WEBVTT - How Hedge Funds Discover the Next Superstar Trader 0:00:03.120 --> 0:00:18.480 Bloomberg Audio Studios, Podcasts, Radio News. 0:00:20.079 --> 0:00:24.040 Hello and welcome to another episode of The Odd Lots Podcast. 0:00:24.120 --> 0:00:26.560 I'm Joe Wisenthal and I'm Tracy Alloway. 0:00:26.800 --> 0:00:30.680 Tracy, do you know this? Sometimes I wonder, like, you know, 0:00:30.760 --> 0:00:33.400 one in the morning, if I can't sleep, I think 0:00:33.440 --> 0:00:35.519 to myself, in a different life, could I have been 0:00:35.560 --> 0:00:39.440 the next Steve Cohen? Yeah? No, for real though, And 0:00:39.640 --> 0:00:41.840 I don't, you know, need to talk about it. There's 0:00:41.880 --> 0:00:43.400 a lot and I've brought it up before, you know. 0:00:43.400 --> 0:00:46.440 I did get an offer at a prop trading shop 0:00:46.520 --> 0:00:49.519 right after college to be a stock trader at this 0:00:49.560 --> 0:00:51.640 place where they're gonna let use to your capital. And 0:00:51.720 --> 0:00:54.400 I think Steve Cohen started off like as a prop 0:00:54.440 --> 0:00:56.440 trader at some shop before being one of the great 0:00:56.480 --> 0:00:58.880 hedge funders of all time. And I didn't take that 0:00:59.040 --> 0:01:02.200 job for reasons I still can't explain to myself twenty 0:01:02.200 --> 0:01:04.800 five years later, But I always wonder whether could have 0:01:04.959 --> 0:01:06.480 cut it. Maybe I could have been a good trader. 0:01:06.520 --> 0:01:06.920 I don't know. 0:01:07.120 --> 0:01:10.400 It's good you have a healthy level of self confidence, Joe. No, 0:01:10.560 --> 0:01:13.920 When I lay awake at night, I think like, oh, shoot, 0:01:13.959 --> 0:01:16.600 what did I say? Something stickid on the podcast, and 0:01:16.600 --> 0:01:19.440 that's what keeps me up. But yes, good for you, Joe. 0:01:19.560 --> 0:01:22.240 No, I don't really think I could have, and I 0:01:22.280 --> 0:01:24.840 actually do not think I would have been a good trader. 0:01:24.880 --> 0:01:27.000 I don't think like that. I'm not that good at 0:01:27.040 --> 0:01:30.800 poker other things. I'm not a natural better I don't 0:01:30.800 --> 0:01:33.320 do like sports betting. I don't think that. But I 0:01:33.319 --> 0:01:36.119 do sort of, you know, wonder about what my life 0:01:36.200 --> 0:01:37.920 had been different if I had said yes to that. 0:01:38.280 --> 0:01:41.680 Yeah, fair enough. I mean we know, we know from 0:01:41.800 --> 0:01:46.039 multiple episodes of the podcast this year alone. Yes, like 0:01:46.120 --> 0:01:48.920 there are a lot of hedge fund traders out there, 0:01:49.080 --> 0:01:51.600 especially in multi strats, who seem to be making a 0:01:51.600 --> 0:01:53.960 lot of money, and everyone's sort of talking about them 0:01:54.160 --> 0:01:56.840 up until recently. Maybe I should say, we're recording this 0:01:57.040 --> 0:02:00.480 on August seventh, so maybe those bonuses a little bit 0:02:00.520 --> 0:02:03.600 less this year given the market sell off. But up 0:02:03.680 --> 0:02:07.720 until this month, people seem to have been doing relatively well, 0:02:07.760 --> 0:02:10.480 and there was all this intrigue and interest in the 0:02:10.520 --> 0:02:13.880 world of traders. And I'm sort of curious. This has 0:02:13.919 --> 0:02:17.720 come up a couple times now, but what makes a 0:02:17.720 --> 0:02:22.280 good trader and how are traders actually evaluated? Because my 0:02:22.360 --> 0:02:25.080 impression was always like, Okay, well it depends on how 0:02:25.160 --> 0:02:29.200 much money you make, but what's the timeframe for making 0:02:29.200 --> 0:02:31.400 that money? And then also what about people who are 0:02:31.440 --> 0:02:35.200 working in for instance, these particular pods who are doing 0:02:35.280 --> 0:02:38.960 one specific thing, like what is the benchmark against which 0:02:39.000 --> 0:02:39.680 they are judged? 