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Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street
In 1956 two Bell Labs scientists discovered the scientific formula for getting rich. One was mathematician Claude Shannon, neurotic father of our digital age, whose genius is ranked with Einstein's. The other was John L. Kelly Jr., a Texas-born, gun-toting physicist. Together they applied the science of information theory—the basis of computers and the Internet—to the problem of making as much money as possible, as fast as possible.
Shannon and MIT mathematician Edward O. Thorp took the "Kelly formula" to Las Vegas. It worked. They realized that there was even more money to be made in the stock market. Thorp used the Kelly system with his phenomenonally successful hedge fund, Princeton-Newport Partners. Shannon became a successful investor, too, topping even Warren Buffett's rate of return. Fortune's Formula traces how the Kelly formula sparked controversy even as it made fortunes at racetracks, casinos, and trading desks. It reveals the dark side of this alluring scheme, which is founded on exploiting an insider's edge.
Shannon believed it was possible for a smart investor to beat the market—and Fortune's Formula will convince you that he was right.
Shannon and MIT mathematician Edward O. Thorp took the "Kelly formula" to Las Vegas. It worked. They realized that there was even more money to be made in the stock market. Thorp used the Kelly system with his phenomenonally successful hedge fund, Princeton-Newport Partners. Shannon became a successful investor, too, topping even Warren Buffett's rate of return. Fortune's Formula traces how the Kelly formula sparked controversy even as it made fortunes at racetracks, casinos, and trading desks. It reveals the dark side of this alluring scheme, which is founded on exploiting an insider's edge.
Shannon believed it was possible for a smart investor to beat the market—and Fortune's Formula will convince you that he was right.
386 pages, Paperback
First published September 19, 2006
About the author
William Poundstone
50 books331 followersWilliam Poundstone is the author of more than ten non-fiction books, including 'Fortune's Formula', which was the Amazon Editors' Pick for #1 non-fiction book of 2005. Poundstone has written for The New York Times, Psychology Today, Esquire, Harpers, The Economist, and Harvard Business Review. He has appeared on the Today Show, The David Letterman Show and hundreds of radio talk-shows throughout the world. Poundstone studied physics at MIT and many of his ideas concern the social and financial impact of scientific ideas. His books have sold over half a million copies worldwide.
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December 3, 2010
This is a very interesting book and one that explains complicated mathematical and economic ideas beautifully and simply. It is a story that involves gangsters, mathematicians, the founder of information theory, more gangsters and politicians and police (both corrupt and, well, even more corrupt).
The formula to make a fortune is essentially this – if you are going to bet you need to be sure you have some sort of edge (not necessarily a ‘sure thing’, but an edge), in this book the gamblers generally count cards or find roulette wheels that are slightly off balance or stock they believe has been under or over priced. Then you place your bets according to how certain you are of winning and in a way that also covers you for ALL eventualities. So, if you are betting on a horse race you would bet on all of the horses running in that race according to how well you believe each might go.
And this is the problem – because you are spreading your bets so widely you need to have a pretty good ‘edge’ to make any money. Generally, in betting situations, the person with the edge is either the house or the stockbroker. No matter if the horse or stock wins or loses, they take their cut.
The book discusses people who have done very well from the stock market and betting on horse races and some of them have done so over a great number of years and despite the market either racing up or falling on its face. All this is very interesting. As one guy says, he has found the perfect method to ‘get rich slow’.
So, why am I not taking out lots of money from my bank account or borrowing heaps so I can leverage my bets on stocks or my investments in blackjack? Because there are other just as compelling arguments made in the book that fortune’s formula (aka Kelly’s formula) doesn’t really work. Let’s say you have proof that someone has beaten the market for 20 years by 5% (people discussed in this book have done better than this) – does that prove their theory true? We like to think so. But does it? The problem is a form of selection bias. As soon as someone fails we know their theory doesn’t work, but how long does it take to know that someone’s theory will never fail? There is a universal law associated with this – any investing strategy will be certain to fail the moment I put money into it. My service to the world is to not invest.
The things that put me off this formula – besides the major inconveniences of having to spend hours and hours of my life studying things like the stock market or horses – was where he showed that you are 1/3 likely to half your money before you double it – whoo-hoo. I’m not a gambler precisely for that reason. Like nearly everyone else in the world, I’m loss averse, but unlike so many of you I find no joy in even a ‘little flutter’.
Some of the cameo appearances in this book are really what make it – everyone from J Edgar Hoover to Paul Newman have walk on roles and these are often very amusing. I loved the gangster who complained about the number of horse races he had to fix to buy off Hoover. The descriptions of casinos in the 1950s in the US – particularly if you had the audacity to actually win in them – was compellingly frightening. Not unlike The Godfather movies.
But the most interesting thing was something I never knew at all. Do you know in 2001 where HAL is having his brain slowly shut down and he reverts to his childhood? (If not, stop reading this review now and borrow the film – it’s ok, I’ll wait) Back yet? Well, he sings a song – Daisy Bell or A Bicycle Made For Two – and what I didn’t know was that this was the first song a computer ever sung. The idea behind getting a computer to sing a song is really interesting too. You see, if you get a computer to say a sentence like this one it needs to be clear what it is saying or the listener won’t have a clue. But it is like listening to someone with an unfamiliar accent – at first you can’t understand a word, but it gets better as it goes along. The guy programming the voice is very familiar with it – so can understand what it is saying, but probably not the person who is first introduced to the computer voice. Programmers got around this by having the computer sing popular songs and because we know what is coming next we basically ‘help the computer along’.
Sorry, need to stop soon, but have to say this – this is the inverse of another test I find very interesting. They get people to sit opposite each other and they give one of them a pencil and that person has to tap out songs they know – no humming or singing along, just tapping the pencil to the tune of the song. Then they ask the person who is doing the tapping how confident they are that the person listening will be able to guess the song – generally people say they are very confident. After all, they have the tune with full orchestration and four part harmonies playing in their head. People are generally outraged when the person listening can’t guess the song, even more so when they guess seemingly completely random songs. I did this in a class this year and it was really fun. I asked a kid to tap out the Australian National Anthem and I think someone guessed it was happy birthday – you should have seen the kids face who had done the tapping.
Much the same argument is made in this book as in Fooled By Randomness – that is, if you are going to bet you need to expect the improbable, because the improbable is never as improbable as you might like to think. In maths talk – 20 sigma events happen all the time. You’d better be prepared for them. Good luck with that.
The formula to make a fortune is essentially this – if you are going to bet you need to be sure you have some sort of edge (not necessarily a ‘sure thing’, but an edge), in this book the gamblers generally count cards or find roulette wheels that are slightly off balance or stock they believe has been under or over priced. Then you place your bets according to how certain you are of winning and in a way that also covers you for ALL eventualities. So, if you are betting on a horse race you would bet on all of the horses running in that race according to how well you believe each might go.
