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Speaker 1: Masters in Business is brought to you by the American

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Speaker 1: Arbitration Association. Business disputes are inevitable, resolve faster with the

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Speaker 1: American Arbitration Association, the global leader in alternative dispute resolution

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Speaker 1: for over ninety years. Learn more at a d R

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Speaker 1: dot org. This is Masters in Business with Barry Ridholtz

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Speaker 1: on Boomberg Radio. This week on the podcast What a Delight,

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Speaker 1: I got to sit down in the offices of Ed

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Speaker 1: Thorpe and chat for two hours about pretty much everything

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Speaker 1: you may know the name Ed Thorpe. I described him

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Speaker 1: as really the first quantitatively driven hedge fund manager UH

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Speaker 1: that of of any import. He was Jim Simons of

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Speaker 1: Renaissance Technologies while Jim Simons was still a undergraduate math professor.

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Speaker 1: He is the author initially of the book Beat the Dealer,

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Speaker 1: where he figured out how to actually beat the house

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Speaker 1: in blackjack. His analysis, his mathematical analysis of of blackjack

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Speaker 1: led Las Vegas and casinos everywhere to completely change how

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Speaker 1: they deal blackjack. They now use half a dozen UH

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Speaker 1: decks instead of a single deck their random reshuffles. They

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Speaker 1: utterly have changed the rules in response to Ed Thorpe.

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Speaker 1: He then figures out the physics behind beating wait for it,

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Speaker 1: Roulette and working with information theorist Claude Shannon at m

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Speaker 1: I T. The two of them come up with a

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Speaker 1: wearable computer. Eventually they get thrown out of old casinos.

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Speaker 1: The casinos don't really like losing money to M I T.

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Speaker 1: Math professors. He relocates to Irvine, you see, in California,

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Speaker 1: and starts thinking about, well, what else is math heavy

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Speaker 1: and filled with inefficiencies that has the potential when statists

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Speaker 1: sical theory is applied properly to generate large amounts of

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Speaker 1: money and finds his way onto Wall Street and and

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Speaker 1: His second book is called Beat the Market and explains

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Speaker 1: how when you can find two related equity issuances that

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Speaker 1: are miss priced, there's an arbitrage opportunity. Believe it or not,

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Speaker 1: Before that, no one was really doing that sort of

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Speaker 1: statistical arbitrage between a stock and a warrant or eventually

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Speaker 1: a stock in an option. Uh. He's had an absolutely

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Speaker 1: fascinating career in a fascinating life. His autobiography is called

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Speaker 1: A Man for All Markets, and it's really quite intriguing. UH.

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Speaker 1: Footnote to this entire conversation. After we finished the interview,

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Speaker 1: my assumption is that, hey, he's got to be tired

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Speaker 1: of hearing my voice. But he invites us to dinner,

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Speaker 1: and and three of us went and had a lovely

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Speaker 1: meal there in Newport Beach. And and he's just an

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Speaker 1: absolutely fascinating UM person and really a character who is

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Speaker 1: beyond influential in the worlds of um, quantz and algorithms

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Speaker 1: and hedge funds and you name it. His impact on

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Speaker 1: finance can't be understated, So can't be overstated. So, with

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Speaker 1: no further ado my conversation with ed Thorpe, I have

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Speaker 1: an extra special guest this week, and I am privileged

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Speaker 1: to be sitting in his offices in glorious Newport Beach, California.

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Speaker 1: Edward oh Thorpe is a legend in finance. He is

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Speaker 1: an American mathematics professor, author, hedge fund manager. He is

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Speaker 1: the person who essentially figured out how to beat Las Vegas.

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Speaker 1: I credit him with more or less inventing card counting. Eventually,

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Speaker 1: Vegas banned him from a ton of a ton of

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Speaker 1: casinos because he was beating the house. And his his

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Speaker 1: first uh first book was Beat the Dealer. And after

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Speaker 1: he did that, he figured out ways of beating baccarat

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Speaker 1: and came up within a wearable computer and figured out

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Speaker 1: how to beat Rolette Roulette. And by that point Las

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Speaker 1: Vegas and Reno wanted nothing to do with him, and

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Speaker 1: he turned his attentions towards the next challenge similar to gambling,

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Speaker 1: and it turned out that was investing in finance in

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Speaker 1: Wall Street, and the book after that became Beat the

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Speaker 1: Market because Edward figured out how to do things in

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Speaker 1: a way that created substantial, substantial returns. I could talk

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Speaker 1: about his curriculum Vita forever. Let me just add PhD

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Speaker 1: in mathematics, taught it m I T in New Mexico

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Speaker 1: and University California, Irvine. Edward Thorpe, Welcome to Bloomberg and

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Speaker 1: thank you for hosting us in your office. Thanks very,

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Speaker 1: It's a pleasure to meet you and be here. I'm

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Speaker 1: thrilled to be here. I'm gonna start with some quotes

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Speaker 1: of yours and just have you respond them. And some

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Speaker 1: of these are from Beat the Dealer. Chance can be

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Speaker 1: thought of as the cards you are dealt in life.

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Speaker 1: Choice is how you play them. That's a pretty deep

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Speaker 1: philosophical perspective. Tell us how you came to that well,

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Speaker 1: if you look back at your own life, you probably

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Speaker 1: have a number of circumstances where you could make an

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Speaker 1: important choice one way or another, and the way you

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Speaker 1: made that choice had a big effect. The woman you're married,

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Speaker 1: if if you infected, get married, the children that you

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Speaker 1: brought up, and how you brought them up, the career

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Speaker 1: you chose in life. A lot of big choices that

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Speaker 1: you make at some point or other. And then there

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Speaker 1: are things that you can't control, like who your parents

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Speaker 1: were and what kind of economic circumstances you were brought

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Speaker 1: up in. Where you where you started. You start twenty

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Speaker 1: yards behind the start line, or twenty yards ahead of

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Speaker 1: it or right on it. People start in different places.

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Speaker 1: Those are cards that are dealt. So along those themes

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Speaker 1: you talk about managing risk as an investor and managing

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Speaker 1: money as a gambler, you wrote, I also believe, then,

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Speaker 1: as I do now have to more than fifty years

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Speaker 1: as a money manager, the surest way to get rich

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Speaker 1: is to play only those gambling games where I have

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Speaker 1: an edge. Let's talk about the edge a little bit, Okay.

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Speaker 1: In the standard dambling games in casinos, you can generally

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Speaker 1: calculate what the casino's edges, or if you figure out

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Speaker 1: how to count cards, you can calculate what your edge

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Speaker 1: over the casino is. So it's a fact, a mathematical fact,

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Speaker 1: that if you play a game like this and the

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Speaker 1: casino has the edge, it will eventually collect all your

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Speaker 1: money if you play long enough. On the other hand,

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Speaker 1: if you have an edge, your bank roll will grow

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Speaker 1: and grow and grow. So basically what happens is you

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Speaker 1: your bankroll either grows or shrinks depending on what your

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Speaker 1: edges or what your disadvantages, and there's luck that pushes

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Speaker 1: it up and down around that growth curve. So that's

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Speaker 1: that's the way things look in the gambling world. So

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Speaker 1: so even when you have an edge, you have to

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Speaker 1: be prepared for Hey, sometimes snake eyes comes up on

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Speaker 1: the dice. To to mix metaphors a little bit. That

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Speaker 1: was one of the early things that I learned, fortunately,

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Speaker 1: which was how much to bet on good situations. If

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Speaker 1: you've got too much, you're likely to be wiped out.

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Speaker 1: If you've got too little, it takes forever to make

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Speaker 1: any money. So there's a happy medium in there, and

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Speaker 1: that was one of the things that I came across

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Speaker 1: quite early. Is there a mathematical solution to what's the

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Speaker 1: right amount and what's too much in too little? Well,

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Speaker 1: I was put onto this by a famous mathematician named

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Speaker 1: Claude Shannon, who I knew at M. I T and

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Speaker 1: I have to add in a man for all markets.

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Speaker 1: You just casually mentioned throughout the book. You casually mentioned

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Speaker 1: these legends in science and physics and mathematics. And oh

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Speaker 1: so I went to Claude Channon with this problem, and

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Speaker 1: the two you spent weekends tell us a little bit

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Speaker 1: about that. Well, let's see it. Um. I got interested

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Speaker 1: in the idea of beating Roulette when I was a

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Speaker 1: high school student, but my ideas weren't very well formed,

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Speaker 1: so I put it aside. And then when I went

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Speaker 1: to u c. L A And gotta a bachelor's and

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Speaker 1: masters in physics, I had about seven more years of

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Speaker 1: physics behind me, and I happened to be talking to

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Speaker 1: people at a study break in my student coopera I

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Speaker 1: lived about Las Vegas. Some people have just come back

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Speaker 1: and said, you can't beat those guys. I said, well,

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Speaker 1: I think you can, and here's how I would go

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Speaker 1: about it. So I described predicting Roulette using physics and

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Speaker 1: they are good back and forth, and half the people

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Speaker 1: believe me in half of them didn't. But I convinced

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Speaker 1: myself from the argument and all the new physics that

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Speaker 1: I had learned in the interim from high school, that yes,

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Speaker 1: this is very likely to work. So I set out

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Speaker 1: to do that, and you created a wearable computer working

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Speaker 1: with Channon and other people. Is that right? What happened

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Speaker 1: was I went to Las Vegas with my wife over

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Speaker 1: Christmas vacation. By this time I had a PhD in

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Speaker 1: math at U c l A. And I was going

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Speaker 1: there just for a cheap, cheap vacation over Christmas. But

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Speaker 1: I wanted to see Roulette wheels because I was busy

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Speaker 1: working on beating Roulette, and I wanted to verify by

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Speaker 1: close up observation of their wheels that what I was

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Speaker 1: doing was likely to work. And on the way, I

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Speaker 1: heard about a blackjack paper that some statisticians had read.

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Speaker 1: So I thought, well, I'll get a little casino experience.

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Speaker 1: I'm gonna need it if I'm going to be playing Roulette.

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Speaker 1: So I sat down at the blackjack table and played

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Speaker 1: for about forty minutes, and I learned enough during that

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Speaker 1: forty minutes to realize I could probably beat blackjack, and

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Speaker 1: I went back and set to work doing that. And

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Speaker 1: black jack is one of those situations. Back then, there

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Speaker 1: was one deck. They didn't shuffle it. They played pretty

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Speaker 1: much through to the fifty two cards. That was astonishing

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Speaker 1: that nobody had sat back and said, oh, we should

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Speaker 1: be able to track the math of this and figure

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Speaker 1: out how to beat the dealer. Well, there have been

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Speaker 1: some savvy gamblers who had figured out that if you

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Speaker 1: kept track of the cards and near the end there

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Speaker 1: are lots of aces and tens, then there'll be more

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Speaker 1: black shacks being dealt. And black jacks then and sometimes

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Speaker 1: still now pay three to two if the player gets one,

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Speaker 1: and just even money if the dealer gets one. He

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Speaker 1: basically a collect your bet. So the more black when

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Speaker 1: the decks rich and aces in tens, the higher frequency

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Speaker 1: of black shacks shifts the edge in favor of the players.

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Speaker 1: So they would sit there and wait and wait and

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Speaker 1: wait until there were lots of aces and tens near

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Speaker 1: the end of the deck. Then they bet a whole

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Speaker 1: lot of money, raised the bets and and hope the

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Speaker 1: statistics just play out over time. Yes, and that worked,

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Speaker 1: but it took a long time, and it meant huge

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Speaker 1: swings in the amounts they beat, and the casino was

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Speaker 1: kind of caught onto that and got rid of these guys.

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Speaker 1: They don't like people who were betting legitimately and winning.

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Speaker 1: They seem to have a problem with that. Well, after

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Speaker 1: I wrote a book about Card County beat the Dealer

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Speaker 1: and it came out, they had a crisis in Las Vegas.

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Speaker 1: Who were tens of thousands of players coming out and

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Speaker 1: a few hundred of which learned how to count cards properly.

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Speaker 1: And these players were upsetting them very much. So on

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Speaker 1: April Fool's Day of they announced a rules changed. The

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Speaker 1: Las Vegas Resort Hotel Association had a big meeting and

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Speaker 1: decided what to do. They had a lot of uh

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Speaker 1: suggestions like break his knees, right, nice guys, But they

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Speaker 1: changed the rules and they went from one deck to

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Speaker 1: six or eight. Is that that was a sort of

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Speaker 1: a separate, parallel evolution. What they did was they restricted

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Speaker 1: what players could do. They couldn't split as many pairs

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Speaker 1: they could and double down as much that sort of thing.

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Speaker 1: And I predicted that the players would not like this,

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Speaker 1: and they leave the tables in droves, which is what happened.

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Speaker 1: So they quietly went back to the old rules, and

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Speaker 1: then they did what you mentioned. They began to introduce

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Speaker 1: what they called professor stoppers, which were four or six

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Speaker 1: or eight decks, and they began to deal them out

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Speaker 1: of shoes, because it's pretty hard to hold for to

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Speaker 1: eight decks in your hand and try to deal professor stoppers.

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Speaker 1: That that's hilarious. So let's talk a little bit about

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Speaker 1: information theory, because there's a line that you have about

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Speaker 1: the great physics professor Richard Feynman, and you went to

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Speaker 1: him with your concept about physical prediction of Roulette, and

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Speaker 1: Vineman said, no, I really don't think anybody can beat Roulette.

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Speaker 1: And you were thrilled with that answer. Yes very much, though,

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Speaker 1: explain why, because I love the thought process here. Well,

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Speaker 1: Fieman had had to graduate students that had figured out

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Speaker 1: a way to be defective Roulette, someones which weren't properly

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Speaker 1: maintained or machined, and they had made several thousand dollars

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Speaker 1: doing this back in the early fifties. So he knew

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Speaker 1: about He knew that fact about Roulette, and he also

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Speaker 1: understood the edge that casino has had and how the

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Speaker 1: mathematical ledge would grind down players who knew that mathematics

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Speaker 1: had shown you couldn't beat roulette in the normal fashion

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Speaker 1: of varying your bets up and down. In other words,

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Speaker 1: it's a game of chance. It's a random outcome, and

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Speaker 1: ultimately the house wins if you play long enough. Exactly.

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Speaker 1: So he knew those two things, and he himself had

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Speaker 1: been in Las Vegas and he saw he saw a

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Speaker 1: guy wandering around betting on red and black and roulette wheels.

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Speaker 1: So he said of the guy, look, I'll bank roll

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Speaker 1: it for you. The guy was betting five dollars a hand.

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Speaker 1: He said, just bet with me. The guy would go

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Speaker 1: over and you say red and the wheel would spend,

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Speaker 1: and if he won, five would pay him five dollars,

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Speaker 1: and if he lost, he paid fiveman five dollars. This

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Speaker 1: went on for a while until Fine one was down

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Speaker 1: eighty dollars or so, at which point Biman quit. So, uh,

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Speaker 1: that's pretty random that he's just banning one way or another.

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Speaker 1: And what Fireman didn't realize was that psychologically his bank

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Speaker 1: roll was eighty dollars and that wasn't big enough to

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Speaker 1: be a casino. It makes a lot of sense. So

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Speaker 1: so when Fynman said I don't think this could be done,

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Speaker 1: that encouraged you, and I figured, here's here's one of

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Speaker 1: the best physicists on earth. He knows about Roulette, he

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Speaker 1: knows about a lot of math, and yet he thinks

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Speaker 1: it's not possible. But I think it's possible. So if

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Speaker 1: he thinks it's not possible, probably everybody else does too.

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Speaker 1: So that means I've got this all to myself. That

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Speaker 1: that's ah, that's a fascinating way. That's like the little

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Speaker 1: Kid and the pony, the old the old joke. Hey,

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Speaker 1: if Fynman doesn't think it, then there's got to be

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Speaker 1: a pony in here nowhere. Um. Another quote from the book,

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Speaker 1: most market participants have no demonstrable advantage for them, just

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Speaker 1: as the cards in black Jacks or the numbers that

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Speaker 1: Roulette seem to appear at random. The market appears to

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Speaker 1: be completely af But you disagree with that assessment. Well,

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Speaker 1: when people talk about efficient markets, I think it's a

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Speaker 1: property of the market, but I think that's not the

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Speaker 1: way to look at it. It's the market is a

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Speaker 1: process that goes on and we have, depending on who

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Speaker 1: we are, different degrees of knowledge about different parts of

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Speaker 1: that process. I'll give you an example. Suppose I take

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Speaker 1: a coin here where we're sitting, and I flip it

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Speaker 1: up and let you call it heads your tails. You'll

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Speaker 1: probably think that the outcome is random, and assuming it's

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Speaker 1: a true coin, sure, yes so, and people can argue

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Speaker 1: about whether coins are true or not, but the distinctions

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Speaker 1: are very minor. Essentially, it's fifty, so you can say, well,

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Speaker 1: you know it's random. I I'm willing to call you

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Speaker 1: the heads their tails. Not. Suppose that I, like Claude Shannon,

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Speaker 1: take a sandbox and a little level with a cup

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Speaker 1: on it and move the level down a measured amount

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Speaker 1: and let it flip the coin. Well, what Shannon found

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Speaker 1: out was that the coin would flip a number of

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Speaker 1: turns that he could calculate depending on how far down

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Speaker 1: he pulled a lever before he released it, so he

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Speaker 1: could have it spend three and a half times, for example,

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Speaker 1: So if heads were up and he spent a three

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Speaker 1: and a half times, it would land tails. A simple

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Speaker 1: problem of force, angular momentum, and an amount of distance

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Speaker 1: it travels exactly. So given the extra information, that coin

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Speaker 1: becomes fairly predictable. So it's the same with markets, depending

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Speaker 1: it depends on any information you have, And of course

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Speaker 1: people who do inside or trading do a pretty good

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Speaker 1: job of predicting a market that seems random to most people.

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Speaker 1: So a few quotes of yours that I find fascinating

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Speaker 1: gambling is investing simplified. I think that's really interesting that

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Speaker 1: investing has become more complex than gambling. Why is that. Well,

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Speaker 1: with most gambling aims, you can calculate probabilities, and so

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Speaker 1: you know what the odds are in any given situation,

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Speaker 1: and you can figure out pretty accurately how much to that.

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Speaker 1: With a security it's more difficult. You only have rough estimates.

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Speaker 1: For example, there's something called the long normal probability distribution

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Speaker 1: that is a rough model for how stock prices change,

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Speaker 1: but it's not an exact model, and it has parameters

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Speaker 1: and like volatility, and if you make a supposition as

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Speaker 1: that what the volatility is going to be in the

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Speaker 1: near future, you can get an idea of the distribution

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Speaker 1: of prices of a particular stock in the future, but

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Speaker 1: you might be off in what you think the volatility

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Speaker 1: is going to be, and so your distribution will be off.

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Speaker 1: So anyhow, it's it's a matter of estimating things that

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Speaker 1: you can't get exactly, you can't know. So so how

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Speaker 1: natural was that progression from the casinos to Wall Street

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Speaker 1: other than they're both big, interesting mathematical riddles to be solved. Well,

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Speaker 1: I learned a lot of things in the casino, I

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Speaker 1: would say, just as an aside um, using a winning

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Speaker 1: gambling system of the casino is per training for the

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Speaker 1: investment world. And the reason is that you learn how

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Speaker 1: to manage money, You learn how much to bet. When

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Speaker 1: you know what your edge is, you get a certain

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Speaker 1: discipline in conducting a strategy that you think is right.

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Speaker 1: For example, let's suppose that you're an index fund investor.

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Speaker 1: If you understand what the probability distribution has been for

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Speaker 1: an index fund in the past, then you have an

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Speaker 1: idea of what kind of ups and downs you're going

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Speaker 1: to be writing in the future. And when something bad

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Speaker 1: happens like two thousand and two tho nine, you don't

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Speaker 1: run out like a scared rabbit at the bottom and

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Speaker 1: miss the whole climb back up to new highs. And

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Speaker 1: yet that seems to be what many people. Many people do,

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Speaker 1: they do, but if they had been car counters a

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Speaker 1: black tack like the professionals I see the blackjack ball

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Speaker 1: once a year if they had been, if they had

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Speaker 1: that experience, they wouldn't flinch at all. So in the book,

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Speaker 1: you describe sort of your first tentative ventures into markets

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Speaker 1: after um the Vegas experience. Tell us how you you

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Speaker 1: began investing well after I had made money both from

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Speaker 1: book royalties and from card counting at blackjack and baccarat

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Speaker 1: in Las Vegas. After I've done that, I had money

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Speaker 1: to invest for the first time in my life. Academics

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Speaker 1: weren't paid a whole lot then and they're not paid

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Speaker 1: a whole lot now, So I find my problem was

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Speaker 1: to figure out how to invest the money. So, knowing nothing,

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Speaker 1: I made my collection of mistakes. I paid a mr

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Speaker 1: Market is rather expensive tuition to learn a bunch of

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Speaker 1: basic things that I'm described in the book, things that

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Speaker 1: behavioral finance people understand now as foolish mistakes. And then

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Speaker 1: I sat down for a whole summer and read everything

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Speaker 1: I could on finance in a big bookstore in Beverly Hills.

