Episode #39: Ed Thorp, Hedge Fund Manager, Author, & Professor, “If You Bet Too Much, You’ll Almost Certainly Be Ruined”

Episode #39: Ed Thorp, Hedge Fund Manager, Author, & Professor, “If You Bet Too Much, You’ll Almost Certainly Be Ruined”



Guest: Ed Thorp is an American mathematics professor, hedge fund manager, and blackjack player. To beat roulette, he and the father of information theory, Claude Shannon, invented the first wearable computer. Along with innovative applications of probability theory, Thorp is also the New York Times bestselling author of Beat the Dealer, the first book to mathematically prove that the house advantage in blackjack could be overcome by card-counting. He also pioneered the use of quantitative investment techniques in the financial markets.

Date Recorded: 2/8/16     |     Run-Time: 58:56

Summary: Ed is a self-made man after having been a child of The Depression. He’s a professor, a renowned mathematician, a fund manager who’s posted one of the lengthiest and best investment track records in all of finance, a best-selling author (his most recent book is A Man for All Markets), the creator of the first wearable computer, and finally, the individual responsible for “counting cards.”

Meb begins the episode in the same place as does Ed in his new book, the Depression. Meb asks how that experience shaped Ed’s world view. Ed tells us about being very poor, and how it forced him to think for himself, as well as teach himself. In fact, Ed even taught himself how to make his own gunpowder and nitroglycerine.

This dovetails into the various pranks that Ed played as a mischievous youth. Ed tells us the story of dying a public pool blood-red, resulting in a general panic.

It’s not long before we talk about Ed’s first Las Vegas gambling experience. He had heard of a blackjack system developed by some quants, that was supposed to give the player a slight mathematical advantage. So Ed hit the tables with a strategy-card based on that system. At first, his decisions caused other players at the table to ridicule him. But when Ed’s strategy ended up causing him to hit “21” after drawing 7 cards, the players’ opinions instantly changed from ridicule to respect.

This was the basis from which Ed would create his own counting cards system. Meb asks for a summary of how it works. Ed gives us the highlights, which involve a number count that helps a player identify when to bet big or small.

Meb then asks why Ed decided to publish his system in academic journals instead of keeping it hush-hush and making himself a fortune. Ed tells us that he was academically-oriented, and the spirit of science is to share.

The conversation turns toward the behavioral side of gambling (and investing). Once we move from theory to practice, the impact of emotions plays a huge role. There’s a psychic burden on morale when you’re losing. Meb asks how Ed handled this.

Ed tells us that his early days spent gambling in the casinos were a great training ground for later, when he would be “gambling” with tens of millions of dollars in the stock market. He said his strategy was to start small, so he could handle the emotions of losing. As he became more comfortable with his level of risk, he would scale his bets to the next level, grow comfortable, then move up again from there. In essence, don’t bet too much too fast.

This dovetails into the topic of how to manage money using the Kelly Criterion, which is a system for deciding the amount to bet in a favorable situation. Ed explains that if you bet too small, won’t make much money, even if you win. However, “if you bet too much, you’ll almost certainly be ruined.” The Kelly Criterion helps you determine the appropriate middle ground for position sizing using probabilities.

It turns out that Ed was so successful with his methods, that Vegas changed the rules and eventually banned Ed from their casinos. To continue playing, Ed turned to disguises, and tells a fun story about growing a beard and using contact lenses to avoid identification.

Meb tells us about one of his own card-counting experiences, which was foiled by his partner’s excessive Bloody Mary consumption.

Next, we move to Wall Street. Meb brings up Ed’s performance record, which boasts one of the highest risk-adjusted returns of all time – in 230 months of investing, Ed had just 3 down months, and all were 1% or less. Annualized, his performance was over 19%.

Ed achieved this remarkable record by hedging securities that were mispriced – using convertible bond and options from the same company. There was also some index arbitraging. Overall, Ed’s strategy was to hedge away as much risk as possible, then let a diversified portfolio of smaller bets play out.

Meb asks, when you have a system that has an edge, yet its returns begin to erode, how do you know when it’s time to give up the strategy, versus when to invest more (banking on mean reversion of the strategy). Ed tells us that he asks himself, “Did the system work in the past, is it working now, and do I believe it will it in the future?” Also “What is the mechanism that’s driving it?” You need to understand whether the less-than-desired current returns are outside the range of usual fluctuation. If you don’t know this, then you won’t know whether you’re experiencing bad luck (yet within statistical reason) or if something has truly changed and your “bad luck” is actually abnormal and concerning.

Next, Meb asks about Ed’s most memorable trade. You’ll want to hear this one for yourself, but it involves buying warrants for $0.27, and the stock price eventually rising to $180.

There’s plenty more in this fantastic episode, including why Ed told his wife that Warren Buffett would be the richest man in America one day (said back in 1968)… What piece of investing advice Ed would give to the average investor today… Ed’s interest in being cryogenically frozen… And finally, Ed’s thoughts on the source of real life-happiness, and how money fits in.

The show ends with Meb revealing that he has bought Ed and himself two lottery Powerball tickets, and provides Ed the numbers. Will Ed win this bet? The drawing is soon, so we’ll see.

Sponsors: Soothe and Lyft

Comments or suggestions? Email us Feedback@TheMebFaberShow.com

Links from the Episode:

A Man for All Markets

Beat the Dealer

Beat the Market

The Kelly Capital Growth Investment Criterion


Transcript of Episode 39:

Welcome Message: Welcome to The Meb Faber Show, where the focus is on helping you grow and preserve your wealth. Join us as we discuss the craft of investing, and uncover new and profitable ideas, all to help you grow wealthier and wiser. Better investing starts here.

Disclaimer: Meb Faber is the co-founder and chief investment officer at Cambria Investment Management. Due to industry regulations, he will not discuss any of Cambria’s funds on this podcast. All opinions expressed by podcast participants are solely their own opinions and do not reflect the opinion of Cambria investment management or its affiliates. For more information, visit cambriainvestments.com.

