How Hedge Funds Discover the Next Superstar Trader

25:05Or comparing someone trading consumer discretionary versus like utilities. Volatility. Totally. And it makes sense to me that there's more than just volatility adjusted return, sharp ratios. And it makes sense to me that punters shouldn't just be measured on pure length because you don't know where their coaches have them punt from. And maybe sometimes you want to punt shorter for various reasons because you want to have a chance at, you know, fair catch or something like that. Okay. I get all of that. Talk to us a little bit more about the art of measuring skill specifically outside of returns, because this is the money ball thing, which is like every day they're coming up with new metrics and vanity metrics.

25:45And they have these conferences where it's like VORP and all these things. And I know that VORP is like that was like 20 years ago that someone invented VORP. Right. But, you know, there's all of these new things. They're always trying to come up with something that will unlock. This is the guy who produces a lot of extra wins or something for the baseball team. What are some of the other techniques or maybe what are the other skills that you can measure a trader on other than just looking at ex post facto returns adjusted by risk? Right. Yes. That's a great question. And I'm laughing as you talk about the acronyms because obviously the sports analytic community is famous for their acronyms.

26:20So I, in creating my framework, I have five skills that explain alpha. And it doesn't reinvent alpha or in any way, it just breaks it down. And of course, I use acronyms to describe and with a nod to the industry that inspired them. I've named them after five different baseball players from the 1970s when I was an impressionable baseball fan. And those skills by name are Seaver, Aaron, Carew, Rose, and then Lum. Lum is something you probably, a name you haven't heard of, but that is named for a... Yeah, just that one. Yeah, that is named for a... Sorry, I'm not. The other four... Ron Carew, Tom Seaver, Pete Rose.

27:08What was the first one? Hank Aaron. Aaron. So all Hall of Fame level players, even though Pete Rose isn't the same. So interestingly, and I won't go in deeply into this, but... Pete Rose measures the degree to which they're betting on the side. How good are they at betting? Well, Pete Rose, it's a good one. So this is actually a descriptive acronym. So Rose stands for return on sector excellence. So why Rose and why this? Well, Pete Rose made more all-star teams at different positions than anybody else in baseball. He made an all-star team at second base, outfield, third base, and first base. So he was good at sector rotation, right?

27:41So that's sort of what that skill is measuring. The LUM stands for Luck Uncontrolled by the Manager, L-U-M. And what that really references, Tracy, it's a lot of what we were talking about in terms of the dispersion and really sort of the average stock in a portfolio versus what the benchmark might be because the average stock is really what the skill neutral performer. Well, those differences are sort of luck that is either a tailwind or headwind uncontrolled by the manager. And Mike Lum was a journeyman player who happened to play on an Atlanta Braves team with Hank Aaron and Davy Johnson, Daryl Evans, when they all hit 40 home runs.

28:19They're the only team that did that. And that inflated all of Mike Lum's performance, too. And obviously it's something he couldn't control. But so these skills, I think the so what they're really measuring is one is is luck to his sector excellence. Third is a consistency measure, and that's the Rod Carew and great batting average. And then there's power, like I talk about what the expected return is of that perfect portfolio. Well, if someone's return is above or below that, what that's really measuring is their ability to identify the best of the outperformers and, crucially, avoid the worst of the outperformers.

28:53And I can quantify that. And then the final one, the siever, is a sizing thing. And you put all five of those together and you might have someone, well, here's a great example of how it's useful. On a multi-manager platform, and I should say that all my work only deals with public equities, public equity MPMs, evaluating them. So on a pod platform, you might have four dozen, five dozen different teams, right? And you generally do not need a model to tell you who the best two or three are. And to a little lesser extent, you don't need a model to tell you who the worst two or three are. They're outliers, and the ones that are really good are out there every year.

