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

Episode Date: October 7, 2024

Multi-strategy hedge funds are still all the rage on Wall Street, but what does it actually mean to be a pod shop and how are they being set up? On this episode, we speak with Dan Morillo, co-founder of Freestone Grove Partners and formerly a partner and head of equity quantitative research at Citadel (one of the most successful multi-strats out there.) While lots of people tend to talk about multi-strategy hedge funds as one big blob, he argues that there are important differences in their business models. We talk about how he identifies top portfolio managers, managing crowding risk, and the math behind compensation, scale and returns.Previously:How Hedge Funds Discover the Next Superstar Trader How to Succeed at Multi-Strategy Hedge FundsOnly Bloomberg.com subscribers can get the Odd Lots newsletter in their inbox each week, plus unlimited access to the site and app. Subscribe at bloomberg.com/subscriptions/oddlotsSee omnystudio.com/listener for privacy information.

Thanks for listening to Odd Lots. Follow the show on Amazon Music for more future episodes or just ask, Alexa, play the Odd Lots podcast on Amazon Music. Bloomberg Audio Studios. Podcasts, Radio News. Hello and welcome to another episode of the Odd Lots podcast. I'm Tracy Alloway. And I'm Joe Wisenthall. Joe, we're back on the multi-strap beat. 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 going to just do 10 episodes about this, I'd be like, yeah, that's fine. Well, I look forward to part 678 in our ongoing attempt to understand multistrat hedge funds.

But, you know, 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, right, on how exactly they can be designed. Right. So there's different sort of 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 like centralize ideas and research. So like I get that. There's 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 would 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. 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 multistrat funds and also the math that actually powers it. We're going to be speaking with Dan Marillo. 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. Thank you.

Thank you for having me. I guess my first question is, why are we talking to you? Yeah, why are we talking to you? 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 us a little bit. So I've been about 25 years now, dating myself in the by-side, on the hedge fund by-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, you know, at some point I was the lead of the global longshore business at Berkeley's global investors before BlackRock acquired them. At BlackRock, I stuck around for a bit. I at some point ran their research group for I shares. 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 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 founded, 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'd tell me after you ask me all these questions. I have a really rudimentary question. What does the word quant mean in finance? 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've run some code and you've got 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 discipline, and that tends to require quantitative modeling tools, whether that's risk models, forecasting, evaluation, attribution, all of these sorts of things, right? And in fact, that's the sort of thing that attracted me, that is sort of a, I guess, a common threat through all of these jobs that I mentioned that I've had, is the idea that you can do this, this sort of systematic modeling work, not just with the numbers themselves, but also with the humans that participated in the market, 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?

You know, how many teams should you have? How should you pay them? You know, 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? Oh, this is exactly what I wanted to ask you about, actually. So if you go to Freestone's website, you can see that there are two partners on the front page, and you are the quantitative. one, and you do have a large number of quant researchers. What's the value added by those

quants to a fundamental equities fund? Yeah, I think the way you want to think about it is that the insight that is associated with understanding the mechanics of a firm, which is the fundamental, in this case for equities, you know, the job of the firm analyst is to understand what drives revenue earnings, margins, et cetera, and in particular, what is likely to be surprising about those next time they announced earnings or over the next couple of quarters, right? The way you make money is you have a view that is different from that of the market, and people come to agree with you, right? That's sort of success, right? In that effort, whether it's modeling the firms, whether it's understanding what about that surprise is

really surprising about the firm versus something that's happening in the broader market, the data that comes into all of this, right? Alternative data stuff, all of that requires a huge amount of investment on the technology side, the analytics side, the forecasting side, right? It's no longer the case that you can be a smart guy, reading 10 Q's and 10 K's, as might have been the case 25 years ago and just kind of see what the surprise is going to be. It requires a significant investment in being the most sophisticated person at doing that job. And that's not a thing you can do without all of that investment on the quantitative tooling, right? There's also all the behavioral stuff, right?

Humans have the ability to really get into the detail of what the firm is doing, right? Many of the people who are very good at this are people who have been covering the same firm literally for a decade, right? they know their CFO, the CEO, the product, they visited the factories. And so they have this ability to pre-up on really subtle patterns, but they're also human, right? And humans come with biases, right? You project your own patterns of sort of your view of the world into what's happening on the ground. And so it is also helpful to think about how do you become as discipline as possible in that process, right?

So you think about risk models, attribution questions. How can you tell luck versus skill, right? Most humans, if you do well, you tend to think it's all about you, and if you do poorly well, it wasn't my fault, my fault, right? And so these process of how do you make sure that humans are as disciplined as possible, again, requires huge investment in that quantitative and analytical capability. So that's kind of what you bring to the table, right? So we'll get into how you go about measuring the skill of your portfolio managers and breaking all these things down, and we'll talk about that a lot. When you founded Freestone Grove, you and your co-founder, Todd Barker, you must think there's an opportunity there, right? You must think there's like some opportunity out there to make money, to have a fund that's different than something that already exists on the market, that you bring something to the table, that you could structure a company in some way that's advantageous.

What is the sort of theory or thesis behind Freestone Grove such that you wanted to build something new? Yeah, so you're correct. think we can compete at the highest level of the industry, right? Otherwise, we wouldn't have started this thing. The way in which we think we can do this isn't some new magic thing, right? Like, oh, only we can do X, Y, and Z, right? A lot of how we, and this is what we tell our clients, is that in having spent all this time looking at what works and what does in the space, I call it the multi-strategy or multi-PM space, we have a view that you can sort of be optimal around key business decisions, right?

