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

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Speaker 1: So I'm I'm really excited about this week's guest. Jason's Wige.

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Speaker 1: He is a reporter, columnist, author. UM. You probably know

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Speaker 1: his work as the Weekend Investor column in the Wall

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Speaker 1: Street Journal. I'm a big fan of some of his books,

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Speaker 1: including Your Money and Your Brain, which is one of

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Speaker 1: the earliest looks at neuroeconomics and neurofinance. He has a

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Speaker 1: book coming up, uh it's an updated version of inspired

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Speaker 1: by Ambrose Bierce's Devil's Dictionary. It's called The Devil's Financial Dictionary.

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Speaker 1: And we we talked about that. We talk a little

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Speaker 1: bit about journalism and what it's gonna be like going forward,

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Speaker 1: or the impact of social media, Twitter, etcetera. I've read

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Speaker 1: him for so many years and know his work for

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Speaker 1: for just so long. It was really interesting sitting down

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Speaker 1: and talking with him about his assess and who his

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Speaker 1: mentors and influences were, and what sort of advice he

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Speaker 1: would give to somebody in the in the finance world.

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Speaker 1: And I just found it to be really quite fascinating.

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Speaker 1: We probably went about ninety minutes, so given how long

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Speaker 1: that is. UM rather than me, Babil, Let me just

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Speaker 1: say here is my conversation with Jason's Why this is

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Speaker 1: Masters in Business with Barry Ridholds on Bloomberg Radio This

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Speaker 1: week on Masters in Business, our special guest is journalist

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Speaker 1: and author Jason's Wage. He is a columnist for The

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Speaker 1: Wall Street Journal. He is also the author of the

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Speaker 1: upcoming The Devil's Financial Dictionary, loosely based on Ambrose Bierce's

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Speaker 1: famous Devil's Dictionary. I actually have read several of his

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Speaker 1: previous books and really enjoyed him, one of which Your

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Speaker 1: Money and Your Brain Might have been one of the

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Speaker 1: first books that looked at behavioral economics from the investor's perspective.

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Speaker 1: He also helped Danny Kahneman edit Thinking Fast and Slow,

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Speaker 1: as well as being the editor of the revised edition

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Speaker 1: of Benjamin Graham's classic The Intelligent Investor. Jason's Wide Welcome

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Speaker 1: to Bloomberg. Great to be here. Mary. So you have

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Speaker 1: a long and storied history and financial journalism, and we'll

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Speaker 1: we'll get into some of the more granular stuff, but

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Speaker 1: let's let's just go a little bit over your background. UM,

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Speaker 1: I would be remiss if I didn't mention in two

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Speaker 1: thousand and thirteen you won the Lobe Award for Personal

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Speaker 1: Finance Writing. You've also been a writer for Money magazine, Time, CNN,

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Speaker 1: you covered mutual funds for Forbes, and today you're the

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Speaker 1: personal finance writer at The Journal. How did you find

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Speaker 1: your way into financial journalism? Well, I think, like a

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Speaker 1: lot of your guests, Barry, I'm gonna talk about luck.

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Speaker 1: I think I'm anybody who has done read intably well

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Speaker 1: in just about any field who doesn't credit luck first

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Speaker 1: and foremost is kidding himself? Or isn't that fascinating? When

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Speaker 1: when I finally sit down and put together the Book

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Speaker 1: of Masters in Business, how many guests, and not just

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Speaker 1: guests who had like one lucky break, but guys who

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Speaker 1: were billionaires like Howard Marks and Leon Cooperman. He'd say, well,

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Speaker 1: you know, you got really lucky. That's an amazing statement,

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Speaker 1: isn't it. It's and I think it's totally true. And

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Speaker 1: we all like to pride ourselves on our abilities and

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Speaker 1: our skills, but without luck, I'm not sure any of

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Speaker 1: us would be where we are today. I mean, I

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Speaker 1: always wanted to be a writer. I was always fascinated

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Speaker 1: by business. I have no formal training in business or economics.

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Speaker 1: You went to Columbia, but you didn't go to Columbia

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Speaker 1: Business school. You went to Columbia College, And would you

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Speaker 1: study undergraduate art history? And so of course that makes

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Speaker 1: perfect sense. You go from that to prest fun right,

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Speaker 1: and and I think it's probably worth pointing out that, uh,

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Speaker 1: one of your former guests, Charlie Ellis, also also spent

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Speaker 1: some time studying art history. What a delightful gentleman. He's great,

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Speaker 1: one of the best. So so you you The real

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Speaker 1: question I always wanted to ask you, and I'll do

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Speaker 1: it now, is how did you manage to avoid a

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Speaker 1: career and asset management. Uh? Well, I think I I

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Speaker 1: learned early enough in my career as a financial journalist

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Speaker 1: that uh, luck is supremely important in investment management, and

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Speaker 1: very early on I had a hard time distinguishing luck

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Speaker 1: from skill. And I still do. You need a long

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Speaker 1: period and lots of data and the right decision making

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Speaker 1: environment to stand a chance of telling whether something is

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Speaker 1: luck or skill. But that's just a long way of

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Speaker 1: saying that I didn't want to go into investment management

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Speaker 1: because I felt I I sort of knew where the

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Speaker 1: fundamental body was buried. And the body that was buried

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Speaker 1: was that a lot of people who think they're skillful

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Speaker 1: are just lucky. And I didn't really want to go

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Speaker 1: through my professional career with that on my conscience. I

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Speaker 1: guess I have other things on my conscience than that.

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Speaker 1: The flip side of that is a lot of people

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Speaker 1: who think they're unlucky merely or unskillful. Correct. So you

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Speaker 1: mentioned you always were fascinated by business. We always interested

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Speaker 1: in stocks and investing or was that something you came

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Speaker 1: to later. Yeah, I bought my first stock when I

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Speaker 1: was sixteen, Barry, it was, and it was a fascinating experience,

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Speaker 1: especially in hindsight. I bought a stock based on an

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Speaker 1: absolutely god awful book that when I was sixteen thought

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Speaker 1: was pretty persuasive. It was called How I Made two

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Speaker 1: Million Dollars in the Stock Market by Nicholas Darvis. I

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Speaker 1: read that a long time. Yeah, and you remember that

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Speaker 1: thing he talks about where you know, increasing volume on

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Speaker 1: rising price proceeds price exactly and you wait for the

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Speaker 1: price to confirm the volume, and then you then you buy.

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Speaker 1: So you're a closet technician, Well I was, at least

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Speaker 1: I was. This is actually an interesting story because I

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Speaker 1: I did exactly what he suggested. I took graph paper

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Speaker 1: and I tracked ten stocks and um I had a

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Speaker 1: copy of the SNP Stock Guide. This was five or six.

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Speaker 1: It's not a great time to be an equity investor

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Speaker 1: in the first place. And I found a stock that

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Speaker 1: was called and I'm saying called mac a F. I

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Speaker 1: didn't know what it was. The SNP Stock Guide said

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Speaker 1: that it uh it produced liquorice and I think ball

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Speaker 1: bearings or something like that, and uh so and it

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Speaker 1: it turned out to be a perfect Darvis stock. And

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Speaker 1: I bought a hundred shares at nine and five eighths

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Speaker 1: with my you know, summer earnings, and it went up

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Speaker 1: to twelve and seven eighths in a matter of days.

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Speaker 1: And I didn't know a thing about the company other

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Speaker 1: than this ridiculous theory that appeared to be confirmed. It

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Speaker 1: turned out later to be mc andrews and Forbes Ron

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Speaker 1: Perlman's original takeover vehicle. And I think this was around

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Speaker 1: the time that he was first amassing shares. So I

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Speaker 1: bought it for the wrong reason. I made money in

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Speaker 1: it for the wrong reason, and I got stopped out

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Speaker 1: at twelve and seven eighths, but this was so I

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Speaker 1: didn't know I had been stopped out until the trade

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Speaker 1: confirm arrived in the mail about a week later, and

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Speaker 1: I went bonkers and I was like, I'm writing this

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Speaker 1: thing all the way to twenty and I called my

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Speaker 1: parents broker and I said, get me back in and

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Speaker 1: then he flat out refused, and my dad had to

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Speaker 1: buy it for me and went to fourteen and a

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Speaker 1: quarter or something, and my dad browbeat me into selling it.

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Speaker 1: And then where did it ultimately go? I it did

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Speaker 1: very well in the long run. I don't think it

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Speaker 1: did too well. Right after that, you're listening to Masters

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Speaker 1: in Business on Bloomberg Radio. My guest this week is

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Speaker 1: Jason's Wide, author of the upcoming The Devil's Financial Dictionary.

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Speaker 1: Before we get into the book, I want to talk

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Speaker 1: a little bit about social media. I see you tweeting

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Speaker 1: pretty regularly. One of the questions I had for you

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Speaker 1: as a professional journalist, what's the impact of Twitter, and

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Speaker 1: to a lesser degree, blobs on the changing nature of

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Speaker 1: financial journalism. Well, I think it's I think it's really big, Barry,

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Speaker 1: and it's it's good and bad, And maybe let me

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Speaker 1: talk about the bad first. You know, uh, up until

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Speaker 1: a few years ago, certainly until ten years ago, probably five,

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Speaker 1: um reading and investing public could rely on major media

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Speaker 1: outlets to be their filters of information. If you saw

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Speaker 1: something in the Wall Street Journal or the New York Times,

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Speaker 1: for that matter, you could be fairly sure it was reliable, vetted,

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Speaker 1: somebody had looked into what it wasn't just a random

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Speaker 1: It's just not somebody spouting or repeating information generated by

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Speaker 1: people with an axe to grind without checking at first.

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Speaker 1: And I think the danger that we're in in a

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Speaker 1: world dominated by social media and kind of insta pundits

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Speaker 1: through the blog of sphere that's literally a blog inst

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Speaker 1: the plan yeah I know, um, but I wasn't referring

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Speaker 1: to them in particular, is that it's it's much harder

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Speaker 1: today to make qualitative judgments as a consumer of news

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Speaker 1: about what's reliable and what's not, because you're just drinking

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Speaker 1: from a fire hose all day long. And I think

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Speaker 1: the value of Twitter is that by giving you those

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Speaker 1: mic o bursts of information and opinion from people, you

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Speaker 1: get to see that, you get to see what they

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Speaker 1: think in short spurts, and you can fairly quickly form

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Speaker 1: a judgment about how objective they are, how reliable their

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Speaker 1: information is, and I think ultimately it's going to help

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Speaker 1: people separate the good from the bad. You know, one

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Speaker 1: of my favorite aspects about Twitter is just how easy

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Speaker 1: it is to derive a conclusion from somebody's Twitter numbers.

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Speaker 1: So if someone's got eighty thousand tweets and a hundred

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Speaker 1: and forty seven followers, that's a pretty fair assessment that

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Speaker 1: this person is not pranking out quality tweets. On the

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Speaker 1: other hands, when and Warren Buffett is probably the extreme

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Speaker 1: opposite of that, He's got four tweets and six million followers.

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Speaker 1: But between those extremes there's a spectrum. And you know,

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Speaker 1: I have under a hundred thousand followers, My partner has

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Speaker 1: over a hundred thousand followers, and we have a lot

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Speaker 1: less tweets than we have followers. So somebody who's looking

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Speaker 1: at that can say, oh, there has to be something

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Speaker 1: worthwhile and substance if here not that followers or everything. Otherwise,

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Speaker 1: if that was the case, Justin Bieber would be philosopher king.

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Speaker 1: And we know that's not the case, but that's a

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Speaker 1: real good shorthand to say, hey, is this person putting

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Speaker 1: out anything of quality? Is there some way to double check, validate,

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Speaker 1: see if the crowd has any wisdom and they've endorsed

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Speaker 1: this person. Isn't it remarkable? Barry? How how hard that

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Speaker 1: simple algorithm you've just expressed is for so many people

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Speaker 1: to understand? I mean every day on Twitter you see

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Speaker 1: recommended lists of people who tweet, and then you look

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Speaker 1: at them and you see, oh, here's somebody they're recommending

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Speaker 1: because he has fifty thou followers without even noting that

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Speaker 1: he follows eighty thousand people and he's tweeted forty thousand times.

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Speaker 1: So you know, a lot of people are still using

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Speaker 1: followers alone. No, it can't be as the metric. You

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Speaker 1: have to it has to be a combination of quantitative

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Speaker 1: and qualities. You have to say, has the crowd vallet

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Speaker 1: you know, it's sort of like when when you go

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Speaker 1: to Metacritic, you basically say what were the reviews? Like

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Speaker 1: what did the audience say? When when you get a

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Speaker 1: movie like Jurassic Park or Mission Impossible and both the

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Speaker 1: reviewers like it and the crowd likes it. So that's

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Speaker 1: how I use the Twitter numbers, and we've now lost

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Speaker 1: half our audience let's let's let's turn to The Devil's

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Speaker 1: Financial Dictionary, which comes out in October. I've got a

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Speaker 1: preliminary copy of of the book, and that's one of

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Speaker 1: the reasons why I wanted to sit down and chat

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Speaker 1: with you. I found it. Let me let me rather

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Speaker 1: than me telling you what I think, let me go

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Speaker 1: some of the early reviews. Bob Schiller, Nobel winner and

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Speaker 1: Professor of Finance at Yale University. The most amusing presentation

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Speaker 1: of principle is the finance I've ever seen. John Bogel,

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Speaker 1: founder of the Vanguard Group. Laugh, cry, and learn as

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Speaker 1: you enjoy the sparkling Devil's Financial Dictionary. The List of

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Speaker 1: people who have given you blurbs Burton Malkiel, author author

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Speaker 1: of Random Walked Down Wall Street, delightfully humorous and stunningly irreverent.

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Speaker 1: James Grant opened this wonderful book to any page, try

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Speaker 1: not to laugh, I dare you. And then Bill Sharp

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Speaker 1: essentially created the Sharp ratio and another Nobel laureate at

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Speaker 1: Stanford The run of People You got to um. I

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Speaker 1: was like disappointed, No Buffett, and then I scrolled down

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Speaker 1: and there's Warren Buffett's blured it. It's Um, it's an

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Speaker 1: amazing run of people. Tell us a little bit about

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Speaker 1: the motivation and thought process behind the book. Well, I've

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Speaker 1: always loved Ambrose Beers's Devil's Dictionary. You know. Beers is

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Speaker 1: maybe the most unjustly elected author of the nineteenth century

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Speaker 1: in America. He was a contemporary of Mark Twain's. He

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Speaker 1: was at least as brilliant. He wrote some of the

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Speaker 1: most devastating epigrams ever written. And um, he was brutally

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Speaker 1: cynical about American society and institutions, and he took on

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Speaker 1: a few He he basically compiled The Devil's Dictionary as

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Speaker 1: a collection of satirical definitions of terms most people think

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Speaker 1: of in a very conventional way. And he just blew

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Speaker 1: up every sort of bit of conventional wisdom and turned

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Speaker 1: them all on their head. And it just occurred to

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Speaker 1: had had in mind for about fifteen years would be

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Speaker 1: easier to do than I realized. And I just started

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Speaker 1: doing it in my spare time. And my wife heard

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Speaker 1: me cackling in the kitchen at night, and I really,

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Speaker 1: if I was bothering her, I must be having fun.

