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Ed Thorp, The Man Who Beat The Dealer and The Market · Masters in Business

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Speaker 1: Masters in Business is brought to you by the American
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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
12:10
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
12:42
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,
12:54
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
13:01
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,
13:52
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
14:23
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
15:15
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
15:21
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
15:30
Speaker 1: a true coin, sure, yes so, and people can argue
15:34
Speaker 1: about whether coins are true or not, but the distinctions
15:36
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
16:06
Speaker 1: he pulled a lever before he released it, so he
16:08
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
16:19
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
16:29
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.
16:36
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
16:45
Speaker 1: investing has become more complex than gambling. Why is that. Well,
16:51
Speaker 1: with most gambling aims, you can calculate probabilities, and so
16:54
Speaker 1: you know what the odds are in any given situation,
16:57
Speaker 1: and you can figure out pretty accurately how much to that.
17:00
Speaker 1: With a security it's more difficult. You only have rough estimates.
17:04
Speaker 1: For example, there's something called the long normal probability distribution
17:09
Speaker 1: that is a rough model for how stock prices change,
17:13
Speaker 1: but it's not an exact model, and it has parameters
17:17
Speaker 1: and like volatility, and if you make a supposition as
17:20
Speaker 1: that what the volatility is going to be in the
17:22
Speaker 1: near future, you can get an idea of the distribution
17:26
Speaker 1: of prices of a particular stock in the future, but
17:30
Speaker 1: you might be off in what you think the volatility
17:32
Speaker 1: is going to be, and so your distribution will be off.
17:34
Speaker 1: So anyhow, it's it's a matter of estimating things that
17:39
Speaker 1: you can't get exactly, you can't know. So so how
17:42
Speaker 1: natural was that progression from the casinos to Wall Street
17:46
Speaker 1: other than they're both big, interesting mathematical riddles to be solved. Well,
17:51
Speaker 1: I learned a lot of things in the casino, I
17:54
Speaker 1: would say, just as an aside um, using a winning
17:58
Speaker 1: gambling system of the casino is per training for the
18:01
Speaker 1: investment world. And the reason is that you learn how
18:05
Speaker 1: to manage money, You learn how much to bet. When
18:07
Speaker 1: you know what your edge is, you get a certain
18:09
Speaker 1: discipline in conducting a strategy that you think is right.
18:13
Speaker 1: For example, let's suppose that you're an index fund investor.
18:16
Speaker 1: If you understand what the probability distribution has been for
18:22
Speaker 1: an index fund in the past, then you have an
18:24
Speaker 1: idea of what kind of ups and downs you're going
18:26
Speaker 1: to be writing in the future. And when something bad
18:28
Speaker 1: happens like two thousand and two tho nine, you don't
18:31
Speaker 1: run out like a scared rabbit at the bottom and
18:33
Speaker 1: miss the whole climb back up to new highs. And
18:37
Speaker 1: yet that seems to be what many people. Many people do,
18:41
Speaker 1: they do, but if they had been car counters a
18:43
Speaker 1: black tack like the professionals I see the blackjack ball
18:46
Speaker 1: once a year if they had been, if they had
18:49
Speaker 1: that experience, they wouldn't flinch at all. So in the book,
18:53
Speaker 1: you describe sort of your first tentative ventures into markets
19:00
Speaker 1: after um the Vegas experience. Tell us how you you
19:04
Speaker 1: began investing well after I had made money both from
19:09
Speaker 1: book royalties and from card counting at blackjack and baccarat
19:14
Speaker 1: in Las Vegas. After I've done that, I had money
19:16
Speaker 1: to invest for the first time in my life. Academics
19:19
Speaker 1: weren't paid a whole lot then and they're not paid
19:20
Speaker 1: a whole lot now, So I find my problem was
19:24
Speaker 1: to figure out how to invest the money. So, knowing nothing,
19:29
Speaker 1: I made my collection of mistakes. I paid a mr
19:33
Speaker 1: Market is rather expensive tuition to learn a bunch of
19:37
Speaker 1: basic things that I'm described in the book, things that
19:40
Speaker 1: behavioral finance people understand now as foolish mistakes. And then
19:45
Speaker 1: I sat down for a whole summer and read everything
19:48
Speaker 1: I could on finance in a big bookstore in Beverly Hills.
19:52
Speaker 1: You described two summers of of of learning in the
19:56
Speaker 1: library and in the books. How many books did you
19:59
Speaker 1: play through? What were you reading. Well, my first go
20:02
Speaker 1: round and the first summer was to read whatever they
20:05
Speaker 1: had in a big Martindale's bookstore on Beverly Hills. So
20:07
Speaker 1: it was newspapers, investment books, charting fundamentals, all kinds of advice,
20:16
Speaker 1: which which leads me to ask a question. And again
20:19
Speaker 1: it's a quote of yours. Most stockpicking stories, advice recommendations
20:25
Speaker 1: are completely worthless. Was that was that your takeaway back
20:28
Speaker 1: then or is that something you learned over the years. Well,
20:31
Speaker 1: it wasn't my instant takeaway, but it was something I
20:34
Speaker 1: came to fairly rapidly as I plowed through all these things.
20:37
Speaker 1: What what is it that led you to the conclusion,
20:41
Speaker 1: which I completely agree with, that a lot of what's
20:44
Speaker 1: out there is just either conflicted or self interested, or
20:48
Speaker 1: noisy or not well informed commentary. Well, I had a
20:53
Speaker 1: science background, and I tend to draw conclusions based on
21:00
Speaker 1: nation facts evidence. That's so old school. We don't do
21:04
Speaker 1: that anymore. It's it's it's now, it's all about the
21:06
Speaker 1: fake facts, fake news. I'm basically immune to all that stuff.
21:12
Speaker 1: It's like water running off a dock or whatever. So anyhow,
21:16
Speaker 1: you brought a fairly critical eye to whatever was being
21:20
Speaker 1: passed off his market advice? Is that a fair statement
21:24
Speaker 1: just because people said something wasn't good enough. I had
21:28
Speaker 1: that set from the time I was a child. I
21:31
Speaker 1: wanted to check it for myself and verify for myself.
21:34
Speaker 1: And you blew up a number of chemistry kits and
21:37
Speaker 1: other such things which you describe in the book UM.
21:41
Speaker 1: So let's talk a little bit about return. So one
21:43
Speaker 1: of the things you discovered are pricing inefficiencies, pricing anomalies
21:49
Speaker 1: that say, hey, here are two related UM stocks. You
21:55
Speaker 1: can either have a stock and a warrants or later
21:58
Speaker 1: on a stock and an option, and they should trade parallel,
22:02
Speaker 1: but they don't. Sometimes one or the other gets cheap
22:05
Speaker 1: and the other gets overvalued. Was anybody else doing those
22:10
Speaker 1: sort of paired or hedged trades at the time. No, Actually,
22:17
Speaker 1: what happened when I I was sent my set. I
22:20
Speaker 1: spent my second summer trying to learn about finance, and
22:23
Speaker 1: I got lucky. The first week or so, I got
22:26
Speaker 1: a little pamp in the mail from something called r
22:28
Speaker 1: h M warrant survey guy named Sydney Freed. I believe
22:32
Speaker 1: that his son still runs this survey, and so it
22:36
Speaker 1: told tales buying warrants for pennies and cashing him out
22:40
Speaker 1: for dollars in the future. So as I read through it,
22:45
Speaker 1: I learned what I warrant was, and I said, hey,
22:47
Speaker 1: wait a minute. This simplifies the investing problem tremendously because
22:51
Speaker 1: the price of a warrant and the price of this
22:53
Speaker 1: underlying stock are related. So I don't have to worry
22:56
Speaker 1: about all these fundamental things. I don't have to go
22:58
Speaker 1: out and talk to CEOs and read balance and income
23:02
Speaker 1: statements and compare companies one to another. If these things
23:07
Speaker 1: moved together, then if they get out of whack, I
23:10
Speaker 1: had to be able to set up some kind of
23:13
Speaker 1: investment short one along the other that will make money
23:15
Speaker 1: for me. So I began to think then about how
23:18
Speaker 1: how to price it warrant, how to know when I
23:20
Speaker 1: was at a proper price or overpriced or underpriced? And
23:24
Speaker 1: you were using some pretty early computer technology at the time. Yes,
23:29
Speaker 1: I had actually got into using computers with Blackjack. I
23:33
Speaker 1: had done that back at M I T and sixty.
23:37
Speaker 1: They had an IBM seven oh four that was available then.
23:40
Speaker 1: So I taught myself for tran and the programmed to
23:45
Speaker 1: figure out the equations solve the equations that I needed
23:50
Speaker 1: to decide which cards were good to have in the
23:54
Speaker 1: deck in blackjack and which cards were good to have
23:57
Speaker 1: out of the deck. So anyhow, I had the experience
24:00
Speaker 1: with the computers, which was fortunate. So then when I
24:04
Speaker 1: got to think about warrants, I had the computer background.
24:09
Speaker 1: Two actually mathematic size and draw graphs and do that
24:13
Speaker 1: sort of thing. Made it much more easy for me.
24:16
Speaker 1: And so you would find when these two related securities
24:20
Speaker 1: were out of out of sync, and basically made the
24:24
Speaker 1: bet that was did any of them ever go against
24:28
Speaker 1: you in an unanticipated way? Or did everything perform as expected.
24:33
Speaker 1: The first things we found were overpriced warrants, and I
24:36
Speaker 1: happened to run into a professor at U see I
24:40
Speaker 1: the first day that you See I opened for classes
24:43
Speaker 1: back in we're both new faculty members, and he had
24:46
Speaker 1: written a thesis about warrants name Machine Kasuf. And so
24:51
Speaker 1: we got together and met every every day for about
24:54
Speaker 1: a year or two to develop the theory further, and
24:57
Speaker 1: then we wrote a book Beat the Market about it
24:59
Speaker 1: and the warrants that we found that were overpriced. We
25:06
Speaker 1: were short and we would buy common stock, long hedge,
25:11
Speaker 1: and every one of those was a winner m hm.
25:15
Speaker 1: And every one of them historically had been winner, so
25:17
Speaker 1: we knew that these overpriced warrants somehow eluded the market.
25:21
Speaker 1: There were warrants with two years or less ago, and
25:23
Speaker 1: investors seemed to think that two years was forever, so
25:26
Speaker 1: they proaced the warrants way high, and then the warrants
25:29
Speaker 1: when they would wake up as the clock ran on
25:32
Speaker 1: the two years, the warrants would collapse in price. So
25:35
Speaker 1: you could short the warrant about the stock. And it
25:37
Speaker 1: was just one annualized event after another and compounded. How
25:43
Speaker 1: did this investment end up running? What did you end
25:47
Speaker 1: up putting up his returns? Well, we made about a
25:52
Speaker 1: year gross and we had investors, faculty members at you
25:56
Speaker 1: see I that sort of thing that rode along with us.
25:59
Speaker 1: So after our our fee, which was the profits and
26:02
Speaker 1: no more, uh, they needed twenty percent a year. And
26:06
Speaker 1: we did this for several years and then um I
26:11
Speaker 1: went into business for myself and she went off and
26:14
Speaker 1: went into business for himself, and you you launched a
26:18
Speaker 1: couple of hedge funds we're going to talk about in
26:20
Speaker 1: a little bit. I want to ask you about one
26:23
Speaker 1: other quote of yours that I really enjoyed. That's related
26:25
Speaker 1: to this. Every stock market system with an edge is
26:29
Speaker 1: necessarily limited in the amount of money it can use
26:33
Speaker 1: and still produce extra returns. In other words, every edge
26:38
Speaker 1: has a scale limit. It can only get so big. Yes,
26:42
Speaker 1: and and how did how did you come to that conclusion?
26:45
Speaker 1: What made you realize, hey, this can only go so far. Well,
26:49
Speaker 1: let me say, first of all, the scale limit could
26:51
Speaker 1: be huge. It could be tens of billions, or could
26:54
Speaker 1: be ten or twenty or fifty million. I have a
26:58
Speaker 1: friend who has a commodity hedge fund that's one of
27:01
Speaker 1: the best performing ones around, and it it's limit is
27:05
Speaker 1: like seventy million dollars. Because trading costs get too high.
27:09
Speaker 1: What happens is if you find a pair of securities
27:12
Speaker 1: that out of line, if one's overpriced and one's underpriced.
