Quant Pioneer James Simons: Breaking Vast Uncertainty into Intermediate Certainties

James Simons, king of high-frequency quantitative trading, has died. Although his investment style was entirely different from venture capital, it represents a more mainstream school of investing in finance, and his approach also offers lessons for entrepreneurship. On May 10, 2024, mathematician and investor James Simons, known as the king of quantitative investing, died aged 86. Simons was the wealthiest among mathematicians and the smartest among investors. He was exceptionally intelligent from childhood, seemingly starting at the top. He graduated from MIT's mathematics department at twenty, received his mathematics doctorate from the University of California, Berkeley, at twenty-three, and won the highest honor in mathematical geometry at thirty-seven. His mathematics was not merely a special talent; it was mastery. Even if he had done nothing and earned nothing after thirty-seven, he could have lived respectably for the rest of his life as a mathematician. Why did someone so intelligent turn to Wall Street rather than devote himself entirely to scholarship? Money certainly mattered; Simons liked earning it. But supposedly he was also provoked by a Wall Street saying: if you are smarter than I am, why aren't you richer? Its implication was that wealth measured intelligence. Perhaps Simons could tolerate someone being richer, but not a claim of intellectual superiority. So he entered investing. He regarded himself first as a mathematician and only second as an investor. The market was a giant mathematical problem, with money the reward for solving it. His investment firm was Renaissance Technologies, whose best-known product was the Medallion Fund. Over the thirty years after its establishment in 1988, the fund's annualized return was 66%, or 39% after management and performance fees. That exceeded the records of the famous Buffett and Soros. Simons kept a very low profile. Anyone joining Renaissance had to sign a lifetime confidentiality agreement, effectively preventing disclosure of its internal affairs for life. Gregory Zuckerman wrote his biography, The Man Who Solved the Market, after collecting more than 400 interviews with former and current Renaissance employees. Zuckerman writes that one thing is certain: Renaissance almost never hired conventional Wall Street finance professionals. It recruited mathematicians, physicists, and computer scientists instead. Even after becoming wealthy, Simons retained a scientist's approach. He established the Simons Foundation and the Flatiron Institute in Manhattan, covering fields including physics, mathematics, and astronomy. The Flatiron Institute's ultimate goal is to make Renaissance's algorithms available to the scientific community, helping research projects detect the faintest patterns in apparently disordered data. Despite their different fields, Renaissance and Flatiron essentially do the same thing: detect faint patterns in seemingly chaotic data. Finance needs this method, and so does science. He used science to make money, then used that money to support science. What exactly was his investment method? Simons and Renaissance kept it strictly secret. But so many people studied them that, over time, outsiders developed a reasonably close conjecture about the broad approach, even without knowing the precise algorithms. Simons probably used high-frequency quantitative trading. High-frequency trading can be summarized simply: always run a little faster than everyone else. Imagine stock trading as a soccer match. Can you predict the result? No one can say which team will ultimately win. But here is the key: although the final result is unpredictable, the teams' current probabilities of winning can be predicted. Ten minutes into a match, for example, a team might deliver a beautiful cross toward the opposing penalty area. At that moment, its probability of scoring is clearly higher. In other words, divide the match into countless short intervals, and a team's probability of winning within each interval becomes relatively determinate. This produces a curious result. Although I do not know the final outcome, I know the intermediate situation each minute: who leads, who is playing well, and who holds the advantage. The match becomes relatively determinate. Conditions change several times a minute, so a shorter forecast horizon means a more accurate prediction. While an ordinary person predicts half an hour ahead, Simons predicts half a second ahead. His forecast horizon was much shorter still. Renaissance's high-frequency trading took place once every one sixty-four-millionth of a second. That is high-frequency trading. Humans cannot sustain that frequency; computers and algorithms are essential. Using computers for quantitative analysis and investment decisions is called quantitative investing. High-frequency refers to how often trades occur; quantitative refers to the method: analysis and decisions are delegated to machines rather than people. Of course, this describes only the theoretical feasibility of Simons's approach. In practice, no investment method can easily guarantee a profit. What can we learn about winning an unequal contest? Simons faced markets much as an ordinary person faces a casino. Humans are a luck-based system; a casino is a mathematical system. This is a mismatch between systems in which the individual seems unable to win. The answer is to upgrade your system from intuition to algorithms, dividing one vast uncertainty into countless intermediate certainties. #DedaoHeadlines20240514 ----- Robin: “Accumulate small wins into a big victory. In entrepreneurship, can you divide an ambitious goal into many intermediate objectives and move steadily toward them, one grounded step at a time? That itself is a methodology for entrepreneurial success.”