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Speaker 1: And I just kept at it. And you know, my

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Speaker 1: goal was to try to do something like what Beers

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Speaker 1: And you know a couple of definitions from the original

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Speaker 1: Devil's Dictionary or worth keeping in mind. I mean these

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Speaker 1: are bear in mind. I'm going to read these. And

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Speaker 1: first comes the word, then comes to the part of speech,

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Speaker 1: then comes to the definition. So this is ambrose beers

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Speaker 1: ambidextrous adjective able to pick with equal skill a right

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Speaker 1: hand pocket or a left I gotta like that. In

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Speaker 1: the in the last thirty seconds we have of this segment, Um,

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Speaker 1: what are some of your favorite ones from the new

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Speaker 1: Financial Devil's Dictionary. I'll give you one of my favorite

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Speaker 1: day trader see idiot. Yeah, that's to get some hate

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Speaker 1: mail on that one. That might be my favorite too.

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Speaker 1: And maybe we can, maybe we can get into some

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Speaker 1: of the longer ones. A bit later, you're listening a

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Speaker 1: Master's in Business on Bloomberg Radio. I'm speaking with jason's Wage,

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Speaker 1: personal finance writer and author of numerous books, his most

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Speaker 1: recent being The Devil's Financial Dictionary. Tell me, uh, some

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Speaker 1: of your favorite selections from this volume? Well, Barry, maybe

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Speaker 1: I can, maybe I can read one quickly, and you know,

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Speaker 1: I'll read the definition, the part of speech, and then

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Speaker 1: I have to warrnt everybody. There are what I call

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Speaker 1: flights of fancy in these, okay, which are these little

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Speaker 1: um imaginary passages featuring people who bear no resemblance to

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Speaker 1: anyone you and I might? Alright, So option nown the

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Speaker 1: right to buy or sell a financial asset at a

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Speaker 1: fixed price on or before a specific time from the

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Speaker 1: Latin opt o. I choose a boon for stockholders whose

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Speaker 1: clients don't understand how options work and generate a fortune

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Speaker 1: in commissions as they attempt to learn. And here comes

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Speaker 1: the flight of fancy stockholders of stockbrokers. Stockbrokers, explained you,

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Speaker 1: asking me, a client of the broker age firm born

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Speaker 1: rich and how I put two children through Harvard by

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Speaker 1: trading options. Unfortunately they were my broker's children. That's great,

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Speaker 1: so um obviously ambrosepers huge, huge influence. You mentioned you

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Speaker 1: were working on this at night. How long did it

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Speaker 1: take you to assemble this is a pretty thorough financial distinary.

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Speaker 1: How how long did this stake? It didn't take very long, actually, U.

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Speaker 1: You know a lot of these I'd had in my

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Speaker 1: head for a long time, so it's just a matter

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Speaker 1: of getting it out and on some page. Yeah. The

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Speaker 1: real what I'm trying to do with this book is

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Speaker 1: in each case when I define a term, I'm saying

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Speaker 1: to myself, you know, after more than a quarter century

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Speaker 1: of you know, researching and reporting on the financial markets,

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Speaker 1: can I take everything I've learned and boil it down

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Speaker 1: into a hundred fifty words or less? And then if

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Speaker 1: I can do that, how about fifty words? And in

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Speaker 1: some cases I got them down to a single word?

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Speaker 1: Give me us an example other than the day trader. Um. Uh, well,

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Speaker 1: there's one which is human adjective biased. We're gonna talk

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Speaker 1: a little more about the biases and cognitive issues of

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Speaker 1: humans coming up in a bit. Uh. Who is the

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Speaker 1: ideal audience for this? Is this scared for the professional?

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Speaker 1: Is this for a mom and pop investor? Who are

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Speaker 1: you thinking about when you were writing this? Well? Really everybody.

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Speaker 1: There's a lot of inside jokes in this book that

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Speaker 1: I think only a professional audience will get and will

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Speaker 1: certainly be annoyed by and I hope amused by. I

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Speaker 1: think one of the wonderful things about the Wall Street

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Speaker 1: community is that people do have an ability to laugh

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Speaker 1: at themselves. UM, at least the better people in the

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Speaker 1: in the field do. And there's a lot to laugh about,

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Speaker 1: so to say the least, I'm curious as to how

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Speaker 1: the research for this win. I know you said this

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Speaker 1: has been kicking around your head for fifteen years, but yeah,

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Speaker 1: I just picture you doing a pretty deep dive, if

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Speaker 1: not wearing the white gloves and the stacks of Yale,

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Speaker 1: certainly working your way through UM either Lexus or Google

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Speaker 1: to try and find some so early ideology of some

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Speaker 1: of these words. Yeah, a lot in a lot of cases,

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Speaker 1: because it is a true dictionary. So I was trying

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Speaker 1: to get at where a lot of these terms derived from.

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Speaker 1: And in many cases, people who use these terms all

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Speaker 1: day long don't really know what they mean, so they're

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Speaker 1: just using them as jargon, not as as true exactly.

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Speaker 1: And and so yes, there's I did a lot of

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Speaker 1: historical and archival research trying to figure out when a

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Speaker 1: certain term was first used, where came from. I spent

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Speaker 1: a lot of time with the Oxford English Dictionary and

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Speaker 1: UM all kinds of historical research as well. And you

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Speaker 1: have quite the background as an editor. One of the

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Speaker 1: questions I had to ask, So, the very famous classic

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Speaker 1: book by Benjamin Graham, The Intelligent Investor, you actually were

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Speaker 1: the editor of the revised edition. How on earth did

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Speaker 1: that invitation come along? Well, I don't think it hurt

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Speaker 1: that I was a Graham afficionado. I mean I I

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Speaker 1: first read The Intelligent Investor in March when I started

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Speaker 1: as a financial journalist at Forbes Magazine, and I probably

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Speaker 1: read it cover to cover at least six times. I'd

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Speaker 1: read almost all of Graham's shorter writings as well, and

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Speaker 1: all the biographies of him that existed at the time.

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Speaker 1: But ultimately it was it was uck like everything else

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Speaker 1: we've been talking about. Um. A friend of mine had

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Speaker 1: written a book for Harbercollins. Uh, one of the top

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Speaker 1: editors at the publishing house, was out to lunch with

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Speaker 1: her and said, uh, you know, we have this book.

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Speaker 1: Who do you think should do it? And she said, oh, well,

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Speaker 1: there was only one person who should do it, and

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Speaker 1: she told him to ask me. And why did she

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Speaker 1: do that? Because we had both been at a party

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Speaker 1: about a month beforehand. We hadn't seen each other in years.

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Speaker 1: I had seen her across the room and I said, oh,

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Speaker 1: I haven't seen her in years, and so I made

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Speaker 1: a special point going to say hi. And if I

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Speaker 1: hadn't said hi to her, she would have recommended somebody else.

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Speaker 1: You're listening to Master's in Business on Bloomberg Radio. We're

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Speaker 1: speaking with Jason's Wide, personal finance writer and author. Um.

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Speaker 1: One of the things I wanted to really go over

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Speaker 1: with you, uh, is your perspective as a personal finance columnist.

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Speaker 1: And I'm going to start by quoting you and then

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Speaker 1: and then we'll discuss it. You once wrote, my job

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Speaker 1: is to write the exact same thing between fifty and

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Speaker 1: one hundred times a year in such a way that

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Speaker 1: neither my editors nor my readers will ever think I'm

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Speaker 1: repeating myself. Explain. Um, Yeah, there's a simple reason for that, Barry.

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Speaker 1: I'm and I think you and I think alike on this.

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Speaker 1: There's there's really only a handful of things you can

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Speaker 1: tell investors about their portfolios that are true before you

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Speaker 1: get into things that are bad for them. And you know,

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Speaker 1: we could disagree about the number. There might be six

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Speaker 1: things they are good for people, there might be thirty,

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Speaker 1: but there aren't a hundred. And I'm and there's at

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Speaker 1: most a couple dozen. So We're not going to see

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Speaker 1: a daily television show for you where an hour day

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Speaker 1: you wax eloquence on the issues of the moment. Well,

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Speaker 1: probably not, because you know, as I, as I also

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Speaker 1: mentioned in the same piece you're quoting from, you know,

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Speaker 1: five per cent of the time, the right thing for

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Speaker 1: people to do is nothing. And uh, there's a financial

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Speaker 1: media that exists all day long, NonStop, having to provide

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Speaker 1: people with what appears to be information, well certainly content content.

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Speaker 1: And if you you can't do that, if you believe

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Speaker 1: that the right thing to tell people to do is nothing,

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Speaker 1: So that Jason's wide TV show would be have a

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Speaker 1: broadly diversified asset allocation model, rebalance regularly, watch your fees,

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Speaker 1: see you tomorrow. Yeah, diversify, buy and hold, minimize your

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Speaker 1: costs and your taxes. And I keep your hands in

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Speaker 1: your pockets, don't. Don't just do something? Sit there right

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Speaker 1: and keep other people's hands out of your pockets. That

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Speaker 1: makes sense. So let me let me re ask that

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Speaker 1: that question in a slightly different way, along the same

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Speaker 1: spirit of that that question, what does good financial advice

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Speaker 1: look like? Good financial advice on keeps people from being

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Speaker 1: their own worst enemies? You know. Benjamin Graham famously wrote

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Speaker 1: about that in the introduction to the Intelligent Investor, and

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Speaker 1: I think it all really boils down to one very

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Speaker 1: very simple thing, which is that markets regrets to the mean,

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Speaker 1: and the way you in the short run, the way

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Speaker 1: you attract attention to yourself if you're providing financial advice,

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Speaker 1: is by encouraging people to bet against regression to the mean.

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Speaker 1: So if an asset is rapidly going up in price,

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Speaker 1: you tell people buy more while you still can, and

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Speaker 1: if it's going down, you tell him to sell before

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Speaker 1: it's too late. But if you're trying to help people

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Speaker 1: get on the right side of regression to the mean,

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Speaker 1: then you give them countercyclical advice. When an asset is

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Speaker 1: going up and Brice, you tell them be cautious, and

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Speaker 1: when it's going down, you tell them to become more aggressive.

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Speaker 1: Quite interesting, So let me ask you the flip side

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Speaker 1: of that. Uh, why is it that so much bad

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Speaker 1: advice seems to hang around and not die? Why why?

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Speaker 1: Or or as slightly differently, why is there so much

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Speaker 1: zombie advice for investors? Well? I think it boils down

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Speaker 1: to something very simple. Barry. I think you know, humans

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Speaker 1: have a profound um, a profound desire to believe in magic.

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Speaker 1: People want to be lied to? And was it you

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Speaker 1: who recently wrote um the three ways to make money

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Speaker 1: as a as a writer lie to. If you, if

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Speaker 1: you tell people to truth, truth who don't want to

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Speaker 1: hear it, you're not gonna make any money. If you

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Speaker 1: tell people truth who want to hear the truth, they're

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Speaker 1: they're gonna make some money. But if you a lot

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Speaker 1: of the people who want to be lied to, you're

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Speaker 1: gonna make the most amount of Yeah, if you, if

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Speaker 1: you tell the truth to people who want to be

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Speaker 1: lied to, you'll starve. That's unbelievable. It's true. And so

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Speaker 1: what is it within our makeup that wants us to

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Speaker 1: be told the soft, easy, comfortable thing and not Hey, here,

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Speaker 1: here's you know, tough love, here's the cold, hard reality.

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Speaker 1: The sooner you learn that, the better off you are. Well,

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Speaker 1: people are people are machines, right, and their machines that

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Speaker 1: are designed to optimize a few things. Pattern recognition, self

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Speaker 1: enhancement m HM, and self delusion. I mean, human beings

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Speaker 1: are really good at those three things. We're we almost

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Speaker 1: certainly have evolved to maximize is those things and that's

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Speaker 1: why people are so susceptible to bad advice, because bad

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Speaker 1: advice praise on that stuff emotionally, praise to those emotional

428
00:27:12,240 --> 00:27:16,960
Speaker 1: hot buttons. Exactly. Interesting. So let's let's talk about some

429
00:27:16,960 --> 00:27:19,639
Speaker 1: more good advice. Who are the young writers that you

430
00:27:19,680 --> 00:27:24,280
Speaker 1: were reading today that you find to be giving the

431
00:27:24,320 --> 00:27:28,199
Speaker 1: sort of good advice or or good writing that is

432
00:27:28,240 --> 00:27:32,320
Speaker 1: beneficial to investors? And yeah, I think I I think

433
00:27:32,359 --> 00:27:35,560
Speaker 1: I I like some of the same people you do.

434
00:27:35,640 --> 00:27:40,040
Speaker 1: Barry Well, it's not a coincidence. You being here is

435
00:27:40,080 --> 00:27:44,879
Speaker 1: just my confirmation bias that works, Yes, exactly, and mine too. Um.

436
00:27:44,920 --> 00:27:47,240
Speaker 1: You know the first person I would name would be Morgan.

437
00:27:47,320 --> 00:27:50,399
Speaker 1: How is only the Moley fool? I think? Um? I

438
00:27:50,440 --> 00:27:54,280
Speaker 1: think he does just incredibly sharp, really insightful stuff. Love

439
00:27:54,320 --> 00:27:58,439
Speaker 1: his work. Ben Carlson a wealth of common sense, really

440
00:27:59,520 --> 00:28:05,240
Speaker 1: that's his does great stuff. James Osborne at Basin Asset

441
00:28:05,320 --> 00:28:08,840
Speaker 1: Management does also really cool stuff. There's a bunch of

442
00:28:08,880 --> 00:28:12,920
Speaker 1: other people as well, I'm who are terrific. It's amazing

443
00:28:13,080 --> 00:28:17,840
Speaker 1: how the meritocracy of financial writing, how these names have

444
00:28:18,000 --> 00:28:21,480
Speaker 1: risen up to the top fairly quickly. Ben Carlson as

445
00:28:21,520 --> 00:28:26,680
Speaker 1: an example, I mean he started blogging. I don't know,

446
00:28:26,840 --> 00:28:30,280
Speaker 1: a year ago, maybe less really relative, maybe it's two

447
00:28:30,359 --> 00:28:34,879
Speaker 1: years ago relatively short time, quickly found an institutional audience

448
00:28:35,320 --> 00:28:38,320
Speaker 1: and puts out really really good stuff on a regular Yeah.

