and
• 55:36
  Speaker 4: it could be. When you do this as a demonstration
• 55:40
  Speaker 4: in the class, you fill up a jar of jelly
• 55:43
  Speaker 4: beans or coins and say it's ten cents for each
• 55:49
  Speaker 4: jelly bean, and it's one hundred dollars in the jar.
• 55:54
  Speaker 4: Now we're going to auction it off. High bid or
• 55:57
  Speaker 4: wins the one hundred dollars. But they don't know what
• 56:00
  Speaker 4: they don't know it's worth one hundred dollars. All they
• 56:02
  Speaker 4: see is a lot of coins or jellybeans. What happens, well,
• 56:08
  Speaker 4: the average bid is less than one hundred dollars because
• 56:11
  Speaker 4: people are risk averse. But the winning bid is always
• 56:16
  Speaker 4: above one hundred dollars if you have enough people, because
• 56:22
  Speaker 4: the most optimistic forecast is likely the highest bid and
• 56:27
  Speaker 4: it's too high. Now, this was not discovered by psychologists
• 56:33
  Speaker 4: in the lab. It was discovered by engineers at Atlantic
• 56:38
  Speaker 4: Richfield Arco, the energy company. The energy company who discovered
• 56:44
  Speaker 4: they were bidding for oil leases in what I continue
• 56:50
  Speaker 4: to insist on calling the Gulf of Mexico. And they
• 56:55
  Speaker 4: realized that the leases they won had less oil than
• 57:02
  Speaker 4: they expected.
• 57:04
  Speaker 3: And they said, we thought we had world class geologists.
• 57:08
  Speaker 3: What's going on? Are they dummies? And then they realized
• 57:13
  Speaker 3: that it's quite subtle that the the what you're trying
• 57:19
  Speaker 3: to do is make a bid that will make you
• 57:23
  Speaker 3: money if you win, and that if you win part
• 57:30
  Speaker 3: if there's one hundred people bidding, gee, do I really
• 57:34
  Speaker 3: want to win because maybe I misunderstood something right. So
• 57:39
  Speaker 3: that's the winner's curse, and.
• 57:44
  Speaker 4: It was found and replicated on bidding for oil leases.
• 57:51
  Speaker 3: It's relevant in book publishing.
• 57:54
  Speaker 2: When they're bidding contests for books for yes, yes, So
• 57:57
  Speaker 2: let me see if I can clarify the way you're
• 58:02
  Speaker 2: describing the winner's carse. So we're bidding for oil leases,
• 58:05
  Speaker 2: we don't know exactly how much oil is going to
• 58:07
  Speaker 2: come out of this hole for or area for the
• 58:10
  Speaker 2: next ten to twenty years, and when there's many people bidding,
• 58:15
  Speaker 2: all of which are advised by geologists. If you make
• 58:18
  Speaker 2: a conservative bet, the odds are you're going to lose.
• 58:21
  Speaker 2: But if you make a bet that's high enough that
• 58:24
  Speaker 2: you're gonna win, the odds are it's not going to
• 58:27
  Speaker 2: be a moneymaker.
• 58:28
  Speaker 3: Right.
• 58:28
  Speaker 2: Coming up, we continue our conversation with Richard Taylor and
• 58:32
  Speaker 2: alex EMUs discussing the book. They have recently updated the
• 58:37
  Speaker 2: Winner's Curse behavioral economics anomalies. Then and now, I'm Barry Ritholtz.
• 58:43
  Speaker 2: You're listening to Masters in Business on Bloomberg Radio. I'm
• 59:06
  Speaker 2: Barry ridolts You're listening to Masters in Business on Bloomberg Radio.
• 59:10
  Speaker 2: My extra special guests this week are Richard Taylor and
• 59:14
  Speaker 2: Alex Emos, both of the Chicago Booth School Business at
• 59:17
  Speaker 2: the University of Chicago. I shared with you an article
• 59:22
  Speaker 2: I saw recently. Some real estate group did a study
• 59:26
  Speaker 2: of tens of thousands of home transactions where there was
• 59:30
  Speaker 2: a bidding war, and they found something very similar. The
• 59:34
  Speaker 2: winners of the bidding war ended up paying much more
• 59:38
  Speaker 2: than the subsequent home value was determined by looking at
• 59:42
  Speaker 2: comparable homes in the neighborhood. So is the purpose of
• 59:47
  Speaker 2: an auction to identify something at a fair value where
• 59:52
  Speaker 2: it's profitable for you, or is the purpose of an
• 59:54
  Speaker 2: auction to win at any cost?
• 59:57
  Speaker 4: Well, two interesting aspects of that. One is suppose you're
• 1:00:04
  Speaker 4: these engineers and you've discovered this, what should you do?
• 1:00:09
  Speaker 4: And if you're losing money every time you win an auction,
• 1:00:14
  Speaker 4: you could not bid, But then you don't have any
• 1:00:16
  Speaker 4: places to drill. And what did they decide to do.