The number of analysts and PMS that you have in your platform, the way you organize them, the way you think about the incentives of how they're compensative, the right mix of quantitative versus fundamental, in a way that sort of is the best of what we've seen around, right? So it's not so much, oh, there's this one thing that is massively different about us, and instead lots of little things that we think you can optimize in a way that many of the other platforms, for various reasons,

haven't gotten to, particularly with the advent of a lot of new ones, right, where you end up with business design that we happen to think is not nearly as optimals as it could be, right? So it's sort of optimized the business as sort of the pitch and then run each piece the best you can. Does that make sense? Yeah. Well, on this note, so there's something that kept coming up when we were preparing for this podcast, but people keep talking about Dan's math. Can you put your professorial hat on and explain to us what exactly is Dan's math and how does it come into play when it comes to design? and optimizing the size of your firm.

Yeah, so first, in my defense, I did not come up with that. I believe it was actually somebody from Bloomberg that came up with that after some interview that they did with us early on. But yeah, to your question, look, the point is that many of the things that you think about, which range from how many people should you have a platform, what sort of risk model should you run, what risks should you take, how should you do capital allocation. These are things that are subject to systematic analysis, right? And so this idea of, quote, the math, is that many of these decisions, you don't have

have to wave your hands around, right? There's sort of reasonably clear answers about them, right? There's a couple of ones that, and we can chase down whichever ones of this you like, but one of the ones that in my mind is the most important is there's been this sort of press in the industry with this idea that more is always better, right? You want to have more portfolio, more analysts, more assets, like that scales this sort of underlying strength, right? It actually goes back to your question around how come do you get good results out of lots

of people, right? And the answer is, your nutrition is actually not wrong, right? There comes a point where adding more people actually doesn't make any difference, right? And so if you just allow me two minutes to set up a little of example, right? So the way this business works is you're hiring individual risk takers, let's call them analysts, right? So there's some potential pool of people you can hire. And assuming you have good hiring practices, you expect to hire people who have some mean performance. Think of that as a sharp ratio. Let's say that sharp ratio is 0.75, right? So A sharp ratio of 0.75 means that if you take a risk of a dollar of risk, you expect to generate 75 cents of return per that amount of risk that you deployed, right?

And so you want to think of performance in sharp ratio space, right? Because in different spaces, people have different risk, right? There's, you know, biotech names are riskier than, say, bank names, and so you want to adjust for that. So typically you want to think in sharp ratio space. So you hire folks, you expect to have some mean distribution, some mean outcome, right? So I hire a person. I don't know what their sharp ratio is going.

going to be. I hope it's good. And on average, I get people who are like, say, 0.75, right? Some people are going to be better than that. Some people are going to be worse than that. Maybe I end up needing to fire them, right? But yeah, I get some distribution of them, right? And then you give them capital and they run capital over the time, right? And so the magic of diversification is that you get a higher sharp ratio as you add people, right? If the correlation was exactly zero, then the more people you add, essentially, the more your sharp ratio increases. It increases with their square root of n, essentially. If there's correlation, however, there's like a maximum limit of how much your aggregator sharp ratio can be, right? So let's take a simple

example. Let's say these 0.75 people that you have on average. Let's say they're correlated by 10%, which most people will tell you that sounds kind of low, not a lot of correlation. Then there's a maximum limit of what your sharp ratio can be, about 2.3, even if you have an infinite number of people. So your intuition that if you add lots and lots of people, you add some, get to some, average return is correct. It's just what is the scale of that average return, right? And so if you add lots and lots of people, you get to that sort of maximum level. And the thing that really matters is a correlation, right? So it is incredibly hard to get zero correlation. Like, that just doesn't really happen, right? So just to be clear what we're talking about when you say

correlation, you hire one PM and they trade semiconductors. You hire another PM and they trade interest rates or maybe they trade banks or something like that. Yeah. But because things in the market are generally correlated, you can have these different people all around the world and implicitly, even though it looks like they have their own focus on the market, they might all implicitly be making money based on their read of the Fed or something like that. And thus, their returns are correlated and therefore, even if they're all really good at their jobs, that caps the amount of firm-wide sharp by virtue of the effect that they're not really adding diversification. That is exactly correct.

So, and it's as simple as if you were to observe somebody's return literally every day, right? And you observe the other persons return every day. You can just computer correlation, put it in Excel, computer correlation. And if that number is low, you get more juice out of adding more people. If that number is high, you get less juice. And just to make the point, it matters enormously. So in that example, that maximum is about 2.4.

If your mean person is 0.75, like with an infinite number, right, at correlation. of 10%. Let's say a correlation is actually 20%, right? You know, it's obviously more, but it's still low in the grand scheme of things. Then that maximum number is only 1.6, right? So a little bit of correlation has an enormous impact on how much you can deliver in the end, right? And more importantly, you get pretty close to that maximum without a lot of people, right? So in the example of 0.75 in a correlation of 10%, if I have 45 risk takers, think of them as analyst. Let's say I put them in teams of three, right? PM team made out of three risk takers. You know, there's not that many teams, right? It's 15 teams. That gives me about 95% of that

ultimate maximum. Right. So I don't need to have 100 teams to get to my maximum. In fact, there comes a point where it is actually more important, let's say you have a million dollars extra to spend on something. And there's something could be, I hire another person. But something could also be, hey, I might produce a better piece of software to help me manage that correlation, to teach people to think about whether their return is really independent of, you know, for example, interest rates as you highlighted them, that actually might be a significantly better investment than adding a team. Because if I reduce my correlation by a little bit, that actually gets me more juice than just adding people, right? And to look back to that

original question, what do we think might be different in terms of how you set up your business, is again that a lot of people have gone for scale, even though you don't have to, at least not for performance reasons, right? There comes a point where you just kind of have the right scale and you're better of investing in other things, right? The reason people have gone for scale is because they want to run more money. It's not because that gives you more performance, right? At least a part past a certain amount, right? And in fact, if you think about scale, scale comes with lots of other issues. It comes to complexity. You'll maybe end up with more management layers. You have to worry a lot more about, you know, offices and coordination and, you know,

management structure, et cetera. You might actually end up reducing your performance that that complexity costs money, right? And so one of the key things that we said are our clients, just as an example, is we look to cap our size so that we can run the right number of people at the minimum complexity of possible while still delivering pretty much that level of performance, right? The news doesn't stop on the weekends. Context changes constantly. And now Bloomberg is the place to stay on top of it all.