27:15
Speaker 1: If you start selling short the overpriced one, you'll drive
27:18
Speaker 1: its priced down so it'll be less overpriced. If you
27:21
Speaker 1: start buying the enterpriced one, you'll lift its price so
27:23
Speaker 1: it will become less underpriced. So your action in trading
27:28
Speaker 1: misprice securities tends to drive them towards the correct price, which,
27:32
Speaker 1: by the way, is a service to the other people
27:34
Speaker 1: in the market because then they see securities that are
27:38
Speaker 1: more fairly priced that they would be otherwise. So not
27:40
Speaker 1: quite shouting those cat you you actually are affecting this
27:44
Speaker 1: by training it as opposed to a bigger liquid equity
27:48
Speaker 1: where a few million dollars and gonna move the price
27:50
Speaker 1: very much. And by the way, that's one of the
27:52
Speaker 1: huge things between that is a difference between finance and physics.
27:57
Speaker 1: In physics, you can do the same experiment over and
27:59
Speaker 1: over and you doesn't affect the natural world. But in finance,
28:02
Speaker 1: what you do affects the human world. You're you're a
28:07
Speaker 1: You're an observer and a participant and affect the outcome
28:10
Speaker 1: of everybody else's opportunities. Let's talk about some of the
28:14
Speaker 1: interesting things you've done and people you've met over over
28:19
Speaker 1: your career. You played bridge with Warren Buffett. Uh tell
28:24
Speaker 1: us how that came about. Well, when I was at
28:28
Speaker 1: U c Irvine, after I had figured out how to
28:31
Speaker 1: value warrants, I started trading for my own account and
28:34
Speaker 1: then the word spread and I signed up people at
28:37
Speaker 1: the university who wanted me to trade for their accounts.
28:40
Speaker 1: And one of the people was the dean of the
28:42
Speaker 1: graduate division, A fellow named Ralph Waldo Gerard, National Academy
28:46
Speaker 1: of Science member, among other things, and so he wanted
28:53
Speaker 1: to get to know me and find out more about
28:55
Speaker 1: this trading. So I was introduced to a friend of
29:00
Speaker 1: his who was going to I didn't know this, but
29:03
Speaker 1: the friend was going to kind of check me out.
29:05
Speaker 1: And the friend was the young Warren Buffett. So we
29:09
Speaker 1: had a dinner at the Gerard house with our wives
29:15
Speaker 1: and then we end up playing bridge, and I went
29:18
Speaker 1: down to Emerald Bay where Warren had one house and
29:20
Speaker 1: later two houses. That's not too far from here. Lodona
29:25
Speaker 1: or Emerald Bay is about four or five miles right
29:29
Speaker 1: down the coast. It's at the north end of Laguna Beach.
29:34
Speaker 1: Lovely part of the world. And you actually said something
29:38
Speaker 1: to your wife, I think that Warren Buffett guy is
29:41
Speaker 1: going to be the wealthiest person in America. Exactly How
29:44
Speaker 1: did that thought process come about? Well, he was cashing
29:47
Speaker 1: out his partnership, which had about a hundred million dollars
29:51
Speaker 1: in it then it was. It was the biggest hedge
29:53
Speaker 1: fund around and also the only really successful one at
29:57
Speaker 1: that point. The runner up is Michael Stronhardt's fund, which
30:03
Speaker 1: was up slightly during a pretty bad period in UM
30:07
Speaker 1: seventy and Buffet's partnership was still doing well, but he
30:10
Speaker 1: said things are so overpriced. I'm cashing out, which is
30:13
Speaker 1: why Ralph Gerard was looking at me. He wanted someplace
30:16
Speaker 1: to put his money that was coming out of Buffett
30:18
Speaker 1: Partners and Buffett was vetting you to see if you
30:21
Speaker 1: were okay to take their money, which is not a
30:24
Speaker 1: bad person to be vetted by. So we got along
30:26
Speaker 1: just fine and talked about a lot of different things,
30:29
Speaker 1: and the way we thought about things was similar, that is,
30:32
Speaker 1: you know, rational, evidence based and so forth. His method
30:36
Speaker 1: of investing, though it was a lot more hands on
30:39
Speaker 1: that I wanted to be. I wasn't an invest I
30:42
Speaker 1: wasn't interested in spending my total percent energy and life
30:46
Speaker 1: just investing. I just thought it was an interesting thing
30:49
Speaker 1: to be involved in because I had some money to
30:51
Speaker 1: invest at the time. So the nine letter from Buffett
30:55
Speaker 1: to his partner's explains he's winding down and eventually he
30:59
Speaker 1: takes Berkshire Hathaway and relaunches it. Um not too not
31:04
Speaker 1: too far off from the distance. You were one of
31:07
Speaker 1: the first investors in in Berkshire Hathway Is that a
31:09
Speaker 1: fair statement, No, not so early. What happened was Buffett
31:13
Speaker 1: didn't tell his limited partners that what he was going
31:16
Speaker 1: to do next. What he was going to do next
31:18
Speaker 1: was turned Berkshire Hathaway into his own private mutual fund.
31:22
Speaker 1: So he had stock to distribute, and he could either
31:24
Speaker 1: distribute stock to the investors or give them money, and
31:29
Speaker 1: he preferred, I think, to keep the stock and give
31:32
Speaker 1: them money. So mostly the investors were cashing out. Some
31:35
Speaker 1: of them took some Burkshire stock and not not a
31:38
Speaker 1: whole lot happened with Berkshire for the next decade, and
31:43
Speaker 1: I was unaware that he was turning it into his
31:45
Speaker 1: own mutual fund. But I was sitting around doing something
31:50
Speaker 1: or other with the hedge fund. I was then and
31:51
Speaker 1: running Princeton Newport Partners, and heard some news item about
31:55
Speaker 1: Berkshire Hathaway and Warren Buffett, so I focused on it,
31:59
Speaker 1: and I realized right away what had happened in the
32:03
Speaker 1: intervening fourteen or fifteen years, that Buffett had turned this
32:08
Speaker 1: uh sick textile company into his own private investment tool.
32:15
Speaker 1: So had I had I known that? I had I
32:20
Speaker 1: been able to invest in Berkshire Hathaway in to I
32:26
Speaker 1: might have invested somewhere in the twelve or fifty dollar range.
32:30
Speaker 1: But but now, but now, the stock with two dollars
32:34
Speaker 1: so most people was a g It's already had a
32:38
Speaker 1: huge multiple. You don't want to buy it now? I said, yes,
32:41
Speaker 1: I want to buy it now. So I began buying
32:44
Speaker 1: in and And how long did you hold on to
32:46
Speaker 1: Berkshire hath You still have it to this day. So
32:50
Speaker 1: I'm compelled to ask, a hundred dollars invested in Berkshire
32:54
Speaker 1: hath Away when you put that in in eighty two,
32:58
Speaker 1: what would a hundred dollar be worth today? Well, it's
33:01
Speaker 1: easier for me to think in hers with a thousand,
33:02
Speaker 1: because that was stocker slutly less. So it's worth roughly two.
33:07
Speaker 1: That's a pretty good return. Well, how does what does
33:10
Speaker 1: that average out compounded that mid twenties? Something like that
33:14
Speaker 1: two D and fifty four thirty five years, two D
33:22
Speaker 1: and fifty thirty five years. I'm guessing mid twenties, but
33:27
Speaker 1: I'm sure I could just punch it into the computer
33:29
Speaker 1: and get the actual answer. But by any stretch of
33:33
Speaker 1: the imagination, it's a fabulous return. And the consistency over
33:37
Speaker 1: such a long time. There is nothing comparable to that,
33:40
Speaker 1: is there that? That's amazing? How did you never get
33:44
Speaker 1: tempted to sell? Let's see if I can figure it out. Okay,
33:47
Speaker 1: I want to take the route of two and fifty
33:52
Speaker 1: so um, so it's about to the five point six
33:58
Speaker 1: over thirty five. So I would say, I'm just this
34:04
Speaker 1: is rough. I could be off a few maybe analyzed,
34:08
Speaker 1: Oh that's no big deal. He's you know, he should
34:11
Speaker 1: have given up for a very long time. That's astonishing.
34:15
Speaker 1: You know. One of the things, and we just talked
34:17
Speaker 1: about it earlier, is eventually everything reaches a capacity. You
34:21
Speaker 1: can't just find an edge and scale it forever unless
34:25
Speaker 1: you're Warren Buffett, whose edges a tremendous amount of um
34:30
Speaker 1: not only intellectual capacity, but discipline and the ability to
34:35
Speaker 1: say I don't care about this, I don't care about that,
34:38
Speaker 1: I don't care about technology. This is what I do well,
34:41
Speaker 1: and I'm going to stick to my knitting. A lot
34:44
Speaker 1: of smart people don't have that ability. Well. Warren Buffett
34:47
Speaker 1: um agrees with the point we made earlier that as
34:52
Speaker 1: you get bigger and bigger, you tend to have capacity problems,
34:55
Speaker 1: and so you can see that in the performance of
34:56
Speaker 1: Burshire Hathway. There's a table in my book which shows
34:59
Speaker 1: how the decade by decade how the return has dropped
35:04
Speaker 1: compared with the SMP. So it's converged too fairly close
35:09
Speaker 1: to the SMP five hundred in this last time period.
35:12
Speaker 1: So at this point you're gonna say he's as big
35:14
Speaker 1: as he can get and continue to outperform. Yes, but
35:19
Speaker 1: Burrshire still has some advantages when you can. Let's suppose
35:22
Speaker 1: that does exactly the same as an index fun going forward,
35:25
Speaker 1: which may not be a bad rough estimate. It doesn't
35:28
Speaker 1: pay dividends, so you don't pay any taxes until you
35:31
Speaker 1: sell your stock. When you sell your stock, if you
35:34
Speaker 1: hold it more than a year, you'll be paying long
35:36
Speaker 1: term capital gain, which is less than you'd pay if
35:39
Speaker 1: you were a trader and generating short term gains or um.
35:45
Speaker 1: And as far as the dividends go, the company that well,
35:52
Speaker 1: I'm not sure where the tax taxation is going to
35:54
Speaker 1: be on dividends now or by the time this broadcasts.
35:57
Speaker 1: Who even knows what I want? But what what is
36:00
Speaker 1: long term capital gains fifteen versus or or higher? I
36:05
Speaker 1: don't know. I've I've lost track. Yeah, but it's considerably
36:09
Speaker 1: lower long term than than short term. I don't plan
36:12
Speaker 1: to pay me for a while, so I'm not paying attention.
36:15
Speaker 1: Were you ever tempted to sell? Did you ever look
36:17
Speaker 1: at this and saying or do you just not pay
36:19
Speaker 1: attention to it? I don't pay a whole lot of
36:22
Speaker 1: attention to me. It's a buy and whole situation. And
36:25
Speaker 1: if I do sell, I'm gonna have to pay all
36:26
Speaker 1: these taxes because I have a low basis, a very
36:29
Speaker 1: low basis, so I get I get punished for selling. Well,
36:32
Speaker 1: that's reason to hold on to it. I'm sure. I'm
36:34
Speaker 1: sure Warrant appreciates that sort of loyalty amongst its stockholders. UM.
36:40
Speaker 1: On a related note, you were at at Citadel Ken Griffins.
36:47
Speaker 1: You were his first limited partner. What what did you
36:51
Speaker 1: see in Ken Griffin that made you say this guy
36:54
Speaker 1: is onto something interesting. He's he's clearly of a mathematical bends.
37:00
Speaker 1: I'm willing to give him money and see what he
37:02
Speaker 1: can do with it. Well, the guy who discovered Ken
37:04
Speaker 1: Griffin is a fellow named Frank Meyer, who was a
37:08
Speaker 1: friend of mine and who I got acquainted with in
37:10
Speaker 1: the early days of Prince and Newport Partners because he
37:13
Speaker 1: was he was representing some investors who wanted to get
37:16
Speaker 1: in to Prince and Newport Partners. So anyhow, when Prince
37:20
Speaker 1: and new Park Partners closed down in nine, Frank had
37:27
Speaker 1: just discovered this um Harvard student who was trading out
37:32
Speaker 1: of his dorm. And what Ken Griffin was doing was
37:35
Speaker 1: trading derivatives, convertibles, lawrants, options and so on, pretty much
37:40
Speaker 1: in the style that I've been trading them. And so
37:43
Speaker 1: Frank decided to set him up in business. So he
37:46
Speaker 1: was his mentor guru, um business organizer. Ken was a
37:51
Speaker 1: young guy then, so smart but still new to all this.
