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<\/p>\nJim Simons died in May 2024 at 86. His Medallion Fund generated 66% average annual returns over 34 years\u2014the best track record in investment history. Not 66% total. 66% per year.<\/p>\n
That number still doesn\u2019t make sense to me. $100 invested in 1988 turned into $398.7 million by 2018 after fees. The same $100 in the S&P 500 would\u2019ve grown to roughly $2,000.<\/p>\n
But here\u2019s what gets me: Simons wasn\u2019t a finance guy. He was a mathematician who won the Oswald Veblen Prize in 1976, one of the highest honors in geometry. He left academia at 40, applied code-breaking techniques to markets, and built something that shouldn\u2019t exist according to efficient market theory.<\/p>\n
I\u2019ve spent the past week reading everything I could find about Simons. Not because I think anyone can replicate Medallion\u2019s returns\u2014they can\u2019t. The fund is closed to outside investors and employs 90 PhDs working on a single unified model that took decades to build.<\/p>\n
I wanted to understand what his career teaches us about investing. What can someone running a traditional portfolio learn from a mathematician who treated markets like cipher codes?<\/p>\n
Turns out, quite a bit.<\/p>\n
Don\u2019t confuse process with intelligence<\/h2>\n<\/p>\n
Simons hired physicists, astronomers, and speech recognition experts. He actively avoided MBAs and Wall Street professionals. When the firm needed a breakthrough in 1993, he recruited Robert Mercer and Peter Brown from IBM\u2019s speech recognition group\u2014people who\u2019d never traded a stock.<\/p>\n
This wasn\u2019t contrarian hiring for the sake of being different. Simons believed markets were pattern recognition problems, not economic forecasting exercises. He wanted people who could prove theorems, not people who could explain why the Fed\u2019s next move mattered.<\/p>\n
The critical hire was Leonard Baum in the early 1980s, creator of the Baum-Welch algorithm used in speech recognition. Baum brought hidden Markov models to finance\u2014mathematical tools designed to find patterns in noisy data. That\u2019s exactly what price movements are.<\/p>\n
Here\u2019s what I think matters for investors: Simons didn\u2019t try to understand\u00a0why<\/em>\u00a0markets moved. He looked for\u00a0what<\/em>\u00a0actually happened in the data. No macro forecasts. No earnings projections. No views on whether a company was well-managed.<\/p>\n Renaissance processed terabytes of data daily, searching for anomalies that repeated. As Simons put it: \u201cWe don\u2019t start with models. We start with data. We look for things that can be replicated thousands of times.\u201d<\/p>\n Robert Mercer once admitted he didn\u2019t know Chrysler no longer existed as a standalone company\u2014the model just signaled when to buy or sell. That\u2019s pure quant investing. No fundamental research required.<\/p>\n I\u2019m not suggesting fundamental analysis is worthless. I use it. But Simons proved there\u2019s another way to generate alpha: find statistical edges that repeat, execute them systematically, and don\u2019t let narrative override data.<\/p>\n Simons repeated this in every interview: his biggest contribution wasn\u2019t the math, it was hiring great people.<\/p>\n Renaissance employed 300-400 people total versus 2,000-5,000 at competitors. Roughly 90 PhDs among 150-200 research staff. Median tenure ran 14-16 years compared to 2-3 years typical in finance.<\/p>\n The compensation structure was unusual: 5% management fee and 44% performance fee, versus the typical 2% and 20%. Yes, Renaissance charged more. But employees got a massive cut of that 44%. The firm\u2019s 401(k) plans could invest directly in Medallion through a special IRS exemption. Combined with lifetime NDAs and non-compete agreements, leaving meant walking away from extraordinary wealth.<\/p>\n Simons also insisted everyone see what everyone else was working on. In 1995, when Mercer and Brown implemented the \u201cone model\u201d system, all researchers began working on a single unified model across all asset classes. Discoveries in currency markets benefited equity trading. Breakthroughs in commodities improved fixed income strategies.<\/p>\n This goes against how most hedge funds operate. Typical structure: portfolio managers compete against each other, guard their best ideas, and get fired if they underperform.<\/p>\n Renaissance did the opposite. Complete transparency. Collaborative research. Long-term incentives. Pay people so well they\u2019d never leave.<\/p>\n The lesson here isn\u2019t \u201chire PhDs\u201d or \u201cpay 44% performance fees.\u201d Most of us can\u2019t do either. But the principle holds: if you can afford to work with people better than you at specific tasks\u2014research, risk management, execution\u2014that\u2019s probably your highest-return investment.