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Denis Dancanet on Cubist

December 2023

Denis Dancanet

Cubist Systematic Strategies President Denis Dancanet sat down with us to discuss the growth of the business and central team buildout, his journey to Point72, and what he looks for in new employees. Here are highlights from the discussion.

Can you share a little about what you do at Point72 and provide an overview of Cubist?

I’m President of Cubist Systematic Strategies – the quant business within Point72. We are approaching 30 years in business and have roughly 500 people with over 40 PM teams and are continuing to grow. Cubist has historically been—and we believe still is—a platform for top investment professionals to come and apply their trade. The pod business is comprised of multiple portfolio management teams pursuing independent research and agendas. We give them a lot of support and invest in a range of asset classes and time horizons.

In addition to the traditional pod model, since I joined in 2020 we’ve built out a central quant team that comes up with its own strategies and trades independently from the other pods and the rest of the firm. The difference compared to the pod model is that it’s much more collaborative and there’s open communication between the various teams across strategies.

The goal is to create a hybrid business structure combining the PM platform and the central team, which we feel would be unique for our industry.


How would you describe the culture of the firm?

I spent most of my career at a small quant hedge fund with a very distinct culture and I was curious what a larger place would be like, and I’m happy to say my experience has been fantastic. The people are smart, they’re focused, and they’re driven in a very commercial way. It’s not like, “Hey, let’s do research for the sake of doing research,” but they tend to be focused on the goal and what we’re trying to achieve.

Another feature that’s been very interesting to me, especially given the size of our firm, is its very entrepreneurial culture. I think that comes from Steve because he is restless. He’s always asking to look at new developments and asking to try new things. And I feel that’s rare for a firm of this size.



What is the Quant Academy and how is it different from the existing Point72 Academy?

In 2021, we launched the Cubist Quant Academy, a year-long rotational program that provides broad training to quantitative researchers and developers to help them build a foundation for long-term success in systematic investing. Unlike the Point72 Academy which is geared toward college students and early-career professionals, we are looking for PhDs and MS graduates in sciences and engineering.

Prior to the Quant Academy, our PMs were hesitant to hire straight from academia because they have relatively small teams and don’t have time to teach somebody the ropes. But so far, we’ve found that if you do these rotations for a year, you will be ready to contribute on either the PM or central teams. Each year our PMs are excited for the new class and understand it will increase their chances of hiring very good people.

We are now in our third year of the Quant Academy, and currently have six members, which is our largest class to date.


Tell us about your path to Cubist.  

I grew up in communist Eastern Europe when there was no notion of entrepreneurship, so when I came to this country, I didn’t have plans to pursue business.

When I was an undergrad, I was only interested in academics. I studied computer science, mathematics, and philosophy. Then I went to Carnegie Mellon for a PhD in Computer Science with the idea that I’ll become an academic. But when I went to these academic conferences, I saw people who had been doing this for 30, 40 years, and they didn’t look happy. That led me to wonder what areas I should be exploring. I read about trading and this notion of the world being like a giant chess board that you interpret and I thought, “Wow, that’s pretty cool.”

I came to Wall Street in ’97, without any knowledge of finance or friends who could advise me. I joined IT at Morgan Stanley, but I ended up on the Treasury desk, supporting bond traders and writing software for them. I didn’t think it was that interesting in terms of scratching my quantitative itch, but then I heard about this place that was actually looking at the world as a chess board by building models and trading systematically. That was PDT, which was a small prop trading group at the time. I joined PDT in 1999, and I ended up spending 17 years there.


What skills do you look for when hiring people?

I would say I tend to weigh heavily in favor of general intelligence rather than specialization in one area because the world changes and needs change. I think if somebody’s smart, they can adapt. Obviously for a quantitative business you need to have some basic level of math knowledge and computer science skills. And from there you need to be able to read scientific papers and recognize why an algorithm sounds nice in theory and identify how it needs to be modified to apply it in practice.

With computer science, you don’t need to be a wizard to do research, but the better you are at it, the more you can iterate through ideas quickly and the more you can create some structure for yourself that allows you to be more efficient.

And most importantly, I look for creativity. Again, the world changes constantly, and you have to come up with new ideas. The models you build don’t just keep working together. Whatever inefficiencies you find in the market, other people are finding them too, so you have to adapt the model to stay ahead.


What is it about systematic trading that keeps you motivated?

It’s this notion that this is essentially an unsolvable problem. You build these models and have a bunch of forecasts to predict a return, and then you combine a lot of these forecasts into a portfolio. So you build this thing and you set it loose in the world and it’s very exciting. But does it work? Does it not work? Why does it not work? Why does it work? Maybe it’s working better than you expected. What’s going on?

It’s just doing science essentially, but a very imperfect science. And you can’t get to 100% accuracy, but if you get to a little over 50%, that’s great. But that’s not enough – you need to know how to manage risk and what happens when it’s not going well. There are a lot of other things that must happen to make it work. It’s a fun game that changes all the time. There’s no set of rules written in stone.

The fact that you work with amazing people helps too.

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