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

23:27And I say, okay, well, do you have a track record of your own estimates, right? So presumably for however many names you 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 the results for all the names you 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?

23:58And it is not uncommon to have folks answer that question by saying, well, I don't really know because I keep my model, say, in 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, right? I change the numbers, I change my assumptions, I maybe even add and subtract lines, add more complexity in the model. And keeping track of what it was at every point in time is hard, right? You don't have to save the file, save the file every day and have some database to figure out what it was every day and chain them and do some analysis, right?

24:28And 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 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 multi-strat world.

25:03You 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, at 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?

25:45And remember, you're competing for your ability to have the resources necessary to do that job really well, right? So rundown of 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 IR for the companies you cover, you know, go to the conferences, do the non-deal roadshow. 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.

26:18In 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, 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?

26:52So 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 profile 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 profile manager's point of view, that's actually not that different in terms of how much risk you might get, but you get better resources, more integrated platform on the technology, risk, corporate access, et cetera.

27:31There's other things that have this flavor, right? And remember, because most folks get paid out of some share 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, a better platform, it's not obvious why it's necessarily an unattractive platform. In fact, we have found that we have hired folks that were portfolio managers at all the places that came to be analysts with us because they understand the benefit of all of those things, right?

28:04As 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 multi-strat firms or pod shops are always going after 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.

28:50The question is, what sort of cultural assistant do you have to make that happen? And in fact, I think you've had guests on the podcast on this podcast talking about those training grounds. 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 performance manager in a way that really matches with your culture and the way you pay and the way the systems work. Part of the problem, though, is that humans are humans. And so even if you train somebody, you can't guarantee that they're going to stay with you.

29:19And 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. 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 I don't, to be entirely honest, I don't think it's terribly different in this industry from any other industry.

29:46Right. 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 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.

30:19So 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 CIO or whatever, 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 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.

31:02Can 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 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 to pick up on subtle patterns about what the likely misunderstandings about that company is from everybody else, right?

31:35So 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 a 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. Right?

32:03So 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, they're overestimating the earnings, right? And if I can repeat that process, my trades are short horizon, but it's not that I have a short horizon view of the firm.

32:33In 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 enormously successful over 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 10-year 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 10 years. 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?

33:13You could shorter now and then buy again after there.

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34:57Brokerage services for U.S.-listed registered securities, options and bonds in a self-directed account are offered by Public Investing, Inc. Member FINRA and SIPC. Crypto trading provided by Backed Crypto Solutions, LLC. Complete disclosures available at public.com slash disclosure. 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 NVIDIA 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.

35:28The pod is blowing up. 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? 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.

35:58Right. If there's the three of us playing poker, pot is not very big. Right. If three more people come in, I'm not worried about, oh, my bed 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 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.

36:32Hopefully the next thing also early and 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 crowding? So I'll give you an example. Let's say two perfect measures. 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 my view, that is very different from somebody who's chasing the idea, right?

37:07They 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?

37:41And 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 decision maker kind of fund, I am going to think more carefully about how do I outkeep capital, how do I distinguish 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.

38:15It's not the way you organize 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 as mass crowding as, you know, some multi-strategy liking NVIDIA. Does that make sense? Like, those are different things. I think the concern is more that like the emphasis on, we talked about the short-term horizon of some of the stuff, and you talked about the focus on the catalyst.

38:50I think the concern is that at turning points, maybe you introduce more volatility because everyone starts to go - The short leash. Yeah, Yeah, exactly. The short leash. Everyone has these very tight stops. They want to keep their job and that that creates a specific type of volatility because everyone – 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 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?

39:24And so whether or not that adds volatility depends on whether that strategy happens to be correlated with 5, 10, 15 others. And it's not obvious why that should happen just because people have this view. Does that make sense? 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. 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 vol move, depending on what the results are. But it's not obvious why they would be all in the same side, right?

39:57Just because they were organized as botchums. 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 of your job, right? And so typically there's a trade-off between making your compensation highly discretionary, I just decide because I like you or don't like you, whatever, versus exactly formulaic, right?

40:3915 % 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 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 to be very focused on the thing you're good at tends to drive better outcomes, right? Now, to be clear, there are trade-offs on the other side business-wise, right? So, and this is something that allocators, I suspect, need to get better at really digging in.

41:16So 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 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 pay 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?

41:54Now 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 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, it's not like you're getting money back, right? And so the total amount of compensation that you're paying is larger than in the first case, right? In fact, in this example, imagine there's 36 people.

42:28Let'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 income 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, right? Now imagine middle ground where you say, okay, I want one-to-one incentives with the thing you're really focused on.

43:00and 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, right? 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, you know, ability to have some discussion or you count, 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 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.

43:34Remember 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?

44:09And 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, OK. So there's I think there's multiple questions in there. One is like a capital allocation, right? So how do I differentiate?

44:48Do I give you more than her or vice versa? stuff, 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 Gappy 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?