• 1:00:20
  Speaker 4: It's really clever. They decided to write a paper.
• 1:00:24
  Speaker 2: In order to get people to stop overbidding.
• 1:00:26
  Speaker 3: Yeah, now that's different than what the owners of Major
• 1:00:32
  Speaker 3: League Baseball did.
• 1:00:33
  Speaker 2: I was going to say, why didn't they just moneyball? Itt,
• 1:00:36
  Speaker 2: Why didn't they just start looking at ugly but productive.
• 1:00:40
  Speaker 4: Well, what the Major League Baseball owners did is they colluded, right,
• 1:00:47
  Speaker 4: and they said, look, let's not bid anymore.
• 1:00:50
  Speaker 2: Well, the salary caps that are in all.
• 1:00:52
  Speaker 3: These no, no, there was just outright collusion when baseball
• 1:00:56
  Speaker 3: players first became free agents. Uh huh, or said hey, we're.
• 1:01:00
  Speaker 4: Losing money on these crazy auctions, let's just not bid,
• 1:01:05
  Speaker 4: and then they got slapped down, right, So what can
• 1:01:11
  Speaker 4: you do?
• 1:01:14
  Speaker 3: You can tried.
• 1:01:16
  Speaker 4: A good strategy is to bid very low on every site,
• 1:01:23
  Speaker 4: and in the data there were lots of sites that
• 1:01:27
  Speaker 4: you could have gotten for a dollar.
• 1:01:29
  Speaker 2: Really, and because because the consensus was is no, they're
• 1:01:35
  Speaker 2: there and were any of them productive? And any of
• 1:01:38
  Speaker 2: these really? So now let's bring this back to sports,
• 1:01:42
  Speaker 2: because you've written papers on NFL drafts.
• 1:01:48
  Speaker 3: So the NFL draft. Every year there's.
• 1:01:54
  Speaker 4: They have a draft for new players. The first pick
• 1:01:58
  Speaker 4: is given to the team with the worst record the
• 1:02:00
  Speaker 4: previous year, with the idea that that's going to be
• 1:02:05
  Speaker 4: a big advantage to them and will help them improve.
• 1:02:09
  Speaker 4: Cade Massey, one of my former students. He and I
• 1:02:12
  Speaker 4: wrote a paper showing that the first pick is actually
• 1:02:20
  Speaker 4: not the most valuable because the league has a salary,
• 1:02:26
  Speaker 4: the first player gets paid the most. And you can
• 1:02:29
  Speaker 4: trade the first pick for the seventh and eighth picks
• 1:02:34
  Speaker 4: or for five second round picks. And what we showed
• 1:02:39
  Speaker 4: is if you trade the first pick for lower picks,
• 1:02:44
  Speaker 4: you get more.
• 1:02:47
  Speaker 3: Value.
• 1:02:48
  Speaker 2: So now this is known like the Arco engineers publishing
• 1:02:52
  Speaker 2: by paper, and yet there still seems to be this
• 1:02:55
  Speaker 2: frenetic war for top top one, top ten, three, top
• 1:03:00
  Speaker 2: five picks. Has the NFL learned any lessons from the research?
• 1:03:07
  Speaker 3: Almost nothing.
• 1:03:09
  Speaker 4: They so they have learned that you should they only
• 1:03:14
  Speaker 4: trade up to get the first pick to pick a quarterback.
• 1:03:19
  Speaker 4: So that's smart because the quarterbacks are more valuable than
• 1:03:26
  Speaker 4: any other player.
• 1:03:26
  Speaker 2: Well, of course you want that first pick, so you
• 1:03:28
  Speaker 2: could get a Tom Brady.
• 1:03:30
  Speaker 4: Yeah, except Tom Brady was taken with one hundred and
• 1:03:33
  Speaker 4: ninety ninth pick. And all the listeners who are football
• 1:03:38
  Speaker 4: fans can have their list of people who were taken
• 1:03:43
  Speaker 4: with the first pick and turned out to be busts.
• 1:03:46
  Speaker 4: The Chicago Bears seemed to specialize in that. Although let's
• 1:03:50
  Speaker 4: hope this current guy.
• 1:03:54
  Speaker 2: Is so new one, So how much of this like
• 1:03:57
  Speaker 2: I'm seeing this through the lens of my book, which
• 1:04:00
  Speaker 2: I don't want to talk about. But how much of
• 1:04:02
  Speaker 2: this is just how difficult it is to predict the future,
• 1:04:07
  Speaker 2: to to have truly expert judgment about these very complex,
• 1:04:13
  Speaker 2: very variable so selecting quarterbacks, identifying oil leases like it
• 1:04:22
  Speaker 2: seems that supposed expert advice ain't all that expert. How
• 1:04:27
  Speaker 2: much of this is? Aren't we better off just being
• 1:04:30
  Speaker 2: a little more humble about our Let's give up the
• 1:04:33
  Speaker 2: top pick and have five second round. Somebody in that
• 1:04:36
  Speaker 2: five is likely to be half decent? Yeah?
