The Math That Explains How Multi-Strategy Hedge Funds Make Money

SPEAKER_1 (0:03)

Bloomberg Audio Studios, podcasts, radio, news.

Tracy Alloway (0:20)

Hello and welcome to another episode of the Odd Lots Podcast. I'm Tracy Alloway.

Joe Weisenthal (0:24)

And I'm Joe Weisenthal.

Tracy Alloway (0:25)

Joe, we're back on the Multistrat beat.

Joe Weisenthal (0:28)

I love this beat. I think it's really interesting. There's a lot we've learned, but there's a lot we haven't learned. I love this beat. If you said we're gonna just do 10 episodes about this, I'd be like, yeah, that's fine.

Tracy Alloway (0:39)

Well, I look forward to part 678 in our ongoing attempt to understand Multistrat hedge funds.

But we've been sort of learning as we go along, and there are a bunch of questions that I still have. One of them is there seem to be a lot of different opinions and variations of pod shops on how exactly they can be designed.

Joe Weisenthal (1:04)

Right. So there's different structures that I understand. There's different compensation structures. There's different degrees to which the different pods, so to speak, coordinate with each other. There's different degrees to which they centralize ideas and research. So I get that. There are still some big questions in my mind. And I'll just say one of the big ones right off the bat, which is if you have a bunch of teams doing a bunch of different strategies and trading a bunch of things, why are the returns good instead of average? Because in my intuition, if you have a bunch of teams, like, okay, you're diversifying alpha across a bunch of things, but great. But then you have a bunch. My gut intuition will be like, you don't get great returns, you get average returns. And yet many of them put up really impressive returns year after year after year. And I don't think I totally have a grasp of why.

Tracy Alloway (1:57)

Well, yes. And this is a question that I have, which is eventually the pod shop, some of them are getting very, very big. And so if you have a thousand pods working under your roof, that's a bit extreme. But at some point, aren't you just sort of replicating the market and that alpha opportunity, as you just described, kind of goes away? Well, on that note, I am happy to say we have the perfect guest to discuss all of this. So these sort of variations behind multi-strat funds and also the math that actually powers it. We're going to be speaking with Dan Morillo. He is the co-founder of Freestone Grove Partners and also ex-Citadel. So again, the perfect person to be speaking to. Dan, welcome to the show.

Daniel Morillo (2:41)

Thank you. Thank you for having me.

Tracy Alloway (2:43)

I guess my first question is, why are we talking to you?

Joe Weisenthal (2:45)

Yeah, why are we talking to you?

Daniel Morillo (2:47)

Well, you're probably in a better position to answer than me, but I guess I'll tell you my background and hopefully that helps a little bit. So I've been about 25 years now dating myself in the buy side, on the hedge fund buy side in particular. And I grew up sort of on the quantitative side of the world. I thought I was going to be a professor. And then I realized that life is more exciting on the industry side of things. And I've done a wide range of roles in the quote quant side of the world. So everything from at some point I was the lead of the global longshore business at Barclays Global Investors before Blackhawk acquired them. At Blackhawk I stuck around for a bit. I at some point ran their research group for iShares. I also was one of the founders of the Model Solutions business there. As you said, I was at Citadel where I had responsibility for the Equity Quantitative Research Group that did a lot of the stuff that you guys have talked about, the risk model stuff and the hedging stuff and all of these sorts of things. I also had responsibility for the Center Book where a lot of that central stuff that you also have talked about happens. And then most recently have co-founded a hedge fund that also does the Pod Long-Short thing. So I'd like to think I have some expertise, but I guess you tell me after you ask me all these questions.

Joe Weisenthal (3:53)

I have a really rudimentary question. What does the word quant mean in finance?

Daniel Morillo (3:58)

Actually, so this is a good point, right? I think it can mean lots of things. From my point of view, the thing that has always been attracted to me about the quant side of things is the idea that you can be disciplined in how you make decisions, right? You can be quantitative in the purely mathematical sense, like you ran some code and you did lots of numbers, while still not actually applying that much judgment. You can also actually be quite disciplined and systematic without using a lot of quant tools, right? I think the right way of doing quant is where you also mix these two together, right? When you have the ability to bring in the judgment that comes from understanding what the humans in the market are doing, but to do so in a way that is repeatable and disciplined. That tends to require quantitative modeling tools, whether that's risk models, forecasting, evaluation, attribution, all of these sorts of things, right? In fact, that's the sort of thing that attracted me, that is sort of a common threat through all of these jobs that I mentioned that I've had, is the idea that you can do this sort of systematic modeling work, not just with the numbers themselves, but also with the humans that participate in the market, right? They are also subject to analysis, right? Whether you think about sentiment measurement or the sorts of questions you guys have asked in this podcast, right? What is the right way to organize a team? How many teams should you have? How should you pay them? What fees should you charge with those? These are all subject to analysis, right? So I like the idea that you can do the quant thing on human behavior, right?