29:35But in the middle, you might have three dozen PMs that are tightly bunched around sort of the average production of all the PMs. What the model is really good at is looking at these very similar returns at the end of the year, looking at the skills that make them up and say, well, I know sizing tends to have a correlation of zero. from year to year. It reverts back to the mean. So if you have two people with the same return, but one of them was adding alpha via their sizing decisions versus someone who was more consistently picking out performers, and this is what you don't see if you're just looking at idiosyncratic alpha, even though you've stripped out all the factors, that's how the framework comes about.

30:19And that's how it is both backward looking in terms of explaining alpha by skill, but then also it becomes a forward predictor by knowing what the correlation is between past and future periods. I have so many questions. For my next one, let me just add a caveat before I ask it, which is everything I know about baseball I learned from that one episode of The Simpsons. So that is to say I don't know very much at all other than don't be mean to Daryl Strawberry. But my impression, and again, I don't remember a lot about Moneyball, but my impression was like part of that strategy was finding players that are underpriced by the market and capable maybe of doing one specific thing very well and then kind of putting them together into a team that can work very well like holistically rather than just going after the expensive players that hit home runs a lot.

31:14Yeah exactly. I guess my question is I get the approach to evaluating individual traders but But is part of your approach also looking at how they like holistically work together and impact each other at all? Or because of the nature of multi-strats and the pod shops, does it not matter so much? That's an insightful question. And I will pick up a topic that Gappy talked about a couple months ago. He talked about the different cultures and how these pod shops and the multi-manager platforms can be different. And a lot of times there's a big culture difference. And I would say that that is absolutely true.

31:53And I have a great sort of answer to your question for that. So at some shops, the philosophy is we are going to strip out everything a PM does. And cynically, they have so many factors and will pay them on what the idiosyncratic alpha that's left is. And they have so many factors they're stripping out that they're trying to get that alpha number down as small as possible so they don't have to pay off bonuses. And I remember joking with a PM one time at one of those shops. And he's like, yeah, I feel like every time I go in there, they tell me like, yeah, you had a good year. But look, year out performance is due to investing in dividend paying companies where the CEO went to an Ivy League school.

32:36And we can get that for free. Right. So at those shops, their philosophy is it doesn't matter because we're taking out everything. I prefer a little different approach, and there are shops that do it this way, which is to say my job as a CIO is to build a multi-manager platform where some of these offset so that there are different skills. and then instead of stripping out factors at each portfolio level, more stripping out the factors once you put them all together. You've got this boule-based stew and then you take the factors out. And that is a different approach because I think the PMs feel a little bit more freedom.

33:20They still have their buffers they have to stay in, but they don't see the ETFs or the factor, anti-factor things getting shoved right into their portfolio. The approach is more higher. So you can take either approach. I do prefer the sort of roster construction idea that you have in sports, but that really is a difference in, I think, in firm culture. In culture and structure. Yeah, that's super interesting.

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35:48Learn more at chase.com forward slash business card. Chase for Business. Make more of what's yours. Accounts subject to credit approval. Restrictions and limitations apply. Cards are issued by JPMorgan Chase Bank and a member FDIC. So in baseball, a general manager looking for players can look at other teams. They can look in the minor leagues. They can look at college sports. They can start scouting in high school, probably. There's a farm system, and they call it a farm system. What you've described so far makes sense for evaluating people in existing seats, either at your shop or perhaps at another shop.

36:26Is there a way to transfer or to apply some of these same ideas to people who don't have the same? because I don't think there's the same equivalent unless trading Ameritrade or Schwab, which actually I do think maybe is kind of a thing. But is there a way to sort of think about how you would evaluate someone who just does not have that much of a track record yet? Yes, because of the way these multi-manager platforms are formed now. They don't hire from the street anymore. I think 20 years ago, 30 years ago, I know when I was on the street, The researchers that were covering the companies, they'd get plucked away sometimes by the shops.

37:06Sometimes traders would get plucked away. That doesn't happen as much anymore because what these huge firms have done, and this also goes to their competitive advantage and their ability to scale, is they are now training these people right out of school. They have universities or academies or these schools, essentially, where they're teaching people to be analysts or PMs. And again, sort of to a culture thing, my favorite ones are the ones where the firms realize it used to just be an up or out thing. Like you became an analyst and then you became a PM. If you weren't a good analyst, you never became a good PM.