And this is the problem – because you are spreading your bets so widely you need to have a pretty good ‘edge’ to make any money. Generally, in betting situations, the person with the edge is either the house or the stockbroker. No matter if the horse or stock wins or loses, they take their cut.
The book discusses people who have done very well from the stock market and betting on horse races and some of them have done so over a great number of years and despite the market either racing up or falling on its face. All this is very interesting. As one guy says, he has found the perfect method to ‘get rich slow’.
So, why am I not taking out lots of money from my bank account or borrowing heaps so I can leverage my bets on stocks or my investments in blackjack? Because there are other just as compelling arguments made in the book that fortune’s formula (aka Kelly’s formula) doesn’t really work. Let’s say you have proof that someone has beaten the market for 20 years by 5% (people discussed in this book have done better than this) – does that prove their theory true? We like to think so. But does it? The problem is a form of selection bias. As soon as someone fails we know their theory doesn’t work, but how long does it take to know that someone’s theory will never fail? There is a universal law associated with this – any investing strategy will be certain to fail the moment I put money into it. My service to the world is to not invest.
The things that put me off this formula – besides the major inconveniences of having to spend hours and hours of my life studying things like the stock market or horses – was where he showed that you are 1/3 likely to half your money before you double it – whoo-hoo. I’m not a gambler precisely for that reason. Like nearly everyone else in the world, I’m loss averse, but unlike so many of you I find no joy in even a ‘little flutter’.
Some of the cameo appearances in this book are really what make it – everyone from J Edgar Hoover to Paul Newman have walk on roles and these are often very amusing. I loved the gangster who complained about the number of horse races he had to fix to buy off Hoover. The descriptions of casinos in the 1950s in the US – particularly if you had the audacity to actually win in them – was compellingly frightening. Not unlike The Godfather movies.
But the most interesting thing was something I never knew at all. Do you know in 2001 where HAL is having his brain slowly shut down and he reverts to his childhood? (If not, stop reading this review now and borrow the film – it’s ok, I’ll wait) Back yet? Well, he sings a song – Daisy Bell or A Bicycle Made For Two – and what I didn’t know was that this was the first song a computer ever sung. The idea behind getting a computer to sing a song is really interesting too. You see, if you get a computer to say a sentence like this one it needs to be clear what it is saying or the listener won’t have a clue. But it is like listening to someone with an unfamiliar accent – at first you can’t understand a word, but it gets better as it goes along. The guy programming the voice is very familiar with it – so can understand what it is saying, but probably not the person who is first introduced to the computer voice. Programmers got around this by having the computer sing popular songs and because we know what is coming next we basically ‘help the computer along’.
Sorry, need to stop soon, but have to say this – this is the inverse of another test I find very interesting. They get people to sit opposite each other and they give one of them a pencil and that person has to tap out songs they know – no humming or singing along, just tapping the pencil to the tune of the song. Then they ask the person who is doing the tapping how confident they are that the person listening will be able to guess the song – generally people say they are very confident. After all, they have the tune with full orchestration and four part harmonies playing in their head. People are generally outraged when the person listening can’t guess the song, even more so when they guess seemingly completely random songs. I did this in a class this year and it was really fun. I asked a kid to tap out the Australian National Anthem and I think someone guessed it was happy birthday – you should have seen the kids face who had done the tapping.
Much the same argument is made in this book as in Fooled By Randomness – that is, if you are going to bet you need to expect the improbable, because the improbable is never as improbable as you might like to think. In maths talk – 20 sigma events happen all the time. You’d better be prepared for them. Good luck with that.
March 2, 2015
This is one of the best books I have read in a long time and the perfect book to read after The Information by James Gleick.
The title and subtitle are pretty overblown and don't really indicate what the book is about. The "fortune's forumula" referred to in the title is pretty dang interesting though -- the Kelly criteria, which is the optimal percentage that should be wagered given the odds.
I didn't realize that there even was a optimal bet, but it turns out that overbetting is actually worse than underbetting. I guess this is intuitive if one sits down and thinks about it, but it's way cooler that it can be mathematically proven.
Anyway, this Kelly bet is the thread that (very loosely) runs through the wide-ranging and fascinating stories in this book.
It begins with information theory and my hero Claude Shannon but soon settles on an even more interesting genius named Ed Thorp: math professor, card counter, and hedge fund manager. In fact, he invented card counting and used an early IBM mainframe to analyze blackjack and prove that card counting could be used to change the odds of the game in favor of the player instead of the house.
He wrote a research paper about this, toning down the title somewhat to increase its chances of being published. Then he reworked it into a book called Beat The Dealer.
There are many interesting side stories in this book, like when Mr. Thorp and a mob-connected guy fly to Nevada to test out his system, or when he and Claude Shannon try to build a device for winning at roulette, or how a parking lot company (also mob-connected) became Time Warner.
Then it gets interesting as any book about mathematics and science I have read. It makes a connection between information theory and finance that never even remotely crossed my mind even though I have spent a lot of time thinking about both.
When the efficient market hypothesis was influencing the field of finance, Mr. Thorp grasped that the math is very similar to information theory. It turns out that a random walk is not terribly different from a compressed signal in noise, at least from a mathematical point of view. In fact, Thorp was able to work out the Black Scholes Merton formula for pricing options (which led to the Nobel prize, by the way) long before their paper was published.
Mr. Thorp was already running a hedge fund and making incredible returns by this time. He may have benefited from efficient market hypothesis, but he had no problem finding market inefficiencies with his early computer and a small staff.
(There is a also a lesson for startups tucked away in this book as Mr. Thorp and his partner, working on different coasts, transformed the financial industry. If they had been more judicious in their choice of brokers they would probably be as celebrated as Buffett and Munger are today.)
The guy brokering his funds trades was Michael Milken, which leads to a bunch of interesting digressions. Ivan Boesky and Rudy Guliani soon get dragged into the story, too. And the LTCM story -- always entertaining no matter how many times I read it -- is told again, though this time with the lesson that the Kelly criterion doesn't go so well with leverage.
The funny thing in this book is that to the eggheads who are floating around Bell Labs and MIT is that horse racing, card games, roulette, and the stock market are treated pretty much interchangeably. They are just games of chance with rules that can be bent with the right kind of insight and equations. It seems like harmless fun, almost honorable, compared to what has been going on in the past decade in finance.
Once slight disappointment is when Claude Shannon's investment portfolio is revealed. He was a smart guy who lived modestly, made some smart stock picks, never sold them, and hit really big on one of them, but I certainly wouldn't call him an investor who beat Wall Street. The Millionaire Next Door has a bunch of stories like that about far more ordinary people. Reading an old computer print out of his holdings felt slightly ghoulish.