<\/p>\n I\u2019ve seen investors waste money on Bloomberg terminals they barely use while refusing to pay for quality research or proper tax advice. That\u2019s backwards. Simons built the world\u2019s most profitable fund by surrounding himself with people who knew things he didn\u2019t.<\/p>\n During the March 2020 selloff, markets dropped 34% in 23 days\u2014the fastest bear market in history. Most funds got crushed. Medallion made money.<\/p>\n How? I don\u2019t know the specifics because Renaissance doesn\u2019t publish details. But I know their philosophy: they don\u2019t predict market direction. They identify patterns that statistically repeat over thousands of trades.<\/p>\n When Medallion returned 152% gross (82% net) during the 2008 financial crisis, they weren\u2019t predicting Lehman\u2019s collapse. When they delivered 128% gross during the 2000 tech bubble, they weren\u2019t forecasting which dotcoms would fail.<\/p>\n They were exploiting short-term anomalies\u2014price discrepancies that existed for hours or days, not months or years. High-frequency statistical arbitrage across thousands of positions. Win rate probably wasn\u2019t that high on individual trades. But with enough trades and proper position sizing, the law of large numbers takes over.<\/p>\n Here\u2019s where this gets relevant for traditional investors: you don\u2019t need to predict the future to make money. You need repeatable processes that work more often than they fail.<\/p>\n I think about this with my own investing. I don\u2019t know if we\u2019re headed into recession. I don\u2019t know if inflation will spike or crater. I don\u2019t know if the Fed will cut rates three times or zero times this year.<\/p>\n What I do know: quality companies with pricing power and low debt tend to outperform during volatility. Rebalancing a diversified portfolio back to target weights forces you to buy low and sell high. Dollar-cost averaging removes timing risk.<\/p>\n None of that requires predicting anything. It\u2019s just pattern recognition\u2014strategies that have worked across multiple market cycles.<\/p>\n Simons took this to an extreme with pure quant methods. Most investors can\u2019t do that. But we can stop pretending we need to forecast the future and instead focus on what actually repeats.<\/p>\n In the early 1970s, Simons collaborated with mathematician Shiing-Shen Chern on what became Chern-Simons theory. They were trying to find a combinatorial formula for something called the first Pontryagin class. They failed at their original goal.<\/p>\n But the work they did instead\u2014creating secondary characteristic classes called Chern-Simons forms\u2014turned out to be fundamental to modern theoretical physics. When Edward Witten showed in 1988 that this mathematics described topological quantum field theory, it became central to string theory, knot theory, and quantum computing.<\/p>\n Simons said: \u201cWe didn\u2019t know any physics. It never occurred to me that it would be applied to physics. But that\u2019s the thing about mathematics\u2014you never know where it\u2019s going to go.\u201d<\/p>\n He carried this philosophy into investing. Renaissance\u2019s approach came from code-breaking, speech recognition, and signal processing\u2014fields completely outside traditional finance. The idea that markets could be treated like encrypted messages or noisy audio data wasn\u2019t obvious in 1978.<\/p>\n Here\u2019s what I take from this: elegant solutions often come from asking different questions than everyone else.<\/p>\n When Simons looked at markets, he didn\u2019t ask \u201cWhat\u2019s this company worth?\u201d or \u201cWhere\u2019s the economy headed?\u201d He asked \u201cWhat patterns exist in price data that repeat with statistical significance?\u201d<\/p>\n That\u2019s a fundamentally different question than what traditional investors ask. It led to fundamentally different answers.<\/p>\n I\u2019m not suggesting everyone should become a quant. But I do think there\u2019s value in questioning assumptions. Why do we analyze companies the way we do? Because it works? Or because that\u2019s how everyone\u2019s always done it?<\/p>\n The investors who\u2019ve generated exceptional long-term returns\u2014Simons with quant, Buffett with value, Dalio with macro\u2014all asked different questions than their peers. They found approaches that matched their skills and temperament.<\/p>\n Simons left academia at 40 after winning the Veblen Prize. The math community thought he\u2019d sold out. One colleague said it was like \u201cselling his soul to the devil.\u201d<\/p>\n But Simons had scratched his itch in mathematics. He\u2019d done work that would influence physics for decades. He wanted to try something else. So he did.<\/p>\n Then after retiring as CEO of Renaissance in 2009, he returned to active mathematical research. At 71. He collaborated with Dennis Sullivan on differential K-theory and published multiple papers through 2024.