37:56
Speaker 1: And since I was shutting down Prince and Newport, it
37:59
Speaker 1: was natural for me to talk with Frank and Ken
38:02
Speaker 1: and explain to them what I did and how I
38:04
Speaker 1: did it, and basically give them my business road map.
38:08
Speaker 1: And they initially followed that, and of course it evolved
38:11
Speaker 1: into its own great thing, and the Citadel is phenomenal success.
38:17
Speaker 1: Are you still an investor in sid Adel? So you
38:19
Speaker 1: are really a buy and hold investor. You look at
38:22
Speaker 1: things over the long haul. That's fascinating. Let's talk about
38:25
Speaker 1: start with a quote of yours. Any edge in the
38:28
Speaker 1: market is limited, small, temporary, and quickly captured by the
38:34
Speaker 1: smartest or best informed investors. Still true today, I'd modify
38:39
Speaker 1: that a little bit. I think that that's true of
38:46
Speaker 1: most of the edges. Some of the small edges have
38:51
Speaker 1: fairly large scope to them. I'll give you one simple example.
38:56
Speaker 1: There's something called a tax loss harvesting, and that's a
39:00
Speaker 1: politically created edge. You could, for instance, set up a
39:05
Speaker 1: thing like an index fund, a thing that tracks an
39:06
Speaker 1: index fund, and at the end of the year, sell
39:10
Speaker 1: your losers and take the tax loss and use the
39:14
Speaker 1: money to buy new stocks. That would keep your collection
39:18
Speaker 1: of stocks tracking the index fairly well, and just keep
39:21
Speaker 1: doing that. You could sell one SMP index and buy
39:24
Speaker 1: a different SMP next. Well, you know that's you can't
39:26
Speaker 1: do that, because be a wash has to be something
39:29
Speaker 1: slightly different. Different. You could take let's say, of the
39:34
Speaker 1: stocks in the SMP index and buy them on day one.
39:38
Speaker 1: You can let it run for a year minus a day,
39:41
Speaker 1: look at the losers, sell them, take the tax loss,
39:45
Speaker 1: use the money to replace those losers with some of
39:48
Speaker 1: the stocks you didn't already hold. So you have four
39:53
Speaker 1: d stocks you haven't bought, you have a hundred that
39:54
Speaker 1: you did have, so you move into some of the
39:57
Speaker 1: four and then you let it run for another year,
40:00
Speaker 1: and you do it again and again, so you keep
40:02
Speaker 1: taking losses and collecting money from the government. And that's
40:07
Speaker 1: an edge that's persists. It's a politically created edge. Got
40:10
Speaker 1: it because of the tax code, Yes, because of the
40:12
Speaker 1: tax code. So nineteen sixty nine was a famously tough
40:17
Speaker 1: year for hedge funds. Carol Loomis wrote a big article
40:21
Speaker 1: about it in in Fortune and and she ultimately ended
40:23
Speaker 1: up being um a really interesting and important journalists. But
40:30
Speaker 1: in in nineteen sixty nine, your hedge fund was just
40:34
Speaker 1: launching or about to launch. We launched in November of
40:38
Speaker 1: nineteen sixty nine, the year that's so terrible. Yes, and
40:41
Speaker 1: there was there were There were articles in Fortune and
40:45
Speaker 1: maybe Forbes, I don't remember which now, which listed twenty
40:49
Speaker 1: some odd of the largest hedge funds and they're all
40:52
Speaker 1: losing going out of business, except for Buffet Partners Limited,
40:56
Speaker 1: which is going out of business for lack of opportunity.
40:59
Speaker 1: And uh, I think that Steinhardt hung in there. He
41:03
Speaker 1: was up maybe five percent, but it was a disaster.
41:08
Speaker 1: So we we started in the middle of a blood bath,
41:12
Speaker 1: and how did you do? How was that first year
41:14
Speaker 1: in business as a hedge fund, Well, we were wondering
41:17
Speaker 1: what was going to happen. So the first two months
41:19
Speaker 1: the market was down four or five and we were
41:22
Speaker 1: up four percent, So that was a That was a
41:24
Speaker 1: good start. And I made as much money then as
41:26
Speaker 1: I was making at the university just in the first
41:28
Speaker 1: two months, so that's a good sign. Then the next
41:32
Speaker 1: year we were up again and the market was down,
41:35
Speaker 1: and so we opened up a big gap on the
41:37
Speaker 1: market fairly early, and that continued. Seventy three the SMP falls,
41:43
Speaker 1: you're up seven. In the SMP five hundred falls, seven,
41:49
Speaker 1: you're up nine percent. At what point do people start
41:53
Speaker 1: knocking on your door saying, Hey, I have some money
41:55
Speaker 1: for you to manage. For us, we had a steady
41:58
Speaker 1: waiting list and we started out with I think a
42:00
Speaker 1: fifty dollar minimum, and it rapidly rose. Um eventually was
42:05
Speaker 1: ten million, and then finally we just couldn't take any
42:07
Speaker 1: more investors. So it grew over the years from initial
42:11
Speaker 1: one point four million to about two hundred and seventy
42:15
Speaker 1: million under management. And you understood all the math behind this.
42:19
Speaker 1: You understood the probabilistic edge you had, but were you
42:22
Speaker 1: ever surprised by, Hey, this is really working out better
42:26
Speaker 1: than we imagined. Uh, that's an interesting question. When I
42:31
Speaker 1: sat down with my then principal partner fellow named J.
42:34
Speaker 1: Reagan back in sixty nine, I estimated what was going
42:38
Speaker 1: to happen, and I said, I think that by five,
42:42
Speaker 1: with the way we're going to grow and the fees
42:44
Speaker 1: we're going to have, will each be worth a million dollars.
42:47
Speaker 1: And you actually hit that number right on time. That's
42:50
Speaker 1: pretty much. Yeah. So in five I sent him a
42:53
Speaker 1: copy of what I've written what I already sent him
42:56
Speaker 1: nine sixty nine, just as a little reminder and at
42:59
Speaker 1: at at what point did it sort of did or
43:03
Speaker 1: I should ask the question, did it ever scale to
43:05
Speaker 1: a point where you said, Gee, this has really gone
43:08
Speaker 1: far beyond what I was expecting. I don't think so. Um.
43:14
Speaker 1: One of the things I learned at the casino table
43:16
Speaker 1: was that scaling is a very interesting psychological thing. When
43:19
Speaker 1: I first started playing blackjack, on my first big trip,
43:23
Speaker 1: I went with a couple of multimillionaires who wanted to
43:27
Speaker 1: bank or on me for a hundred thousand dollars I said, no,
43:30
Speaker 1: I don't know how to handle money. I'm gonna be
43:32
Speaker 1: and if anything goes wrong, you're not gonna like this.
43:35
Speaker 1: You're not gonna like it in spades. So let's only
43:38
Speaker 1: go with ten thousand. And the first eight hours I
43:41
Speaker 1: sat down there betting one dollar when it wasn't favorable
43:45
Speaker 1: and ten dollars when it was favorable, and they just
43:47
Speaker 1: about went out of their skulls with this penny andy betting.
43:51
Speaker 1: But then I got used to it after eight hours,
43:53
Speaker 1: and then I went to two dollars small bets, twenty
43:56
Speaker 1: dollars big bets, and that lasted a couple of hours,
44:00
Speaker 1: and then I got used to that, and then I
44:01
Speaker 1: went to five to fifty and the top bet they
44:10
Speaker 1: would allow at that point. So what I found was
44:12
Speaker 1: you could scale up, and when you get used to scaling,
44:15
Speaker 1: you do the same things, same kind of thinking that
44:18
Speaker 1: you did at the small scale on bigger and bigger scales,
44:21
Speaker 1: So that that stayed with me all the way up.
44:24
Speaker 1: It's just the process getting used to the larger numbers.
44:27
Speaker 1: But that's a physical and a psychological adjustment where when
44:31
Speaker 1: you start out at one and ten five dollars sounds
44:34
Speaker 1: like a lot of money, but you have to stare,
44:36
Speaker 1: step your way up until you're comfortable. Was it was
44:39
Speaker 1: it the same with the hedge fund, starting out relatively
44:42
Speaker 1: small and at a certain point and the biggest you've
44:45
Speaker 1: got in terms of assets under management was we had
44:47
Speaker 1: about a billion long and about a billion short, but
44:49
Speaker 1: we had about two d seventy million behind that. I
44:54
Speaker 1: will I will say this to Bill Gross who used
44:57
Speaker 1: to be here in next door pimp go literally, I'm
44:59
Speaker 1: looking out the window at the Pimco tower right next
45:01
Speaker 1: to yours. So he got his start playing blackjack too,
45:05
Speaker 1: as you got in the book. And so he took
45:07
Speaker 1: two hundred dollars like the book beat the dealer said
45:10
Speaker 1: he would, went out, slaved away for four months, turned
45:13
Speaker 1: into ten thousand like it said he would. And then
45:16
Speaker 1: when he went to Pimco, he was really used to
45:18
Speaker 1: scaling up. And there came a point when PIMCO had
45:22
Speaker 1: just under two trillion under management. So that's a lot
45:25
Speaker 1: of scaling up from from literally a very small division
45:29
Speaker 1: that was wanting was a Pacific life for they were
45:32
Speaker 1: running the money of a local insurance company and decided
45:36
Speaker 1: to set it out as a as a separate entity. Well,
45:39
Speaker 1: the way all this happened was after reading Beat the
45:42
Speaker 1: Dealer bill read Beat the Market, and so he decided
45:47
Speaker 1: to write his thesis in the convertible bonds at U
45:50
Speaker 1: C l A. And then it was one he was
45:53
Speaker 1: looking for a job and his mom said, oh, they're
45:56
Speaker 1: looking for people over at a Pacific Mutual Life. So
45:59
Speaker 1: he went over and they at oh, we don't need
46:00
Speaker 1: any bond guys here. He said, well, I wrote my
46:03
Speaker 1: thesis in convertible bonds. I said, oh, we don't have
46:05
Speaker 1: one of those. So that's how he got high. And
46:08
Speaker 1: literally literally Pacific Life is not that far from here either.
46:11
Speaker 1: It's right up the right up the road. That thing
46:15
Speaker 1: I call the mushroom building. Oh that's it. That's that's
46:17
Speaker 1: so funny. So a couple of really fascinating things that
46:21
Speaker 1: took place with with you and and the hedge funds. First,
46:26
Speaker 1: not only did you never have a losing year, you
46:29
Speaker 1: never had a losing quarter. That's a remarkable run. How
46:34
Speaker 1: is that consistency possible? Well, I was a child of
46:38
Speaker 1: the Depression, so I'm highly risk averse, Okay, So I
46:43
Speaker 1: decided that I wasn't going to lose money, and that's
46:46
Speaker 1: what I liked about hedging, and and by its nature
46:50
Speaker 1: you try and put positions on that even if it
46:53
Speaker 1: goes against you. The downside is is limited due to
46:57
Speaker 1: the hedge. Yes, and I put a lot of positions
46:59
Speaker 1: on uh, so I was diversified over hedges. I kept
47:03
Speaker 1: track of how the hedges did for one period in
47:07
Speaker 1: the earlier mid seventies. I kept track of two of them,
47:11
Speaker 1: and about a hundred eighty of them were winners, about
47:15
Speaker 1: ten of them were pretty close to pushes, and then
47:18
Speaker 1: about ten or more losers, and the winners were bigger
47:22
Speaker 1: than the losers on average. So that that's how stable
47:27
Speaker 1: and solid these hedges were. So with a diversified portfolio
47:31
Speaker 1: of hedges, it was almost inevitable would come out ahead
47:34
Speaker 1: every month. We only had three down months and those
47:37
Speaker 1: are less than one percent, so we could call those
47:40
Speaker 1: a push. Let's let's let's talk about black shoals. So
47:46
Speaker 1: both with the warrants and with the training options against
47:50
Speaker 1: the common stock, you created your own pricing model to
47:54
Speaker 1: figure out when the either the warren or the option
47:58
Speaker 1: was overpriced and priced relative to the underlying security, which
48:03
Speaker 1: essentially was the black shoals option pricing. You invented black
48:09
Speaker 1: shoals option pricing before black shoals. How does that come about?