• 1:04:37
  Speaker 4: Right, So let me stick to the sports for one second,
• 1:04:42
  Speaker 4: because there's one statistic from our paper, and we've just coincidentally,
• 1:04:48
  Speaker 4: we've been in a process of replicating.
• 1:04:50
  Speaker 3: That study, so I have the new data. But here's
• 1:04:53
  Speaker 3: the statistic.
• 1:04:54
  Speaker 4: Take all the players at a given position, say quarterback
• 1:04:58
  Speaker 4: or cornerback or running back, rank them in the order
• 1:05:02
  Speaker 4: in which they're picked, and now ask the fourth guy,
• 1:05:08
  Speaker 4: what's the chance he's better than the fifth guy? So
• 1:05:12
  Speaker 4: for the for the whole thing, so what's the chance
• 1:05:15
  Speaker 4: the earlier player is better than the next one?
• 1:05:19
  Speaker 2: One over two, ten, over eleven, five, over six?
• 1:05:22
  Speaker 4: Now, if they're perfect it'll be one hundred percent. If
• 1:05:26
  Speaker 4: they're coin flipping, it'll be fifty percent. What do you
• 1:05:31
  Speaker 4: think it is.
• 1:05:32
  Speaker 2: I think it's less than fifty percent.
• 1:05:33
  Speaker 3: I think negative. They they know less than nothing, right.
• 1:05:37
  Speaker 2: That's right. I think it's in the thirties or forties.
• 1:05:40
  Speaker 4: Well, they're not that bad, right, I mean, because if
• 1:05:43
  Speaker 4: they were, then you could just you know, you no, no,
• 1:05:48
  Speaker 4: you'd want to what the George Costanza. You wanted to
• 1:05:51
  Speaker 4: be opposite, right, So, so no, they don't have negative knowledge.
• 1:05:58
  Speaker 4: They have a tiny little bay. So but that's your point, really,
• 1:06:05
  Speaker 4: which is they think they know this guy is the
• 1:06:09
  Speaker 4: next Tom Brady, and there's only a fifty three percent
• 1:06:14
  Speaker 4: chance that he's better than the next one. And you know,
• 1:06:20
  Speaker 4: Patrick Mahomes, Josh Allen think none of these were first.
• 1:06:24
  Speaker 2: Right, right, So Mahomes kid is gonna be pretty good one.
• 1:06:27
  Speaker 3: I think he might make it.
• 1:06:29
  Speaker 2: He's got some potential, right.
• 1:06:31
  Speaker 4: So, And I think that is so getting off of sports,
• 1:06:36
  Speaker 4: I think that your general point is exactly right that
• 1:06:40
  Speaker 4: people are look overconfidence. Danny Codeman used to say that's
• 1:06:45
  Speaker 4: the mother of all biases. And we fall into these
• 1:06:51
  Speaker 4: traps because we think we know more than we do,
• 1:06:57
  Speaker 4: and if we add some humility, maybe if we listen
• 1:07:03
  Speaker 4: to our spouses more often, because at least in my house,
• 1:07:10
  Speaker 4: my wife doesn't think that I know anything, so she's
• 1:07:15
  Speaker 4: always bringing me back to fifty percent right, and she's
• 1:07:20
  Speaker 4: usually right.
• 1:07:21
  Speaker 2: My wife is from the same cut, from the same cloth.
• 1:07:24
  Speaker 2: So I want to bring Alex back into this. So
• 1:07:27
  Speaker 2: when we're thinking about the future of behavioral economics and
• 1:07:33
  Speaker 2: what this means for investors or regular people making financial
• 1:07:39
  Speaker 2: decisions or in significant decisions, what direction are we moving in.
• 1:07:44
  Speaker 2: Are are we learning from all of this knowledge that's
• 1:07:49
  Speaker 2: been accumulated, or are we just destined to make the
• 1:07:52
  Speaker 2: same mistakes over and over again.
• 1:07:55
  Speaker 5: I don't think we're destined to do anything. I think
• 1:07:58
  Speaker 5: it's a choice to take, you know, read papers and
• 1:08:03
  Speaker 5: look at papers on kind of published and financial journals
• 1:08:07
  Speaker 5: where people are making mistakes, and then to choose to say, like, look,
• 1:08:10
  Speaker 5: I actually can correct this by having a particular decision
• 1:08:13
  Speaker 5: aid or asking my spouse what to do, or something
• 1:08:17
  Speaker 5: like that. So you know what a paper you mentioned earlier,
• 1:08:21
  Speaker 5: we published this actually just last year, called Selling fast
• 1:08:25
  Speaker 5: and Buying Slow. And in that paper, basically we look
• 1:08:28
  Speaker 5: at institutional investors, So thinking about who in the economy
• 1:08:33
  Speaker 5: are least likely to be exhibiting beha role of biases.
