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Speaker 1: This is Master's in Business with very Ridholts on Bloomberg Radio.

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Speaker 1: This week on the podcast, I have an extra special guest.

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Speaker 1: His name is Ed Mendel. What can I say about Eddie?

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Speaker 1: He is the co founder of ned Davis Research, which

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Speaker 1: is a enormously successful institutional research shop sold to euro

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Speaker 1: Money about six or seven years ago for for a

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Speaker 1: price tag that is uh, google ble, but I could

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Speaker 1: tell you it's in the hundreds of millions of dollars. Uh.

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Speaker 1: He very successfully took the genius that was ned Davis

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Speaker 1: and wrapped an entire business model around it. And while

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Speaker 1: Ed himself is very humble and credits uh, Ned's genius

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Speaker 1: for the success of the firm, really he is one

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Speaker 1: of these um underappreciated UM people in finance who who

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Speaker 1: took a great idea and found a way to turn

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Speaker 1: it into a very very successful business. I don't think

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Speaker 1: there would have been a Nit David's Research without Ed

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Speaker 1: Mendel's contributions. He's also very actively involved in philanthropy. He's

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Speaker 1: one of the minority owners of the Atlanta Falcons and

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Speaker 1: just uh an all around inspirational guy. I've relied on

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Speaker 1: his insights over the years not just for helping to

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Speaker 1: put together the forerunner of Masters in Business. We discussed

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Speaker 1: a little bit about how his contributions actually helped lead

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Speaker 1: to this show, but also his insights about running a

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Speaker 1: business and managing people, and managing capital and assets, and

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Speaker 1: being able to think about the various ways that business

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Speaker 1: has done properly intelligently, ethically, and and um just just

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Speaker 1: being smart about how to run a shop. And so

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Speaker 1: I have tremendous amount of gratitude to him personally for

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Speaker 1: for sharing his insights with me over the years. He

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Speaker 1: mentors a lot of people. Uh and and he's just

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Speaker 1: one of these people who are in a household name

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Speaker 1: but have had an enormous influence on on finance and business.

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Speaker 1: And even though you may not have heard of him,

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Speaker 1: you probably should have. So, with no further ado, here

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Speaker 1: is my conversation with Eddie Mendel. My special guest today

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Speaker 1: is Ed Mendel. He is co founder of NED Davis

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Speaker 1: Research as well as the brokerage firm Davis Mendel and Reeginstein,

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Speaker 1: which we're both founded in nineteen eighty. Collectively, they're known

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Speaker 1: as the NID Davis Research Group. Ed helped to build

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Speaker 1: one of the largest stock and bond research followings on

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Speaker 1: Wall Street, working closely with ned Davis since nineteen seventy one.

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Speaker 1: Their research is best characterized as an objective, disciplined approach

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Speaker 1: to investing, focusing on risk management, primary trends, and avoiding

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Speaker 1: major financial disasters. Ed has been associated philanthropically with numerous

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Speaker 1: national and Atlanta charities. He is also a minority owner

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Speaker 1: of the NFL football team, the Atlanta Falcons. Edmundel, Welcome

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Speaker 1: to Bloomberg. Thank you. So I've been looking forward to

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Speaker 1: this for a while. I think I know you for

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Speaker 1: more than a decade, maybe close to two decades, because

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Speaker 1: of your work at ned Davis Research and and being

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Speaker 1: a partner, Uh to Ned. Let's ask the first question.

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Speaker 1: You guys began in the middle of a bear market

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Speaker 1: in the nineteen seventies in your work in markets, How

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Speaker 1: did that impact your psychology the rest of your career?

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Speaker 1: It's got as focused and uh, we really became an

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Speaker 1: institutional research firm, and we were very fortunate that when

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Speaker 1: we started it was nanti five percent retail. It ended

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Speaker 1: up being n institutional, and so it wasn't a matter

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Speaker 1: of anything but being at the right place at the

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Speaker 1: right time with the right product, and I just knew

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Speaker 1: that Ned was a genius and that we would somehow

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Speaker 1: be successful. The seventies, you guys were at a brokerage firm.

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Speaker 1: How did you and Ned hook up? How did you

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Speaker 1: guys find each other? I started at J. C. Bradford

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Speaker 1: in Nashville, and Ned came home from Harvard and started

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Speaker 1: working for J. C. Bradford. I went and introduced myself

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Speaker 1: to Ned, and we struck a friendship and uh and uh,

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Speaker 1: you know, eight years later we left to start our

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Speaker 1: own firm. You mentioned being launching your career in the

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Speaker 1: nineteen seventies and Ned David's research in eighty. You started

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Speaker 1: mostly at retail, but it eventually morphed two institutional Was

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Speaker 1: that because the retail investor was not a participant? Was

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Speaker 1: it the psychology? How did you shift to such a

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Speaker 1: heavily heavily waited institutional practice. Bradford, I don't think really

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Speaker 1: understood what they had on their hands with Ned. So

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Speaker 1: I was one of the first people to go out

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Speaker 1: and market Ned in Texas, in the state of Texas,

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Speaker 1: and in Houston, and going backwards, I was a retail broker.

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Speaker 1: My career really got helped tremendously. By May Day, uh

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Speaker 1: May one commission structures changed used to be you could,

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Speaker 1: so I became really one of the first discount brokers.

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Speaker 1: And so it was eighty two cents to sell a

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Speaker 1: hundred chairs of IBM. But Mary Lynch and Kidder and

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Speaker 1: pain Webber would not let you discount. So I went

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Speaker 1: around to all the wealthy people I could find in

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Speaker 1: Atlanta and offered him at discount. So I became the

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Speaker 1: biggest producer at Bradford. But then I started asking Med

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Speaker 1: to leave, and we were going to leave in seventy eight,

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Speaker 1: and we ended up leaving in nineteen eighty, which is

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Speaker 1: another story which I'll get to later. Okay, well, let's

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Speaker 1: let's get to it right now. You started in eighty.

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Speaker 1: Why why did you wait till that year? We were

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Speaker 1: gonna leave in seventy eight, and UH Ned was going

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Speaker 1: to move to uh Sarasota, Venice, Florida, and uh we

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Speaker 1: were going to start the firm. But Bradford came to

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Speaker 1: Ned and made him a partner and uh and he stayed.

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Speaker 1: But then in nineteen seventy nine he went to Jimmy

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Speaker 1: Bradford and told Jimmy that he wanted a computer, and

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Speaker 1: Jimmy told him, I've seen computers that you're doing fine,

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Speaker 1: just the way it is. And so Ned went out

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Speaker 1: on his own and spent thirty five thousand dollars on

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Speaker 1: a Hewitt Packard computer that did graphics. And within the

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Speaker 1: six months you could buy a chip that made it

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Speaker 1: ten times faster, and within a year you could buy

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Speaker 1: the computer with the chip in it. It was ten

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Speaker 1: times faster for a total of seven thousand dollars. And

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Speaker 1: uh okay uh. And I recall Ned saying that he

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Speaker 1: had pitched Bradford on technology and computerization and the ability

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Speaker 1: to crunch a lot of numbers, and the response was

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Speaker 1: sort of, hey, what you're doing is working fine? Yeah

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Speaker 1: that Jimmy said, it's working. It's working just fine. But Ned,

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Speaker 1: ned Is was a genius. And you know, among many

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Speaker 1: other things, it was clean data. Uh you know, we

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Speaker 1: cleaned data for Ibbotson and SMP and uh you know,

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Speaker 1: we were known for our charts and our data among

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Speaker 1: many other things. But the other great story uh that

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Speaker 1: came out of that is uh uh Ned told me

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Speaker 1: I think the fourth biggest lie ever told. And he

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Speaker 1: told me when we started that we were going to

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Speaker 1: need a programmer, but just for a year. And I

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Speaker 1: think when we sold the company we had fourteen programmers.

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Speaker 1: So the fourth biggest lie is you we're going to

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Speaker 1: get a programmer, but just for a year, just for

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Speaker 1: a year. Let's talk a little bit about you guys

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Speaker 1: hanging you shingle and in really the final inning innings

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Speaker 1: of a sixteen year bear market. How did you guys

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Speaker 1: have the nerve to launch into that environment and how

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Speaker 1: did you get clients? We started and Ned thought that

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Speaker 1: within four months we would have the products that he

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Speaker 1: had in his head up and running, but it was

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Speaker 1: two years later. So we never took any money out

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Speaker 1: of the business for two years and put it back

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Speaker 1: into the business. Uh, completely and totally. And uh I

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Speaker 1: had a big retail business, and so the retail helped

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Speaker 1: carry us until we got up and running with the

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Speaker 1: institutional business. And that eventually morphed to almost all purely

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Speaker 1: institutional totally, which uh's a blessing. Why do you say

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Speaker 1: that the retail is h you know, where's my check?

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Speaker 1: Where's my dividend? I'm gonna sue you. It's a it's

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Speaker 1: just a it's just and so you know, if you're

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Speaker 1: going to do it, you might as well get paid big.

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Speaker 1: And we we were blessed that as far as the

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Speaker 1: right time, in the right place. Um, we got paid

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Speaker 1: in soft dollars and commissions. So I explained soft dollars

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Speaker 1: because a lot of listeners may not be familiar with it. Well, uh,

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Speaker 1: we would go to the State of Texas and tell

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Speaker 1: them we wanted twenty five thousand dollars. And if I

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Speaker 1: went to you and said, I have this service, and

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Speaker 1: you know you'll like it, but you had to write

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Speaker 1: a check personally for twenty five thousand dollars, you go,

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Speaker 1: I really like a lot of money. But the State

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Speaker 1: of Texas was going to buy a million shares of

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Speaker 1: Boeing through one of our clearing frooms, pain Web Goldman

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Speaker 1: sacks are especially bear Stearns, And so they'd buy a

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Speaker 1: million shares and ten sents a share back then or

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Speaker 1: fifteen and give us ten or fifteen thousand dollars. And

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Speaker 1: it's another word. You guys set up the broker dealer

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Speaker 1: in order too. Hey, we're gonna spend the money on

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Speaker 1: the commission anyway, it might as well pay for the

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Speaker 1: It was other people's money, and so that's what made

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Speaker 1: the business very successful. Were you ever actively involved in

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Speaker 1: trading yourself? Were you mostly doing institutional sales? Mostly institutional sales?

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Speaker 1: And um, so the business was sold in. Do you

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Speaker 1: still have any involvement? Because I know Ned still does.

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Speaker 1: So you're you're free and clear for seven years now,

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Speaker 1: that's right. The falcons and grandchildren is what's keeping you busy? Um.

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Speaker 1: So when you guys launched the Night, who are your competitors?

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Speaker 1: Who was out there selling the sort of quantitative technical

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Speaker 1: research that that you guys were doing. Again, we were

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Speaker 1: just at the right place, at the right time, with

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Speaker 1: with the right product. And one of the products though

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Speaker 1: we ended up by accident, um, was the chart service.

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Speaker 1: We did the charts for Ned, and UH, as long

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Speaker 1: as you're doing it for him, you might as well

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Speaker 1: make we had we really didn't have an idea that

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Speaker 1: that would be such a huge hit for client presentations,

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Speaker 1: for marketing and meetings and brochures, and so we had

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Speaker 1: a huge, uh publishing complex by sending out these huge

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Speaker 1: chart books that were three or four inches thick. And

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Speaker 1: then we got into customized research. So let's talk about

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Speaker 1: customized research because at present and for the past decade

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Speaker 1: or so, ned Davis Research generates two thousand custom research

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Speaker 1: projects a year. Am I getting that right? It's been

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Speaker 1: six years since I've been there, but a lot. So

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Speaker 1: what's a typical request? How is this ramped up over time?