48:14
Speaker 1: The way that happened was, Uh, there was a great book,
48:19
Speaker 1: one of the first quantitative finance books, called The Random
48:22
Speaker 1: Character of Stock Market Prices came out kind of came
48:26
Speaker 1: out of m I T. Paul Kotner, revised, updated, and
48:33
Speaker 1: I happened to read that book. And they had some
48:37
Speaker 1: warrant models in the book, and the warrant models had
48:40
Speaker 1: two parameters that nobody knew what to do with. Nobel
48:45
Speaker 1: Prizewinner Future Nobel Prizewinner Paul Samson didn't know what to
48:48
Speaker 1: do with these parameters, nor did anybody else. And the
48:52
Speaker 1: two parameters were the unknown rate of growth of the
48:55
Speaker 1: stock and the proper discount rate for the uncertain hay
49:00
Speaker 1: off from the warrant or option. And so I understood
49:06
Speaker 1: that model. I had actually worked it out myself, and
49:08
Speaker 1: then I saw that it was already in the book.
49:10
Speaker 1: So I did something everybody already knew. And I thought
49:13
Speaker 1: about for a while, and then I said, you know,
49:17
Speaker 1: I'm doing hedges that are essentially risk neutral. And so
49:25
Speaker 1: is there a world in which those parameters could be chosen? Yes,
49:29
Speaker 1: if the world's risk neutral, then I choose the risks
49:32
Speaker 1: rate for those parameters. So I stuck the risks right
49:35
Speaker 1: THEW and lo and behold, magic, wonderful formula. Tried the
49:39
Speaker 1: formula out all kinds of different ways logically to see
49:42
Speaker 1: if it did what it was supposed to, and it
49:44
Speaker 1: did so. Now I had a formula for value and options,
49:47
Speaker 1: which was the future Black Sholes formula. This Black, by
49:52
Speaker 1: the way, told me later on when we had conversations,
49:55
Speaker 1: that they had figured this thing out in sixty nine,
49:57
Speaker 1: but they were rejected for publication a number of times,
50:02
Speaker 1: and only when they got some help, I think from
50:03
Speaker 1: Samuels then that they were able to get their article
50:06
Speaker 1: in print. They got the first one in print in
50:08
Speaker 1: nineteen seventy two. Now you've been using this model for
50:11
Speaker 1: four or five years at that point, and what what
50:15
Speaker 1: was your Well, I was 'mentilally unaware of the world
50:18
Speaker 1: of academic finance because I was just doing all this
50:20
Speaker 1: stuff on my own myself, like I was used to
50:23
Speaker 1: doing when I was a kid, learning science by myself.
50:26
Speaker 1: And then the cbo EA Chicago Board Options Exchange decided
50:32
Speaker 1: to create list of options, which revolutionized the whole h
50:35
Speaker 1: over the counter options in the warrant market. So in
50:38
Speaker 1: other words, ZSER trading on an exchange, you don't have
50:40
Speaker 1: to go through a specific so that made the cost
50:44
Speaker 1: much more more reasonable, and it also allowed you to
50:50
Speaker 1: trade a much broader set of vehicles. That that was
50:53
Speaker 1: the plan there was supposed to happen in I think
50:57
Speaker 1: April of Vree. So I had been operating my hedge
51:02
Speaker 1: fund for already almost four years, and I had been
51:07
Speaker 1: training options and weren't using the Black Sholes formula among
51:09
Speaker 1: other things, and also value well, you were really training
51:13
Speaker 1: using the ed Thorpe formula because black Sholes didn't exist,
51:16
Speaker 1: and I didn't as far as I knew, that were
51:19
Speaker 1: no such people as black controls. So then I was
51:22
Speaker 1: sitting there about a month before it opened, and I
51:25
Speaker 1: get a brown envelope with a mimeographed article in it
51:30
Speaker 1: and a letter from a FELLO named Fisher Black, who
51:33
Speaker 1: I said, I as I said, I'd never heard of,
51:35
Speaker 1: and he was saying, I'm an admirer of your work.
51:38
Speaker 1: And we took the idea from beat the Market the
51:41
Speaker 1: hedging idea and use dynamic dging, and we've derived this formula.
51:47
Speaker 1: And so I started reading the paper, and I said, gee,
51:50
Speaker 1: this looks a lot like my formula, and so I
51:53
Speaker 1: programmed it on my little helipacker computer which drew graphs,
51:57
Speaker 1: and I drew the curves and they were saying, curves,
51:59
Speaker 1: I've been drawn. So I said, oh, it is the
52:02
Speaker 1: same formula. Let's let's look at it algebraically. So I
52:05
Speaker 1: saw that the algebra translated in the formula. They were
52:08
Speaker 1: the same thing. And I realized more. I actually had
52:13
Speaker 1: a triple of formulas, and not just one. And the
52:17
Speaker 1: triple took care of two cases that were then important
52:23
Speaker 1: in the market, your short warrants and long stock and
52:28
Speaker 1: you can't get the short sale proceeds used that broker
52:31
Speaker 1: pockets it and use it to pay his own debit
52:33
Speaker 1: balances down. And the other formula, uh, that was a
52:38
Speaker 1: second formula. The third formula was the reverse your short
52:42
Speaker 1: stock and long warrants and deprived of the short sale proceeds.
52:45
Speaker 1: Black controls assumed the main formula that you got the
52:49
Speaker 1: use of the short sale proceeds, which you did, or
52:53
Speaker 1: you would when you traded on the exchange. But before
52:56
Speaker 1: the cbo E people did not get the benefit of
53:00
Speaker 1: using the short sale proceeds. So you needed three formulas,
53:03
Speaker 1: and so I had them all and there there's was
53:05
Speaker 1: the middle of the three. And there was something else
53:08
Speaker 1: about the formula I had, which was it used a
53:10
Speaker 1: method that was different. H used something from basically what's
53:15
Speaker 1: now high school calculus, just integration. And that means that
53:21
Speaker 1: my formula could be applied to probability distributions that were
53:25
Speaker 1: different than the long normal distribution, which was the one
53:29
Speaker 1: that underlied the Black Rolls formula. So it was a
53:32
Speaker 1: more general attack on problems. So you get the package
53:36
Speaker 1: from from Black. You see that your secret sauce is
53:40
Speaker 1: now out in the public, even though it's not quite
53:44
Speaker 1: as comprehensive or robust as your version of it. Do
53:48
Speaker 1: you say to yourself, Oh, cats out of the bag
53:50
Speaker 1: and this is no longer going to be a money
53:52
Speaker 1: maker for I figured I don't have the competitive edge
53:54
Speaker 1: that I thought I had. But it turns out that
53:57
Speaker 1: the act American finance world was fairly slow moving, as
54:03
Speaker 1: was the collection of market makers. So when the CBO
54:07
Speaker 1: CBO we opened on day one. We were down there
54:10
Speaker 1: with our traders and our formulas and our charts, and
54:13
Speaker 1: it was it was wonderful there was no competition really.
54:18
Speaker 1: Now you wanted to bring down um small calculators and
54:22
Speaker 1: handheld devices, and they really gave you a lot of
54:25
Speaker 1: grief about that. Well, it was what we wanted to
54:28
Speaker 1: do was bring down hand held calculators so we could
54:30
Speaker 1: just punch out the formula values right on the floor.
54:33
Speaker 1: But they wouldn't let us do it because it was
54:34
Speaker 1: unfair to the old guard that the old Guard didn't
54:38
Speaker 1: have this advantage. And we asked if we could use
54:42
Speaker 1: walkie talkies and they said no, we couldn't do that either.
54:44
Speaker 1: So what we resorted to doing was printing enormous tables
54:49
Speaker 1: that had all the possibilities in them. And these tables
54:52
Speaker 1: were on z fold paper eleven by seventeen. They were
54:56
Speaker 1: probably oh three inches thick because they covered lots of
55:02
Speaker 1: cases and prices and options and that had to be
55:05
Speaker 1: updated daily correct. We ran. We ran printers four or
55:10
Speaker 1: five hours every night, and air freighted our tables to
55:16
Speaker 1: our traders, uh initially on the cbo E and then
55:19
Speaker 1: on the m X the Pacific and Philadelphia exchanges, so
55:25
Speaker 1: it's a day or so behind, but close enough that
55:28
Speaker 1: it was an advantage to them. Well, we covered a
55:30
Speaker 1: wide enough range of prices so that we caught most
55:33
Speaker 1: of the moves for a week or so, and so
55:36
Speaker 1: the tables were usually good for a week, and that
55:38
Speaker 1: gave you guys a statistical edge. And in the actual
55:42
Speaker 1: tables had all our traders had to do was take
55:44
Speaker 1: the tables down to the post flip and decide what
55:46
Speaker 1: trades they wanted to make. And they what What did
55:50
Speaker 1: the CBO we have to say about that? They they
55:53
Speaker 1: they let us do that. They didn't recognize the advantage
55:56
Speaker 1: it was giving you. I think they did. The Wall
55:59
Speaker 1: Street Journal had an article about it in nineteen seventy
56:02
Speaker 1: four front page article. And what was the net result
56:06
Speaker 1: after the article? Did the any more pushback or I
56:09
Speaker 1: think it encouraged people to try to come up to speed.
56:13
Speaker 1: So so even after black Sholes is out, even after
56:17
Speaker 1: all of the mathematical advantages that you had identified were
56:21
Speaker 1: well known, you're still running a big advantage over everybody else.
56:25
Speaker 1: And the fund continues to perform very well for the
56:28
Speaker 1: next how many years, Well, we ran from late nine
56:34
Speaker 1: through the end of eight, and we did fine through
56:36
Speaker 1: the whole period. In fact, we got higher rates of
56:39
Speaker 1: return in the later period that we did in the
56:41
Speaker 1: lower So what made you decide to finally shut that
56:44
Speaker 1: fun fun down? Fellow named Rudy Giuliani made me decide
56:48
Speaker 1: to set up So, so let's talk about that. Because
56:50
Speaker 1: Giuliani and others open and invested an investigation. You, I'm
56:55
Speaker 1: going to cut to the end. You did nothing wrong.
56:58
Speaker 1: You're completely exonerated. However, this is a giant pain in
57:03
Speaker 1: the neck, isn't it. Well, what happened was really Julianni,
57:07
Speaker 1: in my opinion, decided that his career would be advanced
57:11
Speaker 1: greatly if while he was um ahead of the Southern
57:17
Speaker 1: District in New York U S Attorney's of Attorney's Office,
57:21
Speaker 1: that if he prosecuted inside or trading, it would be
57:25
Speaker 1: a great benefit to his future, which it was, And
57:29
Speaker 1: so he found various targets. One of them was Michael Milken,
57:33
Speaker 1: who was everybody's target because Michael Milken was on horsing
57:37
Speaker 1: the old guard by funding drunk bond people who were
57:41
Speaker 1: taking over companies and kicking the old guys out. So
57:44
Speaker 1: his his head had to roll. And fortunately for the
57:48
Speaker 1: people who wanted to kick him out, they were able
57:50
Speaker 1: to find some things to get them with. And Robert
57:53
Speaker 1: Freeman of Goldman. Sachs was another guy that they were after.
57:57
Speaker 1: I don't recall what the reason was. It turned out
58:00
Speaker 1: my partner, Jay Regan, was his roommate at Dartmouth and
58:03
Speaker 1: they were pals. And also we did a fair amount
58:06
Speaker 1: of trading with Michael Milken and his company, so there
58:11
Speaker 1: was a big grade of our offices. The idea was
58:14
Speaker 1: to get Jay Reagan to say something that would help
58:17
Speaker 1: convict a freeman and Milican. Well, what happened was they
58:23
Speaker 1: never got anything from Jay Regan or any of the
58:26
Speaker 1: other people in Princeton, but they're able to make enough
58:29
Speaker 1: of the case so that it dragged through the courts
58:31
Speaker 1: for like two and a half years. Meanwhile, Giuliani split
58:35
Speaker 1: and went on to other things. And there was an
58:37
Speaker 1: initial trial which and conviction which was thrown out of
58:42
Speaker 1: five people in the Princeton office, and then the government
58:45
Speaker 1: elected not to retry. And when all it's more cleared, Uh,
58:48
Speaker 1: nobody paid any money. Nobody did at any time, but
58:51
Speaker 1: they spent ten or fifty million dollars on legal fees.
58:54
Speaker 1: So is it fair to say that you're not a
58:56
Speaker 1: big fan of Rudy Julian and you should you should
58:59
Speaker 1: know that most New Yorkers share your opinion. And I'm
59:02
Speaker 1: not being political when I say that people forget. I'm
59:05
Speaker 1: gonna let me digress. People forget. Right before September eleven,
59:11
Speaker 1: he was cheating on his wife. It was about an
59:13
Speaker 1: ugly divorce. His political career had, you know, had plummeted
59:18
Speaker 1: nine eleven, resurrected his career if it wasn't for that,
59:21
Speaker 1: And you know, the President wasn't very visible then and
59:25
Speaker 1: Rudy stepped into the void, and that basically really gave
59:29
Speaker 1: him a whole second, a second run on a political career.