0:02:40.040 --> 0:02:43.200 You mentioned that maybe they're not making so much money 0:02:43.320 --> 0:02:45.680 this week or this month, But Tracy, I think we're 0:02:45.720 --> 0:02:47.960 told all the time they're so neutral on everything. Their 0:02:48.080 --> 0:02:51.680 market neutral, they're beta neutral, they're neutral every factor you 0:02:51.680 --> 0:02:53.760 can think of. Why should they be losing money right 0:02:53.800 --> 0:02:55.640 now They're supposed to like be neutral all. 0:02:55.560 --> 0:02:56.079 Of this time. 0:02:56.160 --> 0:02:59.000 Yeah, I'm sure they're making loads shore. I'm absolutely sure. 0:02:59.160 --> 0:03:01.160 No, but you're right. And look, we've been doing a 0:03:01.200 --> 0:03:04.640 lot on hedge fund structure, and we did that episode 0:03:04.680 --> 0:03:07.880 with Giuseppe Polyoligo, and we did that episode with Rich 0:03:07.960 --> 0:03:11.880 falk Wallace, various aspects of like how hedge funds measure 0:03:11.960 --> 0:03:14.799 risk and try to isolate alpha and all this stuff. 0:03:14.800 --> 0:03:18.040 But they're just like so many questions in my mind, 0:03:18.160 --> 0:03:20.880 Like I feel like we're just scratching the surface because 0:03:21.240 --> 0:03:24.120 you know, we haven't even really talked about like idea generation. 0:03:24.240 --> 0:03:26.720 So it's one thing to you know, talk about like, okay, 0:03:26.760 --> 0:03:30.160 here's how you like factor out all of these exposures 0:03:30.160 --> 0:03:32.359 that you don't on have like market beta, et cetera. 0:03:32.400 --> 0:03:35.120 It's another thing to talk about like okay, but like 0:03:35.360 --> 0:03:37.600 how do you pick the stocks to buy or go short? 0:03:37.800 --> 0:03:39.760 Well, yeah, we have gotten into this a little bit, 0:03:39.760 --> 0:03:41.280 but you're right, there's more we could do. There are 0:03:41.280 --> 0:03:43.920 all these questions about like how do you size your positions? 0:03:44.000 --> 0:03:46.080 And if you're convinced that one thing is going to 0:03:46.160 --> 0:03:48.120 be the next big thing, then why don't you just 0:03:48.200 --> 0:03:50.040 have like one hundred percent positions? 0:03:50.120 --> 0:03:50.320 Yeah? 0:03:50.600 --> 0:03:52.520 Right, how do you make money if you can't just 0:03:52.560 --> 0:03:55.280 go one hundred percent leverage long and video in video. 0:03:55.400 --> 0:03:55.920 Yeah. 0:03:55.960 --> 0:03:58.080 Anyway, so there's a lot more we can do. But 0:03:58.560 --> 0:04:02.640 to my original, very egotistical start to this episode, I 0:04:02.680 --> 0:04:04.000 do wonder like it's. 0:04:03.840 --> 0:04:07.720 Okay, Joe, it's good to have self confidence. I'm being serious, 0:04:07.840 --> 0:04:08.200 thank you. 0:04:08.760 --> 0:04:11.520 I do wonder like this big question of like you know, 0:04:11.560 --> 0:04:13.000 and a lot of people are probably interested in this 0:04:13.040 --> 0:04:15.720 because these hedge one PM jobs or trader jobs seem 0:04:15.760 --> 0:04:17.960 pretty great and as you mentioned, lucrative, and so it 0:04:18.000 --> 0:04:21.239 would be interesting to know how a fund or anyone 0:04:21.320 --> 0:04:24.640 goes about identifying like the next great trader who gets 0:04:24.680 --> 0:04:26.080 to have that seat, so to speak. 0:04:26.200 --> 0:04:28.839 Well, I also think if you can identify what makes 0:04:28.880 --> 0:04:31.240 a good trader at a hedge fund, then you can 0:04:31.320 --> 0:04:34.400 get more into the business model of what they're actually 0:04:34.440 --> 0:04:36.560 doing on a day to day basis. It helps us 0:04:36.600 --> 0:04:37.480 understand what. 0:04:37.440 --> 0:04:40.159 They're really good at and what they can do specifically. Well, 0:04:40.200 --> 0:04:44.039 I'm very excited today because we really do have the 0:04:44.080 --> 0:04:46.400 perfect guest. We're going to be speaking with, Joe Peta. 0:04:46.520 --> 0:04:49.279 He is the author of a recent book, Moneyball for 0:04:49.360 --> 0:04:51.840 the money set, which is the name sort of implies 0:04:52.279 --> 0:04:54.919 tries to, you