Sponsor: This podcast is sponsored by the Soothe app. We all know how stressful investing in volatile markets can be. That’s why I use Soothe. Soothe delivers five-star, certified massage therapist to your home, office, or hotel in as little as an hour. They bring everything you need for a relaxing spa experience without the hassle of traveling to a spa. Podcast listeners can enjoy $30 to their first Soothe Massage with the promo code “Meb”. Just download the Soothe app, and insert the code before booking. Happy relaxation.

Good morning ladies and gentlemen. We’re here in a rainy and foggy Los Angeles morning. Today we have an incredibly special guest, Professor Ed Thorp. Ed, welcome to the show.

Ed: Thank you very much.

Meb: So, we have a little bit of a younger audience, lot of younger quants. And so just a real super quick intro for those who aren’t familiar, I’m just gonna [inaudible 00:01:45] previous backgrounds, and then we’re gonna hop in because we have so much to cover today. So, Ed is a total self-made man. He’s been a professor, a renowned mathematician, a fund manager who’s supposed to be one of the best track records in all of finance, best-selling author, creator of the world’s first wearable computer, and finally, a successful gambler. Two words you normally don’t hear together. And if you had to pick investors for the Mount Rushmore of investing, Ed would be on there with the likes of Simons and Buffett. And oddly enough, you would probably be on the Mount Rushmore of gambling too, and certainly the only one on both.

So, Ed has just written a great new book, his memoir titled, “A Man for All Markets,” lots of fun stories, don’t want to spoil it. So, let’s jump right in. Ed, I thought we could start where you do in your book. So back in Chicago, and in SoCal in the 1930s, you were born a child of the Depression. You know, my father was actually born around the same time on a farm in Nebraska, no running water, outhouse, all that good stuff. And I saw it kind of color his entire life. The way he thought about things, the way he approached, not only his career but investing. So maybe it would be a good start to talk a little bit about how you thought that experience affected your personality and, in general, worldview growing up.

Ed: Well, I was born in the depths of the Depression, actually. Think the Dow touched its lowest point in July of 1932, and I was born in August of 1932. So, it was uphill all the way, but bumpy in the stock market. And people were very poor then, very much like you picture people in the streets of Moscow when I was there in 1972. Dower, drab clothing, everything was precious. Everything was saved and used, and there was 25% unemployment.

So, it was a grim time in the history of the country. We finally got bailed out, so to speak, with World War II. So, I experienced all that, and that gave me a perspective on later events that I think aren’t shared or appreciated as much by people who are much younger.

Meb: And so, you had to scrap a little bit. You had to take on a few jobs. I think you had the what, 2:00, 4:00 in the morning newspaper route as well, right?

Ed: Yeah, when my family moved to California in 1942, I was about, we’ll pretend that I got to work delivering newspapers at 2:00 a.m. or 3:00 a.m. in order to make a little extra money. And I was going to a high school which was academically, not particularly good. It was ranked 31 out of 32 in the L.A. City school system. So, nobody there went to college, but I was interested in science, math, literature, quite a few things. So, I ended up earning money to buy science equipment and also learned how to teach myself things. And that served me well in later life, the learning how to think for myself and to teach myself.

Meb: And in teaching or so I’m thinking, I remember the book even you referencing making your own gunpowder, nitroglycerin? Is that right?

Ed: Yes, but I learned something, theoretical in science. I also tried to put it to work. I made explosives, gunpowder, nitroglycerin, guncotton, and shot off rockets, that sort of thing.

Meb: And you know, it’s funny, I come from a family of engineers on both sides. And my mom tells a funny story about my uncles who had built pipe bombs, and then hid, set one off, and then buried one in the backyard in North Carolina. And then my grandfather, years later had unearthed it, and thought it was some big plot, and that someone was burying bombs in his backyard. And so, had called the police and I think it even escalated a few levels above that. And eventually, I think my uncles told him many, many decades later. But, you know, it’s interesting because there is oftentimes a link between bright kids and sort of getting into mischief. And you were a bit of a prankster too. I mean and there’s a whole handful of kind of humorous pranks you had kind of pulled when you were a younger guy. Is there any one, in particular, that sticks out? As particular, one of your favorites?

Ed: Well, one of them, it got a fair amount of press. There was a large indoor swimming pool in Long Beach called The Plunge, and it was part of an amusement park area down there. So, I used to go down sometimes, catch the bus when I was a teenager, and go own with myself, all by myself with a friend and look around, and argue with people at one of the outdoor places called the Spit ‘n’ Argue Club, where people would hold forth for 15 minutes in topic.

So, I learned how to reason against people who believed in Flat Earth Society, that sort of thing. Winding down, when I was learning chemistry, I came across a dye called analum red, and this dye could color 6 million times its own weight a deep blood-red. So, I put a little pinch in the goldfish pool in my backyard and it turned it a deep blood-red. And I went back to my little chem lab behind the garage that I had installed there. I heard a lot of screaming. My mother had come out and saw that the pool was blood-red, and she thought I was in there somewhere.

So, I calmed her down, and I thought about it a little more and said, “Well, this could lead to a really fun prank.” So, I went down with a confederate that I’d recruited to the Long Beach Plunge, and we put a little bag of analum red in the pool that was sealed with wax. And we had some strings tied to it so we could tear it apart. Then we walked to the sides of the pool, and a swimmer came by and stretched the strings, and unleashed the dye.

And we got out, dried off, ran upstairs to see what would happen. And there was a large red cloud that had formed. People began to scream. And they hero-dove in and stirred it all up too in an attempt to rescue who was bleeding to death in the middle of this red cloud. And then finally, they realized that, as the cloud dispersed, there was nobody there. But the pool turned kind of a Kool-Aid, a red Kool-Aid color, and everybody basically bailed out. You know, got their arm stamped and they left.

We came back in the afternoon. Not many people checked back. And then the next day, there was an article in the newspaper about pranksters dying the Long Beach club, Plunge, red. So, it was very entertaining to me.

Meb: That’s funny. My favorite go-to growing up was the old-school sink that had the little spray nozzle next to the to the main nozzle, and I used to put rubber bands around it so that anytime anybody would come into the kitchen would get it hosed down. The problem with that one is that it’s much easier to get caught because there’s only a few culprits, than the pool.

