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Speaker 1: This is Masters in Business with Barry Ridholts on Bloomberg

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Speaker 1: Radio this weekend. On the podcast, I have a special guest.

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Speaker 1: Her name is Alison Schraeger, and amongst other things, she

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Speaker 1: is the author of a book that I took with

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Speaker 1: me on vacation and absolutely found intriguing. My my version

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Speaker 1: is just demolished because I plowed through it on a

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Speaker 1: beach uh in Turks and Caicos, and really, despite everything

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Speaker 1: going on around me, I kind of ignored it and

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Speaker 1: just worked my way through the book. I love the

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Speaker 1: title and economists walks into a brothel, which really has

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Speaker 1: nothing to do with sex and everything to do with

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Speaker 1: finding ways to do risk reward analyzes in really unusual places.

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Speaker 1: So whether it's big wave surfing or horse breeding, or

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Speaker 1: poker playing or paparazzi, there are all these unusual um

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Speaker 1: situations where we don't really think about the risk reward analysis,

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Speaker 1: but really the details of that have a major impact

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Speaker 1: on how these industries and these individuals progress. And and

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Speaker 1: once you start looking into that, it changes the way

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Speaker 1: you look at everything from insurance to annuities to hedging

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Speaker 1: to market based portfolios. Risk permanent permeates everything we do

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Speaker 1: and most of us just don't give it enough time

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Speaker 1: and thought to recognize the dangers and advantages it potentially

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Speaker 1: can afford us. So, if you're at all interested in

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Speaker 1: filling the blank investing, insurance, understanding risk, understanding what happens

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Speaker 1: in a brothel from an economic perspective, I think you're

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Speaker 1: gonna find this to be an absolutely fascinating conversation. So,

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Speaker 1: with no further ado, my interview with Alison Traeger. This

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Speaker 1: is Masters in Business with Barry Ridholts on Bloomberg Radio.

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Speaker 1: My special guest this week is Alison Schreeger. She is

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Speaker 1: an economist and adjunct professor at n y U, a

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Speaker 1: journalist at Courts, co founder of life Cycle Finance Partners,

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Speaker 1: which is a risk advisory firm. She is also the

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Speaker 1: author of An Economist Walks Into a Brothel and Other

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Speaker 1: Unexpected Places to Understand Risk. Alison Schreeger, Welcome to Bloomberg.

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Speaker 1: Thank you for having me. My pleasure. So, so let's

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Speaker 1: talk a little bit about your um backgrounds. You describe

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Speaker 1: in the book making a series of what you later

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Speaker 1: called risky career decisions, not exactly sure what you wanted

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Speaker 1: to do. Your assumption was, hey, PhD program and economics.

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Speaker 1: Obviously you're gonna become a professor. How did that work out? Um? Well,

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Speaker 1: in the end, I think it worked out well for me,

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Speaker 1: but the path was a lot rocky year than I

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Speaker 1: would have expected. I think, like a lot of people,

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Speaker 1: especially in this day and age, I fell into this

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Speaker 1: idea that more education was just better and it would

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Speaker 1: open up all these doors. And most of the time

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Speaker 1: it does. I think that is normally a good bet,

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Speaker 1: and certainly an economics PhD is is a good thing

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Speaker 1: to have in life. But I had kind of a

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Speaker 1: rocky transition when I finished grad school because I had

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Speaker 1: invested essentially my whole twenties into learning all these skills

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Speaker 1: and really being cut off from the rest of the world,

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Speaker 1: and then realized what I was investing in was this

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Speaker 1: idea of becoming a professor, and had this realization as

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Speaker 1: I graduated, this was not what I wanted to do

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Speaker 1: at all. So undergrad it and Edinburgh PhD at Columbia University.

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Speaker 1: What sort of thesis were you working on that? Um? Weirdly,

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Speaker 1: even when I was like eighteen, I was always really

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Speaker 1: fascinated by retirement. Uh So my PhD thesis was on

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Speaker 1: risk in retirement, so it was I started around two

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Speaker 1: thousand and so this is as DC Plans had really

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Speaker 1: taken a foothold in the market and I was doing

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Speaker 1: some work in the UK where they were taking over,

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Speaker 1: so really understanding more deeply the risks and define contribution

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Speaker 1: versus define benefit pensions. So eventually you come to the realization, hey,

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Speaker 1: I'm not going to be any sort of professor. And

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Speaker 1: then you happened to have a job interview with a

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Speaker 1: world famous Noble Prize winning economist, Robert Martin. What happened

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Speaker 1: with that? Well, I uh, as I said, I had

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Speaker 1: this hard realization when I was finishing grad school. I

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Speaker 1: didn't want to be professor. I didn't want to be

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Speaker 1: a government bureaucrat all the things you're supposed to do

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Speaker 1: with an economics PhD. So I kind of had this

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Speaker 1: burn it all down, do something new attitude, which also

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Speaker 1: turned out to be helpful for the book. So I

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Speaker 1: was like, I'm gonna do something fun. I've just spent

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Speaker 1: my whole twenties while everyone else was partying solving a

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Speaker 1: math problem. So I went to the economist unpaid, because

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Speaker 1: this is early days with web journalism, and they would

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Speaker 1: take people who had never written before to write for

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Speaker 1: the web back then and what you pay for right,

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Speaker 1: So I was just writing web web stuff for the

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Speaker 1: economists for free with any com PhD. And then someone

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Speaker 1: passed my dissertation along to Bob Merton and turned out

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Speaker 1: retirement was also his main interest. He was working on

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Speaker 1: market solutions to the retirement problem. So so let me

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Speaker 1: interrupt you there and ask some questions here, because this

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Speaker 1: is really kind of fascinating. You're wildly overqualified to turn

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Speaker 1: out web based nonsense where you're not being paid for

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Speaker 1: a website, and you know they treat unpaid volunteers not

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Speaker 1: especially well, and they have the same respect for their content.

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Speaker 1: Who said, oh, I know Allison's pH d thesis. Let

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Speaker 1: me give this to Bob. He's busy, he want a

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Speaker 1: Nobel prize, but he'll like this. How on earth did

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Speaker 1: that come about? Um a friend of mine who I

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Speaker 1: think had him when he was doing his NBA at Harvard.

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Speaker 1: And because my dissertation, that is exactly what Bob was doing.

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Speaker 1: Bob was trying to come up with financial models that

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Speaker 1: took the best to define benefit plans and put them

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Speaker 1: in a defined contribution structure, and that turns out, you know,

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Speaker 1: as I said, as much as I said burn it

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Speaker 1: all down, I always had a good dissertation which I

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Speaker 1: thought about risk in a clever way. So once Bob

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Speaker 1: didn't know, i'd sort of hit bottom career wise. You know,

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Speaker 1: it just goes to show where something you feel like

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Speaker 1: something can feel like it totally blew up. I was thinking,

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Speaker 1: I just really messed up here, you know, I just

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Speaker 1: wasted all this time. And then my paper ended up

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Speaker 1: in front of him and he was like, I really

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Speaker 1: want to do this. He called me in. He's like,

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Speaker 1: if you come work with me, I will teach you finance.

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Speaker 1: Let me tell you when I first was handed a

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Speaker 1: book whose title was an economist walks into a brothel.

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Speaker 1: I'm intrigued. Okay, now, your your thought is they're not

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Speaker 1: going in there as a customer or anything like that.

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Speaker 1: You thought is what sort of wacky economic data are

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Speaker 1: they going to be analyzing? As an economist looking at

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Speaker 1: the sex trade, it's pretty fast spending subject. How did

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Speaker 1: you find your way to Nevada and going to the

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Speaker 1: bunny ranch? So I wrote a story for courts because

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Speaker 1: I'm it's interesting. I've always been in shouting risk even

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Speaker 1: as a journalist. You know, after I started working with Bob,

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Speaker 1: before I was a macro economist, I became I learned finance.

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Speaker 1: I'm like, everything I know about the economy, I was wrong.

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Speaker 1: Risk is a much more rigorous and interesting way to

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Speaker 1: understand the macro economy and every economic problem. So I

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Speaker 1: was applying that in sort of this freakonomics see way

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Speaker 1: as a journalist, and I wrote a story about uh,

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Speaker 1: a friend of a friend who was running an online

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Speaker 1: brothel where her value add was screening clients. This is

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Speaker 1: an illegal operation, so when you work illegally, you have

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Speaker 1: to screen your clients otherwise, especially these were first submissives specifically,

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Speaker 1: so they get they get tied up, so they're particularly vulnerable.

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Speaker 1: So screening has especially important premium. So she was being

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Speaker 1: paid this memium to screen them, and I was like, well,

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Speaker 1: this is pretty cool. Uh. So I wrote the story

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Speaker 1: about it did super well, as you can imagine, And

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Speaker 1: so I got a call from the Bunny ranch saying,

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Speaker 1: if you're gonna be writing about brothels, you should be

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Speaker 1: writing about us, and all, well, I don't write about brothels,

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Speaker 1: but this is an interesting call, So I'm gonna continue

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Speaker 1: this um and so I was talking to them and

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Speaker 1: they were I'm like, how does this all work? They're like, well,

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Speaker 1: the women come in and every we don't upset prices.

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Speaker 1: They negotiate every transaction and they kind of another throwaway

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Speaker 1: thing he said. And I was like, well, that's actually

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Speaker 1: very interesting. So you're telling me you have women who

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Speaker 1: are about twenty years old negotiating with men in their

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Speaker 1: sixties over tens of thousands of dollars. He's like yeah,

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Speaker 1: and it's interesting you say that because most of them

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Speaker 1: come here not knowing how to negotiate. So have a

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Speaker 1: negotiation training program. So the Bunny Ranch in Nevada reaches

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Speaker 1: out to you and says, hey, if you want to

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Speaker 1: have a conversation about a brothel and about risk and

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Speaker 1: analyzing numbers, come to to us. What was your thought

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Speaker 1: process when you got this phone call? Well, at first

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Speaker 1: I was like, this isn't my thing, this isn't what

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Speaker 1: I'm like known for. I'm a retirement economist. But when

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Speaker 1: they said the thing about we have a negotiation training program,

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Speaker 1: which is something I struggle with. Two, Like, you would

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Speaker 1: think a car dealership has a negotiating training program, not

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Speaker 1: a brothel or sales job on Wall Street, you know

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Speaker 1: they and they're like well, and then he even said

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Speaker 1: the headline, the women here don't know their value, so

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Speaker 1: we teach them to know their value and to ask

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Speaker 1: for enough. And I was like, well, I could use that.

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Speaker 1: So I talked to my editor at Courts to sending

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Speaker 1: me there to go through their negotiation training program. It

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Speaker 1: was the immediate reaction, this is a great story. Oh yeah,

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Speaker 1: they send a video crew, all right, so they were

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Speaker 1: they were pretty hip that, hey, this is funky and

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Speaker 1: on you. This was freaky before fre economics, wasn't this

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Speaker 1: freakonomics is already out, but okay, it took it to

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Speaker 1: another level because I don't think Steve Levitt's spent a

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Speaker 1: lot of time in brothels. Now he focused on young

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Speaker 1: drug dealers in the inner city and other similarly freaky stuff.

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Speaker 1: But this is like right up his alley for sure.

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Speaker 1: So you get to the Bunny ranch. How receptive were

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Speaker 1: the women to speaking to you about all these sorts

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Speaker 1: of economic related um issues of of salary and compensation

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Speaker 1: and negotiation and the beginning Some some are very very wary,

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Speaker 1: some are very open, but once you get talking to them,

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Speaker 1: they open up because I think a lot of people

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Speaker 1: go there and they don't really take them seriously as

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Speaker 1: business women. And okay, uh, Dennis when he was alive,

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Speaker 1: this is the guy who was running the bunny ranch. Yes,

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Speaker 1: uh really had a lot of good business training going on.

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Speaker 1: And the women they're are great business women and they're

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Speaker 1: proud of what they know. So once you get them

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Speaker 1: talking about that, they do open up. Because this is

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Speaker 1: a special skill a lot of them have learned, and

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Speaker 1: a skill honestly, I think most people could learn on

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Speaker 1: their jobs and they don't. So how to value the

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Speaker 1: worth of your own work products relative to the mare

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Speaker 1: could place and relative to customer on the other side

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Speaker 1: of the desk from you, and how to ask for it,

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Speaker 1: how to feel comfortable. Especially it's an interesting negotiation because

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Speaker 1: you have to negotiation can be very fraught and afterward

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Speaker 1: you're gonna have this very intimate encounter with this person.

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Speaker 1: So making that transition, which is I mean a more

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Speaker 1: exaggerated version how Valley calls that the pivot, it's a

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Speaker 1: more exactitary vision of what we all do, right, Like,

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Speaker 1: we have to negotiate with someone and we have to

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Speaker 1: work with them. So this is just that on steroids? Huh?

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Speaker 1: And and so what is the secret? What did they

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Speaker 1: do that's somewhat different than what or what did they

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Speaker 1: teach you that you didn't know going into this? I

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Speaker 1: think I always saw negotiation is very adversarial, and what

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Speaker 1: I learned is how to make it not so, how

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Speaker 1: to just put a bunch of things out there, which

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Speaker 1: apparently is a negotiation technique. Here's a here's a men,

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Speaker 1: you choose a be your sake. That's really interesting, and

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Speaker 1: therefore then it's not adversarial. It's just, hey, you know,

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Speaker 1: everyone feels like they're getting what they want. I'm customizing

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Speaker 1: this experience for you. So when I was there, I

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Speaker 1: learned a lot about pricing, sex pricing, and something that

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Speaker 1: fascinated me was how much more they could charge than

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Speaker 1: the illegal market. And now the assumption is, from the

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Speaker 1: John's point of view, they're going into a place where

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Speaker 1: they know the workers have been tested for STDs, they

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Speaker 1: know they're not going to get mummeda, ripped off, they

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Speaker 1: know they're not going to get arrested. That should be

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Speaker 1: worth some sort of premium, shouldn't it. I never thought

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Speaker 1: of it that way before, but that was what always

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Speaker 1: on the lookout for risk fascinated me because I mean,

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Speaker 1: the premium is large. When I went, I went back

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Speaker 1: to the brothel for bookwork and surveyed a lot of

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Speaker 1: the women on their transactions. And then it just turned

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Speaker 1: out that this economist I knew, scraped all the data

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Speaker 1: from the Erotic Review, which is yelped for a legal

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Speaker 1: sex work. So I had one point two million legal

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Speaker 1: sex transactions. So i'd really good robust data set. So

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Speaker 1: I was kind of surprised in reading that chapter, that

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Speaker 1: section of the book the Bunny Ranch. To go to

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Speaker 1: the Bunny Ranch, it's a couple of thousand dollars per

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Speaker 1: um experience. I don't know what's called. The median price

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Speaker 1: I found was four hundred dollars an hour. That's a

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Speaker 1: lot of money. Yeah, uh, you know, especially if you

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Speaker 1: just hire a woman online, even for escorting, which is

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Speaker 1: more high end. I think equivalent to what you get

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Speaker 1: at the Bunny Ranch is three or four hundred dollar

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Speaker 1: an that's all. Yeah, and you don't have to go

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Speaker 1: to Nevada. Yeah, but then the risk is four dollars.

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Speaker 1: But you might get arrested, you might get robbed, you

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Speaker 1: might who knows. You might get killed, so it's worth

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Speaker 1: a premium, one would argue, And you effectively do that

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Speaker 1: in an economist walks into a brothel. Uh that what

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Speaker 1: you're paying is you're paying an insurance premium to eliminate

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Speaker 1: all of the risk associated with legal sex for higher transactions.

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Speaker 1: And on the other side too, because you think these

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Speaker 1: women they're getting so much money, but they're really not. Now,

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Speaker 1: what's the payout to the woman relative to at Ridge.