know, figure out new ways or the 0:04:54.960 --> 0:04:57.920 best ways to identify talent. I'm sure there's a lot 0:04:57.920 --> 0:05:00.600 of old heuristics like they had in Bay, you know, 0:05:00.640 --> 0:05:02.080 and they're like, well, this guy looks like he has 0:05:02.080 --> 0:05:05.200 a good hustle, and then Moneyball came along. He's like, no, actually, 0:05:05.480 --> 0:05:07.320 you want to really look at his like you know, 0:05:07.400 --> 0:05:09.919 on base percentage or whatever it is, and stop looking 0:05:09.920 --> 0:05:12.240 at like his like spirit or you know, his hustle 0:05:12.320 --> 0:05:16.000 ahead of him. Anyway, and prior to that, in his career, 0:05:16.000 --> 0:05:17.680 he's been in this industry for a long time. He 0:05:17.760 --> 0:05:20.520 was the head of performance analytics at point seventy two. 0:05:21.040 --> 0:05:23.400 So this speaks right to the question of how do 0:05:23.440 --> 0:05:26.159 you evaluate traders. We also hit him on years ago 0:05:26.279 --> 0:05:29.200 one of our really early episodes where he talked about 0:05:29.200 --> 0:05:32.200 sports betting with some of these same ideas, et cetera. 0:05:32.800 --> 0:05:36.520 So I'm thrilled to have Joe back to talk about 0:05:36.880 --> 0:05:39.480 this basic question of how it's good trader. So thanks 0:05:39.480 --> 0:05:40.160 for coming back, Joe. 0:05:40.279 --> 0:05:42.320 Oh, it's great to be here Joe and Tracy, and 0:05:42.440 --> 0:05:44.719 nice to do it in person. Seven years ago, Tracy, 0:05:44.800 --> 0:05:47.160 I believe you were in Hong Kong and yeah, Joe, 0:05:47.160 --> 0:05:50.920 you just had a garage band instead of selling out venues. 0:05:50.960 --> 0:05:53.839 Now that's right, that's right. Still mentioned you were head 0:05:53.880 --> 0:05:56.720 of performance analytics at point seventy two. How did you 0:05:56.760 --> 0:05:59.800 get that job at a point seventy two? Steve Cohen's 0:05:59.800 --> 0:06:00.680 big yes. 0:06:00.760 --> 0:06:04.320 So that goes right back to my appearance seven years ago. 0:06:04.680 --> 0:06:06.400 So when I was on in twenty seventeen, I had 0:06:06.440 --> 0:06:09.040 written a book called Trading Basis, which really looked at 0:06:09.080 --> 0:06:13.800 the critical reasoning overlap between asset management, sports betting, and 0:06:13.839 --> 0:06:17.800 the moneyballization of baseball. And you had ask me, Joe, 0:06:17.839 --> 0:06:20.000 I think it was you asked me a specific question 0:06:20.080 --> 0:06:22.640 of well, I mentioned that somebody from all three of 0:06:22.680 --> 0:06:27.120 those constituents could learn something from the other two. And Joe, 0:06:27.160 --> 0:06:29.480 you asked me for a specific example of how they 0:06:29.480 --> 0:06:32.120 look at things differently, and I said, well, if you 0:06:32.320 --> 0:06:35.400 go onto a trading floor, or you go to a 0:06:35.480 --> 0:06:38.359 mutual fund and you ask them, hey, who's your best 0:06:38.360 --> 0:06:42.719 trader or who's your best PM, Inevitably they will point 0:06:42.760 --> 0:06:45.200 to the individual who had the highest return in the 0:06:45.200 --> 0:06:47.560 prior year, either the biggest P and L or the 0:06:47.600 --> 0:06:50.800 highest return on capitol. But I contrasted that that if 0:06:50.839 --> 0:06:52.920 you went into the front office of a baseball team 0:06:52.920 --> 0:06:55.360 and asked them who their best player was, they wouldn't 0:06:55.400 --> 0:06:58.360 look at you know, which picture necessarily had the lowest 0:06:58.600 --> 0:07:01.560 ra or the most wins. They would answer that question 0:07:01.680 --> 0:07:05.640 based on skill sets, and so they it's a subtle difference. 