Odd Lots - How Hedge Funds Discover the Next Superstar Trader

Episode Date: September 5, 2024

One of the problems in investing or trading is that — to use a common disclaimer — past results are no guarantee of future success. Someone can have a great track record in their stock picks, but maybe they just got lucky. Or maybe they were particularly well-dialed into one market regime that inevitably shifts. Or maybe they're actually just better than other traders. For multi-strategy hedge funds or "pod shops," there's an ongoing battle to hire or train the next great portfolio manager. But how can managers tell who is actually good and who isn't? On this episode of the podcast, we speak with Joe Peta, who was previously the head of performance analytics at Point72 Asset Management and has had a long career in the trading world. He's also an avid fan of sports gambling, and the author of the recent book, Moneyball for the Money Set, which attempts to take some of the talent analytical principles that originated in Major League Baseball and apply them to evaluating portfolio managers. He talks us through the traditional approach funds use to find or create superstars, and how these approaches can be improved upon using more rigorous, quantitative methods.Mentioned in this episode: Hedge Fund Talent Schools Are Looking for the Perfect TraderHow to Succeed at Multi-Strategy Hedge Funds Only 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.

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Hello, and welcome to another episode of the Odd Lots podcast. I'm Joe Wisenthall. And I'm Tracy Alaway. Tracy, do you know sometimes I wonder, like, you know, one in the morning if I can't sleep? I think to myself, in a different life, could I have been the next Steve Cohen? No, for real, though. And I don't, you know, need to talk about it. there's a lot, and I've brought it up before. You know, I did get an offer at a prop trading shop

right after college to be a stock trader at this place where they're going to let you to your capital. And I think Steve Cohen started off, like, as a prop trader at some shop before being one of great hedge funders of all time. And I didn't take that job for reasons that I still can't explain to myself 25 years later. But I was wondering whether, could I have cut it? Maybe I could have been a good trader. I don't know. It's good. You have a healthy level of self-confidence, Joe. When I lay awake at night, I think like, oh, shoot, did I say something stupid on the podcast? And that's what keeps me up. But yes, good for you, Joe.

No, I don't really think I could have. And I actually do not think I would have been a good traitor. I don't think like that. I'm not that good of poker or other things. I'm not a natural better. I don't do like sports betting. I don't think that. But I do sort of, you know, wonder about what my life had been different if I had said yes to that.

Yeah, fair enough. I mean, we know from multiple episodes of the podcast this year alone. Yeah. Like there are a lot of hedge fund traders out there, especially in multistrads, who seem to be making a lot of money. And everyone's sort of talking about them up until recently, maybe. I should say we're recording this on August 7th. So maybe those bonuses look a little bit less this year, given the market sell-off. But up until this month, people seem to have been doing relatively well.

and there was all this intrigue and interest in the world of traders. And I'm sort of curious, this has come up a couple times now. But what makes a good trader? And how are traders actually evaluated? Because my impression was always like, okay, well, it depends on how much money you make. But what's the time frame for making that money? And then also, what about people who are working in, for instance, these particular pods who are doing one specific thing? like what is the benchmark against which they are judged?

You mentioned that maybe they're not making so much money this week or this month. But Tracy, I think we're told all the time they're so neutral on everything. They're market neutral. They're neutral. Every factor you can think of. Why should they be losing money right now? They're supposed to be neutral all of this stuff. Yeah, I'm sure they're making loads, Joe.

I'm absolutely sure. No, but you're right. And look, we've been doing a lot on hedge fund structure, and we did that episode with Giuseppe Polyolago, and we did that episode with Rich Falk Wallace, various aspects of like how hedge funds measure risk and try to isolate Alpha and all this stuff. But there are just like so many questions in my mind.

Like I feel like we're just scratching the surface because, you know, we haven't even really talked about like idea generation. So it's one thing to, you know, talk about like, okay, here's how you like factor out all of these exposures that you don't want to have, like market beta, et cetera. It's another thing to talk about like, okay, but like, how do you, pick the stocks to buy or go short. Well, we have gotten into this a little bit, but you're right.

There's more we could do. There are all these questions about, like, how do you size your position? And if you're convinced that one thing is going to be the next big thing, then why don't you just have like 100% position in it, right? How do you make money if you can't just go 100% leverage long in video? In video, yeah. Anyway, so there's a lot more we can do. But to my original, very egotistical start to this episode, I do wonder like. It's okay, Joe.