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What separates good leaders from transformational ones? I'm Jessica Chen, and in season two of Leading By Example, we'll sit down with executives like Grace Chen of Bertie Gray to find out. It's important to understand where you spike, but also really acknowledge where you don't and find people who can fill those gaps. Listen to leading by example, executives making an impact on the IHeart radio app, Apple Podcast, or wherever you get your podcasts. Why do hedge funds promise uncorrelated returns at all? Because it feels to me, as you just said, it's very hard to get correlation down to zero. But the pitch to investors is always, here are a bunch of uncorrelated returns that we can do over and over again. And then what you see repeatedly is that when there is a big event in the market, they all have drawdowns at the same time. So why do they keep pitching uncorrelated returns and why do investors keep putting money in them?

Okay, so there seems to be two questions then there, which is how come are they correlated even though they claim not to be? That's number one. And two is that why is that even a thing in the first place, right? So let me start with the second one. The reality is most correlation is driven by some common effect, right? You know, you've had guests here talking about risk models where you think about sort of common factors, right? And a key reason why, if you're an allocator, say you're a pension fund, you know, in university endowment, is that you get paid for taking risk, right? A lot of the allocation is into things that are risky and you expect to get paid for taking that risk, right?

That's sort of, in a sense, that's the function of a big endowment or a big pension fund, right? The thing is, most of the risks that pay you those returns, whether that's market as a whole, whether it's individual factors like momentum that you can buy separately, you know, interest rate risk, inflation risk, all of these things, you can allocate to those for like essentially like a tenth of a cent on the dollar, right? And so if you're going to make an allocation to something else, you don't want that allocation to be the same thing you already have at essentially no fees, right? So let's say you have a hedge fund who charges you, I don't know, 2 and 20, but that hedge fund has, you know, typically a beta of like, say, 50% on average, right? Then half of the money you're giving that hedge fund is beta that you could buy for essentially no fees, right?

And so the advantage of a hedge fund that is able to, in fact, deliver on quality rate risk is that now you can make cleaner allocation, right? You can say, okay, this is my market risk, this is my interest rate risk, this is my, you know, I don't know, housing, premium, whatever it is. however, you've sort of decided to do your allocation. And then there's a piece that boosts my returns because it is not correlated to those other things, right? And so it is the right objective, if you will, right, if you're an allocator, right? Then the question is whether people can actually execute in delivering that, you know, that outcome, right?

Which is a somewhat separate question. I want to get into how you hire people at Freestone Grove and why a talented PM would come to Freestone Grove from somewhere else and the compensation, etc. But before we get to that, I have to imagine there's certain, like, information asymmetry challenges. You probably have a limited visibility into not just a PM's returns, but exactly how they achieved those returns, whether they achieved those returns in a way that demonstrates their ability to actually extract alpha rather than ride the various betas that you're trying to extract out of them. I assume if you're starting a fund, do you think you're good at identifying the people who come to work for you. What information do you have to use? And when you're accumulating

PMs or analysts, what is the basic process for identifying skill before they show up on your door? That's a really good question. And obviously, it's partly a systematic process, but, you know, like with hiring for everything, it's a bit of an art too, right? Whether you're hiring a portfolio manager or, you know, quantitative research, there's always a bit of an art associated with it, right? I think the key objective that you should have is do you understand and via what mechanism do they deliver this skill that they claim to deliver, right? And so it's a good thing that you typically can't see, you know, a good tracker over returns because then you'd be tempted to base it on past returns, which is not a good idea.

In fact, it's a bad idea. We can talk about that separately. It forces you to think about, okay, if you claim that you can generate good returns via what mechanism do you do that, right? For a typical analyst, at least inequities, it tends to be some form of, I understand what the surprises and fundamentals are going to be, right? I can tell that this firm is going to announce a billion dollars worth of revenue, whereas everybody else is expecting is going to be 900 or whatever.

Right. And if that's a claim, which tends to be the common claim, right, almost by definition in that job, you can then sort of back into what sort of process leads you there, right? What sort of modeling capability you could do, right? Does this sort of get to what you were saying in the beginning when I asked you, like, what is the definition of quant? Where it's not enough to just be able to math that out. There has to be some ability to, like, have the

human intuition understand how these things were. Correct. Right. So just to use this example, right, let's say you tell me, I'm having an interview, I'm interviewing you for an analyst and you tell me, I'm great at knowing what the fundamentals are going to be, right? And I say, okay, well, do you have a track record of your own estimates, right? So presumably for however many names you've covered, you knew you had an estimate in your head about what their revenue is going to be, what the margins are going to be, what the earnings are going to be. I could ask you, okay, what were those estimates back in time, three days before the company announced their results

for all the names are covered back many years, right? And to be clear, I'm not necessarily looking for you to have them and give them to me, but what process did you use to think about even understanding whether you have skill in the first place, right? And it is not uncommon to have folks answer that question by saying, well, I don't really know because I keep my model saying Excel, right?