0:07:05.680 --> 0:07:08.320 Instead of looking at results, they would look at skills 0:07:08.440 --> 0:07:11.960 because they know that the skills, there's so much noise 0:07:12.040 --> 0:07:15.640 and results that the skills. If you can identify the skills, 0:07:15.640 --> 0:07:17.480 you have a better chance of predicting who will do 0:07:17.560 --> 0:07:20.040 better going forward. And as it was told to me, 0:07:20.360 --> 0:07:22.920 a member of the c suite, at zero point seventy 0:07:22.960 --> 0:07:29.680 two listen regular listener heard that episode and played a 0:07:29.720 --> 0:07:31.640 portion of it for Steve. In fact, I think it 0:07:31.680 --> 0:07:34.640 was the part I just mentioned, And I was told, 0:07:34.760 --> 0:07:37.240 as it was relayed to me that Steve said, find him. 0:07:37.280 --> 0:07:40.000 I want to talk to him. And I guess that's 0:07:40.040 --> 0:07:44.280 not a surprise because in twenty twelve, and this is 0:07:44.320 --> 0:07:46.720 all public knowledge. In fact, there's a book by Molly 0:07:46.800 --> 0:07:50.880 Knight called The Best Team Money Can Buy that chronicles 0:07:50.920 --> 0:07:56.800 the Dodger's ownership through the turbulent court years Frank McCourt's ownership, 0:07:57.360 --> 0:08:00.520 and that team was sold in twenty twelve to the 0:08:00.560 --> 0:08:04.000 Guggenheim Group. But Steve also bid for that team and 0:08:04.080 --> 0:08:06.640 came very close to buying the Dodgers in twenty twelve. 0:08:06.680 --> 0:08:08.280 And of course we all know him now as New 0:08:08.360 --> 0:08:11.320 Yorker's nome his uncle Steve owner of the New York Mets. 0:08:11.760 --> 0:08:15.400 So he has, I believe, always had an interest in 0:08:15.440 --> 0:08:18.680 an analytical approach, and I think he always wondered, well 0:08:18.800 --> 0:08:21.440 could that work in the hedge Fund. And I came 0:08:21.480 --> 0:08:25.360 away from those meetings with the bunch of different people 0:08:25.680 --> 0:08:28.360 in the investment committee, and I kind of came away 0:08:28.400 --> 0:08:30.920 with three queries that I thought could sort of be 0:08:31.000 --> 0:08:33.400 my marching orders and how I could help, And that 0:08:33.640 --> 0:08:37.280 was I think at all these pod shops, when somebody 0:08:37.320 --> 0:08:40.480 has a good year, they ask for more money, and 0:08:40.640 --> 0:08:42.760 in terms of buying power, not cash, but in terms 0:08:42.800 --> 0:08:46.960 of buying power. And so the question that management would 0:08:47.000 --> 0:08:51.199 have is, well, is what they did repeatable? And at 0:08:51.200 --> 0:08:55.040 the same time, as you know, there's turnover at these firms, right, 0:08:55.120 --> 0:08:58.240 And I think another question is, well, sometimes we let 0:08:58.320 --> 0:09:01.800 people go too early that then thrive elsewhere just because 0:09:01.840 --> 0:09:04.000 they had a bad start to their career in terms 0:09:04.040 --> 0:09:06.480 of results. Is there a way that we can avoid 0:09:06.480 --> 0:09:09.960 that mistake? And then finally, when a team does well, 0:09:10.040 --> 0:09:12.680 inevitably there's a bit of way, right because we know 0:09:12.800 --> 0:09:14.959 that these four or five huge firms are all very 0:09:15.000 --> 0:09:18.880 competitive and they're trying to steal talent. And so the 0:09:19.000 --> 0:09:22.280 question is I know what a PM and or her 0:09:22.320 --> 0:09:24.200 team may have made me in the past, but what 0:09:24.240 --> 0:09:28.040 are they worth going forward? And all of those queries 0:09:28.080 --> 0:09:30.520 can be answered by looking at skills, which is a 0:09:30.559 --> 0:09:35.120 little different than what the traditional quants do at these firms. 0:09:35.720 --> 0:09:39.120 Okay, so here's my question, who should we bill for 0:09:39.200 --> 0:09:42.720 the finder's fee fee? Should we send it directly to Steez? 0:09:42.800 --> 0:09:44.719 He do you right? 0:09:44.800 --> 0:09:48.040 It would be the firm, right, they probably saved a 0:09:48.080 --> 0:09:51.160 lot of money as opposed to going through a traditional headhunter. 