1 00:00:02,640 --> 00:00:16,239 Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. 2 00:00:18,360 --> 00:00:22,000 Speaker 2: Hello and welcome to another episode of the Odd Lots podcast. 3 00:00:22,079 --> 00:00:24,480 Speaker 3: I'm Joe Wisenthal and I'm Tracy Alloway. 4 00:00:24,720 --> 00:00:28,240 Speaker 2: Tracy, I still want to learn more about how multi 5 00:00:28,240 --> 00:00:29,640 Speaker 2: strategy hedge funds work. 6 00:00:30,080 --> 00:00:31,600 Speaker 3: I thought you were going to say, I still don't 7 00:00:31,600 --> 00:00:35,360 Speaker 3: know anything about multi strategy. I feel like we're slowly 8 00:00:35,440 --> 00:00:39,640 Speaker 3: getting there, and hopefully our listeners don't mind coming along 9 00:00:39,800 --> 00:00:42,800 Speaker 3: with us for the ride. I feel like every time 10 00:00:42,960 --> 00:00:45,960 Speaker 3: we have an episode on multi strategy hedge funds, or 11 00:00:45,960 --> 00:00:48,960 Speaker 3: on the pod shops as they are sometimes called, we 12 00:00:49,040 --> 00:00:52,800 Speaker 3: are deepening our understanding and we're sort of getting into 13 00:00:52,920 --> 00:00:56,400 Speaker 3: more and more detail. And I feel confident that one day, 14 00:00:56,640 --> 00:00:59,320 Speaker 3: after we've done like fifty episodes on this topic, we 15 00:00:59,360 --> 00:00:59,920 Speaker 3: will get there. 16 00:01:00,280 --> 00:01:02,760 Speaker 2: I do think it would take about fifty I think 17 00:01:02,760 --> 00:01:05,000 Speaker 2: that's like an accurate number of what it would actually 18 00:01:05,040 --> 00:01:08,240 Speaker 2: take to get there. But of course, most recently we 19 00:01:08,400 --> 00:01:13,120 Speaker 2: had that episode with Giuseppe Pallioligo Gappy talking about some 20 00:01:13,240 --> 00:01:15,720 Speaker 2: of the big ideas and sort of from a high 21 00:01:15,840 --> 00:01:19,080 Speaker 2: level of how some of these funds actually work. They're 22 00:01:19,160 --> 00:01:22,000 Speaker 2: very popular. They've done some of the big ones that 23 00:01:22,040 --> 00:01:25,480 Speaker 2: people know, like the millenniums, like the Citadels have just 24 00:01:25,520 --> 00:01:29,479 Speaker 2: had incredible runs. Really seems to be displacing a lot 25 00:01:29,520 --> 00:01:33,040 Speaker 2: of the old style quant disrupting the sort of fund 26 00:01:33,040 --> 00:01:36,440 Speaker 2: of funds idea that was popular. I have some sense, 27 00:01:36,520 --> 00:01:38,520 Speaker 2: you know, you have all these managers and you give 28 00:01:38,520 --> 00:01:41,640 Speaker 2: them very specific mandates and they have to really focus 29 00:01:41,680 --> 00:01:44,040 Speaker 2: on that, and then if they're not too correlated with 30 00:01:44,080 --> 00:01:47,480 Speaker 2: each other, you can get above market returns in theory 31 00:01:47,520 --> 00:01:52,360 Speaker 2: and apparently in practice, but like how that actually works, 32 00:01:52,440 --> 00:01:53,360 Speaker 2: I still really don't know. 33 00:01:53,680 --> 00:01:57,640 Speaker 3: Well, Okay, so two things. Number One, everyone should definitely 34 00:01:57,880 --> 00:02:00,960 Speaker 3: go and check out Gappy's book if you haven't already, 35 00:02:01,080 --> 00:02:04,640 Speaker 3: Advanced Portfolio Management. A lot of the references that I'm 36 00:02:04,800 --> 00:02:07,080 Speaker 3: about to throw out on this episode, anything that I 37 00:02:07,160 --> 00:02:11,160 Speaker 3: say that might sound even remotely impressive or like I 38 00:02:11,200 --> 00:02:13,920 Speaker 3: know what I'm talking about, has come up Gappy's book. 39 00:02:14,120 --> 00:02:16,080 Speaker 3: And also I will say I've read that book going 40 00:02:16,120 --> 00:02:19,639 Speaker 3: to and from work on the subway. It's pretty short, 41 00:02:19,680 --> 00:02:21,040 Speaker 3: so I think I did it in like a week. 42 00:02:21,560 --> 00:02:24,840 Speaker 3: And I have never gotten so many people like talking 43 00:02:24,880 --> 00:02:27,480 Speaker 3: to me on the subway when they saw me pull 44 00:02:27,520 --> 00:02:30,079 Speaker 3: out Advance Portfolio Management and they're like, what is. 45 00:02:30,040 --> 00:02:31,560 Speaker 2: That that is very New York. 46 00:02:31,760 --> 00:02:35,000 Speaker 3: Yes. And then secondly, the other thing I will say 47 00:02:35,160 --> 00:02:37,920 Speaker 3: is we've been talking about multi strategy hedge funds. We 48 00:02:37,919 --> 00:02:40,280 Speaker 3: want to learn more about them because they're this new 49 00:02:40,280 --> 00:02:42,920 Speaker 3: thing on Wall Street that everyone seems very excited and 50 00:02:43,000 --> 00:02:46,960 Speaker 3: interested in. But beyond that, there are recent events that 51 00:02:47,080 --> 00:02:50,600 Speaker 3: make this an even more pressing topic. So we've seen 52 00:02:50,800 --> 00:02:53,359 Speaker 3: some of the big winners in the market in recent 53 00:02:53,440 --> 00:02:56,360 Speaker 3: months start to come down, So the big tech names 54 00:02:56,480 --> 00:03:00,400 Speaker 3: things like Nvidia, we've seen small caps shoot up. A 55 00:03:00,440 --> 00:03:02,200 Speaker 3: lot of people are talking about whether or not this 56 00:03:02,360 --> 00:03:06,639 Speaker 3: is a factor rotation, and we'll get into what factors 57 00:03:06,680 --> 00:03:09,919 Speaker 3: actually are. But I think the discussion that we're seeing 58 00:03:10,000 --> 00:03:12,360 Speaker 3: right now, and I should caveat this with it is 59 00:03:12,480 --> 00:03:15,160 Speaker 3: July eighteenth. So we've seen those big moves in the 60 00:03:15,200 --> 00:03:19,840 Speaker 3: market very recently. The discussion that's happening now is how 61 00:03:19,960 --> 00:03:24,640 Speaker 3: much does the I guess growth in factor investing feed 62 00:03:24,720 --> 00:03:28,000 Speaker 3: into some of these moves, and also how does the 63 00:03:28,520 --> 00:03:33,000 Speaker 3: risk models that go alongside this actually impact investor behavior 64 00:03:33,080 --> 00:03:35,720 Speaker 3: and then also feed into these market moves. So is 65 00:03:35,720 --> 00:03:37,920 Speaker 3: it the case that everyone's getting out of big tech 66 00:03:38,240 --> 00:03:39,920 Speaker 3: because their risk models are telling them. 67 00:03:39,840 --> 00:03:43,840 Speaker 2: Too totally and this is like a really important element 68 00:03:43,960 --> 00:03:48,240 Speaker 2: for sort of understanding both how these investment vehicles work 69 00:03:48,280 --> 00:03:49,960 Speaker 2: and the impact that they have on the market. Which 70 00:03:50,000 --> 00:03:52,040 Speaker 2: is one of the things we know is that the 71 00:03:52,120 --> 00:03:56,440 Speaker 2: various portfolio managers within these funds have very tight remits. 