It turns out I've been reading Mr. Poundstone for a long time. I read his Big Secrets and Bigger Secrets a long time ago. (A few years ago I checked them out of the library and found them to be more interesting as an odd pre-Internet relic.) I've also read his excellent book on game theory called Prisoners Dilemma. That book still influences the way that I make decisions. I am definitely planning on reading his other books now.
The title and subtitle are pretty overblown and don't really indicate what the book is about. The "fortune's forumula" referred to in the title is pretty dang interesting though -- the Kelly criteria, which is the optimal percentage that should be wagered given the odds.
I didn't realize that there even was a optimal bet, but it turns out that overbetting is actually worse than underbetting. I guess this is intuitive if one sits down and thinks about it, but it's way cooler that it can be mathematically proven.
Anyway, this Kelly bet is the thread that (very loosely) runs through the wide-ranging and fascinating stories in this book.
It begins with information theory and my hero Claude Shannon but soon settles on an even more interesting genius named Ed Thorp: math professor, card counter, and hedge fund manager. In fact, he invented card counting and used an early IBM mainframe to analyze blackjack and prove that card counting could be used to change the odds of the game in favor of the player instead of the house.
He wrote a research paper about this, toning down the title somewhat to increase its chances of being published. Then he reworked it into a book called Beat The Dealer.
There are many interesting side stories in this book, like when Mr. Thorp and a mob-connected guy fly to Nevada to test out his system, or when he and Claude Shannon try to build a device for winning at roulette, or how a parking lot company (also mob-connected) became Time Warner.
Then it gets interesting as any book about mathematics and science I have read. It makes a connection between information theory and finance that never even remotely crossed my mind even though I have spent a lot of time thinking about both.
When the efficient market hypothesis was influencing the field of finance, Mr. Thorp grasped that the math is very similar to information theory. It turns out that a random walk is not terribly different from a compressed signal in noise, at least from a mathematical point of view. In fact, Thorp was able to work out the Black Scholes Merton formula for pricing options (which led to the Nobel prize, by the way) long before their paper was published.
Mr. Thorp was already running a hedge fund and making incredible returns by this time. He may have benefited from efficient market hypothesis, but he had no problem finding market inefficiencies with his early computer and a small staff.
(There is a also a lesson for startups tucked away in this book as Mr. Thorp and his partner, working on different coasts, transformed the financial industry. If they had been more judicious in their choice of brokers they would probably be as celebrated as Buffett and Munger are today.)
The guy brokering his funds trades was Michael Milken, which leads to a bunch of interesting digressions. Ivan Boesky and Rudy Guliani soon get dragged into the story, too. And the LTCM story -- always entertaining no matter how many times I read it -- is told again, though this time with the lesson that the Kelly criterion doesn't go so well with leverage.
The funny thing in this book is that to the eggheads who are floating around Bell Labs and MIT is that horse racing, card games, roulette, and the stock market are treated pretty much interchangeably. They are just games of chance with rules that can be bent with the right kind of insight and equations. It seems like harmless fun, almost honorable, compared to what has been going on in the past decade in finance.
Once slight disappointment is when Claude Shannon's investment portfolio is revealed. He was a smart guy who lived modestly, made some smart stock picks, never sold them, and hit really big on one of them, but I certainly wouldn't call him an investor who beat Wall Street. The Millionaire Next Door has a bunch of stories like that about far more ordinary people. Reading an old computer print out of his holdings felt slightly ghoulish.
It turns out I've been reading Mr. Poundstone for a long time. I read his Big Secrets and Bigger Secrets a long time ago. (A few years ago I checked them out of the library and found them to be more interesting as an odd pre-Internet relic.) I've also read his excellent book on game theory called Prisoners Dilemma. That book still influences the way that I make decisions. I am definitely planning on reading his other books now.
May 23, 2020
Fortune's Formula begins in the 1950s, when two Bell Lab scientists discovered a formula. It wasn't the usual kind of formula that scientists at Bell Labs found, this one was about how to get rich.
Getting rich maybe isn't the purpose of life, but all else being equal it's probably not a bad idea. In the worse of comedian Bill Hicks, "Money can't buy happiness, but it can buy a jet ski. And you never see an unhappy person riding a jet ski."
Anyhow, one of those scientists was Claude Shannon, the inventor of information theory, and the other was John Kelly, after whom the Kelly Criterion (the aforementioned "get rich" formula) is named.
The Kelly Criterion optimizes for long term wealth (technically called log wealth), not expected wealth. This seems kind of pedantic, but is actually really important.
The general idea behind the Kelly Criterion is that you wager more or less depending on how much "excess information" or "edge" you have. If you know something about the probability of a pharmaceutical company coming out with a new drug, then that equates to potential excess returns. The larger the edge, the more aggressively you should bet.
Also, if you are optimizing for long term wealth, one important thing is to not go broke. Though the Kelly Criterion says you should bet more when you have more edge, it never wagers everything. This has the nice property making it impossible to go bankrupt.
"Bet more when you have more edge and don't go broke" seems kind of obvious, but surprisingly few investors actually do it in practice.
Here's an example. Pretend you are offered a game: a rigged coin flip where you know heads will come up 55% of the time.
Pretty sweet, right?
Obviously, you should bet on heads. But how much? If you bet everything and get unlucky then you could lose it all on the first game: there's no coming back.
If you bet too little then you may not go broke but your wealth will compound slower than it would have otherwise
The Kelly wager for sequential bets on a single coin is 10% of the total bankroll. You can read the book if you want the math on why 10%, but mathematically that bet size maximizes long-term wealth.
It takes into account the possibility that you could have an "unlucky" streak but also maximizes the probability that the total wealth grows over time. By always betting 10%, you reduce your bet as you lose and increase it as you win.
If you start with $100 and lose $10, then you bet 10% of the remaining $90, or $9. If you keep losing, you keep reducing your bet size in absolute terms until the unlucky streak turns around. This is the best system for your long term wealth.
The hard part about sticking with a system like this in the real world is that you never know the exact odds. A coin flip is a trivial example where it's possible to know the odds, real markets are not so transparent. As a result, you are likely to quit playing the game at the worst possible time, after a streak of bad luck.
Even in this very favorable coin flip game, you're going to have losing streaks. When you've lost five times in a row, how do you know it's just unlucky as opposed to the game having fundamentally changed?
Diversification, being able to make simultaneous bets can help. Instead of playing one coin flip after another, what if you could flip 100 coins simultaneously? In that scenario, you would bet almost 1% on each flip (say $0.95/flip in our example starting with $100).
This would greatly reduce the variance in your returns and create a smooth curve of exponential growth. Because of the law of large numbers, you're going to win close to 55% of the tosses each time and so you'll make money more consistently. This is a lot more palatable and easier to stick with.
From an investment portfolio perspective, this means finding different investments which all have a positive expected value (like betting on heads in a game where it wins 55% of the time), but do well at different times.