So, one quick question, and so we’d asked on Twitter. We said, “Hey, we’re having Ed on tomorrow. Anybody have any good questions?” We got all sorts of questions, but one that I thought was interesting, we may include a few as we go along and it says, “You know, a lot of people likely look up to you, quants, gamblers as, you know, kind of their Eider [SP] mentor. Was there anyone that, you know, as a child, and you know, in middle high school, growing up that you looked up to as sort of a role model, you know, in any field or in any sort of regard?

Ed: Well, my parents were busy working in defense industries during the war, and for some years afterwards. And so, one was working swing shift, the other was working graveyard. So, they were either working or sleeping. I saw very little of them. So, I kind of raised myself, but I had a wonderful English teacher at Narbonne High School in Lomita, who said he’d took me under his wing. He saw the results of my IQ tests and sort of honed in on me as somebody who had promise. In any case, he was almost like a second parent or a replacement for my parents. And so, I learned a lot from him, and it’s probably a lot to do with his nurturing that I focused as much as I did.

Meb: You know, it’s interesting that there’s so many commonalities. I remember my father, same sort of thing, was had no intentions of going to college. And if it wasn’t for one teacher that had kind of encouraged him along, it ended up similar to you. So, you had received a scholarship to Berkeley, eventually transferred to UCLA, we’ll mention it later, but ended up being a professor at MIT, and New Mexico State, UC Irvine, all these good things.

But I want to skip forward a little bit to age 26, you’re out of school, assuming grad school at this point, but let’s talk about your first Vegas experience. And this is with blackjack, and I think you had gone to Vegas and with what we would today call a basic strategy system, which is sort of the basics. You know, you can’t really beat the casino, but you’re probably not going to lose a whole lot. And apparently that first time, there was a fair amount of ridicule from other players in this experience. So, I was wondering if you could talk about that or why that was the case.

Ed: Sure. Everything you say is on the mark. What happened was I got a bachelor’s and a master’s in physics, and then while finishing my Ph.D. in physics, I was all done except for the last part of my thesis. I ran into a lot of math problems it was in quantum mechanics, so I started taking more math courses. And then I realized that UCLA was slow graduating people in physics, but math was a relatively quick. So, I changed to math to get my Ph.D. sooner. So, it happened quickly enough so that I wasn’t able to apply for a job immediately. So, I was kept on for another year as an instructor at UCLA while my thesis adviser helping get a good placement which turned out to be MIT.

During that year, I went over Christmas vacation to Las Vegas. It was Christmas 1958 because we didn’t have any money, and they had good accommodations at low prices, and also cheap food. And I happened to hear about a system in blackjack that had been generated by four Army mathematicians. That’s the way they passed their three years at Aberdeen Proving Ground. And they had worked out a way to play blackjack almost even. A casino would have about a six tenths of a percentage, they thought. Their work was approximate, so that wasn’t an exact number. It turned out to be that the casino and the player were just about dead even using the strategy they had generated, but no one knew that at the time.

So, I took their strategy along with me, sat down at a blackjack table, and decided I’d risk $10. The reason I was willing to do that was because I had figured out how to beat the let, and I knew that I’d be having to learn about how to play in casinos, and get some exposure to the environment in a casino. In any case, I played for about 20 minutes with a little strategy card and everybody thought it was ridiculous. And then the card caused me to destroy a good hand. I think it was an ace and a seven and keep drawing cards. And I finally got a terrible hand. I got 12, 13, 14, 16, I kept drawing.

But then I ended up with a 7 card 21, and they all thought this was wonderful. That this was an amazing strategy and when they thought it was a fool’s strategy before. So, I learned from this that the players, at least in that little group, didn’t understand the game. And the people in the casino didn’t understand because once I’d drawn 7 card 21, they all wanted to see what’s going on with the card. And they all changed from ridicule to respect.

I went back to UCLA and grabbed the article written by the so-called Four Horsemen, the mathematicians from Aberdeen Proving Ground, and began to read it. And I realized, almost immediately, that as cards were played, the composition of the deck changed, often quite radically. And so, the odds would change, often quite radically in favor of the casino or the player. So, that was my inspiration. And now the question was how to actually make this into a practical system. So, I thought about that and got to work on it.

Meb: And so, it’s interesting because you had said in the book, there’s a great quote, you said, “Had I been more knowledgeable about the history of gambling and the centuries of effort devoted to the mathematical analysis of games, I might not have tackled blackjack.” And it was only once you sat down with the players, once saw how rational they were, but then also went back, thought about it a little bit, and this is kind of a great comment on I think interdisciplinary work where a lot of the status quo, and a lot of what people think to be, you know, correct, it often takes someone from a somewhat skewed or totally different discipline to think about in a different way.

So, all right. So, you started thinking about moving card counting, which is what people describe it as today. Maybe just give us a super quick summary of how that works on the most basics so the listeners can understand how you then implemented it going forward.

Ed: Okay, what I found out when I first attacked the problem by hand but then by computer after I moved to MIT. They had a new IBM 704 computer that I could use along with 30 New England universities. So pretty crowded. What I found out was that if you took small cards out of the deck, that shifted the odds fairly strongly in favor of the player. If you took big cards out of the deck, aces and 10s, that shifted the odds very strongly in favor of the casino. If you reverse that, a deck rich in big cards is good for the player. A deck poor in big cards is bad for the player. Now they’re mirror images. A deck rich and small cards is bad for the player. A deck poor in small cards is good for the player.

So, the point count that came out of all this was you start with a count of zero, aces and 10s, when they fall, minus 1 each. So, you start counting down if you see aces and 10s going out. If small cards go out, those are two, three, four, five, and six, then you add one to your count for each small card that goes out. And sevens, eights, and nines are fairly neutral so you don’t bother count them. They’re just zeros. And so, the count goes up and down as you see cards during play. And when the count gets positive enough, then you bet a fair amount. And when it gets negative, you bet small just to keep your seat, or you get up and leave or change tables. So, that’s the root idea. And that worked very well initially, and still works now in those casinos that haven’t messed the rules up.

