Jim Simons – Mathematician, Philanthropist and Renaissance Man
What’s the connection between Charlie Munger and Jim Simons, besides acquiring vast amounts of wealth and reputations as super investors? Models. Charlie Munger built them in his head, his latticework of models. Jim Simons builds them with PhDs, algorithms, super computers and big data. After that it’s hard to find the similarities.
Jim Simons, like Munger, Dalio, Graham and Buffett, is a super investor, credited as one of the founders of quant investing. Now 78 and retired, he is a mathematician by training, not from Wall Street and founded Renaissance Technologies in 1982 before there were quant funds. His Medallion Fund, closed now to outside investors, is one of the most secret black boxes in the world and generates incredible returns.
The fabled fund, known for its intense secrecy, has produced about $55 billion in profit over the last 28 years, according to Katherine Burton at Bloomberg, making it about $10 billion more profitable than funds run by billionaires Ray Dalio and George Soros.
Andrew Lo, a finance professor at MIT’s Sloan School of Management and chairman of AlphaSimplex, a quant research firm. Lo credits Jim Simons, the 78-year-old mathematician who founded Renaissance in 1982, for bringing so many scientists together. “They are the pinnacle of quant investing. No one else is even close.” From Burton’s Bloomberg article,
Renaissance’s success, of course, ultimately lies with the people who built, improved upon, and maintain Medallion’s models.
The atmosphere at Renaissance was different than what they’d left behind. “We quickly learned that the financial world is different from IBM,” Brown said at the conference. “It’s ruthless. Either your models work better than the other guy’s, and you make money, or they don’t, and you go broke. That kind of pressure really focuses one’s attention.”
Renaissance also spent heavily collecting, sorting, and cleaning data, as well as making it accessible to its researchers. “If you have an idea, you want to test it quickly. And if you have to get the data in shape, it slows down the process tremendously,” says Patterson.
But as financial sophistication grew and more quants plied their craft at decoding markets, the inefficiencies began disappearing.
Simons determined, almost from the beginning, that the fund’s overall size can affect performance: Too much money destroys returns. Renaissance currently caps Medallion’s assets between $9 billion and $10 billion
Mathematician, Entrepreneur and Philanthropist
Born in 1938 to the Jewish owner of a shoe factory in Brookline Massachusetts, Jim Simons attended MIT, graduating with his BS in Mathematics before UC Berkeley where he received his PhD at the age of 23. Clearly gifted in the field, after graduation he continued his math career teaching at MIT and Harvard while working for the NSA doing code breaking for the Institute for Defense Analysis.
Before leaving for the financial world he served as chairman of the math department at Stoney Brook University. Among a series of achievements in math that continued during his work at Renaissance, Simons received the 1976 AMS Oswald Veblen Prize in Geometry. In 2014 he was named to the National Academy of Sciences. From a 2014 NYT interview,
Restless, in 1978 he founded a predecessor to Renaissance Technologies in a strip mall close to the Stony Brook campus. In 1982, he set up Renaissance, which grew to occupy a 50-acre campus, complete with tennis courts.
In time, his novel approach helped change how the investment world looks at financial markets. The man who “couldn’t write programs” hired a lot of programmers, as well as physicists, cryptographers, computational linguists, and, oh yes, mathematicians. Wall Street experience was frowned on. A flair for science was prized. The techies gathered financial data and used complex formulas to make predictions and trade in global markets.
The company thrived, rewarding investors with double-digit annual returns. It marked an early triumph of the “quants” — quantitative analysts who use advanced math to guide investments — and foreshadowed the ascendency of Big Data.
Today, with a fortune estimated at $18 billion, Dr. Simons now runs a tidy universe of science endeavors, financing not only math teachers but hundreds of the world’s best investigators, even as Washington has reduced its support for scientific research.
At 79, Dr. Simons laughs a lot. He talks of “the fun” of his many careers, as well as his failings and setbacks. From the 2014 New York Times interview with William Broad, he recounted a life full of remarkable twists, including the deaths of two adult children, all of which seem to have left him eager to explore what he calls the mysteries of the universe. His casual manner, which comes across naturally in this 2015 TED interview with Chris Anderson, belies a wide-ranging intellect that seems to resonate with top scientists.
Retired from Renaissance, he and his wife were among the first billionaires to sign the Giving Pledge, promising to devote “the great majority” of their wealth to philanthropy. His giving and charitable work has accelerated and is particularly proud of Math for America. It awards stipends and scholarships of up to $100,000 to train high school math and science teachers and to supplement their regular salaries. He supports the National Math Festival which was held in April 2017. The lead financial sponsor of the festival is the Simons Foundation which Simons started with his wife in 1994 to focus on scientific research.
His passion, however, is basic research — the risky, freewheeling type. He recently financed new telescopes in the Chilean Andes that will look for faint ripples of light from the Big Bang, the theorized birth of the universe. His favorite topics include gene puzzles, the origins of life, the roots of autism, math and computer frontiers, basic physics and the structure of the early cosmos.
Lessons Learned – the Paradox of Skill
While Jim Simons is a great American story, what lessons can you take from his success at Renaissance? Most of us are not world class mathematicians with PhDs from MIT and Harvard. The part that resonates with me is his focus first on getting the right people into his organization and then the rigorous “scientific” approach they took in collecting data and building models. Charlie Munger followed the same principles, as did Ray Dalio, even if they executed in their own way. Simon’s makes reference to the fact that his models worked until they didn’t as the world is in constant evolution, so he assembled a team that continuously built and rebuilt the models, including limiting their gains (greed and egos too?).
The nature of the financial markets is constantly changing, first as investors (and companies) became smarter, then as computers and algorithms changed the game. Howard Lindzon captured this perfectly for all active investors in his recent blog:
Being a rockstar was never easy and now it is harder.
Just today, Tadas posted this awesome piece on how investing was never easy and just getting harder.
The bigger problem for investors may be that the challenge of investing is only likely to get more difficult. Michael Mauboussin in his book and elsewhere has discussed the idea of the ‘paradox of skill.’ Mauboussin describes it:
In investing, as in many other activities, the skill of investors is improving on an absolute basis but shrinking on a relative basis. As a consequence, the variance of excess returns has declined over time and luck has become more important than ever…This process is called the paradox of skill.
Above all, Mauboussin says investors need to play “attractive games” where they are not the obvious patsy. This is easier said than done. For investors who don’t want to play the active investment game there are plenty of options. For those who choose to continue to play, remember that investing wasn’t ever easy and likely won’t be getting easier any time soon
I think this rant should be a meme and I hope some leaders in other industries pick up on it.
I expect more rants like this from older people in every profession.
For some the mission is to play the game, for some the mission is to build the tools for those that still want to play the game. Winners can’t bank luck, they need to be able to extend and amend their models to work at machine scale and speed without compromises in complexity. Success takes academic rigor, historical and social context and an experienced trader’s ability to learn and adapt.
We all can't be Jim Simons, but as long as the world remains complex and at least a little bit irrational, there's money to be made.