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Speaker 1: That sounds like a lot of specific, individualized research or

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Speaker 1: is there a ton of overlap from one to the next. Again,

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Speaker 1: some of the best projects and ideas have come from

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Speaker 1: our clients, and so NED is just incredible about devouring

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Speaker 1: data and information and studies. And so we had a

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Speaker 1: whole research department of people that would do projects that

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Speaker 1: other people thought of doing or uh, they would want

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Speaker 1: the models or stuff built just for them on a

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Speaker 1: proprietary basis. So we once we got in their back pocket,

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Speaker 1: we stayed there. So let me let me share one

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Speaker 1: of my favorite ned Davis quotes and you could perhaps

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Speaker 1: give me a little color on it. We are in

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Speaker 1: the business of making mistakes. The only difference between winners

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Speaker 1: and losers is that winners make small mistakes while losers

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Speaker 1: make big mistakes. Discuss well, you know, Ned's also famous

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Speaker 1: for saying they're the only thing worse than making a

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Speaker 1: forecast is sticking by it. And he wrote famously wrote

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Speaker 1: a book would you Rather be right or Make money? Yes?

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Speaker 1: Being right or making money? Title? And uh, you know,

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Speaker 1: risk control? Uh you know, I'm reminded of one of

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Speaker 1: the biggest hedge fund p pole here in New York

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Speaker 1: once told me he puts on a trade and then

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Speaker 1: it starts worrying about everything that can go wrong. But uh,

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Speaker 1: having a stop loss and controlling risk and not letting

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Speaker 1: a little mistake become a disastrous mistake is incredibly important.

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Speaker 1: The version of that I learned when I began in

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Speaker 1: the business was it's okay to be wrong, it's unforgivable

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Speaker 1: to stay wrong. And I think there's a lot to that. UM.

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Speaker 1: So the I keep coming back to the risk management

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Speaker 1: side of this. How much did really making your bones

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Speaker 1: in the nineties seventies, in the midst of that horrific

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Speaker 1: bear market plus inflation plus uh twelve risk free treasury yields?

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Speaker 1: How much did that impact the psychology of what you

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Speaker 1: guys were doing? Because you keep talking about um and

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Speaker 1: everything I've read from both of you is manage your risk,

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Speaker 1: don't let disasters happen. Pay attention to the primary trend.

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Speaker 1: How formative were the nineties seventies to to Ned David's research. Again, Ned,

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Speaker 1: Ned's genius is what I what I banked on. I

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Speaker 1: realized very early that it wasn't Ed mental research. He

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Speaker 1: was Ned Davis research. So you were, you were, and

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Speaker 1: I appreciate your humility, but you were instrumental in taking

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Speaker 1: his brilliant insights and building a business around it. Because

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Speaker 1: left to his own devices, I get the feeling that

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Speaker 1: Ned would be very happy to just stare at the

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Speaker 1: computer crunch numbers, but perhaps not monetize that in the

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Speaker 1: ways that you've managed to. Well, yeah, I had to

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Speaker 1: go out on the road and hire the people. Ned

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Speaker 1: did not want to deal with lawyers or accountants, and uh,

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Speaker 1: he wanted to do research, which it was is that's

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Speaker 1: his forte and uh he did it unbelievably well, and

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Speaker 1: he wasn't distracted, and so he did not go out

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Speaker 1: on the road a whole lot. And uh so it

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Speaker 1: was left up to me to to to do the

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Speaker 1: marketing and build the firm, and that started in nineteen

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Speaker 1: eighty and then thirty years later the firm was sold

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Speaker 1: to your own money. You guys built a reputation for

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Speaker 1: being fact based, quantitative and really one of the first

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Speaker 1: major technical analysis firms. So, so let's talk about that

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Speaker 1: a little bit. You're an institutional salesperson, NED is looking

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Speaker 1: at charts. How did you perceive the value of technicals

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Speaker 1: for for your institutional clients way back in the seventies

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Speaker 1: and night when you launched, NED would be the first

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Speaker 1: to tell you that technical analysis doesn't work half the time,

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Speaker 1: but neither just fundamentals. And so the genius which NED

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Speaker 1: has is to be able to mesh and look at everything.

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Speaker 1: And so we also, did you know, a incredible deal

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Speaker 1: on on sentiment and uh so we had some of

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Speaker 1: the best ciniment charts you know around. So Ned's geniuses

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Speaker 1: that he looked at everything and he just didn't hang

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Speaker 1: his hat on technical analysis. So, but you were out

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Speaker 1: in the trenches selling the product to institutions. Did you

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Speaker 1: find that when you were discussing charts, sentiment, everything else,

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Speaker 1: that all the work that was being generated in house.

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Speaker 1: Was there an advantage to working with charts and technicals?

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Speaker 1: Was it different than what everybody else was selling? What

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Speaker 1: what made this so successful? Because it's easy to say

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Speaker 1: in retrospect, well, we were more wrong than right. But

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Speaker 1: at the time you're the guy who's selling it and

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Speaker 1: you don't know how much wronger or writer it's going

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Speaker 1: to be. Ned had incredible historical perspective on the market

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Speaker 1: and everything, and so we we we we went out there.

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Speaker 1: We had a broad based service. We had like ten

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Speaker 1: different services and so uh, we did just didn't hang

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Speaker 1: our hat on you know, the bond commentary or the

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Speaker 1: stock rankings, but between Ned's hotline, which is probably the

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Speaker 1: best historic perspective and FED watching around and then the

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Speaker 1: chart service where we got you know, people really dependent

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Speaker 1: on us for their client presentations, marketing and meetings, uh

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Speaker 1: and brochures. And then we added to that we were

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Speaker 1: doing all this customized research. So it's a service business.

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Speaker 1: So you know, I came from a southern town retail

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Speaker 1: and in Little Rock, Arkansas. But the customers always right,

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Speaker 1: and we did we tried to do more than our

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Speaker 1: share in a relationship, and we tried not to goug

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Speaker 1: the customers and for value received, we we gave them

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Speaker 1: a great total product. But we were very lucky that

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Speaker 1: the soft dollars we're able to cover the course and

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Speaker 1: it was painless for them to pay us. And by

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Speaker 1: the time you guys ended up selling in I want

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Speaker 1: to say NED Davis Research was institutionally one of the

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Speaker 1: most widely followed institutional services out there. Is that a

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Speaker 1: Is that a fair statement? Fair statement? We know that

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Speaker 1: the early days NED was attracted to computers, but here

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Speaker 1: we are in TV computers are running everything from high

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Speaker 1: frequency trading to analyzing SEC filings too, just about anything

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Speaker 1: you could think of. How have computers changed the game

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Speaker 1: of institutional investment? Again, I've been out of the business

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Speaker 1: for seven years, and the you know, it's the E

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Speaker 1: T F s and the trading that have diminished everything.

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Speaker 1: And the it's it's what made it wasn't the computer.

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Speaker 1: It was Ned's uh interpretation of the data and the

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Speaker 1: information and so uh you layered upon you know, the

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Speaker 1: computer you had needs needs, historic perspective and uh economic view,

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Speaker 1: monetary view, and that's what made us different. We had

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Speaker 1: to differentiate ourselves and so the computer, you know, and

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Speaker 1: this goes back to two. Reagan deserves a lot of

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Speaker 1: credit for things that he did, but he was very lucky.

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Speaker 1: The personal computer came along and created twenty six million

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Speaker 1: jobs that it also made us unbelievably productive, I mean

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Speaker 1: unbelievably used to take us all day to do a

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Speaker 1: mailing list literally, and once you started working with the computer,

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Speaker 1: it was seconds. I mean, you know, once we got

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Speaker 1: everything up and running. So it's the productivity that made

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Speaker 1: us unbelievably successful. Also, but the pushback to that would be, well,

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Speaker 1: computers were available and they can make everybody productive. Why

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Speaker 1: were you guys able to take advant antage of it

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Speaker 1: when others didn't. Well, again, I alluded to the data.

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Speaker 1: Ned was the first with data and clean data, and

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Speaker 1: so he uh I could look at five charts to

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Speaker 1: day and with a red pen and knows knows when

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Speaker 1: when a chart is six or the slightest bit off,

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Speaker 1: just by eyeballing. Yes, So his one of his many

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Speaker 1: genius things is but we we we were a freak

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Speaker 1: about clean data. And there's a lot of it was

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Speaker 1: a lot of or probably still is a lot of

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Speaker 1: bad data out there. Let's talk a little bit about

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Speaker 1: the modern stock market. How did you guys think about

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Speaker 1: managing risk when you uh launched the firm in in

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Speaker 1: the early eighties. You know, I was thirty years old,

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Speaker 1: and uh I was too naive or stupid to really worry.

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Speaker 1: I knew it would be a big success, and so

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Speaker 1: there wasn't any question that, you know, between my retail

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Speaker 1: business and having Ned as a partner, that it was

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Speaker 1: going to be successful. So it wasn't a question of

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Speaker 1: managing risk. It was it was about putting money back

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Speaker 1: into the business and building it. And so not taking

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Speaker 1: money out for two years and hiring salespeople and hiring

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Speaker 1: people and programmers. Uh and and uh you know that's

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Speaker 1: wold help laid the foundation for the thing to be successful.

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Speaker 1: So you said you didn't take money out for two years,

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Speaker 1: and basically the revenue was coming from the high net

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Speaker 1: worth side, but you guys had to be taking salaries.

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Speaker 1: You weren't just doing nothing, no nothing, not an every

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Speaker 1: dollar went back into the business for two years, and

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Speaker 1: so you were really paying your dues in in that period. Yeah,

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Speaker 1: I remember writing checks for thirty five dollars and seventy

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Speaker 1: dollars and cringing and cringing. So at what point did

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Speaker 1: the firm begin to be able to allow the two

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Speaker 1: of you to take a salary in two years? Took

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Speaker 1: two years? Two years? And what was it that changed

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Speaker 1: the launch of the new products? You know, we were

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Speaker 1: building an institutional base that that was taking off, and

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Speaker 1: we started clearing through bear Stearns. Greenberg was very helpful

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Speaker 1: and convincing the that you know, he took us aside

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Speaker 1: for his eight seconds and said, just come here, we'll

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Speaker 1: take care of everything. That They had an unbelievable clearing operation.

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Speaker 1: This is bear Stons in the early eighties. At the

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Speaker 1: time they were the third or fourth largest broker's farm

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Speaker 1: is that, but they were probably the biggest clearing firm

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Speaker 1: for for what we did, institutional institutional trading, and so

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Speaker 1: we didn't have to have floor brokers or clearing or

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Speaker 1: back office. So you know, the banker New York would

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Speaker 1: call up bear Stearns and buy a million chairs of

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Speaker 1: Boeing for a dime, and they would credit and gave

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Speaker 1: credit that this one, this one, and this center at

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Speaker 1: Davis Research. So so that's a hundred thousand dollar commission

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Speaker 1: on that transaction back then, whatever it was. It made

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Speaker 1: it so easy for people to pay us. So in

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Speaker 1: other words, they would pay for the research via the

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Speaker 1: institutional trades, which they're gonna do anyway, and really, what

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Speaker 1: the heck is tanking? It did not cost them anything

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Speaker 1: ten cents on on bus share of Boeing then, but

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Speaker 1: even then it wasn't their money, so because it's institutional

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Speaker 1: and other people fund of but what they spend I

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Speaker 1: guess it doesn't really matter who there trading for. What

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Speaker 1: matters is who they're UM executing through and where the

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Speaker 1: money man. In other words, whether they paid a nickel

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Speaker 1: or adam, the fifteen cents didn't affect their bottom line.