• 1:08:36
  Speaker 5: You know, maybe retail traders they're like, you know, drinking
• 1:08:38
  Speaker 5: beer in their basement while trading stocks on robin Hood.
• 1:08:41
  Speaker 5: Maybe this is not the sophisticated people we want to
• 1:08:43
  Speaker 5: be looking at. But you know, institutional investors, the average
• 1:08:47
  Speaker 5: portfolio on the data set was like six hundred million,
• 1:08:49
  Speaker 5: seven hundred million dollars or something like that. We had
• 1:08:53
  Speaker 5: a data set where we actually saw every single they
• 1:08:56
  Speaker 5: think they did over something like a twelve or thirteen
• 1:08:58
  Speaker 5: year period far as what they're buying or what they're selling.
• 1:09:02
  Speaker 5: And what we found is because the data is so rich,
• 1:09:05
  Speaker 5: we can actually construct these counterfactual portfolios. We can say, look,
• 1:09:08
  Speaker 5: I see what you're buying. What if you bought something else,
• 1:09:11
  Speaker 5: So it could be something else from your portfolio, you
• 1:09:13
  Speaker 5: can top something up, or you can buy something new
• 1:09:15
  Speaker 5: from the from the universe. On the other hand, we
• 1:09:18
  Speaker 5: could say, look the same thing for selling. I saw
• 1:09:21
  Speaker 5: you sold Apple, I saw you sold Samsung. Let me
• 1:09:25
  Speaker 5: sell something else instead. How how would that perform relative
• 1:09:28
  Speaker 5: to what you actually did. And what we found is
• 1:09:31
  Speaker 5: that on the buying side. People actually did really well.
• 1:09:35
  Speaker 5: I mean, these guys are they.
• 1:09:37
  Speaker 3: Happen really well? But better better than.
• 1:09:39
  Speaker 2: Random Fund managers create some value in their stock selection
• 1:09:44
  Speaker 2: when they're making purchases, yes, but the flip side of
• 1:09:46
  Speaker 2: that not so much.
• 1:09:48
  Speaker 5: No, not so much. We had we really wanted to
• 1:09:50
  Speaker 5: be conservative. We didn't want to, you know, say, compare
• 1:09:54
  Speaker 5: them to the benchmark or something like that. We said,
• 1:09:56
  Speaker 5: let's throw a dart at your portfolio and sell that
• 1:09:59
  Speaker 5: instead of what you act.
• 1:10:00
  Speaker 2: So instead of selling what the manager wants to sell,
• 1:10:03
  Speaker 2: you would sell something else randomly from the rest of the.
• 1:10:06
  Speaker 5: Portfolio, a random selling.
• 1:10:07
  Speaker 2: And the performance difference was how significant?
• 1:10:11
  Speaker 5: Basically same difference but in the opposite direction, meaning they
• 1:10:15
  Speaker 5: were losing a ton of money.
• 1:10:17
  Speaker 2: So one hundred two hundred basis points on a random
• 1:10:20
  Speaker 2: cell better, Yes, performance exactly. And you know the way
• 1:10:24
  Speaker 2: when I read that paper and wrote about it, the
• 1:10:27
  Speaker 2: way I rationalized it, or tried to conceptualize it was
• 1:10:32
  Speaker 2: they're bringing a very objective, quantitative approach to the stock
• 1:10:35
  Speaker 2: selection issue, but it seems that their cells are filled
• 1:10:40
  Speaker 2: with biases and squishy decision making.
• 1:10:43
  Speaker 5: Is that a fair description? Yeah, exactly. So we found
• 1:10:47
  Speaker 5: no evidence for heuristics on their buying decision, Like, we
• 1:10:49
  Speaker 5: couldn't find anything, Like they just seemed to be very
• 1:10:52
  Speaker 5: disciplined and principled about what they're buying. But on the
• 1:10:56
  Speaker 5: selling side we found literally the same biases that we
• 1:11:00
  Speaker 5: found in the lab.
• 1:11:01
  Speaker 2: Has there been any evolution or improvement in this recently?
• 1:11:06
  Speaker 2: That's the question that I keep coming back to. It
• 1:11:09
  Speaker 2: seems that Richard, you've figured a lot of these things
• 1:11:13
  Speaker 2: out twenty five thirty five years ago. Are we any
• 1:11:17
  Speaker 2: better at making unbiased decisions? Or are we still subject
• 1:11:21
  Speaker 2: to the same voibles.
• 1:11:23
  Speaker 5: I think you need something extra right. You can't just
• 1:11:25
  Speaker 5: say I'm not going to do this and I have
• 1:11:28
  Speaker 5: decided not to listen to my psychology. That's what it
• 1:11:30
  Speaker 5: would look.
• 1:11:31
  Speaker 2: Like to be choice architecture of building, exam guardrails and
• 1:11:36
  Speaker 2: but this is a fault.