59:33
Speaker 1: But a lot of New Yorkers, you you asking year, well,
59:37
Speaker 1: you know, they got the bombs off, the the squeegee
59:40
Speaker 1: guys were gone, and there's no begging in the subway.
59:43
Speaker 1: But that second term, he just whatever credibility and goodwill
59:48
Speaker 1: he had accumulated, he completely frittered away. So that leads
59:55
Speaker 1: to an interesting quote of yours um about money and
59:59
Speaker 1: so cess. And I thought this was fascinating quote. Success
1:00:04
Speaker 1: on Wall Street was getting the most money. Success for
1:00:07
Speaker 1: us was having the best life. That is a fairly
1:00:12
Speaker 1: philosophical perspective that is often missing, at least on the
1:00:17
Speaker 1: East Coast. Or in New York. Tell us a little
1:00:20
Speaker 1: bit about that philosophy. Well, what I learned over the
1:00:26
Speaker 1: decades was that it's it's how you live your life
1:00:29
Speaker 1: that matters, and the people that you spend your time
1:00:33
Speaker 1: with that matters, and the stuff you pile up doesn't
1:00:37
Speaker 1: really matter very much. Having money helps, It makes living
1:00:42
Speaker 1: easier pleasanter. You have more fun, better medical care and
1:00:45
Speaker 1: so forth. And your kids and your grandkids, you can
1:00:48
Speaker 1: help take care of them and make sure things are fine.
1:00:51
Speaker 1: But I don't need to own an island in Hawaii.
1:00:53
Speaker 1: I don't need to have villas all over I don't
1:00:56
Speaker 1: need to have private jets. And you know, a lot
1:00:58
Speaker 1: of other people feel the same way. I mean, they'll
1:01:01
Speaker 1: take Warren Buffett for instance. He doesn't spend that much
1:01:03
Speaker 1: money when you consider he is one of the two
1:01:05
Speaker 1: or three richest guys in the world for the longest time. Well,
1:01:09
Speaker 1: what makes him happy is doing his job. He tap
1:01:13
Speaker 1: dances to work, and not a lot of people get
1:01:14
Speaker 1: to do that. And he realizes the same thing as
1:01:17
Speaker 1: he said, it's it's kind of the people you love
1:01:20
Speaker 1: and the people who love you that matter the most,
1:01:22
Speaker 1: to say the least. By the way. Have you seen
1:01:24
Speaker 1: the HBO documentary on Buffett. It's quite charming. He's he's he's.
1:01:31
Speaker 1: I know a lot of people think his stick is, oh,
1:01:35
Speaker 1: he's just being that down home, but he comes across
1:01:38
Speaker 1: is very genuine and he seems like a real person.
1:01:42
Speaker 1: If you get a chance, it's I think it's about
1:01:44
Speaker 1: an hour, it's it's for it. Although you you know
1:01:47
Speaker 1: him personally, so maybe you'll you'll see it differently, But
1:01:50
Speaker 1: I should say I knew him personally. I haven't seen
1:01:51
Speaker 1: it for a long time. But so, um, let's let's
1:01:56
Speaker 1: keep talking about Princeton Newport Partners. That's a better name
1:02:00
Speaker 1: than Convertible Hedge Associates m but that's why, that's why
1:02:04
Speaker 1: we changed it. So so that was simply, oh, this
1:02:07
Speaker 1: is a little uh one partners in Princeton, the other
1:02:10
Speaker 1: partners in Port Beach, and and that's a fair enough name.
1:02:13
Speaker 1: So when you first started it, who were some of
1:02:16
Speaker 1: the early investors. Let's see, there was Bob Evans, who
1:02:21
Speaker 1: later became head of Paramount Studios, was married to McGraw
1:02:25
Speaker 1: and and put out a number of really seminal movies.
1:02:28
Speaker 1: If if memories says um anybody else of note that
1:02:33
Speaker 1: goes Charles Langman of the RITZ. I don't remember much
1:02:36
Speaker 1: about him. Most of these partners were pretty quiet. We
1:02:39
Speaker 1: just kept making money. So then to bother us that
1:02:41
Speaker 1: that those are the best types of partners, especially if
1:02:43
Speaker 1: you keep making them money. Um, So you eventually decide
1:02:47
Speaker 1: to close UM Princeton Newport partners down after the Giuliani
1:02:51
Speaker 1: It was too it was too hard to operate in
1:02:54
Speaker 1: that atmosphere, and it was too difficult to keep reassuring
1:02:57
Speaker 1: them into partners that first of all, he threatened partnership
1:03:00
Speaker 1: with Rico, so they had the fear that through some
1:03:03
Speaker 1: quirk or other, their assets might get confiscated for unknown
1:03:06
Speaker 1: lengths of time. And we knew that there was no
1:03:10
Speaker 1: risk of them losing. But also it was just too harrowing.
1:03:13
Speaker 1: And I said to myself, I don't need more money anyhow.
1:03:17
Speaker 1: So there's a famous quote, and I know I'm mangling this,
1:03:20
Speaker 1: but it's there. There are a few things more dangerous
1:03:23
Speaker 1: than an over zealous prosecutor and run amuck. And I
1:03:27
Speaker 1: think that describes the circumstances with Rudy. Way back when
1:03:32
Speaker 1: you didn't need the money, but at a certain point
1:03:35
Speaker 1: you decided to start PNP. What motivated you to get
1:03:38
Speaker 1: back into hedge fund games? It was let's see Richline
1:03:43
Speaker 1: Partners that I started, and what happened was I took
1:03:45
Speaker 1: a break from and then one of my UM previous partners,
1:03:54
Speaker 1: who was now working for UH, a very large pension
1:03:58
Speaker 1: profit sharing plan, said, you know statistical arbitrage, which we've
1:04:03
Speaker 1: been running, and which I might say, as far as
1:04:06
Speaker 1: I know, we were the first discoverers of back statistical arbitrage.
1:04:10
Speaker 1: You're doing really well. We'd like you to get back
1:04:13
Speaker 1: in this game. So I said, well, I'll take a look,
1:04:16
Speaker 1: and I said, oh, it is doing it quite well.
1:04:18
Speaker 1: Sure we'll we'll run some money for you. And it
1:04:21
Speaker 1: was easy to do. I could do it with about
1:04:23
Speaker 1: three full time equivalence as opposed to a staff of
1:04:28
Speaker 1: eighty which I was using for Princeton Newport Partners. And
1:04:30
Speaker 1: I didn't get the sense you're a big fan of
1:04:32
Speaker 1: managing a large staff. That's a lot of work. It
1:04:35
Speaker 1: turns out what I learned was if you manage five people,
1:04:39
Speaker 1: then you're spending maybe a third or half your time managing,
1:04:43
Speaker 1: and then you've got to find somebody. If you have
1:04:45
Speaker 1: five more people, you've gotta have somebody to help manage them,
1:04:48
Speaker 1: and it sort of grows exponentially. You get this big
1:04:51
Speaker 1: pyramid and you keep getting farther and farther to remove
1:04:54
Speaker 1: from what's really happening. Right, So, so PNP you're doing
1:04:57
Speaker 1: statistical arbitrage? Actually can rich line Partners were doing rich
1:05:02
Speaker 1: Line Partners? And and and what was the performance numbers
1:05:05
Speaker 1: like for rich Line We did about twenty percent a
1:05:07
Speaker 1: year with a little more variability than P m P.
1:05:10
Speaker 1: I don't We didn't make money every month, but we
1:05:12
Speaker 1: made money nearly every month, and it was somewhat event
1:05:15
Speaker 1: driven because the arbitrage opportunities would come along through M
1:05:20
Speaker 1: and A or through what what was the underlying The
1:05:22
Speaker 1: biggest The biggest event that we experienced was in with
1:05:31
Speaker 1: the long term capital management. It turned out that lots
1:05:35
Speaker 1: of people who were competitors now began to bail out,
1:05:39
Speaker 1: and so that caused four or five day dip. We
1:05:42
Speaker 1: had the first loss that we had of any significance.
1:05:46
Speaker 1: It was just three or four and that was a shocker.
1:05:50
Speaker 1: But those those days passed and then we had the
1:05:54
Speaker 1: greatest run we've ever had. We think we made like
1:05:57
Speaker 1: scent in six months. Not to share be and then
1:06:01
Speaker 1: the another hedge fund pnp UM In the twelve months
1:06:06
Speaker 1: ending August, the limited Partners, I guess this is Netta.
1:06:11
Speaker 1: Fees were up over seventy from a market neutral portfolio
1:06:15
Speaker 1: that used less than two to one leverage. That's that's
1:06:18
Speaker 1: Origin Line Partners. Oh so why do I keep calling
1:06:20
Speaker 1: this pnp PPM in so it might be Uh so
1:06:27
Speaker 1: that's the old one. And then Ridgeline took over. So
1:06:30
Speaker 1: back to Ridgeline in you're up se from market neutral,
1:06:35
Speaker 1: very little leverage. How did this come about? And why
1:06:39
Speaker 1: would you ever want to shut that down? Well, the
1:06:41
Speaker 1: reason we had that glorious run was because our competitors
1:06:46
Speaker 1: went away, and as they bailed out, they created opportunity. Okay,
1:06:50
Speaker 1: they disrupt the market places they exit, and that creates
1:06:53
Speaker 1: arbitrage opportunities. And they also don't control the excursions from
1:06:57
Speaker 1: favalue as much, so the excursions got bigger and so
1:07:02
Speaker 1: more more profits to be had. How much longer did
1:07:05
Speaker 1: you keep Ridge Line open after we went from actually
1:07:11
Speaker 1: nineteen two two thousand and two, and then I shut
1:07:15
Speaker 1: down not because we're doing badly, but because we're doing
1:07:19
Speaker 1: just so so, making maybe ten percent a year so
1:07:23
Speaker 1: and the opportunities had had just gone away. Is that
1:07:25
Speaker 1: is that basically I think that there was more competition
1:07:29
Speaker 1: and so the deviations from fair value that we're exploiting
1:07:32
Speaker 1: word is big. And at that point I said to myself,
1:07:35
Speaker 1: you know, why am I doing this? I'd rather just
1:07:38
Speaker 1: manage my own money in a more passive way and
1:07:41
Speaker 1: enjoy life. So let's talk a little bit about that.
1:07:44
Speaker 1: Passive investing has caught on a great deal. UH. It's
1:07:48
Speaker 1: a long time coming. It's finally, um, finally made its
1:07:52
Speaker 1: way to a number of people. Black Rock is one
1:07:55
Speaker 1: of the big underwriters of ETFs in passive investing, not exclusively,
1:08:01
Speaker 1: but they're up to five trillion. Vanguard is probably best
1:08:04
Speaker 1: known as the inventor of the passive index, or at
1:08:08
Speaker 1: least the first company that was created to roll out
1:08:12
Speaker 1: a passive index that people can invest in. They're now
1:08:14
Speaker 1: up to four trillion. Have have mom and pop investors
1:08:19
Speaker 1: finally figured out that for them, a passive investment is
1:08:24
Speaker 1: better than stock picking or market timing, some of them have.
1:08:29
Speaker 1: So you make a convincing argument that if you cannot
1:08:34
Speaker 1: demonstrate your edge, your advantage over a passive uh index,
1:08:40
Speaker 1: then then you shouldn't be doing anything but a passive
1:08:43
Speaker 1: index too. It seems perfectly logical and rational to me.
1:08:48
Speaker 1: Do you get pushed back from people about that? No,
1:08:51
Speaker 1: what I did is probably a lack of alertness discussing it,
1:08:59
Speaker 1: So I have to that's gonna. That leads me to
1:09:01
Speaker 1: a question I didn't ask before, but I have to.
1:09:06
Speaker 1: You seem incredibly grounded and a regular, low key sort
1:09:12
Speaker 1: of guy. You took the California Test of of um
1:09:16
Speaker 1: Mental Maturity. It's an i Q test as a kid.
1:09:20
Speaker 1: You had the highest score they had ever seen. How
1:09:23
Speaker 1: do you manage to keep high school at your at
1:09:27
Speaker 1: your high school, but in the world, not in the world. Okay,
1:09:29
Speaker 1: so that makes it I'm gonna that makes the question easy.