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Speaker 1: Let's call it fifteen hundred dollar average. They're getting what

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Speaker 1: you wrote about half goes to them, Well, not even

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Speaker 1: half goes to the brothel. So they have to give

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Speaker 1: fifty of their cut to the brothel. And then, uh,

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Speaker 1: they're legal sex workers, right, so they ten ninety nine employees,

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Speaker 1: so the self employment tax together cover that. And I

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Speaker 1: mean these are high earners. They're making, you know, probably

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Speaker 1: at least a hundred grand a year, so I mean

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Speaker 1: a lot of them don't live in Nevadas. They might

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Speaker 1: have stayed income taxes on top of it all. So

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Speaker 1: we're talking. So do they do an I RA A

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Speaker 1: or a KYO. They have to have some sort of

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Speaker 1: retirement friends. So Dennis had really had really good financial

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Speaker 1: planners coming in and give them very good financial literacy.

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Speaker 1: I was talking to these women who came from households

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Speaker 1: where no one had a bank account. They're like, I

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Speaker 1: didn't even know the credit score was. And then they're

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Speaker 1: telling me, oh, yeah, my ra is on index funds.

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Speaker 1: You know, why pay the fees for active That's just

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Speaker 1: that's just hilarious. I have a I have a million,

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Speaker 1: a million horrific puns, and I'm not going to touch

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Speaker 1: a single one of them. Wait, so are you saying

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Speaker 1: they were passive investors not active? Is that? Is that

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Speaker 1: what you're saying? They were indexers? Right, They're they're all indexers.

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Speaker 1: That's hilarious. That's that's really hilarious. So what was the

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Speaker 1: single most surprising thing that you came away from the

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Speaker 1: Bunny Ranch with having interviewed all these professional legal sex workers.

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Speaker 1: The most surprising thing? I mean, I know it's sounds

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Speaker 1: almost patronizing, and I knew they'd be smart, but I

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Speaker 1: was shocked at how much I learned from them about business,

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Speaker 1: about negotiation, and about risk. So more than just smart savvy,

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Speaker 1: very savvy. Let's talk a little bit about risk, because

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Speaker 1: I think different people think of risk differently, How can

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Speaker 1: you define what risk is for the average person. Well,

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Speaker 1: I think of risk. I think of it as an

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Speaker 1: estimate of all the different things that could happen and

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Speaker 1: how probable they are. As I say, it's a very

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Speaker 1: technical definition. Let me ask you the same question a

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Speaker 1: little bit differently, then, Since you run a firm who's

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Speaker 1: got the words life cycle in its name, how does

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Speaker 1: risk change over the course of a person's lifetime. Well, um,

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Speaker 1: you have career risk, you have academic risk, you have

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Speaker 1: retirement risk. I mean there has to be a million

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Speaker 1: different points in one's life where the risks that are

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Speaker 1: presented to you are very different with different ramifications. Totally,

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Speaker 1: and how we are able to deal with risk and

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Speaker 1: understand risk and how risk averse we are can also

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Speaker 1: change over a life cycle, which makes it even more complicated.

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Speaker 1: You know, there's all this evidence about behavioral biases, but

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Speaker 1: there's also evidence as people get older those biases tend

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Speaker 1: to be less prevalent. Really, so, is it that we

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Speaker 1: get a little wiser with age or it just matters less?

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Speaker 1: Have we get a little wiser with age? There's experience

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Speaker 1: that just as well. Experience really changes how you perceive risk. Like,

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Speaker 1: once you've seen things blow up for you a couple

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Speaker 1: of times, suddenly you become a little more risk averse

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Speaker 1: or a little more aware of the probabilities you face,

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Speaker 1: or take time to hedge or ensure when you take risks.

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Speaker 1: Let's talk about that, because you have a few chapters

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Speaker 1: in the book on the differences between insurance and hedging.

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Speaker 1: So let's talk a little about hedging, whether we're referring

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Speaker 1: to a market perspective or any other perspective. What is

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Speaker 1: hedging and what should the average person use it for?

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Speaker 1: I think of hedging and is it This is a

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Speaker 1: distinction that a lot of people don't. I think asn't

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Speaker 1: often made very clearly. In fact, I had lunch with

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Speaker 1: the CEO of an insurance firm who even he kept

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Speaker 1: we kept messing up insurance and hedging. So it's it's

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Speaker 1: a very subtle but important difference, and you make it clear.

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Speaker 1: They're two very distinct things. They are, and if you

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Speaker 1: draw a picture of what they mean in on a graph,

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Speaker 1: it's very clear. But intuitively it's a very hard difference.

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Speaker 1: So I think of hedging as you just take less risk.

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Speaker 1: So in basic finance world, that would be of a

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Speaker 1: risky asset and you have a risk free asset. So

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Speaker 1: hedging is just putting more of your portfolio and the

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Speaker 1: risk free asset, the typical sixty stock and bond portfolio.

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Speaker 1: You're not so much hedging your stocks as you're removing

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Speaker 1: some risk and putting it into much lower risk fixed

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Speaker 1: income exactly, So you're you're hedging your portfolio balance. So

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Speaker 1: I've always thought of hedging as I'm willing to give

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Speaker 1: up some upside and an exchange reduce my downside. That's

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Speaker 1: exactly it. Okay, So if you is it, If you

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Speaker 1: in some sixty you do fifty fifty, that's a less

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Speaker 1: upside of stocks do well, but also less downside risk

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Speaker 1: of stocks crash. Or conversely, I'll own X y Z

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Speaker 1: stock and I'll marry a put to it, and that

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Speaker 1: protects it. It costs me something, cost me a couple

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Speaker 1: of percent, but hey, if the stock falls out of bed,

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Speaker 1: the put should cover some percentage of most of that downside. Yeah,

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Speaker 1: they usually think of puts assure a little bit more insurance,

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Speaker 1: as I was saying it. So, so now let's talk

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Speaker 1: about insurance. How do you think of insurance? What? What

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Speaker 1: does purpose does insurance serve. So insurance is a little different.

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Speaker 1: So rather than giving up you know, upside, what you're

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Speaker 1: doing is you're paying someone a fee, So you give

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Speaker 1: up that amount of upsite, but it's a set amount

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Speaker 1: and you eliminate downside risk, So a predetermined costs, and

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Speaker 1: what you're purchasing is putting that risk or eliminating that

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Speaker 1: risk from yourself, putting it onto someone else in a

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Speaker 1: specific state of the world. Yeah, so it's insurance, which

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Speaker 1: is if if X happens, if this, if this stock

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Speaker 1: goes above the strike price, then I get something. So

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Speaker 1: I've never owned a car where I did not have

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Speaker 1: at one point in the ownership of that car, uh,

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Speaker 1: some piece of gravel of rock mo up on our

359
00:20:00,600 --> 00:20:03,840
Speaker 1: local crappy highways here in New York and either ding

360
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Speaker 1: or crack the windshield. So once I could start affording it,

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Speaker 1: I always get glass insurance on the car, and I

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Speaker 1: have replaced literally every single windshield I've ever owned. Um

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Speaker 1: is that a good use of of insurance or am

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Speaker 1: I just padding their profits and wasting money? It sounds

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Speaker 1: like it. I mean, if you're if you're if you're

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Speaker 1: claiming it. Well, but you know, a car you have three,

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Speaker 1: four or five years at least UM, and you're paying

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Speaker 1: whatever it is, a hundred bucks that I guess what

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00:20:34,440 --> 00:20:36,520
Speaker 1: I'm really paying for is, Hey, I don't have to

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Speaker 1: worry about whether or not I break a window of

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Speaker 1: somebody else. Somebody else is responsible for that. Is that

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Speaker 1: a fair definition of insurance? Even though you're paying a

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Speaker 1: fixed amount, that stress and worry goes away exactly because

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Speaker 1: now the stress of breaking the window is on the

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Speaker 1: insurance company. I mean, you have to still go through

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Speaker 1: the rigamarole replacing it, but the financial risk is borne

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Speaker 1: by someone else. So let's talk a little bit about

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Speaker 1: the book and some of the other things, um you describe.

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Speaker 1: Outside of the Bunny Ranch, you spoke to a number

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Speaker 1: of poker players who had some kind of surprising statistic.

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Speaker 1: The one player whose name escapes me, but was notorious

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Speaker 1: for having yes for having these sort of hissy fits

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Speaker 1: when he either wasn't happy with his own play or

384
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Speaker 1: what have you. He said, he should really only be

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Speaker 1: playing a very small percentage of hands. And I would

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Speaker 1: have guessed what percentage does he play? It's twelve percent,

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00:21:34,880 --> 00:21:37,680
Speaker 1: twelve percent. That's in a one out of eight hands,

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00:21:37,680 --> 00:21:40,960
Speaker 1: so seven out of eight hands he's not participating in. Yeah,

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Speaker 1: and there's a lot of poker terminology, which is new

390
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Speaker 1: for me because I'm not a player, and they call

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Speaker 1: that patient. I don't know if that's specific to poker,

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Speaker 1: but you would think if you're passing on seven out

393
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Speaker 1: of eight hands, there's a lot of patients with that,

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Speaker 1: especially for someone who struck me as so volatile and

395
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Speaker 1: out of control. Now is how much of that is

396
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Speaker 1: real and how much of that is just to get

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Speaker 1: into the heads of the other players. I think it's

398
00:22:02,680 --> 00:22:05,359
Speaker 1: a combination of both. I think that's his natural temperament

399
00:22:05,520 --> 00:22:08,959
Speaker 1: and he's noticed it's also an asset. So in the

400
00:22:09,000 --> 00:22:11,119
Speaker 1: beginning it wasn't an asset. It was a bit of

401
00:22:11,200 --> 00:22:14,120
Speaker 1: a problem for him. How did he manage to deal

402
00:22:14,160 --> 00:22:18,000
Speaker 1: with that risk and turn it into a positive Well,

403
00:22:18,000 --> 00:22:21,760
Speaker 1: he tells stories of just being so emotionally overwhelmed by

404
00:22:21,800 --> 00:22:24,399
Speaker 1: trying to keep him in check your pass out. But

405
00:22:24,440 --> 00:22:27,159
Speaker 1: when you really delve into it, you realize, you know,

406
00:22:27,240 --> 00:22:30,679
Speaker 1: this is the classic behavioral bias we called loss a version,

407
00:22:31,080 --> 00:22:34,280
Speaker 1: which is when you're losing, you know you hate you

408
00:22:34,320 --> 00:22:37,840
Speaker 1: hate losses so much more than you value gains that

409
00:22:37,920 --> 00:22:40,760
Speaker 1: you'll take double down and take extra risk to avoid

410
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Speaker 1: a loss. And so this is often shows up a

411
00:22:44,600 --> 00:22:47,040
Speaker 1: lot in poker and why people play too many hands.

412
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Speaker 1: So he has to do is anyway, even though he's down,

413
00:22:49,359 --> 00:22:51,119
Speaker 1: is still not play the hands. So he has to

414
00:22:51,160 --> 00:22:55,760
Speaker 1: stay rational and like overcome this bias. And my found

415
00:22:55,880 --> 00:22:58,959
Speaker 1: is behind the scenes he's doing all these things so

416
00:22:59,000 --> 00:23:01,320
Speaker 1: he feels like there's less set steak so he can

417
00:23:01,359 --> 00:23:04,040
Speaker 1: stay more rational. He does things like he gets he

418
00:23:04,160 --> 00:23:07,520
Speaker 1: called steaked, which is someone else is an investor in

419
00:23:07,640 --> 00:23:10,680
Speaker 1: him and takes a percentage of the winnings exactly. So anyway,

420
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Speaker 1: he claims to be incredibly wealthy, um, he only puts

421
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Speaker 1: in ten tho dollars of his own money in any

422
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Speaker 1: poker tournament. The other thing he does so it's effectively hedging.

423
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Speaker 1: And the other thing he does is he sort of

424
00:23:23,119 --> 00:23:25,919
Speaker 1: gets this weird insurance, which is where he's in the

425
00:23:26,080 --> 00:23:29,080
Speaker 1: in a major game and he takes the other player

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Speaker 1: aside and they cut these little back deals, which is

427
00:23:31,920 --> 00:23:35,240
Speaker 1: apparently allowed. And so in other words, some of these

428
00:23:35,440 --> 00:23:38,119
Speaker 1: tournaments are winner takes all and if you come in

429
00:23:38,200 --> 00:23:41,120
Speaker 1: second you get nothing. So in one one of these

430
00:23:41,119 --> 00:23:43,440
Speaker 1: tournaments he pulled someone aside and said, here's the deal.

431
00:23:44,720 --> 00:23:48,679
Speaker 1: Let's agree to split um a million dollars each of

432
00:23:48,680 --> 00:23:52,240
Speaker 1: the purse, and whoever wins gets the balance. So so

433
00:23:52,440 --> 00:23:55,159
Speaker 1: if you lose, you take half a million, and I

434
00:23:55,200 --> 00:23:57,040
Speaker 1: think it was like two point two millions something like

435
00:23:57,080 --> 00:23:59,040
Speaker 1: that for the winner. So there's a little bit of

436
00:23:59,040 --> 00:24:01,239
Speaker 1: a hedge there as well. Yeah, so he does all

437
00:24:01,240 --> 00:24:03,480
Speaker 1: these things to take a little risk off the table

438
00:24:03,920 --> 00:24:06,879
Speaker 1: and that keeps him rational. What about the concept of

439
00:24:06,920 --> 00:24:11,280
Speaker 1: a player who, after they lose a few hands, instead

440
00:24:11,280 --> 00:24:14,560
Speaker 1: of being patient, has a tendency to try and get

441
00:24:14,600 --> 00:24:17,840
Speaker 1: back to break even. There's something about that concept of

442
00:24:17,920 --> 00:24:20,320
Speaker 1: I gotta get back to break break even. Stock traders

443
00:24:20,359 --> 00:24:22,480
Speaker 1: do the same thing. Hey, I know this stock is

444
00:24:22,480 --> 00:24:24,560
Speaker 1: a piece of junk, but dear Lord, if I get

445
00:24:24,560 --> 00:24:27,000
Speaker 1: back to break even, I swear I'll sell that sort

446
00:24:27,000 --> 00:24:31,160
Speaker 1: of attitude. Poker players do the same thing. They do um,

447
00:24:31,200 --> 00:24:33,480
Speaker 1: but the successful ones are the ones. This is what

448
00:24:33,520 --> 00:24:35,440
Speaker 1: I was saying about. There's evidence as you get older

449
00:24:35,440 --> 00:24:38,440
Speaker 1: and wiser, you are less prey to these behavioral issues.

450
00:24:39,040 --> 00:24:42,399
Speaker 1: And I think it's John List is this economist who

451
00:24:42,480 --> 00:24:46,040
Speaker 1: studies loss of version how impacts behavior? Uh, because the

452
00:24:46,119 --> 00:24:48,720
Speaker 1: break even effect is just sort of a corollary of

453
00:24:48,840 --> 00:24:51,399
Speaker 1: loss a version, which is, when you're down, you're so

454
00:24:51,480 --> 00:24:53,840
Speaker 1: determined to win back, so you didn't experience a loss,

455
00:24:53,840 --> 00:24:56,119
Speaker 1: you double down and take extra risk risky would have

456
00:24:56,200 --> 00:24:58,960
Speaker 1: never taken if you were up. It seems almost opposite

457
00:24:58,960 --> 00:25:02,639
Speaker 1: of risk aversion. You're embracing so much risk I guess

458
00:25:02,640 --> 00:25:05,440
Speaker 1: to try and recover from that loss only when you're down.

459
00:25:05,520 --> 00:25:08,000
Speaker 1: And this is the interesting, you know, sort of um

460
00:25:08,240 --> 00:25:10,880
Speaker 1: deviation from what economists normally think is when you're down,

461
00:25:10,960 --> 00:25:14,000
Speaker 1: you'll take that extra risk to get back. So tell

462
00:25:14,080 --> 00:25:16,240
Speaker 1: us a little bit about how we should be thinking

463
00:25:16,280 --> 00:25:20,560
Speaker 1: about risk in the stock market. Well, actually, I mean

464
00:25:21,480 --> 00:25:25,320
Speaker 1: my background to approach risk is from financial economics, which

465
00:25:25,359 --> 00:25:27,399
Speaker 1: is the study of the stock market. I think in

466
00:25:27,400 --> 00:25:29,600
Speaker 1: a lot of ways, the stock markets the perfect place

467
00:25:29,600 --> 00:25:31,560
Speaker 1: to think about risk because you just have so much data,

468
00:25:32,080 --> 00:25:35,600
Speaker 1: and what financial markets are doing is just finding ways

469
00:25:35,640 --> 00:25:39,280
Speaker 1: to price and move risk around. So I think anyone

470
00:25:39,320 --> 00:25:41,600
Speaker 1: who is in the stock market is someone who's naturally

471
00:25:41,600 --> 00:25:45,120
Speaker 1: thinking about risk all the time. Huh. And and we're

472
00:25:45,160 --> 00:25:49,200
Speaker 1: making more data every day, don't we. Yeah, I think again,

473
00:25:49,280 --> 00:25:52,040
Speaker 1: you you do get people who get away from you know,

474
00:25:52,080 --> 00:25:54,720
Speaker 1: as I said, if they're down there, like, there's all

475
00:25:54,720 --> 00:25:57,640
Speaker 1: this evidence that people won't sell losers, but they'll sell winners.