It's good to have self-confidence. I'm being serious. Thank you. I do wonder, like, this big question of, like, you know, and a lot of people are probably interested in this because these hedge fund PM jobs or trader jobs seem pretty great. And as you mentioned, lucrative. And so it would be interesting to know how a fund or anyone goes about identifying, like, the next great trader. Who gets to have that seat, so to speak? Well, I also think if you can identify what makes a good trader at a hedge fund, then you can get more into the business model of what they're actually doing on a day-to-day basis. It helps to understand what they're really good at and what they can do specifically.

Well, I'm very excited today because we really do have the perfect guest. We're going to be speaking with Joe Pita. He is the author of a recent book, Moneyball for the Money Set, which is the name sort of implies, tries to, you know, figure out new ways or the best ways to identify talent. I'm sure there's a lot of old heuristics like they had in baseball, you know, They're like, well, this guy looks like he has good hustle. And then Moneyball came along. He's like, no, actually, you want to really look at his like, you know, on base percentage or whatever it is and stop looking at like his spirit or, you know, his hustle ahead of him.

Anyway, and prior to that in his career, he's been in this industry for a long time. He was the head of performance analytics at point 72. So this speaks right to the question of how do you evaluate traders. We also had him on years ago, one of our really early episodes where he talked about sports betting with some of these. same ideas, et cetera. So I'm thrilled to have Joe back to talk about this basic question of how it's good traders. So thanks for coming back, Joe. It's great to be here Joe and Tracy and nice to do it in person. Seven years ago, Tracy, I believe you were in Hong Kong. Yeah. Joe, you just had a garage band instead of selling out venues now. That's right. That's right. So you mentioned your head of

performance analytics at 0.72. How did you get that job at 0.72, Steve Cohen's big firm? So that goes right back to my appearance seven years ago. So when I was on in 2017, I had written a book called Trading Bases, which really looked at the critical reasoning overlap between asset management, sports betting, and the money ballization of baseball. And you had asked me, Joe, I think it was you, asked me a specific question of, well, I mentioned that somebody from all three of those constituents could learn something from the other two. And Joe, you asked me for a specific example of how they look at things differently. And I said, well, if you go onto a trading floor or you go to a mutual fund and you ask them, hey, who's your best trader or who's your best PM? Inevitably, they will point to the individual who had the highest return in the prior year, either the biggest P&L or the highest return on capital. But I contrasted that, that if you went into the front office of a baseball team and asked them who their best player was, they wouldn't look at, you know,

know, which picture necessarily had the lowest ERA or the most wins, they would answer that question based on skill sets. And so it's a subtle difference. Instead of looking at results, they would look at skills because they know that the skills, there's so much noise and results that the skills, if you can identify the skills, you have a better chance of predicting who will do better going forward. And as it was told to me, a member of the C-suite at 0.72, listen, a regular listener, heard that episode. Oh, what a coincidence. And played a portion of it for Steve.

In fact, I think it was the part I just mentioned. And I was told, as it was relayed to me, that Steve said, find him. I want to talk to him. And I guess that's not a surprise because in 2012, and this is all public knowledge. In fact, there's a book by Molly Knight called The Best Team Money Can Buy that chronicles the Dodgers ownership through the turbulent McCourt year. Frank McCourt's ownership. And that team was sold in 2012 to the Guggenheim group. But Steve also bid for that team and came very close to buying the Dodgers in 2012.

And of course, we all know him now as New Yorkers, and I'm his Uncle Steve, owner of the New York Mets. So he has, I believe, always had an interest in an analytical approach. And I think he always wondered, well, could that work in the hedge fund? And I came away from those meetings with the bunch of days. different people in the investment committee. And I kind of came away with three queries that I thought could sort of be my marching orders and how I could help. And that was, I think at all these pod shops, when somebody has a good year, they ask for more money. And in terms of buying power, not cash, but in terms of buying power. And so the question that management would have is, well, is what they

did repeatable. And at the same time, as you know, there's turnover at these firms. And I think another question is, well, sometimes we let people go too early that then thrive elsewhere. Just because they had a bad start to their career in terms of results, is there a way that we can avoid that mistake? And then finally, when a team does well, inevitably, there's a bit away, right? Because we know that these four or five huge firms are all very competitive and they're trying to steal talent. And so the question is, I know what a PM and his or her team may have made me in the past, but what are they worth going forward. And all of those queries can be answered by looking at skills, which is a little different than what the traditional quants do at these firms. Okay, so here's my question. Who should we