1:09:32
Speaker 1: How do you keep your ego in check? But still
1:09:35
Speaker 1: you all joking aside? You invent card counting, you invent
1:09:40
Speaker 1: statistical arbitrage, you invent um paired training. Go down the
1:09:46
Speaker 1: list of things that you did that are really unique
1:09:48
Speaker 1: in the world of finance as an investor, how do
1:09:52
Speaker 1: you manage to keep your ego in check? What you
1:09:54
Speaker 1: have to do is look all the other things that
1:09:56
Speaker 1: people do. There's so many great things that people are
1:09:58
Speaker 1: doing all the time. And if you meet any individual,
1:10:01
Speaker 1: the person sitting next to you on a bus or
1:10:03
Speaker 1: a plane or whatever, that person will be able to
1:10:05
Speaker 1: do things or no things that you can't know or
1:10:09
Speaker 1: can't do. Maybe they can sing better, maybe they know
1:10:11
Speaker 1: a language you don't know. Maybe they know how to
1:10:15
Speaker 1: um fix a car. Just you know, one thing after another,
1:10:18
Speaker 1: identify the car has been been rigged against you. And
1:10:22
Speaker 1: you think that's basically helps you keep your ego in check,
1:10:27
Speaker 1: helps anybody keep their ego and check. I find it
1:10:30
Speaker 1: hard to understand why so many people have such big egos. Okay,
1:10:36
Speaker 1: that's that's a fair enough thing there, especially those people
1:10:39
Speaker 1: who haven't created a body of accomplishments. I kind of
1:10:43
Speaker 1: think that maybe the big ego is a substitute for
1:10:47
Speaker 1: feeling like they're falling short here and there, a little
1:10:49
Speaker 1: over compensation. So that's fair enough. Um. In in the book,
1:10:54
Speaker 1: chapter you say, beat most investors by indexing, and chapters one,
1:11:01
Speaker 1: can you beat the market? Should you try? Um? When
1:11:04
Speaker 1: should somebody try to beat the market? Well, my simplified
1:11:09
Speaker 1: views go something like this bit roughly, the three kinds
1:11:13
Speaker 1: of investors. There are guys who don't want to really
1:11:15
Speaker 1: do any work. They just want to have their money grow.
1:11:19
Speaker 1: Those people should be thinking about indexing makes perfect sense.
1:11:22
Speaker 1: Then there are people who really are interested in the
1:11:24
Speaker 1: market and it's kind of fun for them. Those people,
1:11:27
Speaker 1: if they want to learn more, should go out and
1:11:29
Speaker 1: have their go and try to make some money. But
1:11:31
Speaker 1: they shouldn't use the bulk of their resources to do this.
1:11:34
Speaker 1: They should just a fun account, yeah, exactly, And then
1:11:37
Speaker 1: if they find something that really works, then they can
1:11:39
Speaker 1: start putting more money into it. They'll find that most
1:11:43
Speaker 1: of the time they haven't really found anything that really works,
1:11:45
Speaker 1: a little fooled by randomness, exactly. And then there's a
1:11:49
Speaker 1: third group, which are the professional people, some of whom
1:11:52
Speaker 1: actually get on edge, most of whom don't, but some
1:11:54
Speaker 1: of whom do. And those people get a start somehow
1:11:58
Speaker 1: in the market. Just like I gotta start with an
1:12:01
Speaker 1: options formula, and so I have an edge, I get in.
1:12:04
Speaker 1: I build an organization which is small, and it gradually grows,
1:12:07
Speaker 1: It gets more and more skills, it gets into more
1:12:10
Speaker 1: and more kinds of investing. So you basically get over
1:12:14
Speaker 1: the hurdle and get yourself established. If you can do
1:12:17
Speaker 1: that as a professional, then you're kind of on your
1:12:20
Speaker 1: way to collecting what people call alpha access return. Then
1:12:24
Speaker 1: there's a fourth group, which I don't have much interest in,
1:12:27
Speaker 1: and those are the ones who are simply asset gathers
1:12:30
Speaker 1: and they're they're in there to collect feest and get rich,
1:12:34
Speaker 1: but there's nothing really very interesting what they do. That's
1:12:37
Speaker 1: a You've described a huge part of what takes place
1:12:40
Speaker 1: in finance. Most of it people who are trying and
1:12:43
Speaker 1: not getting anywhere. People have just given up and said
1:12:45
Speaker 1: I'm going to go passive. And people who are are
1:12:48
Speaker 1: don't have an a special advantage, but they're just a
1:12:51
Speaker 1: commulating assets and manage them, managing them in some better
1:12:56
Speaker 1: or worse way. Referring to indexes in past of investing,
1:13:01
Speaker 1: people are now describing, oh, this has gotten too large,
1:13:04
Speaker 1: it's a bubble, that it's distorting price discovery. Passive indexes
1:13:09
Speaker 1: are going to cause the next crash. What what do
1:13:11
Speaker 1: you make of that? Well, suppose that half of all
1:13:18
Speaker 1: the list of equities in the United States we're in
1:13:22
Speaker 1: index funds. Then the other half would not be. The
1:13:26
Speaker 1: half has not be that would not be is a
1:13:28
Speaker 1: lot bigger than the market was ten or twenty years ago.
1:13:32
Speaker 1: So I don't see that putting half of the UM
1:13:36
Speaker 1: stocks into the next funds is going to cause any problem,
1:13:38
Speaker 1: except there's one thing. There's the question of the float
1:13:42
Speaker 1: in different companies. Some companies are more closely held than others.
1:13:46
Speaker 1: So you might have a company in which sevent is
1:13:48
Speaker 1: closely held, it never trades and only up for trading.
1:13:53
Speaker 1: So I believe that the index funds, the SMP Vanguard anyhow,
1:13:59
Speaker 1: has changed so that they use a proportion of the
1:14:03
Speaker 1: float rather than a proportion of the total market cap.
1:14:06
Speaker 1: And they've compared how that tracks with UH if they
1:14:11
Speaker 1: had been able to use the proportion of total market
1:14:14
Speaker 1: cap rather than proportion of the float, and the tracks
1:14:17
Speaker 1: has been extremely closed so far, So so far, no problem.
1:14:21
Speaker 1: I think it was Andrew Lowe and might Taste said
1:14:24
Speaker 1: you could get down to passive ten percent active. That's
1:14:28
Speaker 1: enough for price discovery as long as it's sufficient liquidity.
1:14:32
Speaker 1: So there could theoretically be a ways to go if
1:14:34
Speaker 1: he's right sure on the case. There is supposed that
1:14:37
Speaker 1: you had in an exponse not forgetting about the float issue,
1:14:40
Speaker 1: which which might cause some divergence. Well, ten percent of
1:14:45
Speaker 1: the current market is probably a good deal larger than
1:14:48
Speaker 1: the market was fifty years ago. Sure, so you still
1:14:52
Speaker 1: have price discovery even even if that. Here's another quote
1:14:55
Speaker 1: of yours that I really like. Whether or not you
1:14:57
Speaker 1: try to beat the market, you can do better by
1:15:01
Speaker 1: properly managing your wealth. Explain that where was that quote,
1:15:04
Speaker 1: let's take I don't know it was here on my page.
1:15:09
Speaker 1: I think it's from this book, and it's it's in
1:15:11
Speaker 1: the context of of why more people it's either twenty
1:15:14
Speaker 1: five or chapter twenty six about why more people should
1:15:18
Speaker 1: be doing passive. Whether or not you beat the market,
1:15:21
Speaker 1: you could still do better, you know what. Let's see
1:15:23
Speaker 1: if I can find that, I'll tell you what I
1:15:25
Speaker 1: think it is. I think it has to do with
1:15:28
Speaker 1: the fact that there are what Buffett calls fee collectors
1:15:33
Speaker 1: or toll takers. And the upshot is that when you
1:15:37
Speaker 1: think about fees collected by advisors or managers, and also
1:15:43
Speaker 1: the losses due to active trading, both market impact and commissions,
1:15:49
Speaker 1: it adds up to roughly a couple of percent a
1:15:52
Speaker 1: year drain on assets. So if you could make let's
1:15:56
Speaker 1: say ten percent a year in a stock index one
1:15:59
Speaker 1: long term, anything can happen in the short term, then
1:16:03
Speaker 1: maybe you're making eight percent a year before taxes if
1:16:07
Speaker 1: you're paying all these fees and tolls. So just shifting
1:16:12
Speaker 1: to passive investing, get your extra two percent for basically
1:16:17
Speaker 1: no work, and compound that over forty years, and that
1:16:21
Speaker 1: really adds up. Using the UM rule seventy two for instance,
1:16:26
Speaker 1: roughly thirty six years. Actually thirty five closer, because the
1:16:30
Speaker 1: rule varies from seventy two depending what the interest rate is.
1:16:33
Speaker 1: Using the rule of seventy two, in a little less
1:16:37
Speaker 1: than thirty six years, you have twice as much money
1:16:40
Speaker 1: if you invest this way then if you pay the
1:16:43
Speaker 1: two percent toll, So it makes a big difference. We
1:16:46
Speaker 1: have been speaking to Edward Thorpe, professional investor, trader, gambler, mathematician. UH.
1:16:54
Speaker 1: If you want to find out more about the works
1:16:57
Speaker 1: of ed Thorpe, you can read his most recent book,
1:16:59
Speaker 1: I'm and for All Markets Beat The Dealer Beat the
1:17:03
Speaker 1: Markets are both classic books. You may have to hunt
1:17:06
Speaker 1: around to find find them. UM. If anybody wants to
1:17:09
Speaker 1: find anything else you've written, these are really the main
1:17:12
Speaker 1: places to look. Is there to what? What is the website?
1:17:15
Speaker 1: It's uh Edward Ohthorpe dot com, Edward oh Thorpe dot com.
1:17:20
Speaker 1: It also has another second website links to it, a
1:17:23
Speaker 1: Man for All Markets dot com. If you enjoy this conversation,
1:17:27
Speaker 1: be sure and check out our podcast AFTRAS, where we
1:17:29
Speaker 1: keep the tape rolling and continue to discuss all things investing.
1:17:34
Speaker 1: Check out my daily column on Bloomberg at Bloomberg View
1:17:37
Speaker 1: dot com. You can follow me on Twitter at rid Alts.
1:17:41
Speaker 1: We love your comments, feedback and suggestions right to us
1:17:46
Speaker 1: at m IB podcast at Bloomberg dot net. I'm very Ridholts.
1:17:51
Speaker 1: You're listening to Masters in Business on Bloomberg Radio. If
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Speaker 1: A d R dot org resolve faster. Welcome to the podcast.
1:18:38
Speaker 1: Thank you and for doing this. I'm really enjoying this
1:18:41
Speaker 1: conversation and I appreciate you being so generous with your time. Uh,
1:18:45
Speaker 1: you've led a fascinating life. You've also led a well
1:18:49
Speaker 1: lived life. Which are some of the things you talked
1:18:52
Speaker 1: about in the book. It's not about money, it's not
1:18:54
Speaker 1: about accumulating toys, it's not about just piling up junk.
1:18:59
Speaker 1: It's spending time in a way that matters. As you know.
1:19:03
Speaker 1: I totally agree. I read the book. I know. So,
1:19:07
Speaker 1: so let's let's talk a little bit about some of
1:19:10
Speaker 1: um my favorite questions. I ask these of all my guests,
1:19:13
Speaker 1: and we often find some interesting things. What do you
1:19:17
Speaker 1: think is the most interesting or important thing that people
1:19:22
Speaker 1: don't know about your background? I would say that I'm
1:19:28
Speaker 1: consistently rational over a rare wide range of things. And
1:19:35
Speaker 1: you meet a lot of people who are I recall
1:19:38
Speaker 1: locally rational. That is, they do things very sensibly and
1:19:41
Speaker 1: logically in some areas, and then there's a total breakdown
1:19:45
Speaker 1: in some other areas where they act very strange and
1:19:47
Speaker 1: don't use good sense. So, in other words, the logical
1:19:52
Speaker 1: part of their brain only limited to certain subjects. Um. Well,
1:19:56
Speaker 1: I hope we all endeavor to be logical across logical
1:19:59
Speaker 1: growth everything. But I think a lot of people, um
1:20:04
Speaker 1: don't behave that way. Let's let's talk about some of
1:20:06
Speaker 1: your early mentors. Who are the people who really influenced
1:20:09
Speaker 1: you and helped you along your career. Well, I was
1:20:13
Speaker 1: obviously deprived in that way, so I don't have much
1:20:16
Speaker 1: in that category. My father help me a lot from
1:20:19
Speaker 1: ages three to five. Okay, that was enough to really
1:20:22
Speaker 1: get me started. How about colleagues you worked with? Who? Who?