476
00:25:58,200 --> 00:26:00,520
Speaker 1: And you know how, you know that usually not a

477
00:26:00,520 --> 00:26:02,600
Speaker 1: good idea. The stocks going down, it might probably keep

478
00:26:02,600 --> 00:26:04,439
Speaker 1: going down, is supposed to you know, why would you

479
00:26:04,440 --> 00:26:07,119
Speaker 1: sell your winners and keep a loser. But that's supposed

480
00:26:07,160 --> 00:26:09,440
Speaker 1: to be some version of loss of version people think.

481
00:26:09,560 --> 00:26:11,760
Speaker 1: So there was a study that had come out towards

482
00:26:11,800 --> 00:26:16,200
Speaker 1: the end of eighteen that had looked at portfolio managers

483
00:26:16,600 --> 00:26:18,840
Speaker 1: and rather than compare them to a benchmark, what they

484
00:26:18,880 --> 00:26:23,399
Speaker 1: did instead was, let's instead of selling what the portfolio

485
00:26:23,440 --> 00:26:26,680
Speaker 1: manager sold, we're gonna randomly sell anything else from their

486
00:26:26,680 --> 00:26:29,520
Speaker 1: portfolio and then compare and see how they did. And

487
00:26:29,560 --> 00:26:34,239
Speaker 1: it turned out that random sales outperformed manager sales by

488
00:26:34,280 --> 00:26:36,920
Speaker 1: a hundred basis points over the next year. And when

489
00:26:36,920 --> 00:26:40,359
Speaker 1: they looked at what was being sold, they found two

490
00:26:40,440 --> 00:26:44,680
Speaker 1: broad categories that accounted for most of the underperformance. One

491
00:26:44,720 --> 00:26:47,200
Speaker 1: is stocks that had gone up a lot and therefore

492
00:26:47,240 --> 00:26:50,960
Speaker 1: we're benefiting from momentum. Managers had a tendency to sell those,

493
00:26:51,320 --> 00:26:54,719
Speaker 1: but also stocks that had collapsed a lot, rather than

494
00:26:54,760 --> 00:26:57,119
Speaker 1: selling them when they were small losers. They waited till

495
00:26:57,119 --> 00:27:00,960
Speaker 1: they were giant losers and effectively had become deep value

496
00:27:00,960 --> 00:27:04,920
Speaker 1: plays and they were selling them. And those two categories

497
00:27:04,960 --> 00:27:09,200
Speaker 1: were Um, we're determined to be the behavioral errors that

498
00:27:09,320 --> 00:27:14,400
Speaker 1: we're we're driving portfolio losses. So hold that aside. Let's

499
00:27:14,560 --> 00:27:17,439
Speaker 1: let's think of of the stock market in terms of

500
00:27:17,560 --> 00:27:25,520
Speaker 1: individual investors embracing of risk. Are most people overly invested

501
00:27:25,640 --> 00:27:28,320
Speaker 1: in the stock market and the embracing too much risk?

502
00:27:28,840 --> 00:27:33,080
Speaker 1: Or do you perceive the actual Do you perceive risk

503
00:27:33,160 --> 00:27:36,640
Speaker 1: amongst individual investors as just not taking enough of it,

504
00:27:37,040 --> 00:27:39,639
Speaker 1: especially in the early stages of a market, and not

505
00:27:39,760 --> 00:27:42,479
Speaker 1: embracing it until the very latter stages in the market.

506
00:27:43,119 --> 00:27:44,920
Speaker 1: I think it's hard to say. I mean, the rate

507
00:27:45,040 --> 00:27:48,360
Speaker 1: stock allocation really depends on an individual and where they

508
00:27:48,359 --> 00:27:51,800
Speaker 1: are in their life cycle. Um, I think people don't

509
00:27:51,800 --> 00:27:54,560
Speaker 1: appreciate a lot of people don't appreciate how risky the

510
00:27:54,600 --> 00:27:57,960
Speaker 1: stock market is. Like my mother is nearing retirement and

511
00:27:58,000 --> 00:28:00,600
Speaker 1: she expects her portfolio to like double every year, and

512
00:28:00,640 --> 00:28:03,440
Speaker 1: I'm like, sure, but you're gonna have to take on

513
00:28:03,520 --> 00:28:05,359
Speaker 1: a lot of risk for that to happen. And she

514
00:28:05,400 --> 00:28:08,000
Speaker 1: doesn't seem to internalize that. If you want more return

515
00:28:08,320 --> 00:28:10,600
Speaker 1: that comes with something, and you know, in stocks are

516
00:28:10,640 --> 00:28:13,240
Speaker 1: a great investment. They're a great way, especially an index fund,

517
00:28:13,600 --> 00:28:18,679
Speaker 1: to get UH risk exclosure cheaply and efficiently. But you

518
00:28:18,720 --> 00:28:22,360
Speaker 1: know there's you know, there's no guarantees. So we noticed

519
00:28:22,400 --> 00:28:26,560
Speaker 1: that when housing markets are booming, people have the same

520
00:28:26,760 --> 00:28:30,680
Speaker 1: overly optimistic expectations about how fast they are home prices

521
00:28:30,680 --> 00:28:33,680
Speaker 1: are appreciating. I'm assuming your mom is not a big

522
00:28:33,760 --> 00:28:37,400
Speaker 1: big bit clemon investor. Um, why does she think her

523
00:28:37,440 --> 00:28:41,960
Speaker 1: stock her portfolio should double every year given long term

524
00:28:41,960 --> 00:28:45,400
Speaker 1: returns between eight and well disundegrade. It's also you know,

525
00:28:46,080 --> 00:28:49,440
Speaker 1: errors and how we perceive risk. I mean, I think

526
00:28:49,480 --> 00:28:53,480
Speaker 1: people often in assume there's serial correlation where there is none.

527
00:28:54,520 --> 00:28:57,440
Speaker 1: So you know, if if housing prices have gone up

528
00:28:57,480 --> 00:29:00,120
Speaker 1: the last twenty years, people assume they'll stay doing that.

529
00:29:00,240 --> 00:29:03,080
Speaker 1: I think people also make that assumption around interest rates

530
00:29:03,160 --> 00:29:05,680
Speaker 1: that you know, they've been nothing but go down for

531
00:29:05,720 --> 00:29:09,280
Speaker 1: the last thirty years. So I though, it's questionable if

532
00:29:09,280 --> 00:29:11,560
Speaker 1: they can keep doing that, because how negative canields get.

533
00:29:11,800 --> 00:29:15,360
Speaker 1: So this was kind of an interesting um aspect of

534
00:29:15,360 --> 00:29:18,600
Speaker 1: the book that resonated with me personally because we're always

535
00:29:18,600 --> 00:29:23,800
Speaker 1: trying to teach clients to think about portfolios in terms

536
00:29:23,960 --> 00:29:27,560
Speaker 1: of a way that's easily understandable. And if you say

537
00:29:27,600 --> 00:29:32,280
Speaker 1: to somebody, hey, there's the seventy or here here, there's

538
00:29:32,280 --> 00:29:34,600
Speaker 1: a lot of software that can project you out to retirement.

539
00:29:35,000 --> 00:29:41,320
Speaker 1: There's a probability that you'll hit your retirement goals, assuming

540
00:29:41,480 --> 00:29:45,040
Speaker 1: inflation stays under four percent and you continue making your

541
00:29:45,040 --> 00:29:50,520
Speaker 1: regular contributions. I don't know what a confidence interval does

542
00:29:50,640 --> 00:29:53,560
Speaker 1: for most people, but if if you were to say

543
00:29:53,600 --> 00:29:56,920
Speaker 1: to them, hey, in nineteen out of twenty situations, we

544
00:29:57,040 --> 00:29:59,920
Speaker 1: can show you you'll hit your target goals. It's only

545
00:30:00,000 --> 00:30:02,080
Speaker 1: one out of twenty that you don't make it. Why

546
00:30:02,200 --> 00:30:05,280
Speaker 1: is that so much easier? Don't understand than percentile? Yeah.

547
00:30:05,400 --> 00:30:07,920
Speaker 1: I'm not a psychologist, but the research psychologist I talked

548
00:30:07,920 --> 00:30:09,680
Speaker 1: to and said just something about the way our brains

549
00:30:09,680 --> 00:30:12,200
Speaker 1: are programmed. His frequencies just resonate with us more. We

550
00:30:12,240 --> 00:30:18,160
Speaker 1: are so twenty is a better phrase, Yeah, and it

551
00:30:18,200 --> 00:30:20,800
Speaker 1: makes a big difference because we are programmed as humans were,

552
00:30:20,840 --> 00:30:23,600
Speaker 1: not complete like it's supposed to be. Disasters with risk.

553
00:30:23,720 --> 00:30:25,800
Speaker 1: Risk is something that's humans have been facing, you know,

554
00:30:25,840 --> 00:30:28,520
Speaker 1: as long as we've been on the earth. But you know,

555
00:30:28,600 --> 00:30:32,040
Speaker 1: probabilities are a fairly modern invention. They only really came

556
00:30:32,040 --> 00:30:33,720
Speaker 1: along on the Renaissance with a lot of sort of

557
00:30:33,760 --> 00:30:37,760
Speaker 1: brainy people Bernoulli and a whole run of difference calling

558
00:30:37,760 --> 00:30:40,400
Speaker 1: all these people. So, I mean, it's not surprising that

559
00:30:40,440 --> 00:30:43,640
Speaker 1: we aren't just born having this natural intuitive sense of probabilities.

560
00:30:43,680 --> 00:30:45,640
Speaker 1: I mean, we both work in this area all the time,

561
00:30:46,000 --> 00:30:47,600
Speaker 1: and they often don't mean that much to me either.

562
00:30:48,200 --> 00:30:51,600
Speaker 1: So so for the average person, how best should we

563
00:30:51,800 --> 00:30:57,080
Speaker 1: quantify risk? Well, you want to, um, I say converted

564
00:30:57,160 --> 00:30:59,480
Speaker 1: if you're thinking, except when I defined risk to you

565
00:30:59,560 --> 00:31:00,800
Speaker 1: and I was like, it's all the things that you

566
00:31:00,800 --> 00:31:02,760
Speaker 1: can measure happening and now probable they are. That is

567
00:31:02,760 --> 00:31:07,000
Speaker 1: a probability distributions the whole distribution of things that aren't

568
00:31:07,000 --> 00:31:10,520
Speaker 1: intuitive to us. Um So, I mean, part of it

569
00:31:10,520 --> 00:31:13,120
Speaker 1: really is sort of getting more comfortable with those concepts,

570
00:31:13,120 --> 00:31:15,400
Speaker 1: but it's also when you think of probabilities converting them

571
00:31:15,440 --> 00:31:20,320
Speaker 1: to frequencies. The book was really interesting. It reads really well.

572
00:31:20,480 --> 00:31:23,600
Speaker 1: It's sort of like like as I started reading it,

573
00:31:23,680 --> 00:31:29,320
Speaker 1: I immediately um thought of against the Gods because it's

574
00:31:29,360 --> 00:31:34,200
Speaker 1: also so risk focused, but from a historical perspective. This

575
00:31:34,320 --> 00:31:38,800
Speaker 1: is really a twenty one century perspective on risk. I

576
00:31:38,880 --> 00:31:41,280
Speaker 1: love Against the Gods. It was my favorite book, and

577
00:31:42,120 --> 00:31:45,480
Speaker 1: I was actually partially inspired by that because as much

578
00:31:45,520 --> 00:31:47,680
Speaker 1: as I love it, I wouldn't say everyone wants to

579
00:31:47,680 --> 00:31:49,720
Speaker 1: read it. I mean it's oh no, everybody should read

580
00:31:49,760 --> 00:31:51,840
Speaker 1: that book. It's amazing. Everyone should, but not a lot

581
00:31:51,880 --> 00:31:56,440
Speaker 1: of people will. I mean it is dense, um. He

582
00:31:56,920 --> 00:32:01,880
Speaker 1: is a very detailed or in. Every page is filled

583
00:32:01,960 --> 00:32:05,080
Speaker 1: with lots and lots of stuff, which is why ps

584
00:32:05,160 --> 00:32:07,400
Speaker 1: if you go back and reread it twenty years later,

585
00:32:07,960 --> 00:32:11,520
Speaker 1: it's still fresh, and I mean, that is a master work.

586
00:32:11,600 --> 00:32:14,200
Speaker 1: It's beautiful. I mean, I love everything about that book,

587
00:32:14,200 --> 00:32:17,520
Speaker 1: and I also love capital ideas. I like his everything

588
00:32:17,560 --> 00:32:21,600
Speaker 1: he's done. But you know your mom's probably talk about

589
00:32:22,320 --> 00:32:23,880
Speaker 1: you know, your mom is probably not going to read it.

590
00:32:24,040 --> 00:32:26,840
Speaker 1: And I felt like everyone needs to know what's in

591
00:32:26,880 --> 00:32:29,440
Speaker 1: Against the Gods. And that's partially what inspired this book

592
00:32:29,480 --> 00:32:33,600
Speaker 1: is I wanted to take those ideas that we're so resonant,

593
00:32:33,640 --> 00:32:36,479
Speaker 1: and I felt everyone needed to understand and make them

594
00:32:36,520 --> 00:32:40,880
Speaker 1: accessible to even broader audience. So obviously the whole Bunny

595
00:32:41,000 --> 00:32:44,600
Speaker 1: Ranch brothel section is hilarious. And must have been a

596
00:32:44,640 --> 00:32:48,640
Speaker 1: ton of fun to do. Um, what else did you

597
00:32:48,760 --> 00:32:52,200
Speaker 1: do in your research that was kind of fun and surprising?

598
00:32:52,520 --> 00:32:54,440
Speaker 1: It was all fun and surprised I had. I had

599
00:32:54,440 --> 00:32:55,840
Speaker 1: a I mean I was afraid to write a book

600
00:32:55,840 --> 00:32:57,640
Speaker 1: for a long time because my dissertation was such a

601
00:32:57,720 --> 00:33:00,600
Speaker 1: horrible slog. See. I think of my book as as

602
00:33:00,640 --> 00:33:03,840
Speaker 1: my PhD dissertation, and it was a horrible slob. I

603
00:33:03,840 --> 00:33:05,520
Speaker 1: think maybe you have to do that first big research

604
00:33:05,560 --> 00:33:08,560
Speaker 1: project just has to be horrible. Uh So I was like,

605
00:33:08,560 --> 00:33:10,120
Speaker 1: if I'm gonna do this, I'm gonna have a lot

606
00:33:10,160 --> 00:33:12,040
Speaker 1: of fun, which was the other way I wanted to

607
00:33:12,040 --> 00:33:15,160
Speaker 1: approach the book. So I just was like, I'm gonna

608
00:33:15,160 --> 00:33:17,520
Speaker 1: have all these adventures. It seems ceased to go places.

609
00:33:17,960 --> 00:33:20,680
Speaker 1: I went to a risk conference for big wave surfers

610
00:33:20,680 --> 00:33:23,760
Speaker 1: in a y right. That was the whole fascinating segment.