37:46And I think that there are firms now that recognize an analyst can be a career. You may be a great analyst, but not necessarily, you know, the capital committer. You know, there's a different skill set to being the PM. And they find out some of these things in the academies and in the universities, their in-house training schools. This is the farm system that is coming up. Quite literally, this is the bench. and we see that and they don't just get thrown in. They do tend to run paper portfolios or portfolios that feel like they're real because they are entering trades. And their careers depend on them doing well, so they're taking risk even if it's paper money.

38:28Yes, exactly. And you can run the same analytics on these portfolios. And what I definitely have seen is some of the newly graduated PMs, these firms are good at who they're training. And those are the best PMs to find alpha signals from because their portfolios, they tend to be small so they can be replicated. And this is another job of the quants, too. If you have a very senior PM who has a contract that allows he or she to run a$2 billion biotech portfolio, there's not much left for the quants to – because they're probably a little more thinly capitalized. There's not much room to replicate that portfolio at another quant level in the firm.

39:17But the new people that are coming up, they're cheap. They're running small portfolios. But if they're skilled, they're knowing what they're in is just as important as a more senior PM. Yeah, Tracy and listeners, there's a great piece on the Bloomberg from June 19th by our colleagues Nishant Kumar and Liza Tetley about exactly this. Hedge fund talent schools are looking for the perfect trader. and it talks about Point72 and it talks about Citadel building these sort of in-house training things. So all these pieces are coming together, building the own farm system in-house to see who's going to be good one day.

39:51We should go to talent school. Hedge fund talent school, to be clear. Or just general talent school. That's fine, too. That was the joke. Yeah. Okay. Joe, you've talked about sizing and you talked about the general skill set that you're looking for. One thing I'm still unclear on, you alluded to it earlier, but I would love for you to talk more about it in detail, time frame. What is the time frame by which you are evaluating traders? And I guess how much runway do you give people to either prove themselves correct or prove themselves to be disastrously wrong because, you know, the correlation they were betting on suddenly breaks down?

40:32Yeah. So again, great question. And it became a point of frustration for me from when I first started at Novus and building this stuff because I was very used to sports analytics and specifically baseball, but some other sports as well. And I'll touch on golf. When you're evaluating the skill of a picture, and there's three skills that a picture has that are not dependent on anything else, not dependent on his teammates, who's batting, et cetera. Fielding. it's not dependent on fielding, right, would be the strikeout rate of a picture, the walk rate of a picture, and the ground ball rate of a picture.

41:07These are things that the picture controls. And what happens is after about 50 plate appearances, you get the strikeout rate for a picture that is predictive of, you know, it's, you know, from a math standpoint, the correlation is above 0.7, so squared, it's above 0.5, right? The past explains more of the future than factors that we haven't identified. But with PMs, there's much more noise in their result, and it takes a lot longer to find a meaningful correlation. So although I can do work for, like I can, and I do this for a single day, right? So every day I generate a report and I do this for clients now showing their PMs and exactly what their readings of all these skills were each day.

41:49And of course, for one day, it's just trivia. It's no more than trivia. But what it is doing is building a data set. And at the point that you get to six months, which is about 125 trading days, bigger picture, the full model takes the past 500 results. And that's when you start getting very different but more persistent correlations between all these skills. But what I have found is that even after 150 days, if you take for the other year and a half a mean reversion assumption and then just every time a new day comes in, you drop off an assumption, you have a pretty robust skill reading that starts to mean something after six months.

42:38And after two years, that's when it really has some great predictive power for the next quarter. And so you're constantly dropping off. Now, why only two years? I talk about this in the book. I don't have a great answer for that. I suppose you have to start somewhere, right? Yeah. Well, here's what I knew. Two years was better than three years, which in one sense, why would that be? And I have talked to different quants about that, and they have approached this from a much different perspective. And they also have come to somewhat of a two-year conclusion. The reason seems to be regimes within the stock market.