Meb: So, there’s a couple interesting points I want to talk about. And so, you eventually started implementing this. You had some interesting, to say the least, partners that funded you some money. And you started implementing it, won some money. And what probably every entrepreneur in the country would think is a somewhat crazy decision, then decided to publish the work. You know, and one of the most often asked questions on Twitter is said, “Why didn’t Ed just keep this to himself, the secret algorithm, and make a gazillion dollars and never tell the world?” What was your thinking there?

Ed: Well, I was academically oriented. The ideal life for me was to be a full professor at a good university and have all kinds of smart friends and work on interesting problems. So, to me, this was an interesting problem that I was curious about. And the spirit in science is to share what you find out. So, to share my ideas was almost automatic.

However, I ended up actually going to the casinos to play because when I announced the strategy that I’d developed, there was a lot of ridicule both from newspapers and from Las Vegas, and Reno especially. So, the state of Nevada said, this is ridiculous there’s no such thing as a winning system. By the way, mathematicians have thought that was no such thing either.

But when I explained how it worked at a meeting of the American Math Society, then they caught on and they understood what was going on and they realized that it was right. So anyhow, I ended up accepting a bankroll offer from two very wealthy citizens, and we went out to Lake Tahoe and Reno for a test of the system. That was in spring break at MIT, 1961. And we took along $10,000. They wanted to take a $100,000 along, but that was too much in my view because if something went wrong, something might go wrong with me. So anyhow, you might add a zero to these numbers approximately because of inflation. A dollar then is worth about $8, close to $10, now.

I played for about 40 hours, about 20 hours was warm up and getting used to everything, and about 20 hours was serious. About $50 to $500 betting. And I predicted that we double our bankroll, and we actually made $11,000 after it was all over, instead of the $10,000 I predicted. So, everything performed the way I said it was going to, we were never behind more than 1$,300. So, that was the beginning.

Meb: We’re going to skip over some…you have some awesome stories in the book including, one of my favorites which is how you communicated your earnings to your wife over the phone. But listeners, you gotta go read the book to get all these. But here’s a question I wanted to ask and so, you know, a lot of people once it goes from theory to real world implementation, there’s a very real influence which is the impact of emotions. And so, we now know through the work of [inaudible 00:20:38], all these other guys, that there can be a real psychological burden on one’s morale and with losing money and gains don’t have an equal and opposite boost. And so, given this and given the prospect for, you know, runs with cards on either side, how did you stick with the strategy after suffering bad losses? Did you ever let emotions lead you to act in an inconsistent way? And did you ever make any exceptions?

Ed: Well, those are really good questions so I’ll tackle them one at a time here. First, actually having played in a casino to address the questions you just raised was perfect training ground for much bigger scale betting on Wall Street. So, one of the things I learned that I basically taught myself in the casino on our first big gambling trip was start small. Just bet $1 to $10, and play until it doesn’t bother you anymore until you can emotionally handle it. That’s 8 hours of the 20 hours of warm-up.

And then after that, I moved up to $2 to $20. That took about an hour. Then I moved to $5 to $50. That took another hour. And $25 to $300, another couple hours. And then $50 to $500. So, I learned how to handle my emotions, how to be disciplined, how to stick to the system by starting small at a level where it didn’t bother me, and then gradually scaling up as I learned that it worked, then I got confidence, and so on.

And so, there was one thing I learned that was a valuable ever after when the scale became 10s of…even hundreds of millions of dollars on Wall Street. The other thing that was very valuable was how to manage money. And so, I came across something which later was impounded in series of papers I wrote called the Kelly Criterion for deciding how much to bet in favorable situations. And that now has caught on with a lot of people. There’s a big book that I co-edited with tons of math and papers that have been written over actually centuries that relate to this topic. It’s called the “Kelly Capital Growth Investment Criterion.” It’s put up by Will Scientific. It has three editors, Bill Ziemba, Leonard MacLean, and myself. And we wrote, several of the papers in there and a lot of the connective material. But it’s a math thing. You don’t have to do a lot of work for you to…

Meb: And Ziemba’s a great author, by the way, listeners. But so, let’s talk about that real quick. Because I think one of the biggest mistakes gamblers, but also investors make often in their bet sizing is they take way, way too much risk or exposure given, you know, the odds. You see people sit down at a blackjack table, and they have $100 bankroll, and they’re playing with $20 hands. And it doesn’t matter, you know, even if they were counting in that regard because you’re gonna go bankrupt because you have such a high bet size. And so, for so let’s expand real quick, think about risk and return in the Kelly criterion. And so, its main goals, it involves a way to determine the approximate wager or position size when you have a sort of known edge. And I wonder if you’ll explain just simply for a bit for our listeners, is there kind of shorthand version you could summarize that, you know, investors can put into context?

Ed: Sure. The basic idea is that if you bet small in good situations, you won’t make very much money. You should bet really big. There’s a chance you’ll take such a big hit when something bad happens that it’d be ruined. So, there’s an intermediate level that works better than either of the extremes. And the Kelly criterion shows you how to calculate that intermediate level if you know the probabilities. And if you can only estimate them, then you can be somewhat conservative and still get a pretty good result in the Kelly system even with a lack of important information.

Many people don’t understand on how to bet size, and Kelly criterion theory shows you, if you bet too much, you’ll almost certainly be ruined. And you might think that only applies to somebody sitting in a casino or somebody managing a little portfolio, but it actually applies to everybody in quite a dramatic way. Later in the book, I talked about various busts that we’ve had. There was the ’29 Crash. There was the S&L Scandal in ’80s. There was the market meltdown in 1987, there was long-term capital management 1998. And there was the Big Bust in 2008-2009. And one theme that runs through all these is excess leverage.

And one thing the Kelly system tells you is how much leverage to use. And the amount of leverage that should have been used in all these situations is way less than the amount that people actually use. And so, the Kelly system says when you use too much leverage, you’re going to blow up.

Meb: You’re trying to say the hundred to one of long terms is a little too much leverage?