611
00:33:24,080 --> 00:33:27,920
Speaker 1: The guys who invented jet skiing their way onto eighty

612
00:33:27,920 --> 00:33:30,840
Speaker 1: foot waves that before you couldn't even get onto a

613
00:33:30,840 --> 00:33:33,960
Speaker 1: fifty footwave. You just weren't able to get out there

614
00:33:34,000 --> 00:33:36,760
Speaker 1: fast enough. Um, what was that like? You spoke to

615
00:33:36,840 --> 00:33:40,680
Speaker 1: some really interesting big surf names. Yeah, and you know

616
00:33:40,760 --> 00:33:43,920
Speaker 1: they have these this regular conference where they talk about risk,

617
00:33:43,960 --> 00:33:46,040
Speaker 1: and it's like going to a pension risk conference, although

618
00:33:46,080 --> 00:33:50,800
Speaker 1: everyone's better looking everyone Yeah, like I was in the

619
00:33:50,840 --> 00:33:54,200
Speaker 1: worst shape in the room. Um, but but you know

620
00:33:54,280 --> 00:33:56,080
Speaker 1: it is a it is a conference where they're very

621
00:33:56,080 --> 00:34:00,960
Speaker 1: thoughtful about risk in debating uh, you know, regulatory function

622
00:34:01,000 --> 00:34:03,880
Speaker 1: and who bears the responsibility of risk when your behavior

623
00:34:04,280 --> 00:34:07,680
Speaker 1: impacts other people? And you know, it was as intellectual

624
00:34:07,680 --> 00:34:11,400
Speaker 1: discussion as I've seen anywhere. And the guy who I profile,

625
00:34:11,920 --> 00:34:14,840
Speaker 1: Brian cu Lana, is the one who brought jet skis

626
00:34:14,840 --> 00:34:16,840
Speaker 1: to big wave surfing. And much like a lot of

627
00:34:16,840 --> 00:34:20,000
Speaker 1: financial jurve that is initially supposed to be insurance, but

628
00:34:20,040 --> 00:34:22,719
Speaker 1: as you mentioned, you can also use them to lever

629
00:34:22,920 --> 00:34:26,440
Speaker 1: up and take on more risk and take even bigger waves.

630
00:34:26,440 --> 00:34:28,960
Speaker 1: Because anything that can reduce risk can also be flipped

631
00:34:28,960 --> 00:34:32,160
Speaker 1: around to exacerbate risks. We see a plateau ng and

632
00:34:32,239 --> 00:34:37,239
Speaker 1: even an increase in the annual automobile fatality rates, and

633
00:34:37,360 --> 00:34:41,200
Speaker 1: the discussion in your book and and elsewhere has been, well,

634
00:34:41,239 --> 00:34:45,560
Speaker 1: how much of the confidence that people feel about air

635
00:34:45,600 --> 00:34:49,200
Speaker 1: bags and crumple zones and and anti lick breaks is

636
00:34:49,280 --> 00:34:52,160
Speaker 1: leaving them to drive faster and engage in more dangerous

637
00:34:52,200 --> 00:34:56,040
Speaker 1: behavior without these safety provisions. What what are your thoughts

638
00:34:56,040 --> 00:34:58,319
Speaker 1: on that? Yeah, I mean that is I felt like

639
00:34:58,360 --> 00:35:00,759
Speaker 1: an important thing to include an any book on risk

640
00:35:00,880 --> 00:35:03,279
Speaker 1: telling you how to how there's all these tools that

641
00:35:03,280 --> 00:35:04,960
Speaker 1: can reduce your risk, because then you have to be

642
00:35:05,040 --> 00:35:08,439
Speaker 1: mindful of not feeling so safe that you can then

643
00:35:08,480 --> 00:35:11,600
Speaker 1: go and take whatever risks you want, because nothing makes

644
00:35:11,640 --> 00:35:15,000
Speaker 1: the world truly risk free. You know, we're all risk

645
00:35:15,120 --> 00:35:18,680
Speaker 1: is all still an estimate of something that's immeasurable. So

646
00:35:19,080 --> 00:35:21,759
Speaker 1: you know, and you're you're basing your risk strategy on

647
00:35:21,880 --> 00:35:24,800
Speaker 1: something you know it's better than doing nothing. Just because

648
00:35:24,880 --> 00:35:26,719
Speaker 1: it's not perfect doesn't mean you shouldn't do it. But

649
00:35:26,760 --> 00:35:30,239
Speaker 1: you also have to be aware of the limitations. So

650
00:35:30,360 --> 00:35:33,520
Speaker 1: even if you have every safety device in the world

651
00:35:34,000 --> 00:35:36,080
Speaker 1: and you're going to surf an eighty foot wave, it's

652
00:35:36,080 --> 00:35:38,000
Speaker 1: still not going to be safe. There's nothing to make

653
00:35:38,000 --> 00:35:40,920
Speaker 1: that risk free. And you specifically refer to a number

654
00:35:41,040 --> 00:35:45,880
Speaker 1: of big name surface who died even after or perhaps

655
00:35:45,960 --> 00:35:50,680
Speaker 1: because of some of these safety innovations being brought in. Yeah,

656
00:35:50,719 --> 00:35:53,400
Speaker 1: and when I went to the Big Wave Surf Risk Conference,

657
00:35:53,760 --> 00:35:58,160
Speaker 1: you know, they every innovation resurfaces this issue Initially it

658
00:35:58,200 --> 00:36:01,160
Speaker 1: was leashes because you're already hated when they first came

659
00:36:01,200 --> 00:36:03,600
Speaker 1: out totally because I don't know anything about surfing and

660
00:36:03,600 --> 00:36:06,280
Speaker 1: I've never done it. But apparently before there were leashes,

661
00:36:06,360 --> 00:36:08,080
Speaker 1: if you wiped out, you lost your board and you

662
00:36:08,120 --> 00:36:09,760
Speaker 1: had to swim to shore, and that could be maybe

663
00:36:09,760 --> 00:36:13,120
Speaker 1: twenty miles. So you think they were like amazing swimmers.

664
00:36:13,120 --> 00:36:15,319
Speaker 1: Now you can be a pretty mediocre swimmer and still surf. Right,

665
00:36:15,440 --> 00:36:18,480
Speaker 1: we're sending people out who really, if they get into trouble,

666
00:36:18,840 --> 00:36:21,120
Speaker 1: can't swim a half mile back to shore. Yeah. And

667
00:36:21,120 --> 00:36:23,000
Speaker 1: then it was worse with jet skis because now you

668
00:36:23,000 --> 00:36:24,960
Speaker 1: can have a jet ski, you know, push you not

669
00:36:25,000 --> 00:36:26,560
Speaker 1: only an eighty foot wave, but if you only need

670
00:36:26,600 --> 00:36:28,279
Speaker 1: to belong on a five foot wave, Now you can

671
00:36:28,280 --> 00:36:30,759
Speaker 1: go on a twenty foot wave, and you pose risk

672
00:36:30,760 --> 00:36:32,279
Speaker 1: to other people when that happens, if you need to

673
00:36:32,320 --> 00:36:35,160
Speaker 1: be rescued. And now the big debate is on something

674
00:36:35,200 --> 00:36:38,480
Speaker 1: called these inflatable vests right when you go under, and

675
00:36:38,880 --> 00:36:42,080
Speaker 1: they don't always inflate, but it gives people a sense

676
00:36:42,120 --> 00:36:44,399
Speaker 1: that all right, I'm okay, I could do anything now. Yeah,

677
00:36:44,440 --> 00:36:47,520
Speaker 1: And so this because they're new and just starting to

678
00:36:47,520 --> 00:36:50,080
Speaker 1: be sold. I think I mentioned Greg Long this dame

679
00:36:50,080 --> 00:36:52,880
Speaker 1: a surfer who's inflatable fest and open that was I

680
00:36:52,920 --> 00:36:55,480
Speaker 1: think he had a sort of an early version. But

681
00:36:55,560 --> 00:36:59,080
Speaker 1: now they're being widely sold and this is really tearing

682
00:36:59,160 --> 00:37:01,640
Speaker 1: up the surf community of who should be allowed to

683
00:37:01,640 --> 00:37:04,239
Speaker 1: buy these? Is it irresponsible to allow anyone to have

684
00:37:04,280 --> 00:37:06,960
Speaker 1: these vests? So, in other words, we don't want to

685
00:37:06,960 --> 00:37:09,759
Speaker 1: give people a full sense of confidence. Send down an

686
00:37:09,760 --> 00:37:13,959
Speaker 1: amateur with no skills and no minimal swimming ability out

687
00:37:14,000 --> 00:37:16,440
Speaker 1: into a dangerous area, and they feel because they have

688
00:37:16,760 --> 00:37:21,640
Speaker 1: this inflatable vest that they can serve with the big boys,

689
00:37:21,640 --> 00:37:23,279
Speaker 1: so to speak. But then the flip side of that

690
00:37:23,400 --> 00:37:25,880
Speaker 1: is what this is a potentially life saving piece of technology.

691
00:37:25,960 --> 00:37:28,200
Speaker 1: Are you going to deny people buying it? I mean

692
00:37:28,320 --> 00:37:32,160
Speaker 1: it's there aren't easy answers to this, which is why

693
00:37:32,239 --> 00:37:33,920
Speaker 1: they think it's just such debate. But you know, if

694
00:37:33,960 --> 00:37:36,480
Speaker 1: you go to a finance conference and you debate systemic risk,

695
00:37:36,760 --> 00:37:40,080
Speaker 1: there s the answers there. Either what what else do

696
00:37:40,120 --> 00:37:43,640
Speaker 1: you recall from your research that was um, if not

697
00:37:43,719 --> 00:37:48,320
Speaker 1: quite as buff um, really interesting and surprising because you

698
00:37:48,440 --> 00:37:50,319
Speaker 1: cover a lot of ground in the book. I've got

699
00:37:50,400 --> 00:37:53,919
Speaker 1: horse breeding, um that was kind of fascinating. Also, yeah,

700
00:37:54,000 --> 00:37:56,400
Speaker 1: I didn't. Again, I kind of went after things I

701
00:37:56,440 --> 00:37:59,880
Speaker 1: didn't really have much knowledge of before. Um and I

702
00:38:00,120 --> 00:38:01,960
Speaker 1: never been like a horsey person. I wasn't like when

703
00:38:01,960 --> 00:38:06,520
Speaker 1: these little girls who rode horses. Uh, I was so

704
00:38:07,760 --> 00:38:12,480
Speaker 1: I didn't realize that after the tax reform changed the

705
00:38:12,520 --> 00:38:15,480
Speaker 1: whole dynamics of horse breeding and the economics of horse breeding,

706
00:38:15,760 --> 00:38:18,359
Speaker 1: and and all sorts of other tax shelters. These things

707
00:38:18,440 --> 00:38:21,120
Speaker 1: had been devised as a way to hide money from

708
00:38:21,200 --> 00:38:23,759
Speaker 1: Uncle Sam, and suddenly now they have to stand on

709
00:38:23,800 --> 00:38:27,560
Speaker 1: their own feet. They can't just be a foe investment exactly.

710
00:38:27,640 --> 00:38:29,839
Speaker 1: So the fact that there's less return to long term

711
00:38:29,840 --> 00:38:32,319
Speaker 1: capital gains and they want to realize the returns from

712
00:38:32,320 --> 00:38:35,280
Speaker 1: their risk earlier means now that everyone solds a horse

713
00:38:35,360 --> 00:38:37,080
Speaker 1: after when it's one year old, when they don't have

714
00:38:37,120 --> 00:38:41,279
Speaker 1: complete information about how good a racer it's going to be.

715
00:38:41,640 --> 00:38:43,799
Speaker 1: The only information you have is who its parents are.

716
00:38:44,280 --> 00:38:47,000
Speaker 1: So this has led to this increase in inbreeding. So

717
00:38:47,080 --> 00:38:49,960
Speaker 1: we have all these horses that are sprinters, not long

718
00:38:50,000 --> 00:38:52,960
Speaker 1: distance runners, because that's the first thing that will show

719
00:38:53,000 --> 00:38:54,800
Speaker 1: and that will help sell a one year old horse.

720
00:38:55,080 --> 00:38:58,000
Speaker 1: It seems like the incentives are kind of weird and

721
00:38:58,080 --> 00:39:01,759
Speaker 1: not properly aligned. No, because what you really want is

722
00:39:01,800 --> 00:39:03,680
Speaker 1: you want a horse. Well, actually the real money and

723
00:39:03,760 --> 00:39:07,640
Speaker 1: horses is not winning races. It's from the breeding, the

724
00:39:07,680 --> 00:39:10,520
Speaker 1: stud fees. But you know a good race career is

725
00:39:10,560 --> 00:39:13,680
Speaker 1: necessary for that. But none of those things are necessarily

726
00:39:13,680 --> 00:39:16,760
Speaker 1: correlated with There's been studies on this, on the prices

727
00:39:16,800 --> 00:39:19,239
Speaker 1: that you sell for as a year old yearling. So

728
00:39:19,400 --> 00:39:21,880
Speaker 1: as I was reading through the whole horse section, and

729
00:39:21,920 --> 00:39:24,640
Speaker 1: I rode in college, so I was kind of fascinated

730
00:39:24,640 --> 00:39:27,040
Speaker 1: by that. And I'm not a big fan of horse

731
00:39:27,200 --> 00:39:32,239
Speaker 1: racing and betting, but but I enjoy riding. Um, I

732
00:39:32,280 --> 00:39:35,959
Speaker 1: was reminded of a book I read a hundred years

733
00:39:35,960 --> 00:39:42,560
Speaker 1: ago by William Goldman, who was the screenwriter for Butchcasting

734
00:39:42,600 --> 00:39:46,279
Speaker 1: and Sundance Kid, Princess Bride, and Marathon Man Hit. His

735
00:39:46,440 --> 00:39:51,800
Speaker 1: Screenless are Insane and his book was called Adventures in

736
00:39:51,840 --> 00:39:54,480
Speaker 1: the Screen Trade. But a quote of his that I've

737
00:39:54,560 --> 00:39:59,360
Speaker 1: used repeatedly when writing about markets is nobody knows anything.

738
00:40:00,040 --> 00:40:04,160
Speaker 1: And he refers to all the studios that passed on

739
00:40:04,200 --> 00:40:07,080
Speaker 1: Star Wars, all the studios that wanted nothing to do

740
00:40:07,120 --> 00:40:09,840
Speaker 1: with raiders, of the lost art, and he uses example

741
00:40:09,920 --> 00:40:14,520
Speaker 1: after example after example of these people who are supposed

742
00:40:14,520 --> 00:40:17,800
Speaker 1: to be in the film industry and they're throwing darts.

743
00:40:17,880 --> 00:40:23,520
Speaker 1: And I'm reading your right up about the various horses

744
00:40:23,560 --> 00:40:26,839
Speaker 1: that later go on to storied career that were picked

745
00:40:26,920 --> 00:40:30,520
Speaker 1: up for pennies because nobody recognized their value. And all

746
00:40:30,560 --> 00:40:36,920
Speaker 1: of these very storied stud married to uh, these mayors

747
00:40:36,920 --> 00:40:40,560
Speaker 1: who were great runners, and the horses nobody they don't

748
00:40:40,560 --> 00:40:43,160
Speaker 1: win anything, they don't. So is it the same sort

749
00:40:43,200 --> 00:40:46,960
Speaker 1: of situation when it comes to horse breeding nobody knows anything? Well,

750
00:40:47,280 --> 00:40:49,840
Speaker 1: I think it's that the incentives are kind of are off.

751
00:40:50,600 --> 00:40:53,680
Speaker 1: So you know what, people when they breed a horse,

752
00:40:53,719 --> 00:40:55,719
Speaker 1: they're looking for something that's gonna sell after one year,

753
00:40:55,760 --> 00:40:57,320
Speaker 1: which is very different from a horse that's going to

754
00:40:57,360 --> 00:41:00,759
Speaker 1: win a Kentucky Derby. You would think that there's so

755
00:41:00,880 --> 00:41:04,200
Speaker 1: much money in winning these big races that at least

756
00:41:04,360 --> 00:41:08,480
Speaker 1: some subsection of the breeding community would say, hey, if

757
00:41:08,520 --> 00:41:10,040
Speaker 1: you want to buy a horse to sell in a year,

758
00:41:10,080 --> 00:41:14,120
Speaker 1: don't come to us. We're trying to bread triple crown

759
00:41:14,280 --> 00:41:18,400
Speaker 1: competitive racers. How come that hasn't happened? Well, does some degree?