By combining uncorrelated or anti-correlated assets, you can achieve higher returns with lower drawdowns. This is what the dragon portfolio I've written about previously attempts to do.
The classic 60/40 Equity/Bond portfolio is highly reliant on assumptions based on a once-in-a-century bull market in Stocks and Bonds from 1984 to 2020.
Portfolios built primarily around stocks and bonds struggle in periods where interest rates are close to zero (the U.S. in the 1930s, Japan post-1990, Europe today) because there is less room to decrease interest rates (and thus increase bond prices).
They also struggle when inflation is rapidly rising (the U.S. in the 1970s). In those periods, bonds earn very little real return. A bond that pays 10% per year in a currency that is inflating at 10% per year has a real return of zero. Worse yet, bonds can become correlated to risk assets like stocks and real estate causing them to all decline at the same time. This could result in weak performance, if not significant losses, and cause people to abandon their investment strategies at the worst times.
The Kelly Criterion would suggest combining stocks and bonds with anti-correlated assets that do well in those scenarios such as gold, cash, commodity trend, long volatility, and (maybe) Bitcoin.
This combination is, at least in theory, similar to placing multiple bets at the same time. Something in the portfolio should be doing good at any one time and so the overall value of the portfolio should grow in a (comparatively) smooth way to a less diversified portfolio.
The book follows the path of the Kelly Criterion as it first makes its way to Vegas and eventually into the stock market. (It turns out that the stock market is a much more profitable place to use a good betting strategy and it's much less likely for someone to break your knee caps).
The book has a nice narrative flow that keeps it moving along but also goes into helpful detail about exactly how the formula works and how it was applied as both a gambling and investing tool.
If you really want to get into the math, Chapter 6 of Euan Sinclair's Volatility Trading gets into the more nitty gritty details.
Getting rich maybe isn't the purpose of life, but all else being equal it's probably not a bad idea. In the worse of comedian Bill Hicks, "Money can't buy happiness, but it can buy a jet ski. And you never see an unhappy person riding a jet ski."
Anyhow, one of those scientists was Claude Shannon, the inventor of information theory, and the other was John Kelly, after whom the Kelly Criterion (the aforementioned "get rich" formula) is named.
The Kelly Criterion optimizes for long term wealth (technically called log wealth), not expected wealth. This seems kind of pedantic, but is actually really important.
The general idea behind the Kelly Criterion is that you wager more or less depending on how much "excess information" or "edge" you have. If you know something about the probability of a pharmaceutical company coming out with a new drug, then that equates to potential excess returns. The larger the edge, the more aggressively you should bet.
Also, if you are optimizing for long term wealth, one important thing is to not go broke. Though the Kelly Criterion says you should bet more when you have more edge, it never wagers everything. This has the nice property making it impossible to go bankrupt.
"Bet more when you have more edge and don't go broke" seems kind of obvious, but surprisingly few investors actually do it in practice.
Here's an example. Pretend you are offered a game: a rigged coin flip where you know heads will come up 55% of the time.
Pretty sweet, right?
Obviously, you should bet on heads. But how much? If you bet everything and get unlucky then you could lose it all on the first game: there's no coming back.
If you bet too little then you may not go broke but your wealth will compound slower than it would have otherwise
The Kelly wager for sequential bets on a single coin is 10% of the total bankroll. You can read the book if you want the math on why 10%, but mathematically that bet size maximizes long-term wealth.
It takes into account the possibility that you could have an "unlucky" streak but also maximizes the probability that the total wealth grows over time. By always betting 10%, you reduce your bet as you lose and increase it as you win.
If you start with $100 and lose $10, then you bet 10% of the remaining $90, or $9. If you keep losing, you keep reducing your bet size in absolute terms until the unlucky streak turns around. This is the best system for your long term wealth.
The hard part about sticking with a system like this in the real world is that you never know the exact odds. A coin flip is a trivial example where it's possible to know the odds, real markets are not so transparent. As a result, you are likely to quit playing the game at the worst possible time, after a streak of bad luck.
Even in this very favorable coin flip game, you're going to have losing streaks. When you've lost five times in a row, how do you know it's just unlucky as opposed to the game having fundamentally changed?
Diversification, being able to make simultaneous bets can help. Instead of playing one coin flip after another, what if you could flip 100 coins simultaneously? In that scenario, you would bet almost 1% on each flip (say $0.95/flip in our example starting with $100).
This would greatly reduce the variance in your returns and create a smooth curve of exponential growth. Because of the law of large numbers, you're going to win close to 55% of the tosses each time and so you'll make money more consistently. This is a lot more palatable and easier to stick with.
From an investment portfolio perspective, this means finding different investments which all have a positive expected value (like betting on heads in a game where it wins 55% of the time), but do well at different times.
By combining uncorrelated or anti-correlated assets, you can achieve higher returns with lower drawdowns. This is what the dragon portfolio I've written about previously attempts to do.
The classic 60/40 Equity/Bond portfolio is highly reliant on assumptions based on a once-in-a-century bull market in Stocks and Bonds from 1984 to 2020.
Portfolios built primarily around stocks and bonds struggle in periods where interest rates are close to zero (the U.S. in the 1930s, Japan post-1990, Europe today) because there is less room to decrease interest rates (and thus increase bond prices).
They also struggle when inflation is rapidly rising (the U.S. in the 1970s). In those periods, bonds earn very little real return. A bond that pays 10% per year in a currency that is inflating at 10% per year has a real return of zero. Worse yet, bonds can become correlated to risk assets like stocks and real estate causing them to all decline at the same time. This could result in weak performance, if not significant losses, and cause people to abandon their investment strategies at the worst times.
The Kelly Criterion would suggest combining stocks and bonds with anti-correlated assets that do well in those scenarios such as gold, cash, commodity trend, long volatility, and (maybe) Bitcoin.
This combination is, at least in theory, similar to placing multiple bets at the same time. Something in the portfolio should be doing good at any one time and so the overall value of the portfolio should grow in a (comparatively) smooth way to a less diversified portfolio.
The book follows the path of the Kelly Criterion as it first makes its way to Vegas and eventually into the stock market. (It turns out that the stock market is a much more profitable place to use a good betting strategy and it's much less likely for someone to break your knee caps).
The book has a nice narrative flow that keeps it moving along but also goes into helpful detail about exactly how the formula works and how it was applied as both a gambling and investing tool.
If you really want to get into the math, Chapter 6 of Euan Sinclair's Volatility Trading gets into the more nitty gritty details.
November 14, 2018
A disappointing and poorly written book.
Very interesting subject, but author bogged it down with an endless barrage of trivial irrelevant minutiae. All those disparate useless factoids about the Mob, Claude Shannon's marriage life, LTCM, Paul Samuelson's biography - all these seemingly unrelated strands were forcibly meshed together in a disjointed manner. Many chapters flash by without any link to the Kelly Criterion itself.