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Speaker 1: You described two summers of of of learning in the

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Speaker 1: library and in the books. How many books did you

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Speaker 1: play through? What were you reading. Well, my first go

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Speaker 1: round and the first summer was to read whatever they

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Speaker 1: had in a big Martindale's bookstore on Beverly Hills. So

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Speaker 1: it was newspapers, investment books, charting fundamentals, all kinds of advice,

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Speaker 1: which which leads me to ask a question. And again

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Speaker 1: it's a quote of yours. Most stockpicking stories, advice recommendations

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Speaker 1: are completely worthless. Was that was that your takeaway back

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Speaker 1: then or is that something you learned over the years. Well,

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Speaker 1: it wasn't my instant takeaway, but it was something I

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Speaker 1: came to fairly rapidly as I plowed through all these things.

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Speaker 1: What what is it that led you to the conclusion,

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Speaker 1: which I completely agree with, that a lot of what's

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Speaker 1: out there is just either conflicted or self interested, or

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Speaker 1: noisy or not well informed commentary. Well, I had a

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Speaker 1: science background, and I tend to draw conclusions based on

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Speaker 1: nation facts evidence. That's so old school. We don't do

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Speaker 1: that anymore. It's it's it's now, it's all about the

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Speaker 1: fake facts, fake news. I'm basically immune to all that stuff.

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Speaker 1: It's like water running off a dock or whatever. So anyhow,

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Speaker 1: you brought a fairly critical eye to whatever was being

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Speaker 1: passed off his market advice? Is that a fair statement

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Speaker 1: just because people said something wasn't good enough. I had

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Speaker 1: that set from the time I was a child. I

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Speaker 1: wanted to check it for myself and verify for myself.

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Speaker 1: And you blew up a number of chemistry kits and

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Speaker 1: other such things which you describe in the book UM.

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Speaker 1: So let's talk a little bit about return. So one

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Speaker 1: of the things you discovered are pricing inefficiencies, pricing anomalies

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Speaker 1: that say, hey, here are two related UM stocks. You

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Speaker 1: can either have a stock and a warrants or later

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Speaker 1: on a stock and an option, and they should trade parallel,

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Speaker 1: but they don't. Sometimes one or the other gets cheap

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Speaker 1: and the other gets overvalued. Was anybody else doing those

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Speaker 1: sort of paired or hedged trades at the time. No, Actually,

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Speaker 1: what happened when I I was sent my set. I

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Speaker 1: spent my second summer trying to learn about finance, and

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Speaker 1: I got lucky. The first week or so, I got

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Speaker 1: a little pamp in the mail from something called r

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Speaker 1: h M warrant survey guy named Sydney Freed. I believe

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Speaker 1: that his son still runs this survey, and so it

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Speaker 1: told tales buying warrants for pennies and cashing him out

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Speaker 1: for dollars in the future. So as I read through it,

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Speaker 1: I learned what I warrant was, and I said, hey,

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Speaker 1: wait a minute. This simplifies the investing problem tremendously because

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Speaker 1: the price of a warrant and the price of this

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Speaker 1: underlying stock are related. So I don't have to worry

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Speaker 1: about all these fundamental things. I don't have to go

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Speaker 1: out and talk to CEOs and read balance and income

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Speaker 1: statements and compare companies one to another. If these things

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Speaker 1: moved together, then if they get out of whack, I

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Speaker 1: had to be able to set up some kind of

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Speaker 1: investment short one along the other that will make money

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Speaker 1: for me. So I began to think then about how

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Speaker 1: how to price it warrant, how to know when I

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Speaker 1: was at a proper price or overpriced or underpriced? And

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Speaker 1: you were using some pretty early computer technology at the time. Yes,

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Speaker 1: I had actually got into using computers with Blackjack. I

400
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Speaker 1: had done that back at M I T and sixty.

401
00:23:37,119 --> 00:23:40,400
Speaker 1: They had an IBM seven oh four that was available then.

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Speaker 1: So I taught myself for tran and the programmed to

403
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Speaker 1: figure out the equations solve the equations that I needed

404
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Speaker 1: to decide which cards were good to have in the

405
00:23:54,960 --> 00:23:57,280
Speaker 1: deck in blackjack and which cards were good to have

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Speaker 1: out of the deck. So anyhow, I had the experience

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Speaker 1: with the computers, which was fortunate. So then when I

408
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Speaker 1: got to think about warrants, I had the computer background.

409
00:24:09,400 --> 00:24:13,280
Speaker 1: Two actually mathematic size and draw graphs and do that

410
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Speaker 1: sort of thing. Made it much more easy for me.

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Speaker 1: And so you would find when these two related securities

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Speaker 1: were out of out of sync, and basically made the

413
00:24:24,640 --> 00:24:28,200
Speaker 1: bet that was did any of them ever go against

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Speaker 1: you in an unanticipated way? Or did everything perform as expected.

415
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Speaker 1: The first things we found were overpriced warrants, and I

416
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Speaker 1: happened to run into a professor at U see I

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Speaker 1: the first day that you See I opened for classes

418
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Speaker 1: back in we're both new faculty members, and he had

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Speaker 1: written a thesis about warrants name Machine Kasuf. And so

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00:24:51,080 --> 00:24:54,359
Speaker 1: we got together and met every every day for about

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Speaker 1: a year or two to develop the theory further, and

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Speaker 1: then we wrote a book Beat the Market about it

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Speaker 1: and the warrants that we found that were overpriced. We

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Speaker 1: were short and we would buy common stock, long hedge,

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Speaker 1: and every one of those was a winner m hm.

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Speaker 1: And every one of them historically had been winner, so

427
00:25:17,880 --> 00:25:21,280
Speaker 1: we knew that these overpriced warrants somehow eluded the market.

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Speaker 1: There were warrants with two years or less ago, and

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00:25:23,960 --> 00:25:26,600
Speaker 1: investors seemed to think that two years was forever, so

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00:25:26,640 --> 00:25:29,880
Speaker 1: they proaced the warrants way high, and then the warrants

431
00:25:29,920 --> 00:25:32,600
Speaker 1: when they would wake up as the clock ran on

432
00:25:32,640 --> 00:25:35,359
Speaker 1: the two years, the warrants would collapse in price. So

433
00:25:35,400 --> 00:25:37,600
Speaker 1: you could short the warrant about the stock. And it

434
00:25:37,720 --> 00:25:43,880
Speaker 1: was just one annualized event after another and compounded. How

435
00:25:43,920 --> 00:25:47,720
Speaker 1: did this investment end up running? What did you end

436
00:25:47,840 --> 00:25:52,240
Speaker 1: up putting up his returns? Well, we made about a

437
00:25:52,320 --> 00:25:56,240
Speaker 1: year gross and we had investors, faculty members at you

438
00:25:56,359 --> 00:25:58,719
Speaker 1: see I that sort of thing that rode along with us.

439
00:25:59,119 --> 00:26:02,399
Speaker 1: So after our our fee, which was the profits and

440
00:26:02,920 --> 00:26:06,600
Speaker 1: no more, uh, they needed twenty percent a year. And

441
00:26:06,680 --> 00:26:11,080
Speaker 1: we did this for several years and then um I

442
00:26:11,119 --> 00:26:14,639
Speaker 1: went into business for myself and she went off and

443
00:26:14,800 --> 00:26:18,119
Speaker 1: went into business for himself, and you you launched a

444
00:26:18,119 --> 00:26:19,960
Speaker 1: couple of hedge funds we're going to talk about in

445
00:26:20,000 --> 00:26:22,959
Speaker 1: a little bit. I want to ask you about one

446
00:26:23,000 --> 00:26:25,920
Speaker 1: other quote of yours that I really enjoyed. That's related

447
00:26:25,960 --> 00:26:29,800
Speaker 1: to this. Every stock market system with an edge is

448
00:26:29,920 --> 00:26:33,680
Speaker 1: necessarily limited in the amount of money it can use

449
00:26:33,880 --> 00:26:38,640
Speaker 1: and still produce extra returns. In other words, every edge

450
00:26:38,720 --> 00:26:42,200
Speaker 1: has a scale limit. It can only get so big. Yes,

451
00:26:42,840 --> 00:26:45,480
Speaker 1: and and how did how did you come to that conclusion?

452
00:26:45,520 --> 00:26:49,880
Speaker 1: What made you realize, hey, this can only go so far. Well,

453
00:26:49,960 --> 00:26:51,800
Speaker 1: let me say, first of all, the scale limit could

454
00:26:51,800 --> 00:26:54,800
Speaker 1: be huge. It could be tens of billions, or could

455
00:26:54,800 --> 00:26:58,439
Speaker 1: be ten or twenty or fifty million. I have a

456
00:26:58,480 --> 00:27:01,439
Speaker 1: friend who has a commodity hedge fund that's one of

457
00:27:01,480 --> 00:27:05,680
Speaker 1: the best performing ones around, and it it's limit is

458
00:27:05,760 --> 00:27:09,680
Speaker 1: like seventy million dollars. Because trading costs get too high.

459
00:27:09,880 --> 00:27:12,560
Speaker 1: What happens is if you find a pair of securities

460
00:27:12,560 --> 00:27:15,320
Speaker 1: that out of line, if one's overpriced and one's underpriced.

461
00:27:15,520 --> 00:27:18,200
Speaker 1: If you start selling short the overpriced one, you'll drive

462
00:27:18,240 --> 00:27:21,040
Speaker 1: its priced down so it'll be less overpriced. If you

463
00:27:21,080 --> 00:27:23,679
Speaker 1: start buying the enterpriced one, you'll lift its price so

464
00:27:23,720 --> 00:27:28,760
Speaker 1: it will become less underpriced. So your action in trading

465
00:27:28,960 --> 00:27:32,880
Speaker 1: misprice securities tends to drive them towards the correct price, which,

466
00:27:32,880 --> 00:27:34,960
Speaker 1: by the way, is a service to the other people

467
00:27:34,960 --> 00:27:38,239
Speaker 1: in the market because then they see securities that are

468
00:27:38,240 --> 00:27:40,520
Speaker 1: more fairly priced that they would be otherwise. So not

469
00:27:40,640 --> 00:27:44,400
Speaker 1: quite shouting those cat you you actually are affecting this

470
00:27:44,760 --> 00:27:48,280
Speaker 1: by training it as opposed to a bigger liquid equity

471
00:27:48,320 --> 00:27:50,800
Speaker 1: where a few million dollars and gonna move the price

472
00:27:50,920 --> 00:27:52,240
Speaker 1: very much. And by the way, that's one of the

473
00:27:52,320 --> 00:27:56,000
Speaker 1: huge things between that is a difference between finance and physics.

474
00:27:57,160 --> 00:27:59,080
Speaker 1: In physics, you can do the same experiment over and

475
00:27:59,119 --> 00:28:02,760
Speaker 1: over and you doesn't affect the natural world. But in finance,

476
00:28:02,800 --> 00:28:06,919
Speaker 1: what you do affects the human world. You're you're a

477
00:28:07,200 --> 00:28:10,000
Speaker 1: You're an observer and a participant and affect the outcome

478
00:28:10,040 --> 00:28:14,280
Speaker 1: of everybody else's opportunities. Let's talk about some of the

479
00:28:14,400 --> 00:28:19,040
Speaker 1: interesting things you've done and people you've met over over

480
00:28:19,080 --> 00:28:24,320
Speaker 1: your career. You played bridge with Warren Buffett. Uh tell

481
00:28:24,400 --> 00:28:28,200
Speaker 1: us how that came about. Well, when I was at

482
00:28:28,520 --> 00:28:31,320
Speaker 1: U c Irvine, after I had figured out how to

483
00:28:31,400 --> 00:28:34,760
Speaker 1: value warrants, I started trading for my own account and

484
00:28:34,760 --> 00:28:37,960
Speaker 1: then the word spread and I signed up people at

485
00:28:37,960 --> 00:28:40,800
Speaker 1: the university who wanted me to trade for their accounts.

486
00:28:40,800 --> 00:28:42,720
Speaker 1: And one of the people was the dean of the

487
00:28:42,760 --> 00:28:46,800
Speaker 1: graduate division, A fellow named Ralph Waldo Gerard, National Academy

488
00:28:46,840 --> 00:28:53,160
Speaker 1: of Science member, among other things, and so he wanted

489
00:28:53,160 --> 00:28:55,160
Speaker 1: to get to know me and find out more about

490
00:28:55,200 --> 00:29:00,680
Speaker 1: this trading. So I was introduced to a friend of

491
00:29:00,760 --> 00:29:03,560
Speaker 1: his who was going to I didn't know this, but

492
00:29:03,600 --> 00:29:05,600
Speaker 1: the friend was going to kind of check me out.

493
00:29:05,960 --> 00:29:09,360
Speaker 1: And the friend was the young Warren Buffett. So we

494
00:29:09,400 --> 00:29:14,560
Speaker 1: had a dinner at the Gerard house with our wives

495
00:29:15,120 --> 00:29:18,320
Speaker 1: and then we end up playing bridge, and I went

496
00:29:18,360 --> 00:29:20,880
Speaker 1: down to Emerald Bay where Warren had one house and

497
00:29:20,960 --> 00:29:24,800
Speaker 1: later two houses. That's not too far from here. Lodona

498
00:29:25,000 --> 00:29:29,120
Speaker 1: or Emerald Bay is about four or five miles right

499
00:29:29,120 --> 00:29:33,040
Speaker 1: down the coast. It's at the north end of Laguna Beach.

500
00:29:34,480 --> 00:29:38,600
Speaker 1: Lovely part of the world. And you actually said something

501
00:29:38,640 --> 00:29:41,480
Speaker 1: to your wife, I think that Warren Buffett guy is

502
00:29:41,520 --> 00:29:44,800
Speaker 1: going to be the wealthiest person in America. Exactly How

503
00:29:44,840 --> 00:29:47,680
Speaker 1: did that thought process come about? Well, he was cashing

504
00:29:47,720 --> 00:29:51,120
Speaker 1: out his partnership, which had about a hundred million dollars

505
00:29:51,120 --> 00:29:53,800
Speaker 1: in it then it was. It was the biggest hedge

506
00:29:53,800 --> 00:29:57,880
Speaker 1: fund around and also the only really successful one at

507
00:29:57,920 --> 00:30:02,800
Speaker 1: that point. The runner up is Michael Stronhardt's fund, which

508
00:30:03,320 --> 00:30:06,160
Speaker 1: was up slightly during a pretty bad period in UM

509
00:30:07,520 --> 00:30:10,520
Speaker 1: seventy and Buffet's partnership was still doing well, but he

510
00:30:10,560 --> 00:30:13,160
Speaker 1: said things are so overpriced. I'm cashing out, which is

511
00:30:13,200 --> 00:30:16,680
Speaker 1: why Ralph Gerard was looking at me. He wanted someplace

512
00:30:16,680 --> 00:30:18,520
Speaker 1: to put his money that was coming out of Buffett

513
00:30:18,560 --> 00:30:21,280
Speaker 1: Partners and Buffett was vetting you to see if you

514
00:30:21,320 --> 00:30:24,120
Speaker 1: were okay to take their money, which is not a

515
00:30:24,160 --> 00:30:26,440
Speaker 1: bad person to be vetted by. So we got along

516
00:30:26,480 --> 00:30:28,880
Speaker 1: just fine and talked about a lot of different things,

517
00:30:29,360 --> 00:30:32,440
Speaker 1: and the way we thought about things was similar, that is,

518
00:30:32,520 --> 00:30:36,680
Speaker 1: you know, rational, evidence based and so forth. His method

519
00:30:36,720 --> 00:30:39,320
Speaker 1: of investing, though it was a lot more hands on

520
00:30:39,400 --> 00:30:41,920
Speaker 1: that I wanted to be. I wasn't an invest I

521
00:30:42,040 --> 00:30:46,360
Speaker 1: wasn't interested in spending my total percent energy and life

522
00:30:46,520 --> 00:30:48,640
Speaker 1: just investing. I just thought it was an interesting thing

523
00:30:49,080 --> 00:30:51,000
Speaker 1: to be involved in because I had some money to

524
00:30:51,040 --> 00:30:54,960
Speaker 1: invest at the time. So the nine letter from Buffett

525
00:30:55,000 --> 00:30:59,240
Speaker 1: to his partner's explains he's winding down and eventually he

526
00:30:59,280 --> 00:31:04,640
Speaker 1: takes Berkshire Hathaway and relaunches it. Um not too not

527
00:31:04,760 --> 00:31:07,040
Speaker 1: too far off from the distance. You were one of

528
00:31:07,080 --> 00:31:09,760
Speaker 1: the first investors in in Berkshire Hathway Is that a

529
00:31:09,800 --> 00:31:13,160
Speaker 1: fair statement, No, not so early. What happened was Buffett

530
00:31:13,160 --> 00:31:16,480
Speaker 1: didn't tell his limited partners that what he was going

531
00:31:16,480 --> 00:31:18,280
Speaker 1: to do next. What he was going to do next

532
00:31:18,360 --> 00:31:21,240
Speaker 1: was turned Berkshire Hathaway into his own private mutual fund.

533
00:31:22,080 --> 00:31:24,880
Speaker 1: So he had stock to distribute, and he could either

534
00:31:24,920 --> 00:31:29,000
Speaker 1: distribute stock to the investors or give them money, and

535
00:31:29,080 --> 00:31:32,120
Speaker 1: he preferred, I think, to keep the stock and give

536
00:31:32,160 --> 00:31:35,760
Speaker 1: them money. So mostly the investors were cashing out. Some

537
00:31:35,840 --> 00:31:38,400
Speaker 1: of them took some Burkshire stock and not not a

538
00:31:38,400 --> 00:31:41,920
Speaker 1: whole lot happened with Berkshire for the next decade, and

539
00:31:43,120 --> 00:31:45,120
Speaker 1: I was unaware that he was turning it into his

540
00:31:45,160 --> 00:31:50,080
Speaker 1: own mutual fund. But I was sitting around doing something

541
00:31:50,160 --> 00:31:51,760
Speaker 1: or other with the hedge fund. I was then and

542
00:31:51,840 --> 00:31:55,320
Speaker 1: running Princeton Newport Partners, and heard some news item about

543
00:31:55,800 --> 00:31:59,239
Speaker 1: Berkshire Hathaway and Warren Buffett, so I focused on it,

544
00:31:59,720 --> 00:32:03,200
Speaker 1: and I realized right away what had happened in the

545
00:32:03,240 --> 00:32:08,040
Speaker 1: intervening fourteen or fifteen years, that Buffett had turned this

546
00:32:08,680 --> 00:32:13,920
Speaker 1: uh sick textile company into his own private investment tool.

547
00:32:15,400 --> 00:32:20,280
Speaker 1: So had I had I known that? I had I

548
00:32:20,280 --> 00:32:26,120
Speaker 1: been able to invest in Berkshire Hathaway in to I

549
00:32:26,200 --> 00:32:29,040
Speaker 1: might have invested somewhere in the twelve or fifty dollar range.

550
00:32:30,200 --> 00:32:33,400
Speaker 1: But but now, but now, the stock with two dollars

551
00:32:34,560 --> 00:32:37,800
Speaker 1: so most people was a g It's already had a

552
00:32:38,320 --> 00:32:41,320
Speaker 1: huge multiple. You don't want to buy it now? I said, yes,

553
00:32:41,360 --> 00:32:44,040
Speaker 1: I want to buy it now. So I began buying

554
00:32:44,080 --> 00:32:46,640
Speaker 1: in and And how long did you hold on to

555
00:32:46,840 --> 00:32:50,239
Speaker 1: Berkshire hath You still have it to this day. So

556
00:32:50,360 --> 00:32:54,120
Speaker 1: I'm compelled to ask, a hundred dollars invested in Berkshire

557
00:32:54,160 --> 00:32:58,280
Speaker 1: hath Away when you put that in in eighty two,

558
00:32:58,960 --> 00:33:01,120
Speaker 1: what would a hundred dollar be worth today? Well, it's

559
00:33:01,120 --> 00:33:02,760
Speaker 1: easier for me to think in hers with a thousand,

560
00:33:02,760 --> 00:33:06,360
Speaker 1: because that was stocker slutly less. So it's worth roughly two.