760
00:41:18,480 --> 00:41:21,719
Speaker 1: You're right that it is just impossible to know. I mean,

761
00:41:21,800 --> 00:41:25,399
Speaker 1: you're getting more information now with technology because you're able

762
00:41:25,400 --> 00:41:28,600
Speaker 1: to do the genetic profiling of the horses, which gives

763
00:41:28,640 --> 00:41:30,960
Speaker 1: you some information, like you can tell, you know, I

764
00:41:31,000 --> 00:41:32,840
Speaker 1: guess the horses that are best suited for like the

765
00:41:32,920 --> 00:41:36,120
Speaker 1: Kentucky Derby or so of these hybrid half sprinter half

766
00:41:36,120 --> 00:41:40,640
Speaker 1: distance runners, and you can test for that also reference. Um.

767
00:41:40,680 --> 00:41:42,840
Speaker 1: And there's a really fascinating story I'm trying to remember

768
00:41:42,840 --> 00:41:45,080
Speaker 1: where I saw it about the size of one of

769
00:41:45,120 --> 00:41:48,080
Speaker 1: the ventricles and the horses hard you reference. You're they're

770
00:41:48,120 --> 00:41:52,319
Speaker 1: looking for horses with not metaphysical of that horse as hard,

771
00:41:52,719 --> 00:41:57,120
Speaker 1: but genuine larger cardiac pumps. Yeah, although you have to

772
00:41:57,160 --> 00:42:00,719
Speaker 1: be careful because everything you know, horse like everything to

773
00:42:00,760 --> 00:42:03,640
Speaker 1: be like a racer, so of this freakish combination of

774
00:42:03,719 --> 00:42:06,640
Speaker 1: factors have to come together. It's like being a Nobel

775
00:42:06,680 --> 00:42:08,879
Speaker 1: Prize winning supermodel. It's like you just need all these

776
00:42:09,040 --> 00:42:13,640
Speaker 1: genetic components in perfect alignment. Um. That that's the I'm

777
00:42:13,640 --> 00:42:17,560
Speaker 1: sure it's an urban myth, but the Einstein Marilyn Monroe,

778
00:42:17,800 --> 00:42:20,080
Speaker 1: back and forth. You may not get the qualities you

779
00:42:20,120 --> 00:42:24,080
Speaker 1: want from each of the sires of the of the horse. Yeah,

780
00:42:24,120 --> 00:42:27,000
Speaker 1: you may get the worst qualities of both. So you know,

781
00:42:27,000 --> 00:42:28,719
Speaker 1: if you uh, if it was one guy explained to me,

782
00:42:28,760 --> 00:42:31,520
Speaker 1: it's like getting like that perfect heart in the wrong

783
00:42:32,160 --> 00:42:34,800
Speaker 1: um horse could be like having of them mentioned Ferrari

784
00:42:34,840 --> 00:42:39,000
Speaker 1: and a super U. So it's it's really hard to predict.

785
00:42:39,120 --> 00:42:41,440
Speaker 1: So this is why that chapter is about about modern

786
00:42:41,480 --> 00:42:44,240
Speaker 1: portfolio theory. By the way, that that is the super

787
00:42:44,480 --> 00:42:47,040
Speaker 1: super U w r X. They've done that and it's

788
00:42:47,040 --> 00:42:50,719
Speaker 1: been a very successful car. I don't know anything about cars,

789
00:42:50,480 --> 00:42:53,680
Speaker 1: so but that was really that was really a fascinating discussion,

790
00:42:53,680 --> 00:42:57,160
Speaker 1: and I I really enjoyed that last question about the book.

791
00:42:57,719 --> 00:43:01,120
Speaker 1: Anything else sort of stood out it is Wow, that

792
00:43:01,239 --> 00:43:04,040
Speaker 1: was really weird and unusual that I was not expected.

793
00:43:05,520 --> 00:43:08,120
Speaker 1: You know, there was every chapter sort of I guess

794
00:43:08,160 --> 00:43:11,040
Speaker 1: had those moments. I mean there were certainly when the

795
00:43:11,080 --> 00:43:14,480
Speaker 1: time I spent following around the paparazzi, I'm like, also

796
00:43:14,560 --> 00:43:17,800
Speaker 1: a fascinating story. This is weird. You know, I was

797
00:43:17,920 --> 00:43:19,920
Speaker 1: telling someone would say, you know, as I'm like crouching

798
00:43:19,920 --> 00:43:22,360
Speaker 1: behind a garbage can waiting for Alec Baldwin, I'm like,

799
00:43:22,440 --> 00:43:23,880
Speaker 1: this is just not what I expected when I went

800
00:43:23,920 --> 00:43:26,279
Speaker 1: to go out school, you know, to end up here

801
00:43:26,280 --> 00:43:28,520
Speaker 1: of all places. But you know again, they also have

802
00:43:28,600 --> 00:43:31,480
Speaker 1: a fascinating risk story that's going on behind the scenes.

803
00:43:31,520 --> 00:43:33,839
Speaker 1: You would never know because, as you can imagine, they

804
00:43:33,880 --> 00:43:37,480
Speaker 1: faced just crazy amounts of idiosyncratic risk. At the risk

805
00:43:37,600 --> 00:43:39,319
Speaker 1: that you're going to get that one money shot in

806
00:43:39,320 --> 00:43:43,040
Speaker 1: any one day is so large. They have to form

807
00:43:43,080 --> 00:43:47,160
Speaker 1: these complex alliances to share tips and sometimes royalties. It's

808
00:43:47,239 --> 00:43:49,879
Speaker 1: especially its pooling, so you're getting rid of your idiosyncratic risk.

809
00:43:50,320 --> 00:43:52,600
Speaker 1: But because all the money in celebrity photography is from

810
00:43:52,640 --> 00:43:55,000
Speaker 1: getting an exclusive, they also have this incentive to always

811
00:43:55,040 --> 00:43:59,000
Speaker 1: cheat to These alliances are inherently very unstable, so they're

812
00:43:59,040 --> 00:44:02,359
Speaker 1: always reef warming and breaking up these alliances, so they

813
00:44:02,400 --> 00:44:04,520
Speaker 1: all hate each other. So this is something you see

814
00:44:04,520 --> 00:44:07,000
Speaker 1: the paparazzi on the street and you're like, this is

815
00:44:07,000 --> 00:44:09,400
Speaker 1: a much more interesting story that's going on with this

816
00:44:09,400 --> 00:44:12,200
Speaker 1: this lineup of paparazzi than you would ever know. Then

817
00:44:12,360 --> 00:44:15,799
Speaker 1: the paparazzi a more interesting than catching a celebrity takes

818
00:44:15,800 --> 00:44:18,040
Speaker 1: their kid out for ice cream. Yeah, they'd always be surprised.

819
00:44:18,080 --> 00:44:19,919
Speaker 1: Can you me wait for these celebrities for like six

820
00:44:20,000 --> 00:44:22,080
Speaker 1: or eight hours and after maybe an hour or two.

821
00:44:22,120 --> 00:44:23,719
Speaker 1: I'll just get bored and I'm like, I've had a

822
00:44:23,719 --> 00:44:26,480
Speaker 1: good story here. I'm gonna go home. And they'd be shocked.

823
00:44:26,480 --> 00:44:28,480
Speaker 1: They're like, but she has an ampauty. I'm like, well,

824
00:44:28,520 --> 00:44:32,799
Speaker 1: I'm here for supermodel g who was friends with somebody else?

825
00:44:32,840 --> 00:44:38,800
Speaker 1: Want that? That's how suddenly she blew up on on Instagram. Yeah. Well,

826
00:44:38,840 --> 00:44:41,160
Speaker 1: the celebrities who do well with the paparazzi also play

827
00:44:41,239 --> 00:44:45,040
Speaker 1: the game with them. Huh. Quite interesting. You You also

828
00:44:45,120 --> 00:44:50,360
Speaker 1: have been writing regularly on Vox for some time, and

829
00:44:50,360 --> 00:44:56,320
Speaker 1: and some of the columns you've done sort of tangentially

830
00:44:56,320 --> 00:45:02,240
Speaker 1: involved risk in surprising ways. So everybody today is focused

831
00:45:02,280 --> 00:45:06,920
Speaker 1: on the Amazon HQ two disaster that blew up earlier

832
00:45:06,960 --> 00:45:10,319
Speaker 1: this year. Um, but but you looked at it from

833
00:45:10,320 --> 00:45:14,880
Speaker 1: the context of the US has a talent problem and

834
00:45:14,960 --> 00:45:19,680
Speaker 1: that presents a risk. Two corporations explain that. Well, so

835
00:45:20,040 --> 00:45:22,680
Speaker 1: this is as the Richard Florida argument that you need,

836
00:45:23,200 --> 00:45:25,440
Speaker 1: you know, anyway you think anyone would want to work

837
00:45:25,440 --> 00:45:29,480
Speaker 1: for Amazon. They still it's the the competition for sort

838
00:45:29,520 --> 00:45:32,000
Speaker 1: of really good talent is still very stiff, and it

839
00:45:32,120 --> 00:45:36,239
Speaker 1: is does occur globally. And we're talking about engineers and programmers,

840
00:45:36,280 --> 00:45:40,520
Speaker 1: not necessarily the surfs that they have enslaved in their

841
00:45:40,520 --> 00:45:43,520
Speaker 1: in their warehouse. Yeah, on the high skill tech workers.

842
00:45:43,560 --> 00:45:45,960
Speaker 1: I mean, they're kings of the labor market and they

843
00:45:46,000 --> 00:45:47,520
Speaker 1: compete a lot for it, which is one of the

844
00:45:47,560 --> 00:45:50,279
Speaker 1: reasons why Amazon probably wanted to come to New York.

845
00:45:50,320 --> 00:45:53,239
Speaker 1: It wasn't just the tax incentives. It was that you

846
00:45:53,280 --> 00:45:55,359
Speaker 1: could get talent who wanted to move here. It's really

847
00:45:55,440 --> 00:45:58,239
Speaker 1: hard to get a cluster of talented young people to

848
00:45:58,440 --> 00:46:02,080
Speaker 1: want to move to, uh, the middle of the country,

849
00:46:02,160 --> 00:46:04,920
Speaker 1: and that's why Brooklyn is so hot these days. Yeah. Well,

850
00:46:04,960 --> 00:46:07,280
Speaker 1: and you can see why because if you are talented,

851
00:46:07,320 --> 00:46:10,360
Speaker 1: I mean human capital, you know, is something you have

852
00:46:10,400 --> 00:46:12,200
Speaker 1: to work towards your whole career. You don't just go

853
00:46:12,239 --> 00:46:14,120
Speaker 1: to Harvard and then you're just set for life. You

854
00:46:14,160 --> 00:46:18,120
Speaker 1: have to manage a network. You have to keep your

855
00:46:18,160 --> 00:46:20,600
Speaker 1: skills sharp, and that's why you want to be around

856
00:46:20,719 --> 00:46:22,960
Speaker 1: and not just be limited to your own company, be

857
00:46:23,040 --> 00:46:26,680
Speaker 1: around other people, intelligent companies. That way, you keep your

858
00:46:26,680 --> 00:46:29,239
Speaker 1: skills fresh. You have the option of changing jobs. That's

859
00:46:29,239 --> 00:46:32,320
Speaker 1: always a very valuable option. I mean, if if Amazon

860
00:46:32,440 --> 00:46:35,040
Speaker 1: moved to a place where there were no other good jobs.

861
00:46:35,040 --> 00:46:36,560
Speaker 1: You're kind of stuck at Amazon and you give up

862
00:46:36,600 --> 00:46:39,040
Speaker 1: the option of job switching, that's you have to compensate

863
00:46:39,080 --> 00:46:42,319
Speaker 1: people for that. Towns are problematic for that reason. Yeah,

864
00:46:42,360 --> 00:46:45,480
Speaker 1: I mean it worked before when you have people who

865
00:46:45,480 --> 00:46:48,520
Speaker 1: are more middle skill. And also back before when you

866
00:46:48,560 --> 00:46:51,960
Speaker 1: had technology was such before where the skills you would

867
00:46:52,040 --> 00:46:54,560
Speaker 1: learn would be very idustocratic to the company you worked for,

868
00:46:54,880 --> 00:46:57,640
Speaker 1: and now they're very transferable exactly. So if you want

869
00:46:57,680 --> 00:47:02,480
Speaker 1: to maintain competitive competitive really in the labor market, you

870
00:47:02,560 --> 00:47:04,520
Speaker 1: have to be part of sort of these clusters of

871
00:47:04,520 --> 00:47:07,080
Speaker 1: people that allow you to move around. And and Google

872
00:47:07,120 --> 00:47:09,960
Speaker 1: announced they're doubling their New York workforce from seven thousand

873
00:47:10,000 --> 00:47:14,000
Speaker 1: and fourteen thousand. Apple has dramatically expanded its presence. So

874
00:47:14,040 --> 00:47:16,479
Speaker 1: if you're an Amazon work or theoretically in New York,

875
00:47:17,160 --> 00:47:20,560
Speaker 1: they're competing for your skills with some of the biggest

876
00:47:20,640 --> 00:47:23,359
Speaker 1: companies in the world. Yeah, but and you know, they

877
00:47:23,360 --> 00:47:25,719
Speaker 1: don't like the ice work for a company that was

878
00:47:25,880 --> 00:47:28,080
Speaker 1: far outside my cluster, and they kept trying to get

879
00:47:28,120 --> 00:47:30,200
Speaker 1: me to move there, And I'm like, but you're asking

880
00:47:30,239 --> 00:47:32,000
Speaker 1: me to give up a very valuable option. You're gonna

881
00:47:32,040 --> 00:47:34,880
Speaker 1: have to compensate me for it. That never really resonated

882
00:47:34,920 --> 00:47:39,000
Speaker 1: with them. But um, it seems counterintuitive that Amazon would

883
00:47:39,000 --> 00:47:41,279
Speaker 1: want to be close to their job competition, but it's

884
00:47:41,280 --> 00:47:43,719
Speaker 1: also what they need to do to attract talent. It's

885
00:47:43,719 --> 00:47:47,360
Speaker 1: why cities haven't disappeared despite the best predictions of people

886
00:47:47,760 --> 00:47:50,560
Speaker 1: half a century ago. What about I thought this was

887
00:47:50,600 --> 00:47:54,480
Speaker 1: an interesting headline. What is the real reason people regret

888
00:47:54,719 --> 00:47:59,880
Speaker 1: not saving more risk? So, you know, people think about

889
00:48:00,040 --> 00:48:02,799
Speaker 1: saving as something that they're gonna, you know, often for

890
00:48:02,960 --> 00:48:04,600
Speaker 1: something that's gonna happen in the future that they want

891
00:48:04,600 --> 00:48:06,880
Speaker 1: to do. But when they looked at people at the

892
00:48:06,960 --> 00:48:09,799
Speaker 1: end of their life or in retirement, the reason why

893
00:48:09,800 --> 00:48:11,480
Speaker 1: they think they wish had more money. They don't wish

894
00:48:11,520 --> 00:48:13,400
Speaker 1: they could have gone on better vacations when they retired.

895
00:48:13,400 --> 00:48:15,440
Speaker 1: They wish they could have a better lifestyle. It was

896
00:48:15,880 --> 00:48:18,200
Speaker 1: I didn't realize that divorce would blow all my savings.