1:20:25
Speaker 1: Who did you really enjoy working with at at various schools.
1:20:29
Speaker 1: I think the most fun I had was working with
1:20:32
Speaker 1: Claude Shannon at M I T. Because he was a
1:20:35
Speaker 1: very creative thinker, and we could just sit there and
1:20:38
Speaker 1: talk about the widest range of topics, and we kind
1:20:41
Speaker 1: of played off each other, and we we thought alike
1:20:43
Speaker 1: about so many things, but we also had different experiences
1:20:47
Speaker 1: in background, so it made a really good team. And
1:20:49
Speaker 1: you ended up creating was it the Wearable Computer? With him? Well,
1:20:53
Speaker 1: it turned out, yes, we're trying to beat Roulette, and
1:20:55
Speaker 1: so we built a computer to wear on the body
1:20:58
Speaker 1: that was hidden and this computer head switches to input
1:21:03
Speaker 1: information about the Roulette wheel, ball and rotor as they
1:21:07
Speaker 1: were moving, and then it would make an immediate prediction
1:21:10
Speaker 1: as to what was going to happen. So, so you
1:21:12
Speaker 1: know the arrangement of numbers and colors on roulette wheel,
1:21:15
Speaker 1: you're you have a certain spin in one direction of
1:21:18
Speaker 1: the ball and the rotors in the other direction, and
1:21:21
Speaker 1: you basically were able to calculate as long as I
1:21:25
Speaker 1: could get a timing fairly accurate when the ball is
1:21:27
Speaker 1: moving this way, we can have a pretty comfortable guess
1:21:30
Speaker 1: as to the cluster of outcomes. Yeah, basically we were
1:21:34
Speaker 1: able to time the ball and the rotor, so we
1:21:37
Speaker 1: know what speed they were both going at, and the
1:21:40
Speaker 1: computer would know where they were at any time, and
1:21:43
Speaker 1: then it could forecast roughly where the ball was going
1:21:45
Speaker 1: to fall. And there are a lot of uncertainties in
1:21:49
Speaker 1: the wheel that are delivered. The ball will bounce off
1:21:52
Speaker 1: little veins on the side, it will spatter over the
1:21:54
Speaker 1: viders between pockets. But it turned out the prediction, the
1:21:57
Speaker 1: predicting was so strong that we got a forty percent edge,
1:22:01
Speaker 1: which is hute. Yes, so gigantic, you can't believe it.
1:22:05
Speaker 1: So you mentioned Warren Buffett, any other investors influence the
1:22:10
Speaker 1: way you approached investing or was it pretty much all
1:22:14
Speaker 1: based on your own research and math? It was pretty
1:22:18
Speaker 1: much thinking for myself, which was both good and bad.
1:22:23
Speaker 1: It was good because I thought of things that I
1:22:26
Speaker 1: wouldn't have thought of if I had been taught the
1:22:29
Speaker 1: formal academic way. It was bad because I had to
1:22:32
Speaker 1: rediscover some things that I would have known easily had
1:22:36
Speaker 1: I taken, you know, formal training. So let's talk about books.
1:22:40
Speaker 1: This is one of the favorite questions of of listeners.
1:22:44
Speaker 1: What are some of your favorite books, be they finance
1:22:47
Speaker 1: or nonfinance, fiction or nonfiction? What do you what do
1:22:50
Speaker 1: you enjoy reading? What have you enjoyed reading. Well, that's
1:22:54
Speaker 1: a very broadcuestion. I probably have currently in my libraries,
1:22:58
Speaker 1: both here and at home, ten thousand books. And I
1:23:02
Speaker 1: haven't read every page of every book, but I've read
1:23:05
Speaker 1: some pages in every book, and I've read some books
1:23:09
Speaker 1: in their entirety. And human knowledge is so vast that
1:23:13
Speaker 1: I can't just pick out a few books and say
1:23:15
Speaker 1: these are the great books from all those thousands and
1:23:17
Speaker 1: thousands of books. And by the way, besides the ten thousand,
1:23:20
Speaker 1: there are others that I've read that aren't in my library,
1:23:23
Speaker 1: many others. And then so I would say that I
1:23:29
Speaker 1: look at things kind of like Charlie Monker, And it's
1:23:32
Speaker 1: multiple mental models in which you focused on certain ideas
1:23:38
Speaker 1: in certain areas, Like here's one from psychology. There's something
1:23:42
Speaker 1: called the Meyer Briggs personality index, and so, uh, there
1:23:48
Speaker 1: are four four dimensions they type people on the first
1:23:51
Speaker 1: one is easy extrovert, introvert. And so if there are
1:23:56
Speaker 1: four dimensions and there are two choices, then you have
1:23:59
Speaker 1: sixteen pure types of people. None people aren't typically that's simple.
1:24:05
Speaker 1: They don't fall into those categories. But you can get
1:24:07
Speaker 1: a surprising amount of information by estimating what type of
1:24:11
Speaker 1: person is um But there's they're sensing. Sensing versus feeling
1:24:16
Speaker 1: is another dimension, and so a sensing person is very
1:24:22
Speaker 1: aware of everything that's going on around him, and a
1:24:25
Speaker 1: feeling person is a highly emotional person and so forth. Anyhow,
1:24:30
Speaker 1: it's an easy way to have a first cut thought
1:24:33
Speaker 1: about people, and it teaches you that people are very different,
1:24:37
Speaker 1: but there's no good or no bad in the various types.
1:24:41
Speaker 1: They're just different and helps you understand so that that's
1:24:44
Speaker 1: a simple mental model. Or there's anchoring in the stock markets,
1:24:51
Speaker 1: something I learned to my pain very early. I bought
1:24:53
Speaker 1: a stock at forty it went down to twenty I
1:24:57
Speaker 1: didn't want to let go till I came back to
1:24:58
Speaker 1: forty a little. Just let me get break even exactly,
1:25:02
Speaker 1: the losing cries of losing investors everywhere. Four years later,
1:25:06
Speaker 1: I get break even, I get out, but of course
1:25:09
Speaker 1: there's inflations, so I haven't really gotten out even, and
1:25:12
Speaker 1: I've deprived myself of investing the money somewhere else where
1:25:15
Speaker 1: it might have been done a lot better, and so forth.
1:25:17
Speaker 1: So the price I bought at forty is a price
1:25:22
Speaker 1: particular only to meet. There's no relation to anything going
1:25:25
Speaker 1: on in the outside world, and so to be anchored
1:25:28
Speaker 1: at that prices uh idiocy. And and yet people do
1:25:32
Speaker 1: it all the time because they're they're subjective perspective takes
1:25:35
Speaker 1: over from a more global perspective. So you have ten
1:25:39
Speaker 1: thousand books. I'm looking at a handful of books over here.
1:25:44
Speaker 1: Nothing really leaps out, as these aren't necessarily these are
1:25:48
Speaker 1: the five seminal books you have to read. But what
1:25:51
Speaker 1: books did you find interesting? Or or let me make
1:25:54
Speaker 1: it more recent, what's the last book you read that
1:25:57
Speaker 1: you really enjoyed? There are five six struggling Okay, well,
1:26:02
Speaker 1: one of them I enjoyed quite a bit. Was a
1:26:05
Speaker 1: well known one Philip Tatlock's super Forecasters, so by the
1:26:12
Speaker 1: way previous guests on the show, and absolutely a delightful
1:26:15
Speaker 1: human being. Yes. In fact, I was at a conference
1:26:18
Speaker 1: a few years ago that he and I both spoke at,
1:26:20
Speaker 1: so I kind of got to know a little bit
1:26:22
Speaker 1: more about his thoughts and ideas there. But yeah, very
1:26:25
Speaker 1: interesting piece of work, well worth knowing. Another one was
1:26:31
Speaker 1: The Accidental Accidental Superpower by guy named Peter's Eye Hand,
1:26:37
Speaker 1: and he's a Stratford type. By the way, I might
1:26:42
Speaker 1: say that if I read a book and I find
1:26:45
Speaker 1: it interesting, it doesn't mean that I'm endorsing that book
1:26:50
Speaker 1: kind of percent. It's just that I'm finding things that
1:26:53
Speaker 1: are worth thinking about in that book. And I may
1:26:55
Speaker 1: may come out with a different conclusion, but he's provoking
1:26:58
Speaker 1: a thought process and make you think about an issue. Hey,
1:27:01
Speaker 1: I hadn't really considered this quite in this context. That's fascinating. Yes,
1:27:05
Speaker 1: So I'd rather read a book that doesn't just reinforce
1:27:08
Speaker 1: opinions I already hold. I want a book that's going
1:27:10
Speaker 1: to add something that I don't know that's information, if
1:27:13
Speaker 1: it's new, if it's the same stuff over and over,
1:27:15
Speaker 1: that I've already thought about. That's not information, so more
1:27:18
Speaker 1: confirmation biases and how you select books. All right, So
1:27:22
Speaker 1: that's to give us one more and we'll let you
1:27:23
Speaker 1: off the hook with this. Okay. There's a one by
1:27:26
Speaker 1: Paul Wilmock. It's called The Money Formula, and it's it's
1:27:30
Speaker 1: just it's just out and it's about how quants have
1:27:34
Speaker 1: help screw things up in the financial world. Screw things up.
1:27:37
Speaker 1: Well they I don't know if that's the right phrase.
1:27:40
Speaker 1: They they've certainly helped mix things up and change things.
1:27:43
Speaker 1: But I would argue for the better, and I think
1:27:45
Speaker 1: you are going to be an agreement with that. Well,
1:27:49
Speaker 1: perhaps I put that too simplistically, but um, what I
1:27:54
Speaker 1: think part of his thrust is that the cell side
1:27:58
Speaker 1: on Wall Street has taken quant product us to use
1:28:01
Speaker 1: to market to people, and they haven't been discriminating about
1:28:06
Speaker 1: the products that they've marketed, a collateralized mortgage obligations being
1:28:10
Speaker 1: a case in point. Well, any tool could be used
1:28:13
Speaker 1: for good or evil, to say the least, but you know,
1:28:17
Speaker 1: it's it's anything is only a sausage is only as
1:28:19
Speaker 1: good as the meat that goes in. Well, you know,
1:28:21
Speaker 1: take coals, both good and evil. It pollutes, but it
1:28:25
Speaker 1: also keeps us warm and supplies this energy. It got
1:28:28
Speaker 1: us to the point where we can now start looking
1:28:30
Speaker 1: at less polluting energy options. And you know, when we
1:28:35
Speaker 1: look at that transition that's taking place not just with
1:28:38
Speaker 1: cold and natural gas, but quantitative is replacing the old
1:28:43
Speaker 1: qualitative for a reason because it's demonstrably superior in so
1:28:47
Speaker 1: many ways. I think that's a fair statement. I agree. Um,
1:28:51
Speaker 1: So you you started in this industry, in the finance
1:28:54
Speaker 1: industry forty plus years ago. Um, what it? What do
1:29:00
Speaker 1: you think is the most significant change that's taken place
1:29:03
Speaker 1: over that time? There's more than one. One of the
1:29:07
Speaker 1: big ones has been the computerization and the quantification of investing.
1:29:13
Speaker 1: That's really two big ones, right, So technology is everywhere
1:29:17
Speaker 1: and applying it mathematically seems to be the dominant thing.
1:29:22
Speaker 1: What else? The second has been the aggregation of money
1:29:27
Speaker 1: management into huge firms that are offering what seemed to
1:29:34
Speaker 1: be choices but are largely just playing vanilla in different packages.
1:29:41
Speaker 1: Is that is that still going on? Or is that
1:29:44
Speaker 1: now getting bigger and bigger? I think that the big
1:29:47
Speaker 1: firms are getting relatively bigger. Uh. The thing I read
1:29:50
Speaker 1: about hedge funds. Recently on a psyche bi got very riskful.
1:29:55
Speaker 1: I think I've heard of him was the number of
1:30:00
Speaker 1: new hedge funds is smaller than the number of Hatche
1:30:05
Speaker 1: funds that are disappearing, but that the total assets under
1:30:08
Speaker 1: management for hedge funds is increasing over three trillion dollars.
1:30:13
Speaker 1: So you know, Jim, I'm fond of repeating Jim Chanos's quote.