And I have a very complicated Excel model with all the income statement lines and all the balance sheet lines and all these things. And as the firm evolves, I change that model. I changed the numbers, I changed my assumptions, I maybe even add and subtract lines, add more complexity in the model. And keeping track with what it was at every point in time is hard, right? You know, I have to save the file every day and have some database to figure out what it was

every day and chain them and do same analysis, right? And you want to talk to the people who understand that that's a thing they should be doing and have made some effort to move in that direction, right? Meaning there's an interest in being disciplined and understanding your own skill, right? Just that is another significant difference between somebody who does. just does it versus somebody who's interested in understanding how they do it and how they improve, right? So on the flip side of identifying good portfolio managers, how do good portfolio managers or why do good portfolio managers want to come work for you? Because my impression is

there are giants in the multistrat world you used to work for one of them. They can pay millions to a talented PM that they really want. How do you compete with that kind of package? Is it autonomy? Is it the culture of the firm? What is the attraction for good traders? Yeah. So it's a mix of things. Let me give you sort of what I think are the key things that might make you want to talk to us, right, as opposed to stay at your big job, you know, in one of the sort of big name platforms, right? So number one, because of this drive to scale, what has ended to happen at many of the platforms is that if you are, say, a tech portfolio manager, you're one of 10, potentially 15, right? And remember, you're competing for your ability to have the resources

necessary to do that job really well, right? So run down the sort of thing you need, right? You need corporate access, right? So you would like to have the ability to talk to CFO, CEO, you know, even I.R for the companies you cover, you know, go to the conferences, do the non-deal road show. And it doesn't matter how big you are. At some point, the CFO of some firm is not going to talk to a million hedge fund managers, right? So they're going to say to the big names, okay, I'll give you two slots. They're not going to give you 15 slots

just because you have 15 PMs. In fact, they really don't want to talk to you, right? Most companies don't prefer not to talk to the investors. And so you end up in a situation where you're competing for corporate access. You're also competing for data science resources, quantitative resources, portfolio construction and risk management resources.

Meaning as that scale happens, it becomes ever harder to get what I would describe as a truly integrated and sort of partner-like relationship with the resources that you have. have, right? And so it is not atypical to find folks in the big platforms who might like the job, might like the way they get paid, but are actually frustrated about the fact that it's a bit like being a small cog in a big place, right? So that's one aspect of it. The second aspect of it is,

again, the fact that the firm is really large doesn't mean that you necessarily are running any more money at the large place than you would with us. In fact, our reform managers run likely more money than they would run in most other places, right? Because, yes, we're smaller, but we also have fewer people, right? And so we're looking to run as large a scale a team as you could with fewer teams, if that makes sense as a distinction. So from the portfolio manager's point of view, that's actually not that different in terms of how more risk you might get,

but you get better resources, more integrated platform on the technology, risk, corporate access, et cetera. There's other things that have this flavor, right? And remember, because most folks get paid out of some sheet, of the return that they can generate from that amount of assets, it's not like your comp is going to be terribly different, right? If you run just as much assets and your returns are good or better, because you get better resources, more integration, and a better platform,

is not obvious why it's necessarily an unattractive platform. In fact, we have found that we have hired folks that where portfolio managers are all the places that came to be analysts with us because they understand the benefit of all of those things, right, as opposed to be one of, I don't know, 500 analysts in some really large place. Does that make sense? Wait, talk more about that because I'm curious, I get the impression that a lot of multistrat firms or pod shops are always going after like the star portfolio managers or people who have experience.

And I'm curious, is there scope for developing talent in-house? For instance, could you hire me or Joe and train us to be a really good portfolio manager? How much flexibility is there in that career path? There's actually a decent amount of flexibility. So your preference would be not to have to rely on imperfect information, particularly if you have to promise somebody lots of things in order to come to your platform. So you should have a preference to develop talent internally. The question is what sort of cultural and systems do you have to make that happen, right?

And in fact, I think you've had a guest in the podcast talking about those training grounds, right? And so people understand that you should have a preference to, bring in people who you can shape into who you think are going to be the best analyst and the best portfolio manager in a way that really matches with your culture and the way you pay and the way the systems work, right? Part of the problem, though, is that humans are humans, right? And so even if you train somebody, you can't guarantee that they're going to stay with you and vice versa, you might, especially if you're really large and you have to run lots of assets,

in a sense, you're forced into this turnover, right? Because if you have to deploy all those assets and if somebody quits for whatever reason, maybe they just have a personal thing they leave, not because they're going somewhere else, you're sort of forced into this replacement process. And at some point, part of the problem is you might not have the next person ready to be promoted, and therefore you've got to go outside, right? And to be entirely honest, I don't think this is certainly different in this industry from any other industry, right, where you need to hire very talented people,

and there's a limited number of them, and you kind of have to go through that mix of ingrown talent, hiring externally, you know, some mix of the two. And yes, I could train you to be really good profile managers. I was doing. I want to get into soon, like actual how the comp part, because it's nice to talk about access to teams and, you know, lean management and all that. But, you know, it's finance and people care about paychecks a lot. But before we do, there's something you said, and it's come up before, and I still have a hard time wrapping my head around it. So I'd like to hear how you clarify it.

When you talk about a PM having access to a company's management team, that makes sense. I get it. Investing. You want to talk to the CFO or whatever. the CEO, whatever, or the CEO. But, you know, we're not talking Berkshire Hathaway here where you're holding a stock for 25 years, and you really get to know it. In fact, the sort of hold times for a stock within one of the, within a firm like yours, supposedly is extremely short. And sometimes maybe five days or 10 days or one quarter or something like that, in which it's not intuitive to me

that if I'm holding a stock for 20 days, it's particularly important to say, know the management team. the way Warren Buffett gets to know, a management team. Can you explain to me the importance of that sort of insight into a company given the short holding periods, given the high amount of actual trading that you do? Yeah. So this is a really good question. I think I suspect you're munching two things together that don't go together, right? Okay, that's fine. I think you want to separate the investment decision, which might be a short horizon, versus what drives the insight that gets you to that investment decision, right? And so the reason you want to really understand the company is because that allows you.

you to pick up on subtle patterns about what the likely misunderstanding is from everybody else. So I'll repeat, the way you make money is you have a view that is different from the other marginal participant. And the way you make money is you place the trade. And then over time, people come to agree with you, right? And it's either because they eventually see the same thing that you do, right? They see the same data. They do the same analysis.