897
00:48:18,480 --> 00:48:22,200
Speaker 1: It's not a luxury goal. It's a Hey, things happen

898
00:48:22,280 --> 00:48:25,279
Speaker 1: that are just unexpected and I failed to plan for that. Yeah,

899
00:48:25,320 --> 00:48:27,360
Speaker 1: and people, I mean the the like FOURG dollar for

900
00:48:27,400 --> 00:48:30,279
Speaker 1: emergency saving figure is a little controversial, but I don't

901
00:48:30,280 --> 00:48:32,359
Speaker 1: think it is a stretch to say people really don't

902
00:48:32,360 --> 00:48:36,680
Speaker 1: make emergency savings enough of a priority. Right, that's pretty fair. Um,

903
00:48:36,760 --> 00:48:39,439
Speaker 1: let's talk about charities. What's the best way to get

904
00:48:39,480 --> 00:48:41,800
Speaker 1: people to donate to charity? So I think it was

905
00:48:41,880 --> 00:48:44,200
Speaker 1: John List, who I mentioned earlier, did a study of

906
00:48:44,800 --> 00:48:47,319
Speaker 1: the of Alaska. You know, Alaska is just right for

907
00:48:47,440 --> 00:48:49,800
Speaker 1: a lot of economic studies because they get the dividend

908
00:48:49,840 --> 00:48:53,080
Speaker 1: payment from the from all the oil reserves that they're

909
00:48:53,080 --> 00:48:56,759
Speaker 1: selling to or licensing and leasing out to the big

910
00:48:56,800 --> 00:48:59,440
Speaker 1: public oil companies. Yeah, so they have a program where

911
00:48:59,640 --> 00:49:01,920
Speaker 1: you can give some of that money to charity. And

912
00:49:01,960 --> 00:49:05,799
Speaker 1: they had they did a study where you they they

913
00:49:05,880 --> 00:49:08,240
Speaker 1: came with a card and one said, you know, warm

914
00:49:08,280 --> 00:49:10,440
Speaker 1: your heart. The other is like improve Alaska. On the

915
00:49:10,440 --> 00:49:12,680
Speaker 1: other just was nothing. And they found the warm the

916
00:49:12,680 --> 00:49:15,640
Speaker 1: heart group donated the most and we're most likely to donate. So,

917
00:49:15,640 --> 00:49:19,200
Speaker 1: in other words, they made the charitable donation about the

918
00:49:19,239 --> 00:49:23,720
Speaker 1: person as opposed to the recipient exactly. So appeal appeal

919
00:49:23,800 --> 00:49:26,800
Speaker 1: to ego. So over the past year or two, we've

920
00:49:26,840 --> 00:49:31,600
Speaker 1: gone through this giant me too movement. Um, and I

921
00:49:31,640 --> 00:49:35,960
Speaker 1: know that amongst my colleagues we've had debates about what

922
00:49:36,000 --> 00:49:39,400
Speaker 1: do you do with an artist who turns out to

923
00:49:39,440 --> 00:49:44,160
Speaker 1: be a less than um nice guy. Uh. And the

924
00:49:44,239 --> 00:49:47,200
Speaker 1: Michael Jackson HBO documentary just came out. I have yet

925
00:49:47,239 --> 00:49:50,600
Speaker 1: to see it, but I know Michael Jackson fans are

926
00:49:50,680 --> 00:49:53,319
Speaker 1: kind of split. Some are defending him and others are

927
00:49:53,320 --> 00:49:55,759
Speaker 1: a little bereft. I was always a big fan of

928
00:49:55,800 --> 00:49:59,160
Speaker 1: Louis c K. I'm not happy with with what he did.

929
00:49:59,400 --> 00:50:03,879
Speaker 1: Go down the list, you know, it ranges from offensive

930
00:50:03,960 --> 00:50:08,240
Speaker 1: to criminal and everything in between. You raised the question,

931
00:50:08,480 --> 00:50:11,560
Speaker 1: what do you do when a brilliant economist is accused

932
00:50:11,600 --> 00:50:17,399
Speaker 1: of sexually harassing his research assistance? So what's the solution? Well,

933
00:50:17,560 --> 00:50:20,120
Speaker 1: I don't know because the thing about I was writing

934
00:50:20,120 --> 00:50:23,760
Speaker 1: about Rolling Fire, who has been accused but not found guilty. Um.

935
00:50:24,000 --> 00:50:27,920
Speaker 1: But there He's been accused by a number of research assistants,

936
00:50:28,480 --> 00:50:32,879
Speaker 1: some of whom have incredibly claimed that he awarded their

937
00:50:32,920 --> 00:50:37,919
Speaker 1: careers for They're refusing to succumb to his charms. So

938
00:50:38,320 --> 00:50:40,120
Speaker 1: which is hard. It's a terrible way to say. Is

939
00:50:40,120 --> 00:50:43,960
Speaker 1: this is terrible? But and in his what he always

940
00:50:44,040 --> 00:50:46,480
Speaker 1: defended himself with and this is a valid point, although

941
00:50:46,719 --> 00:50:49,680
Speaker 1: doesn't excuse his behavior in any way. Is I do

942
00:50:49,760 --> 00:50:54,000
Speaker 1: research that socially critical. He was the economist who was

943
00:50:54,080 --> 00:50:57,320
Speaker 1: leading the charge of understanding why a lot of minority

944
00:50:57,320 --> 00:51:00,759
Speaker 1: students don't do well in school. So this it's important.

945
00:51:00,800 --> 00:51:02,239
Speaker 1: But what does that have to do with whether or

946
00:51:02,280 --> 00:51:05,839
Speaker 1: not he's a pig. So here's the question is if

947
00:51:05,840 --> 00:51:08,799
Speaker 1: someone is doing research that's socially important, are supposed they're

948
00:51:08,800 --> 00:51:10,640
Speaker 1: on the verge of a cure for cancer and we

949
00:51:10,680 --> 00:51:13,640
Speaker 1: find out they're a pig, you know, do we should

950
00:51:13,719 --> 00:51:15,839
Speaker 1: they still have a career? You know, if there's all

951
00:51:15,840 --> 00:51:20,160
Speaker 1: these other positive externalities for society. I could so you

952
00:51:20,239 --> 00:51:23,880
Speaker 1: just had the bishop six years prison sentence in Australia.

953
00:51:24,320 --> 00:51:28,040
Speaker 1: You could see throughout history that the powerful will say, look,

954
00:51:28,320 --> 00:51:31,480
Speaker 1: there have been some bad behavior, but we're literally doing

955
00:51:31,560 --> 00:51:35,680
Speaker 1: God's work and therefore we should be exempt from this. Um.

956
00:51:37,080 --> 00:51:43,440
Speaker 1: You know, pick a person. Um. John Lennon was supposed

957
00:51:43,440 --> 00:51:46,160
Speaker 1: to be a bit of a hardass, and you don't

958
00:51:46,200 --> 00:51:48,319
Speaker 1: want to go through the history of literature to find

959
00:51:48,320 --> 00:51:51,520
Speaker 1: out how big a jerk half the writers out there are.

960
00:51:52,200 --> 00:51:56,480
Speaker 1: If we but and then artists and paintings and things

961
00:51:56,560 --> 00:51:59,239
Speaker 1: like that, if we're gonna have a moral purity test

962
00:51:59,280 --> 00:52:01,839
Speaker 1: on that stuff, your museums will be empty and there'll

963
00:52:01,840 --> 00:52:05,200
Speaker 1: be nothing on the air waves. However, it doesn't mean

964
00:52:05,200 --> 00:52:08,880
Speaker 1: they shouldn't suffer the ramifications from So should we separate

965
00:52:08,920 --> 00:52:11,480
Speaker 1: the value of the art from the artist. But that

966
00:52:11,560 --> 00:52:13,799
Speaker 1: doesn't give them a free pass in their career, not

967
00:52:13,880 --> 00:52:15,880
Speaker 1: at all. And I didn't honestly know the answer, so

968
00:52:15,960 --> 00:52:18,759
Speaker 1: spoke to philosopher, which was just I love talking to

969
00:52:18,760 --> 00:52:23,160
Speaker 1: philosophers because they always remind you know nothing about anything um.

970
00:52:23,200 --> 00:52:26,200
Speaker 1: And he pointed out that if you are doing important work,

971
00:52:26,440 --> 00:52:30,120
Speaker 1: like scientific works, but either economics or hard science, and

972
00:52:30,160 --> 00:52:32,440
Speaker 1: you're on the verge of really making the world important,

973
00:52:32,760 --> 00:52:35,640
Speaker 1: it's actually the more the pressure on you to behave

974
00:52:35,640 --> 00:52:38,400
Speaker 1: well is even higher because you're letting down especially no

975
00:52:38,400 --> 00:52:41,600
Speaker 1: good work happens alone. You're letting down everyone's effort, and

976
00:52:41,760 --> 00:52:44,640
Speaker 1: it's you know, not everyone gets the opportunity and resources

977
00:52:44,640 --> 00:52:48,480
Speaker 1: to perform research like this, and so if you're threatening

978
00:52:48,480 --> 00:52:50,719
Speaker 1: it with your behavior, it actually should be held to

979
00:52:50,760 --> 00:52:54,400
Speaker 1: an even higher standard. So the question is, according to

980
00:52:54,440 --> 00:52:58,400
Speaker 1: the philosopher, then once this bad behavior is identified, do

981
00:52:58,480 --> 00:53:02,480
Speaker 1: you stop the person from doing research or do you

982
00:53:02,520 --> 00:53:05,640
Speaker 1: just put a higher level of HR scrutiny in and

983
00:53:05,680 --> 00:53:08,440
Speaker 1: sit that person down and say you were putting millions

984
00:53:08,440 --> 00:53:10,560
Speaker 1: of people's lives at risk because you're this close to

985
00:53:10,560 --> 00:53:12,640
Speaker 1: a cure for cancer. And if you can't keep your

986
00:53:12,640 --> 00:53:15,440
Speaker 1: hands off your research assistant, here's what's going to happen.

987
00:53:15,480 --> 00:53:17,359
Speaker 1: I mean, how do you how do you resolve that? Well,

988
00:53:17,400 --> 00:53:21,480
Speaker 1: he was more in favor of just jettising them, I know,

989
00:53:21,600 --> 00:53:27,399
Speaker 1: jetting the assistance. No, the researcher, Um, the philosopher said

990
00:53:27,560 --> 00:53:29,759
Speaker 1: just fire the guy. Yeah, it's like whoever it is. Yeah,

991
00:53:29,760 --> 00:53:31,719
Speaker 1: he's like, we need, we need to have standards, and

992
00:53:31,760 --> 00:53:34,399
Speaker 1: you know, as he said, you know, yeah we might

993
00:53:34,400 --> 00:53:37,919
Speaker 1: have we might have a longer wait before a cure

994
00:53:37,920 --> 00:53:40,040
Speaker 1: for cancer. But he's like, but what about the behavior

995
00:53:40,040 --> 00:53:41,520
Speaker 1: he did all along? Maybe some would have come up

996
00:53:41,520 --> 00:53:44,279
Speaker 1: with a cure for cancer even earlier and he discouraged them,

997
00:53:44,960 --> 00:53:47,560
Speaker 1: and maybe we'd have had a cancer fifteen years ago

998
00:53:47,680 --> 00:53:50,880
Speaker 1: his behavior. So so you can't use as an excuse.

999
00:53:51,160 --> 00:53:53,279
Speaker 1: Bad behavior has to be punished, because you don't know

1000
00:53:53,360 --> 00:53:57,239
Speaker 1: what sort of that's the I love a classic counterfaction

1001
00:53:57,280 --> 00:54:02,000
Speaker 1: on that. That's a perfect one. Um, last one before

1002
00:54:02,040 --> 00:54:05,239
Speaker 1: we get to our favorite questions. Actually there are two here.

1003
00:54:05,239 --> 00:54:09,680
Speaker 1: They're fascinating. Um, let's start with this one. Are millennials

1004
00:54:09,719 --> 00:54:13,560
Speaker 1: the wealthiest generation? They could be I think, you know,

1005
00:54:13,719 --> 00:54:17,319
Speaker 1: I'm tired eventually maybe, but today they certainly don't feel

1006
00:54:17,360 --> 00:54:19,680
Speaker 1: that way. Well, I'm a life cyclist, right, so when

1007
00:54:19,719 --> 00:54:27,040
Speaker 1: I think about wealth lifecle, I think about all your assets.

1008
00:54:27,360 --> 00:54:31,080
Speaker 1: So I think of human capital, which in life cycle

1009
00:54:31,120 --> 00:54:34,400
Speaker 1: economics is the value of your future earnings. So for me,

1010
00:54:34,480 --> 00:54:38,840
Speaker 1: the idea that people don't millennials don't own houses and

1011
00:54:38,880 --> 00:54:41,560
Speaker 1: instead they have student debt doesn't seem to me like

1012
00:54:41,600 --> 00:54:45,719
Speaker 1: they've made a huge investment in their future earnings and education.

1013
00:54:45,840 --> 00:54:49,640
Speaker 1: It's not perfect. Is correlated with much higher lifetime earnings, true,

1014
00:54:49,719 --> 00:54:53,040
Speaker 1: but the issue that they've appropriately raised you're an n

1015
00:54:53,120 --> 00:54:56,440
Speaker 1: y U. It's the most expensive tuition in America sixty

1016
00:54:56,640 --> 00:55:00,279
Speaker 1: three thousand dollars or something insane a year. UM. I

1017
00:55:00,320 --> 00:55:02,560
Speaker 1: went to a state school. I want Stony Brook. My

1018
00:55:02,600 --> 00:55:06,360
Speaker 1: tuition was four fifty semester. Even today it's like five

1019
00:55:06,400 --> 00:55:10,600
Speaker 1: thousand a semester, which seems like a bargain. Um. Students

1020
00:55:10,600 --> 00:55:15,719
Speaker 1: today are paying prices for education that are just so

1021
00:55:15,880 --> 00:55:20,160
Speaker 1: vastly out of whack with what they were like forty fifty,

1022
00:55:20,239 --> 00:55:23,480
Speaker 1: sixty years or even twenty five years ago, it was

1023
00:55:23,600 --> 00:55:26,840
Speaker 1: much much cheaper relative to the total cost of living

1024
00:55:27,239 --> 00:55:30,160
Speaker 1: to go to school. So are they going to get

1025
00:55:30,280 --> 00:55:34,360
Speaker 1: the same return on their investment in education or have

1026
00:55:34,480 --> 00:55:39,480
Speaker 1: things just completely run them up? Well, definitely they're getting Uh. Well,

1027
00:55:39,480 --> 00:55:41,720
Speaker 1: it's hard to say. I mean, we can't predict the future.

1028
00:55:41,760 --> 00:55:44,520
Speaker 1: I mean, so far it does seem I mean, I'm

1029
00:55:44,520 --> 00:55:46,279
Speaker 1: not sure if there's much of value in going to

1030
00:55:46,680 --> 00:55:51,719
Speaker 1: school over Stonybrook. Probably you're you're gonna do just as

1031
00:55:51,760 --> 00:55:54,480
Speaker 1: well going to a good public school. Yes, I couldn't

1032
00:55:54,480 --> 00:55:57,680
Speaker 1: get into stony Brook today with my grades. Back then

1033
00:55:58,440 --> 00:56:00,600
Speaker 1: I was in a little bit of the value between

1034
00:56:01,120 --> 00:56:03,120
Speaker 1: the boomers and the gen xers. And the same thing

1035
00:56:03,120 --> 00:56:06,520
Speaker 1: with grad school. I couldn't get into grad school. Uh

1036
00:56:06,520 --> 00:56:10,480
Speaker 1: with to my grad school. Uh, y you that I

1037
00:56:10,520 --> 00:56:13,719
Speaker 1: got into way back when. So some of it's just

1038
00:56:13,840 --> 00:56:16,320
Speaker 1: dumb luck when you're born. But the other aspect of

1039
00:56:16,520 --> 00:56:21,040
Speaker 1: is what, um, what sort of return are are are

1040
00:56:21,160 --> 00:56:25,720
Speaker 1: these current graduates? What should they expect going forward? Explain

1041
00:56:25,760 --> 00:56:28,440
Speaker 1: why you say potentially they're the wealthiest generation. Well, I

1042
00:56:28,480 --> 00:56:32,359
Speaker 1: think when people look at the outcome firm education, they

1043
00:56:32,360 --> 00:56:35,680
Speaker 1: are too shortsighted because you don't often like I mean,

1044
00:56:35,760 --> 00:56:38,640
Speaker 1: I didn't enter the labor market really officially until I

1045
00:56:38,640 --> 00:56:41,920
Speaker 1: finished grad school was almost thirty. And my earnings well,

1046
00:56:41,920 --> 00:56:45,080
Speaker 1: actually my first job is unpaid. Um, you know, weren't

1047
00:56:45,080 --> 00:56:46,719
Speaker 1: that great. But when you're looking, when you think of

1048
00:56:46,760 --> 00:56:50,440
Speaker 1: the payoff from human capital, it's your lifetime earnings. And

1049
00:56:50,520 --> 00:56:52,879
Speaker 1: often out of school, you're gonna earn less than someone

1050
00:56:52,880 --> 00:56:55,560
Speaker 1: who you know didn't go to college. But the trajectory

1051
00:56:55,560 --> 00:56:57,920
Speaker 1: of your earnings as follows a much steeper path. So

1052
00:56:57,960 --> 00:56:59,840
Speaker 1: they've they've been working for a few years, they have

1053
00:57:00,000 --> 00:57:02,680
Speaker 1: of a series of raises. You're starting up below them,

1054
00:57:03,080 --> 00:57:06,080
Speaker 1: but you're gonna as a college or grad student, you're

1055
00:57:06,120 --> 00:57:08,959
Speaker 1: gonna accelerate way past them. And you also face less risk.