Ed: Yeah.

Meb: Well, you know, I mean it’s been eight years going on now since we’ve had the bear market in the U.S. And it’s funny because, you know, we’ll talk about this in a second but you know, the biggest mistake we see, particularly younger investors make when investing, is they often having not experienced a loss or a devastating loss, in general, they take on way too much risk. And financial advisers we, you know, we think make that mistake. In general, for younger investors, they say put all your money into stocks. But the problem is you neglect the emotional part is can someone sit through a 30%, 50%, 90% loss? It’s tough.

So, all right. So, we got a little more on Vegas. I want to talk about, you know, one of the things that with any business or casinos is that once someone finds a way to take millions of dollars from you, as in the case with Wall Street as well, often the rules change. And so, Vegas started changing the rules. You started to get some chemicals in your coffee. There’s even a pretty scary story about, you know, you didn’t say it was guaranteed, but your car accelerator getting stuck. And then eventually, they just straight-up started banning you from the casino.

And so, you know, that the challenge is not necessarily just having a winning system. It’s the threat of danger that could, and even, you also mentioned the casino outright cheating. And then the rules changed, ability to kick you out. So, I’m curious. You started a couple times to change your appearance a little bit. Was there a crazy, did you have a craziest sort of costume at all? I think one time you said maybe you shaved your beard or added a beard. What was the…

Ed: Well, I didn’t go on many gambling trips because I’m not strongly money-oriented. I’m more life and people-oriented. And I’m interested in doing kind of things that I want to do, rather than just trying to accumulate stuff. I happen to be lucky and also accumulate stuff, but that’s just the way it worked out. In any case, I went on a few more gambling trips, and on one of them I was, a point I made was to always have somebody around you know, to help guarantee my safety. So, there was a couple I didn’t know who volunteered to come along on one of the trips. They were…we have mutual friends.

And so, I decided to experiment with a disguise since I was having trouble getting a decent game looking like myself. So, I grew a beard, and I got contact lenses, and when I walked up to the hotel room where the people who were with me were staying, we were staying in the same hotel. I walked down the hall, knocking on their door, we met, we talked, we went to dinner. And then I played in this outfit with a lion shirt and casual pants, so forth, at a casino nearby in Reno. And I played for several hours, and I kept winning and winning. And eventually, the people and management came by one at a time to ID me, get a good look, so that when they kicked me out, they would know not to let me back in again. And the dealer, who was a young lady who was very interested in me because she saw a lot of money, she wanted to get together at 2:00 a.m. when her shift was off. We didn’t do that.

Meb: My wife wanted me to ask you if you’d ever dressed up as a woman, but I am guessing the answer to that is no.

Ed: No. People have…

Meb: So…

Ed: So…sorry. So, the punchline in the story is that they kicked me out about 1:00 a.m., and she very disappointed. So then shaved off my beard, put on fairly dressy pants and dress jacket, so forth. Put on sunglasses instead of the contact lenses, and came to knock on their door. And when they opened the door they said, “Yes?” They didn’t recognize me. So, I said, “This is gonna be good.” So, I went back to the same table in the same seat the next night, and as it happened, a parade of management started coming by to eyeball me, I thought eyeball me again. But they weren’t looking at me, they were looking at the guy on my right. He was a player cheat who kept trying to either add chips to his pile when he had a good hand or add chips to his bet or drop chips off his bet when he had a bad hand.

So, they jawboned him and criticized him and everybody came by to see who he was. And they kicked him out after about an hour. I made sure that I only spoke in a whisper when they came by to offer me drinks. And I asked for milk, and I didn’t use my voice so that the dealer could hear it. It was the same dealer I had before. And she didn’t recognize me. So, I played on. I won all evening and left. So, the disguise worked very, very well in that instance. And I was, I will say I was greatly entertained by that.

Meb: It’s funny, you know, so I ended up moving to Tahoe out of college, you know, along with a few friends, of course. We’d taught ourselves to count. And had been mildly successful. I think we eventually figured our winnings were about minimum wage per hour. But the biggest challenge for me was that once we learned, you know, you didn’t become almost like a computer.

And once you know that the game can get beat and you get kicked out of about five casinos, like the allure and fun for me, at least, was a little bit gone. And that all of a sudden now you’re sitting at a blackjack table watching people just lose all of their money over and over berate you for the terrible hands you’re playing, smoking cigarettes, and it just wasn’t a whole lot of fun, you know, at that point. And one of the biggest challenges I, you know, added a buddy and we said, “Hey, let’s play as a team. We can spread our bets, breadth,” all that good stuff.

And one of my favorite stories from him and why, you know, I eventually quit doing that, is we came back after about an hour and I saw another of our buddies and I say, “Hey, how’s Chris doing?” And then he said, “Oh man, he’s down a couple thousand.” I said, “How is that even possible?” I don’t even, with the bet sizing we’re doing, I don’t even think that’s possible. He says, “I don’t know, but he’s having a good time. I think he’s had six Bloody Marys already.” I said, “Oh, okay, well that makes sense.”

We’re gonna skip over…so, you gotta go read the book if you want to hear about Ed’s fascinating contribution to roulette, building a wearable computer, he talks about even thinking about wheel of fortune and baccarat. The one question I wanted to have before we move on to the bigger casino is, you don’t ever mention either sports gambling or poker. Were those games, do you ever thought about or had an interest in or in that time not so much?

Ed: Let me share with poker. What I understood, well, I read through a book called, “The Mathematical Theory of Games and Economic Behavior” by John von Neumann and Oskar Morgenstern, the classic fundamental book on mathematical style game theory. And poker is one of the favorite examples in that book, simplified examples that are worked out that launched a lot of people trying to figure out how to mathematically solve poker. And one of the intriguing things about poker is that you can mathematically analyze bluffing.

So, you can get a formula which will tell you know, in a situation with what probability you should bluff, fold, call, or raise. So, people have worked on this pretty hard for a long time. They finally, just now have solved two-person Texas Hold ’em limit poker, and I think they have a very good artificial intelligence program for Texas Hold ’em no limit poker. It’s not perfect but apparently, beats human players. This has just happened.