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Speaker 1: That's right. So o PM and big institutional trading made

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Speaker 1: it easy. But you guys are fairly full service. You

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Speaker 1: guys were, and ned Davis still is a fairly full

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Speaker 1: service UM research shop. What does if I go to

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Speaker 1: nd R and say I want everything? How much can

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Speaker 1: I spend a year with them? Again, I've been out

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Speaker 1: for six years, give me a ballpark from ten years ago? Um,

381
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Speaker 1: you know dollars? Okay, So that's a lot of data,

382
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Speaker 1: a lot of charts, a lot of custom research. But

383
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Speaker 1: you know, Goldman Sachs used to have six hundred cash traders.

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Speaker 1: They have now to the soft dollar business has diminished

385
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Speaker 1: two from six hundred to two. Yeah, and that's computers

386
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Speaker 1: essentially well, and also now eat heat. Everything is going

387
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Speaker 1: to E T F s and UM volume is actually

388
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Speaker 1: way down. It's it's a very different business. So we've

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Speaker 1: seen a move towards passive investing from a lot of

390
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Speaker 1: the mom and pop investors as well as on the

391
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Speaker 1: institutional side, we've seen the rise of ETFs. And as

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Speaker 1: these two things have happened, we've seen a huge decrease

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Speaker 1: in trading volume. What does this mean for what used

394
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Speaker 1: to be called institutional training and now is called I

395
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Speaker 1: don't even know what what do you call it these days?

396
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Speaker 1: Is it just by side research? How do how do

397
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Speaker 1: you describe it? If you have to pay hard dollars

398
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Speaker 1: for a service stuff, it's tough and you have to

399
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Speaker 1: justify and it's tough. So, uh, you know people that

400
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Speaker 1: used to pay us one hundred thousand. I just heard

401
00:25:18,880 --> 00:25:22,920
Speaker 1: of somebody you know, going to forty thousand. So it's

402
00:25:23,680 --> 00:25:28,120
Speaker 1: it's the margins get squeezed, and uh, you know, everything

403
00:25:28,280 --> 00:25:30,600
Speaker 1: was wonderful and you could pay with other people's money

404
00:25:30,880 --> 00:25:34,680
Speaker 1: no longer. Well, there's still some of it out there,

405
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Speaker 1: but you know, Europe is going to the m F

406
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Speaker 1: m F I D and that's gonna you know, that's

407
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Speaker 1: a hard dollar squeeze as well. Yeah. Well but the

408
00:25:44,960 --> 00:25:48,440
Speaker 1: SEC let people off the hook here. But still black

409
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Speaker 1: Rock I think went from two hundred and twenty providers

410
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Speaker 1: down to a hundred And what does that mean in

411
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Speaker 1: terms of hard research dollars drying up? Drying up? So

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Speaker 1: you're no longer in in as involved in the business

413
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Speaker 1: as you are, You're doing other stuff. How closely do

414
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Speaker 1: you watch the stock market? Um? As a retiree. You know,

415
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Speaker 1: I get up in the morning. Uh, and I love reading,

416
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Speaker 1: and so I'm somewhat addicted to the market. So I

417
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Speaker 1: get up very tough to pull that needle. Yes it

418
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Speaker 1: is and it's still the greatest game ever and so uh,

419
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Speaker 1: you know, I love the game and I love reading.

420
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Speaker 1: Uh and uh, the market is still my number one love.

421
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Speaker 1: Are you still invested in the market or have you

422
00:26:37,440 --> 00:26:42,240
Speaker 1: moved more towards a fixed income portfolio? Third? Or third

423
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Speaker 1: or third? And uh? Stock spawns cash? Is that real estate?

424
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Speaker 1: Real estate? Well, the Falcons are a very big investment

425
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Speaker 1: all sud but uh and we have a big, big

426
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Speaker 1: stadium that's that's really nice. Um so uh uh. I

427
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Speaker 1: I'm very big on having goals and uh meaning investing

428
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Speaker 1: towards goals, just personal personal goals. Years ago, I always

429
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Speaker 1: had something in my wallet of all the goals and

430
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Speaker 1: it changes because at thirty, it's very different than its

431
00:27:15,800 --> 00:27:20,280
Speaker 1: sixty or forty and uh. And so you know right now,

432
00:27:20,720 --> 00:27:24,919
Speaker 1: you know, accumulating great more wealth, it won't make me

433
00:27:24,960 --> 00:27:29,560
Speaker 1: any happier. Maybe give more money to charity, but I

434
00:27:29,640 --> 00:27:34,080
Speaker 1: really don't want to accumulate any more things, uh, except

435
00:27:34,119 --> 00:27:37,040
Speaker 1: a Super Bowl ring. So let's talk a little bit

436
00:27:37,080 --> 00:27:39,840
Speaker 1: about the falcons. How do you like them? And what

437
00:27:40,040 --> 00:27:47,200
Speaker 1: changes are coming to the NFL. You know, I'm from Arkansas.

438
00:27:47,600 --> 00:27:49,920
Speaker 1: It's a razor. Back then it was always wait until

439
00:27:49,960 --> 00:27:53,640
Speaker 1: next year. And you know, you don't appreciate how hard

440
00:27:53,680 --> 00:27:56,480
Speaker 1: it is to get to the super Bowl and to

441
00:27:56,520 --> 00:27:59,800
Speaker 1: win it. Uh, it's the most competitive thing in the

442
00:27:59,800 --> 00:28:02,639
Speaker 1: world all to reach that level. So many things have

443
00:28:02,720 --> 00:28:05,840
Speaker 1: to go right, plus you have to get lucky. You

444
00:28:05,920 --> 00:28:09,640
Speaker 1: just appreciate the good times and h if if if

445
00:28:10,000 --> 00:28:14,160
Speaker 1: everything breaks right. But again, two or three key injuries

446
00:28:14,200 --> 00:28:17,840
Speaker 1: and it's it's problematic. So the big issue we won't

447
00:28:17,880 --> 00:28:20,640
Speaker 1: even talk about. We'll save the kneeling and the football

448
00:28:20,680 --> 00:28:24,959
Speaker 1: anthem stuff for for later. In general, we've been seeing

449
00:28:25,200 --> 00:28:30,000
Speaker 1: sports see a lot of pressure with cable people on

450
00:28:30,160 --> 00:28:33,400
Speaker 1: bundling and moving to the internet. What is the future

451
00:28:33,440 --> 00:28:36,719
Speaker 1: of sports broadcast? Look like is it going to be

452
00:28:36,880 --> 00:28:40,440
Speaker 1: mobile and internet is because the idea of television and

453
00:28:40,480 --> 00:28:43,440
Speaker 1: cable and saying here, I'm subscribing to cable and I

454
00:28:43,480 --> 00:28:46,239
Speaker 1: have to get these two channels whether I want them

455
00:28:46,320 --> 00:28:49,720
Speaker 1: or not. Is that going to change? How rapidly is

456
00:28:49,720 --> 00:28:52,120
Speaker 1: that going to change? We know it's changing. What what

457
00:28:52,160 --> 00:28:55,400
Speaker 1: do we think happens? But again, jeff Nis and Baron's

458
00:28:55,480 --> 00:29:00,200
Speaker 1: basically talks about that, you know, the sports entertainment, it's

459
00:29:00,440 --> 00:29:03,480
Speaker 1: just still one of those things football it's in h

460
00:29:03,680 --> 00:29:07,400
Speaker 1: L is something that you want to see live, right,

461
00:29:07,480 --> 00:29:10,320
Speaker 1: you can't you can't read about it afterwards, but you can.

462
00:29:10,400 --> 00:29:12,840
Speaker 1: But it's not it's not the same. It's not the same.

463
00:29:13,240 --> 00:29:17,520
Speaker 1: And so you know, we we have a product that

464
00:29:19,320 --> 00:29:24,160
Speaker 1: uh people want to have and uh, you know, I

465
00:29:24,200 --> 00:29:27,320
Speaker 1: had a partner named John Emily who died recently, but it

466
00:29:27,280 --> 00:29:31,800
Speaker 1: it was one of the most successful venture people in

467
00:29:31,000 --> 00:29:35,240
Speaker 1: in Atlanta and he always told me since Eddie, no

468
00:29:35,280 --> 00:29:38,280
Speaker 1: matter whether we win or whether we lose, the value

469
00:29:38,280 --> 00:29:42,080
Speaker 1: of the franchise goes up every day. That's that's a

470
00:29:42,600 --> 00:29:46,680
Speaker 1: pretty interesting point. We have been speaking with Edmundell of

471
00:29:47,000 --> 00:29:50,480
Speaker 1: Ned David's Research and the Atlanta Falcons. If you enjoy

472
00:29:50,560 --> 00:29:54,000
Speaker 1: this conversation. Be sure and check out our podcast extras,

473
00:29:54,000 --> 00:29:56,800
Speaker 1: where we keep the tape rolling and continue chatting about

474
00:29:56,840 --> 00:30:00,400
Speaker 1: all sorts of interesting things. You can find that wherever

475
00:30:00,520 --> 00:30:06,720
Speaker 1: find podcasts are sold iTunes, Overcast, SoundCloud, Bloomberg dot com, etcetera.

476
00:30:07,000 --> 00:30:09,480
Speaker 1: Be sure and check out my daily column. You'll find

477
00:30:09,480 --> 00:30:11,920
Speaker 1: that on Bloomberg dot Com. You can follow me on

478
00:30:11,960 --> 00:30:15,640
Speaker 1: Twitter at rid Holts. I'm Barry rid Holts. You're listening

479
00:30:15,680 --> 00:30:32,800
Speaker 1: to Masters in Business on Bloomberg Radio. Welcome to the podcast, Eddie.

480
00:30:32,840 --> 00:30:35,400
Speaker 1: Thank you so much for doing this. There are two

481
00:30:35,440 --> 00:30:39,400
Speaker 1: things I have to thank you for. One is when

482
00:30:39,520 --> 00:30:45,000
Speaker 1: I first had the idea a decade ago, for hey,

483
00:30:45,080 --> 00:30:48,440
Speaker 1: let's not ask people what's their favorite stock or where

484
00:30:48,440 --> 00:30:50,280
Speaker 1: the Dow is going to be in a year. Let's

485
00:30:50,400 --> 00:30:53,000
Speaker 1: find out who they are and how they got that way.

486
00:30:53,120 --> 00:30:56,640
Speaker 1: Let's find smart, successful people and say, so, what can

487
00:30:56,640 --> 00:30:59,920
Speaker 1: we learn from you. One of the very first version

488
00:31:00,240 --> 00:31:04,160
Speaker 1: of this show was Ned Davis. We did on the phone.

489
00:31:04,200 --> 00:31:06,720
Speaker 1: You helped to arrange that. I want to say, that's

490
00:31:06,760 --> 00:31:09,480
Speaker 1: almost ten years ago. Am I am? I ballpark with

491
00:31:09,600 --> 00:31:13,160
Speaker 1: that so that that was a fascinating conversation and it

492
00:31:13,240 --> 00:31:15,920
Speaker 1: was so clear after we did it. Even though it

493
00:31:16,040 --> 00:31:18,800
Speaker 1: sounded terrible, it was on the phone and I had

494
00:31:18,840 --> 00:31:21,400
Speaker 1: no idea what the hell I was doing. It was

495
00:31:21,520 --> 00:31:24,880
Speaker 1: clear to me that Wow, not doing four minutes and

496
00:31:24,880 --> 00:31:29,640
Speaker 1: then a commercial, but letting people, hey, tell me about this,

497
00:31:29,880 --> 00:31:34,160
Speaker 1: and letting them speak and share their history, their anecdotes,

498
00:31:34,200 --> 00:31:39,760
Speaker 1: their experiences was really valuable. And that's led to a

499
00:31:39,800 --> 00:31:44,320
Speaker 1: whole run of fascinating people telling me really amazing stories.