1056
00:57:09,000 --> 00:57:11,040
Speaker 1: I mean, the unemployment rates for college grads is much

1057
00:57:11,120 --> 00:57:13,640
Speaker 1: much lower. I'm sensing a theme with you. I don't

1058
00:57:13,680 --> 00:57:15,440
Speaker 1: know what it is, so I only have you for

1059
00:57:15,480 --> 00:57:19,320
Speaker 1: another ten minutes. Let's jump to my favorite questions. These

1060
00:57:19,320 --> 00:57:23,480
Speaker 1: are what we ask all of our guests. Um, tell

1061
00:57:23,600 --> 00:57:26,840
Speaker 1: us the first car you ever owned? Year making model? Well,

1062
00:57:26,880 --> 00:57:29,400
Speaker 1: I've never actually technically owned a car, but in high

1063
00:57:29,400 --> 00:57:33,280
Speaker 1: school I did drive Inde Okay, I love that car.

1064
00:57:33,440 --> 00:57:35,920
Speaker 1: Oh it lasts when two thousand miles, which in the

1065
00:57:35,960 --> 00:57:38,000
Speaker 1: nineties was a really big deal. That was a little

1066
00:57:38,040 --> 00:57:41,080
Speaker 1: two seater. They were you couldn't you couldn't destroy them.

1067
00:57:41,640 --> 00:57:43,360
Speaker 1: It wasn't the fastest car in the world. You could

1068
00:57:43,360 --> 00:57:45,280
Speaker 1: get them with a stick shift, which was nice back

1069
00:57:45,320 --> 00:57:47,800
Speaker 1: then that it was a stick st Oh there you go,

1070
00:57:48,000 --> 00:57:50,400
Speaker 1: so you drive by the way Today. I call stick

1071
00:57:50,440 --> 00:57:55,560
Speaker 1: shifts millennial anti theft divience because that's what they effectively are.

1072
00:57:55,840 --> 00:57:59,240
Speaker 1: So what's the most important thing we don't know about

1073
00:57:59,280 --> 00:58:03,280
Speaker 1: Alison shre ag Um, well, you wouldn't know from the

1074
00:58:03,320 --> 00:58:06,400
Speaker 1: book title. But I'm just really a risk nerd. I mean,

1075
00:58:06,440 --> 00:58:08,600
Speaker 1: I guess I don't know if that's not surprising. Um No,

1076
00:58:08,760 --> 00:58:11,720
Speaker 1: that's not a shocker. You read the book and it's

1077
00:58:11,720 --> 00:58:16,840
Speaker 1: like this is it should say Alison Schraeger comma PhD

1078
00:58:17,160 --> 00:58:20,480
Speaker 1: risk nerd. So that that's not a big surprise that

1079
00:58:20,600 --> 00:58:23,000
Speaker 1: you think of yourself as a risk nerd. Maybe that's

1080
00:58:23,000 --> 00:58:25,720
Speaker 1: a well, I guess. I also I wasn't always Like

1081
00:58:25,840 --> 00:58:29,240
Speaker 1: in college, I had a job in Alaska selling incense.

1082
00:58:30,320 --> 00:58:34,240
Speaker 1: Incense in Alaska? Why would they need incense in Alaska?

1083
00:58:34,320 --> 00:58:36,840
Speaker 1: Is there that much you would think of if anything

1084
00:58:36,880 --> 00:58:40,360
Speaker 1: smells like the Great Outdoor was a fishing village. Oh

1085
00:58:40,520 --> 00:58:44,520
Speaker 1: say no more. Yeah, that's very that's very funny. So

1086
00:58:44,800 --> 00:58:46,320
Speaker 1: I know the answer to this, but I have to

1087
00:58:46,320 --> 00:58:50,080
Speaker 1: ask who are your early mentors. Obviously Bob Merton, So

1088
00:58:50,160 --> 00:58:52,000
Speaker 1: tell us a little bit about what he taught you,

1089
00:58:52,040 --> 00:58:57,600
Speaker 1: because he's clearly fascinating and storied person. Well, he just

1090
00:58:57,720 --> 00:59:00,640
Speaker 1: taught me finance, which you know I went to I

1091
00:59:00,680 --> 00:59:02,720
Speaker 1: did an eCOM PhD, so it wasn't like I wasn't

1092
00:59:02,760 --> 00:59:05,600
Speaker 1: exposed to it, but my program it was very empirical,

1093
00:59:06,080 --> 00:59:09,800
Speaker 1: So I just thought financial economists just crunch data is

1094
00:59:09,800 --> 00:59:12,600
Speaker 1: looking for deviations of the efficient markets hypothesis, and I

1095
00:59:12,640 --> 00:59:15,120
Speaker 1: thought it was not very interesting and sort of corrupt.

1096
00:59:15,760 --> 00:59:18,080
Speaker 1: But then when I met Bob and learned what financial

1097
00:59:18,120 --> 00:59:20,760
Speaker 1: theory was, it really turned me onto theory too. In

1098
00:59:20,760 --> 00:59:23,560
Speaker 1: a way. I was always much more empirically oriented of

1099
00:59:23,960 --> 00:59:25,880
Speaker 1: how to think about the world and how to see

1100
00:59:25,920 --> 00:59:29,120
Speaker 1: the world in terms of a risk lens, and how

1101
00:59:29,160 --> 00:59:32,640
Speaker 1: to see risk problems everywhere and how they're driving all markets,

1102
00:59:32,680 --> 00:59:35,280
Speaker 1: not just financial markets. So he was really like your

1103
00:59:35,320 --> 00:59:37,919
Speaker 1: post doc work. He was. It was a very sort

1104
00:59:37,920 --> 00:59:40,560
Speaker 1: of long, intense postdoc I had good training as an

1105
00:59:40,600 --> 00:59:43,480
Speaker 1: as a graduate student too, but it sort of readied

1106
00:59:43,560 --> 00:59:46,400
Speaker 1: me to really fully embrace all of his lessons. So

1107
00:59:46,600 --> 00:59:50,680
Speaker 1: let's talk about investors and others who influenced the way

1108
00:59:50,720 --> 00:59:52,920
Speaker 1: you look at the world of risk. Who who do

1109
00:59:52,920 --> 00:59:57,200
Speaker 1: you consider to be UM important thinkers that affected the

1110
00:59:57,200 --> 01:00:01,760
Speaker 1: way you perceived the world um, investors, investors, or or anyone.

1111
01:00:01,800 --> 01:00:04,800
Speaker 1: Really you mentioned Peter Bernstein, who who affected the way

1112
01:00:05,240 --> 01:00:09,040
Speaker 1: you perceive UM the world of risk? Certainly? Peter Bernstein

1113
01:00:09,120 --> 01:00:10,800
Speaker 1: also said I worked at d f A for a

1114
01:00:10,880 --> 01:00:14,800
Speaker 1: number of years. So David Booth, Um, when did you

1115
01:00:14,840 --> 01:00:19,960
Speaker 1: work at d f A two thousand ten? Oh? So

1116
01:00:20,040 --> 01:00:23,560
Speaker 1: really that was in fairly They were already fairly developed

1117
01:00:23,560 --> 01:00:25,760
Speaker 1: and running hundreds of billions of dollars by then, and

1118
01:00:25,760 --> 01:00:27,840
Speaker 1: it was it was a great experience for me because

1119
01:00:27,920 --> 01:00:29,800
Speaker 1: I was working on a project that they were all

1120
01:00:29,880 --> 01:00:32,800
Speaker 1: very interested in. So every two weeks I would have

1121
01:00:32,920 --> 01:00:36,680
Speaker 1: these meetings with Gene Fama, Ken French Merton, David booth

1122
01:00:36,680 --> 01:00:39,720
Speaker 1: and EDWARDA. Rippetto who is the U and we would

1123
01:00:39,800 --> 01:00:42,680
Speaker 1: just because we were having to calibrate this very complicated

1124
01:00:42,680 --> 01:00:45,160
Speaker 1: interest rate model I was working on, and we would

1125
01:00:45,200 --> 01:00:47,960
Speaker 1: just debate the path of interest rates, and I learned

1126
01:00:48,080 --> 01:00:49,720
Speaker 1: That's where I learned a lot of my finance was

1127
01:00:49,840 --> 01:00:52,720
Speaker 1: just seeing obviously Geene and Bob go at it and

1128
01:00:52,920 --> 01:00:55,600
Speaker 1: you know, with the influences of David Booth, who's just

1129
01:00:55,640 --> 01:00:59,760
Speaker 1: a real market guy. And this was pre uh will

1130
01:00:59,800 --> 01:01:03,000
Speaker 1: you when when Fama won the Nobel was just before

1131
01:01:06,040 --> 01:01:10,840
Speaker 1: so right before quite quite. That's some collection of mentors

1132
01:01:10,840 --> 01:01:14,720
Speaker 1: and inflot It's interesting because they're smart in such different ways. Huh.

1133
01:01:15,400 --> 01:01:20,240
Speaker 1: So we mentioned Um Against the Gods. What are some

1134
01:01:20,320 --> 01:01:23,520
Speaker 1: of your other favorite books? Well, that obviously anything in

1135
01:01:23,520 --> 01:01:27,439
Speaker 1: Peter Bernstein writes, but I also just I love memoirs. Really, yeah,

1136
01:01:27,560 --> 01:01:30,600
Speaker 1: give us an example. I love just kids like Patti Smith.

1137
01:01:30,720 --> 01:01:33,960
Speaker 1: I just read Educated, which I hate write loving popular books,

1138
01:01:33,960 --> 01:01:36,800
Speaker 1: but I really loved that wrote Educated. Was it, Tara Westover?

1139
01:01:38,040 --> 01:01:40,160
Speaker 1: Don't ask me. Yeah, it's just it's outside of mine.

1140
01:01:40,200 --> 01:01:44,040
Speaker 1: It's just just so beautifully written. Oh no kidding. What

1141
01:01:44,040 --> 01:01:47,280
Speaker 1: What other memoirs have you read that really resonated with you?

1142
01:01:47,560 --> 01:01:50,360
Speaker 1: By the way, I read two books on vacation. Yours

1143
01:01:50,440 --> 01:01:54,920
Speaker 1: was one of them, and then McCullough's Um The Right

1144
01:01:54,960 --> 01:01:58,320
Speaker 1: Brothers was quite fascinating. If you're if you're interested in

1145
01:01:58,400 --> 01:02:02,200
Speaker 1: flight and or um, that's really less of a memoir

1146
01:02:02,360 --> 01:02:05,440
Speaker 1: more of a biography. All right, skip that, give us

1147
01:02:05,440 --> 01:02:09,280
Speaker 1: one more book. So you mentioned you mentioned two, Um

1148
01:02:09,440 --> 01:02:10,920
Speaker 1: hit hit us with the third. By the way, this

1149
01:02:11,000 --> 01:02:13,800
Speaker 1: is the question. I get more email about this point.

1150
01:02:13,840 --> 01:02:16,320
Speaker 1: What was that book? The person I mentioned on? I

1151
01:02:16,360 --> 01:02:18,800
Speaker 1: get more email about this than anything. It's a stressful

1152
01:02:18,880 --> 01:02:23,120
Speaker 1: question because I feel like it's so personal and revealing. Yes, um,

1153
01:02:23,160 --> 01:02:25,640
Speaker 1: and what makes it so good? And especially because I

1154
01:02:25,680 --> 01:02:28,760
Speaker 1: read a lot of crap because you know you finished crap.

1155
01:02:29,560 --> 01:02:32,040
Speaker 1: You know. When I was doing book research, I had

1156
01:02:32,080 --> 01:02:34,600
Speaker 1: this idea that Chris Jenner was this risk mastermind because

1157
01:02:34,600 --> 01:02:37,560
Speaker 1: look at what she's built. And I so I read

1158
01:02:37,600 --> 01:02:39,720
Speaker 1: her biography thinking maybe I'll include her, and it turns

1159
01:02:39,760 --> 01:02:41,200
Speaker 1: out she didn't have a good risk strategy, so I

1160
01:02:41,200 --> 01:02:44,160
Speaker 1: couldn't um right place, right time? Is that all about?

1161
01:02:44,280 --> 01:02:47,120
Speaker 1: She takes advantage? I mean, and it's like this extreme

1162
01:02:47,200 --> 01:02:50,520
Speaker 1: level of diversification where she literally any opportunity that comes

1163
01:02:50,520 --> 01:02:52,920
Speaker 1: her away, she seizes on it and she does work hard.

1164
01:02:53,240 --> 01:02:55,760
Speaker 1: There's nothing like strategic. It's sort of or if it's

1165
01:02:55,760 --> 01:02:57,280
Speaker 1: sort of as well, like a Donald Trump thing. It's

1166
01:02:57,320 --> 01:02:59,560
Speaker 1: like well, if this blows up, I have something else

1167
01:02:59,600 --> 01:03:02,200
Speaker 1: to distry people with, because I've got ninety gazillion things going.

1168
01:03:02,240 --> 01:03:03,920
Speaker 1: So it's like we have a debit card that pour

1169
01:03:04,000 --> 01:03:06,280
Speaker 1: people off. Oh look, here's a sex tape, you know.

1170
01:03:07,800 --> 01:03:10,120
Speaker 1: But I remember I was reading her book on the

1171
01:03:10,160 --> 01:03:13,080
Speaker 1: subway and I had this realization, like I've never read

1172
01:03:13,080 --> 01:03:17,439
Speaker 1: Anna Karna, but I've read this. Um. I'm gonna say

1173
01:03:17,640 --> 01:03:21,320
Speaker 1: you probably picked the wrong one of the two, just

1174
01:03:21,320 --> 01:03:25,000
Speaker 1: just to hypothesize it. Um. Alright, so here's here's a

1175
01:03:25,120 --> 01:03:29,280
Speaker 1: question that also is uh personal and probing. Tell us

1176
01:03:29,480 --> 01:03:31,840
Speaker 1: about a time you failed and what you learned from

1177
01:03:31,840 --> 01:03:36,080
Speaker 1: the experience. And by the way, you detailed some personal failures. Yeah,

1178
01:03:36,280 --> 01:03:38,240
Speaker 1: I fell a lot. You don't. You don't show away

1179
01:03:38,240 --> 01:03:40,520
Speaker 1: from that, No, I mean my first year of grad school,

1180
01:03:40,560 --> 01:03:42,600
Speaker 1: I think I failed almost all my comps. I mean

1181
01:03:42,720 --> 01:03:45,160
Speaker 1: largely because I was doing a quantitative PhD with no

1182
01:03:45,240 --> 01:03:48,720
Speaker 1: math backgrounds UM. And it was the hardest failure I've

1183
01:03:48,760 --> 01:03:51,360
Speaker 1: ever gone through because it was the biggest intellectual achievement

1184
01:03:51,400 --> 01:03:54,240
Speaker 1: I'd ever done, which is I taught myself six years

1185
01:03:54,240 --> 01:03:57,600
Speaker 1: worth of math myself in my free time. My first

1186
01:03:57,640 --> 01:04:00,080
Speaker 1: year of grad school, I never intellectually had grown so

1187
01:04:00,160 --> 01:04:03,400
Speaker 1: much from anything and never achieved so much. And I still,

1188
01:04:03,560 --> 01:04:04,800
Speaker 1: you know, at the end of the day, you're still

1189
01:04:04,800 --> 01:04:07,880
Speaker 1: taking a math exam and you're being judged against Korean

1190
01:04:07,880 --> 01:04:11,160
Speaker 1: math champion. No matter how much math you learned quickly,

1191
01:04:11,960 --> 01:04:13,760
Speaker 1: you're not going to stack up, so you're going to fail.