Maybe you got there because your data is better. Your analysis is more sophisticated, et cetera. Or the firm tells you. The firm literally comes and says, here's our earnings and here's our revenue and you turn out to be correct versus other folks. So you need that catalyst, right? And so you're playing

in the same firm over and over again, but the nature of the insight is what's changing, right? And so because you know the firm that well and because you've been following it for 10 years and go to the conferences and talk to the management, et cetera, you are able to tell that, gee, well, this quarter, my suspicion is that people are underestimating the earnings, maybe the next quarter

are overestimating the earnings, right? And if I can repeat that process, might trade their short horizon, but it's not that I have a short horizon view of the firm. In fact, if you're going to do this well, you should have a long view of what the firm is likely to do. In fact, some of your hypothesis might be, hey, people are thinking that the XYZ product is going to be, you know, enormously successful of the next five years or 10 years, aka long-term view. But if you think that that's going to be slightly disappointing this quarter, why hold it now? So like a company like Nvidia, everyone has a big,

tenure horizon for it. So that's not that you're not going to gain an edge just knowing that AI is going to be bigger for the next year. Correct. The edge is going to be, you might want to be long on average in this example in Nvidia, but if you think that they're going to miss those very high expectations the next quarter, why are you holding it down? You could shorter now and then buy again, you know, after there. You can get the news whenever you want it with Bloomberg News Now. I'm Amy Morris. And I'm Karen Moscow here to tell you about our new on-demand news report, delivered right to your podcast feed. Bloomberg News Now is a short five-minute audio report on the day's top stories. Episodes are published throughout the day with the latest information and data to keep you informed.

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executives making an impact on the IHeart Radio app, Apple Podcast, or wherever you get your podcasts. So one of the criticisms of multistrats and their phenomenal growth has been this idea that we're getting more crowding risk in the markets. And you brought up in video, just then, and to some extent, that's kind of the perfect example of some of this.

It feels like whenever Nvidia has a big move now, there's some talk about like, oh, there's a pod behind it. Yeah, that's right. Or like some sort of factor is changing. Talk to us how you actually see the impact of the growth of multistrats and factor investing on the market. Yes. Okay, so I'm going to separate this into two pieces.

One is it, how do you think about it as an individual manager and then what impact that has on the market? because I think it's important to make that distinction, right? So on the first one, I think crowding is one of those things that you should manage rather than be worried about, right? The analogy that we sometimes use is this idea of sitting at a poker table, right? If there's the three of us playing poker, pod's not very big, right? If three more people come in, I'm not worried about, oh, my better is going to be the same as you. If I think I'm better than you and the three people who've shown up, having more people at the table is great, right?

Meaning the way in which you make money, again, I'll repeat, which is you have a third, different view from the rest of the market participants and they come to agree with you, that looks like crowding. Remember, I come into a position before it's crowded, and the way I make money is it becomes crowded. And at some point I say, okay, I've gotten paid for my view and I rotate into the next thing, hopefully the next thing also early in whatever the idea is, right? And so crowding in a sense is the mechanical way in which you get paid from being early in an idea, right? And so for a manager, an individual portfolio manager or a firm like ours, we want to think about how do you manage the crowd. So I'll give you an example.

Let's say two performance managers, they both have the same, quote, crowding exposure, right, measured in some way that we all agree is a good way of measuring. If I got there because I was early and then I got paid slowly as people came to be in my view, that is very different from somebody who's chasing the idea, right? They weren't early. They just see it happening and then they chase. and is different because if there's a crowding unwind, we both might have some negative returns, but I likely have less negative returns because some of my ideas are new, some part of my portfolio is not as crowded,

and two, I got paid on the way up, right? And so how you get there is super critical, right? Now, to your market question, if there's more participants doing anything, whatever it is, the mean return, of course, comes down. That doesn't mean that the people who are at the high end of skill are affected by it. In fact, they might even make more money

if there's enough people on the other side of their skill, if that makes sense, right? And the last thing that I would say is that being a multi-strategy fund is a way of organizing yourself, right? It's a way of deciding that instead of running a traditional integrated sort of single-de-de-de-de-de-de-de-de-de-a-cept capital, I am going to think more carefully about how do I out-cap capital, how do I this thing there's talent, how do I manage all of these things that we talk about the way people get paid and all the incentives. It's a way of organizing yourself. It's not an investment strategy. You could organize itself that way

and have lots of different ways of investing. And it's the coincidence of the investment strategy being the same that drives crowding. It's not the way you're organizing yourself. So it's not obvious to me. And I'm not sure that the data supports the idea that somehow there's more crowding. In fact, the biggest crowding event that we've ever had

was back in 2007, which is the great crowding unwind, right? Crowding is a thing no matter where it comes from, right? So if I have a bunch of long-only active managers, like Nvidia, that's just a small. mass crowding as, you know, so multi-strategy liking in Vivida. Does that make sense? Like, yeah, I think the concern is more that, like, the emphasis on, we, we talked about the short-term horizon of some of the stuff, and you talked about the focus on the catalyst. I think the concern is that at turning points, maybe you introduce more volatility because everyone starts to go.

The short leash. Yeah, exactly. The short leash. Everyone has these very tight stops. They want to keep their job, and that be, and that that creates a specific type of volatility. volatility, the speed with which they have to cut positions, et cetera. Yeah, I don't disagree. But again, that's something that happens at the individual level, right? So let's say you have, you know, whatever your stop loss is. Some firms don't even have that. They do their risk control differently.

That is specific to a particular strategy, right? And so whether or not that adds volatility depends on whether that strategy happens to be correlated with five, 10, 15 others, right? And it's not obvious why that should happen just because people have this view. Does that make sense, right? Yeah. So let's say that there's 100 people playing for the next earnings from, I'll make it up, I don't know, Bank of America, right? Like they're going to report something and there's a lot of people playing them.