So anyhow, my analysis of all this when I thought about it was, I could spend years studying poker and trying to get good at it, but then my life would be a poker analyst and a poker player. And that wasn’t the kind of life I had any particular interest in. So, I passed on poker because it was too much work for what I was going to get back out of it.

Meb: Yeah, poker for me, I love playing, having a few beers, playing with friends. But sitting in a casino, same thing. I did like these tournaments that go on for like 10 hours. It’s just that it’s almost like torture to me. It’s the most boring thing in the world. Anyway…

Ed: So, I asked myself the question, what kind of life you want to have, and it wasn’t the kind of life I wanted to have. But let me…you mentioned sports betting. In the ’90s, I happened to hire a Ph.D. candidate in computer science who had a sports betting program, and it looked quite good to me. And there were a few ways to maybe simplify it a little bit, and add just a little bit to it.

And so, we gave it a try in Las Vegas, and we had a lady there, betting for us, a very smart, capable person. And she spent about five months using the system. We had about a 6% edge, and we ran $50,000 up to $173,000. But I decided to kill the program because people who were carrying living betting tickets were being killed and robbed in Vegas. And I didn’t want to risk her person in an operation like this. I basically just shut it down. It wasn’t worth it.

Meb: There’s definitely some unsavory characters. Well, let’s move on to the even bigger casino, Wall Street and investing. And so eventually you redirected your focus to the financial markets. And in the book, you said, “Gambling is investing simplified.” And so maybe you talk a little bit what you mean by that.

Ed: Sure. In gambling, you put money down and there’s an uncertain outcome and you get a payoff. Same thing in Wall Street. Put money down, uncertain outcome, you get a payoff. There are differences in details. Gambling is a much faster series of bets typically than on Wall Street. Not anymore, of course, with high-frequency traders trading in milliseconds and microseconds. But in the old days, humans were not so rapid in the way they did things.

The difference between the casinos and Wall Street is more one of scale than anything else, and the fact that at least in some casino games, you can calculate odds very accurately, not all of them. In most Wall Street situations, you can only estimate. So, if were to ask you for instance, where will the S&P 500 be? Or where will the DOW be, let’s say, at the end of this year? So, I could ask you what you think the midpoint estimate is, probability half higher probability half lower. You’d come up with something fast, someone else will come up with a slightly different number and so forth.

But we don’t know it’s going to be spread around that. And events could cause the spread, the outcome to be very, very far away from our estimate or very, very close. Typically, the estimate is, you know, any given year up 10% over the course of the year because that’s what history has shown. But in many years, the move is far greater or far less. So, on Wall Street, you’re busy estimating things that you can’t know exactly, whereas, with blackjack, you can calculate the exact probability that the dealer, if he’s honest, will deal you a blackjack on the next hand.

Meb: And so, you interestingly enough, picked an area that relies a little bit less on safe forecasting and a little bit more on, certainty is the wrong word, but the opportunity in arbitrage. And so, you know, so Ed started a convertible hedge which eventually became Princeton Newport, and concocted one of the near highest, if not risk-adjusted returns over 20 years plus another 10 with Ridgeline. And Princeton Newport had something like no down years and three down months, I think, or three down…no, three down months, I think. So, you talk a little bit about that strategy that you implemented and as a gazillion readers always were interested if that’s something they could still replicate today?

Ed: Okay, a lot of questions there. Let me tackle one at time. First record Princeton Newport, we ran a [inaudible 00:39:13] as I recall, 230 months, and we had three down months. They were 1% or less. All the other months were winning, all the quarters were winning, all the years were winning. And we annualized before fees a little over 19%, and the Dow did about half that. So, lots of people have had records in profits that good, few, if any, have had such a low risk associated with that kind of a record.

And the way we got that low risk was that I specialized in hedging securities that were mispriced against each other. Not securities in the same company. So, I might, for instance, buy a convertible bond in the company and sell short options against that bond, if I thought the bond was underpriced. And then the underlying risk of the company price-changing would largely be hedged away. And so, then I built a portfolio of a very large number of these things, and as money became available or as our positions were cashed out, I put more of them on.

And then we developed and branched out into other profit areas like index arbitrage, and locking in profits in futures markets and so forth. But always things in which the risk was hedged away as much as I could. And when you had a portfolio in which each little part had most of its risk hedged away, what was left was a diversification among many low-risk things. And the total risk would kind of wash out by the law of large numbers. So, that’s why the returns were so stable over this time.

Meb: And so, here’s an interesting…there’s nothing that breeds competition more than success. And so, you had a really long successful track record. And you talk in the book about, you know, eventually having conversations and helping to start firms like Ken Griffin’s Citadel as one of the first LLP user. First LLP, and then D.E. Shaw, and a bunch of others. One of the questions I have so, you know, if you look up a lot of these multi-factor models today or even the gazillion hedge funds, you’ll often see, you know, you pull the stock and AQR owns it, D.E. Shaw owns it, yadda, yadda all the way down, LSV owns it.

Let’s say you have a system that has an edge, you know, how do you…and it starts to degrade or do poorly. And this, I actually struggle with this. And I don’t know that I have a great answer, is it how do, you know, when it’s time to put a system to pasture versus say when it’s in a drawdown that it’s actually time to invest more in the belief that it’s a mean reversion sort of opportunity. Is that something you could comment about?

Ed: Sure. That’s a hard question that many people [inaudible 00:42:04] over the years. And the way I’ve addressed it is, if I’m doing something that I think gives me an edge, I asked myself did it work in the past? Is it working now? Do I think it’s going to work in the future? And I want to know what the mechanism is that’s driving it.

For instance, somebody who is a commodity trend follower and doing pretty well right now, worked for him a couple years, and we worked on various systems for trading commodities. And they did. They had mixed results, but on the whole, somewhat good. But I said to myself, I’m afraid to invest in this thing in a big way because I will never know if, when I have significant drawdown whether it’s just bad luck, you know, random chance. I’ll come back and explain what I mean by that in a minute. Or whether something has changed and things don’t work anymore.