1192
01:04:14,080 --> 01:04:19,480
Speaker 1: Meaning the rest of the student base was hardcore math people. Yeah,

1193
01:04:19,480 --> 01:04:21,680
Speaker 1: and I was just reading math textbooks in my free time,

1194
01:04:21,720 --> 01:04:24,640
Speaker 1: which wasn't all much. I wasn't sleeping. I was just reading,

1195
01:04:25,040 --> 01:04:27,800
Speaker 1: working through entire math textbooks to try to do my homework.

1196
01:04:28,200 --> 01:04:30,720
Speaker 1: That sounds horrible, and yeah, it was horrible. I was

1197
01:04:30,760 --> 01:04:33,320
Speaker 1: such an unhappy person. I kind of started developing weird

1198
01:04:33,360 --> 01:04:36,040
Speaker 1: social tics. And then I went through all of this,

1199
01:04:36,600 --> 01:04:38,880
Speaker 1: never slept for a year, just working through math textbooks,

1200
01:04:38,920 --> 01:04:41,560
Speaker 1: and I still just failed everything. And I mean that

1201
01:04:41,640 --> 01:04:45,320
Speaker 1: was obviously just I was devastated. I'm gonna blame the

1202
01:04:45,400 --> 01:04:47,680
Speaker 1: lack of sleep because that affects God want to function it,

1203
01:04:47,960 --> 01:04:49,880
Speaker 1: you know. And it was like I had time to

1204
01:04:50,240 --> 01:04:52,600
Speaker 1: get to retake them. And when I got to finally

1205
01:04:52,680 --> 01:04:55,880
Speaker 1: rest and have all that knowledge should have marinate in me,

1206
01:04:56,000 --> 01:04:58,400
Speaker 1: I realized how much I knew and I mean, I

1207
01:04:58,440 --> 01:05:02,080
Speaker 1: think what I learned from that experience is, you know,

1208
01:05:02,200 --> 01:05:04,439
Speaker 1: if you really want something, you know, I mean, there's

1209
01:05:04,440 --> 01:05:07,120
Speaker 1: a point you have to cut cut loose, you just

1210
01:05:07,640 --> 01:05:10,760
Speaker 1: you you can't take the first failure because you know,

1211
01:05:10,960 --> 01:05:14,800
Speaker 1: no one remembers that you failed your first year exam.

1212
01:05:14,880 --> 01:05:18,040
Speaker 1: All they remembers that you graduated. Huh. That's that's an

1213
01:05:18,080 --> 01:05:21,600
Speaker 1: interesting observation. So now let me ask you the flip question.

1214
01:05:22,040 --> 01:05:23,480
Speaker 1: What do you do for fun? When do you do

1215
01:05:23,560 --> 01:05:28,520
Speaker 1: when you're not failing math exams? I play bridge? Really yeah,

1216
01:05:28,560 --> 01:05:31,280
Speaker 1: my mother plays bridge, my wife plays bridge. This has

1217
01:05:31,320 --> 01:05:34,200
Speaker 1: become like a giant thing. Now I'm part of this

1218
01:05:34,520 --> 01:05:38,360
Speaker 1: great bridge group of all these intellectuals. There's a Field

1219
01:05:38,200 --> 01:05:40,880
Speaker 1: medal winner, last of the last one, but mixed it.

1220
01:05:40,880 --> 01:05:43,200
Speaker 1: But it's just a very humble, low key group. H

1221
01:05:43,680 --> 01:05:47,680
Speaker 1: really quite quite interesting. Um what are you most excited

1222
01:05:47,720 --> 01:05:52,840
Speaker 1: about in the future direction of the risk industry? I

1223
01:05:52,840 --> 01:05:56,200
Speaker 1: hank technology. I mean everyone else is sort of scared

1224
01:05:56,280 --> 01:05:58,280
Speaker 1: of it, but I think it's gonna sort of take

1225
01:05:58,320 --> 01:06:03,960
Speaker 1: us to some interesting places. Interest interesting. I don't necessarily disagree. Um,

1226
01:06:04,000 --> 01:06:06,560
Speaker 1: So if a millennial or recent college student came up

1227
01:06:06,600 --> 01:06:10,560
Speaker 1: to you and asked you for career advice about going

1228
01:06:10,680 --> 01:06:15,120
Speaker 1: into economics, risk or academia. What what sort of advice

1229
01:06:15,160 --> 01:06:17,440
Speaker 1: would you give them? Find the smartest person you can

1230
01:06:17,520 --> 01:06:20,520
Speaker 1: and attach yourself to them. It's funny you say that

1231
01:06:20,520 --> 01:06:22,640
Speaker 1: that was my farthest advice to me when I went

1232
01:06:22,680 --> 01:06:26,840
Speaker 1: to gratz. Did it work? Um? More or less? Actually

1233
01:06:26,960 --> 01:06:30,600
Speaker 1: it began as joining track in high school. Find the

1234
01:06:30,600 --> 01:06:33,120
Speaker 1: fastest guy, keep up with him. And then when I

1235
01:06:33,160 --> 01:06:35,560
Speaker 1: went to grad school, he said, Hey, remember the advice

1236
01:06:35,560 --> 01:06:38,200
Speaker 1: I gave you about track. I'm going to give you

1237
01:06:38,240 --> 01:06:41,480
Speaker 1: the same exact advice, find the smartest And I'm like, Dad,

1238
01:06:41,520 --> 01:06:43,600
Speaker 1: I'm way ahead of you. I already I already had

1239
01:06:43,600 --> 01:06:47,080
Speaker 1: thought about that. So how does that manifest itself for

1240
01:06:47,800 --> 01:06:50,880
Speaker 1: millennial or college graduate? How would they actually go about

1241
01:06:50,920 --> 01:06:52,960
Speaker 1: doing that? When you're in a job, I said, find

1242
01:06:52,960 --> 01:06:54,680
Speaker 1: the smartest person in the room and it should never

1243
01:06:54,720 --> 01:06:59,040
Speaker 1: be you, um or there's something wrong um, and try

1244
01:06:59,080 --> 01:07:02,040
Speaker 1: to get them to mentor you and be open to it.

1245
01:07:02,040 --> 01:07:04,760
Speaker 1: And you know, anyway, contrary all these perceptions of millennials

1246
01:07:04,840 --> 01:07:06,880
Speaker 1: being no it alls, I find that the millennials I

1247
01:07:06,920 --> 01:07:10,120
Speaker 1: work with are always looking to learn. Um. Maybe I've

1248
01:07:10,120 --> 01:07:11,720
Speaker 1: just been lucky. I don't know if no it alls

1249
01:07:11,840 --> 01:07:16,800
Speaker 1: is the right, uh right description. They're definitely hard working,

1250
01:07:16,880 --> 01:07:20,240
Speaker 1: and they have areas that they have great strengths in,

1251
01:07:20,360 --> 01:07:23,840
Speaker 1: and I think the the biggest knock is their weaknesses.

1252
01:07:23,840 --> 01:07:28,440
Speaker 1: They're not interested in working on UM. But I think

1253
01:07:28,480 --> 01:07:31,680
Speaker 1: they've gotten a bad having worked with them for five

1254
01:07:31,800 --> 01:07:34,960
Speaker 1: years in a firm, H do you think they've gotten

1255
01:07:34,960 --> 01:07:36,760
Speaker 1: a bad rap. Yeah. I think they're the same as

1256
01:07:36,760 --> 01:07:39,600
Speaker 1: every other generation. You got some noisy outliers, and I

1257
01:07:39,640 --> 01:07:42,320
Speaker 1: think with social media the noisy outliers voices are amplified.

1258
01:07:42,520 --> 01:07:44,880
Speaker 1: That's a great observation. I think on average, they're just

1259
01:07:44,960 --> 01:07:48,320
Speaker 1: like everyone else. That that makes perfect sense. UM. Although

1260
01:07:48,320 --> 01:07:52,080
Speaker 1: there they grew up in such a unique like think

1261
01:07:52,120 --> 01:07:54,360
Speaker 1: about what you grew up in and then what people

1262
01:07:54,400 --> 01:07:56,840
Speaker 1: born in ten or twenty years after you growing up

1263
01:07:56,880 --> 01:07:59,680
Speaker 1: in technology is this thing that's kind of cool on

1264
01:07:59,760 --> 01:08:02,440
Speaker 1: this id. They're immersed in it from birth. It's a

1265
01:08:02,440 --> 01:08:06,520
Speaker 1: whole different headspace. So yeah, I mean it's it's very different.

1266
01:08:06,520 --> 01:08:08,560
Speaker 1: I think their brains probably formed differently to some degree.

1267
01:08:08,600 --> 01:08:11,360
Speaker 1: But again and again, I think ultimately even college students,

1268
01:08:11,400 --> 01:08:14,560
Speaker 1: the ones I've interacted with from teaching are also really

1269
01:08:14,560 --> 01:08:18,439
Speaker 1: open to debate in new ideas and even uncomfortable ideas. Again,

1270
01:08:18,479 --> 01:08:23,440
Speaker 1: it's just the noisy outliers. That's that's quite fascinating. Um

1271
01:08:23,479 --> 01:08:26,000
Speaker 1: and our final question, what do you know about the

1272
01:08:26,040 --> 01:08:30,040
Speaker 1: world of risk and economics today that you wish you

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01:08:30,120 --> 01:08:35,240
Speaker 1: knew fifteen twenty years ago when you were just graduating. Well,

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01:08:35,280 --> 01:08:38,880
Speaker 1: that's when I was starting grad school, and you know,

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01:08:39,200 --> 01:08:44,120
Speaker 1: I chose macro, and macro traditionally has not incorporated risk

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01:08:44,160 --> 01:08:47,120
Speaker 1: at all. I didn't realize how fundamental that it was

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01:08:47,200 --> 01:08:49,759
Speaker 1: to the economy. I thought, you know, I was studying

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01:08:49,800 --> 01:08:52,880
Speaker 1: sort of either the neo classical Kensie or new Kynsian models,

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01:08:52,880 --> 01:08:55,160
Speaker 1: where you know, if you do government spending, this is

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01:08:55,200 --> 01:08:57,639
Speaker 1: what happens. Where if I was doing finance, I would

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01:08:57,640 --> 01:08:59,680
Speaker 1: think of how does that impact markets? What are the

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01:08:59,760 --> 01:09:02,679
Speaker 1: range of things that happened. So I wish I knew

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01:09:02,680 --> 01:09:05,839
Speaker 1: about risk because I didn't really, So, Matt, I'm shocked

1284
01:09:05,840 --> 01:09:09,160
Speaker 1: to even hear that I don't have an economics background.

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01:09:09,240 --> 01:09:14,400
Speaker 1: It's just I play an economist on TV. How how

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01:09:14,479 --> 01:09:19,519
Speaker 1: is it possible that macro economics does not incorporate any

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01:09:19,560 --> 01:09:23,439
Speaker 1: analysis or study of risk? That seems shocking. It is

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01:09:23,600 --> 01:09:25,960
Speaker 1: is actually, you know, people don't talk about this because

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01:09:25,960 --> 01:09:28,400
Speaker 1: other things get attention. But I just went to a

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01:09:28,400 --> 01:09:30,439
Speaker 1: conference two or three weeks ago that Lars Hanson and

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01:09:30,400 --> 01:09:34,320
Speaker 1: Antilope put on about how can we incorporate finance into

1292
01:09:34,360 --> 01:09:37,160
Speaker 1: macro how can we put risk into macro models? And

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01:09:37,200 --> 01:09:39,800
Speaker 1: I think this was amongst academics, the big takeaway from

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01:09:39,840 --> 01:09:43,120
Speaker 1: the financial crisis. Then law macro models have no meaningful

1295
01:09:43,200 --> 01:09:45,080
Speaker 1: rule for financial sector, so how are you gonna even

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01:09:45,160 --> 01:09:50,240
Speaker 1: capture systemic risk? So this is really where like hardcore economists,

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01:09:50,240 --> 01:09:52,920
Speaker 1: but they've been working on post crisis, and it's it's

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01:09:52,920 --> 01:09:55,400
Speaker 1: a hard problem because any economic model has to make

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01:09:55,439 --> 01:09:58,520
Speaker 1: choices and once you bring risk in, they get more complicated.

1300
01:09:58,840 --> 01:10:00,880
Speaker 1: This is a controversial thing to say. I mean, I'm

1301
01:10:00,880 --> 01:10:03,519
Speaker 1: not gonna sound sexy to most people, but to small

1302
01:10:03,560 --> 01:10:06,280
Speaker 1: group economist is I now feel strongly I don't think

1303
01:10:06,280 --> 01:10:09,920
Speaker 1: anyone agrees with me that finance basic finance should be

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01:10:10,120 --> 01:10:13,600
Speaker 1: part of basic economics training because it is such a

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01:10:13,640 --> 01:10:16,519
Speaker 1: fundamental part of understanding how the economy works. Meaning basic

1306
01:10:16,600 --> 01:10:19,680
Speaker 1: finance plus an understanding of risk. Well yeah, well you

1307
01:10:19,800 --> 01:10:22,519
Speaker 1: you get a basic understanding of risk if you learn

1308
01:10:22,560 --> 01:10:24,840
Speaker 1: finance because finance is all the principles of risk, because

1309
01:10:25,160 --> 01:10:28,400
Speaker 1: finance and economics is just howcom study risk. It just

1310
01:10:28,439 --> 01:10:30,640
Speaker 1: happens to be in financial markets because that's where the

1311
01:10:30,720 --> 01:10:33,519
Speaker 1: data is. So I think it should be, you know,

1312
01:10:33,960 --> 01:10:39,360
Speaker 1: undergraduate macro micro finance. Quite fascinating. We have been speaking

1313
01:10:39,439 --> 01:10:43,960
Speaker 1: to Alison Schraeger. She is the author of An Economist

1314
01:10:44,240 --> 01:10:48,080
Speaker 1: Walks Into a Brothel, as well as co founder of

1315
01:10:48,120 --> 01:10:53,840
Speaker 1: the life Cycle Financial Partners UH and an adject professor

1316
01:10:53,880 --> 01:10:58,000
Speaker 1: at n y U and journalists at Courts. If you

1317
01:10:58,120 --> 01:11:00,599
Speaker 1: enjoy this conversation, we'll be sure look up an Inch

1318
01:11:00,680 --> 01:11:04,240
Speaker 1: or down an inch on Apple iTunes, UH, Stitcher, Overcast,

1319
01:11:04,240 --> 01:11:08,200
Speaker 1: Bloomberg dot com, wherever you find your favorite podcast. We

1320
01:11:08,320 --> 01:11:12,960
Speaker 1: love your comments, feedback and suggestions right to us at

1321
01:11:13,640 --> 01:11:16,600
Speaker 1: m IB podcast at Bloomberg dot net. Be sure and

1322
01:11:16,680 --> 01:11:19,280
Speaker 1: go to Apple iTunes and give us a delightful review

1323
01:11:19,920 --> 01:11:22,880
Speaker 1: for sharing all this time and information with you. I

1324
01:11:22,960 --> 01:11:25,679
Speaker 1: would be remiss if I did not thank the crack

1325
01:11:25,800 --> 01:11:31,240
Speaker 1: staff that helps put together UH these weekly conversations UH.

1326
01:11:31,280 --> 01:11:35,680
Speaker 1: Attica Valburon is our project manager. Michael Batnick is my

1327
01:11:35,800 --> 01:11:39,800
Speaker 1: head of research. I'm Barry Riholts. You've been listening to

1328
01:11:39,960 --> 01:11:42,519
Speaker 1: pastors of business on Bloomberg Radio.