• 1:11:37
  Speaker 5: This is where overconfidence comes in. When you read a
• 1:11:39
  Speaker 5: paper about somebody doing something silly, your first reaction is
• 1:11:43
  Speaker 5: not me right, that's the blind spot. This is called
• 1:11:47
  Speaker 5: the bias blind spot exactly. So this is a well
• 1:11:50
  Speaker 5: replicated finding. When you ask people to what extent do
• 1:11:53
  Speaker 5: you exhibit a bias? I don't obviously I'm a smart person,
• 1:11:57
  Speaker 5: but to what extent to other people. Of course people
• 1:12:00
  Speaker 5: are bad at selling. I'm really good. But in order
• 1:12:02
  Speaker 5: to adopt choice architecture to help you out when making decisions,
• 1:12:06
  Speaker 5: you actually have to be have some You have to
• 1:12:09
  Speaker 5: have a lot of humility to say, look these institutional investors,
• 1:12:13
  Speaker 5: to say, look, looks like I'm not really doing so
• 1:12:16
  Speaker 5: well on selling. I'm going to adopt some choice architectures
• 1:12:20
  Speaker 5: so I don't suffer from these biases. Maybe I'll hire
• 1:12:22
  Speaker 5: somebody else to help me out. Maybe I'll think longer,
• 1:12:26
  Speaker 5: or use the same sort of research technology for my
• 1:12:29
  Speaker 5: selling as I'm doing for my buying. And because that
• 1:12:32
  Speaker 5: requires humility, which most people don't have a lot of,
• 1:12:36
  Speaker 5: that's really hard to do. So I think that's why
• 1:12:38
  Speaker 5: we're seeing a lot of these biases just be perpetuated
• 1:12:42
  Speaker 5: forward to the point where we're running the same analysis
• 1:12:46
  Speaker 5: now as we did thirty years ago and finding the
• 1:12:47
  Speaker 5: exact same thing.
• 1:12:49
  Speaker 2: So the question that since we were talking about sports
• 1:12:52
  Speaker 2: and lack of knowledge, and then you mentioned Robin Hood,
• 1:12:57
  Speaker 2: one of the things that's a little concerning is how
• 1:13:02
  Speaker 2: some companies are putting our knowledge of biases and bad
• 1:13:06
  Speaker 2: behavior to work for their own profit. So when we
• 1:13:09
  Speaker 2: see the gamification of investing with Robinhood or just the
• 1:13:14
  Speaker 2: incredible rise not just of sports books and gambling, but
• 1:13:18
  Speaker 2: you could bet on every play, it's reached a point
• 1:13:20
  Speaker 2: where it's ridiculous, and there is a robust evidence that,
• 1:13:25
  Speaker 2: especially young men, are having all sorts of how can
• 1:13:29
  Speaker 2: we how can we deal with what seems to be
• 1:13:33
  Speaker 2: not a good use of choice architecture, but a bad
• 1:13:36
  Speaker 2: use of choice choice architecture, at least as far as
• 1:13:40
  Speaker 2: the public is concerned.
• 1:13:41
  Speaker 4: Yeah, it's a very good question, Barry, and one to
• 1:13:44
  Speaker 4: which I don't have a pad answer. I mean, it's
• 1:13:48
  Speaker 4: tempting to say, look, all these sports betting apps and
• 1:13:55
  Speaker 4: the gamification of investing are bad for people. On the
• 1:14:00
  Speaker 4: other hand, people like doing it. They're mostly adults, and
• 1:14:08
  Speaker 4: you know, prohibition basically didn't work. So I think some
• 1:14:15
  Speaker 4: disclosure would help. It's difficult to find out what the odds.
• 1:14:23
  Speaker 3: Are and a lot of these things. But it's a
• 1:14:28
  Speaker 3: tough question.
• 1:14:30
  Speaker 4: I had a conversation on this book when Nate Silver
• 1:14:35
  Speaker 4: a couple of weeks ago, and we talked a lot
• 1:14:38
  Speaker 4: about sports gambling. He's a professional gambler and he spent
• 1:14:44
  Speaker 4: a year betting on NBA games and basically broke even.
• 1:14:48
  Speaker 4: So you know, if Nate can't make money doing this,
• 1:14:53
  Speaker 4: chances are you can't. And you know, my advice would be, look,
• 1:14:58
  Speaker 4: if you really think you doing this, do it on
• 1:15:03
  Speaker 4: a small scale.
• 1:15:05
  Speaker 3: You know, don't bet the house list, you don't write.
• 1:15:09
  Speaker 3: And the same with weekly options or daily.
• 1:15:12
  Speaker 5: That's one of the most popular. One of our colleagues
• 1:15:14
  Speaker 5: at University of Chicago she did an analysis of what
• 1:15:17
  Speaker 5: retail traders are actually doing on Robinhood and one of
• 1:15:20
  Speaker 5: the most popular products, which because it's pushed by Robinhood,
• 1:15:24
  Speaker 5: is weekly options.