500
00:31:44,760 --> 00:31:46,680
Speaker 1: And I have you to help for sending that up

501
00:31:46,760 --> 00:31:50,520
Speaker 1: to begin with. Um, the other thing I had to

502
00:31:50,560 --> 00:31:54,000
Speaker 1: say is when I was thinking about launching my shop,

503
00:31:54,600 --> 00:31:56,880
Speaker 1: I came to you and said, Hey, I'm I'm wrestling

504
00:31:56,920 --> 00:31:59,160
Speaker 1: with these ideas, and you gave me a lot of

505
00:31:59,200 --> 00:32:01,960
Speaker 1: really good advice and I wanna thank you for that.

506
00:32:02,040 --> 00:32:05,120
Speaker 1: It was it was very very help remembering. Oh, I

507
00:32:05,400 --> 00:32:09,400
Speaker 1: trust me, I remember everything. UM So so let's talk

508
00:32:09,400 --> 00:32:12,880
Speaker 1: a little bit about about football. Um, are we going

509
00:32:12,920 --> 00:32:18,000
Speaker 1: to ever see live football on Facebook, Twitter, etcetera. I

510
00:32:18,000 --> 00:32:20,520
Speaker 1: know there have been some contracts and some announcements made.

511
00:32:20,880 --> 00:32:23,040
Speaker 1: Are we going to ever be in a situation where

512
00:32:23,360 --> 00:32:25,320
Speaker 1: I don't need to be home in front of a television.

513
00:32:25,360 --> 00:32:27,680
Speaker 1: I could just pull up my phone and watch a game.

514
00:32:28,320 --> 00:32:33,960
Speaker 1: I'm a minority owner, but you have some insight. Is that?

515
00:32:34,120 --> 00:32:37,880
Speaker 1: Is that something that we're thinking about constantly. Yes, you

516
00:32:37,880 --> 00:32:41,080
Speaker 1: know they're looking at all aspects of of of everything

517
00:32:41,600 --> 00:32:46,000
Speaker 1: to monetize this, and you know, to keep millennials and

518
00:32:46,080 --> 00:32:52,520
Speaker 1: keep people interested in in uh in football. But you know, UH,

519
00:32:52,680 --> 00:32:55,680
Speaker 1: we have a lot of people at the NFL home office,

520
00:32:55,760 --> 00:33:01,560
Speaker 1: and they're constantly working away, working away. So there's a

521
00:33:01,600 --> 00:33:06,480
Speaker 1: couple of really interesting things going on in football. UM.

522
00:33:06,480 --> 00:33:08,880
Speaker 1: And I know you don't speak on behalf of league

523
00:33:08,880 --> 00:33:11,400
Speaker 1: of the team, so I'm being circumspected in what I

524
00:33:11,440 --> 00:33:15,080
Speaker 1: can ask you. You're not an authorized spokesperson for NFL,

525
00:33:15,560 --> 00:33:20,400
Speaker 1: but you're certainly an astute observer. Um. Three things that

526
00:33:20,960 --> 00:33:24,680
Speaker 1: I've been thinking about aware of. The first is, uh,

527
00:33:24,720 --> 00:33:29,320
Speaker 1: the idea of people's phones being the most important screen

528
00:33:29,360 --> 00:33:31,480
Speaker 1: in their life. I have to think we're going to

529
00:33:31,560 --> 00:33:37,200
Speaker 1: see Netflix or Twitter or Facebook eventually doing a regular broadcast.

530
00:33:37,560 --> 00:33:39,600
Speaker 1: The other thing, which we haven't heard a lot about

531
00:33:39,680 --> 00:33:43,520
Speaker 1: this year, but was pretty big the past couple of years, UH,

532
00:33:43,600 --> 00:33:46,080
Speaker 1: and I've been reading about some of the new technologies,

533
00:33:47,080 --> 00:33:50,960
Speaker 1: was the concussion issues. And I've been reading about these

534
00:33:51,080 --> 00:33:58,120
Speaker 1: new helmets that supposedly lighter, stronger, more dissipating of energy.

535
00:33:59,240 --> 00:34:02,200
Speaker 1: How much is technology going to be the basis of

536
00:34:02,240 --> 00:34:06,520
Speaker 1: a solution for you know, keeping players safer over time?

537
00:34:07,320 --> 00:34:10,120
Speaker 1: You know, we recognize it as a big deal. And

538
00:34:10,160 --> 00:34:12,480
Speaker 1: I think that this is a year, a couple of

539
00:34:12,760 --> 00:34:15,440
Speaker 1: four million dollars a year. I think we were spending

540
00:34:15,640 --> 00:34:20,759
Speaker 1: on research to come up with safer, better, you know, helmets.

541
00:34:20,800 --> 00:34:23,960
Speaker 1: But you know, it is, it is. It's a big

542
00:34:23,960 --> 00:34:28,040
Speaker 1: issue in uh uh, you know, it's a major concern.

543
00:34:28,239 --> 00:34:31,640
Speaker 1: The most recent piece of technology I was reading about

544
00:34:31,760 --> 00:34:34,239
Speaker 1: is this new helmet. A couple of I remember was

545
00:34:34,440 --> 00:34:36,680
Speaker 1: M I T or cal Tech, but a couple of

546
00:34:36,680 --> 00:34:40,040
Speaker 1: professors developed this helmet. If the standard helmet is three

547
00:34:40,080 --> 00:34:45,320
Speaker 1: hundred dollars, this is nine hundred dollars. But supposedly, the

548
00:34:45,320 --> 00:34:51,560
Speaker 1: the physics underlying um impact energy dissipation has progressed to

549
00:34:51,600 --> 00:34:55,200
Speaker 1: the point where there's we should really expect to see

550
00:34:55,239 --> 00:34:59,040
Speaker 1: some significant changes going forward. Is that I know you

551
00:34:59,080 --> 00:35:02,440
Speaker 1: can't speak public really officially, but is it fair to

552
00:35:02,480 --> 00:35:05,040
Speaker 1: say that technology is going to be a big part

553
00:35:05,040 --> 00:35:08,960
Speaker 1: of the solution. Just about has to be. But you know,

554
00:35:09,200 --> 00:35:12,320
Speaker 1: they're they're founding from junior high school, high school, college.

555
00:35:12,520 --> 00:35:15,000
Speaker 1: You know this, this is a cumulative effect. It's not

556
00:35:15,080 --> 00:35:19,359
Speaker 1: just it's not just the NFL. So that that's an

557
00:35:19,400 --> 00:35:24,080
Speaker 1: issue that um, uh kind of got overwhelmed this this

558
00:35:24,200 --> 00:35:27,279
Speaker 1: year by by other stuff. Um, what else do you

559
00:35:27,320 --> 00:35:30,680
Speaker 1: see that's interesting taking place in football? I I just

560
00:35:30,800 --> 00:35:40,000
Speaker 1: read a fascinating review of of the the commissioner, Um Goodell,

561
00:35:40,640 --> 00:35:44,680
Speaker 1: and he's about to re up his contract. Basically the

562
00:35:44,760 --> 00:35:48,480
Speaker 1: owners seem to think he's doing a tremendous job in

563
00:35:48,560 --> 00:35:51,960
Speaker 1: a very difficult environment. Is that a fair, fair assessment?

564
00:35:52,400 --> 00:35:55,440
Speaker 1: You know again, I'm not I'm still the minority partner

565
00:35:55,520 --> 00:35:59,480
Speaker 1: from fifteen minutes ago, right, So so you're not, um

566
00:35:59,680 --> 00:36:03,920
Speaker 1: turn around to uh to say anything. If you google,

567
00:36:04,719 --> 00:36:07,680
Speaker 1: there was a recent story about on ESPN and a

568
00:36:07,680 --> 00:36:12,040
Speaker 1: handful of other places. The consensus seems to be very

569
00:36:12,120 --> 00:36:15,600
Speaker 1: challenging couple of years, especially under this president. Um, but

570
00:36:15,719 --> 00:36:18,520
Speaker 1: he's done about as good a job as anybody really

571
00:36:18,640 --> 00:36:23,200
Speaker 1: is can expect of him. Fair fair assessment. But like

572
00:36:23,560 --> 00:36:27,080
Speaker 1: you know this better than anybody, everything changes, So you know,

573
00:36:27,360 --> 00:36:29,359
Speaker 1: there's never been a time when that when there wasn't

574
00:36:29,400 --> 00:36:32,680
Speaker 1: something to worry about. Of course, and the marketer in life,

575
00:36:32,719 --> 00:36:36,240
Speaker 1: so we were. We were just discussing this the other day,

576
00:36:36,280 --> 00:36:42,480
Speaker 1: that we are biologically programmed to notice bad news. And

577
00:36:42,600 --> 00:36:45,200
Speaker 1: one of my colleagues in the office, Mike Batnick, wrote

578
00:36:45,239 --> 00:36:48,840
Speaker 1: a piece why good news is overlooked. Good news is

579
00:36:48,880 --> 00:36:51,480
Speaker 1: never a threat, but bad news is a threat, and

580
00:36:51,560 --> 00:36:57,040
Speaker 1: that's why we basically place such disproportionate weight on that. Well,

581
00:36:57,080 --> 00:36:59,239
Speaker 1: except for right now, I think good news in the

582
00:36:59,239 --> 00:37:03,200
Speaker 1: market is good news and bad news just isn't true. Well,

583
00:37:03,239 --> 00:37:05,399
Speaker 1: what sort of bad news in the market isn't true?

584
00:37:05,480 --> 00:37:08,520
Speaker 1: What what do you see as as the memes that

585
00:37:08,560 --> 00:37:11,399
Speaker 1: are out there that are negative for the market. But

586
00:37:11,560 --> 00:37:14,879
Speaker 1: we're over emphasizing, we're putting too much weight on We'll

587
00:37:14,880 --> 00:37:19,480
Speaker 1: get back to black swan events. I'm not big on, right,

588
00:37:19,880 --> 00:37:24,080
Speaker 1: but they're in Japanese folklore. There's a thing called white swans,

589
00:37:25,360 --> 00:37:27,279
Speaker 1: and a white swan is something right in front of

590
00:37:27,320 --> 00:37:30,279
Speaker 1: you that you just can't see. We don't just stop

591
00:37:30,320 --> 00:37:34,640
Speaker 1: paying attention to and so uh, this next time around,

592
00:37:35,200 --> 00:37:37,799
Speaker 1: just like in O one, the dot com was right

593
00:37:37,840 --> 00:37:39,840
Speaker 1: in front of you, and I said, didn't see it,

594
00:37:39,920 --> 00:37:43,319
Speaker 1: and they saw it. They just said, what can we do?