bill for the finder's fee? For our finder's fee. Should we send it directly to Steve? Or do you? Right. It would be the firm, right? They probably saved a lot of money as opposed to going through a traditional headhunter. Okay, I'm joking, obviously. That's fantastic to hear. I've love stories like that. Before we get into the existing model of compensation, there's one question that I wonder, because I think we've done a number of Moneyball episodes at this point, but it's been a while since we've talked about that approach. And all I remember is the movie and Brad Pitt kind of unconvincingly playing a guy that understands math. Could you maybe explain like what it is about the Moneyball approach that seems to attract people?

people in finance? Like, why is there that analogy that seems to come up again and again? Yeah. I think if you're attracted to critical reasoning, that's the big thing. And all of this industry is, you know, Joe said, what have I succeeded here? And I always think the biggest question is, do you have the mentality in the stomach to make decisions and commit capital based on incomplete information? Whether you have the skills to, you know, build models for, you know, and and understand companies and read documents. It's really, can you make decisions based on incomplete information? And it's true at the poker table, right?

And it's certainly true when you're building a sports team, right? You're like, how much is this free agent worth? And before, there were a lot of, Joe, like you say, heuristics. And I even mentioned that in the book. I feel that still goes on at the allocator level in this industry. Allocators, they do the interviews, and you will hear things like, well, he just got divorced, you know, or there's a Bentley in the parking lot. He must not be hungry anymore. Oh, absolutely.

And one of the reasons is because they don't have, they don't take a different approach that might be more database. The whole idea of the money ball approach is to tease out skill from, or the signal, from these very noisy. results because both athletes and asset managers, their results are filled with influences over which they have no control. I'd love to answer this question later. You both were talking about like market neutral PMs, neutral everything PMs. Why would they be having a worse week this week than before? And there's an actual real answer to that that has nothing to do with their skills. Why don't you just tell us the answer right now? Yeah. So this can apply to any time period We're looking at days or months a year.

Let's go to sort of an economics 101, like holding all else equal. Let's say we have a PM that has one long and one short, and that's their entire portfolio. And, of course, they never would. This goes to something else you said in the intro because of career risk, right? Even if it's their best idea long and best idea short, they'll still fill it. But let's say this is their portfolio. And on any given day or any period we could measure, but let's keep it at a day, it's a perfect portfolio in that the long. in that the long produces alpha and the short produces alpha.

So the long outperforms the market and the short underperforms the market, right? So that's a perfect portfolio. What is the expected return for that portfolio for, like I say, any period, but for a day? And the answer is there's a way to figure it out. And Tracy, you're going to love this because the answer is dispersion. And I know you light up when you have the derivative. But this is a little different dispersion than the quants and the derivative traders. make their life around. This dispersion is, and it's going to be very context specific for where the

PM toils, right? So we know at these pod shops, they tend to be, they have, you know, subject matter expertise in sectors, right? So you might have an energy PM. So let's say this is a consumer discretionary PM. And you would say, okay, well, I'm going to look at his or her universe, and maybe that's the S&P 1500 consumer discretionary. Maybe it's a portfolio of just consumer discretionary stocks that he has modeled, so there might only be 80 or so he and his team. But whatever it is, we'll say that it's all the consumer discretionary stocks in the S&P 500 or 1500. Well, the way to figure out what the expected return is, is to simply look at all those stocks and say, here's the skill neutral return, which would be the average return of all those

holdings, and then you look at the ones that outperformed, what was their average, and you look at all the stocks that underperformed and what was their average. And the difference between those two numbers is the dispersion between outperformers and underperformers. And that varies greatly from day to day. And it can very greatly from year to year. Is that like the maximum that you can produce that dispersion? Not the maximum because you could have the very best outperformer and the very best underperformer. Right. But if you're looking at a pod, so I'm taking all the pod from all the shops across the street that are focused on consumer discretionary, I'm going to be dead on by saying the average of all those, of all those perfect portfolios, is going to be the average

of all the outperformers and the average of all the underperformers. And it's invisible to investment committees, to CIOs, to the PMs themselves. They can be just as skilled from one day or one period and one year to the next, but the payoff is different. And this is sort of the money ball look at hey, once we get this all context neutral, we might say that a neutral everything PM that had a 7% return one year and a 5% return the next year, he may have even been more skilled in the 5% year. But the dispersion wasn't there to pay off that skill. Oh, I see what you're saying. So in other words, it's like, okay, this person's up 5%. In order to establish whether that's good or bad or not, you have to have some sort of.