561
00:33:07,720 --> 00:33:10,120
Speaker 1: That's a pretty good return. Well, how does what does

562
00:33:10,160 --> 00:33:13,200
Speaker 1: that average out compounded that mid twenties? Something like that

563
00:33:14,800 --> 00:33:22,800
Speaker 1: two D and fifty four thirty five years, two D

564
00:33:22,920 --> 00:33:27,360
Speaker 1: and fifty thirty five years. I'm guessing mid twenties, but

565
00:33:27,400 --> 00:33:29,600
Speaker 1: I'm sure I could just punch it into the computer

566
00:33:29,720 --> 00:33:33,600
Speaker 1: and get the actual answer. But by any stretch of

567
00:33:33,640 --> 00:33:37,560
Speaker 1: the imagination, it's a fabulous return. And the consistency over

568
00:33:37,600 --> 00:33:40,640
Speaker 1: such a long time. There is nothing comparable to that,

569
00:33:40,760 --> 00:33:44,600
Speaker 1: is there that? That's amazing? How did you never get

570
00:33:44,640 --> 00:33:47,120
Speaker 1: tempted to sell? Let's see if I can figure it out. Okay,

571
00:33:47,520 --> 00:33:51,120
Speaker 1: I want to take the route of two and fifty

572
00:33:52,120 --> 00:33:58,280
Speaker 1: so um, so it's about to the five point six

573
00:33:58,400 --> 00:34:04,240
Speaker 1: over thirty five. So I would say, I'm just this

574
00:34:04,320 --> 00:34:07,800
Speaker 1: is rough. I could be off a few maybe analyzed,

575
00:34:08,080 --> 00:34:11,960
Speaker 1: Oh that's no big deal. He's you know, he should

576
00:34:11,960 --> 00:34:15,359
Speaker 1: have given up for a very long time. That's astonishing.

577
00:34:15,400 --> 00:34:17,240
Speaker 1: You know. One of the things, and we just talked

578
00:34:17,239 --> 00:34:21,719
Speaker 1: about it earlier, is eventually everything reaches a capacity. You

579
00:34:21,760 --> 00:34:25,720
Speaker 1: can't just find an edge and scale it forever unless

580
00:34:25,760 --> 00:34:29,839
Speaker 1: you're Warren Buffett, whose edges a tremendous amount of um

581
00:34:30,000 --> 00:34:35,759
Speaker 1: not only intellectual capacity, but discipline and the ability to

582
00:34:35,800 --> 00:34:38,719
Speaker 1: say I don't care about this, I don't care about that,

583
00:34:38,760 --> 00:34:41,120
Speaker 1: I don't care about technology. This is what I do well,

584
00:34:41,760 --> 00:34:44,040
Speaker 1: and I'm going to stick to my knitting. A lot

585
00:34:44,080 --> 00:34:47,480
Speaker 1: of smart people don't have that ability. Well. Warren Buffett

586
00:34:47,960 --> 00:34:52,080
Speaker 1: um agrees with the point we made earlier that as

587
00:34:52,120 --> 00:34:55,200
Speaker 1: you get bigger and bigger, you tend to have capacity problems,

588
00:34:55,200 --> 00:34:56,920
Speaker 1: and so you can see that in the performance of

589
00:34:56,960 --> 00:34:59,560
Speaker 1: Burshire Hathway. There's a table in my book which shows

590
00:34:59,560 --> 00:35:03,680
Speaker 1: how the decade by decade how the return has dropped

591
00:35:04,000 --> 00:35:09,720
Speaker 1: compared with the SMP. So it's converged too fairly close

592
00:35:09,760 --> 00:35:12,320
Speaker 1: to the SMP five hundred in this last time period.

593
00:35:12,440 --> 00:35:14,839
Speaker 1: So at this point you're gonna say he's as big

594
00:35:14,880 --> 00:35:19,319
Speaker 1: as he can get and continue to outperform. Yes, but

595
00:35:19,560 --> 00:35:22,840
Speaker 1: Burrshire still has some advantages when you can. Let's suppose

596
00:35:22,920 --> 00:35:25,320
Speaker 1: that does exactly the same as an index fun going forward,

597
00:35:25,680 --> 00:35:28,840
Speaker 1: which may not be a bad rough estimate. It doesn't

598
00:35:28,840 --> 00:35:31,800
Speaker 1: pay dividends, so you don't pay any taxes until you

599
00:35:31,840 --> 00:35:34,239
Speaker 1: sell your stock. When you sell your stock, if you

600
00:35:34,280 --> 00:35:36,480
Speaker 1: hold it more than a year, you'll be paying long

601
00:35:36,600 --> 00:35:39,520
Speaker 1: term capital gain, which is less than you'd pay if

602
00:35:39,520 --> 00:35:44,879
Speaker 1: you were a trader and generating short term gains or um.

603
00:35:45,000 --> 00:35:51,480
Speaker 1: And as far as the dividends go, the company that well,

604
00:35:52,160 --> 00:35:53,960
Speaker 1: I'm not sure where the tax taxation is going to

605
00:35:54,000 --> 00:35:57,239
Speaker 1: be on dividends now or by the time this broadcasts.

606
00:35:57,360 --> 00:36:00,200
Speaker 1: Who even knows what I want? But what what is

607
00:36:00,280 --> 00:36:05,520
Speaker 1: long term capital gains fifteen versus or or higher? I

608
00:36:05,520 --> 00:36:09,480
Speaker 1: don't know. I've I've lost track. Yeah, but it's considerably

609
00:36:09,520 --> 00:36:12,279
Speaker 1: lower long term than than short term. I don't plan

610
00:36:12,320 --> 00:36:13,960
Speaker 1: to pay me for a while, so I'm not paying attention.

611
00:36:15,400 --> 00:36:17,560
Speaker 1: Were you ever tempted to sell? Did you ever look

612
00:36:17,600 --> 00:36:19,480
Speaker 1: at this and saying or do you just not pay

613
00:36:19,520 --> 00:36:22,520
Speaker 1: attention to it? I don't pay a whole lot of

614
00:36:22,560 --> 00:36:25,359
Speaker 1: attention to me. It's a buy and whole situation. And

615
00:36:25,400 --> 00:36:26,880
Speaker 1: if I do sell, I'm gonna have to pay all

616
00:36:26,920 --> 00:36:29,080
Speaker 1: these taxes because I have a low basis, a very

617
00:36:29,080 --> 00:36:32,359
Speaker 1: low basis, so I get I get punished for selling. Well,

618
00:36:32,400 --> 00:36:34,200
Speaker 1: that's reason to hold on to it. I'm sure. I'm

619
00:36:34,239 --> 00:36:40,320
Speaker 1: sure Warrant appreciates that sort of loyalty amongst its stockholders. UM.

620
00:36:40,480 --> 00:36:47,239
Speaker 1: On a related note, you were at at Citadel Ken Griffins.

621
00:36:47,280 --> 00:36:51,080
Speaker 1: You were his first limited partner. What what did you

622
00:36:51,120 --> 00:36:54,240
Speaker 1: see in Ken Griffin that made you say this guy

623
00:36:54,440 --> 00:36:59,760
Speaker 1: is onto something interesting. He's he's clearly of a mathematical bends.

624
00:37:00,560 --> 00:37:02,319
Speaker 1: I'm willing to give him money and see what he

625
00:37:02,360 --> 00:37:04,479
Speaker 1: can do with it. Well, the guy who discovered Ken

626
00:37:04,480 --> 00:37:08,120
Speaker 1: Griffin is a fellow named Frank Meyer, who was a

627
00:37:08,160 --> 00:37:10,680
Speaker 1: friend of mine and who I got acquainted with in

628
00:37:10,719 --> 00:37:13,120
Speaker 1: the early days of Prince and Newport Partners because he

629
00:37:13,200 --> 00:37:16,439
Speaker 1: was he was representing some investors who wanted to get

630
00:37:16,440 --> 00:37:20,200
Speaker 1: in to Prince and Newport Partners. So anyhow, when Prince

631
00:37:20,200 --> 00:37:27,799
Speaker 1: and new Park Partners closed down in nine, Frank had

632
00:37:27,840 --> 00:37:32,640
Speaker 1: just discovered this um Harvard student who was trading out

633
00:37:32,640 --> 00:37:35,920
Speaker 1: of his dorm. And what Ken Griffin was doing was

634
00:37:35,920 --> 00:37:40,399
Speaker 1: trading derivatives, convertibles, lawrants, options and so on, pretty much

635
00:37:40,400 --> 00:37:43,040
Speaker 1: in the style that I've been trading them. And so

636
00:37:43,239 --> 00:37:45,880
Speaker 1: Frank decided to set him up in business. So he

637
00:37:46,040 --> 00:37:51,000
Speaker 1: was his mentor guru, um business organizer. Ken was a

638
00:37:51,000 --> 00:37:55,680
Speaker 1: young guy then, so smart but still new to all this.

639
00:37:56,600 --> 00:37:59,640
Speaker 1: And since I was shutting down Prince and Newport, it

640
00:37:59,719 --> 00:38:02,880
Speaker 1: was natural for me to talk with Frank and Ken

641
00:38:02,960 --> 00:38:04,759
Speaker 1: and explain to them what I did and how I

642
00:38:04,800 --> 00:38:08,520
Speaker 1: did it, and basically give them my business road map.

643
00:38:08,920 --> 00:38:11,600
Speaker 1: And they initially followed that, and of course it evolved

644
00:38:11,640 --> 00:38:16,800
Speaker 1: into its own great thing, and the Citadel is phenomenal success.

645
00:38:17,040 --> 00:38:19,839
Speaker 1: Are you still an investor in sid Adel? So you

646
00:38:19,880 --> 00:38:22,040
Speaker 1: are really a buy and hold investor. You look at

647
00:38:22,040 --> 00:38:25,720
Speaker 1: things over the long haul. That's fascinating. Let's talk about

648
00:38:25,880 --> 00:38:28,480
Speaker 1: start with a quote of yours. Any edge in the

649
00:38:28,520 --> 00:38:34,239
Speaker 1: market is limited, small, temporary, and quickly captured by the

650
00:38:34,360 --> 00:38:39,640
Speaker 1: smartest or best informed investors. Still true today, I'd modify

651
00:38:39,719 --> 00:38:46,000
Speaker 1: that a little bit. I think that that's true of

652
00:38:46,080 --> 00:38:51,560
Speaker 1: most of the edges. Some of the small edges have

653
00:38:51,760 --> 00:38:56,560
Speaker 1: fairly large scope to them. I'll give you one simple example.

654
00:38:56,960 --> 00:39:00,719
Speaker 1: There's something called a tax loss harvesting, and that's a

655
00:39:00,760 --> 00:39:04,920
Speaker 1: politically created edge. You could, for instance, set up a

656
00:39:05,000 --> 00:39:06,880
Speaker 1: thing like an index fund, a thing that tracks an

657
00:39:06,920 --> 00:39:10,520
Speaker 1: index fund, and at the end of the year, sell

658
00:39:10,560 --> 00:39:14,400
Speaker 1: your losers and take the tax loss and use the

659
00:39:14,400 --> 00:39:18,960
Speaker 1: money to buy new stocks. That would keep your collection

660
00:39:18,960 --> 00:39:21,520
Speaker 1: of stocks tracking the index fairly well, and just keep

661
00:39:21,520 --> 00:39:24,439
Speaker 1: doing that. You could sell one SMP index and buy

662
00:39:24,480 --> 00:39:26,879
Speaker 1: a different SMP next. Well, you know that's you can't

663
00:39:26,880 --> 00:39:29,400
Speaker 1: do that, because be a wash has to be something

664
00:39:29,440 --> 00:39:34,719
Speaker 1: slightly different. Different. You could take let's say, of the

665
00:39:34,719 --> 00:39:37,799
Speaker 1: stocks in the SMP index and buy them on day one.

666
00:39:38,080 --> 00:39:41,480
Speaker 1: You can let it run for a year minus a day,

667
00:39:41,680 --> 00:39:44,280
Speaker 1: look at the losers, sell them, take the tax loss,

668
00:39:45,120 --> 00:39:48,840
Speaker 1: use the money to replace those losers with some of

669
00:39:48,880 --> 00:39:52,960
Speaker 1: the stocks you didn't already hold. So you have four

670
00:39:53,200 --> 00:39:54,920
Speaker 1: d stocks you haven't bought, you have a hundred that

671
00:39:54,960 --> 00:39:57,480
Speaker 1: you did have, so you move into some of the

672
00:39:57,480 --> 00:39:59,960
Speaker 1: four and then you let it run for another year,

673
00:40:00,200 --> 00:40:02,080
Speaker 1: and you do it again and again, so you keep

674
00:40:02,480 --> 00:40:07,359
Speaker 1: taking losses and collecting money from the government. And that's

675
00:40:07,360 --> 00:40:10,960
Speaker 1: an edge that's persists. It's a politically created edge. Got

676
00:40:10,960 --> 00:40:12,880
Speaker 1: it because of the tax code, Yes, because of the

677
00:40:12,920 --> 00:40:17,840
Speaker 1: tax code. So nineteen sixty nine was a famously tough

678
00:40:17,960 --> 00:40:21,160
Speaker 1: year for hedge funds. Carol Loomis wrote a big article

679
00:40:21,200 --> 00:40:23,879
Speaker 1: about it in in Fortune and and she ultimately ended

680
00:40:23,960 --> 00:40:29,000
Speaker 1: up being um a really interesting and important journalists. But

681
00:40:30,520 --> 00:40:34,759
Speaker 1: in in nineteen sixty nine, your hedge fund was just

682
00:40:34,920 --> 00:40:37,920
Speaker 1: launching or about to launch. We launched in November of

683
00:40:38,040 --> 00:40:41,640
Speaker 1: nineteen sixty nine, the year that's so terrible. Yes, and

684
00:40:41,680 --> 00:40:45,000
Speaker 1: there was there were There were articles in Fortune and

685
00:40:45,160 --> 00:40:49,920
Speaker 1: maybe Forbes, I don't remember which now, which listed twenty

686
00:40:49,960 --> 00:40:52,839
Speaker 1: some odd of the largest hedge funds and they're all

687
00:40:52,880 --> 00:40:56,680
Speaker 1: losing going out of business, except for Buffet Partners Limited,

688
00:40:56,680 --> 00:40:59,120
Speaker 1: which is going out of business for lack of opportunity.

689
00:40:59,760 --> 00:41:03,719
Speaker 1: And uh, I think that Steinhardt hung in there. He

690
00:41:03,840 --> 00:41:07,920
Speaker 1: was up maybe five percent, but it was a disaster.

691
00:41:08,640 --> 00:41:12,000
Speaker 1: So we we started in the middle of a blood bath,

692
00:41:12,480 --> 00:41:14,759
Speaker 1: and how did you do? How was that first year

693
00:41:14,920 --> 00:41:17,120
Speaker 1: in business as a hedge fund, Well, we were wondering

694
00:41:17,160 --> 00:41:19,000
Speaker 1: what was going to happen. So the first two months

695
00:41:19,400 --> 00:41:22,200
Speaker 1: the market was down four or five and we were

696
00:41:22,280 --> 00:41:24,319
Speaker 1: up four percent, So that was a That was a

697
00:41:24,320 --> 00:41:26,680
Speaker 1: good start. And I made as much money then as

698
00:41:26,680 --> 00:41:28,680
Speaker 1: I was making at the university just in the first

699
00:41:28,680 --> 00:41:31,919
Speaker 1: two months, so that's a good sign. Then the next

700
00:41:32,000 --> 00:41:34,680
Speaker 1: year we were up again and the market was down,

701
00:41:35,239 --> 00:41:37,040
Speaker 1: and so we opened up a big gap on the

702
00:41:37,080 --> 00:41:42,120
Speaker 1: market fairly early, and that continued. Seventy three the SMP falls,

703
00:41:43,040 --> 00:41:48,800
Speaker 1: you're up seven. In the SMP five hundred falls, seven,

704
00:41:49,800 --> 00:41:53,040
Speaker 1: you're up nine percent. At what point do people start

705
00:41:53,360 --> 00:41:55,839
Speaker 1: knocking on your door saying, Hey, I have some money

706
00:41:55,840 --> 00:41:58,000
Speaker 1: for you to manage. For us, we had a steady

707
00:41:58,000 --> 00:42:00,680
Speaker 1: waiting list and we started out with I think a

708
00:42:00,719 --> 00:42:05,400
Speaker 1: fifty dollar minimum, and it rapidly rose. Um eventually was

709
00:42:05,400 --> 00:42:07,680
Speaker 1: ten million, and then finally we just couldn't take any

710
00:42:07,680 --> 00:42:11,720
Speaker 1: more investors. So it grew over the years from initial

711
00:42:11,760 --> 00:42:15,360
Speaker 1: one point four million to about two hundred and seventy

712
00:42:15,400 --> 00:42:19,040
Speaker 1: million under management. And you understood all the math behind this.

713
00:42:19,200 --> 00:42:22,440
Speaker 1: You understood the probabilistic edge you had, but were you

714
00:42:22,480 --> 00:42:26,080
Speaker 1: ever surprised by, Hey, this is really working out better

715
00:42:26,120 --> 00:42:30,960
Speaker 1: than we imagined. Uh, that's an interesting question. When I

716
00:42:31,000 --> 00:42:34,239
Speaker 1: sat down with my then principal partner fellow named J.

717
00:42:34,360 --> 00:42:38,839
Speaker 1: Reagan back in sixty nine, I estimated what was going

718
00:42:38,920 --> 00:42:42,359
Speaker 1: to happen, and I said, I think that by five,

719
00:42:42,800 --> 00:42:44,360
Speaker 1: with the way we're going to grow and the fees

720
00:42:44,400 --> 00:42:46,880
Speaker 1: we're going to have, will each be worth a million dollars.

721
00:42:47,480 --> 00:42:50,080
Speaker 1: And you actually hit that number right on time. That's

722
00:42:50,120 --> 00:42:53,360
Speaker 1: pretty much. Yeah. So in five I sent him a

723
00:42:53,400 --> 00:42:56,319
Speaker 1: copy of what I've written what I already sent him

724
00:42:56,400 --> 00:42:59,480
Speaker 1: nine sixty nine, just as a little reminder and at

725
00:42:59,680 --> 00:43:02,840
Speaker 1: at at what point did it sort of did or

726
00:43:03,000 --> 00:43:05,880
Speaker 1: I should ask the question, did it ever scale to

727
00:43:05,880 --> 00:43:08,960
Speaker 1: a point where you said, Gee, this has really gone

728
00:43:08,960 --> 00:43:13,920
Speaker 1: far beyond what I was expecting. I don't think so. Um.

729
00:43:14,120 --> 00:43:16,200
Speaker 1: One of the things I learned at the casino table

730
00:43:16,360 --> 00:43:19,839
Speaker 1: was that scaling is a very interesting psychological thing. When

731
00:43:19,840 --> 00:43:23,319
Speaker 1: I first started playing blackjack, on my first big trip,

732
00:43:23,840 --> 00:43:27,680
Speaker 1: I went with a couple of multimillionaires who wanted to

733
00:43:27,719 --> 00:43:29,920
Speaker 1: bank or on me for a hundred thousand dollars I said, no,

734
00:43:30,000 --> 00:43:31,920
Speaker 1: I don't know how to handle money. I'm gonna be

735
00:43:32,440 --> 00:43:34,839
Speaker 1: and if anything goes wrong, you're not gonna like this.

736
00:43:35,080 --> 00:43:38,399
Speaker 1: You're not gonna like it in spades. So let's only

737
00:43:38,440 --> 00:43:41,480
Speaker 1: go with ten thousand. And the first eight hours I

738
00:43:41,480 --> 00:43:45,280
Speaker 1: sat down there betting one dollar when it wasn't favorable

739
00:43:45,520 --> 00:43:47,480
Speaker 1: and ten dollars when it was favorable, and they just

740
00:43:47,560 --> 00:43:51,000
Speaker 1: about went out of their skulls with this penny andy betting.

741
00:43:51,520 --> 00:43:53,560
Speaker 1: But then I got used to it after eight hours,

742
00:43:53,560 --> 00:43:56,880
Speaker 1: and then I went to two dollars small bets, twenty

743
00:43:56,920 --> 00:43:59,760
Speaker 1: dollars big bets, and that lasted a couple of hours,

744
00:44:00,200 --> 00:44:01,440
Speaker 1: and then I got used to that, and then I

745
00:44:01,480 --> 00:44:10,399
Speaker 1: went to five to fifty and the top bet they

746
00:44:10,440 --> 00:44:12,600
Speaker 1: would allow at that point. So what I found was

747
00:44:12,640 --> 00:44:15,480
Speaker 1: you could scale up, and when you get used to scaling,

748
00:44:15,760 --> 00:44:18,040
Speaker 1: you do the same things, same kind of thinking that

749
00:44:18,080 --> 00:44:21,000
Speaker 1: you did at the small scale on bigger and bigger scales,

750
00:44:21,120 --> 00:44:24,160
Speaker 1: So that that stayed with me all the way up.

751
00:44:24,520 --> 00:44:27,640
Speaker 1: It's just the process getting used to the larger numbers.