43:13Just something about where you are skilled. You know, I haven't been able to identify it. And I also know that we could do a, you know, we could run the numbers and find out that, oh, you know, it's not two years. It's the most predictive thing for the last three months would have been two years and 43 days. When you try to get that precise, that's not going to be what the perfect. So two years does seem to work because you're constantly rolling off whatever happened two years ago. And so there's some regime change that that seems to work. But that is an unanswered sort of question I have, too.

43:51So one thing about baseball is that every GM in baseball has basically perfect visibility into the performance of every player on every other team because it's all out on the field and it's all measured and we all have the same information. You know, one of the most measure heart. Yeah, right, right. You can't measure hardware. We all can see players on base percentage and ops and slop and all of this stuff, right? You know, some of the most popular alerts that always read Spike on the terminal are it's like consumer discretionary manager, Paley Asney goes to Citadel, whatever. People love, people eat that stuff up.

44:25Just from an industry perspective, setting aside whether you want to use a traditional sharp ratio perspective or rose or lum or whatever, how much visibility does one shop have into the performance of a pod at another shop that can then be ported over? Or how much insight can you have if maybe there is an undervalued player somewhere else and you want to bring them over and give them more capital than they're getting? Extremely limited in terms of - And how do they solve that problem? Well, and I'll tell you who can solve that problem. What you will have is, of course, if a team is marketing itself or being recruited by another firm, they bring over their returns.

45:01They don't bring over. They might talk about portfolio construction, but I'm pretty sure they shouldn't and probably don't bring over their last two years. Right. What the portfolio, what their holdings have been for the last two years. So you don't get that type of visibility. But let me tell you who can. OK, and this is, I think, one of the most important constituents in our industry because I think they have the purest motive, and that is the allocators, right? Allocators, I'm talking about the huge multibillion dollar entities which provide the blood that keeps the heart pumping, right? At all these hedge funds, sure, we know that Ken Griffin has a tremendous amount of the AUM is his money, and we hear that about some other people too.

45:52But in general, these firms, it's outside money which keep these firms afloat. But the allocators, many of them, though, and what I'm talking about here are foundations, university endowments, sovereign wealth funds, pension plans, and they have a very pure motive. They are trying to get returns for the retirees or reduce tuition for future students, et cetera, or in the case of Norway, the citizens of the country. So they are a treasured investor if you run a hedge fund. So when they are doing manager selection, they have the ability to go to hedge funds. Now, maybe not Citadel and Millennium, but to all these non-multi-manager platforms, they have the ability to go to them and say, hey, if you want us to really evaluate you, we need to see.

46:43Pot-level returns. Yeah, we need to see, we need position-level transparency for the last two years. Hey, if you don't want to give us yesterday, start a quarterback so that it's on a lag. But now they have the leverage to get those returns, especially if you're talking about emerging managers, right? young managers they're trying to that you know to build a hedge fund and i don't feel they use that leverage and this is to me is like well joe you know you you talk about this framework and it's it's applicable to multi-manager platforms and you know an endowment isn't a leveraged portfolio so how could they use it well this is how they could use it because they can get that joe and they do ask those questions about the bentley and and the in fact can i give you an example can i all right I really like this.

47:29I've never worked with them, I should say that. I have worked with their brethren, and I've worked with the endowments that they would measure themselves against. But the MIT endowment, Matimco is the name of the entity. They have something between$20 and$30 billion under management, so we know that a portion of that is dedicated to public equities. And we know because, and I won't mention his name because I'm not trying to call him out, but we know that one of those gentlemen that looks for equity managers is a presence on Fintwit. He's actually a great follow, very earnest. And so he'll talk about things and sometimes he'll post job postings, right?