• 1:15:26
  Speaker 2: And there's now end of day options where it expires
• 1:15:30
  Speaker 2: you have till four o'clock to either make money or not.
• 1:15:34
  Speaker 3: So you know, if you want to risk one month's
• 1:15:37
  Speaker 3: pay on.
• 1:15:38
  Speaker 2: That, fine, not just not every month.
• 1:15:42
  Speaker 3: No, yes, yeah, that's in your lifetime budget and when
• 1:15:47
  Speaker 3: it goes to zero, switch to something else.
• 1:15:51
  Speaker 2: I'm a big fan of the cowboy account, where you
• 1:15:54
  Speaker 2: take three or four percent of your portfolio and if
• 1:15:57
  Speaker 2: you want to fool around with options, whatever, knock your
• 1:16:00
  Speaker 2: out and if it makes money, great, But like we've
• 1:16:03
  Speaker 2: seen you mentioned Apple, if it was your whole portfolio.
• 1:16:06
  Speaker 2: You would never have been able to ride it to
• 1:16:09
  Speaker 2: be a five extra tenants. You would have taken I'm
• 1:16:11
  Speaker 2: up twenty bucks. I'm taking the money off the table.
• 1:16:14
  Speaker 3: Yeah yeah.
• 1:16:14
  Speaker 4: So you know, people long ago would adopt the strategy
• 1:16:20
  Speaker 4: of bringing a certain amount of money to the casino, right,
• 1:16:24
  Speaker 4: and then of course the casinos put ATM on the floor.
• 1:16:28
  Speaker 3: So it's a battle. But mental accounting.
• 1:16:32
  Speaker 4: You have a gambling account, but that's it.
• 1:16:37
  Speaker 3: Yeah. I mean it would be better if it were zero, right,
• 1:16:40
  Speaker 3: but otherwise set it up.
• 1:16:43
  Speaker 4: That's something you can afford to lose. And when you've
• 1:16:46
  Speaker 4: lost it all.
• 1:16:47
  Speaker 5: Your stump, don't go to the ATM.
• 1:16:49
  Speaker 3: That's right.
• 1:16:50
  Speaker 2: So I only have you for a few more minutes.
• 1:16:52
  Speaker 2: I want to ask two of my favorite questions that
• 1:16:55
  Speaker 2: I want to ask each of you, that I ask
• 1:16:57
  Speaker 2: all of my guests, starting with what sort of advice
• 1:17:01
  Speaker 2: would you give a recent college grad interest in the
• 1:17:04
  Speaker 2: career and either behavioral finance or economics. Alse you're the
• 1:17:08
  Speaker 2: more recent grad, what would you what would your advice be?
• 1:17:12
  Speaker 5: Get tacked up? Really get tacked up. I think that's
• 1:17:16
  Speaker 5: the biggest kind of difference between even when I was
• 1:17:18
  Speaker 5: in graduate school and when I'm seeing hiring preducts and
• 1:17:21
  Speaker 5: ras the sort of work that you're doing in modern
• 1:17:24
  Speaker 5: behavioral economics, modern finance, it just requires a different level
• 1:17:28
  Speaker 5: of analysis.
• 1:17:29
  Speaker 2: So is this learning to code or is this becoming
• 1:17:31
  Speaker 2: a prompt engineer for AI?
• 1:17:33
  Speaker 5: What? I think you still got to learn to code.
• 1:17:35
  Speaker 5: I think you know where I work in the Applied
• 1:17:39
  Speaker 5: A I group at booth, you know, you still got
• 1:17:42
  Speaker 5: to learn to code. And a lot of this sort
• 1:17:44
  Speaker 5: of modern analyzes that people are doing, particularly as behavioral
• 1:17:47
  Speaker 5: finance behavioral ekon has moved out of the lab into
• 1:17:50
  Speaker 5: the field. These data sets are huge machine learning AI tools.
• 1:17:55
  Speaker 5: The type of people who are getting hired are doing
• 1:17:58
  Speaker 5: sophisticated analys So it's still.
• 1:18:00
  Speaker 2: Basically STEM groups science, technology, engineering, and math for people
• 1:18:05
  Speaker 2: who don't know the acronym, but applied specifically to the Feeah.
• 1:18:09
  Speaker 5: Yeah, And like you know, you take your economics courses,
• 1:18:11
  Speaker 5: you take your finance courses, take some CS courses on
• 1:18:14
  Speaker 5: the side. Those are this is what I wish I
• 1:18:18
  Speaker 5: would have done. Right when I was getting my PhD.
• 1:18:20
  Speaker 5: It wasn't on my radar to take, you know, a
• 1:18:22
  Speaker 5: coding class in the CS department. People coming out now
• 1:18:25
  Speaker 5: the ones who are really successful, you have to have
• 1:18:27
  Speaker 5: good ideas. That's a necessary condition. It's not a sufficient condition.