752
00:44:27,680 --> 00:44:31,920
Speaker 1: But that's a physical and a psychological adjustment where when

753
00:44:31,920 --> 00:44:34,280
Speaker 1: you start out at one and ten five dollars sounds

754
00:44:34,320 --> 00:44:36,719
Speaker 1: like a lot of money, but you have to stare,

755
00:44:36,800 --> 00:44:39,440
Speaker 1: step your way up until you're comfortable. Was it was

756
00:44:39,480 --> 00:44:42,719
Speaker 1: it the same with the hedge fund, starting out relatively

757
00:44:42,719 --> 00:44:45,040
Speaker 1: small and at a certain point and the biggest you've

758
00:44:45,040 --> 00:44:47,480
Speaker 1: got in terms of assets under management was we had

759
00:44:47,480 --> 00:44:49,640
Speaker 1: about a billion long and about a billion short, but

760
00:44:49,719 --> 00:44:54,400
Speaker 1: we had about two d seventy million behind that. I

761
00:44:54,440 --> 00:44:57,080
Speaker 1: will I will say this to Bill Gross who used

762
00:44:57,080 --> 00:44:59,040
Speaker 1: to be here in next door pimp go literally, I'm

763
00:44:59,080 --> 00:45:01,399
Speaker 1: looking out the window at the Pimco tower right next

764
00:45:01,440 --> 00:45:05,120
Speaker 1: to yours. So he got his start playing blackjack too,

765
00:45:05,120 --> 00:45:07,799
Speaker 1: as you got in the book. And so he took

766
00:45:07,840 --> 00:45:10,359
Speaker 1: two hundred dollars like the book beat the dealer said

767
00:45:10,360 --> 00:45:13,479
Speaker 1: he would, went out, slaved away for four months, turned

768
00:45:13,520 --> 00:45:16,239
Speaker 1: into ten thousand like it said he would. And then

769
00:45:16,400 --> 00:45:18,920
Speaker 1: when he went to Pimco, he was really used to

770
00:45:18,960 --> 00:45:22,440
Speaker 1: scaling up. And there came a point when PIMCO had

771
00:45:22,640 --> 00:45:25,359
Speaker 1: just under two trillion under management. So that's a lot

772
00:45:25,360 --> 00:45:29,760
Speaker 1: of scaling up from from literally a very small division

773
00:45:29,800 --> 00:45:32,239
Speaker 1: that was wanting was a Pacific life for they were

774
00:45:32,320 --> 00:45:36,440
Speaker 1: running the money of a local insurance company and decided

775
00:45:36,480 --> 00:45:39,759
Speaker 1: to set it out as a as a separate entity. Well,

776
00:45:39,760 --> 00:45:42,840
Speaker 1: the way all this happened was after reading Beat the

777
00:45:42,880 --> 00:45:47,960
Speaker 1: Dealer bill read Beat the Market, and so he decided

778
00:45:47,960 --> 00:45:50,480
Speaker 1: to write his thesis in the convertible bonds at U

779
00:45:50,480 --> 00:45:53,120
Speaker 1: C l A. And then it was one he was

780
00:45:53,160 --> 00:45:56,160
Speaker 1: looking for a job and his mom said, oh, they're

781
00:45:56,200 --> 00:45:59,120
Speaker 1: looking for people over at a Pacific Mutual Life. So

782
00:45:59,160 --> 00:46:00,640
Speaker 1: he went over and they at oh, we don't need

783
00:46:00,640 --> 00:46:02,960
Speaker 1: any bond guys here. He said, well, I wrote my

784
00:46:03,000 --> 00:46:05,319
Speaker 1: thesis in convertible bonds. I said, oh, we don't have

785
00:46:05,360 --> 00:46:08,520
Speaker 1: one of those. So that's how he got high. And

786
00:46:08,560 --> 00:46:11,560
Speaker 1: literally literally Pacific Life is not that far from here either.

787
00:46:11,600 --> 00:46:14,960
Speaker 1: It's right up the right up the road. That thing

788
00:46:15,000 --> 00:46:17,799
Speaker 1: I call the mushroom building. Oh that's it. That's that's

789
00:46:17,800 --> 00:46:21,279
Speaker 1: so funny. So a couple of really fascinating things that

790
00:46:21,320 --> 00:46:25,560
Speaker 1: took place with with you and and the hedge funds. First,

791
00:46:26,239 --> 00:46:29,759
Speaker 1: not only did you never have a losing year, you

792
00:46:29,920 --> 00:46:34,360
Speaker 1: never had a losing quarter. That's a remarkable run. How

793
00:46:34,520 --> 00:46:38,880
Speaker 1: is that consistency possible? Well, I was a child of

794
00:46:38,920 --> 00:46:43,440
Speaker 1: the Depression, so I'm highly risk averse, Okay, So I

795
00:46:43,520 --> 00:46:46,960
Speaker 1: decided that I wasn't going to lose money, and that's

796
00:46:46,960 --> 00:46:50,560
Speaker 1: what I liked about hedging, and and by its nature

797
00:46:50,880 --> 00:46:53,360
Speaker 1: you try and put positions on that even if it

798
00:46:53,400 --> 00:46:57,360
Speaker 1: goes against you. The downside is is limited due to

799
00:46:57,440 --> 00:46:59,680
Speaker 1: the hedge. Yes, and I put a lot of positions

800
00:46:59,719 --> 00:47:03,680
Speaker 1: on uh, so I was diversified over hedges. I kept

801
00:47:03,680 --> 00:47:07,120
Speaker 1: track of how the hedges did for one period in

802
00:47:07,200 --> 00:47:11,000
Speaker 1: the earlier mid seventies. I kept track of two of them,

803
00:47:11,080 --> 00:47:15,160
Speaker 1: and about a hundred eighty of them were winners, about

804
00:47:15,640 --> 00:47:18,839
Speaker 1: ten of them were pretty close to pushes, and then

805
00:47:18,840 --> 00:47:22,760
Speaker 1: about ten or more losers, and the winners were bigger

806
00:47:22,800 --> 00:47:27,360
Speaker 1: than the losers on average. So that that's how stable

807
00:47:27,600 --> 00:47:30,960
Speaker 1: and solid these hedges were. So with a diversified portfolio

808
00:47:31,000 --> 00:47:34,239
Speaker 1: of hedges, it was almost inevitable would come out ahead

809
00:47:34,719 --> 00:47:37,359
Speaker 1: every month. We only had three down months and those

810
00:47:37,400 --> 00:47:40,560
Speaker 1: are less than one percent, so we could call those

811
00:47:40,600 --> 00:47:45,200
Speaker 1: a push. Let's let's let's talk about black shoals. So

812
00:47:46,160 --> 00:47:50,360
Speaker 1: both with the warrants and with the training options against

813
00:47:50,400 --> 00:47:54,560
Speaker 1: the common stock, you created your own pricing model to

814
00:47:54,600 --> 00:47:58,760
Speaker 1: figure out when the either the warren or the option

815
00:47:58,840 --> 00:48:03,360
Speaker 1: was overpriced and priced relative to the underlying security, which

816
00:48:03,560 --> 00:48:09,879
Speaker 1: essentially was the black shoals option pricing. You invented black

817
00:48:09,960 --> 00:48:14,680
Speaker 1: shoals option pricing before black shoals. How does that come about?

818
00:48:14,880 --> 00:48:18,320
Speaker 1: The way that happened was, Uh, there was a great book,

819
00:48:19,480 --> 00:48:22,680
Speaker 1: one of the first quantitative finance books, called The Random

820
00:48:22,760 --> 00:48:26,799
Speaker 1: Character of Stock Market Prices came out kind of came

821
00:48:26,800 --> 00:48:33,160
Speaker 1: out of m I T. Paul Kotner, revised, updated, and

822
00:48:33,239 --> 00:48:36,560
Speaker 1: I happened to read that book. And they had some

823
00:48:37,080 --> 00:48:40,280
Speaker 1: warrant models in the book, and the warrant models had

824
00:48:40,880 --> 00:48:44,920
Speaker 1: two parameters that nobody knew what to do with. Nobel

825
00:48:45,000 --> 00:48:48,400
Speaker 1: Prizewinner Future Nobel Prizewinner Paul Samson didn't know what to

826
00:48:48,440 --> 00:48:52,040
Speaker 1: do with these parameters, nor did anybody else. And the

827
00:48:52,080 --> 00:48:55,400
Speaker 1: two parameters were the unknown rate of growth of the

828
00:48:55,440 --> 00:49:00,160
Speaker 1: stock and the proper discount rate for the uncertain hay

829
00:49:00,160 --> 00:49:06,759
Speaker 1: off from the warrant or option. And so I understood

830
00:49:06,800 --> 00:49:08,920
Speaker 1: that model. I had actually worked it out myself, and

831
00:49:08,960 --> 00:49:10,239
Speaker 1: then I saw that it was already in the book.

832
00:49:10,280 --> 00:49:13,759
Speaker 1: So I did something everybody already knew. And I thought

833
00:49:13,760 --> 00:49:16,160
Speaker 1: about for a while, and then I said, you know,

834
00:49:17,000 --> 00:49:23,319
Speaker 1: I'm doing hedges that are essentially risk neutral. And so

835
00:49:25,080 --> 00:49:29,719
Speaker 1: is there a world in which those parameters could be chosen? Yes,

836
00:49:29,880 --> 00:49:32,720
Speaker 1: if the world's risk neutral, then I choose the risks

837
00:49:32,800 --> 00:49:35,560
Speaker 1: rate for those parameters. So I stuck the risks right

838
00:49:35,600 --> 00:49:39,080
Speaker 1: THEW and lo and behold, magic, wonderful formula. Tried the

839
00:49:39,120 --> 00:49:42,359
Speaker 1: formula out all kinds of different ways logically to see

840
00:49:42,400 --> 00:49:43,960
Speaker 1: if it did what it was supposed to, and it

841
00:49:44,000 --> 00:49:47,440
Speaker 1: did so. Now I had a formula for value and options,

842
00:49:47,480 --> 00:49:52,160
Speaker 1: which was the future Black Sholes formula. This Black, by

843
00:49:52,160 --> 00:49:54,959
Speaker 1: the way, told me later on when we had conversations,

844
00:49:55,000 --> 00:49:57,200
Speaker 1: that they had figured this thing out in sixty nine,

845
00:49:57,719 --> 00:50:02,200
Speaker 1: but they were rejected for publication a number of times,

846
00:50:02,200 --> 00:50:03,960
Speaker 1: and only when they got some help, I think from

847
00:50:03,960 --> 00:50:06,560
Speaker 1: Samuels then that they were able to get their article

848
00:50:06,719 --> 00:50:08,480
Speaker 1: in print. They got the first one in print in

849
00:50:08,520 --> 00:50:11,640
Speaker 1: nineteen seventy two. Now you've been using this model for

850
00:50:11,680 --> 00:50:14,800
Speaker 1: four or five years at that point, and what what

851
00:50:15,080 --> 00:50:18,560
Speaker 1: was your Well, I was 'mentilally unaware of the world

852
00:50:18,560 --> 00:50:20,400
Speaker 1: of academic finance because I was just doing all this

853
00:50:20,480 --> 00:50:23,160
Speaker 1: stuff on my own myself, like I was used to

854
00:50:23,239 --> 00:50:25,920
Speaker 1: doing when I was a kid, learning science by myself.

855
00:50:26,840 --> 00:50:31,600
Speaker 1: And then the cbo EA Chicago Board Options Exchange decided

856
00:50:32,040 --> 00:50:35,680
Speaker 1: to create list of options, which revolutionized the whole h

857
00:50:35,960 --> 00:50:38,400
Speaker 1: over the counter options in the warrant market. So in

858
00:50:38,440 --> 00:50:40,520
Speaker 1: other words, ZSER trading on an exchange, you don't have

859
00:50:40,600 --> 00:50:43,920
Speaker 1: to go through a specific so that made the cost

860
00:50:44,040 --> 00:50:49,600
Speaker 1: much more more reasonable, and it also allowed you to

861
00:50:50,040 --> 00:50:53,880
Speaker 1: trade a much broader set of vehicles. That that was

862
00:50:53,920 --> 00:50:57,520
Speaker 1: the plan there was supposed to happen in I think

863
00:50:57,560 --> 00:51:02,319
Speaker 1: April of Vree. So I had been operating my hedge

864
00:51:02,320 --> 00:51:06,960
Speaker 1: fund for already almost four years, and I had been

865
00:51:07,000 --> 00:51:09,879
Speaker 1: training options and weren't using the Black Sholes formula among

866
00:51:09,880 --> 00:51:13,080
Speaker 1: other things, and also value well, you were really training

867
00:51:13,160 --> 00:51:16,480
Speaker 1: using the ed Thorpe formula because black Sholes didn't exist,

868
00:51:16,960 --> 00:51:19,640
Speaker 1: and I didn't as far as I knew, that were

869
00:51:19,680 --> 00:51:22,960
Speaker 1: no such people as black controls. So then I was

870
00:51:22,960 --> 00:51:25,520
Speaker 1: sitting there about a month before it opened, and I

871
00:51:25,560 --> 00:51:29,960
Speaker 1: get a brown envelope with a mimeographed article in it

872
00:51:30,520 --> 00:51:32,960
Speaker 1: and a letter from a FELLO named Fisher Black, who

873
00:51:33,000 --> 00:51:34,960
Speaker 1: I said, I as I said, I'd never heard of,

874
00:51:35,200 --> 00:51:38,160
Speaker 1: and he was saying, I'm an admirer of your work.

875
00:51:38,520 --> 00:51:41,319
Speaker 1: And we took the idea from beat the Market the

876
00:51:41,360 --> 00:51:46,800
Speaker 1: hedging idea and use dynamic dging, and we've derived this formula.

877
00:51:47,320 --> 00:51:50,120
Speaker 1: And so I started reading the paper, and I said, gee,

878
00:51:50,160 --> 00:51:53,080
Speaker 1: this looks a lot like my formula, and so I

879
00:51:53,120 --> 00:51:56,880
Speaker 1: programmed it on my little helipacker computer which drew graphs,

880
00:51:57,480 --> 00:51:59,520
Speaker 1: and I drew the curves and they were saying, curves,

881
00:51:59,520 --> 00:52:02,080
Speaker 1: I've been drawn. So I said, oh, it is the

882
00:52:02,120 --> 00:52:05,160
Speaker 1: same formula. Let's let's look at it algebraically. So I

883
00:52:05,480 --> 00:52:08,359
Speaker 1: saw that the algebra translated in the formula. They were

884
00:52:08,400 --> 00:52:12,799
Speaker 1: the same thing. And I realized more. I actually had

885
00:52:13,440 --> 00:52:17,279
Speaker 1: a triple of formulas, and not just one. And the

886
00:52:17,360 --> 00:52:23,200
Speaker 1: triple took care of two cases that were then important

887
00:52:23,200 --> 00:52:28,279
Speaker 1: in the market, your short warrants and long stock and

888
00:52:28,360 --> 00:52:30,920
Speaker 1: you can't get the short sale proceeds used that broker

889
00:52:31,080 --> 00:52:33,880
Speaker 1: pockets it and use it to pay his own debit

890
00:52:33,920 --> 00:52:38,600
Speaker 1: balances down. And the other formula, uh, that was a

891
00:52:38,640 --> 00:52:42,480
Speaker 1: second formula. The third formula was the reverse your short

892
00:52:42,520 --> 00:52:45,440
Speaker 1: stock and long warrants and deprived of the short sale proceeds.

893
00:52:45,640 --> 00:52:49,520
Speaker 1: Black controls assumed the main formula that you got the

894
00:52:49,600 --> 00:52:53,360
Speaker 1: use of the short sale proceeds, which you did, or

895
00:52:53,440 --> 00:52:56,600
Speaker 1: you would when you traded on the exchange. But before

896
00:52:56,680 --> 00:52:59,880
Speaker 1: the cbo E people did not get the benefit of

897
00:53:00,040 --> 00:53:02,640
Speaker 1: using the short sale proceeds. So you needed three formulas,

898
00:53:03,080 --> 00:53:05,880
Speaker 1: and so I had them all and there there's was

899
00:53:05,960 --> 00:53:08,439
Speaker 1: the middle of the three. And there was something else

900
00:53:08,440 --> 00:53:10,440
Speaker 1: about the formula I had, which was it used a

901
00:53:10,480 --> 00:53:15,600
Speaker 1: method that was different. H used something from basically what's

902
00:53:15,600 --> 00:53:21,160
Speaker 1: now high school calculus, just integration. And that means that

903
00:53:21,320 --> 00:53:25,160
Speaker 1: my formula could be applied to probability distributions that were

904
00:53:25,560 --> 00:53:29,279
Speaker 1: different than the long normal distribution, which was the one

905
00:53:29,480 --> 00:53:32,279
Speaker 1: that underlied the Black Rolls formula. So it was a

906
00:53:32,320 --> 00:53:36,400
Speaker 1: more general attack on problems. So you get the package

907
00:53:36,520 --> 00:53:40,440
Speaker 1: from from Black. You see that your secret sauce is

908
00:53:40,480 --> 00:53:44,040
Speaker 1: now out in the public, even though it's not quite

909
00:53:44,040 --> 00:53:47,880
Speaker 1: as comprehensive or robust as your version of it. Do

910
00:53:48,000 --> 00:53:50,839
Speaker 1: you say to yourself, Oh, cats out of the bag

911
00:53:50,920 --> 00:53:52,520
Speaker 1: and this is no longer going to be a money

912
00:53:52,520 --> 00:53:54,560
Speaker 1: maker for I figured I don't have the competitive edge

913
00:53:54,560 --> 00:53:57,399
Speaker 1: that I thought I had. But it turns out that

914
00:53:57,840 --> 00:54:03,120
Speaker 1: the act American finance world was fairly slow moving, as

915
00:54:03,320 --> 00:54:06,800
Speaker 1: was the collection of market makers. So when the CBO

916
00:54:07,640 --> 00:54:10,799
Speaker 1: CBO we opened on day one. We were down there

917
00:54:10,800 --> 00:54:13,560
Speaker 1: with our traders and our formulas and our charts, and

918
00:54:13,600 --> 00:54:17,840
Speaker 1: it was it was wonderful there was no competition really.

919
00:54:18,080 --> 00:54:22,000
Speaker 1: Now you wanted to bring down um small calculators and

920
00:54:22,080 --> 00:54:25,000
Speaker 1: handheld devices, and they really gave you a lot of

921
00:54:25,000 --> 00:54:28,319
Speaker 1: grief about that. Well, it was what we wanted to

922
00:54:28,320 --> 00:54:30,919
Speaker 1: do was bring down hand held calculators so we could

923
00:54:30,960 --> 00:54:33,239
Speaker 1: just punch out the formula values right on the floor.

924
00:54:33,280 --> 00:54:34,759
Speaker 1: But they wouldn't let us do it because it was

925
00:54:34,840 --> 00:54:38,799
Speaker 1: unfair to the old guard that the old Guard didn't

926
00:54:38,840 --> 00:54:42,160
Speaker 1: have this advantage. And we asked if we could use

927
00:54:42,160 --> 00:54:44,319
Speaker 1: walkie talkies and they said no, we couldn't do that either.

928
00:54:44,880 --> 00:54:49,560
Speaker 1: So what we resorted to doing was printing enormous tables

929
00:54:49,600 --> 00:54:52,880
Speaker 1: that had all the possibilities in them. And these tables

930
00:54:52,920 --> 00:54:56,359
Speaker 1: were on z fold paper eleven by seventeen. They were

931
00:54:56,400 --> 00:55:02,360
Speaker 1: probably oh three inches thick because they covered lots of

932
00:55:02,400 --> 00:55:05,359
Speaker 1: cases and prices and options and that had to be

933
00:55:05,719 --> 00:55:10,920
Speaker 1: updated daily correct. We ran. We ran printers four or

934
00:55:10,960 --> 00:55:16,279
Speaker 1: five hours every night, and air freighted our tables to

935
00:55:16,320 --> 00:55:19,680
Speaker 1: our traders, uh initially on the cbo E and then

936
00:55:19,719 --> 00:55:25,000
Speaker 1: on the m X the Pacific and Philadelphia exchanges, so

937
00:55:25,080 --> 00:55:28,479
Speaker 1: it's a day or so behind, but close enough that

938
00:55:28,640 --> 00:55:30,840
Speaker 1: it was an advantage to them. Well, we covered a

939
00:55:30,880 --> 00:55:33,759
Speaker 1: wide enough range of prices so that we caught most

940
00:55:33,800 --> 00:55:36,360
Speaker 1: of the moves for a week or so, and so

941
00:55:36,440 --> 00:55:38,680
Speaker 1: the tables were usually good for a week, and that

942
00:55:38,719 --> 00:55:41,960
Speaker 1: gave you guys a statistical edge. And in the actual

943
00:55:42,239 --> 00:55:44,080
Speaker 1: tables had all our traders had to do was take

944
00:55:44,080 --> 00:55:46,840
Speaker 1: the tables down to the post flip and decide what

945
00:55:46,920 --> 00:55:50,239
Speaker 1: trades they wanted to make. And they what What did

946
00:55:50,280 --> 00:55:52,440
Speaker 1: the CBO we have to say about that? They they

947
00:55:53,200 --> 00:55:56,960
Speaker 1: they let us do that. They didn't recognize the advantage

948
00:55:56,960 --> 00:55:59,359
Speaker 1: it was giving you. I think they did. The Wall

949
00:55:59,360 --> 00:56:01,920
Speaker 1: Street Journal had an article about it in nineteen seventy

950
00:56:02,000 --> 00:56:05,920
Speaker 1: four front page article. And what was the net result

951
00:56:06,000 --> 00:56:09,000
Speaker 1: after the article? Did the any more pushback or I

952
00:56:09,040 --> 00:56:12,200
Speaker 1: think it encouraged people to try to come up to speed.