Of course, if everybody of these 100 people that I'm describing is on one side of it, you might get a big ball move, depending on what the results are. But it's not obvious why they would be all in the same side, right, just because they were organized as potchums. Does that make sense? Yeah, yeah. Let's talk about comp and making money. You mentioned very kindly that in theory you think you could mold me and Tracy into decent traders or analysts or PMs. Maybe analysts, that's fine. Okay, so Tracy and I are there and we seem to deliver something that resembles Alpha over time.

What's our paycheck? How is our paycheck derived? Yeah, so typically you want to have an incentive for you to focus on the mechanics or your job, right? And so typically there's a tradeoff between making your compensation. highly discretionary, I just decide because I like you or don't like you or whatever, versus exactly formulaic, right? 15% of your gross returns or whatever it is, right? Typically, what you find is that the more you can separate the job to be about these 40

names in the context of, you know, some particular boundaries of risk and capital deployment and concentration rules, et cetera, it becomes easier to give that direct incentive, right? And so what you'll find is that most places end up in a circumstance where that incentive it to be very focused on the thing you're good at, tends to drive better outcomes, right? Now, to be clear, there are tradeoffs on the other side business-wise, right? So, and this is something that allocators, I suspect,

need to get better at really digging in. So let's say you have 36 risk-takers, let's call them analysts, right? And imagine three ways of potentially paying them. One way is you net everybody's returns first, and, you know, some of them did well, some of them, they're poorly, maybe even negative. You get some total return at the end across everybody,

And then some fraction of that is everybody's comp, and then you sort of paid discretionary, right? It's probably not as good from the firm's point of view because it makes it hard to have that sort of one-to-one incentive and really focusing on the thing you're good at. But to be clear, from the allocator's point of view, it might be the best because you're only paying for the returns that were delivered in total, right?

Now let's go to the... Oh, I see, yep. Now let's go to the other extreme, which is typical now with many platforms, which is each risk taker runs a small team, Each analyst has like an associate that helps them. And each of them you pay, let's say the same 15% of whatever the share is. So now you have this thing that people in the industry would call netting risk, right?

Which is you pay 15% of the people who did well. And the people who did poorly is not like you're getting money back, right? And so the total amount of compensation they're paying is larger than in the first case, right? In fact, in this example, imagine there's 36 people. let's say they each have the 0.75 that example that I've been using before. If that's what's happening, you pay about 25% more in comp costs

in this second case as compared to the first case. So if you say this is great because everybody has a direct incentive on what they're doing, that's not free, right? It costs you literally 25% more cost, right? And in a situation

where you're passing through all this to your investors, your investors are worse off by a decent amount. Yeah. Now imagine middle ground where you say, okay, I want one-to-one incentives with the thing you're really focused on. And so I'm going to put these 36 people into teams, right?

So I'm going to make teams of three, right? And within that team, they net with each other. So maybe one of them has a poor year, the other two do well. And now you pay the team, that same share of 15%. And within the team, there's maybe some ability to have some discussion at your comp, right? It's still more expensive than netting everybody, but it's only 5%, 5% to 6% more expensive. So that version of the world gets you almost all of the, the benefit of that direct focus on your job with much less cost, right?

And so if you're an allocator, you should be asking this. Remember, in this example, these are the same 36 people with the same skill, with the same total capital matters. And from the allocator's point of view, it makes a huge difference which of these you're doing. Tracy, I find this to be so fascinating that you could basically have the same structure and that the math works out so differently just if you sort of change the size of the set where you do the netting. Like, this is really interesting.

I have another money-related question, but how much money would you give us as PMs? Not in terms of direct comp, but how would you decide how much we actually have to play around with? And then related to that, one thing I'm always unclear on with multi-strap firms, it seems like the size of the available capital pool

is sometimes a draw for individual PMs, like, oh, I get to play with, I don't know, like 50 million or, I don't even know what a normal number is for them. But on the other hand, you sometimes see headlines about how Citadel or Millennium have to limit new investor funds. So I'm wondering, like, how do you right size the available capital for trading? Yeah, okay. So there's, I think there's multiple questions in there. One is like a capital allocation, right?

So how do I differentiate, do I give you more than her or vice versa? Right. So that's like whatever the amount I have, there's an allocation question. so we can get there in a second. And then there's also the, is there such a thing as like an optimal amount for an individual person, right? Let me start with the second one.

The answer is generally yes. And I think it was Gapy, who made this point that there's a human and sort of psychology aspect of how much money you can comfortably run, right? And so typically past a certain amount, literally the psychology of seeing however much you're making or losing every day

gets really large and uncomfortable for a lot of people, right? I get anxious just looking at my 401k. Yes, exactly. And to be clear, that's a thing, right? Let's say you start somebody running, I'll make it up $100 million of just dollars, right? And they're 50 of them are long, 50 of them are short. And maybe every day they go up by half a million, you know, they're made down by half a million, right?

That's sort of the range. Now you make that 10x. In return space, it might be literally identical. But the psychology of you walk into the morning, the market's open and now you're down $5 million, it comes a point where people, where that's a thing, right? I always thought, like, when I, like, play poker, I wonder if, like, it would be nice if they would just lie to me and say, you're playing a one-two game, you're buying it for 200. And then at the end, they're like, oh, it turns out you're playing for $2,000 because

the chips are the same. Yeah, and the psychology, the way the psychology plays is not just on the amount of money you can comfortably run. And remember, the bigger, the amounts, you have to worry about things other than your, say, fundamental views, you have to worry more about T-Cross and implementation questions and liquidity questions and, you know, how can you, do you get to play on smaller cap names where you maybe feel you have an edge, but now you can't really do as much of it. So there's all these sort of things that have to do with scale. The other thing that happens is a psychology and

compensation, right? It is not uncommon for folks to prefer, I could give you a billion dollars and pay you 15% of, say, you're not in returns, or maybe half a billion dollars and pay you 30%, right? The economics are the same. Many people might prefer the latter rather than the former, Right. So psychology does play a significant role in this. We tend to find that good portfolio managers can actually run, assuming they have a good team with them, in the billions of dollars, but it's not necessarily the most common situation. Most platforms find themselves running smaller teams with lots of little allocations. We then have all these netting issues, right? So you do want to think about that. The second question is, okay, however big eat portfolio

might get, how do you separate? Like, do I give you more than other person, right? The reality is, is you want to make your capital allocation based on your expectation of return, right? Will you have good sharp ratio in the future, right? The problem is you don't know the true sharp ratio. Most people are tempted to use some realized sharp ratio. What was your sharp ratio last year? And the problem is there's a huge amount of noise in that, right? And I find the intuition of this really interesting.