• 1:18:33
  Speaker 5: You still you got you need to be TechEd up
• 1:18:36
  Speaker 5: to a level that I don't think we were seeing
• 1:18:38
  Speaker 5: when I was graduating.
• 1:18:39
  Speaker 3: Really interesting.
• 1:18:40
  Speaker 4: I'll reinforce that with the following. I think you need
• 1:18:44
  Speaker 4: some practical experience. Yeah, because the part that you don't
• 1:18:48
  Speaker 4: learn in a textbook is you you get this gigantic
• 1:18:53
  Speaker 4: data set and it's noisy, and there are errors and
• 1:18:59
  Speaker 4: so so learning how to clean up a data set.
• 1:19:06
  Speaker 3: You got to learn that through experience.
• 1:19:08
  Speaker 2: Really interesting. And our final question, what do you know
• 1:19:12
  Speaker 2: about the world of behavioral economics today, would have been
• 1:19:15
  Speaker 2: useful back in the nineteen seventies and eighties when you
• 1:19:19
  Speaker 2: were getting started, and in the two thousands when you
• 1:19:22
  Speaker 2: were getting started.
• 1:19:24
  Speaker 3: So I think if we go back.
• 1:19:26
  Speaker 4: We talked about the changes that I helped make in
• 1:19:31
  Speaker 4: the retirement plans, and what I wish I had been
• 1:19:37
  Speaker 4: able to accomplish more of is making retirement saving at
• 1:19:43
  Speaker 4: the workplace available to the possibly forty percent of American
• 1:19:48
  Speaker 4: workers whose firms don't offer that option. And there were
• 1:19:55
  Speaker 4: plans around and they didn't get passed.
• 1:20:01
  Speaker 3: I think.
• 1:20:03
  Speaker 4: The system they have in the UK is a reasonable model,
• 1:20:07
  Speaker 4: which is there's a requirement that any firm with more
• 1:20:12
  Speaker 4: than I'm not show the number, say twenty employees, has
• 1:20:17
  Speaker 4: to offer a plan and automatically enroll people into the plan,
• 1:20:25
  Speaker 4: and the government has like a generic plan they can use,
• 1:20:29
  Speaker 4: like the government thrift program. So and this is useful
• 1:20:35
  Speaker 4: because big firms like Fidelity and Vanguard don't really want
• 1:20:39
  Speaker 4: tiny accounts, so make it easy for an employer. They
• 1:20:46
  Speaker 4: don't have to do anything. They just have to let
• 1:20:49
  Speaker 4: their employees enroll in this and then when they change
• 1:20:53
  Speaker 4: jobs they can keep it there. Because the real problem
• 1:20:58
  Speaker 4: is they go to war for a while at this
• 1:21:02
  Speaker 4: firm and they worked there for a year and they've
• 1:21:05
  Speaker 4: saved six hundred dollars and then they leave and.
• 1:21:08
  Speaker 3: They take the cash out.
• 1:21:10
  Speaker 4: So we need automatic roll over. So that's the piece
• 1:21:16
  Speaker 4: of the puzzle that I don't know whether I could
• 1:21:20
  Speaker 4: have done anything about, but it's what I wish we
• 1:21:23
  Speaker 4: could work on now.
• 1:21:24
  Speaker 2: So related to that, what do you think of these
• 1:21:26
  Speaker 2: new baby accounts? Every newborn in America next year gets
• 1:21:30
  Speaker 2: one thousand dollars has to be invested domestically, which you know,
• 1:21:34
  Speaker 2: we can have an argument. We were talking about home
• 1:21:37
  Speaker 2: country bias, but still you're starting every infant off with
• 1:21:41
  Speaker 2: a portfolio. What are your thoughts on that.
• 1:21:45
  Speaker 3: I don't know the details of how that's going to work.
• 1:21:47
  Speaker 3: You could.
• 1:21:50
  Speaker 4: My friend and sometimes colleague when I'm in Berkeley, Ulrica
• 1:21:55
  Speaker 4: Melmendie has made a similar proposal in Germany, where she's
• 1:21:59
  Speaker 4: on the German Council of Economic Advisors.
• 1:22:03
  Speaker 3: I think the idea is to give kids.
• 1:22:08
  Speaker 4: Some experience with the stock market, and I think that
• 1:22:13
  Speaker 4: could be useful. I don't know how this is going
• 1:22:19
  Speaker 4: to wash out and all of these things end up
• 1:22:23
  Speaker 4: being tilted toward the rich. I mean, there were big
• 1:22:28
  Speaker 4: reforms made recently to the retirement plans. One of the
• 1:22:33
  Speaker 4: reforms was to let you wait longer to start making withdrawals.
• 1:22:39
  Speaker 3: Who do you think that helps people who.
• 1:22:41
  Speaker 2: Are wealthy, healthy and going to have a longer lifespan.