0:09:51.440 --> 0:09:54.400 Okay, I'm joking. Obviously that's fantastic to hear. I've loved 0:09:54.440 --> 0:09:58.840 stories like that. Before we get into the existing model 0:09:58.920 --> 0:10:02.080 of compensation. There's one question that I wonder because I 0:10:02.080 --> 0:10:06.000 think we've done a number of Moneyball episodes at this point, 0:10:06.040 --> 0:10:08.800 but it's been a while since we've talked about that approach, 0:10:08.920 --> 0:10:12.360 and all I remember is the movie and Brad Pitt 0:10:12.440 --> 0:10:15.960 kind of unconvincingly playing a guy that understands math. Could 0:10:16.040 --> 0:10:20.640 you maybe explain, like what it is about the Moneyball 0:10:20.760 --> 0:10:25.840 approach that seems to attract people in finance, Like why 0:10:25.920 --> 0:10:28.520 is there that analogy that seems to come up again 0:10:28.559 --> 0:10:29.000 and again. 0:10:29.200 --> 0:10:32.120 Yeah, I think if you're attracted to critical reasoning, and 0:10:32.760 --> 0:10:37.360 that's the big thing and all of this industry is 0:10:37.440 --> 0:10:39.720 you know, Joe said, what have I succeeded here? And 0:10:39.720 --> 0:10:42.600 I always think the biggest question is do you have 0:10:42.760 --> 0:10:46.760 the mentality in the stomach to make decisions and commit 0:10:46.840 --> 0:10:50.960 capital based on incomplete information? Whether you have the skills 0:10:50.960 --> 0:10:54.160 to you know, build models for you know, and and 0:10:55.040 --> 0:10:58.839 understand companies and read documents. It's really can you make 0:10:58.880 --> 0:11:02.400 decisions based on income information? And it's true at the 0:11:02.400 --> 0:11:06.120 poker table, all right, and it's certainly true when you're 0:11:06.120 --> 0:11:08.599 building a sports team, right you're like, how much is 0:11:08.640 --> 0:11:13.040 this free agent worth? And before there were a lot 0:11:13.080 --> 0:11:16.160 of Joe like you say heuristics, and I even mentioned 0:11:16.160 --> 0:11:18.120 that in the book. I feel that still goes on 0:11:18.200 --> 0:11:21.840 at the allocator level in this industry. Allocators they do 0:11:21.880 --> 0:11:25.880 the interviews and you will hear things like, well he 0:11:26.040 --> 0:11:29.280 just got divorced, you know, or there's a Bentley in 0:11:29.360 --> 0:11:32.800 the parking lot. He must not be hungry anymore. Oh absolutely. 0:11:33.320 --> 0:11:35.120 And one of the reasons is because they don't have 0:11:35.320 --> 0:11:40.360 they don't take a different approach that might be more databased. 0:11:40.679 --> 0:11:45.840 The whole idea of the moneyball approach is to tease 0:11:45.920 --> 0:11:51.040 out skill from or the signal from these very noisy results, 0:11:51.080 --> 0:11:55.760 because both athletes and asset managers, their results are filled 0:11:55.840 --> 0:12:00.440 with influences over which they have no control. To answer 0:12:00.480 --> 0:12:04.520 this question later, you both were talking about like market 0:12:04.640 --> 0:12:09.400 neutral PMS, neutral everything pms, Why would they be having 0:12:09.400 --> 0:12:11.800 the worst week this week than before. And there's an 0:12:11.840 --> 0:12:14.640 actual real answer to that that has nothing to do 0:12:14.720 --> 0:12:19.360 with their skills. Yeah, so this can apply to any 0:12:19.440 --> 0:12:22.200 time period. We're looking at days or months, a year. 0:12:22.520 --> 0:12:24.760 Let's go to sort of an economics one OHO one 0:12:24.800 --> 0:12:28.000 like holding all else equal. Let's say we have a 0:12:28.040 --> 0:12:30.960 PM that has one long and one short, okay, and 0:12:30.960 --> 0:12:33.480 that's their entire portfolio. And of course they never would 0:12:33.480 --> 0:12:35.079 this goes to something else you said in the intro 0:12:35.320 --> 0:12:38.120 because of career risk. Right, even if it's their best 0:12:38.160 --> 0:12:40.320 idea long and best idea short, they'll still fill it. 0:12:40.320 --> 0:12:42.960 But let's say this is their portfolio and on any 0:12:42.960 --> 0:12:45.880 given day or any period we could measure, but let's 0:12:45.960 --> 0:12:46.640 keep it at a day. 0:12:47.120 --> 0:12:48.360 It's a perfect. 