449
00:28:38,360 --> 00:28:40,360
Speaker 1: And I think this is a very hopeful sign for

450
00:28:40,400 --> 00:28:42,120
Speaker 1: the future. You know, there are a lot of people

451
00:28:42,200 --> 00:28:48,920
Speaker 1: and Charlie Munger is one worrying what does he blog away?

452
00:28:50,200 --> 00:28:54,000
Speaker 1: I know I recognized that he. Uh, he's very worried

453
00:28:54,040 --> 00:28:57,840
Speaker 1: about the institutional impact of the decline of the traditional

454
00:28:58,360 --> 00:29:01,360
Speaker 1: great newspaper. And we don't know yet what's going to

455
00:29:01,480 --> 00:29:05,840
Speaker 1: replace it. But the fact that that really talented, bright

456
00:29:05,960 --> 00:29:09,160
Speaker 1: young people have been able to sort of rise to

457
00:29:09,240 --> 00:29:12,400
Speaker 1: the top, I think is is a hopeful sign for

458
00:29:12,440 --> 00:29:16,760
Speaker 1: the future. I can't say I I disagree. Um, what

459
00:29:16,840 --> 00:29:19,160
Speaker 1: are the writers do you read? Who else have you

460
00:29:19,200 --> 00:29:22,800
Speaker 1: read over the years that really stood out as as

461
00:29:22,840 --> 00:29:27,240
Speaker 1: a powerful and influential voice to you? Well, a lot

462
00:29:27,280 --> 00:29:32,840
Speaker 1: of them are are now of another generation. Um, Charlie

463
00:29:32,840 --> 00:29:38,080
Speaker 1: Ellis was a very big influence on me. I'm Ben Graham,

464
00:29:38,200 --> 00:29:44,680
Speaker 1: of course. Uh. Probably the single biggest influences on the

465
00:29:44,920 --> 00:29:49,600
Speaker 1: on the way I try to think, uh would be

466
00:29:49,680 --> 00:29:58,800
Speaker 1: Richard Feineman, the physicist m yes um uh. Who's wonderful

467
00:29:58,840 --> 00:30:01,720
Speaker 1: books these oral streets, And there's a series of them.

468
00:30:01,720 --> 00:30:04,240
Speaker 1: There's four or five of them I think are among

469
00:30:04,280 --> 00:30:08,520
Speaker 1: the most engaging books on how to think. I believe

470
00:30:08,560 --> 00:30:12,440
Speaker 1: you could find anywhere. And of course Danny Kahneman the

471
00:30:13,240 --> 00:30:17,200
Speaker 1: Thinking Fast Uh and Slow Ye was a huge influence

472
00:30:17,240 --> 00:30:20,280
Speaker 1: on me as well, and I was very fortunate to

473
00:30:20,320 --> 00:30:22,040
Speaker 1: be able to work with him and help him on

474
00:30:22,080 --> 00:30:25,600
Speaker 1: the book that that was a fascinating book, which leads

475
00:30:25,640 --> 00:30:29,120
Speaker 1: to a question. So you and I both found behavioral

476
00:30:29,160 --> 00:30:32,560
Speaker 1: economics fairly early in our career. How did you stumble

477
00:30:32,640 --> 00:30:42,160
Speaker 1: into that space? Uh? Well, I realized around that I

478
00:30:42,200 --> 00:30:46,120
Speaker 1: was running out of interesting questions. I didn't think. I

479
00:30:46,120 --> 00:30:48,600
Speaker 1: I didn't think by any means I had all the answers.

480
00:30:49,480 --> 00:30:53,320
Speaker 1: But as someone who didn't have professional training in finance

481
00:30:54,040 --> 00:30:57,680
Speaker 1: or economics, but who sort of sat at the feet

482
00:30:57,720 --> 00:31:00,600
Speaker 1: of the smartest people, I could find a lot of

483
00:31:00,640 --> 00:31:04,320
Speaker 1: the questions were starting to feel repetitive to me, my

484
00:31:04,520 --> 00:31:09,880
Speaker 1: questions and other people's questions. And there was still this

485
00:31:09,880 --> 00:31:12,760
Speaker 1: this fundamental mystery that nobody seemed to have an answer to,

486
00:31:12,840 --> 00:31:16,120
Speaker 1: which is why do smart people do such stupid things?

487
00:31:16,480 --> 00:31:19,920
Speaker 1: When it comes to money. It's amazing. History is replete

488
00:31:20,320 --> 00:31:24,760
Speaker 1: with examples, whether it's Isaac Newton, Mark Twain, go down

489
00:31:24,760 --> 00:31:29,040
Speaker 1: the list of various brilliant, insightful people who will manage

490
00:31:29,080 --> 00:31:32,280
Speaker 1: to just blow up in the stock mark. Ye. And

491
00:31:32,280 --> 00:31:35,800
Speaker 1: and that's really the central question that has obsessed me

492
00:31:36,520 --> 00:31:39,360
Speaker 1: for the past twenty years is why do smart people

493
00:31:39,480 --> 00:31:44,440
Speaker 1: become stupid when money comes into the room. Well, we've

494
00:31:44,480 --> 00:31:46,920
Speaker 1: run out of time for this segment. If you enjoy

495
00:31:47,000 --> 00:31:49,920
Speaker 1: this conversation, be sure and check out our podcast extras

496
00:31:50,240 --> 00:31:54,400
Speaker 1: with a tape keeps rolling and we continue our conversation. Uh.

497
00:31:54,440 --> 00:31:57,080
Speaker 1: If you want to catch more of Jason's wives work,

498
00:31:57,400 --> 00:31:59,880
Speaker 1: you can follow him on Twitter. Your Twitter handle is

499
00:32:00,240 --> 00:32:03,800
Speaker 1: Jason's wag w s J. I'm Barry RIH Halts. Be

500
00:32:03,840 --> 00:32:06,320
Speaker 1: sure and check out my daily column on Bloomberg View

501
00:32:06,360 --> 00:32:10,000
Speaker 1: dot com. Follow me on Twitter at rid Halts. You've

502
00:32:10,000 --> 00:32:14,760
Speaker 1: been listening to Masters in Business on Bloomberg Radio. Welcome

503
00:32:14,840 --> 00:32:18,200
Speaker 1: to the podcast Extras. My guest this week is Jason's

504
00:32:18,200 --> 00:32:20,440
Speaker 1: Wage and there's so much stuff I want to ask

505
00:32:20,480 --> 00:32:23,320
Speaker 1: you about. You know, it's funny those segments that are

506
00:32:24,120 --> 00:32:28,440
Speaker 1: seven minutes, eight minutes, six minutes, eleven minutes, I lose

507
00:32:28,480 --> 00:32:30,760
Speaker 1: a little bit of rhythm because I'm always aware of

508
00:32:30,800 --> 00:32:32,960
Speaker 1: the time. And then we get to this segment and

509
00:32:33,200 --> 00:32:36,080
Speaker 1: we could kind of take our shoes off and kick

510
00:32:36,160 --> 00:32:38,640
Speaker 1: back and relax. So let me go over some of

511
00:32:38,680 --> 00:32:42,720
Speaker 1: the questions that I skipped that I wanted to to

512
00:32:42,760 --> 00:32:46,800
Speaker 1: ask you. And these are really print real questions printed out.

513
00:32:47,360 --> 00:32:49,760
Speaker 1: You know you can you can get the home copies

514
00:32:49,840 --> 00:32:53,360
Speaker 1: if you want. Um. We talked briefly about Twitter and

515
00:32:53,440 --> 00:32:57,520
Speaker 1: blogs and social media. Um. What I wanted to ask

516
00:32:57,680 --> 00:33:00,640
Speaker 1: was not so much about commentary in and now lissis,

517
00:33:01,120 --> 00:33:05,400
Speaker 1: which is always its own separate page in a newspaper,

518
00:33:05,440 --> 00:33:08,520
Speaker 1: but what about just straight up reporting. We keep seeing

519
00:33:08,640 --> 00:33:15,680
Speaker 1: various media outlets just ending various local reporting, city hall

520
00:33:15,840 --> 00:33:18,600
Speaker 1: that the other thing, what does this mean for the

521
00:33:18,640 --> 00:33:22,360
Speaker 1: future of of actual not what you and I do,

522
00:33:22,600 --> 00:33:25,400
Speaker 1: but actual reporting with people go out and try and

523
00:33:25,480 --> 00:33:29,240
Speaker 1: find the facts in current events and tell the rest

524
00:33:29,240 --> 00:33:31,080
Speaker 1: of the world about it. Yeah, I think that I

525
00:33:31,120 --> 00:33:36,520
Speaker 1: think you know, all of this is like is sort

526
00:33:36,520 --> 00:33:40,360
Speaker 1: of like a Newton's law of thermodynamics, right, Barry. I mean,

527
00:33:40,400 --> 00:33:46,320
Speaker 1: for everything created, something is destroyed. And you know, the

528
00:33:46,320 --> 00:33:50,680
Speaker 1: the Internet and the advent of social media have democratized

529
00:33:51,400 --> 00:33:54,360
Speaker 1: the spread and for that matter, in the generation of

530
00:33:55,160 --> 00:33:58,960
Speaker 1: news and information, so that anyone with a cell phone

531
00:34:00,080 --> 00:34:05,200
Speaker 1: hand become a broadcaster or a reporter. The problem is

532
00:34:05,680 --> 00:34:11,960
Speaker 1: that anybody who has done shoe leather investigative reporting in

533
00:34:12,160 --> 00:34:14,799
Speaker 1: one of the great newsrooms of America like the Wall

534
00:34:14,840 --> 00:34:17,960
Speaker 1: Street Journal or or the New York Times or the

535
00:34:18,000 --> 00:34:21,720
Speaker 1: Washington Post or the l A Times, any of the

536
00:34:21,760 --> 00:34:26,120
Speaker 1: great or or once great newspapers in this country or

537
00:34:26,200 --> 00:34:30,200
Speaker 1: magazines for that manner, can tell you that reporting is

538
00:34:30,520 --> 00:34:34,759
Speaker 1: brutally hard work to get it right. It's really hard

539
00:34:34,800 --> 00:34:38,360
Speaker 1: work to do. It's really hard work to prevent yourself

540
00:34:38,440 --> 00:34:44,160
Speaker 1: from injecting your own viewpoint, bias, personality. It's and it's

541
00:34:44,160 --> 00:34:48,040
Speaker 1: absolutely essential to a functioning democrat. It's I mean, it's not.

542
00:34:48,280 --> 00:34:51,200
Speaker 1: The press is not called the fourth Estate for nothing.

543
00:34:51,760 --> 00:34:55,520
Speaker 1: And Charlie Munger's fear, and this is the fear that

544
00:34:55,680 --> 00:35:01,760
Speaker 1: I share, is that when reporting gets atomized the way

545
00:35:01,800 --> 00:35:06,640
Speaker 1: we see it unfolding on the Internet and in social media,

546
00:35:07,239 --> 00:35:12,400
Speaker 1: you don't have that institutional memory and historical tradition, and

547
00:35:12,480 --> 00:35:18,239
Speaker 1: most importantly, that professional training in newsrooms the way you

548
00:35:18,280 --> 00:35:23,040
Speaker 1: once did. You don't have reporters doing two three five

549
00:35:23,239 --> 00:35:29,840
Speaker 1: years of understudy with unmasters, and and so the attention

550
00:35:29,920 --> 00:35:37,320
Speaker 1: to fairness and detail and proper methodology falls by the wayside.

551
00:35:37,360 --> 00:35:40,000
Speaker 1: And if you think about House of Cards, for example,

552
00:35:40,120 --> 00:35:42,600
Speaker 1: the you know, the TV I'm going to call it

553
00:35:42,600 --> 00:35:46,640
Speaker 1: a melodrama with that's very compelling, with Kevin Spacey and

554
00:35:46,760 --> 00:35:51,520
Speaker 1: Robin Wright, House of Cards, House of Games, House of Cards, um,

555
00:35:52,280 --> 00:35:54,799
Speaker 1: you know, in that show, over and over again, you

556
00:35:54,840 --> 00:35:58,520
Speaker 1: see these journalists at a newspaper clearly modeled after the

557
00:35:58,520 --> 00:36:03,640
Speaker 1: Washington Post doing things that are so unscrupulous that if

558
00:36:03,680 --> 00:36:06,840
Speaker 1: you did them once at the Wall Street Journal, you

559
00:36:06,880 --> 00:36:12,759
Speaker 1: would be summarily fired. And it's it now just goes

560
00:36:12,840 --> 00:36:16,439
Speaker 1: without passing, without mention. I think the American public sort

561
00:36:16,440 --> 00:36:20,800
Speaker 1: of takes it for granted that journalists are sloppy creeps who,

562
00:36:21,200 --> 00:36:25,520
Speaker 1: you know, invade people's privacy and lie to their sources regularly,

563
00:36:25,640 --> 00:36:30,040
Speaker 1: and in fact, believe it or not, journalists in a

564
00:36:30,080 --> 00:36:36,600
Speaker 1: traditional newsroom environment are phenomenally ethical people who who would

565
00:36:36,800 --> 00:36:40,640
Speaker 1: you could put most journalists thumb in a screw and

566
00:36:40,719 --> 00:36:43,040
Speaker 1: tighten it before you would get them to tell a lie.

567
00:36:43,880 --> 00:36:47,120
Speaker 1: That I don't disagree with that it's always the bed

568
00:36:47,160 --> 00:36:51,279
Speaker 1: eggs that that spoil everybody's perception. But I want to

569
00:36:51,440 --> 00:36:55,120
Speaker 1: mention something you just said earlier, that anybody with a

570
00:36:55,200 --> 00:37:00,239
Speaker 1: cell phone can can broadcast and and literally, I'm gonna

571
00:37:00,320 --> 00:37:03,280
Speaker 1: do this. I'm gonna do this right now. I'm gonna

572
00:37:03,440 --> 00:37:08,200
Speaker 1: use periscope and broadcast a few seconds of our interview

573
00:37:08,920 --> 00:37:13,120
Speaker 1: off of a phone. That this is fascinating technology that

574
00:37:13,600 --> 00:37:16,520
Speaker 1: did not exist forget twenty five years ago, this didn't

575
00:37:16,520 --> 00:37:21,600
Speaker 1: exist eight months ago. Exactly. It's amazing that the technology

576
00:37:21,640 --> 00:37:25,280
Speaker 1: for this exists today. And so how do you compete

577
00:37:25,280 --> 00:37:29,319
Speaker 1: with that? How do you build a television station and

578
00:37:29,400 --> 00:37:33,920
Speaker 1: go through the training of reporters and journalists when everyone

579
00:37:33,960 --> 00:37:36,799
Speaker 1: and anyone can be a citizen journalist. What happens to

580
00:37:36,880 --> 00:37:42,239
Speaker 1: that business model that digital technology is challenging. It's amazing. Well,

581
00:37:42,360 --> 00:37:44,480
Speaker 1: first of all, Barry, thank you for not pointing the

582
00:37:44,600 --> 00:37:47,640
Speaker 1: camera at your feet. Um. And secondly, I said, can't

583
00:37:47,680 --> 00:37:51,040
Speaker 1: off of shoes and get comfortable, and uh, literally, that's

584
00:37:51,040 --> 00:37:54,560
Speaker 1: what we did. And secondly, I'm you know, look, you're

585
00:37:54,640 --> 00:37:58,880
Speaker 1: raising an incredibly troubling question and we don't yet know

586
00:37:58,960 --> 00:38:00,719
Speaker 1: the answer because we don't how this is going to

587
00:38:00,800 --> 00:38:06,720
Speaker 1: play out. I think ultimately the better emerging news organizations.

