Renaissance Technologies’ Medallion fund, a hedge fund that deploys systematic trading using mathematical models, is known to have the best investing track record in history. In 2019, Gregory Zuckerman’s book titled “The Man Who Solved the Market” about Jim Simons, the legendary founder of RenTech, said this about the fund’s record: “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.” However, the world still doesn’t know the secret behind its returns. Whilst the current CEO Peter Brown doesn’t give out any of their IP in this interview with Goldman Sachs, he does share the principles that drive the firm and even more fascinatingly, lets us in some very interesting aspects of Simons’ personality and Brown’s own quirks.
What according to Brown was the principles that drove the firm:
“First. Science. The company was founded by scientists. It’s owned by scientists. It’s run by scientists. We employ scientists. Guess what, we take a scientific approach to investing and treat the entire problem as a giant problem in mathematics.
Second. Collaboration. Science is best done through collaboration. If you go to a physics department, it would be absurd to imagine that the scientist in one office doesn’t speak to the scientist in the office next door about what he or she is working on. So, we strongly encourage collaboration between our scientists. For example, we encourage people to work in teams. We constantly change those teams up so that people get to know others within the firm. We pay everyone from the same pot instead of paying different groups in accordance with how much money they’ve made for us and so forth.
Third. Infrastructure. We want our scientists to be as productive as possible. And that means providing them with the best infrastructure money can buy…
…fourth principle is no interference. We don’t impose our own judgment on how the markets behave. Now, there’s a danger that comes along with success. To avoid this, we try to remember that we know how to build large mathematical models and that’s all we know. We don’t know any economics. We don’t have any insights in the markets. We just don’t interfere with our trading systems. Yes, of course there are a few occasions where something’s going on in the world and so we’ll cut back because we think the model doesn’t appropriately appreciate the risk of what’s going on. But those occasions are pretty rare.
And finally, and most importantly, the last principle is time. We’ve been doing this for a very long time. For me, this is my 30th year with the firm. And Jim and others were doing it for a decade before I arrived. This is really important because the markets are complicated and there are a lot of details one has to get straight in order to trade profitably. If you don’t get those details straight, the transaction costs will just eat you alive. So, time and experience really matters.”
Brown shares a few anecdotes that throw light on Simons’ personality.
About the time when Brown lost money during the dot com bubble:
“I went into Jim’s office and told him that I’d screwed up in not appreciating the risk we were taking and said that if he wanted me to resign, I would resign. But he responded, “Peter, quite the opposite. Now that you’ve been through such a stressful losing period, you’re far more valuable to me and to the firm than you were before.” Now, that response really tells you something about Jim Simons”
About the time when Brown persuaded Simons not to cut back on risk during the quant quake of 2007:
“…what we learned from that was to always make sure we have enough on reserve to just hang on. Later, when Jim was about to retire, I reminded him of this period and asked if he was concerned that I was going to be so aggressive that I was going to blow the place up. But Jim responded that the only reason I was so aggressive was because I knew he was determined to reduce risk, another example of Jim’s insight into human nature.”
About the time during the Lehmann crisis when they were concerned about counterparty risk:
“….we spent a lot of time with those counterparties and examined their CDS rates and so forth. I remember at one point, two senior executives from some firm we did business with came into our New York City office to meet with us. They assured us that the funds we had in our margin account were safe with them. And I was inclined to believe them. Why not? But after the meeting, Jim said, “Peter, they wouldn’t have come to our office. They wouldn’t have requested the meeting unless they were in real trouble. It’s time to get out.” So, we did. And Jim was right because shortly thereafter, that firm just disappeared.”
Prior to joining Renaissance, Peter Brown was at IBM and is known to have seeded the idea and given the name for Deep Blue, the chess playing computer that eventually beat Gary Kasparov. At a time AI is threatening to replace humans, Brown shares an anecdote involving the great investor Seth Klarman, about technology in investing. Brown was set to speak at Harvard Business School right after Klarman:
“At the end of his talk, someone asked him [Klarman] his thoughts on quantitative investing. I suppose it was a set up for my talk. I don’t know. And I carefully noted his answer which was, “To do what I do takes a certain amount of creativity and finesse that a computer will never have.” And all those Harvard Business School MBAs seemed to really like that response. So, when it was my time to speak, right after him, I began by pointing out that after defeating Deep Blue in the first match, Kasparov was elated and gave a press conference at which he said, “To play chess at my level takes a certain amount of creativity and finesse that a computer will never have.” I then went on to point out that two years later we crushed him. Now, I’m not sure that’s how things will evolve. But whether it’s speech recognition, machine translation, or building large language models, or chess, or making investment decisions, I continue to love the process of showing that human intelligence, intuition, creativity, and finesse are nothing more than computation.”
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