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JS: In the old days -- this is kind of a graph from the old days, commodities or currencies had a tendency to trend. Not necessarily the very light trend you see here, but trending in periods. And if you decided, OK, I'm going to predict today, by the average move in the past 20 days -- maybe that would be a good prediction, and I'd make some money. And in fact, years ago, such a system would work -- not beautifully, but it would work. You'd make money, you'd lose money, you'd make money. But this is a year's worth of days, and you'd make a little money during that period. It's a very vestigial system. 10:55 CA: So you would test a bunch of lengths of trends in time and see whether, for example, a 10-day trend or a 15-day trend was predictive of what happened next. 11:05 JS: Sure, you would try all those things and see what worked best. Trend-following would have been great in the '60s, and it was sort of OK in the '70s. By the '80s, it wasn't. 11:19 CA: Because everyone could see that. So, how did you stay ahead of the pack? 11:26 JS: We stayed ahead of the pack by finding other approaches -- shorter-term approaches to some extent. The real thing was to gather a tremendous amount of data -- and we had to get it by hand in the early days. We went down to the Federal Reserve and copied interest rate histories and stuff like that, because it didn't exist on computers. We got a lot of data. And very smart people -- that was the key. I didn't really know how to hire people to do fundamental trading. I had hired a few -- some made money, some didn't make money. I couldn't make a business out of that. But I did know how to hire scientists, because I have some taste in that department. So, that's what we did. And gradually these models got better and better, and better and better. 12:17 CA: You're credited with doing something remarkable at Renaissance, which is building this culture, this group of people, who weren't just hired guns who could be lured away by money. Their motivation was doing exciting mathematics and science. 12:30 JS: Well, I'd hoped that might be true. But some of it was money. 12:36 CA: They made a lot of money. 12:38 JS: I can't say that no one came because of the money. I think a lot of them came because of the money. But they also came because it would be fun. 12:45 CA: What role did machine learning play in all this? 12:47 JS: In a certain sense, what we did was machine learning. You look at a lot of data, and you try to simulate different predictive schemes, until you get better and better at it. It doesn't necessarily feed back on itself the way we did things. But it worked. 13:07 CA: So these different predictive schemes can be really quite wild and unexpected. I mean, you looked at everything, right? You looked at the weather, length of dresses, political opinion. 13:16 JS: Yes, length of dresses we didn't try. 13:19 CA: What sort of things? 13:21 JS: Well, everything. Everything is grist for the mill -- except hem lengths. Weather, annual reports, quarterly reports, historic data itself, volumes, you name it. Whatever there is. We take in terabytes of data a day. And store it away and massage it and get it ready for analysis. You're looking for anomalies. You're looking for -- like you said, the efficient market hypothesis is not correct. 13:51 CA: But any one anomaly might be just a random thing. So, is the secret here to just look at multiple strange anomalies, and see when they align? 14:00 JS: Any one anomaly might be a random thing; however, if you have enough data you can tell that it's not. You can see an anomaly that's persistent for a sufficiently long time -- the probability of it being random is not high. But these things fade after a while; anomalies can get washed out. So you have to keep on top of the business. 14:23 CA: A lot of people look at the hedge fund industry now and are sort of ... shocked by it, by how much wealth is created there, and how much talent is going into it. Do you have any worries about that industry, and perhaps the financial industry in general? Kind of being on a runaway train that's -- I don't know -- helping increase inequality? How would you champion what's happening in the hedge fund industry? 14:53 JS: I think in the last three or four years, hedge funds have not done especially well. We've done dandy, but the hedge fund industry as a whole has not done so wonderfully. The stock market has been on a roll, going up as everybody knows, and price-earnings ratios have grown. So an awful lot of the wealth that's been created in the last -- let's say, five or six years -- has not been created by hedge funds. People would ask me, "What's a hedge fund?" And I'd say, "One and 20." Which means -- now it's two and 20 -- it's two percent fixed fee and 20 percent of profits. Hedge funds are all different kinds of creatures. 15:34 CA: Rumor has it you charge slightly higher fees than that. 15:38 JS: We charged the highest fees in the world at one time. Five and 44, that's what we charge. 15:44 CA: Five and 44. So five percent flat, 44 percent of upside. You still made your investors spectacular amounts of money. 15:52 JS: We made good returns, yes. People got very mad: "How can you charge such high fees?" I said, "OK, you can withdraw." But "How can I get more?" was what people were -- (Laughter) -- But at a certain point, as I think I told you, we bought out all the investors because there's a capacity to the fund. 16:10 CA: But should we worry about the hedge fund industry attracting too much of the world's great mathematical and other talent to work on that, as opposed to the many other problems in the world? 16:21 JS: Well, it's not just mathematical. We hire astronomers and physicists and things like that. I don't think we should worry about it too much. It's still a pretty small industry. And in fact, bringing science into the investing world has improved that world. It's reduced volatility. It's increased liquidity. Spreads are narrower because people are trading that kind of stuff. So I'm not too worried about Einstein going off and starting a hedge fund." |
Jim Simons: A rare interview with the mathematician who cracked Wall Street | TED
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