588
00:38:07,480 --> 00:38:09,799
Speaker 1: And you know, we could all think of you know,

589
00:38:09,840 --> 00:38:12,360
Speaker 1: some of the candidates right like, you know, maybe Vice

590
00:38:12,520 --> 00:38:16,239
Speaker 1: or some some outfit like that. Um, we'll get the

591
00:38:16,280 --> 00:38:21,680
Speaker 1: capital and the experience and the professionalism that they need

592
00:38:21,680 --> 00:38:25,120
Speaker 1: to build something that competes with the traditional newsroom like

593
00:38:25,160 --> 00:38:29,759
Speaker 1: the Wall Street Journal or Bloomberg, and um, that could

594
00:38:29,760 --> 00:38:33,880
Speaker 1: take a long time now, and ultimately those organizations are

595
00:38:33,880 --> 00:38:39,680
Speaker 1: going to have to cannibalize the existing great news organizations

596
00:38:40,680 --> 00:38:45,320
Speaker 1: to staff themselves up with people who have the experience

597
00:38:45,560 --> 00:38:52,319
Speaker 1: and the ethical background to run uh, proper news organization.

598
00:38:52,520 --> 00:38:54,520
Speaker 1: I'm running, I'm running out of battery here, so I'm

599
00:38:54,560 --> 00:38:58,759
Speaker 1: gonna kill periscope and I think that that's done. But um,

600
00:38:58,920 --> 00:39:02,759
Speaker 1: that was just thirty of that. UM, I think you're

601
00:39:02,800 --> 00:39:05,400
Speaker 1: onto something with that, which which leads me to a

602
00:39:05,480 --> 00:39:09,320
Speaker 1: question I wanted to get to earlier, um, and didn't.

603
00:39:09,719 --> 00:39:13,040
Speaker 1: It's actually two related questions. The first is this comes

604
00:39:13,120 --> 00:39:16,400
Speaker 1: right from you, from your money and your brain. People

605
00:39:16,400 --> 00:39:20,520
Speaker 1: who received frequent news updates on their investments earn lower

606
00:39:20,560 --> 00:39:25,600
Speaker 1: returns than those who get no news whatsoever. Discuss Yeah,

607
00:39:25,640 --> 00:39:29,280
Speaker 1: this is based on a series of fascinating experiments done

608
00:39:29,320 --> 00:39:33,280
Speaker 1: by a psychologist named Paul Andreas and in the nineteen

609
00:39:33,400 --> 00:39:36,840
Speaker 1: eighties he was at Columbia for a while and Harvard,

610
00:39:37,440 --> 00:39:42,360
Speaker 1: and he took UH. He took experimental subjects and divided

611
00:39:42,400 --> 00:39:46,800
Speaker 1: them into two groups. Some got essentially no no updated

612
00:39:46,840 --> 00:39:53,000
Speaker 1: information on their chosen investment portfolios, and others got frequent,

613
00:39:53,440 --> 00:39:57,480
Speaker 1: in fact, almost constant news updates. And then he tracked

614
00:39:57,480 --> 00:40:01,000
Speaker 1: their investment performance. And he found that the more often

615
00:40:01,040 --> 00:40:04,200
Speaker 1: people got news, the more often they traded. And we

616
00:40:04,280 --> 00:40:06,120
Speaker 1: know what the net outcome of that is for the

617
00:40:06,200 --> 00:40:08,759
Speaker 1: average invest The more you trade, the less you learn.

618
00:40:09,480 --> 00:40:13,840
Speaker 1: And it's a direct relationship. It's just if A then B.

619
00:40:14,520 --> 00:40:19,560
Speaker 1: If you get more news information, more updates, you will

620
00:40:19,640 --> 00:40:24,000
Speaker 1: act on it because it will feel informative to you,

621
00:40:24,080 --> 00:40:26,920
Speaker 1: even if it's just even if the news is just noise,

622
00:40:27,560 --> 00:40:29,600
Speaker 1: you will feel you have to act on it because

623
00:40:29,640 --> 00:40:33,880
Speaker 1: it seems like news and it seems significant. I call

624
00:40:33,920 --> 00:40:39,040
Speaker 1: a lot of what gets reported falls into the recency effects. Look,

625
00:40:39,040 --> 00:40:42,320
Speaker 1: we get a non farm payroll every month. It doesn't

626
00:40:42,320 --> 00:40:45,200
Speaker 1: mean that it changes the value of what your investment is.

627
00:40:45,960 --> 00:40:48,520
Speaker 1: And half of the time when it's an aberration to

628
00:40:48,560 --> 00:40:52,040
Speaker 1: the upisode of the downside, it's still within the statistical

629
00:40:52,200 --> 00:40:57,240
Speaker 1: range of probabilities that reflect nothing other than oh, something

630
00:40:57,320 --> 00:40:59,839
Speaker 1: is slightly often this reporting, and so it's a little

631
00:40:59,840 --> 00:41:02,239
Speaker 1: more were a little less than people were expecting, and

632
00:41:02,320 --> 00:41:06,279
Speaker 1: yet everybody reacts and overreacts to it. Yeah, I mean

633
00:41:06,719 --> 00:41:10,400
Speaker 1: we're you know, everyone sort of comes with a default

634
00:41:10,920 --> 00:41:14,319
Speaker 1: hair trigger mechanism. I mean, this example has been used

635
00:41:14,360 --> 00:41:17,120
Speaker 1: so many times it's it's almost dull at this point.

636
00:41:17,200 --> 00:41:22,240
Speaker 1: But you know, in evolutionary time, when our ancestors were

637
00:41:22,280 --> 00:41:25,799
Speaker 1: evolving on the you know, the planes of the African savannah,

638
00:41:26,280 --> 00:41:29,000
Speaker 1: you know, if you thought you might see a lion

639
00:41:29,680 --> 00:41:32,680
Speaker 1: and you overreacted to what you thought was the lion,

640
00:41:33,600 --> 00:41:38,200
Speaker 1: well you lived to you know, pass on, and if

641
00:41:38,239 --> 00:41:42,200
Speaker 1: person was blase about that, not a whole lot of progeny. Right.

642
00:41:42,360 --> 00:41:45,640
Speaker 1: The thing is, most of the time it wasn't a lion.

643
00:41:46,400 --> 00:41:48,359
Speaker 1: Most of the time it was just the wind, you know,

644
00:41:48,400 --> 00:41:51,080
Speaker 1: waving a patch of grass. But that one time it

645
00:41:51,160 --> 00:41:55,799
Speaker 1: was a lion. If you overreacted, you survived and you

646
00:41:55,880 --> 00:41:58,520
Speaker 1: passed your jeans on. And if you underreacted, you were

647
00:41:58,600 --> 00:42:03,040
Speaker 1: lion food. That that makes perfect sense. So I've used

648
00:42:03,080 --> 00:42:09,600
Speaker 1: the phrase behavioral economics repeatedly your behavioral finance. But the

649
00:42:09,719 --> 00:42:13,200
Speaker 1: book Your Your Money, or Your Brain, which you talked

650
00:42:13,200 --> 00:42:18,720
Speaker 1: about the New Science of Neuroeconomics, describe the difference between

651
00:42:18,760 --> 00:42:22,680
Speaker 1: behavioral economics and neurofinance. How do you how do you

652
00:42:22,680 --> 00:42:24,880
Speaker 1: define that? Yeah, it's really I think it's a I

653
00:42:24,920 --> 00:42:29,520
Speaker 1: think it's a very simple distinction. In neuroeconomics or or

654
00:42:29,560 --> 00:42:38,000
Speaker 1: neurofinance simply uses the tools and techniques of neuroscience. Two

655
00:42:38,640 --> 00:42:45,000
Speaker 1: extend existing research and findings in psychology and economics, and

656
00:42:45,320 --> 00:42:50,160
Speaker 1: by those tools and techniques, it's primarily methods of measuring

657
00:42:50,840 --> 00:42:55,560
Speaker 1: brain activity m R. Yeah, like a functional MRI I,

658
00:42:55,800 --> 00:42:59,160
Speaker 1: a pet scan, a cat scan, um. It could also

659
00:42:59,280 --> 00:43:06,480
Speaker 1: be uh various um um uh tools for monitoring brain

660
00:43:06,520 --> 00:43:10,319
Speaker 1: waves or electromagnetic activity in the brain. So so the

661
00:43:10,360 --> 00:43:12,920
Speaker 1: way I like to define that behavioral economics is you

662
00:43:12,960 --> 00:43:18,120
Speaker 1: can see the results of this issue in someone's actual behavior.

663
00:43:18,200 --> 00:43:21,240
Speaker 1: They did this, they bought that, they sold that, whereas

664
00:43:21,280 --> 00:43:24,880
Speaker 1: neuro you're actually looking inside their brain in some way

665
00:43:25,239 --> 00:43:30,000
Speaker 1: and seeing what's going on organically as opposed to behaviorally. Yeah,

666
00:43:30,080 --> 00:43:33,920
Speaker 1: And if you think about it, uh, there's there's not

667
00:43:34,040 --> 00:43:38,959
Speaker 1: much difference between the two in a way behavioral economics

668
00:43:39,120 --> 00:43:43,839
Speaker 1: is telling you what minds do, and neuroeconomics is telling

669
00:43:43,840 --> 00:43:47,680
Speaker 1: you what brains do. It's we already a great distinction.

670
00:43:48,000 --> 00:43:51,879
Speaker 1: We can already observe the behavior. Because somebody buys high

671
00:43:51,960 --> 00:43:54,680
Speaker 1: and sells low. They made a decision in their mind

672
00:43:54,760 --> 00:43:56,880
Speaker 1: to do this. We see, we see their action, we

673
00:43:56,920 --> 00:44:00,040
Speaker 1: see what they did, so we know what happened. What

674
00:44:00,200 --> 00:44:04,279
Speaker 1: neuroeconomics does is it says, these are the specific circuits

675
00:44:04,320 --> 00:44:09,279
Speaker 1: in the brain that generated the behavior you've observed in

676
00:44:09,280 --> 00:44:12,440
Speaker 1: in your psychological experiments. Let me shift gears on you,

677
00:44:12,520 --> 00:44:15,400
Speaker 1: and and let's talk a little bit about the problem

678
00:44:15,440 --> 00:44:20,400
Speaker 1: with predictions. What's the problem? So we all make forecasts. Everybody,

679
00:44:20,400 --> 00:44:22,560
Speaker 1: everybody in will state has a Here's where the d

680
00:44:22,680 --> 00:44:24,760
Speaker 1: will be in a year. Here's what I think these

681
00:44:25,040 --> 00:44:29,160
Speaker 1: earnings are gonna be. What's the problem with predictions. Well,

682
00:44:29,200 --> 00:44:32,440
Speaker 1: I think that there's a there's many problems with them, Barry.

683
00:44:32,600 --> 00:44:35,640
Speaker 1: I think the first problem is that people are people

684
00:44:35,680 --> 00:44:41,560
Speaker 1: are very poor at learning from their predictive errors, particularly

685
00:44:42,160 --> 00:44:45,719
Speaker 1: when the consequences aren't high or the feedback is noisy.

686
00:44:45,800 --> 00:44:48,720
Speaker 1: To define that, what do you mean by that? Well,

687
00:44:49,280 --> 00:44:54,000
Speaker 1: think about it this way. I'm you know. Uh, think

688
00:44:54,040 --> 00:44:57,440
Speaker 1: of the quarterfinal match in the US Open between the

689
00:44:57,520 --> 00:45:01,520
Speaker 1: Williams sisters. You know, Venus and Serena are playing. Every

690
00:45:01,560 --> 00:45:08,360
Speaker 1: stroke has three characteristics. The feedback their feedback of every stroke.

691
00:45:08,880 --> 00:45:13,560
Speaker 1: So if Venus hits the ball, it's in or it's out,

692
00:45:14,560 --> 00:45:17,760
Speaker 1: and she knows that immediately. So the feedback is prompt

693
00:45:17,840 --> 00:45:24,120
Speaker 1: and it's unambiguous. Secondly, every stroke matters. It's consequential if

694
00:45:24,800 --> 00:45:27,440
Speaker 1: if you miss too many shots, you lose, and you

695
00:45:27,560 --> 00:45:30,440
Speaker 1: lose millions of dollars in front of millions of people.

696
00:45:30,800 --> 00:45:33,640
Speaker 1: Other than that, though, really it's just a game. Other

697
00:45:33,680 --> 00:45:36,600
Speaker 1: than that, it's just a game. So in in a

698
00:45:36,719 --> 00:45:41,880
Speaker 1: sport like professional tennis, feedback is extremely reliable, in a

699
00:45:42,000 --> 00:45:46,000
Speaker 1: very powerful way of learning from your mistakes. If you

700
00:45:46,080 --> 00:45:49,640
Speaker 1: take it seriously and you practice, you will get good

701
00:45:49,680 --> 00:45:54,600
Speaker 1: at it. You'll get a lot better. The financial markets

702
00:45:54,600 --> 00:46:01,440
Speaker 1: are different. The feedback is not prompt out nor isn't unambiguous.

703
00:46:02,000 --> 00:46:05,799
Speaker 1: I mean, let's say you and I decide today Netflix

704
00:46:06,400 --> 00:46:10,319
Speaker 1: is going up, so we buy Netflix. Well, on the

705
00:46:10,360 --> 00:46:15,040
Speaker 1: next tick, Netflix goes up, so we're right. But the

706
00:46:15,120 --> 00:46:18,560
Speaker 1: next ticket goes down and now we're wrong, so we

707
00:46:18,600 --> 00:46:20,399
Speaker 1: get up. We go to the bathroom. We come back,

708
00:46:20,400 --> 00:46:23,120
Speaker 1: it's up. We're right. We go out for lunch, we

709
00:46:23,200 --> 00:46:25,600
Speaker 1: come back, it's down. Now we're wrong. What is the

710
00:46:25,640 --> 00:46:29,680
Speaker 1: feedback telling us? It's really noisy. The feedback is noise.