<\/p>\n This is rare. Most people who leave a field don\u2019t come back. Simons did, because he genuinely loved mathematics\u2014not for career advancement, but for its own sake.<\/p>\n I think about this with investing. How many people are doing it because they love it versus because they think they should? How many are following strategies that don\u2019t fit their personality because some expert said it\u2019s \u201cbest practice\u201d?<\/p>\n Simons succeeded in investing partly because he approached it like mathematics: pattern recognition, rigorous testing, collaboration with smart people, willingness to be wrong. Those were his strengths.<\/p>\n If you\u2019re naturally good at fundamental analysis and enjoy reading 10-Ks, lean into that. If you\u2019re quantitatively minded and prefer data to narratives, build systematic approaches. If you\u2019re patient and disciplined, maybe passive indexing fits better than active trading.<\/p>\n The worst investment strategy is one you can\u2019t stick with. Simons found an approach that matched his skills and temperament. That\u2019s why it worked for 34 years.<\/p>\n Simons and his wife Marilyn gave away approximately $6 billion during their lifetimes. The Simons Foundation holds over $5 billion in assets and distributes roughly $450 million annually in grants.<\/p>\n The June 2023 gift of $500 million to Stony Brook University was the largest unrestricted gift to any American university in history. Math for America supports roughly 1,000 STEM teachers annually in NYC public schools. The Simons Foundation Autism Research Initiative has identified approximately 100 genes related to autism versus one when they started.<\/p>\n Here\u2019s what gets me: Simons could\u2019ve kept it all. He\u2019d earned it. 66% annual returns over 34 years isn\u2019t luck\u2014it\u2019s skill applied systematically over decades.<\/p>\n Instead, he redirected his fortune toward basic scientific research. Not applied research with obvious commercial applications. Basic research\u2014the kind that asks fundamental questions about the universe without knowing where it\u2019ll lead.<\/p>\n Just like his mathematical work on Chern-Simons theory had no obvious application until decades later, Simons funded research trusting that good science takes you places you don\u2019t expect.<\/p>\n His own summary shortly before death: \u201cI did a lot of math, I made a lot of money, and I gave almost all of it away.\u201d<\/p>\n I\u2019m not here to tell anyone what to do with their money. But I do think Simons\u2019 approach to philanthropy reflected the same philosophy that made him successful in mathematics and investing: be guided by beauty, surround yourself with smart people, trust that you don\u2019t know where good work will lead.<\/p>\n He wasn\u2019t trying to optimize tax deductions or build a legacy. He funded what he found beautiful and important.<\/p>\n Jim Simons proved that markets aren\u2019t perfectly efficient. They\u2019re discoverable. Patterns exist. Data matters more than narrative.<\/p>\n But I think the deeper lesson is this: success comes from finding approaches that match your strengths, building teams smarter than you, and having the conviction to ignore conventional wisdom when you\u2019ve got a better answer.<\/p>\n Simons left academia when colleagues thought he was crazy. He hired scientists instead of traders when Wall Street thought he was naive. He charged 5-and-44 when the standard was 2-and-20. He gave away billions to basic research when others were buying yachts and sports teams.<\/p>\n Every decision was contrarian. Every decision was right.<\/p>\n I won\u2019t achieve 66% annual returns. Nobody will. Medallion\u2019s performance was a unique combination of brilliant people, proprietary data, decades of model refinement, and probably some luck in finding anomalies before they got arbitraged away.<\/p>\n But I can ask better questions. I can focus on process over prediction. I can work with people smarter than me. I can stick with approaches that fit my temperament.<\/p>\n That\u2019s what Jim Simons taught me about investing. Not the math\u2014I\u2019ll never understand the Baum-Welch algorithm or hidden Markov models. The philosophy: be rigorous, be systematic, be humble enough to learn from data, and be bold enough to ignore everyone when you know you\u2019re right.<\/p>\n Sixty-six percent per year for 34 years. That\u2019s the exclamation point on one hell of an argument.<\/p>\nThe best investment is in people smarter than you<\/h2>\n<\/p>\n
Pattern recognition beats prediction<\/h2>\n<\/p>\n
Beautiful solutions often come from unexpected places<\/h2>\n<\/p>\n
Know when to walk away (and when to come back)<\/h2>\n<\/p>\n
What he did with the money matters more<\/h2>\n<\/p>\n
What I\u2019ll remember<\/h2>\n<\/p>\n