953
00:56:13,280 --> 00:56:16,720
Speaker 1: So so even after black Sholes is out, even after

954
00:56:17,560 --> 00:56:20,919
Speaker 1: all of the mathematical advantages that you had identified were

955
00:56:21,040 --> 00:56:24,960
Speaker 1: well known, you're still running a big advantage over everybody else.

956
00:56:25,600 --> 00:56:28,680
Speaker 1: And the fund continues to perform very well for the

957
00:56:28,719 --> 00:56:34,040
Speaker 1: next how many years, Well, we ran from late nine

958
00:56:34,120 --> 00:56:36,480
Speaker 1: through the end of eight, and we did fine through

959
00:56:36,480 --> 00:56:39,120
Speaker 1: the whole period. In fact, we got higher rates of

960
00:56:39,200 --> 00:56:41,080
Speaker 1: return in the later period that we did in the

961
00:56:41,080 --> 00:56:44,319
Speaker 1: lower So what made you decide to finally shut that

962
00:56:44,400 --> 00:56:48,160
Speaker 1: fun fun down? Fellow named Rudy Giuliani made me decide

963
00:56:48,200 --> 00:56:50,400
Speaker 1: to set up So, so let's talk about that. Because

964
00:56:50,840 --> 00:56:55,759
Speaker 1: Giuliani and others open and invested an investigation. You, I'm

965
00:56:55,760 --> 00:56:58,120
Speaker 1: going to cut to the end. You did nothing wrong.

966
00:56:58,160 --> 00:57:02,960
Speaker 1: You're completely exonerated. However, this is a giant pain in

967
00:57:03,000 --> 00:57:07,080
Speaker 1: the neck, isn't it. Well, what happened was really Julianni,

968
00:57:07,360 --> 00:57:11,720
Speaker 1: in my opinion, decided that his career would be advanced

969
00:57:11,719 --> 00:57:17,560
Speaker 1: greatly if while he was um ahead of the Southern

970
00:57:17,600 --> 00:57:21,400
Speaker 1: District in New York U S Attorney's of Attorney's Office,

971
00:57:21,800 --> 00:57:25,240
Speaker 1: that if he prosecuted inside or trading, it would be

972
00:57:25,320 --> 00:57:29,200
Speaker 1: a great benefit to his future, which it was, And

973
00:57:29,320 --> 00:57:33,240
Speaker 1: so he found various targets. One of them was Michael Milken,

974
00:57:33,680 --> 00:57:37,640
Speaker 1: who was everybody's target because Michael Milken was on horsing

975
00:57:37,640 --> 00:57:41,840
Speaker 1: the old guard by funding drunk bond people who were

976
00:57:41,840 --> 00:57:44,800
Speaker 1: taking over companies and kicking the old guys out. So

977
00:57:44,920 --> 00:57:48,280
Speaker 1: his his head had to roll. And fortunately for the

978
00:57:48,320 --> 00:57:50,040
Speaker 1: people who wanted to kick him out, they were able

979
00:57:50,040 --> 00:57:53,440
Speaker 1: to find some things to get them with. And Robert

980
00:57:53,480 --> 00:57:57,680
Speaker 1: Freeman of Goldman. Sachs was another guy that they were after.

981
00:57:57,760 --> 00:58:00,560
Speaker 1: I don't recall what the reason was. It turned out

982
00:58:00,560 --> 00:58:03,560
Speaker 1: my partner, Jay Regan, was his roommate at Dartmouth and

983
00:58:03,600 --> 00:58:06,600
Speaker 1: they were pals. And also we did a fair amount

984
00:58:06,600 --> 00:58:11,680
Speaker 1: of trading with Michael Milken and his company, so there

985
00:58:11,720 --> 00:58:13,960
Speaker 1: was a big grade of our offices. The idea was

986
00:58:14,000 --> 00:58:17,840
Speaker 1: to get Jay Reagan to say something that would help

987
00:58:17,920 --> 00:58:23,400
Speaker 1: convict a freeman and Milican. Well, what happened was they

988
00:58:23,440 --> 00:58:26,480
Speaker 1: never got anything from Jay Regan or any of the

989
00:58:26,480 --> 00:58:29,280
Speaker 1: other people in Princeton, but they're able to make enough

990
00:58:29,320 --> 00:58:31,640
Speaker 1: of the case so that it dragged through the courts

991
00:58:31,680 --> 00:58:34,680
Speaker 1: for like two and a half years. Meanwhile, Giuliani split

992
00:58:35,040 --> 00:58:37,560
Speaker 1: and went on to other things. And there was an

993
00:58:37,560 --> 00:58:42,000
Speaker 1: initial trial which and conviction which was thrown out of

994
00:58:42,080 --> 00:58:45,280
Speaker 1: five people in the Princeton office, and then the government

995
00:58:45,280 --> 00:58:48,360
Speaker 1: elected not to retry. And when all it's more cleared, Uh,

996
00:58:48,480 --> 00:58:51,120
Speaker 1: nobody paid any money. Nobody did at any time, but

997
00:58:51,200 --> 00:58:54,200
Speaker 1: they spent ten or fifty million dollars on legal fees.

998
00:58:54,480 --> 00:58:56,520
Speaker 1: So is it fair to say that you're not a

999
00:58:56,520 --> 00:58:59,640
Speaker 1: big fan of Rudy Julian and you should you should

1000
00:58:59,680 --> 00:59:02,840
Speaker 1: know that most New Yorkers share your opinion. And I'm

1001
00:59:02,840 --> 00:59:05,880
Speaker 1: not being political when I say that people forget. I'm

1002
00:59:05,880 --> 00:59:10,680
Speaker 1: gonna let me digress. People forget. Right before September eleven,

1003
00:59:11,240 --> 00:59:13,400
Speaker 1: he was cheating on his wife. It was about an

1004
00:59:13,440 --> 00:59:17,560
Speaker 1: ugly divorce. His political career had, you know, had plummeted

1005
00:59:18,040 --> 00:59:21,000
Speaker 1: nine eleven, resurrected his career if it wasn't for that,

1006
00:59:21,560 --> 00:59:25,200
Speaker 1: And you know, the President wasn't very visible then and

1007
00:59:25,280 --> 00:59:29,480
Speaker 1: Rudy stepped into the void, and that basically really gave

1008
00:59:29,560 --> 00:59:33,680
Speaker 1: him a whole second, a second run on a political career.

1009
00:59:33,760 --> 00:59:37,919
Speaker 1: But a lot of New Yorkers, you you asking year, well,

1010
00:59:37,960 --> 00:59:40,880
Speaker 1: you know, they got the bombs off, the the squeegee

1011
00:59:40,880 --> 00:59:43,560
Speaker 1: guys were gone, and there's no begging in the subway.

1012
00:59:43,600 --> 00:59:48,120
Speaker 1: But that second term, he just whatever credibility and goodwill

1013
00:59:48,200 --> 00:59:55,040
Speaker 1: he had accumulated, he completely frittered away. So that leads

1014
00:59:55,040 --> 00:59:59,640
Speaker 1: to an interesting quote of yours um about money and

1015
00:59:59,720 --> 01:00:04,080
Speaker 1: so cess. And I thought this was fascinating quote. Success

1016
01:00:04,120 --> 01:00:07,560
Speaker 1: on Wall Street was getting the most money. Success for

1017
01:00:07,720 --> 01:00:11,600
Speaker 1: us was having the best life. That is a fairly

1018
01:00:12,320 --> 01:00:17,800
Speaker 1: philosophical perspective that is often missing, at least on the

1019
01:00:17,840 --> 01:00:20,320
Speaker 1: East Coast. Or in New York. Tell us a little

1020
01:00:20,360 --> 01:00:26,120
Speaker 1: bit about that philosophy. Well, what I learned over the

1021
01:00:26,200 --> 01:00:29,800
Speaker 1: decades was that it's it's how you live your life

1022
01:00:29,840 --> 01:00:33,200
Speaker 1: that matters, and the people that you spend your time

1023
01:00:33,240 --> 01:00:37,200
Speaker 1: with that matters, and the stuff you pile up doesn't

1024
01:00:37,200 --> 01:00:42,080
Speaker 1: really matter very much. Having money helps, It makes living

1025
01:00:42,120 --> 01:00:45,760
Speaker 1: easier pleasanter. You have more fun, better medical care and

1026
01:00:45,800 --> 01:00:48,480
Speaker 1: so forth. And your kids and your grandkids, you can

1027
01:00:48,520 --> 01:00:50,959
Speaker 1: help take care of them and make sure things are fine.

1028
01:00:51,520 --> 01:00:53,920
Speaker 1: But I don't need to own an island in Hawaii.

1029
01:00:53,960 --> 01:00:56,560
Speaker 1: I don't need to have villas all over I don't

1030
01:00:56,560 --> 01:00:58,560
Speaker 1: need to have private jets. And you know, a lot

1031
01:00:58,600 --> 01:01:01,040
Speaker 1: of other people feel the same way. I mean, they'll

1032
01:01:01,040 --> 01:01:03,720
Speaker 1: take Warren Buffett for instance. He doesn't spend that much

1033
01:01:03,720 --> 01:01:05,640
Speaker 1: money when you consider he is one of the two

1034
01:01:05,720 --> 01:01:09,800
Speaker 1: or three richest guys in the world for the longest time. Well,

1035
01:01:09,800 --> 01:01:12,959
Speaker 1: what makes him happy is doing his job. He tap

1036
01:01:13,040 --> 01:01:14,840
Speaker 1: dances to work, and not a lot of people get

1037
01:01:14,840 --> 01:01:17,120
Speaker 1: to do that. And he realizes the same thing as

1038
01:01:17,120 --> 01:01:19,960
Speaker 1: he said, it's it's kind of the people you love

1039
01:01:20,000 --> 01:01:21,760
Speaker 1: and the people who love you that matter the most,

1040
01:01:22,600 --> 01:01:24,520
Speaker 1: to say the least. By the way. Have you seen

1041
01:01:24,600 --> 01:01:30,400
Speaker 1: the HBO documentary on Buffett. It's quite charming. He's he's he's.

1042
01:01:31,480 --> 01:01:35,440
Speaker 1: I know a lot of people think his stick is, oh,

1043
01:01:35,480 --> 01:01:38,840
Speaker 1: he's just being that down home, but he comes across

1044
01:01:38,960 --> 01:01:41,480
Speaker 1: is very genuine and he seems like a real person.

1045
01:01:42,000 --> 01:01:44,280
Speaker 1: If you get a chance, it's I think it's about

1046
01:01:44,280 --> 01:01:47,360
Speaker 1: an hour, it's it's for it. Although you you know

1047
01:01:47,480 --> 01:01:49,880
Speaker 1: him personally, so maybe you'll you'll see it differently, But

1048
01:01:50,040 --> 01:01:51,720
Speaker 1: I should say I knew him personally. I haven't seen

1049
01:01:51,720 --> 01:01:56,160
Speaker 1: it for a long time. But so, um, let's let's

1050
01:01:56,240 --> 01:02:00,640
Speaker 1: keep talking about Princeton Newport Partners. That's a better name

1051
01:02:00,640 --> 01:02:04,760
Speaker 1: than Convertible Hedge Associates m but that's why, that's why

1052
01:02:04,760 --> 01:02:07,120
Speaker 1: we changed it. So so that was simply, oh, this

1053
01:02:07,200 --> 01:02:10,680
Speaker 1: is a little uh one partners in Princeton, the other

1054
01:02:10,680 --> 01:02:13,400
Speaker 1: partners in Port Beach, and and that's a fair enough name.

1055
01:02:13,880 --> 01:02:16,000
Speaker 1: So when you first started it, who were some of

1056
01:02:16,040 --> 01:02:21,120
Speaker 1: the early investors. Let's see, there was Bob Evans, who

1057
01:02:21,240 --> 01:02:24,680
Speaker 1: later became head of Paramount Studios, was married to McGraw

1058
01:02:25,920 --> 01:02:28,760
Speaker 1: and and put out a number of really seminal movies.

1059
01:02:28,800 --> 01:02:32,880
Speaker 1: If if memories says um anybody else of note that

1060
01:02:33,000 --> 01:02:36,400
Speaker 1: goes Charles Langman of the RITZ. I don't remember much

1061
01:02:36,400 --> 01:02:39,160
Speaker 1: about him. Most of these partners were pretty quiet. We

1062
01:02:39,240 --> 01:02:41,200
Speaker 1: just kept making money. So then to bother us that

1063
01:02:41,200 --> 01:02:43,640
Speaker 1: that those are the best types of partners, especially if

1064
01:02:43,640 --> 01:02:47,240
Speaker 1: you keep making them money. Um, So you eventually decide

1065
01:02:47,320 --> 01:02:51,320
Speaker 1: to close UM Princeton Newport partners down after the Giuliani

1066
01:02:51,800 --> 01:02:53,800
Speaker 1: It was too it was too hard to operate in

1067
01:02:54,040 --> 01:02:57,640
Speaker 1: that atmosphere, and it was too difficult to keep reassuring

1068
01:02:57,640 --> 01:03:00,440
Speaker 1: them into partners that first of all, he threatened partnership

1069
01:03:00,480 --> 01:03:03,640
Speaker 1: with Rico, so they had the fear that through some

1070
01:03:03,760 --> 01:03:06,880
Speaker 1: quirk or other, their assets might get confiscated for unknown

1071
01:03:06,960 --> 01:03:10,520
Speaker 1: lengths of time. And we knew that there was no

1072
01:03:10,640 --> 01:03:13,560
Speaker 1: risk of them losing. But also it was just too harrowing.

1073
01:03:13,880 --> 01:03:16,360
Speaker 1: And I said to myself, I don't need more money anyhow.

1074
01:03:17,480 --> 01:03:20,120
Speaker 1: So there's a famous quote, and I know I'm mangling this,

1075
01:03:20,720 --> 01:03:23,400
Speaker 1: but it's there. There are a few things more dangerous

1076
01:03:23,440 --> 01:03:27,000
Speaker 1: than an over zealous prosecutor and run amuck. And I

1077
01:03:27,040 --> 01:03:31,120
Speaker 1: think that describes the circumstances with Rudy. Way back when

1078
01:03:32,080 --> 01:03:34,919
Speaker 1: you didn't need the money, but at a certain point

1079
01:03:35,000 --> 01:03:38,439
Speaker 1: you decided to start PNP. What motivated you to get

1080
01:03:38,480 --> 01:03:42,840
Speaker 1: back into hedge fund games? It was let's see Richline

1081
01:03:43,120 --> 01:03:45,720
Speaker 1: Partners that I started, and what happened was I took

1082
01:03:45,720 --> 01:03:54,480
Speaker 1: a break from and then one of my UM previous partners,

1083
01:03:54,480 --> 01:03:58,720
Speaker 1: who was now working for UH, a very large pension

1084
01:03:58,800 --> 01:04:03,800
Speaker 1: profit sharing plan, said, you know statistical arbitrage, which we've

1085
01:04:03,800 --> 01:04:06,560
Speaker 1: been running, and which I might say, as far as

1086
01:04:06,600 --> 01:04:10,560
Speaker 1: I know, we were the first discoverers of back statistical arbitrage.

1087
01:04:10,560 --> 01:04:13,080
Speaker 1: You're doing really well. We'd like you to get back

1088
01:04:13,120 --> 01:04:16,000
Speaker 1: in this game. So I said, well, I'll take a look,

1089
01:04:16,160 --> 01:04:17,959
Speaker 1: and I said, oh, it is doing it quite well.

1090
01:04:18,320 --> 01:04:20,880
Speaker 1: Sure we'll we'll run some money for you. And it

1091
01:04:21,000 --> 01:04:23,400
Speaker 1: was easy to do. I could do it with about

1092
01:04:23,440 --> 01:04:28,080
Speaker 1: three full time equivalence as opposed to a staff of

1093
01:04:28,120 --> 01:04:30,680
Speaker 1: eighty which I was using for Princeton Newport Partners. And

1094
01:04:30,720 --> 01:04:32,400
Speaker 1: I didn't get the sense you're a big fan of

1095
01:04:32,440 --> 01:04:35,200
Speaker 1: managing a large staff. That's a lot of work. It

1096
01:04:35,200 --> 01:04:38,720
Speaker 1: turns out what I learned was if you manage five people,

1097
01:04:39,200 --> 01:04:42,680
Speaker 1: then you're spending maybe a third or half your time managing,

1098
01:04:43,240 --> 01:04:45,640
Speaker 1: and then you've got to find somebody. If you have

1099
01:04:45,760 --> 01:04:48,040
Speaker 1: five more people, you've gotta have somebody to help manage them,

1100
01:04:48,320 --> 01:04:51,720
Speaker 1: and it sort of grows exponentially. You get this big

1101
01:04:51,760 --> 01:04:54,320
Speaker 1: pyramid and you keep getting farther and farther to remove

1102
01:04:54,360 --> 01:04:57,200
Speaker 1: from what's really happening. Right, So, so PNP you're doing

1103
01:04:57,240 --> 01:05:01,960
Speaker 1: statistical arbitrage? Actually can rich line Partners were doing rich

1104
01:05:02,000 --> 01:05:05,480
Speaker 1: Line Partners? And and and what was the performance numbers

1105
01:05:05,520 --> 01:05:07,680
Speaker 1: like for rich Line We did about twenty percent a

1106
01:05:07,760 --> 01:05:10,520
Speaker 1: year with a little more variability than P m P.

1107
01:05:10,600 --> 01:05:12,320
Speaker 1: I don't We didn't make money every month, but we

1108
01:05:12,400 --> 01:05:15,680
Speaker 1: made money nearly every month, and it was somewhat event

1109
01:05:15,760 --> 01:05:20,040
Speaker 1: driven because the arbitrage opportunities would come along through M

1110
01:05:20,080 --> 01:05:22,920
Speaker 1: and A or through what what was the underlying The

1111
01:05:22,960 --> 01:05:31,080
Speaker 1: biggest The biggest event that we experienced was in with

1112
01:05:31,240 --> 01:05:35,400
Speaker 1: the long term capital management. It turned out that lots

1113
01:05:35,400 --> 01:05:38,320
Speaker 1: of people who were competitors now began to bail out,

1114
01:05:39,040 --> 01:05:42,360
Speaker 1: and so that caused four or five day dip. We

1115
01:05:42,400 --> 01:05:46,440
Speaker 1: had the first loss that we had of any significance.

1116
01:05:46,480 --> 01:05:50,240
Speaker 1: It was just three or four and that was a shocker.

1117
01:05:50,520 --> 01:05:53,560
Speaker 1: But those those days passed and then we had the

1118
01:05:54,280 --> 01:05:57,600
Speaker 1: greatest run we've ever had. We think we made like

1119
01:05:57,960 --> 01:06:01,040
Speaker 1: scent in six months. Not to share be and then

1120
01:06:01,560 --> 01:06:05,880
Speaker 1: the another hedge fund pnp UM In the twelve months

1121
01:06:06,000 --> 01:06:10,959
Speaker 1: ending August, the limited Partners, I guess this is Netta.