711
00:46:30,000 --> 00:46:34,680
Speaker 1: Prices fluctuated, that's what it's telling you. So there is

712
00:46:34,880 --> 00:46:38,960
Speaker 1: useful feedback in market prices, but it takes a long

713
00:46:39,080 --> 00:46:42,479
Speaker 1: time to unfold, and you have to develop a real

714
00:46:42,560 --> 00:46:45,680
Speaker 1: skill in interpreting the feedback, and a lot of people

715
00:46:45,760 --> 00:46:48,320
Speaker 1: can't do that. And the task at the same time

716
00:46:48,640 --> 00:46:52,360
Speaker 1: manage the money or uh, you know, pick the particular

717
00:46:52,440 --> 00:46:56,520
Speaker 1: securities and the consequences. The consequences for a lot of

718
00:46:56,560 --> 00:46:59,480
Speaker 1: people are enormous. It depends how much skin you have

719
00:46:59,520 --> 00:47:02,879
Speaker 1: in the game, right, how much of your reputation, your

720
00:47:02,920 --> 00:47:07,319
Speaker 1: personal reputation is riding on the line. If you're an employee,

721
00:47:08,480 --> 00:47:11,040
Speaker 1: just a face in a crowd at a giant asset

722
00:47:11,120 --> 00:47:16,040
Speaker 1: management firm, uh, consequences might not be all that great.

723
00:47:16,440 --> 00:47:20,120
Speaker 1: The consequences for you aren't in being right or wrong.

724
00:47:20,719 --> 00:47:23,520
Speaker 1: It's whether you're right or wrong relative to the crowd.

725
00:47:25,000 --> 00:47:29,160
Speaker 1: And so you're not concerned with your absolute performance, whether

726
00:47:29,200 --> 00:47:31,880
Speaker 1: what you did is right or wrong. You're concerned whether

727
00:47:31,880 --> 00:47:35,520
Speaker 1: you're gonna stand out for doing something different that might

728
00:47:35,680 --> 00:47:38,319
Speaker 1: make you or the firm look foolish. Career risk is

729
00:47:38,360 --> 00:47:41,359
Speaker 1: that is that what you're It's career risk. And so

730
00:47:41,880 --> 00:47:47,400
Speaker 1: if avoiding career risk steers you toward making timid or

731
00:47:47,520 --> 00:47:54,240
Speaker 1: sub optimal decisions, you can't learn from normal feedback because

732
00:47:54,239 --> 00:47:57,560
Speaker 1: the feedback you care about is am I increasing or

733
00:47:57,640 --> 00:48:01,600
Speaker 1: decreasing my career risk as opposed to am I doing

734
00:48:01,680 --> 00:48:06,520
Speaker 1: better or worse? As an investor invest investments. That's quite fascinating.

735
00:48:07,040 --> 00:48:11,560
Speaker 1: So so when we look at predictions in general, you're

736
00:48:11,600 --> 00:48:16,319
Speaker 1: looking at them with that three pronged analysis, similar to

737
00:48:16,320 --> 00:48:18,560
Speaker 1: the way you would look at a sporting event, only

738
00:48:19,400 --> 00:48:22,480
Speaker 1: the results, the timeline, and the all three things are

739
00:48:22,640 --> 00:48:27,279
Speaker 1: totally different. Yeah, and you know there's I think the

740
00:48:27,320 --> 00:48:30,000
Speaker 1: other thing that makes all of this baffling is we

741
00:48:30,600 --> 00:48:33,400
Speaker 1: in in daily life, we have a general belief and

742
00:48:33,440 --> 00:48:37,120
Speaker 1: it's been popularized as sort of the ten thousand hours rule,

743
00:48:37,440 --> 00:48:41,440
Speaker 1: you know, outlawyer rule, Mountain Glass exactly if you if

744
00:48:41,480 --> 00:48:45,680
Speaker 1: you just practice anything long enough and hard enough, you'll

745
00:48:45,680 --> 00:48:48,319
Speaker 1: get good at it. If that was true, wouldn't all

746
00:48:48,360 --> 00:48:50,600
Speaker 1: the best fund managers be the guys who've been around

747
00:48:50,640 --> 00:48:53,720
Speaker 1: the longest. Yes. And also if that were true, everyone

748
00:48:53,760 --> 00:48:57,560
Speaker 1: would be the Beatles. Everyone who's ever everyone who's ever

749
00:48:57,600 --> 00:49:00,160
Speaker 1: spent a lot of time plucking on and get are

750
00:49:00,320 --> 00:49:05,680
Speaker 1: would would be Eric Clapton. And it's not really true

751
00:49:05,840 --> 00:49:11,600
Speaker 1: because the second essential criterion, the other thing you have

752
00:49:11,680 --> 00:49:14,080
Speaker 1: to have, is good feedback. I mean, if you you

753
00:49:14,120 --> 00:49:17,760
Speaker 1: could spend thirty hours playing guitar, but if you're tone

754
00:49:17,800 --> 00:49:21,719
Speaker 1: deaf and you don't know you stink and nobody will

755
00:49:21,760 --> 00:49:25,040
Speaker 1: tell you stink, you'll never get good. Well, you'll figure

756
00:49:25,040 --> 00:49:26,879
Speaker 1: it out when they're not buying your albums or going

757
00:49:26,920 --> 00:49:29,279
Speaker 1: to your concerts, or when they're paying or when they're

758
00:49:29,280 --> 00:49:31,920
Speaker 1: paying you not to play. That's a whole different Uh,

759
00:49:32,480 --> 00:49:37,919
Speaker 1: that's a whole different issue. Um. Another random question. Does

760
00:49:38,000 --> 00:49:42,240
Speaker 1: money by happiness or is it the anticipation of money

761
00:49:42,280 --> 00:49:45,160
Speaker 1: that leads to happiness? Well, yeah, I talked about this

762
00:49:45,320 --> 00:49:48,319
Speaker 1: quite a bit in in my earlier book Your Money

763
00:49:48,360 --> 00:49:54,799
Speaker 1: in Your Brain and anticipation based on the on the

764
00:49:54,880 --> 00:49:58,319
Speaker 1: research that's been done, I think is more of what

765
00:49:58,360 --> 00:50:03,640
Speaker 1: I would call a hot state than I'm actually receiving

766
00:50:03,640 --> 00:50:09,840
Speaker 1: a gain. So thinking about or imagining or contemplating the money,

767
00:50:09,880 --> 00:50:12,440
Speaker 1: you'll make if you turn out to be right, is

768
00:50:12,719 --> 00:50:16,839
Speaker 1: probably more at least as rewarding as getting the money is.

769
00:50:17,600 --> 00:50:22,440
Speaker 1: I've used that analysis as an explanation for sell the News,

770
00:50:22,520 --> 00:50:25,280
Speaker 1: and you you kind of imply that in the book

771
00:50:25,280 --> 00:50:29,120
Speaker 1: as well. Ye. So, so put it into context of investing.

772
00:50:29,680 --> 00:50:33,680
Speaker 1: Why do stock prices run up to a quarterly earning

773
00:50:34,040 --> 00:50:36,600
Speaker 1: then you get a good earnings number and the stock

774
00:50:36,719 --> 00:50:41,760
Speaker 1: goes down? Yeah, there's there's There was a fascinating study

775
00:50:41,800 --> 00:50:45,759
Speaker 1: published in Science magazine, which, as many of our listeners know,

776
00:50:46,000 --> 00:50:49,960
Speaker 1: is the is the pre eminent journal of science in

777
00:50:50,320 --> 00:50:53,719
Speaker 1: the US, and it it looked at monkey brains and

778
00:50:53,760 --> 00:50:58,600
Speaker 1: they recorded from single neurons deep in the dopamine centers

779
00:50:58,680 --> 00:51:03,160
Speaker 1: of the monkey's brains how they responded. And essentially, when

780
00:51:03,400 --> 00:51:11,600
Speaker 1: the monkey receives an expected reward, the dopamine generation at

781
00:51:11,600 --> 00:51:15,480
Speaker 1: the time the reward is received goes down, So the

782
00:51:15,520 --> 00:51:20,479
Speaker 1: dopamine activity ramps up as the monkey recognizes the predictive queue.

783
00:51:20,719 --> 00:51:23,480
Speaker 1: Here comes the banana. Here comes the banana. In this

784
00:51:23,520 --> 00:51:26,280
Speaker 1: case it was it was sugar water, but same idea.

785
00:51:26,360 --> 00:51:28,920
Speaker 1: Here comes here comes the banana. I'm going to get

786
00:51:28,920 --> 00:51:33,800
Speaker 1: the banana. The bananas coming, and that's when the dopamine peaks,

787
00:51:34,520 --> 00:51:37,560
Speaker 1: then the monkey actually gets the banana and he's like, oh,

788
00:51:37,640 --> 00:51:40,759
Speaker 1: I got the banana. I've been wanting all along. And

789
00:51:41,360 --> 00:51:45,000
Speaker 1: so that activity peaks and then it drops. And I

790
00:51:45,040 --> 00:51:48,759
Speaker 1: think that's why so often in the stock market you

791
00:51:48,840 --> 00:51:51,600
Speaker 1: see the price of you see the company's stock price

792
00:51:51,800 --> 00:51:55,840
Speaker 1: just ramp up and and go parabolic when people are

793
00:51:55,880 --> 00:51:58,719
Speaker 1: expecting a positive earning surprise, the company is going to

794
00:51:58,840 --> 00:52:02,920
Speaker 1: exceed analysts expectations. Then the actual learnings come out and

795
00:52:03,120 --> 00:52:06,440
Speaker 1: exactly what people thought they would be just as good,

796
00:52:06,840 --> 00:52:09,160
Speaker 1: and it goes down. It's a disappointment. There's a Spock

797
00:52:09,320 --> 00:52:12,080
Speaker 1: quote from Star Trek which I won't even attempt, but

798
00:52:12,120 --> 00:52:16,719
Speaker 1: it's the same concept of wanting generating more satisfaction than

799
00:52:16,760 --> 00:52:21,719
Speaker 1: actually having. And I find that that fascinating so related

800
00:52:21,760 --> 00:52:25,480
Speaker 1: to some of um these cognitive issues. Um, what was

801
00:52:25,520 --> 00:52:29,280
Speaker 1: it like working with Danny Kahneman on thinking fast and Slow? What?

802
00:52:29,280 --> 00:52:32,879
Speaker 1: What were your contributions, uh to the book? He I'm

803
00:52:32,920 --> 00:52:34,680
Speaker 1: trying to remember if he thanked you in his Nobel

804
00:52:34,760 --> 00:52:39,440
Speaker 1: speech or not, but I didn't, But I I I

805
00:52:39,920 --> 00:52:43,000
Speaker 1: read the book, or I read the first three quarters

806
00:52:43,040 --> 00:52:46,000
Speaker 1: of the book on a beach some years ago and

807
00:52:46,000 --> 00:52:50,480
Speaker 1: found it just absolutely fascinating stuff. Well, we're you know,

808
00:52:50,560 --> 00:52:52,640
Speaker 1: working with Danny was one of the was one of

809
00:52:52,640 --> 00:52:56,480
Speaker 1: the great experiences of of my life. Um, certainly one

810
00:52:56,520 --> 00:53:00,439
Speaker 1: of the great learning experiences. UM. You know. He he's

811
00:53:00,480 --> 00:53:06,960
Speaker 1: got a remarkable mind and um, he questions everything, and

812
00:53:07,080 --> 00:53:13,400
Speaker 1: he has he has that wonderful ability that most of

813
00:53:13,480 --> 00:53:18,759
Speaker 1: us losing our intellectual lifetime of putting ourselves almost into

814
00:53:18,800 --> 00:53:23,840
Speaker 1: the shoes of a child and saying just asking why

815
00:53:23,880 --> 00:53:30,040
Speaker 1: why is that? And he's often completely baffled by things

816
00:53:30,239 --> 00:53:35,160
Speaker 1: that everyone takes for granted as an as an accepted truth,

817
00:53:35,360 --> 00:53:37,319
Speaker 1: and he he just sort of scratches his head in

818
00:53:37,360 --> 00:53:40,640
Speaker 1: front of it, says, well why do people think that?

819
00:53:40,920 --> 00:53:43,400
Speaker 1: And it's not that he's doubting it, he just genuinely

820
00:53:43,480 --> 00:53:46,640
Speaker 1: doesn't know. And part of that is because he has

821
00:53:47,640 --> 00:53:51,640
Speaker 1: the most perfectly calibrated sense of what he knows. I

822
00:53:51,719 --> 00:53:54,680
Speaker 1: think of anybody I've ever certainly anybody I've ever worked with,

823
00:53:54,760 --> 00:53:58,520
Speaker 1: no Dunning Kruger effect. They're very, very very little. I

824
00:53:58,640 --> 00:54:02,319
Speaker 1: only saw it once, which was the first day we

825
00:54:02,360 --> 00:54:07,439
Speaker 1: worked physically together on the book in his apartment, when

826
00:54:08,000 --> 00:54:11,200
Speaker 1: he talked about the planning fallacy which I know you're

827
00:54:11,239 --> 00:54:15,920
Speaker 1: familiar with, Barry, but I'll explain it for for our listeners. Uh.

828
00:54:15,960 --> 00:54:18,440
Speaker 1: He likes to tell a famous a story that's become

829
00:54:18,520 --> 00:54:23,359
Speaker 1: famous about one of the first major projects that he

830
00:54:23,480 --> 00:54:27,320
Speaker 1: undertook when he was a young psychologist on the rise

831
00:54:28,640 --> 00:54:32,839
Speaker 1: in Israel at the Hebrew University in Jerusalem. And um,

832
00:54:32,920 --> 00:54:36,640
Speaker 1: he and some of his colleagues were working on rewriting

833
00:54:37,960 --> 00:54:42,120
Speaker 1: the standard high school psychology textbook for students in Israel.

834
00:54:42,719 --> 00:54:45,880
Speaker 1: And that's a big undertaking because it's a you know,

835
00:54:46,000 --> 00:54:49,799
Speaker 1: it's it's basically a core part of the curriculum. You

836
00:54:49,800 --> 00:54:54,839
Speaker 1: have to start the textbook from scratch and um. So

837
00:54:56,080 --> 00:54:59,160
Speaker 1: they asked each other, well, how long we should this take?