So if you have a good basic way of thinking about it, let's say you cover 40 names, in your views about these names, let's say, I like this, I don't like this every day, are correlated with actual returns by 1%. So not a lot of predictability. Like 99% of what's happening you don't know, but you have 1% predictability. If you do this and trade based on these views,

you will have a sharp ratio of 1 at the end of the year, which is pretty good for 40 names, right? Meaning the little amount of predictability, 1% in this case is what people call the IC, the correlation between your views and next day returns, get to a pretty good outcome at the end of the year. It also tells you that there's a huge amount noise, right? So if you think about, let's say that we all three of us agree that, you know,

we have a crystal and we know for a fact that there's a person that has 1% correlation between views and returns. And we observe a year worth of returns, and we observe that for 100 years. The average sharp will be one, but some years will be low because, you know, of the 90% you're not predicting, you might be unlucky some year and you end up with a sharp of zero. Some years you get really lucky and you end up with a sharp of two. So realize returns, realize sharp, have a huge amount of variation. So you don't know what the true sharp is. You only observe the real eye sharp.

And so if you make allocations based on the real eye sharp, you're mostly allocating on noise, especially if you only do it over a short period of time, right? And so the way you want to start is to say, look, I'm going to ignore the past returns and do equal risk. That's essentially the same as saying, I am going to assume that the two of you have the same I see, the same sharp, because I don't know what it is, right?

It's sort of a Bayesian statistics kind of thing, right? And then I deviate away from that benchmark of equal risk as I get to learn not so much more about your returns, but what drive you as returns. So over time, I might be able to observe that actually, as it turns out, one of you is really good at the margin parts of thinking about earnings, right? And where names where there's a lot of room to think about differences in views about margin, you happen to do really well, right? Whereas, you know, somebody else might have, you know, high expertise on product questions, right? will a product fly or not fly in a particular space, right? And I collect data about the stuff. So let me give you an example.

Let's say you tell me, the reason I generate 1% correlation between my views and returns is because I'm good at predicting surprises, right? Earning surprises. Okay. And you told me that you can predict surprises at 10% correlation. So every time you have a prediction for 40 names, they are correlated 10% with actual surprises. So this is not much better because if I collect data about your predictions of earnings,

not returns, I can distinguish 10%. percent from zero much better than 1% from zero, right? The second thing that is true is that I knew that returns are correlated with earnings surprises by about 10%. And to be clear, that I can do with lots of data. I can go back in time and think about the correlation of returns and earnings surprises for every stock going back in time for 50 years, right? And these are transitive. So if you predict earnings by 10% and returns are correlated with earnings surprises by 10%, you get the 1% that you're looking for. But I can look at your earnings and do much better analysis because those are 10% related with actual earnings. Does that make sense?

Mm-hmm. So as I get time, I can get to understand your investment, the underlying things that drive those returns much better. This seems like a very big theme throughout this conversation, that the more you can understand why things work. Correct. The better you are, the easier many other decisions become. And I have one last question for you.

Say, we have some students, college students, listen to odd lots from time to time. I'm a freshman in college. I'm interested in finance. It sounds like a fun career. I want to make a lot of money working for a multi-strategy hedge fund one day. What's the best decision I could make right now as a freshman or sophomore in college that would most likely open a future door for me for something in this career? Yeah, that's a good question. We run an internship program, so we get asked this thing all the time.

I would say two things. Number one is you, I think, need to have a good mix of liking and being reasonably good at the I'm going to call it the data part of it, right? These jobs are all about do I understand the data that tells me something about this farms, right? And so, you know, whether it's, you know, I cover consumer firms and I'm looking at credit card data and, you know, thinking about, you know, what is the color of the fall and how I might get, you know, data about whose color is going to be the important one and what sort of am I running and all these sorts of things. So there's a lot of data analysis that you have to do. And you have to be sort of both good at it and really like it because it becomes sort of your day to day, right? The second thing is you have to be willing to understand that there's sort of a grind aspect of the job, right?

It sounds really exciting to think about predicting things and potentially making a lot of money. But the reality is that the day-to-day job can be a bit of a grind, right? You're covering these 40 names, and they're the same 40 names every year, right? And you're listening to every conference call and listening to every earnings announcement and you're looking for like tiny little bits of differences. It's like, well, you know, last time around they describe the nature of the particular product that they're working on in this way. Now describing it slightly differently. I wonder if that means something about their strategy. And so there's this sort of Todd, my partner uses the word of coal mining,

right? It can be a bit of a grind, right? Down in the minds of multistraths. Exactly, right? It's not all the excitement of I show up in the morning and have an idea and now I make a bunch of money. And then I watch a line go up and down. Exactly, yes. Wait, speaking of the grind and interns, is there a future where I know you spoke earlier about the importance of the human factor in a lot of this, but could you switch the emphasis to more AI? Oh, Tracy, you still, this was going to be my other thing that I wasn't going to get to. Well, because I'm thinking, I am happy to talk about AI stuff.