For a better book on Edward Thorp, read his biography A Man For All Markets. For a better book on Claude Shannon, read A Mind At Play. For a better book on applying the Kelly Criterion to investing, read Concentrated Investing. For a better take on the geek mathematics behind the Kelly Criterion, go read some academic papers, or even the Wikipedia page.
Very interesting subject, but author bogged it down with an endless barrage of trivial irrelevant minutiae. All those disparate useless factoids about the Mob, Claude Shannon's marriage life, LTCM, Paul Samuelson's biography - all these seemingly unrelated strands were forcibly meshed together in a disjointed manner. Many chapters flash by without any link to the Kelly Criterion itself.
For a better book on Edward Thorp, read his biography A Man For All Markets. For a better book on Claude Shannon, read A Mind At Play. For a better book on applying the Kelly Criterion to investing, read Concentrated Investing. For a better take on the geek mathematics behind the Kelly Criterion, go read some academic papers, or even the Wikipedia page.
May 7, 2016
Hard to implement, but an awesome read.
January 26, 2020
Is it possible to not quite understand anything in the book, but still profit from it? Turns out, it is. If you're uninitiated to gambling, stocks trade, sports betting and other fields that seem like pure chaos and any yield they might ever give is pure luck, you'll get lost in terminology and phrasing pretty damn quickly. This is book that needs to be read like Algebra 101.
However, what I did manage to get from it that there's potential order behind the "luck", and that stories about "systems" so many gamblers claim to have are not complete self delusions. Turns out, if you're very, very disciplined, skilled and dedicated, it is possible to exploit those games and stock trade to your benefit. Pay attention to parts where it says "most people find it hard to believe", and you'll find you're one of those people. Lots of completely counterintuitive revelations here.
However, in order to stand some chances in those circumstances, the nature of the game must be such that the player has so called edge, not "the house". If "the edge" is on your side, you can exploit it even if it's as small as 0,09464098.
Fascinating read.
However, what I did manage to get from it that there's potential order behind the "luck", and that stories about "systems" so many gamblers claim to have are not complete self delusions. Turns out, if you're very, very disciplined, skilled and dedicated, it is possible to exploit those games and stock trade to your benefit. Pay attention to parts where it says "most people find it hard to believe", and you'll find you're one of those people. Lots of completely counterintuitive revelations here.
However, in order to stand some chances in those circumstances, the nature of the game must be such that the player has so called edge, not "the house". If "the edge" is on your side, you can exploit it even if it's as small as 0,09464098.
Fascinating read.
December 23, 2020
Went into this book without knowing much about it. At first I thought it was going to be a biography of Claude Shannon, then I thought it would be about using maths to cheat at casinos, but it turned out to be mostly about finance; or broadly, I guess, about modelling uncertainty for fun and profit. Shannon - to begin at the beginning - liked fun. His early career was something of a phenomenon. He wrote perhaps the most important Master's thesis of all time, creating the field of Information Theory (the basis of huge swathes of modern telecommunication) basically out of whole cloth. "It’s said that it is one of the few times in history where somebody founded the field, asked all the right questions, and proved most of them and answered them all at once." After that Shannon got jobs at MIT and Bell Labs, where he basically spent the rest of his life riding unicycles, juggling, and building odd gadgets like a computer that used Roman numerals, or a box which, when opened, would reach out a hand and close itself. Occasionally people would come to show Shannon a new discovery of theirs in Information Theory, whereupon he would show them that he had already figured it out but neglected to publish.
At Bell Labs Shannon met J.L. Kelly, who was working on voice synthesis, successfully building a machine that could sing "Daisy, daisy give me your answer do" which Kubrick saw and incorporated into 2001: A Space Odyssey. (Apparently using a song is somewhat cheating, since the brain recognises the melody and guesses the lyrics; the voice synthesis of the time was not very good. Modern technology is a lot better but this is far from a solved problem.) Together with Shannon and a mathematician named Ed Thorp, Kelly came up with an optimal gambling strategy based on Information Theory, known today as the Kelly criterion. They initially raised money from an eccentric backer and tried the method in Reno casinos, and the book tells of their shenanigans trying to fool pit bosses with false beards and hidden computers. They started with blackjack ("then considered to be a woman’s game, offered to give wives something to do while their men played craps." Why!?) . Trying to "hack" gambling all seems a bit of a waste of time, because the house always structures the game to win, and if you find a loophole they will escort you out and then close it, which is what happened to our heroes. They gave up on gambling with the Kelly criterion, though Thorp did write a successful book called Beat the Dealer explaining how to win by card counting. There are intermittent stories about early Jewish and Italian mobsters, who had a national organisation known as The Combination (or Murder, Inc.) and the Kefauver hearings; entertaining in themselves, but I cannot remember what they had to do with the rest of the book.
The core of the Kelly criterion is using additional information (an edge) and the public odds, but it also involves maximising the expected value of the log of the geometric mean of your winnings - not the arithmetic mean. Since the arithmetic mean is always equal or greater, this is a more conservative approach; a nice application of an odd bit of middle school maths. (Is there any possible use for the harmonic mean, though? Answers on a postcard please.) Poundstone briefly outlines the history of mathematical ideas about gambling, including the idea of "gambler's ruin": given negative expected value, or positive expected value but finite liquidity, a gambler will always eventually go bust. The Kelly criterion finds a way around this under ideal conditions by constantly adjusting the stake in proportion to one's remaining funds (presuming there is no minimum bet size). Whether all this works in practice is a bit controversial; it seems to be rejected by mainstream academia, but also is allegedly used by Warren Buffett and Jim Simons.
At this point - as might be clear - the book takes an abrupt turn into finance. Thorp started one of the first hedge funds, given the deliberately white-shoe-sounding name of Princeton-Newport (a deliberate misdirection: no connection to the university, and Newport was not after the one in Rhode Island but Newport Beach, California, where the firm's second office was). This was the time when financial engineering started to explode: they had the idea of selling a bond "stripped" of its coupons and selling the coupons separately (this back when the bond was a physical piece of paper, and you would actually tear off the individual coupons), decoupling interest and principal into separate commodities. The bondholder could also then conveniently sell the stripped bond for much less and declare a loss to offset capital gains. After the IRS noticed, this was outlawed by Congress. Princeton-Newport got involved with Michael Milken, the "junk bond king", leading to a RICO trial by up-and-coming Southern District prosecutor Rudy Giuliani. The RICO act was ostensibly passed to go after racketeering, but was usually used against organised crime (hence, according to Poundstone, the tortured acronym resembling an Italian name) until Giuliani turned it against financial crooks, using high-profile Wall Street trials to promote his successful run for mayor. Princeton-Newport was ultimately cleared of wrongdoing, but the firm closed down. (Times have changed: in today's strange monetary environment the junk bond market, once the demesne of crooks and cranks, is now effectively backstopped by the Federal Reserve.)
