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벌써 오래전으로 느껴집니다. 2014년 서울 수학자 대회입니다. 이 때만큼 ‘수학’이라는 단어가 신문을 요란하게 장식한 때가 없었습니다. 투자와 관련한 업무를 하는 분이면 이름을 들어본 적이 있을 ‘르네상스 테크놀로지의 제임스 사이먼스’도 이 때 방한을 하였습니다. 수학자 대회 기간중 James H. Simons – My Life in Mathematics을 주제로 대중강연을 하였습니다.
수학자대회 조직위원장인 박형주 교수가 진행한 인터뷰를 보면 이런 대목이 나옵니다.
“한마디로 ‘협력과 공유’라는 팀 문화였다고 생각한다. 우리 직원들은 주로 수학 물리학 천문학 전산학 통계학 같은 자연과학자들과 공학 전공자들인데 일주일에 한 번 모두 모여 서로가 하는 일을 모두 공유했다(실제로 르네상스 테크놀로지가 금융 경제 경영 전공자들을 선호하지 않는다는 것은 월가에서 유명한 이야기다). 어떤 팀이 성공 모델을 만들면 바로 매매 시스템으로 적용해 누구나 사용할 수 있도록 했다. 연봉도 당장 한 해 실적을 바탕으로 주지 않는다. 몇 년에 걸쳐 한 일을 종합 판단하고 다른 사람의 성공의 일부를 나눌 수 있도록 회사 이익 중 일부를 지급한다. 그래야 서로가 잘될 수 있도록 협력하기 때문이다. 또 컴퓨터의 지시를 절대 무시하지 않는다. 개개인의 의견은 서로 다를 수 있기 때문에 철저히 데이터를 바탕으로 한 수학적인 분석만을 토대로 의사결정을 한다.”
[초대석] 수학자에서 세계 최고 펀드매니저로 변신한 제임스 사이먼스중에서
2010년 은퇴를 한 이후 대중 강연을 주로 합니다. 그 중 Numberphile을 통하여 소개된 Jim Simons Transcript: Quantitative Finance and Building a Firm을 소개합니다. Numberphile은 수학과 관련한 동영상을 전문으로 제작하는 공인단체입니다
위의 인터뷰를 영어로 옮겨놓은 글입니다.
Simons: My father had made a little bit of money, and I had the opportunity to try investing it. And that was interesting. And I thought, you know, I’m going to try another career altogether, and so I went into the money management business, so to speak.
Interviewer: So you started with some of your dad’s money and that got you a taste for, an interest in it?
Simons: Yes, some family money, and then some other people put up some money. And I did that. No models. No models for the first two years.
Interviewer: So what were you doing then? You were just using cunning and, you know, just like normal people do?
Simons: Like normal people do. And I brought in a couple people to work with me, and we were extremely successful. I think it was just plain good luck, but nonetheless we were very successful. But I could see this was a very gut-wrenching business. You know you come in one morning, you think you’re a genius. The markets are for you. We were trading currencies and commodities and financial instruments and so on, not stocks, but those kinds of things. And the next morning you come in, you feel like a jerk. The markets are against you. It was very gut-wrenching. And in looking at the patterns of prices, I could see that there was something you could study here, that there were maybe some ways to predict prices, mathematically and statistically. And I started working on that, and then brought in some other people. And gradually, we built models. And the models got better and better and finally the models replaced the fundamental stuff. So it took awhile.
Interviewer: I would have thought with your background as a mathematician, this would have almost occurred to you immediately. Like you would have straightaway seen this. What was the two year delay?
Simons: Well two things. I saw it pretty early, but, and I brought in a guy from the code cracking place. And he was, I thought, together we’ll start building models. That was fairly early. But it wasn’t right away. But he got more interested in the fundamental stuff. And he says, “the models aren’t going to be very strong,” and so on and so forth. So we didn’t get very far. But I knew there were models to be made. Then I brought in another mathematician, and a couple more, and a better computer guy. And then we started making models which really worked. But you know, the general, there’s something called the efficient market theory which says that there’s nothing in the data, let’s say price data, which will indicate anything about the future, because the price is sort of always right, the price is always right in some sense. But that’s just not true. So there are anomalies in the data. Even in the price history data. For one thing, commodities especially, used to trend. Not dramatically trend, but trend. So if you could get the trend right, you’d bet on the trend. And you’d make money more often than you wouldn’t, whether it was going down or going up. That was an anomaly in the data. But gradually we found more and more and more and more anomalies. None of them is so overwhelming that you’re going to clean up on a particular anomaly. Because if they were, other people would have seen them, so they have to be subtle things. And you put together a collection of these subtle anomalies and you begin to get something that will predict pretty well.