like holistic view of what dispersion on average look like in that particular area. Exactly. Well, that makes sense. It also seems kind of obvious. I'm Francine Lacqua, 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 Lacquois wherever you get your podcasts. You know, I know that divorce into Bentley is probably like extreme examples. It's sort of funny. But, you know, thinking about the moneyball thing and I mentioned in the old days, like, oh, that guy looks, he is a good eye or whatever, just like all these sort of unquantive eye, he is hustle, you know, his heart, whatever. And then, you know, Brad Pitt or the Billy Bee. came along and actually put some numbers on it. If they're not doing that, what are the sort of like old heuristics that aren't the extreme ones that the investment committees or the hiring committees or the firing committees would have been using to a very price? So there's no question

that it seems obvious. And it's just the first building block. And this isn't Black Shoals stuff in terms of complexity. I started this sort of journey in analytics by working for a company called Novus. And Novus was one of, about 15 years ago, was at the forefront of portfolio analytics. And in fact, they had read my book and I'm like, hey, this is what we try to do. And I worked for them. So I've seen just about every package out there, whether it is from a vendor in terms of analytics or, you know, inside firms. I've, you know, worked with allocators. I have never seen dispersion quoted. Michael Mobeson has written a paper on it.

So there are academics who are aware of it, but I don't think people realize that is the calculation for the fruit on the tree, the meat on the bone for these pod shops. There has to be dispersion to pay off a non-factor, you know, a factor neutral portfolio. So what the quant really do, and this is like what Gapie touched on when he talked about, the day in the life of a quant and your other guest within the last month whose name I can't recall. Rich Falk Wallace. Yes. There was lots of talk about risk management, right? Because of course, it's of utmost importance when you have a leveraged firm, right? You have to understand every factor that's bouncing around in there. And that's really their job. And they will, of course,

because drawdowns in a leveraged firm, drawdowns are to be avoided as much as possible. So the sharp ratio really drives the way the quants are looking at PMs, but they're all backwards looking sort of in my view. So they do strip out everything, but once they get alpha, or as I know one firm calls it idiosyncratic alpha, what I then do is the next step. I don't change the definition of alpha, but then I break that into a skill framework so that once you get different skills, you can say, this one's more repeatable than another skill, et cetera. So like dispersion weighted, basically, like weighted by the opportunity that's available to you? Yes, exactly. And that's what allows you, Tracy, to compare the energy trader to the consumer discretionary trader because, and I make an analogy in the book, it's like looking at NFL punters, right? You know, PM's job is to make as much money as possible. And essentially, a punter's job is to kick the ball as far as possible. So before sports analytics came along, punters were judged on. And in fact, I think there was even award for, the punter that had the biggest average at the end of the year, right? The distance of all his punts divided by total number of punts.

But what sports analytics people quickly figured out is that, well, hey, if the best punter is averaging 44 yards a punt, and you've got another punter whose coach is so conservative that he's constantly punning from the opponent's 35-yard line or the opponent's 40-yard line, he can't even get a 44-yard punt off. Right, right. So the way to measure that is to say, okay, When a punter punts from his own 15-yard line, I'm going to measure that against every other punt from the 15-yard line. And now each punt is then evaluated. And I think what's really important to the work I do too is, or to note, you don't measure it now by the distance.

You measure it by plus or minus the average punter. So you can say someone is on average one and a half yards better per punt. And then you can put a value on that. And that's the same way a lot of, you know, my framework is it's not saying, you know, it is especially sort of like that canned package, you will see a canned batting average on all analytics platform. It's meaningless. In fact, it's, it's worthless.

But if you express it the way I just talked about punters, sort of the skill neutral and to say, oh, his batting average is one or two percent above, you know, over the year, he averages one percent a day. well, you know, in a 50% portfolio, that would be, you know, one more winner than expected every other day. Then you can compare that to the dispersion world that he lives in. And you can put an absolute value on his skill. Now, it might differ from the actual, but that's because of stuff out of the PM's control. So that's the approach. And it's sort of marrying the sports analytics approach.