838
00:55:00,120 --> 00:55:02,520
Speaker 1: And so one of them said, oh, I mean, how

839
00:55:02,520 --> 00:55:05,240
Speaker 1: could it take more than seven or eight months? Another

840
00:55:05,239 --> 00:55:08,480
Speaker 1: one said maybe a year? And somebody else said, I

841
00:55:08,520 --> 00:55:10,640
Speaker 1: don't know, a year and a half or something. And

842
00:55:10,719 --> 00:55:14,239
Speaker 1: Danny's just sitting there listening, puzzled the same way I

843
00:55:14,360 --> 00:55:17,680
Speaker 1: just described. And then he so he turned, he gets

844
00:55:17,719 --> 00:55:19,840
Speaker 1: a lightbulb and he over his head and he turns

845
00:55:19,920 --> 00:55:25,240
Speaker 1: to the dean of education at the at the school

846
00:55:25,239 --> 00:55:29,280
Speaker 1: they were affiliated with, the Humor University, and says to him,

847
00:55:29,320 --> 00:55:33,759
Speaker 1: you know, are you aware of other teams, other groups

848
00:55:33,760 --> 00:55:38,480
Speaker 1: that have done similar projects like this one? And the

849
00:55:38,520 --> 00:55:41,799
Speaker 1: dean says, well, yeah, you know now that you mention it,

850
00:55:42,920 --> 00:55:46,040
Speaker 1: I am, And that didn't occur to me before because

851
00:55:46,120 --> 00:55:47,920
Speaker 1: he was one of the people who had given this

852
00:55:48,120 --> 00:55:51,759
Speaker 1: low estimate of ten months or something like that. So

853
00:55:51,880 --> 00:55:57,600
Speaker 1: Danny says, oh, so, of those groups, what what was

854
00:55:57,680 --> 00:56:02,000
Speaker 1: the average length of time took them to complete the textbook?

855
00:56:02,800 --> 00:56:05,280
Speaker 1: So the dean sort of pulls his chin for a while,

856
00:56:05,400 --> 00:56:08,600
Speaker 1: and everyone in the room is sort of shifting uneasily

857
00:56:08,680 --> 00:56:11,919
Speaker 1: in their seats, and after a long pause, he says, well,

858
00:56:13,560 --> 00:56:19,960
Speaker 1: I guess of those who completed a similar project, I

859
00:56:20,000 --> 00:56:24,880
Speaker 1: think it took on average about four years, not counting

860
00:56:24,920 --> 00:56:27,200
Speaker 1: the guys who were still working and having closed the

861
00:56:27,960 --> 00:56:32,120
Speaker 1: and so and so. Then Danny says, oh, okay, and

862
00:56:32,640 --> 00:56:35,799
Speaker 1: what about the ones and how many how many of

863
00:56:35,840 --> 00:56:41,239
Speaker 1: these teams would you say never completed it? And he said, oh,

864
00:56:41,440 --> 00:56:44,239
Speaker 1: I guess forty or fifty percent, And so the room

865
00:56:44,320 --> 00:56:49,120
Speaker 1: goes dead silent. And so the corollary to this is

866
00:56:49,160 --> 00:56:52,879
Speaker 1: that they finally did finish the textbook, it took either

867
00:56:53,120 --> 00:56:58,759
Speaker 1: seven or nine years, and if I think there's a

868
00:56:58,760 --> 00:57:01,560
Speaker 1: second correla, I think I'm remembering this right was never

869
00:57:01,600 --> 00:57:07,560
Speaker 1: actually used and yeah, it was out of it. And

870
00:57:07,640 --> 00:57:09,840
Speaker 1: so so the first day Danny and I are working

871
00:57:09,880 --> 00:57:11,759
Speaker 1: on the book, he says to me, I really want

872
00:57:11,760 --> 00:57:14,319
Speaker 1: to avoid the planning fallacy. And by the way, I

873
00:57:14,360 --> 00:57:17,400
Speaker 1: forgot Barry the just to sort of take this the

874
00:57:17,440 --> 00:57:22,360
Speaker 1: final turn. The reason the planning fallacy is relevant because

875
00:57:22,360 --> 00:57:27,640
Speaker 1: it brings out a basic flaw in human cognition, which is,

876
00:57:27,680 --> 00:57:32,120
Speaker 1: when we're faced with any challenge or any set of data,

877
00:57:32,320 --> 00:57:34,480
Speaker 1: what we tend to do is we think in very

878
00:57:34,600 --> 00:57:38,240
Speaker 1: narrow framing. So you and I are going to write

879
00:57:38,240 --> 00:57:41,080
Speaker 1: this textbook. So we think about and we talked to

880
00:57:41,080 --> 00:57:43,720
Speaker 1: each other and we and you say, well, you know,

881
00:57:43,840 --> 00:57:47,120
Speaker 1: I'm good at this, and I say, well you're good

882
00:57:47,120 --> 00:57:51,520
Speaker 1: at it, I'm better. And we think about how qualified

883
00:57:51,600 --> 00:57:54,440
Speaker 1: we are to do this, how excited we are about

884
00:57:54,560 --> 00:57:57,680
Speaker 1: performing this task, and all we can really focus on

885
00:57:58,360 --> 00:58:01,800
Speaker 1: is how we're going to do it better, faster and

886
00:58:02,480 --> 00:58:06,720
Speaker 1: um and more perfectly than anybody who's ever done it before.

887
00:58:07,000 --> 00:58:10,320
Speaker 1: But we don't ask ourselves what's the success rate of

888
00:58:10,400 --> 00:58:14,520
Speaker 1: people who have tried this. There's a database of all

889
00:58:14,520 --> 00:58:17,000
Speaker 1: the other people who've tried it, what is it? What's

890
00:58:17,000 --> 00:58:19,720
Speaker 1: in the data? And so we don't look at the

891
00:58:19,720 --> 00:58:24,280
Speaker 1: base rate. And that's Danny Kahneman is all about base rates.

892
00:58:24,360 --> 00:58:27,520
Speaker 1: And so the wonderful line he gave me that I

893
00:58:27,600 --> 00:58:29,880
Speaker 1: use all the time in my thinking and in my

894
00:58:29,920 --> 00:58:33,520
Speaker 1: writing is he once said to me, the single most

895
00:58:33,600 --> 00:58:37,600
Speaker 1: important question is what is the base rate? It's so

896
00:58:37,680 --> 00:58:41,160
Speaker 1: funny you you bring this up today. This is absolutely true.

897
00:58:41,720 --> 00:58:44,000
Speaker 1: I was having a discussion on the train on the

898
00:58:44,000 --> 00:58:48,080
Speaker 1: way in about a woman I work with work. A

899
00:58:48,120 --> 00:58:52,680
Speaker 1: woman I commute with is in fashion, and she's she's

900
00:58:52,760 --> 00:58:56,360
Speaker 1: on the train. She's always having these angry like she

901
00:58:56,480 --> 00:59:01,520
Speaker 1: pulls out her her phone so annoyed and she sends

902
00:59:02,000 --> 00:59:05,760
Speaker 1: nasty emails to people, and I go, what's today? She goes,

903
00:59:05,920 --> 00:59:08,720
Speaker 1: I get this email from somebody saying that they were

904
00:59:08,920 --> 00:59:11,320
Speaker 1: so they do these long. I won't mention the brand,

905
00:59:11,720 --> 00:59:15,280
Speaker 1: but one of us is wearing it, and it's um

906
00:59:15,320 --> 00:59:18,920
Speaker 1: it's a very well known, famous brand. And she'll get

907
00:59:18,960 --> 00:59:23,440
Speaker 1: this run off of a production line and three of

908
00:59:23,480 --> 00:59:27,320
Speaker 1: them were terrible. Oh we have three, and she I go, so,

909
00:59:27,880 --> 00:59:29,920
Speaker 1: why are you angry? She goes, three out of what

910
00:59:30,080 --> 00:59:32,680
Speaker 1: three out of fifty three a fifty thousand. I don't

911
00:59:32,680 --> 00:59:34,880
Speaker 1: know what this means. And I said to her that's

912
00:59:34,880 --> 00:59:39,440
Speaker 1: called numerator blindness or denominator Basically, they're giving you the

913
00:59:40,000 --> 00:59:43,840
Speaker 1: numerator and ignoring the context of how many of this? So,

914
00:59:43,960 --> 00:59:46,960
Speaker 1: so the denominator blindness is So, if you want to

915
00:59:47,000 --> 00:59:50,560
Speaker 1: impress the person who's sending you this adiotic there were

916
00:59:50,640 --> 00:59:54,240
Speaker 1: three bad units out of the production run, tell them

917
00:59:54,280 --> 00:59:57,240
Speaker 1: you have denominator blindness. You're not aware of the context

918
00:59:57,240 --> 00:59:59,880
Speaker 1: from the lower You're only giving me the top number

919
01:00:00,040 --> 01:00:02,880
Speaker 1: with no frame of reference. Correct. But but that that

920
01:00:02,960 --> 01:00:06,000
Speaker 1: reminds me very similar to what is the basics? What

921
01:00:06,120 --> 01:00:09,520
Speaker 1: is what is the base rate? Exactly? So you know.

922
01:00:09,640 --> 01:00:13,600
Speaker 1: So Danny says to me that first day, of all people,

923
01:00:13,800 --> 01:00:15,600
Speaker 1: I don't want to be the one who commits the

924
01:00:15,600 --> 01:00:19,120
Speaker 1: planning fallacy. I don't want us in a book on

925
01:00:19,240 --> 01:00:21,520
Speaker 1: cognitive bias, in a book that is going to talk

926
01:00:21,560 --> 01:00:25,200
Speaker 1: about the planning house. So, uh, I want us to

927
01:00:25,240 --> 01:00:28,280
Speaker 1: figure out together how long this is likely to take,

928
01:00:28,560 --> 01:00:30,840
Speaker 1: so that we know what we're in for. And so

929
01:00:30,880 --> 01:00:33,680
Speaker 1: we had a long conversation and he has a method

930
01:00:33,760 --> 01:00:37,400
Speaker 1: for d biasing the planning fallacy, and we went through

931
01:00:37,440 --> 01:00:42,680
Speaker 1: really perfectly, and at the end we concluded it should

932
01:00:42,680 --> 01:00:45,280
Speaker 1: take a year and a half to two years, two years,

933
01:00:45,800 --> 01:00:47,760
Speaker 1: and so I worked on the project with him for

934
01:00:47,800 --> 01:00:50,600
Speaker 1: two years until you know, I had other things that

935
01:00:50,960 --> 01:00:54,560
Speaker 1: were demanding my attention. Um, but for those two years

936
01:00:54,600 --> 01:00:56,480
Speaker 1: it was pretty much a full time job for me

937
01:00:56,640 --> 01:00:59,800
Speaker 1: and for him for that matter. And so at the

938
01:00:59,880 --> 01:01:03,600
Speaker 1: end to those two years, he said, I'm about a

939
01:01:03,640 --> 01:01:08,960
Speaker 1: third done, I think. And it took another I think

940
01:01:10,040 --> 01:01:13,440
Speaker 1: three or four years after that, no kidding. So so

941
01:01:13,480 --> 01:01:15,640
Speaker 1: he was right when he when he said he was

942
01:01:15,680 --> 01:01:19,480
Speaker 1: a third done. Uh yeah, except he was wrong in

943
01:01:19,520 --> 01:01:22,840
Speaker 1: the beginning when we when we decided it would take

944
01:01:22,880 --> 01:01:28,280
Speaker 1: two years. So you know, when I said that, he

945
01:01:28,280 --> 01:01:33,360
Speaker 1: he he has a remarkable sense of his own limitations.

946
01:01:33,400 --> 01:01:36,240
Speaker 1: That was one issue where I think, but even there,

947
01:01:36,240 --> 01:01:38,400
Speaker 1: he was aware of the fact that that was a problem.

948
01:01:38,520 --> 01:01:40,440
Speaker 1: He knew it was a problem. He it's just the

949
01:01:40,520 --> 01:01:44,680
Speaker 1: d biasing method didn't work well. And it's very difficult

950
01:01:44,720 --> 01:01:49,720
Speaker 1: for most people to the bias, especially considering so many

951
01:01:49,760 --> 01:01:53,000
Speaker 1: people are so unaware of their own biases. How can

952
01:01:53,040 --> 01:01:58,000
Speaker 1: you protect yourself if you simply have no conception that hey,

953
01:01:58,040 --> 01:02:00,400
Speaker 1: I have a cognitive bias he or or I have

954
01:02:00,600 --> 01:02:04,040
Speaker 1: some aside from the fact that most many of these

955
01:02:04,040 --> 01:02:07,760
Speaker 1: are already hardwired into you. If you're unaware of your

956
01:02:07,960 --> 01:02:10,920
Speaker 1: confirmation bias, how are you going to avoid it? I

957
01:02:11,360 --> 01:02:13,840
Speaker 1: tell people the reason we study this is so at

958
01:02:13,920 --> 01:02:17,360
Speaker 1: least you have a fighting chance to be biasues. That

959
01:02:17,520 --> 01:02:21,560
Speaker 1: is key. I mean, the biggest problem with the findings

960
01:02:21,640 --> 01:02:26,240
Speaker 1: of behavioral economics is that most of the biases it

961
01:02:26,360 --> 01:02:31,520
Speaker 1: identifies are unconscious, and by definition, you don't know you

962
01:02:31,640 --> 01:02:34,920
Speaker 1: have this bias. So the single most powerful thing that

963
01:02:35,000 --> 01:02:37,080
Speaker 1: you can do as an investor or for that manner,

964
01:02:37,160 --> 01:02:41,080
Speaker 1: a consumer of information, or just an intelligent citizen is

965
01:02:41,440 --> 01:02:44,520
Speaker 1: to use techniques to try to surface those biases in

966
01:02:44,560 --> 01:02:47,360
Speaker 1: your own mind, at least make yourself aware of them.

967
01:02:47,440 --> 01:02:50,640
Speaker 1: You know, It's funny because coming out of a legal background,

968
01:02:51,200 --> 01:02:55,320
Speaker 1: where the mood court exercises, basically, you prepare a case

969
01:02:55,840 --> 01:02:58,280
Speaker 1: and at any point in time you may be forced

970
01:02:58,320 --> 01:03:00,600
Speaker 1: to argue the other side of the case, and it

971
01:03:00,720 --> 01:03:04,800
Speaker 1: really gives you some degree of objectivity as to the

972
01:03:04,840 --> 01:03:08,840
Speaker 1: strength and weaknesses of your arguments. And I started as

973
01:03:08,880 --> 01:03:11,480
Speaker 1: a trader, and this was always something that was drilled

974
01:03:11,520 --> 01:03:14,640
Speaker 1: into me. Hey, it doesn't matter if you're long or short.

975
01:03:14,680 --> 01:03:18,800
Speaker 1: You have to be ready when the circumstances change to

976
01:03:18,880 --> 01:03:20,920
Speaker 1: get out of that position to take the other position.

