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November 30, 2019 - August 22, 2020
God gave me a tail to keep off the flies. But I’d rather have had no tail and no flies.’ That’s kind of the way I feel about publicity.”
Early on, Simons made a decision to dig through mountains of data, employ advanced mathematics, and develop cutting-edge computer models, while others were still relying on intuition, instinct, and old-fashioned research for their own predictions.
Do Simons’s achievements prove human judgment and intuition are inherently flawed, and that only models and automated systems can handle the deluge of data that seems to overwhelm us?
Simons never took a single finance class, didn’t care very much for business, and, until he turned forty, only dabbled in trading. A decade later, he still hadn’t made much headway.
“The lesson was: Do what you like in life, not what you feel you ‘should’ do,” Simons says. “It’s something I never forgot.”
“Jim understood at an early age that money is power,” Barbara says. “He didn’t want people to have power over him.”
The IDA taught Simons how to develop mathematical models to discern and interpret patterns in seemingly meaningless data. He began using statistical analysis and probability theory, mathematical tools that would influence his work.
“Bad ideas is good, good ideas is terrific, no ideas is terrible.”
Mathematicians who focus on theoretical questions often immerse themselves in their work—walking, sleeping, even dreaming about problems for years on end. Those with no exposure to this kind of mathematics, which can be described as abstract or pure, are liable to dismiss it as pointless.
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.
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.
The whys didn’t matter, Simons and his colleagues seemed to suggest, just the strategies to take advantage of the inferred states.
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.
Until then, investors generally sought an underlying economic rationale to explain and predict stock moves, or they used simple technical analysis, which involved employing graphs or other representations of past price movements to discover repeatable patterns. 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.
Markov chains, which are sequences of events in which the probability of what happens next depends only on the current state, not past events.
In a Markov chain, it is impossible to predict future steps with certainty, yet one can observe the chain to make educated guesses about possible outcomes. Baseball can be seen as a Markov game. If a batter has three balls and two strikes, the order in which they came and the number of fouls in between don’t matter. If the next pitch is a strike, the batter is out.
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”...
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“The Baum-Welch algorithm gets you closer to the final answer by giving you better probabilities,” Welch explains.
Baum-Welch enabled the first effective speech recognition system and even Google’s search engine.