Quant funds were like the original users of machine learning or one of the big original users. So it seems fairly natural for them to use more AI in order to spot potential patterns or potential catalysts for big moves. Tell us what's real and what's BS. There's always a mix. But I do want to say something before we get to A specifically, this sort of job is always a bit

around an arms race, right? Meaning this sort of thing that made you money, let's say, 20 years ago, 20 years ago you could have been an analyst that figured out that in order to understand particular, say, retail firms, you could go look at footnotes about whether, you know, be you owned or leased your retail space

where you sold your T-shirts or whatever it was. and that might have had some consequence, right, depending on how your finance and what that meant for, you know, et cetera, like early data stuff, right? You don't do that now, and the reason you don't do that now

is because that's all in a database that everybody can go mechanically look at, right? And so there's this sort of, you need to become ever more sophisticated data and analytics-wise, and AI is sort of one more step in that direction, right? So I don't think of it as something inherently different from this sort of constant evolution

of always being more sophisticated and understanding the firms, right? The one thing that I would say about AI is that at least up until this point, if you think about how AI is trained, right? You feed it all this text, essentially, mostly from the Internet. And the job that is trying to do is it is trying to predict the most likely answer to a question or the most likely thing that comes after some prompt, right? That's essentially what you're doing. And what that means by definition is that if you ask it, hey, what is different about company X, by definition, by definition, it's going to tell you what everybody else thinks

is different about companies, which means it's actually not the different thing, aka you're getting the consensus, right? And so, that could be quite useful in the way you think about doing data analysis as lots of ways, and we have a bunch of investment in AI work within the firm, but that is not the same as assuming

that AI will have insight about the firm, because it's been trained on the average of things, kind of by definition, right? And so, this step of going from, it helps me summarize or potentially, you know, kind of clarify what themes are people talking about. There's lots of things that you might be able to do with it. That is not quite the same as they jump to and therefore here's a difference in

view versus everybody else's views. Does that make sense? Yeah, absolutely. Dan, thank you so much for coming on all thoughts. That was great. That was amazing. You explained the maths perfectly. The Dan maths. No, it was really great. Thank you for having me. I feel like a million questions we answered newer great game to really work us through all of them with us. So appreciate you coming out. Thank you. Joe, that was fun. That was so fun. I like talking about maths and multistrap funds.

The Dan maths. Yeah, the Dan maths. So there are a few things to pick out of there. I really like the emphasis, and this has come up before, but the idea that crowding in is not necessarily a bad thing for individual managers because what you're trying to do is identify that catalyst that will get everyone crowding into your position. Crowding in is how you get paid. Yeah. Like eventually, you just want to be there before the crowding, but the crowding is ultimately what delivers the paycheck. Right. Now, does that maybe have a less desirable effect on the overall market? I mean, I kind of take the point about, well, if you have a bunch of long-only funds that are in something and then something bad happens, they'll all retreat, that that's like the same effect as multi-strats crowding in. But it does feel to me just observing the market in recent years that you are getting these sort of shorter and sharper turning points or reactions.

Totally. There were so many things that I took from that conversation. I thought that was fantastic. And all of our conversations about this topic have been good, but to talk to an actual founder of a fund, I thought it was great. You know, there was the big conceptual thing that he kept coming back to, which is that the more you can know why something works, the better. I think I'm pretty good at my job of co-hosting outlaws. I think you are too. But I do think, and they're like, you know, I know other people are good at their jobs. But to be able to articulate why you. you are good at your jobs and provably be able to articulate why you're good at your jobs, which you, why you didn't just get lucky. Yeah, why it's not lucky, why you were able to identify something like, oh, I am very good at identifying earning surprises, setting aside the question of, am I good at picking stocks? That's a really interesting way to think about it. Like, okay, we know that earning surprises are correlated to stock performance. If I can prove that I'm good at X, then I can probably prove that I'm good at stock selection.

That was really interesting. I love hearing about the math of why you want to avoid correlation between managers and how powerful that effect is and how few pods you need to get optimal. So much good stuff. The part about compensation is super interesting. Well, I do think in general a good piece of life advice is identify your comparative advantage early on, right? And play up to it. Like figure out what you do well and why you do it well.

that's a really good thing to do early in your career. You know, I figured out early in my career that my one competitive advantage in journalism was waking up at 4 a.m. before everyone. And now I'm spending thousands of dollars a year on therapy to allow myself to sleep in a little bit more. So there are some drawbacks depending on what thing you identify. All right.

Everyone stop telling Joe what he missed because it's just compounding this problem. this problem. All right. Shall we leave it there? Let's leave it there. This has been another episode of the Odd Thoughts podcast. I'm Tracy Allaway. You can follow me at Tracy Allaway. And I'm Joe Wisenthal. You can follow me at the stalwart. Follow our producers, Carmen Rodriguez, at Kermann, Dashel Bennett at Dashbot at Kail Brooks. Thank you to our producer, Moses, Andam. For more Oddlots content, go to Bloomberg.com slash oddlots, where we have transcripts, a blog, and a newsletter. And you can chat about all of these topics 24-7 in our

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I'm Francie Lacquan, an award-winning journalist. And I've got a new podcast, Leaders with Francine Lacqua from Bloomberg Podcasts. I've interviewed everyone from heads of state to fashion icons about the news of the moment. But I've always been curious who are these people as leaders. I don't think there's one right way to be a leader. Make decisions. A poor decision is always better than no decision. Listen to new episodes every other Monday.

Follow leaders with Francine Lacroix wherever you get your podcasts. What separates good leaders from transformational ones? I'm Jessica Chen, and in season two of Leading By Example, we'll sit down with executives like Grace Chen of Bertie Gray to find out. It's important to understand where you spike, but also really acknowledge where you don't and find people who can fill those gaps. Listen to Leading by Example,

executives making an impact on the IHeart Radio app, Apple Podcast, or wherever you get your podcasts.