0:12:47.960 --> 0:12:52.240 Portfolio in that the long produces alpha and the short 0:12:52.240 --> 0:12:55.200 produces alpha. So the long outperforms the market and the 0:12:55.240 --> 0:12:58.160 short underperforms the market. Right, So that's a perfect portfolio. 0:12:58.760 --> 0:13:01.599 What is the expected re turn for that portfolio for 0:13:01.920 --> 0:13:04.640 like I say, any period, but for a day, And 0:13:04.679 --> 0:13:07.120 the answer is there's a way to figure out. And Tracey, 0:13:07.120 --> 0:13:10.840 you're gonna love this because the answer is dispersion. And 0:13:10.880 --> 0:13:13.160 I know you light up when when you have the 0:13:13.720 --> 0:13:17.240 But this is a little different dispersion than the quants 0:13:17.240 --> 0:13:21.160 and the derivative traders make their life around. This dispersion 0:13:21.440 --> 0:13:25.280 is and it's going to be very context specific for 0:13:25.480 --> 0:13:28.520 where the PM toils. Right, so we know what these 0:13:28.559 --> 0:13:30.840 pod shops they tend to be. They have you know, 0:13:31.520 --> 0:13:34.240 subject matter expertise in sectors. Right, So you might have 0:13:34.240 --> 0:13:36.600 an energy PM, and so let's say this is a consumer 0:13:36.600 --> 0:13:39.720 discretionary PM, and you would say, okay, well, I'm going 0:13:39.800 --> 0:13:42.319 to look at his or her universe and maybe that's 0:13:42.360 --> 0:13:45.360 the S and P fifteen hundred consumer discretionary Maybe it's 0:13:45.360 --> 0:13:49.320 say a portfolio of just consumer discretionary stocks that he 0:13:49.400 --> 0:13:51.320 has modeled, so there might only be eighty or so 0:13:51.360 --> 0:13:53.640 he and his team, but whatever it is, we'll say 0:13:53.640 --> 0:13:57.240 that it's the all the consumer discretionary stocks in the 0:13:57.280 --> 0:13:59.319 S and P five hundred or fifteen hundred. Well, the 0:13:59.360 --> 0:14:01.400 way to figure out what the expected return is is 0:14:01.440 --> 0:14:04.520 to simply look at all those stocks and say, here's 0:14:04.559 --> 0:14:07.520 the skill neutral return, which would be the average return 0:14:07.600 --> 0:14:10.280 of all those holdings. And then you look at the 0:14:10.320 --> 0:14:12.760 ones that outperformed what was their average And you look 0:14:12.840 --> 0:14:15.199 at all the stocks that underperformed and what was their average. 0:14:15.240 --> 0:14:18.359 And the difference between those two numbers is the dispersion 0:14:18.480 --> 0:14:25.520 between outperformers and underperformers, and that varies greatly from day 0:14:25.520 --> 0:14:28.680 to day, and it can very greatly from year to year. 0:14:28.720 --> 0:14:32.720 It's not like the maximum that you can produce dispersions. 0:14:32.200 --> 0:14:34.480 Not the maximum, because you could have the very best 0:14:34.480 --> 0:14:39.080 outperformer and the very best underperformer. But if you're looking 0:14:39.080 --> 0:14:42.240 at a POD, so I'm taking all the pod from 0:14:42.280 --> 0:14:45.040 all the shops across the street that are focused on 0:14:45.680 --> 0:14:48.840 consumer discretionary, I'm going to be dead on by saying 0:14:48.880 --> 0:14:52.160 the average of all those of all those perfect portfolios 0:14:52.400 --> 0:14:54.119 is going to be the average of all the outperformers 0:14:54.120 --> 0:14:57.640 and the average of all the underperformers. And it's invisible 0:14:58.160 --> 0:15:03.240 to investment commits, to CIOs, to the pms themselves. They 0:15:03.280 --> 0:15:06.640 can be just as skilled from one day or one 0:15:06.720 --> 0:15:09.240 period and one year to the next, but the payoff 0:15:09.320 --> 0:15:12.280 is different. And this is sort of the moneyball. Look 0:15:12.320 --> 0:15:15.560 at hey, once we get this all context neutral, we 0:15:15.720 --> 0:15:19.400 might say that a neutral everything PM that had a 0:15:19.400 --> 0:15:21.920 seven percent return one year and a five percent cent 0:15:22.000 --> 0:15:24.480 return the next year, he may have even been more 0:15:24.480 --> 0:15:27.920 skilled than the five percent year, but the dispersion wasn't 0:15:27.920 --> 0:15:29.760 there to pay off that skill. 