Early mathematically inclined traders also took advantage of basic arbitrage and mispriced financial instruments. Options, such as warrants and convertible bonds, existed but were valued by intuition, often incorrectly. One of the notable achievements of modern mathematical finance was the Black-Scholes formula which formalised this procedure: Texas Instruments immediately added it to their calculators, and Scholes won a Nobel (Black had already died). Poundstone discusses the Efficient Market Hypothesis and its discontents, and a bit of portfolio theory. The Long Term Capital Management debacle also comes up. The lesson traditionally drawn from it is that even Nobel laureate eggheads can't plan around a big market correction, but Poundstone notes that the academics involved had no real-world trading experience; the reason Thorp declined to invest in the fund. Today he is a successful hedge fund manager, who claims he would be a billionaire had it not been for the Milken contretemps. RenTech makes money hand over fist, seemingly with risk-free "stat arb" and (according to Kelly's Wikipedia page) the Kelly criterion. But it does not appear overall to be a secret to instant wealth.
In sum this book is a bit of a curate's egg, with an odd mix of gangsters, glamour and finance geekery. Poundstone is an engaging writer, and the length of this review shows that I found it full of great tidbits.
At Bell Labs Shannon met J.L. Kelly, who was working on voice synthesis, successfully building a machine that could sing "Daisy, daisy give me your answer do" which Kubrick saw and incorporated into 2001: A Space Odyssey. (Apparently using a song is somewhat cheating, since the brain recognises the melody and guesses the lyrics; the voice synthesis of the time was not very good. Modern technology is a lot better but this is far from a solved problem.) Together with Shannon and a mathematician named Ed Thorp, Kelly came up with an optimal gambling strategy based on Information Theory, known today as the Kelly criterion. They initially raised money from an eccentric backer and tried the method in Reno casinos, and the book tells of their shenanigans trying to fool pit bosses with false beards and hidden computers. They started with blackjack ("then considered to be a woman’s game, offered to give wives something to do while their men played craps." Why!?) . Trying to "hack" gambling all seems a bit of a waste of time, because the house always structures the game to win, and if you find a loophole they will escort you out and then close it, which is what happened to our heroes. They gave up on gambling with the Kelly criterion, though Thorp did write a successful book called Beat the Dealer explaining how to win by card counting. There are intermittent stories about early Jewish and Italian mobsters, who had a national organisation known as The Combination (or Murder, Inc.) and the Kefauver hearings; entertaining in themselves, but I cannot remember what they had to do with the rest of the book.
The core of the Kelly criterion is using additional information (an edge) and the public odds, but it also involves maximising the expected value of the log of the geometric mean of your winnings - not the arithmetic mean. Since the arithmetic mean is always equal or greater, this is a more conservative approach; a nice application of an odd bit of middle school maths. (Is there any possible use for the harmonic mean, though? Answers on a postcard please.) Poundstone briefly outlines the history of mathematical ideas about gambling, including the idea of "gambler's ruin": given negative expected value, or positive expected value but finite liquidity, a gambler will always eventually go bust. The Kelly criterion finds a way around this under ideal conditions by constantly adjusting the stake in proportion to one's remaining funds (presuming there is no minimum bet size). Whether all this works in practice is a bit controversial; it seems to be rejected by mainstream academia, but also is allegedly used by Warren Buffett and Jim Simons.
At this point - as might be clear - the book takes an abrupt turn into finance. Thorp started one of the first hedge funds, given the deliberately white-shoe-sounding name of Princeton-Newport (a deliberate misdirection: no connection to the university, and Newport was not after the one in Rhode Island but Newport Beach, California, where the firm's second office was). This was the time when financial engineering started to explode: they had the idea of selling a bond "stripped" of its coupons and selling the coupons separately (this back when the bond was a physical piece of paper, and you would actually tear off the individual coupons), decoupling interest and principal into separate commodities. The bondholder could also then conveniently sell the stripped bond for much less and declare a loss to offset capital gains. After the IRS noticed, this was outlawed by Congress. Princeton-Newport got involved with Michael Milken, the "junk bond king", leading to a RICO trial by up-and-coming Southern District prosecutor Rudy Giuliani. The RICO act was ostensibly passed to go after racketeering, but was usually used against organised crime (hence, according to Poundstone, the tortured acronym resembling an Italian name) until Giuliani turned it against financial crooks, using high-profile Wall Street trials to promote his successful run for mayor. Princeton-Newport was ultimately cleared of wrongdoing, but the firm closed down. (Times have changed: in today's strange monetary environment the junk bond market, once the demesne of crooks and cranks, is now effectively backstopped by the Federal Reserve.)
Early mathematically inclined traders also took advantage of basic arbitrage and mispriced financial instruments. Options, such as warrants and convertible bonds, existed but were valued by intuition, often incorrectly. One of the notable achievements of modern mathematical finance was the Black-Scholes formula which formalised this procedure: Texas Instruments immediately added it to their calculators, and Scholes won a Nobel (Black had already died). Poundstone discusses the Efficient Market Hypothesis and its discontents, and a bit of portfolio theory. The Long Term Capital Management debacle also comes up. The lesson traditionally drawn from it is that even Nobel laureate eggheads can't plan around a big market correction, but Poundstone notes that the academics involved had no real-world trading experience; the reason Thorp declined to invest in the fund. Today he is a successful hedge fund manager, who claims he would be a billionaire had it not been for the Milken contretemps. RenTech makes money hand over fist, seemingly with risk-free "stat arb" and (according to Kelly's Wikipedia page) the Kelly criterion. But it does not appear overall to be a secret to instant wealth.
In sum this book is a bit of a curate's egg, with an odd mix of gangsters, glamour and finance geekery. Poundstone is an engaging writer, and the length of this review shows that I found it full of great tidbits.
January 5, 2016
This book is a bit of an odd combination of history-biography like The Idea Factory: Bell Labs and the Great Age of American Innovation or One Summer: America, 1927 and finance / gambling. Fortunately, the long rambles about various people involved in the ideas covered by this book are diverting. It's just not as cohesive as I would prefer. Claude Shannon gets significant coverage, although his direct contribution to finance is somewhat unclear; coverage of Ed Thorp is totally appropriate (and his story is inherently interesting). Also, the author is comfortable laying out related stories regarding gambling and organized crime in a direct way that is as easy to read as it is fascinating.
I'm a little surprised I haven't run across the Kelly criterion long before this, even if it has been unpopular with the major economic theorists of the last 50 years. Like cancer research and certain other areas of science, it serves as a reminder that science can be a popularity contest and the quality of the work is very much not the primary concern when it comes to who gets funding. It appears that a few of the prominent adherents to using Kelly quietly go about making money, while one of the only real-life examples from its detractors flamed out spectacularly (that would be Long Term Capital Management). In any case, Kelly and geometric mean / logarithmic utility are definitely concepts to understand for any economics / finance enthusiast interested in securities.