1:30:18
Speaker 1: He said when he started in hedge funds thirty years ago,
1:30:21
Speaker 1: there were a couple of hundred hedge funds. They all
1:30:23
Speaker 1: created alpha. Now there's almost ten thousand hedge funds and
1:30:26
Speaker 1: those same thirty hedge funds of the alpha generators. And
1:30:30
Speaker 1: there is some truth to that. There is. It is
1:30:33
Speaker 1: not a true Gaussing distribution. That's very much a fat
1:30:36
Speaker 1: head in a long tail um and not not everybody
1:30:40
Speaker 1: who's running a hedge fund is capable of putting up
1:30:43
Speaker 1: the sort of numbers that you put up. Well. In
1:30:45
Speaker 1: the nineties, I could find hetche funds when I didn't
1:30:48
Speaker 1: feel like managing money of my own. I could find
1:30:50
Speaker 1: hedge funds that we're making annualized. And then in the
1:30:55
Speaker 1: two thousands they kept dying. The numbers drop for Cliff
1:31:00
Speaker 1: and I want to say it's Simon Lacks book The
1:31:04
Speaker 1: hedge fund He said in the book, the losses hedge
1:31:10
Speaker 1: funds suffered in O eight oh nine had wiped out
1:31:13
Speaker 1: all of their previous profits in total. It didn't wipe
1:31:16
Speaker 1: out manager's fees, but it wiped out all the all
1:31:19
Speaker 1: the profits and other than those top let's call it
1:31:24
Speaker 1: five hedge funds, it's certainly not thirty these days. The
1:31:28
Speaker 1: balance really don't seem to be generating any sort of alpha.
1:31:31
Speaker 1: So you have a skewed when you look at the returns.
1:31:34
Speaker 1: They're so skewed not by the big hedge funds, but
1:31:37
Speaker 1: by all the rest that are under performing. Well. I
1:31:40
Speaker 1: think it's probably a first apement to say that all
1:31:42
Speaker 1: the money that have ever been put into hedge funds
1:31:45
Speaker 1: and then put it into X funds. The investors who
1:31:48
Speaker 1: did that would be far better off than they actually were.
1:31:52
Speaker 1: Renaissance technologies accepting in a handful of others. But that
1:31:55
Speaker 1: you could you could very well be right. Um, let's
1:32:00
Speaker 1: let's go to the next question. Tell us about a
1:32:02
Speaker 1: time you failed. And I know you wrote a few
1:32:05
Speaker 1: times in the book about things he failed and what
1:32:08
Speaker 1: you learned from the experience. Well back when I was
1:32:11
Speaker 1: in high school, I heard about a chemistry contest. It
1:32:15
Speaker 1: was the Southern California Chemistry Teachers Contest, and I happened
1:32:19
Speaker 1: to love chemistry, and I was at a school where
1:32:23
Speaker 1: basically there weren't any academics. But I decided that I
1:32:27
Speaker 1: would study for this test because if I were able
1:32:29
Speaker 1: to win it, I could choose a scholarship to a
1:32:34
Speaker 1: place I could not otherwise afford to go, like calchech
1:32:37
Speaker 1: or UC Berkeley or whatever. So I trained up for
1:32:41
Speaker 1: this test really hard, and the typical winning score was
1:32:46
Speaker 1: about Basically there was a couple of hundred of the
1:32:50
Speaker 1: best high school chemistry students from southern California would compete
1:32:54
Speaker 1: each year, and I competed as a fifteen year old
1:32:58
Speaker 1: as a junior. People who were basically seniors who were
1:33:02
Speaker 1: seventeen and eighteen years old, and I thought I was
1:33:05
Speaker 1: going to get the test were higher. So the best
1:33:09
Speaker 1: score by far they had ever seen. And I took
1:33:12
Speaker 1: old tests that my chemistry teacher had assembled over the years,
1:33:17
Speaker 1: and I was scoring that way on the old tests
1:33:19
Speaker 1: one after another. I went into the test and I
1:33:22
Speaker 1: just rolled through the exam. I got just about everything right,
1:33:25
Speaker 1: and then I came to the last part, which was
1:33:28
Speaker 1: a lot of calculating, and they allowed slide rules that year,
1:33:32
Speaker 1: and they didn't say they were necessary, only that you
1:33:34
Speaker 1: could bring one if you wanted to. Turned out the
1:33:36
Speaker 1: tests were designed so that only with a good slide
1:33:39
Speaker 1: rule could you complete that part of the test. I
1:33:42
Speaker 1: had a tense cent slide rule I brought along was worthless.
1:33:44
Speaker 1: It was so inaccurate that there was no chance of
1:33:47
Speaker 1: even bothering. So I did what I could by hand,
1:33:50
Speaker 1: and I completed I think eight hundred and seventy three
1:33:53
Speaker 1: points on the test. I got eight hundred sixty right,
1:33:56
Speaker 1: and but um, I was just crushed by the fact
1:34:00
Speaker 1: that I didn't have a good slide rule, but I
1:34:03
Speaker 1: hadn't prepared and covered that base properly. So that that
1:34:06
Speaker 1: taught me that one of the things you wanted to
1:34:08
Speaker 1: do is look at things redundantly when you can and
1:34:12
Speaker 1: try to cover all the downside possibilities that might occur
1:34:16
Speaker 1: and eliminate them. So that worked in very well later
1:34:19
Speaker 1: when I played in the casinos and when I ran
1:34:23
Speaker 1: hedge fund. Did you end up taking the test again
1:34:26
Speaker 1: when you were a senior Um, that's a good interesting question.
1:34:30
Speaker 1: I asked him if I could, and they said, no,
1:34:31
Speaker 1: you can only take it once. So I thought about that,
1:34:34
Speaker 1: and I found that there was a physics physics test
1:34:38
Speaker 1: that was also given by It was analogous to the
1:34:41
Speaker 1: chemistry test who was given by the physics teachers, and
1:34:44
Speaker 1: another couple of hundred of the best students southern California
1:34:48
Speaker 1: took that. So I crammed for that test. I only
1:34:52
Speaker 1: heard about a short time before, and I wasn't able
1:34:55
Speaker 1: to finish all the studying that I wanted to do,
1:34:57
Speaker 1: but I was able to win that one. Really, so
1:34:59
Speaker 1: and you that, Um, so, that's like a tuition scholarship.
1:35:03
Speaker 1: And you I had a choice of cal Techer Berkeley.
1:35:06
Speaker 1: I found out I wanted to go to cal Tech,
1:35:08
Speaker 1: but I couldn't because I didn't have the money to
1:35:10
Speaker 1: live in your passage, you know. So you went to Berkeley?
1:35:12
Speaker 1: And how did that work out? Well? It worked out
1:35:15
Speaker 1: just fine. I got a full scholarship, and after a
1:35:18
Speaker 1: year at Berkeley, I changed the U c l A
1:35:20
Speaker 1: because I had more friends down there, and I went
1:35:23
Speaker 1: through the UC system and everything went was fine for me.
1:35:26
Speaker 1: You're physically fit, you seem to be in pretty good shape.
1:35:29
Speaker 1: Tell us what you do to stay physically fit and
1:35:31
Speaker 1: what do you do to keep mentally fit? Well physically.
1:35:36
Speaker 1: I've been a long distance I had had been a
1:35:40
Speaker 1: long distance runner for about twenty five years. I used
1:35:43
Speaker 1: to run marathons and have sent ten ks in five ks.
1:35:47
Speaker 1: I'd run about forty miles a week, and then I
1:35:50
Speaker 1: hurt my back weightlifting, so now I just walk. When
1:35:54
Speaker 1: when did you stop running around? What you How old
1:35:56
Speaker 1: were you? I stopped running when I was sixty eight,
1:36:01
Speaker 1: ran my last marathon in New York in and so
1:36:07
Speaker 1: that's twenty years ago. But you're not eighty eight. No,
1:36:14
Speaker 1: I'm doing the math. You sixty eight? Well, seventeen years ago? Seven? Okay?
1:36:19
Speaker 1: And you do you say you do you still lift
1:36:21
Speaker 1: weights or yes? I go to the gym with a
1:36:24
Speaker 1: trainer twice a week. I walk about ten to fifteen
1:36:27
Speaker 1: miles a week, and then I do some hiking in
1:36:31
Speaker 1: the hills. What do you do to to keep mentally active? What?
1:36:35
Speaker 1: What do you enjoy? What? What keeps your focus these days?
1:36:40
Speaker 1: I'd like I still like problems in finance, so I
1:36:43
Speaker 1: take time after work on some of them. I also
1:36:47
Speaker 1: black math problems and chest problems and so forth, play
1:36:51
Speaker 1: word games, um, travel, read a lot, talked to really
1:36:58
Speaker 1: interesting people. Okay, I have a very smart family and
1:37:01
Speaker 1: so they're very stimulating. That sounds like I have a
1:37:06
Speaker 1: I have grandchildren who are all very talented. Three of
1:37:09
Speaker 1: them are triplets, and they're all out of I t Oh, really,
1:37:13
Speaker 1: so you're you're busy all the time. There's there's no
1:37:16
Speaker 1: depth of things to keep you occupying. The last time
1:37:20
Speaker 1: I was born, I think was when I was eleven.
1:37:23
Speaker 1: That's a fair fair statement. So if the triplets and
1:37:28
Speaker 1: M I T or any other millennials or recent graduates
1:37:32
Speaker 1: were to come up to you and say, uh, Ed Thorpe,
1:37:36
Speaker 1: I'm thinking about a career in finance or investing, what
1:37:39
Speaker 1: sort of advice would you give them? I'd say, play
1:37:44
Speaker 1: to your strengths and your skills, do what you like
1:37:47
Speaker 1: to do. And if you do what you like to do,
1:37:49
Speaker 1: then you're gonna do better than if you do something
1:37:51
Speaker 1: that you think you should do but don't want to do.
1:37:54
Speaker 1: And try to plan your life so that you're spending
1:37:59
Speaker 1: your time with good people. That sounds like that sounds
1:38:02
Speaker 1: like good advice. And and my final question what do
1:38:06
Speaker 1: you know today about investing that you wish you knew
1:38:10
Speaker 1: fifty years ago when you were getting started. I would
1:38:15
Speaker 1: like to have known that I could have bought Berkshire
1:38:18
Speaker 1: Hathaway at twelve instead of it instead. So so that's
1:38:25
Speaker 1: the key thing is precience about the success of Warren Buffett.
1:38:30
Speaker 1: I will tell you a lot of people would have
1:38:32
Speaker 1: liked to buy Berkeshire Hathaway under a thousand. So even
1:38:37
Speaker 1: twelve is even even when you got in, is better
1:38:40
Speaker 1: than when most people got in. Um. Ed Thorpe, thank
1:38:43
Speaker 1: you so much for being so this is this has
1:38:46
Speaker 1: been an absolute delight we have been speaking with Ed Thorpe.
1:38:49
Speaker 1: I I will pound the table on a couple of
1:38:52
Speaker 1: his books. A Man for All Markets is his most
1:38:55
Speaker 1: recent book. It's really it's more than than a biograph
1:39:00
Speaker 1: fee It's a history of what's happened in finance over
1:39:03
Speaker 1: the past half century, including the rise of hedge funds,
1:39:07
Speaker 1: the rise of quants, the rise of all sorts of things,
1:39:10
Speaker 1: told from a unique perspective of somebody who not only
1:39:14
Speaker 1: beat the deal or and beat the markets, but as
1:39:16
Speaker 1: as Um, learned a lot of secrets about life that
1:39:20
Speaker 1: that many of us should learn um. If you enjoy
1:39:23
Speaker 1: this conversation, be sure and look up and inch or
1:39:25
Speaker 1: down an inch on Apple iTunes, SoundCloud overcast, or Bloomberg
1:39:31
Speaker 1: dot com and you could see the other hundred and
1:39:34
Speaker 1: fifty or so such conversations we've had. We love your comments,
1:39:39
Speaker 1: feedback and suggestions right to us at m IB podcast
1:39:44
Speaker 1: at Bloomberg dot net. I would be remiss if I
1:39:47
Speaker 1: did not thank some of the people who helped to
1:39:50
Speaker 1: put this podcast together. Michael Batnick is my head of research.
1:39:54
Speaker 1: Taylor Riggs is our producer. Booker Medina Partwada is our
1:40:00
Speaker 1: audio engineer who helps make these things sound as good
1:40:03
Speaker 1: as they actually do. I'm Barry Ridhults. You've been listening
1:40:07
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