595
00:37:43,400 --> 00:37:46,759
Speaker 1: It's easy to clean up after O eight, No money down,

596
00:37:46,800 --> 00:37:50,319
Speaker 1: pay what you want, interest only, no documentation. How can

597
00:37:50,360 --> 00:37:53,600
Speaker 1: anybody not be surprised? But it was right in front

598
00:37:53,640 --> 00:37:56,960
Speaker 1: of you, and so this this, in fact, I have

599
00:37:57,040 --> 00:38:00,880
Speaker 1: to again give kudos to Ned David's research. One of

600
00:38:00,920 --> 00:38:04,560
Speaker 1: my favorite charts that if you looked at you couldn't

601
00:38:04,560 --> 00:38:08,920
Speaker 1: help but not see something coming. There were three charts

602
00:38:08,960 --> 00:38:11,960
Speaker 1: that I tracked in the early two thousands, some of

603
00:38:11,960 --> 00:38:15,040
Speaker 1: which came from you. One was cost of owning versus

604
00:38:15,080 --> 00:38:17,880
Speaker 1: cost of renting. That was a pretty standard chart. The

605
00:38:17,920 --> 00:38:21,440
Speaker 1: one that I first noticed from ned davis research was

606
00:38:21,600 --> 00:38:25,640
Speaker 1: median income versus median home price. Ned was putting that

607
00:38:25,719 --> 00:38:28,960
Speaker 1: out and for decades it you know, moved up and

608
00:38:28,960 --> 00:38:31,520
Speaker 1: down a little bit, and then in oh four oh

609
00:38:31,600 --> 00:38:36,040
Speaker 1: five it went straight up through the roof and it

610
00:38:36,160 --> 00:38:40,400
Speaker 1: was clear something strange was going on. There was no

611
00:38:40,440 --> 00:38:44,000
Speaker 1: other way to describe that. UM. And then the third

612
00:38:44,080 --> 00:38:47,080
Speaker 1: one was, and I think this also might have been

613
00:38:47,160 --> 00:38:54,640
Speaker 1: you guys, was total value of the UM total value

614
00:38:54,640 --> 00:38:57,400
Speaker 1: of the housing stock, meaning all the homes in America

615
00:38:57,920 --> 00:39:00,960
Speaker 1: relative to g d P and some really it was,

616
00:39:01,560 --> 00:39:04,720
Speaker 1: you know, pretty steady for decades, and then suddenly three

617
00:39:04,760 --> 00:39:09,040
Speaker 1: standard orders of magnitude. If you were I called the

618
00:39:09,160 --> 00:39:14,280
Speaker 1: financial crisis the jumping dolphin of crises. Do you remember

619
00:39:14,320 --> 00:39:17,319
Speaker 1: the three D paintings that people used to have. If

620
00:39:17,320 --> 00:39:19,160
Speaker 1: you stared at it right, suddenly you would see the

621
00:39:19,760 --> 00:39:22,759
Speaker 1: leaping dolphin. Those charts with the leaping dolphin. If you

622
00:39:22,800 --> 00:39:25,239
Speaker 1: saw those charts, it was obvious, hey, this is all

623
00:39:25,239 --> 00:39:27,640
Speaker 1: going to blow up. Um. But if you weren't looking

624
00:39:27,680 --> 00:39:30,160
Speaker 1: in the right place, well, the market keeps going higher.

625
00:39:30,200 --> 00:39:32,640
Speaker 1: It must the market must know that this is an

626
00:39:32,680 --> 00:39:37,960
Speaker 1: important I heard over and over again. Well, getting back

627
00:39:38,000 --> 00:39:42,359
Speaker 1: to the the white swan, Uh, you know, things are

628
00:39:42,520 --> 00:39:46,080
Speaker 1: really great right now. There's no economists not saying, you know,

629
00:39:46,239 --> 00:39:49,800
Speaker 1: there's no recession for a couple of years, and uh,

630
00:39:49,840 --> 00:39:57,200
Speaker 1: you know, earnings are record highs growing, and so the

631
00:39:57,239 --> 00:40:00,440
Speaker 1: white swan that's out there is that maybe things are

632
00:40:00,480 --> 00:40:03,239
Speaker 1: too good and you're you know, you're gonna have a

633
00:40:03,280 --> 00:40:09,279
Speaker 1: blow off that's gonna tighten economy. You can't lower unemployment

634
00:40:09,280 --> 00:40:15,359
Speaker 1: any further, can you? Again? Uh, you know there's laves

635
00:40:15,440 --> 00:40:21,520
Speaker 1: damn license statistics. Uh uh, nothing would surprise me. But uh,

636
00:40:22,880 --> 00:40:31,000
Speaker 1: you know the you know this thing is somewhat manipulated EU,

637
00:40:31,600 --> 00:40:36,520
Speaker 1: especially jan and Japan. You know they owned sixty of

638
00:40:36,520 --> 00:40:38,920
Speaker 1: all the e t s and sixty percent of the topics,

639
00:40:38,960 --> 00:40:42,759
Speaker 1: and Switzerland owns two billion dollars worth of stock out

640
00:40:42,800 --> 00:40:45,720
Speaker 1: of thin air. Mother nature doesn't like to be fooled.

641
00:40:45,800 --> 00:40:50,080
Speaker 1: So whatever happens will come out in inflation or currencies

642
00:40:50,800 --> 00:40:53,560
Speaker 1: or the bond market. Uh So let me push back.

643
00:40:53,680 --> 00:40:56,200
Speaker 1: If I had to guess the white swan that's right

644
00:40:56,239 --> 00:40:59,640
Speaker 1: in front of us, it's just everything is good, maybe

645
00:40:59,640 --> 00:41:03,160
Speaker 1: two it. So let me push back against that. Every

646
00:41:03,200 --> 00:41:06,840
Speaker 1: time there's some sort of financial crisis, be at O

647
00:41:07,000 --> 00:41:09,960
Speaker 1: eight oh nine or two thousand or seventy four or

648
00:41:10,840 --> 00:41:13,480
Speaker 1: twenty nine, or pick your crisis. In the United States,

649
00:41:13,840 --> 00:41:16,759
Speaker 1: the government always steps in to do something. You know,

650
00:41:16,840 --> 00:41:19,080
Speaker 1: we created the SEC, we created the fd I C.

651
00:41:19,840 --> 00:41:24,880
Speaker 1: Isn't that standard that? Uh, some disaster befalls us and

652
00:41:24,880 --> 00:41:29,239
Speaker 1: and uh the free marketers suddenly become maybe we can

653
00:41:29,480 --> 00:41:34,759
Speaker 1: manage this a little more aggressively. Well again, I had

654
00:41:34,960 --> 00:41:38,320
Speaker 1: breakfast with the president of the Doubt of the Atlanta

655
00:41:38,360 --> 00:41:42,720
Speaker 1: Third and uh I asked him the next speed bump,

656
00:41:42,800 --> 00:41:45,360
Speaker 1: what are you gonna do? This is last year at breakfast,

657
00:41:45,680 --> 00:41:48,520
Speaker 1: and he says, we're just gonna do more que And

658
00:41:48,600 --> 00:41:51,200
Speaker 1: so that's now the solution to everything that it must

659
00:41:51,239 --> 00:41:53,239
Speaker 1: be because they don't they don't ask if there's a

660
00:41:53,239 --> 00:41:56,440
Speaker 1: plan B. There is there isn't a plan B. And

661
00:41:56,520 --> 00:42:00,640
Speaker 1: so you know you so they'll buy real estate in

662
00:42:00,760 --> 00:42:03,359
Speaker 1: E T S or whatever this next time. But they're

663
00:42:03,360 --> 00:42:06,320
Speaker 1: already doing that in Europe. So every general fights the

664
00:42:06,400 --> 00:42:10,759
Speaker 1: last battle. QUI worked fine when the issue was a

665
00:42:10,760 --> 00:42:14,719
Speaker 1: frozen credit market. But if you just have a cyclical

666
00:42:14,760 --> 00:42:17,960
Speaker 1: slowdown in a recession, what is quigan? That's that's the

667
00:42:18,160 --> 00:42:22,600
Speaker 1: that's the question, and uh, it's going to be very interesting. Yeah,

668
00:42:22,640 --> 00:42:25,160
Speaker 1: to say the least, I want to get to our

669
00:42:25,239 --> 00:42:29,239
Speaker 1: favorite questions. But there's one thing, Um, I have to

670
00:42:29,320 --> 00:42:33,560
Speaker 1: ask about sentiment. You're raising that issue. Um, things are

671
00:42:33,600 --> 00:42:37,319
Speaker 1: too good? Do you think the investing public thinks things

672
00:42:37,400 --> 00:42:41,360
Speaker 1: are too good? Because up until recently they did not

673
00:42:41,480 --> 00:42:45,200
Speaker 1: embrace this rally. They were not The market tripled since

674
00:42:45,239 --> 00:42:48,839
Speaker 1: the March O nine lows, and they seem to constantly

675
00:42:48,880 --> 00:42:51,480
Speaker 1: be waiting for the black swan, not the white swan.

676
00:42:52,840 --> 00:42:55,600
Speaker 1: You know, but you have record low mutu fun cash

677
00:42:55,760 --> 00:42:58,200
Speaker 1: and you know the markets tripled, revenue is only up,

678
00:43:00,000 --> 00:43:02,720
Speaker 1: and so there's but profits are higher, Yeah, No, profits

679
00:43:02,719 --> 00:43:05,600
Speaker 1: are higher, but some of that's financial engineering, meanings share

680
00:43:05,640 --> 00:43:10,160
Speaker 1: by backs like that. Yeah, I just read the two

681
00:43:10,200 --> 00:43:13,719
Speaker 1: and a half trillion dollars overseas. You know, Apple and

682
00:43:14,040 --> 00:43:16,319
Speaker 1: Oracle on all these companies already spent five hundred and

683
00:43:16,320 --> 00:43:23,160
Speaker 1: sixty big and yeah they still have stellar balance sheets.

684
00:43:23,160 --> 00:43:28,200
Speaker 1: But uh, you know the it's but it's not the public,

685
00:43:28,239 --> 00:43:30,560
Speaker 1: it's it's but the public or whoever is buying twenty

686
00:43:30,600 --> 00:43:33,320
Speaker 1: billion dollars at the e t s a month and

687
00:43:33,560 --> 00:43:38,239
Speaker 1: the sixty four threeon dollar questions, who's gonna when they want?

688
00:43:38,480 --> 00:43:41,919
Speaker 1: If they ever start selling the ets, it could get

689
00:43:42,040 --> 00:43:47,960
Speaker 1: ugly very fast. But you know, uh, you know, you

690
00:43:47,600 --> 00:43:50,799
Speaker 1: you know, in the bullmarkt be bullish. So this is

691
00:43:51,080 --> 00:43:53,759
Speaker 1: this is just a good time to be bullish the least.

692
00:43:54,120 --> 00:43:57,880
Speaker 1: Let's jump to our favorite questions. These are what I

693
00:43:57,920 --> 00:44:00,800
Speaker 1: asked all of our guests tell me the most important

694
00:44:00,800 --> 00:44:11,120
Speaker 1: thing people don't know about your background? Um um. I

695
00:44:11,120 --> 00:44:14,600
Speaker 1: had a grandfather who got me interested in the market

696
00:44:14,640 --> 00:44:18,080
Speaker 1: when I was seven, and I started buying stocks when

697
00:44:18,120 --> 00:44:21,920
Speaker 1: I was seven, and he gave me a healthy disrespect

698
00:44:21,960 --> 00:44:25,759
Speaker 1: for the banking system. Yes, and uh, he had all

699
00:44:25,840 --> 00:44:28,520
Speaker 1: his money in a safety deposit box when the when

700
00:44:28,560 --> 00:44:31,839
Speaker 1: the when the crash came. And he also taught me

701
00:44:32,960 --> 00:44:39,480
Speaker 1: about being a scavenger buyer fixed income because he made

702
00:44:39,520 --> 00:44:42,640
Speaker 1: his money buying railroad, mind's opinion and nickel on the dollar.

703
00:44:43,480 --> 00:44:47,600
Speaker 1: And then he thought he invented uh text rattles back

704
00:44:47,840 --> 00:44:50,960
Speaker 1: in the thirties, Did he or he did it? So

705
00:44:51,000 --> 00:44:53,520
Speaker 1: he was way ahead of his time. He also was

706
00:44:53,600 --> 00:44:58,040
Speaker 1: a socialist and uh a socialist market trader, Yes he was.