In any case, the fundamental takeaway: don't actively trade unless you have some kind of reason to believe you know better than the market about something, and only trade in proportion to your edge and likely return. Also, avoid trading in a way that can completely eliminate your money. It sounds so common sense and it's crazy that modern trading techniques ignore this (portfolio insurance, to name another historical example that blew up), but that's how it is.
I'm a little surprised I haven't run across the Kelly criterion long before this, even if it has been unpopular with the major economic theorists of the last 50 years. Like cancer research and certain other areas of science, it serves as a reminder that science can be a popularity contest and the quality of the work is very much not the primary concern when it comes to who gets funding. It appears that a few of the prominent adherents to using Kelly quietly go about making money, while one of the only real-life examples from its detractors flamed out spectacularly (that would be Long Term Capital Management). In any case, Kelly and geometric mean / logarithmic utility are definitely concepts to understand for any economics / finance enthusiast interested in securities.
In any case, the fundamental takeaway: don't actively trade unless you have some kind of reason to believe you know better than the market about something, and only trade in proportion to your edge and likely return. Also, avoid trading in a way that can completely eliminate your money. It sounds so common sense and it's crazy that modern trading techniques ignore this (portfolio insurance, to name another historical example that blew up), but that's how it is.
August 19, 2015
The nerd in me expected more discussion of how/why the Kelly criterion formula works. More of a history book than a math/gambling book.
October 24, 2023
Fantastic book. Describes the intersection of statistics, information theory, gambling, and markets but does so in a Liars Poker type narrative. Very enjoyable and interesting. Also want to use this platform to call all believers in the Efficient Maket Hypothesis nerds. Thank you for reading.
February 17, 2021
4/5, this book provides a strategic and measured approach to a world of risk-taking that has become overwhelmed with misinformation and disorder.
A brilliant methodology is presented that reshaped the way I look at risk taking in all forms — the concept of edge. Our ‘edge’ is our unique skill, knowledge, alpha and the bit that separates us from market competitors. By learning to understand our edge, we can learn to size risk and scale wealth responsibly while, at the same time, protecting ourselves from Gambler’s Ruin.
What really interested me is that this concept of ‘edge’ was something that was always discussed in my world, such as in athletics, politics and world history, but I wasn’t fully aware of it’s applications prior to this book.
A very, very worthwhile read!
A brilliant methodology is presented that reshaped the way I look at risk taking in all forms — the concept of edge. Our ‘edge’ is our unique skill, knowledge, alpha and the bit that separates us from market competitors. By learning to understand our edge, we can learn to size risk and scale wealth responsibly while, at the same time, protecting ourselves from Gambler’s Ruin.
What really interested me is that this concept of ‘edge’ was something that was always discussed in my world, such as in athletics, politics and world history, but I wasn’t fully aware of it’s applications prior to this book.
A very, very worthwhile read!
November 20, 2015
Interesting history tying together Claude Shannon, information theory, organized crime, probability, investment theory, and the early history of hedge funds. In general I found it interesting but too glib for its own good. The title is misleading, there isn't a single scientific betting system that beat the casinos and Wall Street. The title does refer to the Kelly Criterion, a particular method for choosing how much of your portfolio to bet in a way that simultaneously avoids any possibility of bankruptcy [assuming infinitely divisible bets] and maximizes expected compound growth. According to this book, smart real-world folks have often used the Kelly Criterion or variants, while academic economists vehemently slammed it. I felt the book ends up dancing around the debate rather than really getting into the meat. It comes down on the pro-Kelly side, but the arguments it makes are not especially convincing.
October 8, 2008
After enjoying Poundstone's Gaming the Vote, I picked this up from the local library. Quite a timely read, given the current financial disarray, this traces the history of the Kelly criterion from Bell Labs (where John Kelly, Jr. worked in the 50's and 60's), to Ed Thorp's application of it in his classic Beat the Dealer, to Wall Street in recent decades.
I feel I came away with a better understanding of the hedge fund/junk bond scandals of the 80's and 90's, as well as an appreciation of the economic theory of rational markets. There were times when I'd wished Poundstone had gone into a slightly deeper explanation -- as if I was just on the verge of fully grasping the concepts -- but I suspect I'd need an economics textbook for that. This was excellent as an overview; tracing the connections between math, the Mob, and Wall Street never fails to entertain.
I feel I came away with a better understanding of the hedge fund/junk bond scandals of the 80's and 90's, as well as an appreciation of the economic theory of rational markets. There were times when I'd wished Poundstone had gone into a slightly deeper explanation -- as if I was just on the verge of fully grasping the concepts -- but I suspect I'd need an economics textbook for that. This was excellent as an overview; tracing the connections between math, the Mob, and Wall Street never fails to entertain.
November 6, 2018
Some interesting stories revolving around the science of information, investments sometimes using this science (and sometimes not), and the law revolving around RICO. These things fit together loosely, and the author does a good job of tying stories back to the scientists described early on in the book. But there is a lot of ground covered, in many directions. This is a book of stories related to information theory, not a how-to book. I did not pick up on enough science to be useful for my own investing and gambling, but this book does give you the basics on how it can be applied, and whets the appetite for learning more. I thought the variety of stories helped the narrative – I wasn’t expecting Rudy Giuliani to make such a large appearance.
December 8, 2008
I really enjoyed this book. Who knew that MIT, Vegas Casinos, mobsters, and Wall Street all had so much in common? The stories in this book are fascinating! However, the most useful part of the book is the explanation of the relationship between information theory and betting/investing. I wish I had read this book 10 years ago.
August 23, 2018
O livro começa meio superficial com umas historinhas de teoria da informação e tal, mas no meio, quando começa a falar do mercado financeiro é impossível de largar. A história é super bem contada e, mesmo para um super noob de finanças como eu, ultra interessante. Imagino que o leitor com algum conhecimento vai pirar mais ainda.
December 14, 2017
Unfortunately, this book is too formulaic. It's a typical who has done what book with a lot of history and trivia. I was expecting how and why Kelly Crriterion and information theory works. Had to skim through most of the book because its mostly filled with trivia. I am baffled by its popularity and top rating.
July 19, 2015
Excellent. ' The Idea Factory: Bell Labs ...." led me to this book. Blends Mafia stories, Bell Lab star's anecdotes, gambling, a compelling betting and investment strategy via Kelly criterion and Wall Street greed stories.
July 19, 2010
Every bettor/investor should know the Kelly Criteria!
February 16, 2022
Fun read! I've always been enamored by the Kelly Criterion so it was nice to explore that in more detail, especially. Overall I learnt quite a bit from the book and of course it was very dramatic.