48:13And what you will find is everybody who works in that division, and in fact, you can even see this publicly. I know this. Yale's management company has the resumes of every person who's in that division, and they're all the same. Here's what they will say. They will say things like, you know, was president of the investment club at the University of Virginia, right? And they've been investing in stock since I had a paper route, right? They always have this, right? So when they go to do manager selection, and I've been on that side too as a marketer, they will sit down with the PM and they'll ask, they'll go over each position in the portfolio.

48:51And make no mistake about it, they're passing judgment, right? Because if they're not frustrated or want to be PMs, this is how they think about the market. All right, so I'm going to put full stop there. Now let's go to the general manager of the Philadelphia 76ers, a gentleman named Daryl Morey. Oh, yeah. I like Daryl. Right. He was with Houston. And in fact, while he was at Houston, he really brought Moneyball to the NBA. Mark Cuban was probably maybe the second, but Daryl Morey, right down to the fact that Michael Lewis did a piece on him in the Sunday New York Times maybe 20 years ago. So Daryl Moore is the GM of the 76ers, and he has juniors too, right?

49:31And when they're doing their equivalent of manager selection, whether it'll be drafting a player or looking at free agents, can you imagine how absurd it would be for Daryl and the analysts to go down and shoot free throws with the prospective player, right? And to judge the player based on that. But I guarantee you at the endowments, they go back and say, can you believe that manager's short Netflix, right? So now, why did I pick those two? And the example is this. Before Daryl got into basketball, he is a proud graduate of MIT Sloan. He got his MBA at Sloan School of Management. And he started, along with a woman named Jessica Gelman, he started the Sloan Sports Conference.

50:15which started as Bill Simmons when he was at Grantland described it as Dorkapalooza, right? It was just people, kids, guys. It was almost all guys back then talking about sports analytics. And it has morphed into a massive event. And it's a job fair where all these sports teams from all different leagues are looking for talent, right? And they're essentially looking for performance analytics people, right? Voros McCracken was the one from what's it called? Exactly. Exactly. So this is think now. Now look across the campus at the MIT Sloan Endowment. What they're actually trying to find is performance analytics.

50:59Do you think there might be anybody right across the campus who may have never invested in stocks but gets the profit motive? They would take my work and probably take it three steps more. But I don't think there's an endowment out there that thinks like that. Like, I'm sure they've never walked across the campus. And even I'm sure Daryl has never thought to invite them over to the, you know, to, hey, why don't you interview some of some of our people? So that's, again, sort of how I look at, like, hey, this this is how some of this work, how you somebody who doesn't have the data can get it at the allocator level.

51:33We've been talking very much about, you know, performance evaluation and metrics from a sort of managerial level. If I am a trader or quant, you know, a sort of junior or medium level quant, I guess, at one of these multistrat hedge funds, how am I viewing the performance of others and competition? Is it the case that I'm trying to move into a particular sector that maybe has more of an opportunity set in terms of dispersion where maybe there's more volatility or more relative value opportunities or something like that? How am I like viewing my competition? It's a good question. Even the work that I do, well, I think there's definitely a comparison, right?

52:19You do, again, to culture, some of the pods, and I think Gappy mentioned this, some of the pods, there's a sharing of information, right? And at some shops, there's not. This is a case where I actually prefer the not sharing of information, right? Because I would rather, I think the quants would rather know that maybe two different PMs came to the same conclusion independently as opposed to they both went to the same idea dinner and then both decided to buy the stock. There's more of a signal in somebody coming to it independently. So I believe they're aware of what the returns are of their other PMs.

53:02In addition, and I don't know if this was in that article, Joe, you just referenced, but these firms all have coaching teams, too. Yeah. And I certainly found that the older PMs that, you know, had been in the business since the 90s, they're set in their ways, right? They don't want a quant to come in with a laptop and start telling them that spin rate, the spin rate of their pictures. But the younger people, I think there's more of an, hey, if there's data that you can give me to help me get better, I think maybe in some ways they might be looking for that. Joe Pita, this was super fun. Thank you so much for coming on the podcast again.