Let me go back to the random chance thing. If you have something that trends upward historically like, let’s say the S&P 500, and let’s say it goes up 10% a year. There are random fluctuations around that, and some of them are fairly large. If you have an idea of the level of the random fluctuations, then you can tell whether something is really extraordinary out of line or not.

The same in blackjack. I learned that early in the casinos. If I have a certain edge, I can tell by comparing my results with the amount I should have made on average whether what’s happening is extraordinarily bad, so bad that it suggests something else is going on like cheating, for instance. Or sometimes it’s extraordinarily good, maybe a dealer is throwing cards my way. I’ve never seen that happen but were it be extraordinarily good, I’d also question that. In any case, you need to understand what the underlying average result ought to be, and how much normal chance fluctuations up. Down is bad luck. Up is good luck. How much that can be, and then see whether what is happening is outside that range. And if it is, then you wanna know why. So anyhow, if you don’t have a reason for knowing why something works, if it goes bad, you don’t know whether it’s bad luck or whether something changed.

Meb: Yeah, and we often talk about in on the podcast about how important it is to at least understand history even if you’re a buy and hold investor, you know, and we talk often about as a equity investor, you need to be able to accept 50. Like you mentioned in the 30s, the stock market went down over 80%. And ask anyone in Russia, Brazil, Greece, etc., but a lot of people may acknowledge that fact, but then of course when it happens they don’t believe that it’s really happening and so are unprepared for it.

All right. So, I’m wanna hit a few more topics before…we only have you for so long today. So, over your career, you mentioned you did all sorts of trading, convertible, Warren R, risk, thrift conversion, futures R, stat R, multi-factor models, trend following, all this stuff, do you have a most memorable trade over, you know, the last 40-plus years?

Ed: Well, one of my favorites, there are quite a few I mention in the book, one of my favorites was some warrants I bought way back in the early ’70s. When I first learned about warrants when I was educating myself about the market, I got a little book which told about how once in awhile you’d buy warrants for pennies, and they’d be worth dollars. You’d make a hundred times your money. And I thought, I’ve done a lot of warrant trading, and I thought that would never happen to me. But we picked up some warrants for something like 27 cents a warrant by, I have the exact numbers in the book. Then I bought about10,800 of them. And as for…and the stock, underlying stock was, I think, $8.

So, being a hedger, I hedged even this tiny amount on which we’d spent just a few thousand dollars. And the stock went down to something like one and a half. So, I covered the stock which paid for the entire cost of position, left a small profit and a couple thousand dollars. And the warrants were so cheap, I said to my partner just, you know, “Put them in a box and leave them there. They don’t expire for another 10 years or so.” So, we did, and then time went by, a couple of years, and we started getting phone calls. They wanted to buy our warrants. Are you looking to sell? The stocks moved up. It’s like $10 or $15 now, they want to pay us maybe $3 or $4. And I said, “No, it’s not enough. The warrants are worth more than that.” And as the stock moved up, the warrants became more and more valuable theoretically. And the people who wanted to buy it kept raising their offer, but never enough. So, I said you know, just to sit on ’em.

Pretty soon the stock got to 40 which was the exercise price for the warrants. So now they’re moving into the money and then the stock kept moving up. The stock finally moved up to, I think 180 or so. And the warrants were carried along with it. And so, we sold these 10,000 warrants on the way up, mostly near the top of our profit at more than a million dollars.

And the company it was sort of interesting. It was something called Mary Carter Paint Company, and they had purchased a bunch of land down in the Bahamas. They decided to see if they could erect a casino down there. And they got early permission to do that and they changed the name to Resorts International. And that was the cause of all the action in the warrants and the great explosion [inaudible 00:47:50] company. So anyhow, that was fun to see that happen.

Meb: I love it. You should’ve completed the circle by then going down to the casino and taking them for millions of dollars and making the stock go down. You know, I thought for sure you might have said the trade where you bought part of an oil tanker with Bruce Kovner, the Empress Demurs. But readers or listeners are going to have to read the book to hear about that story. It’s pretty awesome.

All right. We’re gonna do a couple, super quick questions. We only have you for about 10 more minutes or so. The great story in the book is you talk about getting to meet Warren Buffett. It’s who everyone’s gonna be familiar with, and you said you later told your wife that you thought he’d be, one day, the richest man in America. What did you see in Buffet in that meeting or that time that kind of led you to that conclusion?

Ed: Well, I learned that he had started in the stock market when he was something like 11 years old. And he was devoted to it, and extremely knowledgeable, and he’d already made a lot of money at that point. He was worth about $25 million at the point I met him and that was back in 1968. Now, in 1982, it took $100 million to get on the Forbes’ 400 list. Twenty-five million in 1968 probably would have gotten him on if they’d had that list then. Forbes, of course, didn’t know about him. They didn’t discover him until 1965 when he was well up the list. Pardon me, 1985, when we was off the list. They’d been running for three years before they even knew he was around.

But he was smart and knowledgeable. He was good with numbers. He understood long-term compounding. And he was gonna spend his life doing it. And he was an encyclopedia of information. So, I thought he had everything it took, and he’d already gone very far when I met him. Although a few people, except his investors and immediate friends, knew how far he’d already come.

Meb: You know, it’s interesting we talk a lot about Buffett, and we have done a ton of modeling that just goes and tracks his holdings through public 13F filings, and show that you could easily beat the market by a mile just by following his holdings once a quarter when they come out publicly delayed. But at the same time, like anyone in like any strategy, he goes through these periods of under and outperformance.

And so, it’s something like his stock picks, his long stock picks, not Berkshire, has underperformed the market 8 of the last 10 years. But if you go back to 2000, he’s outperformed the market on those stock picks by something like 5% a year, which would have beaten 99% of all mutual funds. And it just goes to show a lot of people’s edge, and in his case, I think, for example, is that is his ability to stick to his system, you know, much like you talk about in blackjack where he says, “Look, this is what I do and realize there’s gonna be times of underperformance,” and not changing his whole approach when markets are down or he’s doing poorly.