707
00:44:59,040 --> 00:45:02,400
Speaker 1: And he claimed he was secretary treasurer of the Socialist

708
00:45:02,400 --> 00:45:05,840
Speaker 1: Party between nineteen fifteen and nineteen eighteen and booty for

709
00:45:05,920 --> 00:45:09,319
Speaker 1: Eugene Debs five times. And he said they disbanded the

710
00:45:09,360 --> 00:45:11,880
Speaker 1: Socialist Party when FDR was elected because he was the

711
00:45:11,880 --> 00:45:16,040
Speaker 1: biggest socialist that ever lived. Well, we created we created

712
00:45:16,080 --> 00:45:18,840
Speaker 1: social scar We did a lot of things under under FDO,

713
00:45:19,400 --> 00:45:23,000
Speaker 1: medicaid um, certainly the sec There's a ton of stuff

714
00:45:23,040 --> 00:45:25,840
Speaker 1: that that he did. I find a lot of people

715
00:45:25,880 --> 00:45:30,520
Speaker 1: who have been successful in the market and have accumulated

716
00:45:30,600 --> 00:45:36,320
Speaker 1: wealth are starting to get pretty concerned about inequality because

717
00:45:36,400 --> 00:45:40,719
Speaker 1: they would rather the public be fairly satisfied and not

718
00:45:41,400 --> 00:45:44,080
Speaker 1: calling for revolution. If you asked me for the one

719
00:45:44,160 --> 00:45:47,440
Speaker 1: thing that you know bothers me is that this this

720
00:45:47,480 --> 00:45:51,320
Speaker 1: has not been distributed. It's been great for wealthy people.

721
00:45:51,840 --> 00:45:55,759
Speaker 1: I think, uh, blue collar, middle class people are struggling.

722
00:45:56,160 --> 00:45:59,640
Speaker 1: And you're you're this is the white Swan that you

723
00:46:00,040 --> 00:46:02,319
Speaker 1: saw it in Trump being elected, but you saw it

724
00:46:02,320 --> 00:46:05,399
Speaker 1: in the British exit, sat in Spain, but last week

725
00:46:05,440 --> 00:46:10,360
Speaker 1: in Calonia. Absolutely, Czechoslovakia and Austria, but you saw France

726
00:46:10,760 --> 00:46:15,719
Speaker 1: the two major parties were cut off. Even Merkel had

727
00:46:15,719 --> 00:46:20,759
Speaker 1: her niece buckled and so and then nationalism is out there,

728
00:46:21,760 --> 00:46:25,360
Speaker 1: and uh, you know, bad things happen when you know that.

729
00:46:26,360 --> 00:46:29,239
Speaker 1: And you're tracing this to income inequality, and you know,

730
00:46:29,400 --> 00:46:32,319
Speaker 1: I'm just tracing it that. I think that that just

731
00:46:32,360 --> 00:46:34,840
Speaker 1: what you said. The whole there's a whole class of

732
00:46:34,880 --> 00:46:38,760
Speaker 1: people out there that haven't participated and are not happy.

733
00:46:38,800 --> 00:46:41,760
Speaker 1: And if the numbers are right of fifty one or something,

734
00:46:41,880 --> 00:46:44,360
Speaker 1: can't you know, come up with two d dollars or whatever.

735
00:46:45,360 --> 00:46:49,040
Speaker 1: It's it's there. You know, there there's a problem out there,

736
00:46:49,200 --> 00:46:52,360
Speaker 1: and uh, you know, I haven't got the answer, but

737
00:46:52,600 --> 00:46:54,879
Speaker 1: you know you're seeing something right in front of you.

738
00:46:55,160 --> 00:46:58,160
Speaker 1: And you know, you got the people in Brussels, unelected

739
00:46:59,200 --> 00:47:03,319
Speaker 1: bureaucrats that are sort of like Monty Python Black Night

740
00:47:03,440 --> 00:47:06,040
Speaker 1: that lost one arm, one leg, the other arm the

741
00:47:06,080 --> 00:47:08,960
Speaker 1: other leg and says it's just a no, he said,

742
00:47:09,080 --> 00:47:11,719
Speaker 1: it's just a flesh with But they're not paying any

743
00:47:11,719 --> 00:47:16,840
Speaker 1: attention that right underneath. What's what's happening right underneath their noses?

744
00:47:17,239 --> 00:47:21,440
Speaker 1: But you de Greece and Italy have these parties in

745
00:47:21,520 --> 00:47:27,600
Speaker 1: the pin nationalism, popularism, nationalism. People are pushing back against

746
00:47:27,640 --> 00:47:32,160
Speaker 1: globalization and the loss of jobs to low cost providers.

747
00:47:32,200 --> 00:47:34,799
Speaker 1: It's not just China, but it's Turkey and Vietnam, and

748
00:47:34,840 --> 00:47:39,239
Speaker 1: els and the computers and and shobots and no, it

749
00:47:39,360 --> 00:47:41,839
Speaker 1: is it is. If you had to pick one thing

750
00:47:41,920 --> 00:47:44,680
Speaker 1: that is worrisome, is you hit on it that it's it's,

751
00:47:44,800 --> 00:47:48,680
Speaker 1: it's it's This has not been a broad based, wonderful

752
00:47:48,719 --> 00:47:51,880
Speaker 1: thing for everybody, and and that is a of a concern.

753
00:47:52,000 --> 00:47:54,400
Speaker 1: So that may perhaps that's the whites one you talked about.

754
00:47:54,760 --> 00:47:57,279
Speaker 1: Let's let's talk about your early mentors. Who were the

755
00:47:57,320 --> 00:48:03,719
Speaker 1: people who affected the way you think about business and markets. Well,

756
00:48:04,120 --> 00:48:07,520
Speaker 1: first was my grandfather, and then you know, second was

757
00:48:07,680 --> 00:48:12,480
Speaker 1: ned and U Marty's wag. What was your relationship with

758
00:48:12,520 --> 00:48:15,440
Speaker 1: Marty's wife? For you young uns, listenings. Wig was a

759
00:48:15,600 --> 00:48:19,040
Speaker 1: very famous technician owned I think it was the most

760
00:48:19,040 --> 00:48:22,000
Speaker 1: expensive home in America at one point in time, the

761
00:48:22,000 --> 00:48:27,480
Speaker 1: top of room and regular on Rukaiser and he had

762
00:48:27,480 --> 00:48:31,160
Speaker 1: all the Beatles outfits and Marl Monroe's outfit and uh

763
00:48:31,680 --> 00:48:35,319
Speaker 1: was quite a remarkable individual. And how did Marty uh

764
00:48:35,840 --> 00:48:39,120
Speaker 1: affect you? Just just his studies and you know, he

765
00:48:39,480 --> 00:48:43,400
Speaker 1: was just brilliant and you know, uh also I was

766
00:48:43,480 --> 00:48:45,880
Speaker 1: would tell you that this is under Ripley's believe it

767
00:48:45,960 --> 00:48:51,600
Speaker 1: or not that uh talking about failures, that I was

768
00:48:51,680 --> 00:48:54,279
Speaker 1: partners with Marty and ned and not one but to

769
00:48:54,480 --> 00:49:00,200
Speaker 1: retail letters and both went south, really, which I would

770
00:49:00,239 --> 00:49:02,960
Speaker 1: have thought would be impossible to do. Let me tell

771
00:49:03,000 --> 00:49:06,520
Speaker 1: you that's a that's question number seven. Let's ask it now.

772
00:49:06,680 --> 00:49:08,360
Speaker 1: Tell me about a time you failed and what you

773
00:49:08,480 --> 00:49:11,680
Speaker 1: learned from it. Marty's wide and that Davis two of

774
00:49:11,719 --> 00:49:15,400
Speaker 1: the most successful technicians in history. How did you not

775
00:49:15,560 --> 00:49:20,840
Speaker 1: make a go of those news letters? Uh? Good question?

776
00:49:21,800 --> 00:49:25,000
Speaker 1: But I think you know one was a bond letter

777
00:49:25,520 --> 00:49:28,680
Speaker 1: and it goes back to you know, no, well Einstein

778
00:49:28,800 --> 00:49:31,919
Speaker 1: has it goes up to heaven and Holy One calls

779
00:49:31,960 --> 00:49:33,960
Speaker 1: him in and says, can you explain the bond market

780
00:49:34,040 --> 00:49:36,960
Speaker 1: to me. So I don't think anybody can explain the

781
00:49:36,960 --> 00:49:41,640
Speaker 1: bond market. NY, we had a bond letter and uh,

782
00:49:41,800 --> 00:49:45,319
Speaker 1: for whatever reason that I think ned Marty's Forte was

783
00:49:45,480 --> 00:49:47,920
Speaker 1: not in bonds. But I don't, I really don't remember.

784
00:49:48,040 --> 00:49:52,200
Speaker 1: I tried to, you know, suppressed suppress it. But I

785
00:49:52,239 --> 00:49:56,600
Speaker 1: think more importantly, I think it was the retail is

786
00:49:57,040 --> 00:50:02,080
Speaker 1: a much more difficult market than selling two institutions like So,

787
00:50:02,560 --> 00:50:04,759
Speaker 1: you know, I learned. The one lesson I learned from

788
00:50:04,800 --> 00:50:08,080
Speaker 1: that is that, uh, you know sticks, you know, dance

789
00:50:08,200 --> 00:50:12,000
Speaker 1: with them who bring you. That's a that's a fair question. Um,

790
00:50:12,040 --> 00:50:15,359
Speaker 1: what about investors, Any investors affect the way you look

791
00:50:15,400 --> 00:50:20,880
Speaker 1: at business or finance. Um, you know, I'm a big

792
00:50:20,920 --> 00:50:24,799
Speaker 1: believer in blink going out and finding people that can

793
00:50:24,840 --> 00:50:30,080
Speaker 1: do things that that you cannot do. And uh, and

794
00:50:30,160 --> 00:50:33,400
Speaker 1: there's plenty of those. But I'm not a big believer

795
00:50:33,440 --> 00:50:36,200
Speaker 1: in hitch funds at eight or nine at one time,

796
00:50:37,760 --> 00:50:41,160
Speaker 1: but meaning money invested in not running. Yeah, but they

797
00:50:41,719 --> 00:50:45,680
Speaker 1: they can't. They can't perform now for whatever whatever reason,

798
00:50:45,840 --> 00:50:48,400
Speaker 1: the conditions have changed. At one point in time, some

799
00:50:48,520 --> 00:50:52,799
Speaker 1: of them were creating alpha. Very few these days, very few, Yeah,

800
00:50:52,920 --> 00:50:56,279
Speaker 1: maybe two or three percent. Wow, that's fascinating. Um, let's

801
00:50:56,280 --> 00:51:00,239
Speaker 1: talk about books. This is everybody's famous favorite question. Uh,

802
00:51:00,360 --> 00:51:03,160
Speaker 1: tell us about books that you've you read? What what

803
00:51:03,239 --> 00:51:06,440
Speaker 1: sort of stuff do you like? Fiction? Nonfiction? Well? I

804
00:51:06,520 --> 00:51:09,200
Speaker 1: read three or four hours every day, but really I

805
00:51:09,239 --> 00:51:13,040
Speaker 1: don't read fiction and nonfiction anymore. So what are you reading?

