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| Jim Simons looked to math and computers as ways to eliminate the emotional ups and downs of investing. “I don’t want to have to worry about the market every minute. I want models that will make money while I sleep.” ELLEN MCDERMOTT |
Jim Simons was a middle-aged mathematician in a strip mall who knew little about finance. He had to overcome his own doubts to turn Wall Street on its head.
Jim Simons sat in a storefront office in a dreary Long Island strip mall. He was next to a women’s clothing boutique, two doors from a pizza joint and across from a tiny, one-story train station. His office had beige wallpaper, a single computer terminal, and spotty phone service.
It was early summer 1978, weeks after Mr. Simons ditched a distinguished mathematics career to try his hand trading currencies. Forty years old, with a slight paunch and long, graying hair, the former professor hungered for serious wealth. But this wry, chain-smoking teacher had never taken a finance class, didn’t know much about trading, and had no clue how to estimate earnings or predict the economy.
For a while, Mr. Simons traded like most everyone else, relying on intuition and old-fashioned research. But the ups and downs left him sick to his stomach. Mr. Simons recruited renowned mathematicians and his results improved, but the partnerships eventually crumbled amid sudden losses and unexpected acrimony. Returns at his hedge fund were so awful he had to halt its trading and employees worried he’d close the business.
Today, Mr. Simons is considered the most successful money maker in the history of modern finance. Since 1988, his flagship Medallion fund has generated average annual returns of 66% before charging hefty investor fees — 39% after fees — racking up trading gains of more than $100 billion. No one in the investment world comes close. Warren Buffett, George Soros, Peter Lynch, Steve Cohen, and Ray Dalio all fall short.
Doing the Math
Jim Simons, a pioneer in the use of quantitative analysis, has outperformed the biggest names in the investment world over the past three decades — even after deducting investor fees that are much higher than those of rivals.
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| *Returns have fallen in recent years as Soros has stopped investing
money for others †Averaged 62% gains investing his personal money from
1951-57, starting with less than $10,000, and saw average gains of
24.3% for a partnership managed from 1957-69. Doesn’t charge fees. Source: ‘The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution’ |
A radical investing style was behind Mr. Simons’s rise. He built computer programs to digest torrents of market information and select ideal trades, an approach aimed at removing emotion and instinct from the investment process. Mr. Simons and colleagues at his firm, Renaissance Technologies LLC, sorted data and built sophisticated predictive algorithms — years before Mark Zuckerberg and his peers in Silicon Valley began grade school.
“If we have enough data, I know we can make predictions,” Simons told a colleague.
Mr. Simons amassed a $23 billion fortune — enough to wield influence in the worlds of politics, science, education, and philanthropy. Robert Mercer, a Renaissance executive who helped the firm achieve some of its most remarkable breakthroughs, was a leading supporter of Donald Trump during Mr. Trump’s 2016 presidential election victory.
Mr. Simons both anticipated and inspired a revolution. Today, investors have embraced his mathematical, computer-oriented approach. Quantitative investors are the market’s largest players, controlling 31% of stock trading, according to the Tabb Group, a research firm. Just 15% of stock trading is done by “fundamental” stock traders, according to JPMorgan Chase & Co., as many forsake once-dependable tactics, such as grilling corporate managers, scrutinizing balance sheets and predicting global economic shifts.
Mr. Simons’s pioneering methods have been embraced in the halls of government, sports stadiums, doctors’ offices, and pretty much everywhere else forecasting is required. M.B.A.s once scoffed at the notion of relying on computer models, confident they could hire coders if they were needed. Today, coders say the same about M.B.A.s, if they think about them at all.
Source: The Wall Street Journal
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