977
01:03:21,240 --> 01:03:23,360
Speaker 1: I think a lot of people don't go through those

978
01:03:23,400 --> 01:03:26,200
Speaker 1: sort of processes of saying because we have this whole

979
01:03:26,240 --> 01:03:29,120
Speaker 1: conversation all the time, Hey, are you a bull or

980
01:03:29,120 --> 01:03:32,960
Speaker 1: a bear? Uh? You know, I'm whatever makes sense at

981
01:03:32,960 --> 01:03:35,880
Speaker 1: the moment, and don't feel the need to declare a

982
01:03:35,960 --> 01:03:38,720
Speaker 1: major and stick to that regardless of what what the

983
01:03:38,760 --> 01:03:41,680
Speaker 1: evidence says. Yeah, and it isn't an interesting barrier that,

984
01:03:41,760 --> 01:03:46,880
Speaker 1: despite how badly reviled, the legal profession is almost as

985
01:03:46,880 --> 01:03:52,360
Speaker 1: badly as financial journalism. When people are trying to figure

986
01:03:52,400 --> 01:03:55,000
Speaker 1: out whether there's a conflict of interest at hand, they

987
01:03:55,040 --> 01:03:58,560
Speaker 1: do call in lawyers because lawyers are better at evaluating

988
01:03:58,600 --> 01:04:02,120
Speaker 1: that than most other profession. Doctors are not good at it.

989
01:04:02,440 --> 01:04:04,720
Speaker 1: People on Wall Street are not good at it, and

990
01:04:04,760 --> 01:04:07,080
Speaker 1: a lot of financial journalists, for that matter, are not

991
01:04:07,120 --> 01:04:09,840
Speaker 1: good at it. You have to be able to step

992
01:04:09,880 --> 01:04:12,800
Speaker 1: back and look at both sides of the argument, and

993
01:04:12,880 --> 01:04:16,480
Speaker 1: that's a bigger challenge. That's that's a skill set that

994
01:04:16,520 --> 01:04:19,600
Speaker 1: you have to learn. It doesn't necessarily come easy. Once

995
01:04:19,640 --> 01:04:21,520
Speaker 1: you kind of get into a groove of it, it

996
01:04:21,600 --> 01:04:24,080
Speaker 1: becomes something that can be done. All right, I know

997
01:04:24,160 --> 01:04:25,960
Speaker 1: I don't have you all day, so let me get

998
01:04:25,960 --> 01:04:31,120
Speaker 1: to some of my favorite questions that I ask everybody. UM.

999
01:04:31,440 --> 01:04:34,560
Speaker 1: In the last ten or fifteen minutes we have, UM,

1000
01:04:34,600 --> 01:04:38,080
Speaker 1: you've mentioned some of your mentors. Who are your earliest mentors,

1001
01:04:38,120 --> 01:04:45,720
Speaker 1: either as a journalist or a UM neuroeconomic neuro economists,

1002
01:04:46,120 --> 01:04:49,560
Speaker 1: neuro finances as a journalist who your early mentioned I'm

1003
01:04:49,600 --> 01:04:52,120
Speaker 1: gonna I think I'm just going to mention one people,

1004
01:04:52,480 --> 01:04:56,160
Speaker 1: one person out of all the people I could mention.

1005
01:04:56,840 --> 01:05:00,640
Speaker 1: UM my beloved editor at Forbes, jim My Coles, who

1006
01:05:00,720 --> 01:05:05,600
Speaker 1: was the long time editor of the magazine and far

1007
01:05:05,640 --> 01:05:08,360
Speaker 1: and away one of the most brilliant editors I or

1008
01:05:08,400 --> 01:05:14,040
Speaker 1: anyone else I have ever encountered. And in when I

1009
01:05:14,120 --> 01:05:18,400
Speaker 1: was a young reporter at Forbes, Jim uh decided that

1010
01:05:18,480 --> 01:05:24,560
Speaker 1: I would be the mutual funds editor, and I'm uh.

1011
01:05:24,680 --> 01:05:27,200
Speaker 1: The one thing I remembered in the nick of time

1012
01:05:27,400 --> 01:05:30,240
Speaker 1: was that that had been his first major job when

1013
01:05:30,320 --> 01:05:33,200
Speaker 1: he came to the magazine in the nineteen fifties. So

1014
01:05:33,360 --> 01:05:37,160
Speaker 1: I knew better than to show disappointment on my face

1015
01:05:37,240 --> 01:05:42,280
Speaker 1: at having been handed such a boring assignment. So I

1016
01:05:42,320 --> 01:05:44,560
Speaker 1: said to him, do you have any advice for me?

1017
01:05:44,680 --> 01:05:47,720
Speaker 1: You did this job once, and he thought about it

1018
01:05:47,760 --> 01:05:49,439
Speaker 1: for a second. He looked at me and he sort

1019
01:05:49,440 --> 01:05:52,440
Speaker 1: of half smiled, and he said, don't get anybody's blood

1020
01:05:52,440 --> 01:05:56,760
Speaker 1: on your hands. And I never forgot it, and I

1021
01:05:57,360 --> 01:06:00,520
Speaker 1: know exactly what he meant by it, which was, you know,

1022
01:06:00,840 --> 01:06:05,120
Speaker 1: don't do your best not to give people advice. You

1023
01:06:05,120 --> 01:06:09,720
Speaker 1: wouldn't take yourself the hypocritic first, do first, do no harm,

1024
01:06:09,920 --> 01:06:12,520
Speaker 1: not not a bad thing. We've been talking a lot

1025
01:06:12,640 --> 01:06:16,160
Speaker 1: about books and investors, and you mentioned The Intelligent Investor

1026
01:06:16,200 --> 01:06:19,800
Speaker 1: by Benjamin Graham. What other books really stood out to

1027
01:06:19,840 --> 01:06:25,920
Speaker 1: you as you developed your your your your BS Detector,

1028
01:06:25,960 --> 01:06:29,360
Speaker 1: your your journalism chops, and your finance jops. Yeah, there's

1029
01:06:29,400 --> 01:06:31,280
Speaker 1: a couple of books I really love. I mean, we

1030
01:06:31,360 --> 01:06:34,480
Speaker 1: talked about Richard Feynman before. I think anybody can pick

1031
01:06:34,560 --> 01:06:39,400
Speaker 1: up any of the oral history books. You just Mr

1032
01:06:39,480 --> 01:06:44,120
Speaker 1: fineman or um, what do you care what other people think?

1033
01:06:44,280 --> 01:06:48,800
Speaker 1: Is another wonderful one. Uh, those books are fabulous even

1034
01:06:48,840 --> 01:06:51,560
Speaker 1: for someone with no background in science. They can teach

1035
01:06:51,600 --> 01:06:53,880
Speaker 1: you a little bit about how to think like a scientist,

1036
01:06:54,400 --> 01:06:58,560
Speaker 1: and that's critical. There's a wonderful little book called um

1037
01:06:59,160 --> 01:07:02,680
Speaker 1: How to Lie with Statistics by Darryl Hoff. It's literally

1038
01:07:02,720 --> 01:07:05,680
Speaker 1: sitting on my night tap, which is such a fabulous book.

1039
01:07:06,480 --> 01:07:08,160
Speaker 1: You know that came in out and I want to

1040
01:07:08,160 --> 01:07:09,960
Speaker 1: say the late fifties. Yeah, it came out in the

1041
01:07:10,000 --> 01:07:14,080
Speaker 1: fifties as a wonderful book. It doesn't seem dated at all.

1042
01:07:14,120 --> 01:07:16,720
Speaker 1: It's it's not, it's not. It will never go out

1043
01:07:16,720 --> 01:07:20,440
Speaker 1: of style. And where are the customers? Yachts? By Fred

1044
01:07:20,440 --> 01:07:25,280
Speaker 1: Schwade Jr. Which was published in and I've never read that,

1045
01:07:25,920 --> 01:07:28,600
Speaker 1: have the book, and I've been dying to get the

1046
01:07:28,680 --> 01:07:30,800
Speaker 1: free time. I can't believe I came on your show

1047
01:07:30,920 --> 01:07:33,280
Speaker 1: and you've never read that. I'm not I'm leaving right now.

1048
01:07:33,320 --> 01:07:36,920
Speaker 1: I'm not exaggerating that. I actually mentioned that to someone

1049
01:07:37,240 --> 01:07:40,080
Speaker 1: in the beginning of the summer, that that's my book

1050
01:07:40,120 --> 01:07:42,320
Speaker 1: to read the summer, and know you're going to read

1051
01:07:42,360 --> 01:07:48,120
Speaker 1: it tonight. It's I started the first chapter and really

1052
01:07:48,200 --> 01:07:50,320
Speaker 1: enjoyed it. What I want you to do is read

1053
01:07:50,360 --> 01:07:52,800
Speaker 1: it aloud. I want you to read it aloud to you.

1054
01:07:53,000 --> 01:07:56,680
Speaker 1: The second person who's mentioned that technique, we didn't allowed

1055
01:07:56,720 --> 01:08:00,160
Speaker 1: to yourself, all right, or to other people. You I'm

1056
01:08:00,200 --> 01:08:02,400
Speaker 1: walk in a room start reading aloud from it. People

1057
01:08:02,480 --> 01:08:05,320
Speaker 1: stop what they're doing, really, and now look at you. Well, normally,

1058
01:08:05,320 --> 01:08:06,919
Speaker 1: when you walk into a room and just start reading

1059
01:08:06,960 --> 01:08:08,760
Speaker 1: a lot from a book, people stop what they're doing.

1060
01:08:08,800 --> 01:08:10,919
Speaker 1: And yeah, but but after they listen for a while,

1061
01:08:10,960 --> 01:08:12,520
Speaker 1: they'll go back to what they're doing. They won't do

1062
01:08:12,560 --> 01:08:15,080
Speaker 1: that with this book. All right, I'm gonna I'm not

1063
01:08:15,160 --> 01:08:17,679
Speaker 1: exaggerating when I say, of all the Wall Street books

1064
01:08:17,920 --> 01:08:20,360
Speaker 1: I have not read, this is the one that I've

1065
01:08:20,520 --> 01:08:24,000
Speaker 1: been most looking forward to reading, not on my night table,

1066
01:08:24,160 --> 01:08:27,960
Speaker 1: on my dresser, but literally in the bedroom. It's it

1067
01:08:28,120 --> 01:08:31,799
Speaker 1: is easily one of the five best books ever written,

1068
01:08:31,840 --> 01:08:35,880
Speaker 1: so ahead of the Goldman sax Elevator book. But move

1069
01:08:35,960 --> 01:08:41,120
Speaker 1: move this up the queue. Okay, it's in the top three. Beforehand,

1070
01:08:41,600 --> 01:08:45,240
Speaker 1: I'm I'm finishing, had a lie with statistics and this

1071
01:08:45,360 --> 01:08:49,640
Speaker 1: was next in my You have to I'm embarrassed to

1072
01:08:49,680 --> 01:08:52,840
Speaker 1: admit that I've had this sitting waiting to be read

1073
01:08:52,880 --> 01:08:55,200
Speaker 1: for six months and I just haven't read it. It's

1074
01:08:55,200 --> 01:09:00,760
Speaker 1: a wonderful book. And uh what other investors influenced you? Uh?

1075
01:09:01,200 --> 01:09:04,759
Speaker 1: You know. Fairly early in my career, I was lucky

1076
01:09:04,880 --> 01:09:09,400
Speaker 1: enough to get to know a bunch of really great

1077
01:09:09,520 --> 01:09:16,720
Speaker 1: wise people, UM, Sir John Templeton, m Phil Coray, Irving Khan.

1078
01:09:17,560 --> 01:09:20,120
Speaker 1: These were all people who had lived through the crash

1079
01:09:20,160 --> 01:09:26,639
Speaker 1: of and had really benefited from it and developed sort

1080
01:09:26,640 --> 01:09:32,080
Speaker 1: of incredibly powerful long term perspective. UM. And I learned

1081
01:09:32,080 --> 01:09:36,960
Speaker 1: a lot from from those guys, Uh and UM, and

1082
01:09:37,040 --> 01:09:39,639
Speaker 1: also from some other people I was lucky to spend

1083
01:09:39,640 --> 01:09:42,040
Speaker 1: time with when it was easier to spend time with them,

1084
01:09:42,080 --> 01:09:47,840
Speaker 1: Michael Price, Bill Miller. UM. Folks like that who you know,

1085
01:09:47,920 --> 01:09:52,240
Speaker 1: sort of gone on to fame and fortune and in

1086
01:09:52,280 --> 01:09:55,280
Speaker 1: some cases some some misfortunes. I'm trying to get Templeton

1087
01:09:55,360 --> 01:09:57,840
Speaker 1: on the show, but as people never response, yeah, he's

1088
01:09:57,880 --> 01:10:01,160
Speaker 1: not returning calls anywhere. U. Miller is a guy who's

1089
01:10:01,160 --> 01:10:04,400
Speaker 1: actually enjoying a little bit of a resurgence. He would

1090
01:10:04,400 --> 01:10:07,400
Speaker 1: be interesting to have a conversation. UM. So we talked

1091
01:10:07,400 --> 01:10:11,040
Speaker 1: a little bit about the changing nature of financial journalism

1092
01:10:11,120 --> 01:10:14,679
Speaker 1: and and media. What do you see as the next

1093
01:10:14,720 --> 01:10:18,720
Speaker 1: major shifts that are that are coming in this space? Well,

1094
01:10:18,760 --> 01:10:22,840
Speaker 1: I think I think what is still desperately needed, UM

1095
01:10:23,120 --> 01:10:28,000
Speaker 1: is UM filters. Um. You know, Twitter does a lot

1096
01:10:28,000 --> 01:10:30,840
Speaker 1: of that, but if you set it upright, if you

1097
01:10:30,960 --> 01:10:33,719
Speaker 1: use it, you have to you have to approach Twitter

1098
01:10:34,040 --> 01:10:37,479
Speaker 1: very very deliberately and intelligently to make it work for

1099
01:10:37,600 --> 01:10:41,240
Speaker 1: you instead of against you. And I think the time

1100
01:10:41,360 --> 01:10:46,400
Speaker 1: is coming when there will be platforms or applications that

1101
01:10:46,520 --> 01:10:51,559
Speaker 1: will intelligently filter material for people and do a real service.