1122
01:06:11,000 --> 01:06:15,440
Speaker 1: Fees were up over seventy from a market neutral portfolio

1123
01:06:15,520 --> 01:06:18,240
Speaker 1: that used less than two to one leverage. That's that's

1124
01:06:18,320 --> 01:06:20,520
Speaker 1: Origin Line Partners. Oh so why do I keep calling

1125
01:06:20,520 --> 01:06:27,640
Speaker 1: this pnp PPM in so it might be Uh so

1126
01:06:27,720 --> 01:06:29,840
Speaker 1: that's the old one. And then Ridgeline took over. So

1127
01:06:30,640 --> 01:06:34,960
Speaker 1: back to Ridgeline in you're up se from market neutral,

1128
01:06:35,560 --> 01:06:39,439
Speaker 1: very little leverage. How did this come about? And why

1129
01:06:39,440 --> 01:06:41,960
Speaker 1: would you ever want to shut that down? Well, the

1130
01:06:41,960 --> 01:06:46,160
Speaker 1: reason we had that glorious run was because our competitors

1131
01:06:46,160 --> 01:06:50,240
Speaker 1: went away, and as they bailed out, they created opportunity. Okay,

1132
01:06:50,280 --> 01:06:53,200
Speaker 1: they disrupt the market places they exit, and that creates

1133
01:06:53,600 --> 01:06:57,640
Speaker 1: arbitrage opportunities. And they also don't control the excursions from

1134
01:06:57,640 --> 01:07:02,360
Speaker 1: favalue as much, so the excursions got bigger and so

1135
01:07:02,480 --> 01:07:05,680
Speaker 1: more more profits to be had. How much longer did

1136
01:07:05,760 --> 01:07:10,760
Speaker 1: you keep Ridge Line open after we went from actually

1137
01:07:11,000 --> 01:07:15,080
Speaker 1: nineteen two two thousand and two, and then I shut

1138
01:07:15,120 --> 01:07:19,040
Speaker 1: down not because we're doing badly, but because we're doing

1139
01:07:19,160 --> 01:07:22,880
Speaker 1: just so so, making maybe ten percent a year so

1140
01:07:23,000 --> 01:07:25,560
Speaker 1: and the opportunities had had just gone away. Is that

1141
01:07:25,680 --> 01:07:28,760
Speaker 1: is that basically I think that there was more competition

1142
01:07:29,200 --> 01:07:32,480
Speaker 1: and so the deviations from fair value that we're exploiting

1143
01:07:32,520 --> 01:07:35,800
Speaker 1: word is big. And at that point I said to myself,

1144
01:07:35,840 --> 01:07:38,800
Speaker 1: you know, why am I doing this? I'd rather just

1145
01:07:38,920 --> 01:07:41,560
Speaker 1: manage my own money in a more passive way and

1146
01:07:41,640 --> 01:07:44,280
Speaker 1: enjoy life. So let's talk a little bit about that.

1147
01:07:44,480 --> 01:07:48,919
Speaker 1: Passive investing has caught on a great deal. UH. It's

1148
01:07:48,920 --> 01:07:52,640
Speaker 1: a long time coming. It's finally, um, finally made its

1149
01:07:52,640 --> 01:07:55,560
Speaker 1: way to a number of people. Black Rock is one

1150
01:07:55,600 --> 01:08:01,040
Speaker 1: of the big underwriters of ETFs in passive investing, not exclusively,

1151
01:08:01,200 --> 01:08:04,680
Speaker 1: but they're up to five trillion. Vanguard is probably best

1152
01:08:04,720 --> 01:08:08,160
Speaker 1: known as the inventor of the passive index, or at

1153
01:08:08,240 --> 01:08:12,160
Speaker 1: least the first company that was created to roll out

1154
01:08:12,200 --> 01:08:14,840
Speaker 1: a passive index that people can invest in. They're now

1155
01:08:14,920 --> 01:08:19,920
Speaker 1: up to four trillion. Have have mom and pop investors

1156
01:08:19,960 --> 01:08:24,160
Speaker 1: finally figured out that for them, a passive investment is

1157
01:08:24,200 --> 01:08:27,800
Speaker 1: better than stock picking or market timing, some of them have.

1158
01:08:29,360 --> 01:08:34,080
Speaker 1: So you make a convincing argument that if you cannot

1159
01:08:34,520 --> 01:08:40,040
Speaker 1: demonstrate your edge, your advantage over a passive uh index,

1160
01:08:40,160 --> 01:08:43,400
Speaker 1: then then you shouldn't be doing anything but a passive

1161
01:08:43,400 --> 01:08:47,720
Speaker 1: index too. It seems perfectly logical and rational to me.

1162
01:08:48,160 --> 01:08:50,840
Speaker 1: Do you get pushed back from people about that? No,

1163
01:08:51,080 --> 01:08:58,160
Speaker 1: what I did is probably a lack of alertness discussing it,

1164
01:08:59,080 --> 01:09:01,519
Speaker 1: So I have to that's gonna. That leads me to

1165
01:09:01,560 --> 01:09:05,120
Speaker 1: a question I didn't ask before, but I have to.

1166
01:09:06,040 --> 01:09:12,080
Speaker 1: You seem incredibly grounded and a regular, low key sort

1167
01:09:12,120 --> 01:09:16,599
Speaker 1: of guy. You took the California Test of of um

1168
01:09:16,800 --> 01:09:19,400
Speaker 1: Mental Maturity. It's an i Q test as a kid.

1169
01:09:20,360 --> 01:09:23,799
Speaker 1: You had the highest score they had ever seen. How

1170
01:09:23,840 --> 01:09:27,040
Speaker 1: do you manage to keep high school at your at

1171
01:09:27,040 --> 01:09:29,759
Speaker 1: your high school, but in the world, not in the world. Okay,

1172
01:09:29,800 --> 01:09:32,519
Speaker 1: so that makes it I'm gonna that makes the question easy.

1173
01:09:32,560 --> 01:09:35,000
Speaker 1: How do you keep your ego in check? But still

1174
01:09:35,080 --> 01:09:40,320
Speaker 1: you all joking aside? You invent card counting, you invent

1175
01:09:40,360 --> 01:09:46,360
Speaker 1: statistical arbitrage, you invent um paired training. Go down the

1176
01:09:46,400 --> 01:09:48,839
Speaker 1: list of things that you did that are really unique

1177
01:09:48,840 --> 01:09:52,040
Speaker 1: in the world of finance as an investor, how do

1178
01:09:52,080 --> 01:09:54,840
Speaker 1: you manage to keep your ego in check? What you

1179
01:09:54,880 --> 01:09:56,360
Speaker 1: have to do is look all the other things that

1180
01:09:56,400 --> 01:09:58,760
Speaker 1: people do. There's so many great things that people are

1181
01:09:58,760 --> 01:10:01,360
Speaker 1: doing all the time. And if you meet any individual,

1182
01:10:01,840 --> 01:10:03,760
Speaker 1: the person sitting next to you on a bus or

1183
01:10:03,760 --> 01:10:05,800
Speaker 1: a plane or whatever, that person will be able to

1184
01:10:05,800 --> 01:10:09,600
Speaker 1: do things or no things that you can't know or

1185
01:10:09,640 --> 01:10:11,800
Speaker 1: can't do. Maybe they can sing better, maybe they know

1186
01:10:11,920 --> 01:10:14,320
Speaker 1: a language you don't know. Maybe they know how to

1187
01:10:15,120 --> 01:10:18,559
Speaker 1: um fix a car. Just you know, one thing after another,

1188
01:10:18,680 --> 01:10:22,080
Speaker 1: identify the car has been been rigged against you. And

1189
01:10:22,760 --> 01:10:27,160
Speaker 1: you think that's basically helps you keep your ego in check,

1190
01:10:27,600 --> 01:10:30,840
Speaker 1: helps anybody keep their ego and check. I find it

1191
01:10:30,920 --> 01:10:36,240
Speaker 1: hard to understand why so many people have such big egos. Okay,

1192
01:10:36,479 --> 01:10:39,240
Speaker 1: that's that's a fair enough thing there, especially those people

1193
01:10:39,280 --> 01:10:43,680
Speaker 1: who haven't created a body of accomplishments. I kind of

1194
01:10:43,680 --> 01:10:46,559
Speaker 1: think that maybe the big ego is a substitute for

1195
01:10:47,240 --> 01:10:49,639
Speaker 1: feeling like they're falling short here and there, a little

1196
01:10:49,680 --> 01:10:54,000
Speaker 1: over compensation. So that's fair enough. Um. In in the book,

1197
01:10:54,479 --> 01:10:59,880
Speaker 1: chapter you say, beat most investors by indexing, and chapters one,

1198
01:11:01,280 --> 01:11:04,840
Speaker 1: can you beat the market? Should you try? Um? When

1199
01:11:04,880 --> 01:11:09,760
Speaker 1: should somebody try to beat the market? Well, my simplified

1200
01:11:09,800 --> 01:11:13,479
Speaker 1: views go something like this bit roughly, the three kinds

1201
01:11:13,520 --> 01:11:15,800
Speaker 1: of investors. There are guys who don't want to really

1202
01:11:15,840 --> 01:11:18,639
Speaker 1: do any work. They just want to have their money grow.

1203
01:11:19,160 --> 01:11:22,320
Speaker 1: Those people should be thinking about indexing makes perfect sense.

1204
01:11:22,479 --> 01:11:24,760
Speaker 1: Then there are people who really are interested in the

1205
01:11:24,840 --> 01:11:27,240
Speaker 1: market and it's kind of fun for them. Those people,

1206
01:11:27,479 --> 01:11:29,479
Speaker 1: if they want to learn more, should go out and

1207
01:11:29,680 --> 01:11:31,280
Speaker 1: have their go and try to make some money. But

1208
01:11:31,320 --> 01:11:34,120
Speaker 1: they shouldn't use the bulk of their resources to do this.

1209
01:11:34,120 --> 01:11:37,120
Speaker 1: They should just a fun account, yeah, exactly, And then

1210
01:11:37,320 --> 01:11:39,800
Speaker 1: if they find something that really works, then they can

1211
01:11:39,800 --> 01:11:43,439
Speaker 1: start putting more money into it. They'll find that most

1212
01:11:43,479 --> 01:11:45,879
Speaker 1: of the time they haven't really found anything that really works,

1213
01:11:45,920 --> 01:11:49,519
Speaker 1: a little fooled by randomness, exactly. And then there's a

1214
01:11:49,520 --> 01:11:52,559
Speaker 1: third group, which are the professional people, some of whom

1215
01:11:52,720 --> 01:11:54,880
Speaker 1: actually get on edge, most of whom don't, but some

1216
01:11:54,920 --> 01:11:58,839
Speaker 1: of whom do. And those people get a start somehow

1217
01:11:58,840 --> 01:12:01,000
Speaker 1: in the market. Just like I gotta start with an

1218
01:12:01,000 --> 01:12:03,800
Speaker 1: options formula, and so I have an edge, I get in.

1219
01:12:04,640 --> 01:12:07,479
Speaker 1: I build an organization which is small, and it gradually grows,

1220
01:12:07,880 --> 01:12:10,080
Speaker 1: It gets more and more skills, it gets into more

1221
01:12:10,080 --> 01:12:14,679
Speaker 1: and more kinds of investing. So you basically get over

1222
01:12:14,720 --> 01:12:17,559
Speaker 1: the hurdle and get yourself established. If you can do

1223
01:12:17,600 --> 01:12:20,519
Speaker 1: that as a professional, then you're kind of on your

1224
01:12:20,560 --> 01:12:24,720
Speaker 1: way to collecting what people call alpha access return. Then

1225
01:12:24,760 --> 01:12:27,880
Speaker 1: there's a fourth group, which I don't have much interest in,

1226
01:12:27,960 --> 01:12:30,120
Speaker 1: and those are the ones who are simply asset gathers

1227
01:12:30,840 --> 01:12:33,439
Speaker 1: and they're they're in there to collect feest and get rich,

1228
01:12:34,280 --> 01:12:37,000
Speaker 1: but there's nothing really very interesting what they do. That's

1229
01:12:37,040 --> 01:12:40,000
Speaker 1: a You've described a huge part of what takes place

1230
01:12:40,040 --> 01:12:42,920
Speaker 1: in finance. Most of it people who are trying and

1231
01:12:43,000 --> 01:12:45,639
Speaker 1: not getting anywhere. People have just given up and said

1232
01:12:45,640 --> 01:12:48,320
Speaker 1: I'm going to go passive. And people who are are

1233
01:12:48,560 --> 01:12:51,560
Speaker 1: don't have an a special advantage, but they're just a

1234
01:12:51,640 --> 01:12:56,479
Speaker 1: commulating assets and manage them, managing them in some better

1235
01:12:56,560 --> 01:13:00,960
Speaker 1: or worse way. Referring to indexes in past of investing,

1236
01:13:01,520 --> 01:13:04,599
Speaker 1: people are now describing, oh, this has gotten too large,

1237
01:13:04,640 --> 01:13:09,080
Speaker 1: it's a bubble, that it's distorting price discovery. Passive indexes

1238
01:13:09,120 --> 01:13:11,360
Speaker 1: are going to cause the next crash. What what do

1239
01:13:11,400 --> 01:13:17,960
Speaker 1: you make of that? Well, suppose that half of all

1240
01:13:18,000 --> 01:13:22,280
Speaker 1: the list of equities in the United States we're in

1241
01:13:22,360 --> 01:13:26,240
Speaker 1: index funds. Then the other half would not be. The

1242
01:13:26,320 --> 01:13:28,680
Speaker 1: half has not be that would not be is a

1243
01:13:28,680 --> 01:13:31,599
Speaker 1: lot bigger than the market was ten or twenty years ago.

1244
01:13:32,479 --> 01:13:36,160
Speaker 1: So I don't see that putting half of the UM

1245
01:13:36,400 --> 01:13:38,679
Speaker 1: stocks into the next funds is going to cause any problem,

1246
01:13:38,840 --> 01:13:41,960
Speaker 1: except there's one thing. There's the question of the float

1247
01:13:42,120 --> 01:13:46,559
Speaker 1: in different companies. Some companies are more closely held than others.

1248
01:13:46,920 --> 01:13:48,960
Speaker 1: So you might have a company in which sevent is

1249
01:13:48,960 --> 01:13:52,400
Speaker 1: closely held, it never trades and only up for trading.

1250
01:13:53,479 --> 01:13:59,439
Speaker 1: So I believe that the index funds, the SMP Vanguard anyhow,

1251
01:13:59,479 --> 01:14:03,120
Speaker 1: has changed so that they use a proportion of the

1252
01:14:03,160 --> 01:14:05,519
Speaker 1: float rather than a proportion of the total market cap.

1253
01:14:06,280 --> 01:14:11,479
Speaker 1: And they've compared how that tracks with UH if they

1254
01:14:11,479 --> 01:14:14,920
Speaker 1: had been able to use the proportion of total market

1255
01:14:14,920 --> 01:14:17,799
Speaker 1: cap rather than proportion of the float, and the tracks

1256
01:14:17,840 --> 01:14:21,360
Speaker 1: has been extremely closed so far, So so far, no problem.

1257
01:14:21,400 --> 01:14:24,360
Speaker 1: I think it was Andrew Lowe and might Taste said

1258
01:14:24,920 --> 01:14:28,599
Speaker 1: you could get down to passive ten percent active. That's

1259
01:14:28,720 --> 01:14:32,080
Speaker 1: enough for price discovery as long as it's sufficient liquidity.

1260
01:14:32,120 --> 01:14:34,400
Speaker 1: So there could theoretically be a ways to go if

1261
01:14:34,400 --> 01:14:36,960
Speaker 1: he's right sure on the case. There is supposed that

1262
01:14:37,000 --> 01:14:40,559
Speaker 1: you had in an exponse not forgetting about the float issue,

1263
01:14:40,600 --> 01:14:45,000
Speaker 1: which which might cause some divergence. Well, ten percent of

1264
01:14:45,040 --> 01:14:48,760
Speaker 1: the current market is probably a good deal larger than

1265
01:14:48,800 --> 01:14:52,200
Speaker 1: the market was fifty years ago. Sure, so you still

1266
01:14:52,240 --> 01:14:55,479
Speaker 1: have price discovery even even if that. Here's another quote

1267
01:14:55,479 --> 01:14:57,760
Speaker 1: of yours that I really like. Whether or not you

1268
01:14:57,840 --> 01:15:00,880
Speaker 1: try to beat the market, you can do better by

1269
01:15:01,000 --> 01:15:04,599
Speaker 1: properly managing your wealth. Explain that where was that quote,

1270
01:15:04,680 --> 01:15:07,920
Speaker 1: let's take I don't know it was here on my page.

1271
01:15:09,120 --> 01:15:11,120
Speaker 1: I think it's from this book, and it's it's in

1272
01:15:11,160 --> 01:15:14,920
Speaker 1: the context of of why more people it's either twenty

1273
01:15:14,920 --> 01:15:18,040
Speaker 1: five or chapter twenty six about why more people should

1274
01:15:18,080 --> 01:15:21,839
Speaker 1: be doing passive. Whether or not you beat the market,

1275
01:15:21,880 --> 01:15:23,800
Speaker 1: you could still do better, you know what. Let's see

1276
01:15:23,800 --> 01:15:25,439
Speaker 1: if I can find that, I'll tell you what I

1277
01:15:25,479 --> 01:15:28,080
Speaker 1: think it is. I think it has to do with

1278
01:15:28,120 --> 01:15:33,400
Speaker 1: the fact that there are what Buffett calls fee collectors

1279
01:15:33,479 --> 01:15:37,760
Speaker 1: or toll takers. And the upshot is that when you

1280
01:15:37,800 --> 01:15:43,200
Speaker 1: think about fees collected by advisors or managers, and also

1281
01:15:43,800 --> 01:15:49,200
Speaker 1: the losses due to active trading, both market impact and commissions,

1282
01:15:49,920 --> 01:15:52,080
Speaker 1: it adds up to roughly a couple of percent a

1283
01:15:52,160 --> 01:15:56,280
Speaker 1: year drain on assets. So if you could make let's

1284
01:15:56,280 --> 01:15:59,040
Speaker 1: say ten percent a year in a stock index one

1285
01:15:59,439 --> 01:16:03,120
Speaker 1: long term, anything can happen in the short term, then

1286
01:16:03,160 --> 01:16:06,920
Speaker 1: maybe you're making eight percent a year before taxes if

1287
01:16:07,000 --> 01:16:12,080
Speaker 1: you're paying all these fees and tolls. So just shifting

1288
01:16:12,439 --> 01:16:17,920
Speaker 1: to passive investing, get your extra two percent for basically

1289
01:16:17,920 --> 01:16:20,960
Speaker 1: no work, and compound that over forty years, and that

1290
01:16:21,040 --> 01:16:25,639
Speaker 1: really adds up. Using the UM rule seventy two for instance,

1291
01:16:26,920 --> 01:16:30,200
Speaker 1: roughly thirty six years. Actually thirty five closer, because the

1292
01:16:30,280 --> 01:16:33,240
Speaker 1: rule varies from seventy two depending what the interest rate is.

1293
01:16:33,760 --> 01:16:37,080
Speaker 1: Using the rule of seventy two, in a little less

1294
01:16:37,080 --> 01:16:40,280
Speaker 1: than thirty six years, you have twice as much money

1295
01:16:40,560 --> 01:16:43,600
Speaker 1: if you invest this way then if you pay the

1296
01:16:43,640 --> 01:16:46,160
Speaker 1: two percent toll, So it makes a big difference. We

1297
01:16:46,280 --> 01:16:54,040
Speaker 1: have been speaking to Edward Thorpe, professional investor, trader, gambler, mathematician. UH.

1298
01:16:54,160 --> 01:16:56,759
Speaker 1: If you want to find out more about the works

1299
01:16:57,439 --> 01:16:59,800
Speaker 1: of ed Thorpe, you can read his most recent book,

1300
01:16:59,800 --> 01:17:03,200
Speaker 1: I'm and for All Markets Beat The Dealer Beat the

1301
01:17:03,280 --> 01:17:06,479
Speaker 1: Markets are both classic books. You may have to hunt

1302
01:17:06,520 --> 01:17:09,600
Speaker 1: around to find find them. UM. If anybody wants to

1303
01:17:09,640 --> 01:17:12,080
Speaker 1: find anything else you've written, these are really the main

1304
01:17:12,080 --> 01:17:15,519
Speaker 1: places to look. Is there to what? What is the website?

1305
01:17:15,800 --> 01:17:19,599
Speaker 1: It's uh Edward Ohthorpe dot com, Edward oh Thorpe dot com.

1306
01:17:20,000 --> 01:17:23,760
Speaker 1: It also has another second website links to it, a

1307
01:17:23,840 --> 01:17:27,120
Speaker 1: Man for All Markets dot com. If you enjoy this conversation,

1308
01:17:27,240 --> 01:17:29,479
Speaker 1: be sure and check out our podcast AFTRAS, where we

1309
01:17:29,600 --> 01:17:34,040
Speaker 1: keep the tape rolling and continue to discuss all things investing.

1310
01:17:34,520 --> 01:17:37,800
Speaker 1: Check out my daily column on Bloomberg at Bloomberg View

1311
01:17:37,840 --> 01:17:41,240
Speaker 1: dot com. You can follow me on Twitter at rid Alts.

1312
01:17:41,720 --> 01:17:46,200
Speaker 1: We love your comments, feedback and suggestions right to us

1313
01:17:46,400 --> 01:17:51,040
Speaker 1: at m IB podcast at Bloomberg dot net. I'm very Ridholts.

1314
01:17:51,120 --> 01:18:04,040
Speaker 1: You're listening to Masters in Business on Bloomberg Radio. If

1315
01:18:04,040 --> 01:18:06,800
Speaker 1: you're having a business dispute, the process can be slow

1316
01:18:06,880 --> 01:18:09,840
Speaker 1: and drawn out, especially if you rely on litigation in

1317
01:18:09,840 --> 01:18:12,479
Speaker 1: the courts. You can wait for years before your case

1318
01:18:12,520 --> 01:18:15,599
Speaker 1: is resolved, and the longer your case proceeds, the more

1319
01:18:15,680 --> 01:18:19,840
Speaker 1: your case can cost. Not with the American Arbitration Association.