Okay, a couple more really quick questions, and then we’ll let you go. One Twitter question that we got like in six different variants was, if you could give a piece of investing advice to say a child, grandchild, you know, maybe with a slant towards kind of what’s the best strategy for the average investor to grow wealthy, what would you say?

Ed: For the average investor? He shouldn’t spend his time and life trying to beat the stock market. He should just buy a no-load low-fee index fund, like, you know, Vanguard, S&P 500, or VTSAX.

Meb: Well, that’s easy. That’s great advice. We certainly sympathize with that. A couple other ones, not necessarily finance-related and we’ll let go. You talk a lot about…I mean, I’m sure you get asked a ton about gambling, investing, written multiple books now. So, you must enjoy the writing process. Could you talk a little bit about, you know, are you a regimented writer or what’s your sort of writing routine?

Ed: Well, what I do is I make an outline of what I’m going to write. And I think about the outline and decide if this is really what I want to write about and how I want to do it. And then I begin to flesh in the outline. Now, I finally get a piece, a draft piece. After that, I look at it and think about it for a while, and decide how to try to improve the writing and the quality of the writing. And as one friend said, “All writing is really rewriting.” And I find that with each pass, I can make the sentence structure better. I get a few more ideas. It becomes more conversational, and so forth.

Meb: I think that’s true, you know, in my experience, it has always been to have a total amount of writer’s block and panic, and then to go totally insane and then kind of write it all at the same time. But the first draft is, you know, so many people think that’s the most work, but it’s really the 200 rewrites after that. So, you’ve said, I’ve seen recently a note that you’re thinking about or maybe decided to be frozen once you pass on and, you know, 20, 30, 40 years from now once science has caught up. It seems like in, you know, many ways everything you do is about defying the odds and proving that the impossible is anything but. What sort of odds are you thinking here and in what year do you think that the science may catch up if you had to give us an estimate?

Ed: There’s no telling. I think the probabilities are perhaps as low as 2% in succeeding, but that could be much higher. It’s a subjective thing. There’s no way of estimating. It could be 50% or 60%. As far as how long it takes, it’s a matter of how science rolls forward at what pace, and that’s a very uncertain thing, difficult to forecast which areas of science will move faster and which not so fast. So, I think one could be in storage for anywhere from 50 to 200 years.

Meb: It seems like a call option, right? There’s not a whole lot of downside, really only upside. So even at a 50 to 1, it’s not too bad. My uncle who’s a pilot and an engineer always gives his kids a ton of anxiety by saying he’s written in his will that he wants to be stuffed and put into a knight coat of arms in the hallway and that they have do that in his house, and they spend most of the time at Christmas worrying if he’s actually really put that in the will. Well…

Ed: It’s something like what you mentioned. Something like, well, you mentioned a call option. There’s the Resorts International warrants that I bought a long time ago. I’d say it’s something like that purchase.

Meb: So, in your book…

Ed: [inaudible 00:54:39] the same way.

Meb: Right. In your book, you touched on kinda that there’s a consistent theme that, you know, I found so refreshing because you see so much on Wall Street. So many people so obsessed, you know, with just the dollar and with just money. And with all this behavioral research you spend so much time and people thinking about how to make money. And then there’s been a lot of good books like Happy Money, and some others that say that people are actually really fairly terrible at optimizing on how to spend it. So, they make all the money and they do all the wrong things. They buy a bunch of yachts, and things that may not necessarily, you know, kind of drive the happiness. And so, in the book you touched on an example of referencing J. Paul Getty and said, you know, super wealthy but the happiest he ever was when he was 16 surfing in Malibu.

And just one last question, I want to talk about, you know, maybe you can mention, because I think it’s great for a lot of the younger investors and quants, you know, as they think about their life. You know, how do the success of your funds really, you know, as the years went on, affect your perspectives on the source of real happiness?

Ed: Well, I’ve I thought that the important thing in life is how you spend our time, who you spend it with, and what you do. And money is something which can make that much more agreeable and pleasant and make you much happier, but I don’t think it’s an end in itself. You should do what you want to do and what you like to do, and I think good things will follow.

Meb: We put a great quote in my first book by the climber George Mallory, and then it’s just been on the blog ever since and it says, “Enjoy is after all the end of life. We do not eat to live and make money. We eat and make money to be able to live. That is what life means and what life is for.” One of my favorite quotes.

Ed, one more question for you and then we’re gonna let you go, I saw somewhere that you said, I know you’re a rational science-based guy, you’ve never bought a lottery ticket, is that still true?

Ed: I bought five lottery tickets once when the pool was so large, carried over that I had an edge.

Meb: Well, I wanted to thank you for coming on today, so we said we were going to alleviate the pain of you making a negative expectancy bet. So, I picked up two Powerball tickets on the way to work, do you want the first entry or the second? Because I’m gonna take the other one?

Ed: Okay, I’ll take the first one.

Meb: All right, first one, just so the listeners can hold us accountable, it’s tonight’s drawing. I think it’s 200 million. Your numbers are 25, 28, 37, 41, 62, and Powerball of 20. Ed, good luck on that, by the way. You’ve been a gentleman. I would love to keep you here for six more hours and ask a hundred more questions, but I know you have wonderful, better things to do. Thank you, so much for taking the time today.

Ed: My pleasure. Thank you Meb.

Meb: Listeners, thanks for listening. We always welcome feedback and questions to the mailbag at feedback at the mebfabershow.com. As a reminder, you can always find the show notes and other episodes at mebfaber.com/podcast. You can subscribe to the show on iTunes and if you’re enjoying the podcast, please leave a review. We’ll have a link to the book, “A Man for All Markets: From Las Vegas to Wall Street How I Beat the Dealer in the Market with Ed Thorp”. Thanks for listening friends, and good investing.

Sponsor: Today’s podcast is sponsored by the ride-sharing app Lyft. I only live about two miles from work. My favorite means of getting around traffic-clogged Los Angeles is to use the various ride-sharing apps, and Lyft is my favorite. Today, if you go to lyft.com/invite/meb, you get a free $50 credit to your first rides. Again, that’s lyft.com/invite/meb.