January 27, 2019
I wanted to read this book for a while and did have the patience to start it because I thought it will be complicated and hard to read. I was wrong!! It explains complicated mathematical and economic ideas simply. The language is not over the top, but easy to understand and the explanations and examples are spot on. The author takes the time to explain all the concepts in detail, also offering short biographies on the mathematicians that worked on it and how their life and society influenced their research in some degree.
The topic is interesting on its own, but the explanations and the connections made in this book make it even more attractive. There is something for everyone here: mathematicians, economists, Nobel laureates, politicians, gangsters (old and new), police (more corrupt and less corrupt). In this book you go from betting on horse races and counting cards to complex hedge funds, such as LTCM. The book presents people who have "bit the market" and people that were destroyed by it.
The topic is interesting on its own, but the explanations and the connections made in this book make it even more attractive. There is something for everyone here: mathematicians, economists, Nobel laureates, politicians, gangsters (old and new), police (more corrupt and less corrupt). In this book you go from betting on horse races and counting cards to complex hedge funds, such as LTCM. The book presents people who have "bit the market" and people that were destroyed by it.
March 15, 2021
Fortune's Formula - is an interesting yet not so easy book to read. Author does an excellent job in describing the complex theories by simplifying them to the average reader. At the same time, there is a lot of unrelated yet interesting stuff in the book.
Fortune's Formula is about Kelly's Criterion - Jonh Kelly a scientist at Bell Labs derived a formula to help the telecom industry with noise over long distance calls. The formula eventually slipped out of the Telecom industry and found its way into the investment world. Book explains the journey of the formula. While doing so the author brings lots of information seemingly unrelated to the formula yet enjoyable chapters.
Fortune's Formula is about Kelly's Criterion - Jonh Kelly a scientist at Bell Labs derived a formula to help the telecom industry with noise over long distance calls. The formula eventually slipped out of the Telecom industry and found its way into the investment world. Book explains the journey of the formula. While doing so the author brings lots of information seemingly unrelated to the formula yet enjoyable chapters.
November 11, 2020
One of my all time favorite books and a large reason I have been interested in the various topics the book discusses for decades. William Poundstone is a wonderful story teller, and the stories he wove around characters like Claude Shannon and John Kelly. Kelly’s Criterion has been a concept that has stuck with me as clearly as any has throughout the years.
February 12, 2018
Heard many times elsewhere, but still cool as a history.
January 6, 2019
Investing, math, scientific discovery, economic feuds, gambling, the mob... what's not to love?
A well-researched, entertaining narrative spanning from the early days of Bell Labs and the information age, to wall street scandals and racketeering charges. Full of history and biographical nuggets, it's a tale ultimately about risk, uncertainty, and putting your money where your mouth is.
A well-researched, entertaining narrative spanning from the early days of Bell Labs and the information age, to wall street scandals and racketeering charges. Full of history and biographical nuggets, it's a tale ultimately about risk, uncertainty, and putting your money where your mouth is.
August 5, 2023
Need to get back on the reading grind for this year. This book was good, I thought the author did a good job assembling all of the stories together and connecting anything but missed the big “push” for one large story and takeaway. Would recommend
March 26, 2024
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Fortune’s Formula by William Poundstone (pp. 400)
Utterly fascinating. Gambling, mobsters, mathematicians, economists, hedge funds, greed, and how it’s linked together by some early genius and freak timing. Part history lesson, part text book, part novel, all true, it flows beautifully. For anyone who is interested in math, the financial markets, or Las Vegas, this book is a fun read.
Poundstone tells the story of how one formula changed the way casinos look at card counters, how a mathematical concept influenced the height of junk bond trading and ultimately took it down, how current electronic trading was made possible, and how a couple of academics jumped from publishing obscure papers to making millions in the financial markets.
A lot of the elements of this book have been told in depth as their own subjects, but the joy of reading Poundstone’s account is seeing how they are all related. Ivan Boesky, the early Las Vegas mobsters, Rudolph Giuliani, Warren Buffet and the original card counting book, Beat the Dealer are not something you’d expect to be rolled into a book about based on a formula created at the height of the Bell Labs heyday, but here it is.
This book is what all science pop culture books should strive to be and is even more relevant today given the daily news of our financial markets.
Utterly fascinating. Gambling, mobsters, mathematicians, economists, hedge funds, greed, and how it’s linked together by some early genius and freak timing. Part history lesson, part text book, part novel, all true, it flows beautifully. For anyone who is interested in math, the financial markets, or Las Vegas, this book is a fun read.
Poundstone tells the story of how one formula changed the way casinos look at card counters, how a mathematical concept influenced the height of junk bond trading and ultimately took it down, how current electronic trading was made possible, and how a couple of academics jumped from publishing obscure papers to making millions in the financial markets.
A lot of the elements of this book have been told in depth as their own subjects, but the joy of reading Poundstone’s account is seeing how they are all related. Ivan Boesky, the early Las Vegas mobsters, Rudolph Giuliani, Warren Buffet and the original card counting book, Beat the Dealer are not something you’d expect to be rolled into a book about based on a formula created at the height of the Bell Labs heyday, but here it is.
This book is what all science pop culture books should strive to be and is even more relevant today given the daily news of our financial markets.
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May 1, 2023 Best poker lay down ever at Emu Casino
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September 3, 2008
I love books like this.
It's non fictional account of a few people who were behind "fortune's formula". This formula tells you how much of your total net worth to invest in any particular investment. All you have to do is figure out the expected payout of the investment based on information you have.
Does it work? Yes, if you have accurate data.
The investments have to have an expected payoff of greater than 0: meaning you will make some money on average--even though there is some chance of losing money. So for all the casino games in Vegas, since the expected return is less than 0, the formula says "don't wager anything". However if you have some inside information (like how many Ace's are left in the deck) then it may make sense to wager.
It also explains how the system was developed that met the formula, but didn't get you kicked out of the casino. For example, they won't let you play only a few hands of blackjack near the end of each deck.
I also like this book because it blurs the line between "wagering" and "investing". Mathematically, this makes the theory and formula simpler. They are really the same thing. In gambling you often have no "edge" (expected return). In investing, you generally do have an positive expected return.
It's non fictional account of a few people who were behind "fortune's formula". This formula tells you how much of your total net worth to invest in any particular investment. All you have to do is figure out the expected payout of the investment based on information you have.
Does it work? Yes, if you have accurate data.
The investments have to have an expected payoff of greater than 0: meaning you will make some money on average--even though there is some chance of losing money. So for all the casino games in Vegas, since the expected return is less than 0, the formula says "don't wager anything". However if you have some inside information (like how many Ace's are left in the deck) then it may make sense to wager.
It also explains how the system was developed that met the formula, but didn't get you kicked out of the casino. For example, they won't let you play only a few hands of blackjack near the end of each deck.
I also like this book because it blurs the line between "wagering" and "investing". Mathematically, this makes the theory and formula simpler. They are really the same thing. In gambling you often have no "edge" (expected return). In investing, you generally do have an positive expected return.
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