1320
01:18:20,200 --> 01:18:24,320
Speaker 1: Arbitration or mediation with the American Arbitration Association is faster.

1321
01:18:24,600 --> 01:18:28,280
Speaker 1: In fact, nearly fifty of our cases settled prior to hearings.

1322
01:18:29,000 --> 01:18:38,000
Speaker 1: A d R dot org resolve faster. Welcome to the podcast.

1323
01:18:38,439 --> 01:18:41,040
Speaker 1: Thank you and for doing this. I'm really enjoying this

1324
01:18:41,120 --> 01:18:45,439
Speaker 1: conversation and I appreciate you being so generous with your time. Uh,

1325
01:18:45,680 --> 01:18:49,320
Speaker 1: you've led a fascinating life. You've also led a well

1326
01:18:49,439 --> 01:18:52,000
Speaker 1: lived life. Which are some of the things you talked

1327
01:18:52,000 --> 01:18:54,760
Speaker 1: about in the book. It's not about money, it's not

1328
01:18:54,840 --> 01:18:59,080
Speaker 1: about accumulating toys, it's not about just piling up junk.

1329
01:18:59,280 --> 01:19:03,760
Speaker 1: It's spending time in a way that matters. As you know.

1330
01:19:03,800 --> 01:19:07,559
Speaker 1: I totally agree. I read the book. I know. So,

1331
01:19:07,560 --> 01:19:09,840
Speaker 1: so let's let's talk a little bit about some of

1332
01:19:10,040 --> 01:19:13,839
Speaker 1: um my favorite questions. I ask these of all my guests,

1333
01:19:13,920 --> 01:19:17,800
Speaker 1: and we often find some interesting things. What do you

1334
01:19:17,800 --> 01:19:22,080
Speaker 1: think is the most interesting or important thing that people

1335
01:19:22,400 --> 01:19:28,720
Speaker 1: don't know about your background? I would say that I'm

1336
01:19:28,760 --> 01:19:35,160
Speaker 1: consistently rational over a rare wide range of things. And

1337
01:19:35,840 --> 01:19:38,200
Speaker 1: you meet a lot of people who are I recall

1338
01:19:38,600 --> 01:19:41,800
Speaker 1: locally rational. That is, they do things very sensibly and

1339
01:19:41,840 --> 01:19:45,360
Speaker 1: logically in some areas, and then there's a total breakdown

1340
01:19:45,360 --> 01:19:47,760
Speaker 1: in some other areas where they act very strange and

1341
01:19:47,800 --> 01:19:51,680
Speaker 1: don't use good sense. So, in other words, the logical

1342
01:19:52,040 --> 01:19:56,599
Speaker 1: part of their brain only limited to certain subjects. Um. Well,

1343
01:19:56,640 --> 01:19:59,599
Speaker 1: I hope we all endeavor to be logical across logical

1344
01:19:59,720 --> 01:20:03,640
Speaker 1: growth everything. But I think a lot of people, um

1345
01:20:04,080 --> 01:20:06,360
Speaker 1: don't behave that way. Let's let's talk about some of

1346
01:20:06,400 --> 01:20:09,200
Speaker 1: your early mentors. Who are the people who really influenced

1347
01:20:09,240 --> 01:20:12,840
Speaker 1: you and helped you along your career. Well, I was

1348
01:20:13,120 --> 01:20:16,200
Speaker 1: obviously deprived in that way, so I don't have much

1349
01:20:16,280 --> 01:20:19,240
Speaker 1: in that category. My father help me a lot from

1350
01:20:19,280 --> 01:20:22,599
Speaker 1: ages three to five. Okay, that was enough to really

1351
01:20:22,640 --> 01:20:25,559
Speaker 1: get me started. How about colleagues you worked with? Who? Who?

1352
01:20:25,560 --> 01:20:28,559
Speaker 1: Who did you really enjoy working with at at various schools.

1353
01:20:29,160 --> 01:20:32,320
Speaker 1: I think the most fun I had was working with

1354
01:20:32,439 --> 01:20:35,320
Speaker 1: Claude Shannon at M I T. Because he was a

1355
01:20:35,439 --> 01:20:38,160
Speaker 1: very creative thinker, and we could just sit there and

1356
01:20:38,200 --> 01:20:41,120
Speaker 1: talk about the widest range of topics, and we kind

1357
01:20:41,120 --> 01:20:43,400
Speaker 1: of played off each other, and we we thought alike

1358
01:20:43,400 --> 01:20:47,120
Speaker 1: about so many things, but we also had different experiences

1359
01:20:47,120 --> 01:20:49,160
Speaker 1: in background, so it made a really good team. And

1360
01:20:49,240 --> 01:20:53,160
Speaker 1: you ended up creating was it the Wearable Computer? With him? Well,

1361
01:20:53,200 --> 01:20:55,479
Speaker 1: it turned out, yes, we're trying to beat Roulette, and

1362
01:20:55,520 --> 01:20:58,439
Speaker 1: so we built a computer to wear on the body

1363
01:20:58,600 --> 01:21:02,960
Speaker 1: that was hidden and this computer head switches to input

1364
01:21:03,160 --> 01:21:07,559
Speaker 1: information about the Roulette wheel, ball and rotor as they

1365
01:21:07,560 --> 01:21:09,960
Speaker 1: were moving, and then it would make an immediate prediction

1366
01:21:10,040 --> 01:21:12,000
Speaker 1: as to what was going to happen. So, so you

1367
01:21:12,040 --> 01:21:15,200
Speaker 1: know the arrangement of numbers and colors on roulette wheel,

1368
01:21:15,800 --> 01:21:18,720
Speaker 1: you're you have a certain spin in one direction of

1369
01:21:18,840 --> 01:21:21,800
Speaker 1: the ball and the rotors in the other direction, and

1370
01:21:21,880 --> 01:21:25,320
Speaker 1: you basically were able to calculate as long as I

1371
01:21:25,360 --> 01:21:27,920
Speaker 1: could get a timing fairly accurate when the ball is

1372
01:21:27,960 --> 01:21:30,840
Speaker 1: moving this way, we can have a pretty comfortable guess

1373
01:21:30,840 --> 01:21:34,360
Speaker 1: as to the cluster of outcomes. Yeah, basically we were

1374
01:21:34,360 --> 01:21:37,519
Speaker 1: able to time the ball and the rotor, so we

1375
01:21:37,600 --> 01:21:40,200
Speaker 1: know what speed they were both going at, and the

1376
01:21:40,240 --> 01:21:43,120
Speaker 1: computer would know where they were at any time, and

1377
01:21:43,160 --> 01:21:45,840
Speaker 1: then it could forecast roughly where the ball was going

1378
01:21:45,880 --> 01:21:49,320
Speaker 1: to fall. And there are a lot of uncertainties in

1379
01:21:49,320 --> 01:21:52,320
Speaker 1: the wheel that are delivered. The ball will bounce off

1380
01:21:52,400 --> 01:21:54,720
Speaker 1: little veins on the side, it will spatter over the

1381
01:21:54,800 --> 01:21:57,760
Speaker 1: viders between pockets. But it turned out the prediction, the

1382
01:21:57,840 --> 01:22:01,519
Speaker 1: predicting was so strong that we got a forty percent edge,

1383
01:22:01,560 --> 01:22:04,920
Speaker 1: which is hute. Yes, so gigantic, you can't believe it.

1384
01:22:05,840 --> 01:22:10,640
Speaker 1: So you mentioned Warren Buffett, any other investors influence the

1385
01:22:10,720 --> 01:22:14,839
Speaker 1: way you approached investing or was it pretty much all

1386
01:22:14,920 --> 01:22:18,559
Speaker 1: based on your own research and math? It was pretty

1387
01:22:18,640 --> 01:22:22,880
Speaker 1: much thinking for myself, which was both good and bad.

1388
01:22:23,240 --> 01:22:26,600
Speaker 1: It was good because I thought of things that I

1389
01:22:26,600 --> 01:22:29,200
Speaker 1: wouldn't have thought of if I had been taught the

1390
01:22:29,280 --> 01:22:32,320
Speaker 1: formal academic way. It was bad because I had to

1391
01:22:32,320 --> 01:22:36,519
Speaker 1: rediscover some things that I would have known easily had

1392
01:22:36,560 --> 01:22:40,320
Speaker 1: I taken, you know, formal training. So let's talk about books.

1393
01:22:40,320 --> 01:22:44,560
Speaker 1: This is one of the favorite questions of of listeners.

1394
01:22:44,960 --> 01:22:47,439
Speaker 1: What are some of your favorite books, be they finance

1395
01:22:47,560 --> 01:22:50,479
Speaker 1: or nonfinance, fiction or nonfiction? What do you what do

1396
01:22:50,520 --> 01:22:54,640
Speaker 1: you enjoy reading? What have you enjoyed reading. Well, that's

1397
01:22:54,680 --> 01:22:58,840
Speaker 1: a very broadcuestion. I probably have currently in my libraries,

1398
01:22:58,920 --> 01:23:02,120
Speaker 1: both here and at home, ten thousand books. And I

1399
01:23:02,160 --> 01:23:05,559
Speaker 1: haven't read every page of every book, but I've read

1400
01:23:05,840 --> 01:23:09,160
Speaker 1: some pages in every book, and I've read some books

1401
01:23:09,280 --> 01:23:12,880
Speaker 1: in their entirety. And human knowledge is so vast that

1402
01:23:13,200 --> 01:23:15,000
Speaker 1: I can't just pick out a few books and say

1403
01:23:15,040 --> 01:23:17,240
Speaker 1: these are the great books from all those thousands and

1404
01:23:17,320 --> 01:23:20,360
Speaker 1: thousands of books. And by the way, besides the ten thousand,

1405
01:23:20,640 --> 01:23:23,400
Speaker 1: there are others that I've read that aren't in my library,

1406
01:23:23,439 --> 01:23:29,800
Speaker 1: many others. And then so I would say that I

1407
01:23:29,840 --> 01:23:32,320
Speaker 1: look at things kind of like Charlie Monker, And it's

1408
01:23:32,400 --> 01:23:38,880
Speaker 1: multiple mental models in which you focused on certain ideas

1409
01:23:38,920 --> 01:23:42,240
Speaker 1: in certain areas, Like here's one from psychology. There's something

1410
01:23:42,240 --> 01:23:48,360
Speaker 1: called the Meyer Briggs personality index, and so, uh, there

1411
01:23:48,360 --> 01:23:51,360
Speaker 1: are four four dimensions they type people on the first

1412
01:23:51,400 --> 01:23:56,559
Speaker 1: one is easy extrovert, introvert. And so if there are

1413
01:23:56,560 --> 01:23:59,000
Speaker 1: four dimensions and there are two choices, then you have

1414
01:23:59,040 --> 01:24:04,960
Speaker 1: sixteen pure types of people. None people aren't typically that's simple.

1415
01:24:05,040 --> 01:24:07,080
Speaker 1: They don't fall into those categories. But you can get

1416
01:24:07,080 --> 01:24:11,479
Speaker 1: a surprising amount of information by estimating what type of

1417
01:24:11,520 --> 01:24:16,880
Speaker 1: person is um But there's they're sensing. Sensing versus feeling

1418
01:24:16,960 --> 01:24:22,080
Speaker 1: is another dimension, and so a sensing person is very

1419
01:24:22,080 --> 01:24:25,200
Speaker 1: aware of everything that's going on around him, and a

1420
01:24:25,280 --> 01:24:29,200
Speaker 1: feeling person is a highly emotional person and so forth. Anyhow,

1421
01:24:30,360 --> 01:24:33,439
Speaker 1: it's an easy way to have a first cut thought

1422
01:24:33,840 --> 01:24:37,479
Speaker 1: about people, and it teaches you that people are very different,

1423
01:24:37,880 --> 01:24:41,080
Speaker 1: but there's no good or no bad in the various types.

1424
01:24:41,120 --> 01:24:44,400
Speaker 1: They're just different and helps you understand so that that's

1425
01:24:44,400 --> 01:24:51,599
Speaker 1: a simple mental model. Or there's anchoring in the stock markets,

1426
01:24:51,600 --> 01:24:53,880
Speaker 1: something I learned to my pain very early. I bought

1427
01:24:53,920 --> 01:24:57,280
Speaker 1: a stock at forty it went down to twenty I

1428
01:24:57,280 --> 01:24:58,840
Speaker 1: didn't want to let go till I came back to

1429
01:24:58,880 --> 01:25:01,960
Speaker 1: forty a little. Just let me get break even exactly,

1430
01:25:02,960 --> 01:25:06,720
Speaker 1: the losing cries of losing investors everywhere. Four years later,

1431
01:25:06,760 --> 01:25:09,000
Speaker 1: I get break even, I get out, but of course

1432
01:25:09,560 --> 01:25:12,360
Speaker 1: there's inflations, so I haven't really gotten out even, and

1433
01:25:12,520 --> 01:25:15,240
Speaker 1: I've deprived myself of investing the money somewhere else where

1434
01:25:15,240 --> 01:25:17,320
Speaker 1: it might have been done a lot better, and so forth.

1435
01:25:17,920 --> 01:25:22,000
Speaker 1: So the price I bought at forty is a price

1436
01:25:22,880 --> 01:25:25,840
Speaker 1: particular only to meet. There's no relation to anything going

1437
01:25:25,880 --> 01:25:28,120
Speaker 1: on in the outside world, and so to be anchored

1438
01:25:28,120 --> 01:25:32,280
Speaker 1: at that prices uh idiocy. And and yet people do

1439
01:25:32,320 --> 01:25:35,840
Speaker 1: it all the time because they're they're subjective perspective takes

1440
01:25:35,880 --> 01:25:39,800
Speaker 1: over from a more global perspective. So you have ten

1441
01:25:39,880 --> 01:25:43,599
Speaker 1: thousand books. I'm looking at a handful of books over here.

1442
01:25:44,439 --> 01:25:48,800
Speaker 1: Nothing really leaps out, as these aren't necessarily these are

1443
01:25:48,800 --> 01:25:51,840
Speaker 1: the five seminal books you have to read. But what

1444
01:25:51,880 --> 01:25:54,720
Speaker 1: books did you find interesting? Or or let me make

1445
01:25:54,760 --> 01:25:57,280
Speaker 1: it more recent, what's the last book you read that

1446
01:25:57,320 --> 01:26:02,160
Speaker 1: you really enjoyed? There are five six struggling Okay, well,

1447
01:26:02,439 --> 01:26:05,880
Speaker 1: one of them I enjoyed quite a bit. Was a

1448
01:26:05,920 --> 01:26:12,479
Speaker 1: well known one Philip Tatlock's super Forecasters, so by the

1449
01:26:12,479 --> 01:26:15,320
Speaker 1: way previous guests on the show, and absolutely a delightful

1450
01:26:15,400 --> 01:26:18,080
Speaker 1: human being. Yes. In fact, I was at a conference

1451
01:26:18,600 --> 01:26:20,559
Speaker 1: a few years ago that he and I both spoke at,

1452
01:26:20,680 --> 01:26:22,720
Speaker 1: so I kind of got to know a little bit

1453
01:26:22,720 --> 01:26:25,439
Speaker 1: more about his thoughts and ideas there. But yeah, very

1454
01:26:25,439 --> 01:26:31,080
Speaker 1: interesting piece of work, well worth knowing. Another one was

1455
01:26:31,800 --> 01:26:36,160
Speaker 1: The Accidental Accidental Superpower by guy named Peter's Eye Hand,

1456
01:26:37,240 --> 01:26:42,599
Speaker 1: and he's a Stratford type. By the way, I might

1457
01:26:42,600 --> 01:26:45,200
Speaker 1: say that if I read a book and I find

1458
01:26:45,200 --> 01:26:50,240
Speaker 1: it interesting, it doesn't mean that I'm endorsing that book

1459
01:26:50,280 --> 01:26:53,320
Speaker 1: kind of percent. It's just that I'm finding things that

1460
01:26:53,400 --> 01:26:55,599
Speaker 1: are worth thinking about in that book. And I may

1461
01:26:55,640 --> 01:26:58,240
Speaker 1: may come out with a different conclusion, but he's provoking

1462
01:26:58,240 --> 01:27:01,599
Speaker 1: a thought process and make you think about an issue. Hey,

1463
01:27:01,640 --> 01:27:05,599
Speaker 1: I hadn't really considered this quite in this context. That's fascinating. Yes,

1464
01:27:05,640 --> 01:27:08,840
Speaker 1: So I'd rather read a book that doesn't just reinforce

1465
01:27:08,920 --> 01:27:10,720
Speaker 1: opinions I already hold. I want a book that's going

1466
01:27:10,760 --> 01:27:13,080
Speaker 1: to add something that I don't know that's information, if

1467
01:27:13,080 --> 01:27:15,760
Speaker 1: it's new, if it's the same stuff over and over,

1468
01:27:15,800 --> 01:27:18,160
Speaker 1: that I've already thought about. That's not information, so more

1469
01:27:18,160 --> 01:27:21,960
Speaker 1: confirmation biases and how you select books. All right, So

1470
01:27:22,040 --> 01:27:23,840
Speaker 1: that's to give us one more and we'll let you

1471
01:27:23,880 --> 01:27:25,919
Speaker 1: off the hook with this. Okay. There's a one by

1472
01:27:26,040 --> 01:27:30,320
Speaker 1: Paul Wilmock. It's called The Money Formula, and it's it's

1473
01:27:30,360 --> 01:27:33,400
Speaker 1: just it's just out and it's about how quants have

1474
01:27:34,360 --> 01:27:37,280
Speaker 1: help screw things up in the financial world. Screw things up.

1475
01:27:37,320 --> 01:27:40,000
Speaker 1: Well they I don't know if that's the right phrase.

1476
01:27:40,000 --> 01:27:43,120
Speaker 1: They they've certainly helped mix things up and change things.

1477
01:27:43,479 --> 01:27:45,599
Speaker 1: But I would argue for the better, and I think

1478
01:27:45,680 --> 01:27:48,400
Speaker 1: you are going to be an agreement with that. Well,

1479
01:27:49,640 --> 01:27:54,280
Speaker 1: perhaps I put that too simplistically, but um, what I

1480
01:27:54,360 --> 01:27:57,920
Speaker 1: think part of his thrust is that the cell side

1481
01:27:58,000 --> 01:28:01,120
Speaker 1: on Wall Street has taken quant product us to use

1482
01:28:01,520 --> 01:28:06,200
Speaker 1: to market to people, and they haven't been discriminating about

1483
01:28:06,200 --> 01:28:10,519
Speaker 1: the products that they've marketed, a collateralized mortgage obligations being

1484
01:28:10,560 --> 01:28:13,920
Speaker 1: a case in point. Well, any tool could be used

1485
01:28:13,960 --> 01:28:16,960
Speaker 1: for good or evil, to say the least, but you know,

1486
01:28:17,120 --> 01:28:19,760
Speaker 1: it's it's anything is only a sausage is only as

1487
01:28:19,760 --> 01:28:21,680
Speaker 1: good as the meat that goes in. Well, you know,

1488
01:28:21,800 --> 01:28:25,680
Speaker 1: take coals, both good and evil. It pollutes, but it

1489
01:28:25,720 --> 01:28:28,400
Speaker 1: also keeps us warm and supplies this energy. It got

1490
01:28:28,439 --> 01:28:30,280
Speaker 1: us to the point where we can now start looking

1491
01:28:30,320 --> 01:28:35,200
Speaker 1: at less polluting energy options. And you know, when we

1492
01:28:35,240 --> 01:28:38,600
Speaker 1: look at that transition that's taking place not just with

1493
01:28:38,680 --> 01:28:43,000
Speaker 1: cold and natural gas, but quantitative is replacing the old

1494
01:28:43,120 --> 01:28:47,320
Speaker 1: qualitative for a reason because it's demonstrably superior in so

1495
01:28:47,360 --> 01:28:51,639
Speaker 1: many ways. I think that's a fair statement. I agree. Um,

1496
01:28:51,880 --> 01:28:54,879
Speaker 1: So you you started in this industry, in the finance

1497
01:28:54,920 --> 01:29:00,120
Speaker 1: industry forty plus years ago. Um, what it? What do

1498
01:29:00,200 --> 01:29:03,120
Speaker 1: you think is the most significant change that's taken place

1499
01:29:03,200 --> 01:29:07,479
Speaker 1: over that time? There's more than one. One of the

1500
01:29:07,520 --> 01:29:12,920
Speaker 1: big ones has been the computerization and the quantification of investing.

1501
01:29:13,360 --> 01:29:17,240
Speaker 1: That's really two big ones, right, So technology is everywhere

1502
01:29:17,280 --> 01:29:21,479
Speaker 1: and applying it mathematically seems to be the dominant thing.