0:15:30.480 --> 0:15:33.640 Oh, I see what you're saying. So in other words, 0:15:33.840 --> 0:15:37.200 it's like, Okay, this person's up five percent. In order 0:15:37.680 --> 0:15:40.520 to establish like whether that's good or bad or not, 0:15:40.960 --> 0:15:43.920 you have to have some sort of like holistic view 0:15:44.080 --> 0:15:47.360 of what dispersion on average looked like. In that that's 0:15:47.920 --> 0:16:04.880 exactly well, that makes sense. It also seems kind of obvious, 0:16:07.560 --> 0:16:09.920 you know, I know, the divorce and the Bentley is 0:16:09.960 --> 0:16:14.520 probably like extreme examples. They're sort of funny. But you know, 0:16:14.600 --> 0:16:16.680 thinking about the moneyball thing, and I mentioned in the 0:16:16.680 --> 0:16:18.480 old days like oh, that guy looks he's a good 0:16:18.520 --> 0:16:21.080 ey or whatever, just like all these sort of unquantified 0:16:21.120 --> 0:16:24.520 his hustle, you know, his heart whatever. And then you know, 0:16:24.560 --> 0:16:27.480 Brad Pitt or the being came along and actually put 0:16:27.520 --> 0:16:30.400 some numbers on this. If they're not doing that, what 0:16:30.560 --> 0:16:33.840 are the sort of like old heuristics that aren't the 0:16:33.880 --> 0:16:37.400 extreme ones that the investment committees or the hiring committees 0:16:37.440 --> 0:16:40.600 of the firing committees would have been using two aviliate. 0:16:40.160 --> 0:16:44.440 Pro So there's no question that it seems obvious, and 0:16:44.480 --> 0:16:46.840 it's just the first building block. And this isn't Black 0:16:46.880 --> 0:16:48.600 Shawl stuff in terms of complexity. 0:16:48.680 --> 0:16:49.239 Yeah. 0:16:49.280 --> 0:16:52.000 I started this sort of journey and analytics by working 0:16:52.000 --> 0:16:54.880 for a company called Novus, and Novas was one of 0:16:55.000 --> 0:16:58.840 about fifteen years ago, was at the forefront of portfolio analytics, 0:16:59.520 --> 0:17:01.400 and in fact, they had read my book and I'm like, hey, 0:17:01.440 --> 0:17:03.160 this is what we try to do. And I worked 0:17:03.160 --> 0:17:06.960 for them. So I've seen just about every package out there, 0:17:07.160 --> 0:17:11.160 whether it is from a vendor in terms of analytics 0:17:11.280 --> 0:17:16.400 or you know inside firms. I've you know, worked with allocators. 0:17:17.200 --> 0:17:22.840 I have never seen dispersion quoted Michael Mobison has written 0:17:22.880 --> 0:17:25.840 a paper on it. So there are academics who are 0:17:25.960 --> 0:17:29.560 aware of it, but I don't think people realize that 0:17:29.720 --> 0:17:32.439 is the calculation for the fruit on the tree, the 0:17:32.480 --> 0:17:35.680 meat on the bone, for these pod shops, there has 0:17:35.760 --> 0:17:39.399 to be dispersion to pay off a non factor you know, 0:17:39.440 --> 0:17:44.199 a factor neutral portfolio. So what the quants really do 0:17:44.400 --> 0:17:46.679 and this is like what Gappy touched on when he 0:17:46.720 --> 0:17:48.280 talked about you know, the day in the life of 0:17:48.280 --> 0:17:50.600 a quant and your other guest within the last month 0:17:50.640 --> 0:17:54.000 whose name I can't read. Yes, there was lots of 0:17:54.040 --> 0:17:57.840 talk about risk management, right because of course it's of 0:17:58.200 --> 0:18:01.240 utmost importance when you have a leveraged firm, right, you 0:18:01.400 --> 0:18:03.880 have to understand every factor that's bouncing around in there, 0:18:04.240 --> 0:18:07.480 and that's really their job, and they will, of course 0:18:07.560 --> 0:18:10.919 because draw downs in a leverage firm, drawdowns are to 0:18:10.960 --> 0:18:14.240 be avoided as much as possible. So the sharp ratio 0:18:14.520 --> 0:18:18.440 really drives the way the quants are looking at pms. 0:18:18.600 --> 0:18:21.359 But they're all backwards looking, sort of in my view, 0:18:22.280 --> 0:18:26.240 So they do strip out everything. But once they get alpha, 0:18:26.359 --> 0:18:28.960 or as I know, one firm calls it idiosyncratic alpha. 