806
00:51:13,160 --> 00:51:18,000
Speaker 1: But I do? My favorite Arthur is Malcolm Gladwell Blink

807
00:51:18,040 --> 00:51:22,080
Speaker 1: and tipping Point, and the tipping point is AIDS, epidemics,

808
00:51:22,080 --> 00:51:26,239
Speaker 1: syphilis epidemics, crime epidemics, stop market, bond markets. Everything goes

809
00:51:26,320 --> 00:51:31,040
Speaker 1: to a tipping point, and life goes to a tipping point,

810
00:51:31,280 --> 00:51:35,399
Speaker 1: and it's always good to remember that. Uh, you know,

811
00:51:36,000 --> 00:51:38,680
Speaker 1: it's there. There's a tipping point out there, you know,

812
00:51:38,719 --> 00:51:43,399
Speaker 1: at some point for everything, for everything, for everything, things

813
00:51:43,440 --> 00:51:47,000
Speaker 1: that cannot go on eventually stop stop. And then Blink

814
00:51:47,160 --> 00:51:49,920
Speaker 1: is getting one of my favorite books. But whether it's

815
00:51:50,080 --> 00:51:53,719
Speaker 1: an electrician or car mechanic, finding these people that can

816
00:51:53,920 --> 00:51:56,560
Speaker 1: do things and they know things in a blink and

817
00:51:56,600 --> 00:52:00,440
Speaker 1: they're keeping them dear, you know, is really real. Make

818
00:52:00,560 --> 00:52:02,719
Speaker 1: makes your life a lot easier. Have you have you

819
00:52:02,719 --> 00:52:05,359
Speaker 1: read Outliers yet? Yes? I have. That was a really

820
00:52:05,400 --> 00:52:09,760
Speaker 1: interesting everything he does is great, always, always fascinating. Although

821
00:52:09,880 --> 00:52:13,719
Speaker 1: I WI will tell you, yes, the Beatles played the

822
00:52:13,760 --> 00:52:17,000
Speaker 1: Cavern Club eight hours a day for a year, I

823
00:52:17,040 --> 00:52:19,239
Speaker 1: can play the Cavern Club eight hours a day for

824
00:52:19,280 --> 00:52:21,880
Speaker 1: a hundred years. I will never be the Beatles, and

825
00:52:22,040 --> 00:52:25,279
Speaker 1: vice versa. There are people who pick stuff up so

826
00:52:25,440 --> 00:52:29,680
Speaker 1: rapidly that they're just built for certain things. But I

827
00:52:29,719 --> 00:52:33,000
Speaker 1: find all his books thought provoking and interesting. And he's

828
00:52:33,040 --> 00:52:37,080
Speaker 1: an excellent writer. I mean, his pros is just lovely.

829
00:52:37,239 --> 00:52:40,160
Speaker 1: His podcast from last year were great. Only maybe two

830
00:52:40,200 --> 00:52:43,600
Speaker 1: of the ones this year were the ones from last

831
00:52:43,640 --> 00:52:47,080
Speaker 1: year was um, a history of what what? What's the

832
00:52:47,160 --> 00:52:51,239
Speaker 1: name of his podcast? Revisionist history. Revisionist History, That's what

833
00:52:51,320 --> 00:52:52,879
Speaker 1: it was. Yeah, I listened to a few of them.

834
00:52:52,880 --> 00:52:56,160
Speaker 1: I thought they were very interesting. He's always interesting. Who

835
00:52:56,200 --> 00:52:59,920
Speaker 1: else do you read anybody else? I read you at

836
00:53:00,040 --> 00:53:04,120
Speaker 1: think that you have the best service out there, and

837
00:53:04,280 --> 00:53:06,960
Speaker 1: especially like your ten A m Reads The Morning ten

838
00:53:07,000 --> 00:53:10,960
Speaker 1: things each day the Morning Reads. Thank you for saying that. Um,

839
00:53:11,000 --> 00:53:14,279
Speaker 1: they're usually out seven seven thirty in the morning. I

840
00:53:14,280 --> 00:53:16,520
Speaker 1: try and get them out as early as possible. I

841
00:53:16,719 --> 00:53:19,560
Speaker 1: I sift through a lot of jump to get to that. Um,

842
00:53:19,600 --> 00:53:21,680
Speaker 1: there's a long story behind the reeds. I'll have to

843
00:53:21,680 --> 00:53:23,600
Speaker 1: share them one day, but thank you so much for

844
00:53:23,640 --> 00:53:27,200
Speaker 1: saying that it is um. It is an interesting thing

845
00:53:27,280 --> 00:53:33,320
Speaker 1: that forces me to realize how much of what's produced

846
00:53:33,440 --> 00:53:39,680
Speaker 1: is just noise, and finding something that is insightful, educational

847
00:53:39,960 --> 00:53:43,040
Speaker 1: and and helpful is is hard. So out of the

848
00:53:43,080 --> 00:53:46,480
Speaker 1: thousands of things, identifying those ten things each day is

849
00:53:46,600 --> 00:53:49,480
Speaker 1: a little bit of a challenge. Well, I appreciate. I

850
00:53:49,520 --> 00:53:52,640
Speaker 1: appreciate the kind words. What do you do outside of

851
00:53:52,680 --> 00:53:57,960
Speaker 1: the office to uh stay either mentally or physically fit? Well,

852
00:53:58,600 --> 00:54:02,080
Speaker 1: the mental part is eating. But you know, in the morning,

853
00:54:02,120 --> 00:54:07,239
Speaker 1: I all the CBS sports uh, Bleacher Report, you know,

854
00:54:07,400 --> 00:54:10,840
Speaker 1: I'm a I'm a sports junkie. But also no surprise

855
00:54:11,120 --> 00:54:13,920
Speaker 1: and so. But also there's lots of things that I

856
00:54:13,960 --> 00:54:16,560
Speaker 1: like reading in the morning. What else? What else do

857
00:54:16,560 --> 00:54:21,400
Speaker 1: you read? The Wall Street Journal? You know? Barons Zulov? Uh.

858
00:54:22,480 --> 00:54:24,759
Speaker 1: You know there's ten or twenty people that you know,

859
00:54:24,800 --> 00:54:27,600
Speaker 1: I really like getting their stuff Zuolov, give me a

860
00:54:27,640 --> 00:54:29,440
Speaker 1: couple of other names. Who else do you would like

861
00:54:29,520 --> 00:54:35,600
Speaker 1: to read? I like to read the Pimco people. Um.

862
00:54:35,760 --> 00:54:40,160
Speaker 1: The one person that is is brilliant and uh uh

863
00:54:40,760 --> 00:54:49,560
Speaker 1: is um double line. Uh, Jeff fascinating, fascinating, Uh fascinating

864
00:54:49,920 --> 00:54:56,319
Speaker 1: and uh uh he's uh he's out there thinking on

865
00:54:56,360 --> 00:54:59,399
Speaker 1: another on another level. He he definitely is. And every

866
00:54:59,440 --> 00:55:02,360
Speaker 1: now and then I'll read something of his and I'm like, Wow,

867
00:55:02,400 --> 00:55:05,080
Speaker 1: he's not afraid to really put it all out there,

868
00:55:05,480 --> 00:55:07,799
Speaker 1: and as often as not, he's right on some of

869
00:55:07,840 --> 00:55:11,360
Speaker 1: these real outlawyer calls that you would think is a

870
00:55:11,520 --> 00:55:14,120
Speaker 1: much lower success. He was one of the first people

871
00:55:14,160 --> 00:55:17,680
Speaker 1: come out on Trump. That's exactly right. I remember saying, Hey,

872
00:55:17,760 --> 00:55:21,600
Speaker 1: you're ender. I remember reading him saying, you're underestimating the

873
00:55:21,680 --> 00:55:26,000
Speaker 1: anger in the country, and you're underestimating, uh, the potential

874
00:55:26,080 --> 00:55:28,719
Speaker 1: for a change candidate to win. And Trump is a

875
00:55:29,040 --> 00:55:31,560
Speaker 1: change candidate. And he wanted to buy the Buffalo Bills

876
00:55:32,360 --> 00:55:35,960
Speaker 1: and uh they didn't let him. He I don't know

877
00:55:36,080 --> 00:55:39,720
Speaker 1: what what happened, but he would be a fantastic owner.

878
00:55:39,840 --> 00:55:43,960
Speaker 1: But he he uh to two things. He uh he

879
00:55:44,320 --> 00:55:47,000
Speaker 1: forecasts that they wouldn't win many many games this year,

880
00:55:47,160 --> 00:55:49,440
Speaker 1: which has not been true. They have a good team.

881
00:55:49,520 --> 00:55:52,400
Speaker 1: And the other most fascinating thing, which we could have

882
00:55:52,400 --> 00:55:55,080
Speaker 1: a whole another segment on he talks about his autism.

883
00:55:55,440 --> 00:55:58,920
Speaker 1: Uh huh uh and I found that lots of the

884
00:55:59,320 --> 00:56:03,200
Speaker 1: really unbelievable people on the street, just like in the

885
00:56:03,239 --> 00:56:07,000
Speaker 1: Big Short, have Aspergers as Bergers are somewhere on the

886
00:56:07,040 --> 00:56:11,680
Speaker 1: spectrum across across the board. Um, I've heard that about

887
00:56:12,480 --> 00:56:17,200
Speaker 1: Ken Fisher mentioned his dad was, you know, pre diagnosis,

888
00:56:17,200 --> 00:56:21,120
Speaker 1: he thought he was Asperger's. Ken himself very easily could

889
00:56:21,200 --> 00:56:24,399
Speaker 1: be go down the list of of of people who

890
00:56:24,480 --> 00:56:28,240
Speaker 1: have the ability to remove their emotions from the decision

891
00:56:28,280 --> 00:56:31,680
Speaker 1: making process. And if you could do that, there might

892
00:56:31,719 --> 00:56:33,920
Speaker 1: be a little spectrum going on with that, for sure.

893
00:56:34,520 --> 00:56:36,080
Speaker 1: That's and that's what you need to do to be

894
00:56:37,280 --> 00:56:40,040
Speaker 1: to be great, you know, you need to be different

895
00:56:40,080 --> 00:56:44,520
Speaker 1: from the average human and information completely and totally different

896
00:56:44,800 --> 00:56:49,280
Speaker 1: than than than than a normal person. Not to name drop,

897
00:56:49,320 --> 00:56:52,359
Speaker 1: but I'm going to name drop. Marc Andreessen had has

898
00:56:52,400 --> 00:56:56,480
Speaker 1: pointed out that Mark Zuckerberg at Facebook is a learning machine.

899
00:56:56,600 --> 00:57:01,200
Speaker 1: He said he's never met anybody who learned as aggressively

900
00:57:01,280 --> 00:57:05,799
Speaker 1: as him, constantly reading, constantly assimilating data and and your

901
00:57:05,800 --> 00:57:10,040
Speaker 1: description immediately made me think of him though the jobs

902
00:57:10,080 --> 00:57:16,720
Speaker 1: socially totally. But he didn't have any committees and he

903
00:57:16,760 --> 00:57:20,680
Speaker 1: did all those things him, but he knew things other

904
00:57:20,720 --> 00:57:23,240
Speaker 1: people didn't didn't know any knew it in a blink.

905
00:57:23,600 --> 00:57:28,960
Speaker 1: And you can't teach that. It's just that's intuitive a curse.

906
00:57:30,360 --> 00:57:36,840
Speaker 1: Why your socially inept? And uh, you know the big,

907
00:57:36,920 --> 00:57:41,560
Speaker 1: the big short, big short life is I don't do this,

908
00:57:41,680 --> 00:57:44,520
Speaker 1: I don't do that, I don't shave, I don't. It's

909
00:57:44,640 --> 00:57:48,240
Speaker 1: just kind of man, I'm looking for. Michael Lewis is

910
00:57:48,280 --> 00:57:51,320
Speaker 1: another one of those writers who was just unbelievable. So

911
00:57:51,400 --> 00:57:55,520
Speaker 1: any any favorite Lewis books. While everything he's put out everything,

912
00:57:55,520 --> 00:57:58,080
Speaker 1: he's everything. So it's Lewis and Gladwell and those are

913
00:57:58,120 --> 00:58:01,960
Speaker 1: your favorite, But I'm more I'm more interested in life,

914
00:58:02,160 --> 00:58:09,400
Speaker 1: in infects life. Did you read um The Undoing Project

915
00:58:09,440 --> 00:58:12,120
Speaker 1: by Lewis? The book about Oh well, I'm gonna give

916
00:58:12,120 --> 00:58:15,760
Speaker 1: you a book recommendation. Lewis wrote about Danny Khneman and

917
00:58:15,800 --> 00:58:21,720
Speaker 1: Amos Tversky who discovered all of these behavioral issues that

918
00:58:21,840 --> 00:58:26,080
Speaker 1: not just economists and economic behavior, but across the board

919
00:58:26,120 --> 00:58:29,880
Speaker 1: the way humans behave. And it's a fascinating story. If

920
00:58:29,960 --> 00:58:33,040
Speaker 1: you like Michael Lewis. It's a little different than some

921
00:58:33,120 --> 00:58:36,080
Speaker 1: of his other books, but it's just as fascinating. It's

922
00:58:36,080 --> 00:58:39,840
Speaker 1: really a fact, I now envy you for not having

923
00:58:39,880 --> 00:58:42,200
Speaker 1: read it, because you get to read it. I've already

924
00:58:42,200 --> 00:58:44,360
Speaker 1: read it. So let's get to our last two questions.