1503
01:29:22,040 --> 01:29:27,439
Speaker 1: What else? The second has been the aggregation of money

1504
01:29:27,439 --> 01:29:34,200
Speaker 1: management into huge firms that are offering what seemed to

1505
01:29:34,240 --> 01:29:39,480
Speaker 1: be choices but are largely just playing vanilla in different packages.

1506
01:29:41,080 --> 01:29:44,559
Speaker 1: Is that is that still going on? Or is that

1507
01:29:44,600 --> 01:29:47,360
Speaker 1: now getting bigger and bigger? I think that the big

1508
01:29:47,400 --> 01:29:50,880
Speaker 1: firms are getting relatively bigger. Uh. The thing I read

1509
01:29:50,880 --> 01:29:55,559
Speaker 1: about hedge funds. Recently on a psyche bi got very riskful.

1510
01:29:55,600 --> 01:30:00,760
Speaker 1: I think I've heard of him was the number of

1511
01:30:00,800 --> 01:30:05,160
Speaker 1: new hedge funds is smaller than the number of Hatche

1512
01:30:05,200 --> 01:30:08,760
Speaker 1: funds that are disappearing, but that the total assets under

1513
01:30:08,800 --> 01:30:12,920
Speaker 1: management for hedge funds is increasing over three trillion dollars.

1514
01:30:13,640 --> 01:30:17,479
Speaker 1: So you know, Jim, I'm fond of repeating Jim Chanos's quote.

1515
01:30:18,000 --> 01:30:20,720
Speaker 1: He said when he started in hedge funds thirty years ago,

1516
01:30:21,600 --> 01:30:23,360
Speaker 1: there were a couple of hundred hedge funds. They all

1517
01:30:23,439 --> 01:30:26,960
Speaker 1: created alpha. Now there's almost ten thousand hedge funds and

1518
01:30:26,960 --> 01:30:30,320
Speaker 1: those same thirty hedge funds of the alpha generators. And

1519
01:30:30,760 --> 01:30:33,720
Speaker 1: there is some truth to that. There is. It is

1520
01:30:33,760 --> 01:30:36,679
Speaker 1: not a true Gaussing distribution. That's very much a fat

1521
01:30:36,720 --> 01:30:40,920
Speaker 1: head in a long tail um and not not everybody

1522
01:30:40,920 --> 01:30:43,599
Speaker 1: who's running a hedge fund is capable of putting up

1523
01:30:43,600 --> 01:30:45,360
Speaker 1: the sort of numbers that you put up. Well. In

1524
01:30:45,400 --> 01:30:48,360
Speaker 1: the nineties, I could find hetche funds when I didn't

1525
01:30:48,400 --> 01:30:50,960
Speaker 1: feel like managing money of my own. I could find

1526
01:30:50,960 --> 01:30:55,720
Speaker 1: hedge funds that we're making annualized. And then in the

1527
01:30:55,720 --> 01:31:00,639
Speaker 1: two thousands they kept dying. The numbers drop for Cliff

1528
01:31:00,840 --> 01:31:04,800
Speaker 1: and I want to say it's Simon Lacks book The

1529
01:31:04,880 --> 01:31:10,360
Speaker 1: hedge fund He said in the book, the losses hedge

1530
01:31:10,400 --> 01:31:13,000
Speaker 1: funds suffered in O eight oh nine had wiped out

1531
01:31:13,040 --> 01:31:16,840
Speaker 1: all of their previous profits in total. It didn't wipe

1532
01:31:16,880 --> 01:31:19,280
Speaker 1: out manager's fees, but it wiped out all the all

1533
01:31:19,320 --> 01:31:24,000
Speaker 1: the profits and other than those top let's call it

1534
01:31:24,080 --> 01:31:28,519
Speaker 1: five hedge funds, it's certainly not thirty these days. The

1535
01:31:28,560 --> 01:31:31,759
Speaker 1: balance really don't seem to be generating any sort of alpha.

1536
01:31:31,840 --> 01:31:34,240
Speaker 1: So you have a skewed when you look at the returns.

1537
01:31:34,240 --> 01:31:37,640
Speaker 1: They're so skewed not by the big hedge funds, but

1538
01:31:37,720 --> 01:31:40,280
Speaker 1: by all the rest that are under performing. Well. I

1539
01:31:40,280 --> 01:31:42,800
Speaker 1: think it's probably a first apement to say that all

1540
01:31:42,840 --> 01:31:45,280
Speaker 1: the money that have ever been put into hedge funds

1541
01:31:45,600 --> 01:31:48,679
Speaker 1: and then put it into X funds. The investors who

1542
01:31:48,680 --> 01:31:51,439
Speaker 1: did that would be far better off than they actually were.

1543
01:31:52,200 --> 01:31:55,639
Speaker 1: Renaissance technologies accepting in a handful of others. But that

1544
01:31:55,920 --> 01:31:59,840
Speaker 1: you could you could very well be right. Um, let's

1545
01:32:00,240 --> 01:32:02,760
Speaker 1: let's go to the next question. Tell us about a

1546
01:32:02,800 --> 01:32:05,479
Speaker 1: time you failed. And I know you wrote a few

1547
01:32:05,520 --> 01:32:08,240
Speaker 1: times in the book about things he failed and what

1548
01:32:08,320 --> 01:32:11,439
Speaker 1: you learned from the experience. Well back when I was

1549
01:32:11,479 --> 01:32:15,559
Speaker 1: in high school, I heard about a chemistry contest. It

1550
01:32:15,640 --> 01:32:19,840
Speaker 1: was the Southern California Chemistry Teachers Contest, and I happened

1551
01:32:19,880 --> 01:32:22,920
Speaker 1: to love chemistry, and I was at a school where

1552
01:32:23,720 --> 01:32:27,200
Speaker 1: basically there weren't any academics. But I decided that I

1553
01:32:27,200 --> 01:32:29,599
Speaker 1: would study for this test because if I were able

1554
01:32:29,640 --> 01:32:34,240
Speaker 1: to win it, I could choose a scholarship to a

1555
01:32:34,280 --> 01:32:37,280
Speaker 1: place I could not otherwise afford to go, like calchech

1556
01:32:37,439 --> 01:32:41,320
Speaker 1: or UC Berkeley or whatever. So I trained up for

1557
01:32:41,320 --> 01:32:46,280
Speaker 1: this test really hard, and the typical winning score was

1558
01:32:46,280 --> 01:32:50,040
Speaker 1: about Basically there was a couple of hundred of the

1559
01:32:50,080 --> 01:32:54,759
Speaker 1: best high school chemistry students from southern California would compete

1560
01:32:54,760 --> 01:32:58,760
Speaker 1: each year, and I competed as a fifteen year old

1561
01:32:58,880 --> 01:33:02,200
Speaker 1: as a junior. People who were basically seniors who were

1562
01:33:02,200 --> 01:33:05,080
Speaker 1: seventeen and eighteen years old, and I thought I was

1563
01:33:05,080 --> 01:33:09,479
Speaker 1: going to get the test were higher. So the best

1564
01:33:09,640 --> 01:33:12,000
Speaker 1: score by far they had ever seen. And I took

1565
01:33:12,120 --> 01:33:16,960
Speaker 1: old tests that my chemistry teacher had assembled over the years,

1566
01:33:17,360 --> 01:33:19,240
Speaker 1: and I was scoring that way on the old tests

1567
01:33:19,320 --> 01:33:22,439
Speaker 1: one after another. I went into the test and I

1568
01:33:22,479 --> 01:33:25,360
Speaker 1: just rolled through the exam. I got just about everything right,

1569
01:33:25,640 --> 01:33:27,840
Speaker 1: and then I came to the last part, which was

1570
01:33:28,240 --> 01:33:31,639
Speaker 1: a lot of calculating, and they allowed slide rules that year,

1571
01:33:32,400 --> 01:33:34,559
Speaker 1: and they didn't say they were necessary, only that you

1572
01:33:34,560 --> 01:33:36,400
Speaker 1: could bring one if you wanted to. Turned out the

1573
01:33:36,400 --> 01:33:39,439
Speaker 1: tests were designed so that only with a good slide

1574
01:33:39,479 --> 01:33:42,320
Speaker 1: rule could you complete that part of the test. I

1575
01:33:42,320 --> 01:33:44,680
Speaker 1: had a tense cent slide rule I brought along was worthless.

1576
01:33:44,880 --> 01:33:47,840
Speaker 1: It was so inaccurate that there was no chance of

1577
01:33:47,960 --> 01:33:50,640
Speaker 1: even bothering. So I did what I could by hand,

1578
01:33:50,840 --> 01:33:53,080
Speaker 1: and I completed I think eight hundred and seventy three

1579
01:33:53,120 --> 01:33:56,120
Speaker 1: points on the test. I got eight hundred sixty right,

1580
01:33:56,760 --> 01:34:00,479
Speaker 1: and but um, I was just crushed by the fact

1581
01:34:00,880 --> 01:34:03,320
Speaker 1: that I didn't have a good slide rule, but I

1582
01:34:03,320 --> 01:34:06,800
Speaker 1: hadn't prepared and covered that base properly. So that that

1583
01:34:06,920 --> 01:34:08,720
Speaker 1: taught me that one of the things you wanted to

1584
01:34:08,760 --> 01:34:12,240
Speaker 1: do is look at things redundantly when you can and

1585
01:34:12,520 --> 01:34:16,639
Speaker 1: try to cover all the downside possibilities that might occur

1586
01:34:16,720 --> 01:34:19,479
Speaker 1: and eliminate them. So that worked in very well later

1587
01:34:19,520 --> 01:34:23,200
Speaker 1: when I played in the casinos and when I ran

1588
01:34:23,320 --> 01:34:26,360
Speaker 1: hedge fund. Did you end up taking the test again

1589
01:34:26,400 --> 01:34:30,000
Speaker 1: when you were a senior Um, that's a good interesting question.

1590
01:34:30,120 --> 01:34:31,800
Speaker 1: I asked him if I could, and they said, no,

1591
01:34:31,840 --> 01:34:34,679
Speaker 1: you can only take it once. So I thought about that,

1592
01:34:34,920 --> 01:34:37,160
Speaker 1: and I found that there was a physics physics test

1593
01:34:38,120 --> 01:34:41,800
Speaker 1: that was also given by It was analogous to the

1594
01:34:41,880 --> 01:34:44,800
Speaker 1: chemistry test who was given by the physics teachers, and

1595
01:34:44,840 --> 01:34:48,200
Speaker 1: another couple of hundred of the best students southern California

1596
01:34:48,240 --> 01:34:52,000
Speaker 1: took that. So I crammed for that test. I only

1597
01:34:52,479 --> 01:34:55,760
Speaker 1: heard about a short time before, and I wasn't able

1598
01:34:55,760 --> 01:34:57,280
Speaker 1: to finish all the studying that I wanted to do,

1599
01:34:57,320 --> 01:34:59,160
Speaker 1: but I was able to win that one. Really, so

1600
01:34:59,400 --> 01:35:03,360
Speaker 1: and you that, Um, so, that's like a tuition scholarship.

1601
01:35:03,400 --> 01:35:05,559
Speaker 1: And you I had a choice of cal Techer Berkeley.

1602
01:35:06,040 --> 01:35:07,840
Speaker 1: I found out I wanted to go to cal Tech,

1603
01:35:08,000 --> 01:35:09,960
Speaker 1: but I couldn't because I didn't have the money to

1604
01:35:10,000 --> 01:35:12,559
Speaker 1: live in your passage, you know. So you went to Berkeley?

1605
01:35:12,840 --> 01:35:15,320
Speaker 1: And how did that work out? Well? It worked out

1606
01:35:15,360 --> 01:35:18,519
Speaker 1: just fine. I got a full scholarship, and after a

1607
01:35:18,560 --> 01:35:20,479
Speaker 1: year at Berkeley, I changed the U c l A

1608
01:35:20,520 --> 01:35:23,000
Speaker 1: because I had more friends down there, and I went

1609
01:35:23,040 --> 01:35:25,680
Speaker 1: through the UC system and everything went was fine for me.

1610
01:35:26,240 --> 01:35:29,160
Speaker 1: You're physically fit, you seem to be in pretty good shape.

1611
01:35:29,200 --> 01:35:31,640
Speaker 1: Tell us what you do to stay physically fit and

1612
01:35:31,680 --> 01:35:35,600
Speaker 1: what do you do to keep mentally fit? Well physically.

1613
01:35:36,560 --> 01:35:40,200
Speaker 1: I've been a long distance I had had been a

1614
01:35:40,240 --> 01:35:43,760
Speaker 1: long distance runner for about twenty five years. I used

1615
01:35:43,760 --> 01:35:47,080
Speaker 1: to run marathons and have sent ten ks in five ks.

1616
01:35:47,560 --> 01:35:50,320
Speaker 1: I'd run about forty miles a week, and then I

1617
01:35:50,400 --> 01:35:54,200
Speaker 1: hurt my back weightlifting, so now I just walk. When

1618
01:35:54,200 --> 01:35:56,639
Speaker 1: when did you stop running around? What you How old

1619
01:35:56,680 --> 01:36:00,880
Speaker 1: were you? I stopped running when I was sixty eight,

1620
01:36:01,360 --> 01:36:07,800
Speaker 1: ran my last marathon in New York in and so

1621
01:36:07,880 --> 01:36:13,080
Speaker 1: that's twenty years ago. But you're not eighty eight. No,

1622
01:36:14,160 --> 01:36:18,160
Speaker 1: I'm doing the math. You sixty eight? Well, seventeen years ago? Seven? Okay?

1623
01:36:19,120 --> 01:36:21,599
Speaker 1: And you do you say you do you still lift

1624
01:36:21,640 --> 01:36:24,080
Speaker 1: weights or yes? I go to the gym with a

1625
01:36:24,120 --> 01:36:27,719
Speaker 1: trainer twice a week. I walk about ten to fifteen

1626
01:36:27,760 --> 01:36:31,439
Speaker 1: miles a week, and then I do some hiking in

1627
01:36:31,479 --> 01:36:35,719
Speaker 1: the hills. What do you do to to keep mentally active? What?

1628
01:36:35,720 --> 01:36:38,960
Speaker 1: What do you enjoy? What? What keeps your focus these days?

1629
01:36:40,400 --> 01:36:43,720
Speaker 1: I'd like I still like problems in finance, so I

1630
01:36:43,800 --> 01:36:46,280
Speaker 1: take time after work on some of them. I also

1631
01:36:47,280 --> 01:36:51,960
Speaker 1: black math problems and chest problems and so forth, play

1632
01:36:51,960 --> 01:36:58,320
Speaker 1: word games, um, travel, read a lot, talked to really

1633
01:36:58,360 --> 01:37:01,840
Speaker 1: interesting people. Okay, I have a very smart family and

1634
01:37:01,960 --> 01:37:05,240
Speaker 1: so they're very stimulating. That sounds like I have a

1635
01:37:06,320 --> 01:37:09,280
Speaker 1: I have grandchildren who are all very talented. Three of

1636
01:37:09,320 --> 01:37:13,120
Speaker 1: them are triplets, and they're all out of I t Oh, really,

1637
01:37:13,600 --> 01:37:16,120
Speaker 1: so you're you're busy all the time. There's there's no

1638
01:37:16,240 --> 01:37:20,200
Speaker 1: depth of things to keep you occupying. The last time

1639
01:37:20,240 --> 01:37:22,200
Speaker 1: I was born, I think was when I was eleven.

1640
01:37:23,960 --> 01:37:28,479
Speaker 1: That's a fair fair statement. So if the triplets and

1641
01:37:28,640 --> 01:37:32,160
Speaker 1: M I T or any other millennials or recent graduates

1642
01:37:32,200 --> 01:37:36,000
Speaker 1: were to come up to you and say, uh, Ed Thorpe,

1643
01:37:36,040 --> 01:37:39,680
Speaker 1: I'm thinking about a career in finance or investing, what

1644
01:37:39,800 --> 01:37:44,080
Speaker 1: sort of advice would you give them? I'd say, play

1645
01:37:44,120 --> 01:37:47,200
Speaker 1: to your strengths and your skills, do what you like

1646
01:37:47,320 --> 01:37:49,400
Speaker 1: to do. And if you do what you like to do,

1647
01:37:49,520 --> 01:37:51,639
Speaker 1: then you're gonna do better than if you do something

1648
01:37:51,680 --> 01:37:54,040
Speaker 1: that you think you should do but don't want to do.

1649
01:37:54,800 --> 01:37:59,000
Speaker 1: And try to plan your life so that you're spending

1650
01:37:59,000 --> 01:38:02,719
Speaker 1: your time with good people. That sounds like that sounds

1651
01:38:02,720 --> 01:38:06,760
Speaker 1: like good advice. And and my final question what do

1652
01:38:06,800 --> 01:38:10,120
Speaker 1: you know today about investing that you wish you knew

1653
01:38:10,240 --> 01:38:15,320
Speaker 1: fifty years ago when you were getting started. I would

1654
01:38:15,320 --> 01:38:18,320
Speaker 1: like to have known that I could have bought Berkshire

1655
01:38:18,320 --> 01:38:25,360
Speaker 1: Hathaway at twelve instead of it instead. So so that's

1656
01:38:25,439 --> 01:38:30,320
Speaker 1: the key thing is precience about the success of Warren Buffett.

1657
01:38:30,560 --> 01:38:32,240
Speaker 1: I will tell you a lot of people would have

1658
01:38:32,280 --> 01:38:37,240
Speaker 1: liked to buy Berkeshire Hathaway under a thousand. So even

1659
01:38:37,280 --> 01:38:40,160
Speaker 1: twelve is even even when you got in, is better

1660
01:38:40,240 --> 01:38:43,599
Speaker 1: than when most people got in. Um. Ed Thorpe, thank

1661
01:38:43,640 --> 01:38:46,400
Speaker 1: you so much for being so this is this has

1662
01:38:46,439 --> 01:38:49,960
Speaker 1: been an absolute delight we have been speaking with Ed Thorpe.

1663
01:38:49,960 --> 01:38:52,800
Speaker 1: I I will pound the table on a couple of

1664
01:38:52,840 --> 01:38:55,519
Speaker 1: his books. A Man for All Markets is his most

1665
01:38:55,520 --> 01:39:00,000
Speaker 1: recent book. It's really it's more than than a biograph

1666
01:39:00,120 --> 01:39:03,080
Speaker 1: fee It's a history of what's happened in finance over

1667
01:39:03,120 --> 01:39:07,439
Speaker 1: the past half century, including the rise of hedge funds,

1668
01:39:07,439 --> 01:39:10,840
Speaker 1: the rise of quants, the rise of all sorts of things,

1669
01:39:10,960 --> 01:39:14,559
Speaker 1: told from a unique perspective of somebody who not only

1670
01:39:14,600 --> 01:39:16,639
Speaker 1: beat the deal or and beat the markets, but as

1671
01:39:16,760 --> 01:39:20,200
Speaker 1: as Um, learned a lot of secrets about life that

1672
01:39:20,200 --> 01:39:23,439
Speaker 1: that many of us should learn um. If you enjoy

1673
01:39:23,560 --> 01:39:25,720
Speaker 1: this conversation, be sure and look up and inch or

1674
01:39:25,800 --> 01:39:31,880
Speaker 1: down an inch on Apple iTunes, SoundCloud overcast, or Bloomberg

1675
01:39:31,920 --> 01:39:33,920
Speaker 1: dot com and you could see the other hundred and

1676
01:39:34,000 --> 01:39:39,720
Speaker 1: fifty or so such conversations we've had. We love your comments,

1677
01:39:39,760 --> 01:39:44,640
Speaker 1: feedback and suggestions right to us at m IB podcast

1678
01:39:44,680 --> 01:39:47,840
Speaker 1: at Bloomberg dot net. I would be remiss if I

1679
01:39:47,880 --> 01:39:50,240
Speaker 1: did not thank some of the people who helped to

1680
01:39:50,439 --> 01:39:54,000
Speaker 1: put this podcast together. Michael Batnick is my head of research.

1681
01:39:54,400 --> 01:39:59,840
Speaker 1: Taylor Riggs is our producer. Booker Medina Partwada is our

1682
01:40:00,000 --> 01:40:03,400
Speaker 1: audio engineer who helps make these things sound as good

1683
01:40:03,439 --> 01:40:06,960
Speaker 1: as they actually do. I'm Barry Ridhults. You've been listening

1684
01:40:07,000 --> 01:40:18,040
Speaker 1: to Masters in Business on Bloomberg Radio. Masters in Business

1685
01:40:18,080 --> 01:40:21,479
Speaker 1: is brought to you by the American Arbitration Association. Business

1686
01:40:21,479 --> 01:40:25,920
Speaker 1: disputes are inevitable, resolve faster with the American Arbitration Association,

1687
01:40:26,280 --> 01:40:30,679
Speaker 1: the global leader in alternative dispute resolution for over ninety years.

1688
01:40:31,160 --> 01:40:33,800
Speaker 1: Learn more at a d R dot org.