45:22I get anxious just looking at my 401k. Yes, exactly. And that, 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 50 of them are long, 50 of them are short. And maybe every day they go up by half a million, they're maybe done 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, now you're down$5 million. There 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.

46:04And 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-cost and implementation questions and liquidity questions. And, you know, how 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.

46:31The other thing that happens is there's 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, your net 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.

47:08We then have all these netting issues, right? So you do want to think about that. The second question is, okay, however big each, but if I make it, how do you separate? Like, how do I give you more than other person, right? The reality is you want to make your capital allocation based on your expectation of return, right? Will you have good Sharpe ratio in the future, right? The problem is you don't know the true Sharpe ratio. Most people are tempted to use some realized Sharpe ratio. What was your Sharpe ratio last year, right? And the problem is there's a huge amount of noise in that, right?

47:37And 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 what 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 about 1 % at the end of the year, which is pretty good for 40 names, right? Meaning a little amount of predictability, 1 % in this case is what people call the IC, the correlation between your views and next day returns, gets you a pretty good outcome at the end of the year.

48:15It also tells you that there's a huge amount of noise, right? So if you think about, let's say that we all three of us agree that, you know, we have a crystal ball 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 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 realized returns, realized sharps, have a huge amount of variation.

48:49So you don't know what the true sharp is. You only observe the realized sharp. And so if you make allocations based on the realized 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 IC, 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 drives those returns.

49:24So 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 for names where there's a lot of room to think about differences in views about margin, you happen to do really well, right? Whereas somebody else might have high expertise on product questions, right? Will a product fly or not fly in a particular space, right? And I collect data about this 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?

49:57Earning surprises. Okay. And you tell me that you can predict surprise is a 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 % from zero, much better than 1 % from zero, right? The second thing that is true is that I knew that returns are correlated with earning 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 earning surprises for every stock, going back in time for 50 years, right?

50:33And 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 % correlated with actual earnings. Does that make sense? 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. The better you are, the easier many other decisions become. All right, I have one last question for you.

51:06Say 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. This 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?

51:46These jobs are all about, do I understand the data that tells me something about these firms, right? And so whether it's I cover consumer firms and I'm looking at credit card data and thinking about what is the color of the fall and how I might get data about whose color is going to be the important one and what survey 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?

52:19It 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 described the nature of the particular product that they're working on in this way. Now they're describing it slightly differently. I wonder if that means something about their strategy.

52:46And 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 multistrats. Exactly, right? It's not all the excitement of I show up in the morning and have an idea and now I make up on tomorrow. 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, this was going to be my other thing that I wasn't going to get to.

53:19Well, because I'm thinking... I'm 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 in AI. There's always a mix. But I do want to say something before we get to AI specifically. This sort of job is always a bit of an arms race, right? Meaning the 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, you owned or leased your retail space where you sold your t-shirts or whatever it was.

54:05And 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. 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. 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.

54:33right? 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 it's 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, it's going to tell you what everybody else thinks is different about companies, which means it's actually not the different thing.

55:09AKA, you're getting the consensus, right? And so that could be quite useful in the way you think about doing data analysis in 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, but it's not quite the same as the jump to. And therefore, here's a difference in view versus everybody else's views.

55:46Does 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. Yeah. No, it was really great. Thank you for having me. This is like, I feel like a million questions we answered in your game to really work us through. We're through all of them with us. I appreciate you coming on. Thank you.

56:17Joe, that was fun. That was so fun. I like talking about maths and multi-strap 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. Yeah, I like how he said crowding in is how you get paid. Yeah, that's right. Eventually, you just want to be there before the crowding, but the crowding is ultimately what delivers the paycheck.

56:50Right. 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 are 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.

57:30You 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 I know other people are good at their jobs, but to be able to articulate why 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're 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?

58:05That'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 was super interesting. Well, I do think in general, a good piece of life advice is identify your comparative advantage early on and play up to it.

58:42Figure out what you do well and why you do it well. that's a really good thing to do early in your career. All right. 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 like allow myself to sleep in a little bit more. So there are some drawbacks depending on what thing you identify. All right. Everyone stop asking Joe or stop telling Joe what he missed because it's just compounding this problem. All right, shall we leave it there? Let's leave it there.

59:18This has been another episode of the Odd Lots podcast. I'm Tracy Alloway. You can follow me at Tracy Alloway. And I'm Joe Weisenthal. You can follow me at The Stalwart. Follow our producers, Carmen Rodriguez at Carmen Ehrman, Dashiell Bennett at Dashbot, at Kell Brooks at Kell Brooks. Thank you to our producer, Moses Andam. For more Odd Lots content, go to Bloomberg.com slash Odd Lots, where you have transcripts, a blog, and a newsletter. And you can chat about all of these topics 24-7 in our Discord, discord.gg slash oddlots. And if you enjoy Oddlots, if you like our ongoing exploration of multi-strategy hedge funds, then please leave us a positive review on your favorite podcast platform.

59:58And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you need to do is find the Bloomberg channel on Apple Podcasts and then follow the instructions there. Thanks for listening. Thank you.

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