53:41It's always great to be here. And I'll see you in seven years. Yeah, exactly. Whatever your next job is from this one. Thanks so much, Joe. Yeah. Even though there were baseball references, I enjoyed it. Yeah.

54:05Tracy that was a really fun conversation I love hearing stories when people get jobs off the back of all thoughts appearances that's nothing sort of flatters our ego sense of self-importance I also like it when people say they're listening to all thoughts episodes while going to the gym oh yeah because I hate going to the gym I hate running and things like that but it makes me feel nice that like people are listening to us to offset something that's kind of like a chore no but beyond all that it was very fun i feel like we could just talk about these businesses forever it seems so rich uh you know like we still have to do something on like compensation structure etc but also like just the sort of like fundamental point that everyone nodes, which is like manager identification is really difficult because, and you know, first of all, there's all these questions about like, well, is beating the market really possible because of efficient markets and stuff?

55:01And then you can identify someone, well, this person beat the market seven years in a row, but if a pool of a thousand managers, there's going to be a lot of people who beat the market seven years in a row. And so it seems like a very interesting problem to solve. It's kind of funny that you're trying to like select traders on a factor neutral basis who are themselves able to be factor neutral in some respects. Like you're kind of you're trying to separate them from like these circumstances that they are operating in or trying to weight them against the value of the opportunity that they are currently facing.

55:36Right. That dispersion that Joe was mentioning. That's kind of funny. I've thought about it not to go all media naval gaisy but like you know it's sort of similar to journalist beats in some respects where you can get really lucky and be on a really interesting beat where there's tons happening and suddenly you know all your stuff is getting read and you're getting all these major scoops and then maybe two years later to go back to that time frame point it's sort of faded into the distance and there's not as much to write about and how do you judge the talent of a particular journalist or a trader from their particular set of circumstances.

56:14That's a great example. I remember, you know, like when I was at a Business Insider years ago, it's like the reporters who covered Apple on days of like iPhone announcement, like they got we were like measured on traffic back then. They got all the traffic, you know, it's like, oh, this isn't fair. Like I'm talking about like the Bank of England decision. This is nonsense. I just read a really good analysis of like U.S. payrolls and people only want to read about the next iPhone. I explained Mario Draghi's new OMT thing really well and like 10 people read it. But no, like this is like, it's all like versioned to the same problem.

56:47By the way, my hedge fund media metaphor that I use in my head is like alpha decay. So it's like the first person who ever came up with like, here's what you need to know or the answer will shock you. Like probably like did crazy well. That was you, wasn't it, Joe? That was me. But then by the millionth person who did like the answer will shock you, it stopped working. so it's like there's the same thing of like alpha decay where it's like you can be the first on a strategy and then everyone discovers it and then the excess returns from that move on the crowding in effect yeah no i did think actually that time frame point was really interesting and the fact that joe kind of i guess gravitated towards two years or 500 trading days but then he was talking about how others seem to have sort of alighted on that same time period yeah i wonder why that I mean, I get that you have to at some point you just have to choose like a horizon, but it is.

57:39Yeah, it's an interesting one. Very interesting stuff. Plenty more to come on this topic. All right. Shall we leave it there? Let's leave it there. This has been another episode of the All Thoughts 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 guest, Joe Pita. He's at MagicRatSF. And check out his book, Moneyball for the Money Set. Follow our producers, Carmen Rodriguez at CarmenArmand-O-Bennett at Dashbot and Kale Brooks at Kale 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.

58:15And you can chat about all of these topics 24-7 in our Discord, Discord.gg slash OddLots. And if you enjoy OddLots, then please leave us a positive review on your favorite podcast platform. We will accept positive reviews in lieu of finders fees. And remember, if you are a Bloomberg subscriber, you can listen to all of our episodes absolutely ad-free. All you need to do is connect your Bloomberg account with Apple Podcasts. In order to do that, just find the Bloomberg channel on Apple Podcasts and follow the instructions there. Thanks for listening.

59:02Thank you.

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