925
00:58:44,400 --> 00:58:48,000
Speaker 1: My favorite questions. So if in then a millennial or

926
00:58:48,040 --> 00:58:50,880
Speaker 1: someone who just graduated college came to you and said, Hey,

927
00:58:50,880 --> 00:58:55,080
Speaker 1: I'm looking for some advice on career and finance, what

928
00:58:55,160 --> 00:58:57,640
Speaker 1: sort of advice would you give him? Well, I actually

929
00:58:57,800 --> 00:59:02,560
Speaker 1: try to mentor millennials, and I have a packet. I

930
00:59:02,600 --> 00:59:06,160
Speaker 1: have found that most do not have a clue about interviewing.

931
00:59:06,760 --> 00:59:08,800
Speaker 1: So I give him a packet on how to interview,

932
00:59:09,040 --> 00:59:10,720
Speaker 1: and I try to take into the lunch or dinner,

933
00:59:11,280 --> 00:59:13,880
Speaker 1: and I can tell you there's five or ten things

934
00:59:13,880 --> 00:59:17,480
Speaker 1: that I reinforce that they must do to make a

935
00:59:17,480 --> 00:59:20,400
Speaker 1: good first impression. And then there's three or four or

936
00:59:20,400 --> 00:59:22,800
Speaker 1: five questions. I try to teach some of the questions

937
00:59:22,880 --> 00:59:26,480
Speaker 1: they need to ask. But helping them in the interview

938
00:59:26,520 --> 00:59:31,560
Speaker 1: processes is very, very important because they really do not

939
00:59:31,720 --> 00:59:35,080
Speaker 1: show up ready to make a good first impression. Give

940
00:59:35,120 --> 00:59:37,880
Speaker 1: us an example of each what what do you suggest

941
00:59:37,960 --> 00:59:40,520
Speaker 1: they do on their interview and what sort of questions

942
00:59:42,480 --> 00:59:47,760
Speaker 1: they need to put their resume on slightly thicker paper? Um,

943
00:59:47,800 --> 00:59:50,280
Speaker 1: they need to have at the bottom like nice bonded

944
00:59:50,360 --> 00:59:55,520
Speaker 1: linen paper that fuel substantial. That's right, it's noticed. And

945
00:59:55,520 --> 00:59:58,080
Speaker 1: then that they need to have community service at the

946
00:59:58,080 --> 01:00:01,280
Speaker 1: bottom of the resume. They have to have no spelling

947
01:00:01,360 --> 01:00:06,280
Speaker 1: or punctuation or grammar zero, no errors whatsoever. They need

948
01:00:06,320 --> 01:00:10,640
Speaker 1: to walk in with a briefcase. Open up the briefcase.

949
01:00:10,680 --> 01:00:13,280
Speaker 1: Here's my resume. Well, it has to be organized. They

950
01:00:13,320 --> 01:00:16,360
Speaker 1: walk into the left hand and shake a firm handshake,

951
01:00:16,480 --> 01:00:18,040
Speaker 1: which a lot of them don't know how to handshake

952
01:00:18,440 --> 01:00:21,760
Speaker 1: with a with a hand with a firm handshake. Uh.

953
01:00:21,920 --> 01:00:27,640
Speaker 1: If they go to lunch or dinner and no, but yeah,

954
01:00:27,640 --> 01:00:32,440
Speaker 1: I don't order spaghetty. But uh if the boss orders

955
01:00:33,000 --> 01:00:35,400
Speaker 1: fish and chips and scotch on the rocks, you order

956
01:00:35,440 --> 01:00:39,080
Speaker 1: the same exact thing. Commonality, it's the most important thing.

957
01:00:39,600 --> 01:00:42,040
Speaker 1: And so you want to learn everything you can about

958
01:00:42,080 --> 01:00:46,479
Speaker 1: that person in the company. Everything. And if you walk

959
01:00:46,520 --> 01:00:49,280
Speaker 1: in and see that he's a tennis player, you're love

960
01:00:49,360 --> 01:00:56,960
Speaker 1: tennis and uh um, and then you know the you

961
01:00:57,000 --> 01:01:04,520
Speaker 1: know the the the process. Also the second thing is

962
01:01:04,880 --> 01:01:09,560
Speaker 1: you want to ask them why is this position? Open um?

963
01:01:10,240 --> 01:01:15,560
Speaker 1: How do you measure success? Um? Um? Good questions? Yeah,

964
01:01:15,760 --> 01:01:19,360
Speaker 1: and what's the next what's the next stage in the process.

965
01:01:20,360 --> 01:01:22,320
Speaker 1: You also want to take a card when you leave

966
01:01:22,360 --> 01:01:24,440
Speaker 1: and write them a personal thank you and telling them

967
01:01:24,480 --> 01:01:26,200
Speaker 1: you want to be part of their team and how

968
01:01:26,240 --> 01:01:29,600
Speaker 1: impressed you were. And even if you get rejected, you

969
01:01:29,640 --> 01:01:31,720
Speaker 1: want to go back again send the thank you note

970
01:01:31,720 --> 01:01:33,960
Speaker 1: for being rejected and please keep me in mind in

971
01:01:34,000 --> 01:01:38,040
Speaker 1: the future. But persistence pays off. Good good advice for

972
01:01:38,080 --> 01:01:41,480
Speaker 1: any millennial. And our final question, what is it that

973
01:01:41,520 --> 01:01:44,960
Speaker 1: you know about the world investing today that you wish

974
01:01:44,960 --> 01:01:47,760
Speaker 1: you knew forty years ago when you were first starting.

975
01:01:49,320 --> 01:01:51,680
Speaker 1: That's that is a good question, isn't that? It's my

976
01:01:51,760 --> 01:01:57,080
Speaker 1: favorite question? Is it really that? That is my encore?

977
01:01:57,760 --> 01:01:59,760
Speaker 1: After the everybody cheers, you come out, you do the

978
01:02:00,200 --> 01:02:04,160
Speaker 1: song that's my last song. Well, you know, forty forty

979
01:02:04,240 --> 01:02:08,240
Speaker 1: years ago, I think I was too naive and stupid

980
01:02:08,720 --> 01:02:13,080
Speaker 1: to to to know how much risk I was taking,

981
01:02:13,720 --> 01:02:16,440
Speaker 1: uh you know, at the time. But you know, it

982
01:02:16,520 --> 01:02:20,320
Speaker 1: also goes back to uh the women neuronet at twenty

983
01:02:20,360 --> 01:02:23,600
Speaker 1: five and men at thirty, and so thirty is just

984
01:02:23,680 --> 01:02:29,000
Speaker 1: a wonderful aged too matriculate and to to to do things.

985
01:02:29,280 --> 01:02:35,800
Speaker 1: And so I'll tell you one final story about Jesus. Uh,

986
01:02:36,200 --> 01:02:40,360
Speaker 1: Jesus is the Son of God. Average life expectancy back

987
01:02:40,360 --> 01:02:43,600
Speaker 1: then it was thirty two years of age. It's a

988
01:02:43,680 --> 01:02:47,920
Speaker 1: misnomer because half the people died in childbirth two So

989
01:02:48,040 --> 01:02:51,400
Speaker 1: did you ever wonder? Uh? They don't they know anything

990
01:02:51,400 --> 01:02:54,720
Speaker 1: about it between the age of three and thirty zero.

991
01:02:55,160 --> 01:02:57,320
Speaker 1: Why at thirty all of a sudden did he emerge

992
01:02:57,720 --> 01:03:04,240
Speaker 1: Because he picked at thirty quite but he Jewish people

993
01:03:04,440 --> 01:03:06,520
Speaker 1: do not allow you to become a rabbi until your

994
01:03:06,560 --> 01:03:11,480
Speaker 1: twenty nine or thirty. You cannot teach life until you've experienced.

995
01:03:12,120 --> 01:03:15,680
Speaker 1: Same thing with the medicine. Okay, you don't want a

996
01:03:15,760 --> 01:03:19,840
Speaker 1: doctor that was no doogie howser you do him? So

997
01:03:20,560 --> 01:03:24,160
Speaker 1: you know the third thirty you know lots of things

998
01:03:24,200 --> 01:03:27,000
Speaker 1: and Judaism are well thought out. And this is why

999
01:03:27,080 --> 01:03:29,880
Speaker 1: at thirty he emerged because nobody would listen to him

1000
01:03:29,960 --> 01:03:34,760
Speaker 1: until he was thirty. So I tell millennials also that

1001
01:03:35,560 --> 01:03:38,200
Speaker 1: college is the time to grow up and mature and

1002
01:03:38,240 --> 01:03:41,520
Speaker 1: then go out and get your and be men. Beach

1003
01:03:41,640 --> 01:03:45,440
Speaker 1: you know, in experienced life before you go out and

1004
01:03:45,440 --> 01:03:48,480
Speaker 1: and try to do something. So wait, so eventually you're

1005
01:03:48,520 --> 01:03:51,640
Speaker 1: you're team yourself up to become somebody a thirty when

1006
01:03:51,640 --> 01:03:55,560
Speaker 1: you can have a more successful chance of being successful.

1007
01:03:55,600 --> 01:03:59,080
Speaker 1: I think this is just me. I like that. That's fascinating.

1008
01:03:59,720 --> 01:04:01,840
Speaker 1: Ed's thank you so much for doing this. We have

1009
01:04:02,000 --> 01:04:05,400
Speaker 1: been speaking with Ed Mandel. He is a co founder

1010
01:04:05,400 --> 01:04:08,560
Speaker 1: of ned Davis Research as well as minority owner of

1011
01:04:08,600 --> 01:04:12,400
Speaker 1: the Atlanta Falcons. If you enjoy this conversation, be sure

1012
01:04:12,440 --> 01:04:17,400
Speaker 1: and look up an introd down an inch on Apple iTunes, Overcast, SoundCloud,

1013
01:04:17,440 --> 01:04:21,600
Speaker 1: Bloomberg dot com wherever final podcasts are sold, and you

1014
01:04:21,640 --> 01:04:24,560
Speaker 1: could see the other hundred and sixty or so such

1015
01:04:24,640 --> 01:04:29,760
Speaker 1: conversations that we've had previously. We love your comments, feedback

1016
01:04:29,800 --> 01:04:33,880
Speaker 1: and suggestions right to us at m IB podcast at

1017
01:04:33,880 --> 01:04:37,360
Speaker 1: Bloomberg dot net. I would be remiss if I forgot

1018
01:04:37,400 --> 01:04:41,400
Speaker 1: to mention our crack staff that helps put together UH

1019
01:04:41,440 --> 01:04:46,000
Speaker 1: these weekly podcasts. Medina Parwana is our audio producer, Taylor

1020
01:04:46,080 --> 01:04:49,120
Speaker 1: Riggs is in charge of booking UH, and Michael Batnick

1021
01:04:49,280 --> 01:04:53,320
Speaker 1: is our head of research. I'm Barry Riholts. You've been

1022
01:04:53,320 --> 01:05:00,960
Speaker 1: listening to Masters in Business on Bloomberg Radio. Take a

1023
01:05:01,120 --> 01:05:02,680
Speaker 1: tablet in days