1102
01:10:51,720 --> 01:10:53,559
Speaker 1: I don't think we're there yet, and I don't know

1103
01:10:53,600 --> 01:10:55,840
Speaker 1: what it's going to look like, but I know it's

1104
01:10:55,880 --> 01:10:59,280
Speaker 1: coming and I hope it comes soon. That's that's that's

1105
01:10:59,280 --> 01:11:02,840
Speaker 1: really interesting. And I find that a handful of UM

1106
01:11:02,880 --> 01:11:07,400
Speaker 1: aggregators are are enormously helpful. I not only read a

1107
01:11:07,439 --> 01:11:09,679
Speaker 1: ton of stuff when I put together my morning reads,

1108
01:11:09,920 --> 01:11:12,640
Speaker 1: but I also look at a handful of other aggregators

1109
01:11:12,640 --> 01:11:15,559
Speaker 1: and see what they're looking at. And I don't want

1110
01:11:15,560 --> 01:11:17,360
Speaker 1: to have too much overlap. I want to look and

1111
01:11:17,439 --> 01:11:22,679
Speaker 1: find interesting. But but that's human driven, that's not robot driven. UM.

1112
01:11:22,880 --> 01:11:26,160
Speaker 1: Twitter is really something that I think has so much potential.

1113
01:11:26,680 --> 01:11:30,599
Speaker 1: So long as you mute the annoying and block the

1114
01:11:30,680 --> 01:11:34,840
Speaker 1: intellectually dishonest, that that eliminates a lot of the noises.

1115
01:11:35,160 --> 01:11:37,679
Speaker 1: And I think there's one other. There's one other tip,

1116
01:11:37,960 --> 01:11:39,920
Speaker 1: Barry and and I know you do this and I

1117
01:11:40,240 --> 01:11:42,439
Speaker 1: work very hard at it, which is you really want

1118
01:11:42,479 --> 01:11:46,559
Speaker 1: to use a service like Twitter. Two feed yourself as

1119
01:11:46,680 --> 01:11:50,479
Speaker 1: much disconfirmation as you can. You really want to follow

1120
01:11:50,600 --> 01:11:54,400
Speaker 1: people and try to be followed by people you don't

1121
01:11:54,400 --> 01:11:57,559
Speaker 1: agree with, people you think are wrong, but you think

1122
01:11:57,600 --> 01:12:01,840
Speaker 1: they're intelligently wrong. I have that cast has always had

1123
01:12:01,880 --> 01:12:04,800
Speaker 1: that role with me. We are frequently on the opposite

1124
01:12:04,840 --> 01:12:08,280
Speaker 1: side of the trade. And if I like a market,

1125
01:12:08,400 --> 01:12:10,800
Speaker 1: or if I'm long term bullish and Doug this short

1126
01:12:10,920 --> 01:12:16,680
Speaker 1: term bearish, he forces me to sharpen my argument. On

1127
01:12:16,720 --> 01:12:20,679
Speaker 1: the other hands, I've been negative on gold for about

1128
01:12:20,800 --> 01:12:24,400
Speaker 1: four or five years, and when someone challenges me and

1129
01:12:24,960 --> 01:12:28,639
Speaker 1: their Twitter handle has either the word gold or bullion

1130
01:12:28,720 --> 01:12:34,520
Speaker 1: in it, I know I'm not getting a honest, objective analysis.

1131
01:12:34,560 --> 01:12:36,559
Speaker 1: On the other side, I want someone to explain to

1132
01:12:36,560 --> 01:12:39,400
Speaker 1: me why I should own gold when I don't want

1133
01:12:39,439 --> 01:12:43,639
Speaker 1: to own gold. Um, in a way that's objective and

1134
01:12:43,960 --> 01:12:47,639
Speaker 1: neutral and dispassionate. Don't go looking for that on Twitter.

1135
01:12:47,680 --> 01:12:50,760
Speaker 1: You're not gonna find them. And UM my favorite two

1136
01:12:50,840 --> 01:12:54,600
Speaker 1: questions I asked of all my guests. First, what advice

1137
01:12:54,600 --> 01:12:57,640
Speaker 1: would you give to a millennial or recent college graduate

1138
01:12:58,040 --> 01:13:05,200
Speaker 1: who is just beginning their career in financial journalism? Well,

1139
01:13:05,600 --> 01:13:08,160
Speaker 1: I would First of all, I guess the first advice

1140
01:13:08,200 --> 01:13:11,720
Speaker 1: I would give you is to make sure you're in

1141
01:13:11,760 --> 01:13:14,639
Speaker 1: the right field. There are a lot of smart people

1142
01:13:14,680 --> 01:13:17,640
Speaker 1: who would tell you you're crazy for trying to be

1143
01:13:17,720 --> 01:13:20,280
Speaker 1: a financial journalist right now, and you might want to

1144
01:13:20,320 --> 01:13:23,400
Speaker 1: listen to them. They could be right. The second thing is,

1145
01:13:23,680 --> 01:13:30,439
Speaker 1: I'm I arrive earlier and leave later than everybody else.

1146
01:13:30,560 --> 01:13:36,200
Speaker 1: Every day I'm find the smartest people you can possibly

1147
01:13:36,320 --> 01:13:40,000
Speaker 1: find and learn everything you can from them. Ask everyone,

1148
01:13:40,320 --> 01:13:45,320
Speaker 1: what do you read? Whom do you admire? Who's the

1149
01:13:45,360 --> 01:13:49,080
Speaker 1: smartest person you know? Um? You know, when I started

1150
01:13:49,080 --> 01:13:52,439
Speaker 1: at Forbes in seven, every day I would I would

1151
01:13:52,479 --> 01:13:55,320
Speaker 1: read the Wall Street Journal and I would circle in

1152
01:13:55,400 --> 01:14:00,200
Speaker 1: red pen every quote from somebody who knew more about

1153
01:14:00,200 --> 01:14:03,439
Speaker 1: whatever it was than I did, and then I would

1154
01:14:03,439 --> 01:14:05,960
Speaker 1: call that person up, because in those days you had

1155
01:14:06,000 --> 01:14:08,559
Speaker 1: to call and I would say, I want to buy

1156
01:14:08,560 --> 01:14:12,160
Speaker 1: you breakfast, lunch, dinner, drinks, and then I would just

1157
01:14:12,320 --> 01:14:15,439
Speaker 1: download from the person's brain and then I you know,

1158
01:14:15,479 --> 01:14:18,800
Speaker 1: I learned something I didn't know before. That's quite fascinating,

1159
01:14:18,840 --> 01:14:20,439
Speaker 1: and that worked. You can pick up the phone and

1160
01:14:20,520 --> 01:14:24,519
Speaker 1: call somebody. It worked then, and I think it works

1161
01:14:24,560 --> 01:14:29,400
Speaker 1: now too, although email is easier obviously, to say the least.

1162
01:14:29,520 --> 01:14:32,519
Speaker 1: And and our last question, what is it that you

1163
01:14:32,560 --> 01:14:37,760
Speaker 1: know today about financial reporting, investing and journalism that you

1164
01:14:37,800 --> 01:14:42,240
Speaker 1: wish you knew thirty years ago? Well, my dad, who

1165
01:14:42,320 --> 01:14:47,080
Speaker 1: was a very wise man, had a wonderful expression, which

1166
01:14:47,240 --> 01:14:51,160
Speaker 1: was that, And I think I remember this verbatim. It's

1167
01:14:51,200 --> 01:14:56,160
Speaker 1: remarkable how much you have to learn about something in

1168
01:14:56,280 --> 01:15:01,040
Speaker 1: order to discover how little you need to know about it.

1169
01:15:02,439 --> 01:15:05,360
Speaker 1: He goes right back to Dunnan Krueger. There's the experts

1170
01:15:05,520 --> 01:15:08,920
Speaker 1: know their own skill set. Amateurs have no clue, right,

1171
01:15:09,240 --> 01:15:11,920
Speaker 1: And I think the other the other thing he was

1172
01:15:12,000 --> 01:15:15,759
Speaker 1: driving at is that in order to learn those few

1173
01:15:16,240 --> 01:15:20,040
Speaker 1: true things that are at the core of everything, you

1174
01:15:20,160 --> 01:15:24,400
Speaker 1: have to learn an enormous amount about them. And I

1175
01:15:24,400 --> 01:15:27,599
Speaker 1: guess the thing I most wish I had known at

1176
01:15:27,600 --> 01:15:33,920
Speaker 1: the beginning was where all this would come out, which

1177
01:15:34,000 --> 01:15:40,320
Speaker 1: is that ultimately success as an investor depends on one thing,

1178
01:15:41,080 --> 01:15:44,840
Speaker 1: really only depends on one thing self control. M hm.

1179
01:15:45,479 --> 01:15:51,760
Speaker 1: And you know when Benjamin Graham said, uh, the investor's

1180
01:15:51,760 --> 01:15:57,800
Speaker 1: worst enemy is himself. He wasn't kidding, And in a

1181
01:15:57,800 --> 01:16:01,439
Speaker 1: lot of ways, the entire book The Intelligent Investor boils

1182
01:16:01,479 --> 01:16:06,839
Speaker 1: down to that one sentence. And great investors have self control,

1183
01:16:07,840 --> 01:16:12,240
Speaker 1: and everyone else can't be a great investor. If you

1184
01:16:12,280 --> 01:16:15,439
Speaker 1: don't have self control, you don't belong in the game

1185
01:16:15,520 --> 01:16:18,800
Speaker 1: where you're not going to prevail at least. Hmm. That's

1186
01:16:18,880 --> 01:16:22,280
Speaker 1: quite fascinating. Jason, Thank you so much for spending so

1187
01:16:22,360 --> 01:16:25,559
Speaker 1: much time with us and being being so generous. If

1188
01:16:25,600 --> 01:16:29,479
Speaker 1: people want to find your work, the entire body of

1189
01:16:29,479 --> 01:16:32,559
Speaker 1: your work, I would be remiss if I didn't mention

1190
01:16:32,720 --> 01:16:36,320
Speaker 1: Jason's Wide dot com. For those of you who follow

1191
01:16:36,400 --> 01:16:39,639
Speaker 1: me on Twitter, and you will notice each morning around

1192
01:16:39,840 --> 01:16:45,639
Speaker 1: six ish, you occasionally will see a Today in seven

1193
01:16:46,640 --> 01:16:49,559
Speaker 1: and I will reference what happened that day, and I

1194
01:16:49,600 --> 01:16:53,479
Speaker 1: probably do that once or twice a week. That comes

1195
01:16:53,479 --> 01:16:57,640
Speaker 1: directly from this day in financial history, UM, which I

1196
01:16:57,680 --> 01:16:59,960
Speaker 1: then have to edit down to a hundred forty characters

1197
01:17:00,200 --> 01:17:03,720
Speaker 1: because it usually starts at about three or four hundred characters,

1198
01:17:03,800 --> 01:17:06,880
Speaker 1: and there is an art to getting a paragraph into

1199
01:17:06,960 --> 01:17:11,519
Speaker 1: a tweetable sentence. UM. But I find that fascinating. There's

1200
01:17:11,520 --> 01:17:14,599
Speaker 1: a lot of great quotes there, there's basically who you are,

1201
01:17:14,760 --> 01:17:17,880
Speaker 1: what you've done. Is it safe to say just about

1202
01:17:17,920 --> 01:17:21,200
Speaker 1: every column you've written is either there or linked to

1203
01:17:21,320 --> 01:17:23,800
Speaker 1: from there? That it can be gotten? Yeah, I mean,

1204
01:17:23,880 --> 01:17:26,880
Speaker 1: I'm plus your books plus everything. I like a good

1205
01:17:26,920 --> 01:17:31,920
Speaker 1: hedge fund manager. I'm backfilling. But it but it, but

1206
01:17:32,000 --> 01:17:36,160
Speaker 1: it takes time. I think I have everything back to

1207
01:17:36,200 --> 01:17:39,960
Speaker 1: two thousand thirteen and a lot of stuff back uh ten,

1208
01:17:40,080 --> 01:17:43,520
Speaker 1: fifteen or twenty years or more. In fact, I recently

1209
01:17:44,520 --> 01:17:49,960
Speaker 1: pulled something from your site that you had re added there.

1210
01:17:49,960 --> 01:17:52,080
Speaker 1: I'm trying to remember. It was was like fifteen years

1211
01:17:52,080 --> 01:17:55,040
Speaker 1: ago as a speech you had given, and I linked

1212
01:17:55,080 --> 01:17:57,000
Speaker 1: to it in the Weekend Reads because it was the

1213
01:17:57,080 --> 01:18:01,320
Speaker 1: longer form thing and it I love when I will

1214
01:18:01,360 --> 01:18:04,320
Speaker 1: find something that people haven't seen yet and then it

1215
01:18:04,439 --> 01:18:07,519
Speaker 1: starts just hanging around going viral, and I'm like, oh,

1216
01:18:07,600 --> 01:18:09,920
Speaker 1: I guess everybody else liked it. Also, it was I

1217
01:18:09,960 --> 01:18:12,759
Speaker 1: wasn't out on a limb. That was a speech you gave.

1218
01:18:13,040 --> 01:18:14,840
Speaker 1: Do you know what I'm referring to? I think it

1219
01:18:14,920 --> 01:18:18,120
Speaker 1: was a speech I gave that that I called fat

1220
01:18:18,160 --> 01:18:22,599
Speaker 1: tails thin ice. That the one and and that fifteen

1221
01:18:22,640 --> 01:18:26,000
Speaker 1: years not seen online for a long time and suddenly

1222
01:18:26,080 --> 01:18:29,320
Speaker 1: that goes viral. It's a speech I gave at the

1223
01:18:29,360 --> 01:18:35,519
Speaker 1: Morning Star Investment conferency and UH two thousand one, I think, UM,

1224
01:18:36,080 --> 01:18:40,880
Speaker 1: where I sort of talked about UH, I guess what

1225
01:18:40,920 --> 01:18:46,880
Speaker 1: would I say? A few risks that I'm materialized later

1226
01:18:47,240 --> 01:18:51,720
Speaker 1: and UH that I thought the financial advisor community was

1227
01:18:53,760 --> 01:18:58,160
Speaker 1: dangerously overlooking. Well, I'm We're out of time, and I

1228
01:18:58,240 --> 01:19:01,760
Speaker 1: just wanted to thank you again for spending so much time. UM,

1229
01:19:02,040 --> 01:19:05,280
Speaker 1: you've been listening to Masters in Business on Bloomberg Radio.

1230
01:19:05,320 --> 01:19:08,439
Speaker 1: If you enjoy this conversation, look up an Inch or

1231
01:19:08,479 --> 01:19:11,639
Speaker 1: down an inch on iTunes and you could see any

1232
01:19:11,720 --> 01:19:15,000
Speaker 1: of the other five dozen such podcasts we've had over

1233
01:19:15,040 --> 01:19:19,320
Speaker 1: the past year and change. UM, Jason's wife, thank you

1234
01:19:19,360 --> 01:19:22,000
Speaker 1: again for for coming by my pleasure. Parry, thanks for

1235
01:19:22,040 --> 01:19:25,559
Speaker 1: having me. You're listening to Masters in Business with Barry

1236
01:19:25,640 --> 01:19:27,639
Speaker 1: Ridholts on Bloomberg Radio.