Interviewer: How elaborate are these things? Because in my head I’m imagining, you know, some equation. Like Pythagoras’s equation. You put a few numbers in and something spits out. But are these giant beasts of equations and algorithms, or are they simple things?
Simons: Well the system as it is today is extraordinarily elaborate. But it’s not a whole lot of, you know it’s, it’s what’s called machine learning. So you find things that are predictive. You might guess, oh, such and such should be predictive, might be predictive, and you test it out on the computer and maybe it is and maybe it isn’t. You test it out on long-term historical data, and price data, and other things. And then you add to the system, this, if it works and if it doesn’t you throw it out. So there aren’t elaborate equations, at least not for the prediction part, but the prediction part is not the only part. You have to know what you’re costs are when you trade. You’re going to move the market when you trade. Now the average person can buy 200 shares of something, and he’s not going to move the market at all because he’s too small. But if you want to buy 200,000 shares you’re going to push the price. How much are you going to push the price? How are you going to, you know, are you going to push it so far that you can’t make any money because you’ve distorted things so much? So you have to understand costs, and that’s something that’s important. And then you have to understand how to minimize the volatility of the whole, of the whole assembly of positions that you have, and be, so you have to do that. That last part takes some fairly sophisticated applied mathematics, not earth-shattering, but fairly sophisticated.
Interviewer: What discipline of mathematics, or disciplines — is it multi-disciplinary? Or are we talking…
Simons: It’s mostly statistics. It’s mostly statistics and some probability theory. And, but, I can’t get into what things we do use, and what things we don’t use. We reach for different things that come, that might be effective. So we’re very universal, we don’t have any, but it’s a big computer model. For one thing there is a capacity to the major model. It can manage a certain amount of money, which is rather large. But it can’t manage an enormous amount of money because you’re pushing, you’re going to end up pushing the market around too much, so it was kind of a sweet spot as to how much it’s reasonable to manage. Therefore it would never grow into some behemoth, which would, you know, take everybody out and you’d be the only player. I mean, well of course, if you were the only player there would be no one to play against. There are limitations, at least the way we see it. But we keep improving it. We have about 100 PhDs working for the firm.
Interviewer: That’s what I mean, I mean how did you get to that point? Did you start to think, we need this we need that. What did..?
Simons: We just hired smart people. My algorithm has always been, you get smart people together. You give them a lot of freedom. Create an atmosphere where everyone talks to everyone else. They’re not hiding in a corner with their own little thing. They talk to everybody else. And you provide the best infrastructure, the best computers and so on that people can work with. And make everyone partners. So that was the model that we used in Renaissance. So we would bring in smart folks and they didn’t know anything about finance, but they learned.
Jim Simons Transcript: Quantitative Finance and Building a Firm중에서
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또다른 대중 강연은 MIT에서 Mathematics, Common Sense, and Good Luck: My Life and Careers입니다.
Secret Sauce: Jim Simons on Life and Building a Business은 강연의 일부를 글로 옮긴 것입니다.
앞서 박형주교수가 인터뷰한 부분과 겹치는 내용이 나옵니다. 르네상스 테크놀로지의 비밀을 소개한 부분입니다.
People always ask me, “well, what’s the secret?”
The real secret sauce is that we start with great scientists. We start with first class people who’ve done first class work, or we believe…will do first class work.
The second thing is we provide people with a great infrastructure. And I’ve had people come to us from all over, and when they come to work they say…it’s more easy to get to work here than any place else.,,give people good infrastructure.
The most important thing we do is have an open atmosphere. My belief is that the best way to conduct research is on a broad scale…make sure that as much as possible they everybody knows what everybody else is doing, at least as quickly as possible… The sooner the better, start talking to other people about what you’re doing, because that’s what will stimulate things the fastest. No compartmentization …everybody meets once a week. All the researchers meet. Any new idea gets brought up, discussed, vetted, and hopefully put into production…It’s an open atmosphere…and people get paid based on the overall profits…so everyone has an interest in everyone else’s success.