• 1:22:44
  Speaker 4: Right, So most people start taking the money out at
• 1:22:50
  Speaker 4: fifty nine and a half. Raising the data at which
• 1:22:54
  Speaker 4: you have to start from seventy to seventy two doesn't
• 1:22:59
  Speaker 4: help anybody that's in any trouble.
• 1:23:01
  Speaker 2: Yeah, that makes a lot of sense, Alex, What do
• 1:23:04
  Speaker 2: you know today about behavioral finance that you wish you
• 1:23:07
  Speaker 2: knew when you were getting started.
• 1:23:10
  Speaker 5: I think what I when I was getting started, I
• 1:23:13
  Speaker 5: wasn't really thinking about kind of target being able to
• 1:23:16
  Speaker 5: target these big institutional investors and thinking about getting data
• 1:23:21
  Speaker 5: sets on smart money in the economy. So I think
• 1:23:26
  Speaker 5: when I was starting out, I was really focused on
• 1:23:28
  Speaker 5: lab experiments. I was really focused on kind of the
• 1:23:30
  Speaker 5: data that's that was available. And if I was starting
• 1:23:34
  Speaker 5: out now, I would I would start my PhD trying
• 1:23:37
  Speaker 5: to get trying to get data sets that I eventually
• 1:23:39
  Speaker 5: was able to get because the types of as far
• 1:23:42
  Speaker 5: as like looking at my research, what has had the
• 1:23:44
  Speaker 5: largest impact, what has had you know, people in finance
• 1:23:48
  Speaker 5: in the professional world calling me up and saying, hey,
• 1:23:51
  Speaker 5: what do you think about this? Or that it's been
• 1:23:54
  Speaker 5: looking at the population that people are actually interested in,
• 1:23:57
  Speaker 5: which are you know, the smart money in the economy.
• 1:23:59
  Speaker 5: So I think and this is I think, you know,
• 1:24:04
  Speaker 5: you have this analogy of looking under the spot under
• 1:24:08
  Speaker 5: the street light, where is a lot of behavioral finance
• 1:24:11
  Speaker 5: for the missing exactly? You know where are you're looking? Oh,
• 1:24:14
  Speaker 5: it's where the data sets already are. So you know,
• 1:24:17
  Speaker 5: Terry o'dean had this genius idea in ninety eight when
• 1:24:20
  Speaker 5: he published his paper. What did he do? He made
• 1:24:22
  Speaker 5: that big data set available. Now it's easy to just oh,
• 1:24:26
  Speaker 5: I got an idea, Why don't I look at Terry's
• 1:24:28
  Speaker 5: data set. Terry's data set is great, but it's three
• 1:24:30
  Speaker 5: years in the nineties with you know, a couple hundred
• 1:24:33
  Speaker 5: retail traders, and that tells you about that's specific population.
• 1:24:37
  Speaker 5: But you can't have a field evolve looking at three
• 1:24:41
  Speaker 5: years of retail traders with ten thousand dollars portfolios. So
• 1:24:44
  Speaker 5: I think if I was going to going into the
• 1:24:46
  Speaker 5: field now and thinking about, you know, what have I learned?
• 1:24:50
  Speaker 5: It's the power of getting data sets and running analyzes
• 1:24:54
  Speaker 5: on on populations that are important for the economy and
• 1:24:59
  Speaker 5: for finance.
• 1:25:00
  Speaker 2: Really really fascinating. Gentlemen, Thank you so much for doing this.
• 1:25:03
  Speaker 2: We have been speaking with Richard Taylor and Alex Emos,
• 1:25:07
  Speaker 2: both of the Booth School of Business, about their updated
• 1:25:11
  Speaker 2: version of The Winner's Curse. Strong recommendation if you enjoy
• 1:25:15
  Speaker 2: this conversation, well check out any of the five hundred
• 1:25:18
  Speaker 2: and ninety two we've done over the past twelve years.
• 1:25:22
  Speaker 2: You can find those at iTunes, Spotify, Bloomberg YouTube, wherever
• 1:25:27
  Speaker 2: you get your favorite podcasts, and be sure to check
• 1:25:30
  Speaker 2: out my new book, How Not to Invest The ideas, numbers,
• 1:25:33
  Speaker 2: and behavior that destroys wealth and how to avoid them.
• 1:25:37
  Speaker 5: I would be remiss if.
• 1:25:38
  Speaker 2: I thank the Crack team that helps put these conversations
• 1:25:41
  Speaker 2: together each week. Alexis Noriega is my video producer. Sean
• 1:25:46
  Speaker 2: Russo is my researcher. Anna Luke is my producer. Safe
• 1:25:50
  Speaker 2: Bauman is the head of podcasts here at Bloomberg. I'm
• 1:25:54
  Speaker 2: Barry Ritolts. You've been listening to Masters in Business on
• 1:25:58
  Speaker 2: Bloomberg Radio.
• 1:26:00
  Speaker 5: Brast transud to reco