And again, you kind of said, like, why isn't this done? I do have some thoughts on that because I got dropped. into a fish out of water quantivision, and they're brilliant, right? They are brilliant, but they're not very flexible in their thinking. They tend to think the same way. And I found that when I was interviewing for a quant developer, you know, because I'm sort of building my framework on Excel and then you need some production around it to make it usable in a big firm or to clients.

And I was, you know, in interviewing for a quant developer, I couldn't get them to stop talking about factors because that's sort of the way they're trained. And I'm like, okay, right, we're going to strip out factors. How would you evaluate skill? And again, it came down. It was very hard. Just start talking about factors again. Yeah. Yeah. And like I say, they're brilliant, but I think sort of an approach outside the industry. Yeah. It can really help. You can uncover different stuff by sort of marrying two different industries. So a lot of this stuff so far as you've described it is intuitive as you describe it like yeah it makes a lot of sense that you know you have to if you're going to compare two different pods that are trading consumer discretionary you have to understand that dispersion and how they compare to each other or comparing someone trading consumer discretionary versus like utilities totally and it makes sense to me that there's more than just volatility adjusted returns 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 punch shorter for various reasons because you want to have a chance that, you know,

a fair catcher, 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 and they have these conferences where it's like VORP and all these things. I know that VORP is like, that was like 20 years ago that someone invented forp. 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 traitor on other than just looking at ex post facto returns adjusted by risk? Right. Yes. So 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. So 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 Siever, 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 Hay.

Sorry, I'm not. The other Ford. Rod Caroo, Tom, Siever, Pete Rose. What was the first? Hank Aaron. So all Hall of Fame level players, even though Pete Rose isn't willing. So interestingly, and I won't go in deeply into this. Pete Rose measures the degree to which the first.

They're betting on the side. Right. How good are they at, yes, 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, you know, 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? So that 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 headwin 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, and Darrell Evans, when they all hit 40 home runs. They'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, two is 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 measure, is their ability to identify the best of the outperformers and crucially avoid the worst of the outperformers, and I can quantify that. And then the final one, the seaver, 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 at PMS 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. But 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, 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 outperformers, and this is what you don't see if you're just looking at idiosyncratic alpha, even though you've stripped at all the factors. That's how the framework comes about, and 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.

Yeah, exactly. I guess my question is I get the approach to evaluating individual traders, 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 multistrats and the pod shops doesn't not matter so much? That's an insightful question. And I will pick up a topic that Gapie talked about a couple months ago. He talked about the different cultures and how like 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. And 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 we'll pay them on what the idiosyncratic alpha that's left is. And they have so many factors they're stripping. out that, you know, they're trying to get that alpha number down as small as possible so they don't have to pay out 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 performances due to investing in dividend paying companies where the CEO went to an Ivy league school. And we can get that for free. Right. So that is, 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 Boliabay 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. They 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. That really is a difference in, you know, I think in culture. In culture and structure. Yeah, that's super interesting. 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. Yes. other products like this from a variety of news organizations. But they usually rerun their radio newscasts throughout the day. That's not what we do. We create customized episodes that can only be heard on Bloomberg News Now. And we don't wait an hour to publish breaking news. When news breaks,

we'll have an episode up in your podcast feed within minutes. So you're always getting the latest stories and developments. Get the reporting and the context from Bloomberg's 3,000 journalists and analysts, we're all over the world. Listen to the latest from Bloomberg News Now on Apple, Spotify, or anywhere you listen. 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 at 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, is there a way to transfer it 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, you know, an merit trader, Schwab, which actually I do think maybe is kind of a thing, but is there a way to sort of think about like 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. Sometimes traders would get plucked away, right? 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, right? They have, you know, universities or academies or, you know, 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, right? Like you became an analyst and then you became a PM. Or if you weren't a good analyst,

you never became a good PM. And 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 portfolio. 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. Yes. 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 dollar biotech portfolio, there's not much left for the quants to, you know, 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. But the new people that are coming up, they're cheap. they're running small portfolios.

But if they're skilled, 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 0.72 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. We should go to talent school. Yeah, we should do. That sounds fun talent school to be clear. That's general talent school. 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?

