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August 16 - September 30, 2020
“If we have enough data, I know we can make predictions,” Simons
“The lesson was: Do what you like in life, not what you feel you ‘should’ do,” Simons
“I had no interest in business, which is not to say I had no interest in money.”
Q: What’s the difference between a PhD in mathematics and a large pizza? A: A large pizza can feed a family of four.
“Bad ideas is good, good ideas is terrific, no ideas is terrible.”
Lenny Baum
Albert Einstein argued that there is a natural order in the world; mathematicians like Simons can be seen as searching for evidence of that structure. There is true beauty to their work, especially when it succeeds in revealing something about the universe’s natural order.
Simons and the code-breakers proposed a similar approach to predicting stock prices, relying on a sophisticated mathematical tool called a hidden Markov model. Just as a gambler might guess an opponent’s mood based on his or her decisions, an investor might deduce a market’s state from its price movements.
Simons and his colleagues were proposing a third approach, one that had similarities with technical trading but was much more sophisticated and reliant on tools of math and science. They were suggesting that one could deduce a range of “signals” capable of conveying useful information about expected market moves.
“It would make us a stronger country to rebuild Watts than it would to bomb Hanoi,” Simons wrote. “It would make us stronger to construct decent transportation on our East Coast than it would to destroy all the bridges in Vietnam.”
He had little idea what he was going to do next, but getting fired so abruptly convinced him that he needed to gain some control over his future.
Getting fired can be a good thing. You just don’t want to make a habit of it. Jim Simons
A hidden Markov process is one in which the chain of events is governed by unknown, underlying parameters or variables. One sees the results of the chain but not the “states” that help explain the progression of the chain.
“I don’t want to have to worry about the market every minute. I want models that will make money while I sleep,” Simons said. “A pure system without humans interfering.”
“He had the buy-low part, but he didn’t always have the sell-high part,” Simons
Truth . . . is much too complicated to allow for anything but approximations. John von Neumann
Simons wondered if the technology was yet available to trade using mathematical models and preset algorithms, to avoid the emotional ups and downs that come with betting on markets with only intelligence and intuition.
Ax had long believed financial markets shared characteristics with Markov chains, those sequences of events in which the next event is only dependent on the current state. In a Markov chain, each step along the way is impossible to predict with certainty, but future steps can be predicted with some degree of accuracy if one relies on a capable model.
Carmona says. “The name of the game is not to always be right, but to be right often enough.”
I strongly believe, for all babies and a significant number of grownups, curiosity is a bigger motivator than money. Elwyn Berlekamp
though a passion for literature was mostly extinguished by a teacher who insisted on spending half the semester analyzing the novel Gone With the Wind.
“Truth in life is broad and nuanced; you can make all kinds of arguments, such as whether a president or person is fantastic or awful,” he says. “That’s why I love math problems—they have clear answers.”
Berlekamp
Since price movements often resembled those of the past, that data enabled the firm to more accurately determine when trends were likely to continue and when they were ebbing.
Scientists are human, often all too human. When desire and data are in collision, evidence sometimes loses out to emotion. Brian Keating, cosmologist, Losing the Nobel Prize
Their goal remained the same: scrutinize historic price information to discover sequences that might repeat, under the assumption that investors will exhibit similar behavior in the future.
Berlekamp also argued that buying and selling infrequently magnifies the consequences of each move. Mess up a couple times, and your portfolio could be doomed. Make a lot of trades, however, and each individual move is less important, reducing a portfolio’s overall risk.
Some of the trading signals they identified weren’t especially novel or sophisticated. But many traders had ignored them. Either the phenomena took place barely more than 50 percent of the time, or they didn’t seem to yield enough in profit to offset the trading costs. Investors moved on, searching for juicier opportunities, like fishermen ignoring the guppies in their nets, hoping for bigger catch. By trading frequently, the Medallion team figured it would be worthwhile to hold on to all the guppies they were collecting.
when you smell smoke, you get the hell out!” Simons
“That which has been is that which shall be . . . there is nothing new under the sun.”
simple-yet-profound Bayes’ theorem of probability, which argues that, by updating one’s initial beliefs with new, objective information, one can arrive at improved understandings.
“What you’re really modeling is human behavior,” explains Penavic, the researcher. “Humans are most predictable in times of high stress—they act instinctively and panic. Our entire premise was that human actors will react the way humans did in the past . . . we learned to take advantage.”
“Our very good results have made us well known, and this may be our most serious challenge,” Simons
“Visibility invites competition, and, with all due respect to the principles of free enterprise—the less the better.”
Simons
No one ever made a decision because of a number. They need a story. Daniel Kahneman,
“I took this as an indication that one of the most important goals of government-financed research is not so much to get answers as it is to consume the computer budget,” Mercer
After reading several hundred papers, Simons and his colleagues gave up. The tactics sounded tantalizing, but when Medallion’s researchers tested the efficacy of the strategies proposed by the academics, the trade recommendations usually failed to pan out. Reading so many disappointing papers reinforced a certain cynicism within the firm about the ability to predict financial moves.
By 1997, Medallion’s staffers had settled on a three-step process to discover statistically significant moneymaking strategies, or what they called their trading signals. Identify anomalous patterns in historic pricing data; make sure the anomalies were statistically significant, consistent over time, and nonrandom; and see if the identified pricing behavior could be explained in a reasonable way.
Never place too much trust in trading models.
“If you didn’t make so much money, you wouldn’t pay so much in taxes,” Simons
All models are wrong, but some are useful. George Box, statistician
“It’s a very big exercise in machine learning, if you want to look at it that way. Studying the past, understanding what happens and how it might impinge, nonrandomly, on the future.”
Many serious contributors want something from politicians, and it’s usually reasonably clear what they’re after.
Ayn Rand might have imagined a hero like Mercer—a tall, ruggedly handsome individualist who was a huge fan of capitalism and always rational and in control.
Renaissance doesn’t see all the market’s hues, but they see enough of them to make a lot of money, thanks in part to the firm’s reliance on ample amounts of leverage.
Renaissance has gathered, the firm only profits on barely more than 50 percent of its trades, a sign of how challenging it is to try to beat the market—and how foolish it is for most investors to try.
“Work with the smartest people you can, hopefully smarter than you . . . be persistent, don’t give up easily. “Be guided by beauty . . . it can be the way a company runs, or the way an experiment comes out, or the way a theorem comes out, but there’s a sense of beauty when something is working well, almost an aesthetic to it.”
Simons
