The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution

by Gregory Zuckerman

Markov models, kernel methods of machine learning, and stochastic differential equations,

the direction of investments, financial markets, and global economies,

didn’t have a clue how to estimate cash flows, identify new products, or forecast interest rates.

data cleansing, signals, and backtesting, terms most Wall Street pros were wholly unfamiliar with.

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Marcia

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“The lesson was: Do what you like in life, not what you feel you ‘should’ do,” Simons says. “It’s something I never forgot.”

United Fruit Company,

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“On the Transitivity of Holonomy Systems,”

Dick Leibler,

“Probabilistic Models for and Prediction of Stock Market Behavior”

They posited that the market had as many as eight underlying “states”—such as “high variance,” when stocks experienced larger-than-average moves, and “good,” when shares generally rose.

a sophisticated mathematical tool called a hidden Markov model.

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.

Getting fired can be a good thing. You just don’t want to make a habit of it.

analyze Markov chains,

which are sequences of events in which the probability of what happens next depends only on the current state, not past events.

Markov chain, it is impossible to predict future steps with certainty, yet one can observe the chain to make educated...

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.

Baum’s algorithm, which allows a computer to teach itself states and probabilities, is seen as one of the twentieth century’s notable advances in machine learning,

Greg Hullender

“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.”

machine-learning specialist for Amazon and Microsoft.

drifted from predictive mathematical models to a more traditional trading style.

“quantitative” style of trading seemed a waste of time.

“If I don’t have a reason for doing something, I leave things as they are and do nothing,”

“If you make money, you feel like a genius,” he told a friend. “If you lose, you’re a dope.”

Ax-Kochen theorem.

Data on public sentiment and the holdings of fellow futures traders also yielded few dependable sequences.

simple linear regressions,

A model dependent on running simple linear regressions through data points generally does a poor job predicting future prices in complex, volatile markets marked by freak snowstorms, panic selling, and turbulent geopolitical events, all of which can play havoc with commodity and other prices.

Carmona decided they needed regressions that might capture nonlinear relationships in market data.

Just as one can infer what a missing jigsaw puzzle piece might look like by observing pieces already in place,

In a sense, he was proposing an early machine-learning system.

higher dimensional kernel regression approaches,

“Try to get on a great team,” he says.

advanced calculus class taught by John Nash, the game theorist and mathematician who later would be immortalized in Sylvia Nasar’s book A Beautiful Mind.

Elias and Shannon were pioneers of information theory, the groundbreaking approach to quantifying, encoding, and transmitting telephone signals, text, pictures, and other kinds of information that would provide the underpinnings for computers, the internet, and all digital media.

Kelly’s work underscored the importance of sizing one’s bets, a lesson Berlekamp would draw on later in life.

Sometimes, it chased prices, or bought various commodities that were moving higher or lower on the assumption that the trend would continue.

Other times, the model wagered that a price move was petering out and would reverse, a reversion strategy.

John Murphy had published a book called Technical Analysis of the Financial Markets,

Warren Buffett and other big-name investors embraced that value style of investing.

Paul Tudor Jones, had adopted trend following strategies similar to those Simons’s team relied on.

Edward Thorp, the pioneering quantitative trader.

It seemed quantitative investing didn’t come naturally,

quantum mechanics written by Simon Kochen