0:18:29.920 --> 0:18:31.640 What I then do is the next step. I don't 0:18:31.720 --> 0:18:34.280 change the definition of alpha, but then I break that 0:18:34.400 --> 0:18:38.160 into a skill framework so that once you get different skills, 0:18:38.200 --> 0:18:41.120 you can say this one's more repeatable than another skill, 0:18:41.160 --> 0:18:41.600 et cetera. 0:18:42.320 --> 0:18:46.440 So like dispersion weighted basically like weighted by the opportunity 0:18:46.480 --> 0:18:47.760 that's available. 0:18:47.280 --> 0:18:50.919 To you, Yes, exactly, And that's what allows you Tracy 0:18:51.040 --> 0:18:55.480 to compare the energy trader to the consumer discussionary trader 0:18:55.720 --> 0:18:58.320 because and I make an analogy in the book, it's 0:18:58.359 --> 0:19:01.320 like looking at NFL punt. You know, PM's job is 0:19:01.359 --> 0:19:03.480 to make as much money as possible, and essentially a 0:19:03.480 --> 0:19:05.600 punter's job is to kick the ball as far as possible. 0:19:05.920 --> 0:19:09.679 So before sports analytics came along, punters were judged on 0:19:09.760 --> 0:19:11.439 and in fact, I think there was even award for 0:19:11.840 --> 0:19:14.119 the punter that had the biggest average at the end 0:19:14.160 --> 0:19:15.840 of the year, right the distance of all his punts 0:19:15.840 --> 0:19:19.320 divided by total number of punts. But what sports analytics 0:19:19.359 --> 0:19:22.560 people quickly figured out is that, well, hey, if the 0:19:22.880 --> 0:19:25.639 best punter is averaging forty four yards to punt, and 0:19:25.680 --> 0:19:29.159 you've got another punter whose coach is so conservative that 0:19:29.200 --> 0:19:33.040 he's constantly punning from the opponent's thirty five yard line 0:19:33.119 --> 0:19:35.240 or the opponent's forty yard line, he can't even get 0:19:35.240 --> 0:19:38.600 a forty four yard punt off. So the way to 0:19:38.720 --> 0:19:43.160 measure that is to say, okay, when a punts from 0:19:43.160 --> 0:19:45.840 his own fifteen yard line, I'm going to measure that 0:19:46.160 --> 0:19:50.160 against every other punt from the fifteen yard line. And 0:19:50.440 --> 0:19:54.160 now you each punt is then evaluated. And I think 0:19:54.160 --> 0:19:56.800 what's really important to the work I do too, is 0:19:56.920 --> 0:19:59.920 or to note, you don't measure it now by the distance. 0:20:00.320 --> 0:20:03.720 You measure it by plus or minus the average punter. 0:20:03.840 --> 0:20:07.080 So you can say someone is on average one and 0:20:07.080 --> 0:20:09.760 a half yards better per punt, and then you can 0:20:09.760 --> 0:20:11.920 put a value on that. And that's the same way 0:20:11.960 --> 0:20:14.679 a lot of you know, my framework is, it's not 0:20:15.200 --> 0:20:19.520 saying you know it especially sort of like that that 0:20:20.000 --> 0:20:22.879 canned package. You will see a canned batting average on 0:20:22.960 --> 0:20:27.720 all analytics platform. It's meaningless. In fact, it's worthless. But 0:20:27.800 --> 0:20:30.800 if you express it the way I just talked about punters, 0:20:30.800 --> 0:20:33.840 sort of this skill neutral and to say, oh, his 0:20:33.960 --> 0:20:36.800 batting average is one or two percent above, you know, 0:20:36.920 --> 0:20:39.879 over the year he averages one percent a day. Well, 0:20:40.080 --> 0:20:42.360 you know, in a fifty percent portfolio, that would be 0:20:42.800 --> 0:20:45.480 you know, one more winner than expect it every other day. 0:20:45.760 --> 0:20:48.520 Then you can compare that to the dispersion world that 0:20:48.560 --> 0:20:51.320 he lives in, and you can put an absolute value 0:20:51.760 --> 0:20:55.320 on his skill. Now it might differ from the actual, 0:20:55.640 --> 0:20:57.919