Yeah. 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 its teammates, who's batting, et cetera. Fielding. Right, 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. These 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, So 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, their PMSs. and exactly what their readings of all these skills for each day. And 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 days, trading days, bigger picture,

the full model takes 500, the past 500 results. And that's when you start getting very different but more persistent correlations between all these skills, right? 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'd get, you have a pretty robust skill reading that starts to mean something after six months. And after two years, that's when it really has, you know, has some great predictive power for the next. 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. Just 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, 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 seems to work, but that is an unanswered sort of question I have, too. So 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...

You can't measure heart, Joe. Yeah, right, right. You can't measure heart. 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, Paliasne, goes to Citadel, whatever. People love, people eat that stuff up. Just from an industry perspective, setting aside whether you want to use a traditional sharp ratio perspective or rose or lung 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? So, 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, right? But they 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 their 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. Okay. 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 multi-billion 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, you know, we hear that about some other people, too. But 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 foundation. universities endowments, sovereign wealth funds, right, pension plans. And they have a very pure motive, right? They are trying to get returns for the retirees or, you know, reduce tuition for future students, et cetera, or, you know, in the case of Norway, the citizens of the country, right? So they are a treasured investor, right, 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-multim 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. Pot level returns. Yeah, we need to see, we need position level transparency for the last two years. Hey, if you want, 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, you know, to build a hedge fund. And I don't feel they use that leverage. And this, to me, is like, well, Joe, you're, you know, 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 the, in fact, can I give you an example. Can I really like this. I'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, 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 FinTwitt. He's actually a great follow, very earnest. And so he'll talk about things. And sometimes he'll post job postings, right? And what you will find is everybody who works in that division or, 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. And 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 full stop there. Now let's go to a, the general manager of the Philadelphia 76. There's a gentleman named Darry. Oh, yeah. I like Darrell. Right. He was with Houston. And in fact, while he was at Houston, he really brought money ball to the NBA. Mark Cuban was probably maybe the second. But Darry, right down to the fact that Michael Lewis did a piece on him in the Sunday New York Times maybe 20 years ago. So Darry is the GM of the 76ers and he has juniors too, right?

And 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 Darrell 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 short Netflix, right? So now, why did I pick those two? And the example is this. Before Darrell 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. which 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.

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? Vauros 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. Do 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 it 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 our people.

So that's, again, sort of how I look at like, hey, this is how some of this work, how you, somebody who doesn't have the data can get it at the allocator level. We've been talking very much about, you know, performance evaluation and metrics from a sort of managerial level. If I am a traitor or quant, you know, a sort of, 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? You do, again, to culture some of the pods, and I think, I think Gapby mentioned that some of the pods there is a, 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 stuck. 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. In addition, and I don't

know if this was in that article, Joe, you just referenced, but these firms all have coaches. teams too. Yeah. And I certainly found that the older PMs that, you know, had been in the business since the 90s, they're setting their ways, right? They don't want a quant to come in with a laptop and start telling them that spin rate, like 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 Peter, this is super fun. Thank you so much for coming on the podcast again. Oh, it'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. Tracy, that was a really fun conversation. I love hearing stories when people get jobs off the back of Odd Thoughts appearance. That's nothing sort of flatters our ego. It's in sense of self-important. Well, no, I also like it when people say they're listening to Odd 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. You know, like we still have to do something on, like, compensation structure, et cetera. But also, like, just the sort of, like, fundamental point that everyone knows, 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? And then you can identify someone, well, this person beat the market seven years in a row, but in 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, right? That dispersion that Joe was mentioning. That's kind of funny. I've thought about it not to go all media, naval gasey, 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? That's a great example. I remember, you know, like when I was at a business insider years ago, it's like the report orders 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 wrote a really good analysis of like U.S. payrolls. And people only want to read about the next iPhone. I explained, uh, 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 version to the same problem. By the way, my hedge fund media metaphor. that I used 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. But by the time. That was you, wasn't it? Yeah. 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 seemed to have sort of alighted on that same time period as well. Yeah. I wonder why that it. I mean, I get that you have to, at some point, you just have to choose like a time horizon.

But it is, yeah, 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 Wisenthall. You can follow me at the stalwart. Follow our guest, Joe Pita. He's at Magic Rat S.F. And check out his book, Moneyball for the Money Set. Follow our producers, Carmen Rodriguez, at Carmen Erman. Dashobennett at Dashop at Kail Brooks.

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