72 00:03:56,480 --> 00:04:00,680 Speaker 2: It's like, your team is responsible for trading chip stocks, 73 00:04:00,720 --> 00:04:03,520 Speaker 2: and your team is responsible for trading the short end 74 00:04:03,560 --> 00:04:07,320 Speaker 2: of the Brazilian yield curve, and your team is responsible 75 00:04:07,480 --> 00:04:10,600 Speaker 2: for international oil plays. And then we know that like 76 00:04:10,680 --> 00:04:12,880 Speaker 2: and then you're not allowed to take any sector beta, 77 00:04:12,880 --> 00:04:14,920 Speaker 2: and you're not allowed to take any market beta and 78 00:04:14,960 --> 00:04:17,359 Speaker 2: all these things, and so you see fact you're neutral 79 00:04:17,560 --> 00:04:20,719 Speaker 2: factor neutral, and then you know tight risk limits. So 80 00:04:20,720 --> 00:04:22,840 Speaker 2: if something starts to go down, you don't want to 81 00:04:22,880 --> 00:04:25,479 Speaker 2: lose your job, and you like get out of positions, 82 00:04:25,480 --> 00:04:28,120 Speaker 2: and that can create interesting moves for the market. Anyway, 83 00:04:28,200 --> 00:04:29,880 Speaker 2: suffice to say, there is much more to learn. 84 00:04:30,000 --> 00:04:32,720 Speaker 3: Yes, well, the other thing, just one more thing, Yeah, 85 00:04:32,960 --> 00:04:34,839 Speaker 3: the other other thing. The other other thing that I 86 00:04:34,839 --> 00:04:37,359 Speaker 3: think is kind of funny now is remember whenever you 87 00:04:37,440 --> 00:04:40,680 Speaker 3: had weird market moves. Yeah, like I guess it would 88 00:04:40,680 --> 00:04:42,960 Speaker 3: have been fifteen years ago or something like that, it 89 00:04:43,000 --> 00:04:46,480 Speaker 3: was always quant funds like the quant quake before two 90 00:04:46,520 --> 00:04:49,880 Speaker 3: thousand and eight, and then it became CTA's and then 91 00:04:49,920 --> 00:04:53,200 Speaker 3: it was risk parody, and now it's very much the 92 00:04:53,240 --> 00:04:56,040 Speaker 3: pod shops that people point to when we start to 93 00:04:56,040 --> 00:04:58,239 Speaker 3: see sketchiness in the market. So I think we should 94 00:04:58,240 --> 00:05:00,280 Speaker 3: talk about, you know, what are the technicalities that are 95 00:05:00,360 --> 00:05:01,960 Speaker 3: driving that pod shop behavior. 96 00:05:02,120 --> 00:05:04,800 Speaker 2: Every time there's some big move in the market, someone 97 00:05:04,880 --> 00:05:06,200 Speaker 2: tweets like, I hear a. 98 00:05:06,120 --> 00:05:08,440 Speaker 4: Pod is blowing up. Yeah, Oh I hear some pods. 99 00:05:08,120 --> 00:05:10,400 Speaker 2: Are blowing up. That's like, that's how to sound like 100 00:05:10,440 --> 00:05:12,520 Speaker 2: an in guy on a finance twit. 101 00:05:13,440 --> 00:05:15,760 Speaker 3: Little do they know the pod that's blowing up is off? 102 00:05:16,080 --> 00:05:18,520 Speaker 2: If you don't a good one. If you don't know 103 00:05:18,560 --> 00:05:22,440 Speaker 2: the pod that's blowing up, you're Anyway, we have the 104 00:05:22,440 --> 00:05:24,680 Speaker 2: perfect guest. I'm very excited. We are going to be 105 00:05:24,720 --> 00:05:28,760 Speaker 2: speaking with rich falk Wallace, who was previously a portfolio manager, 106 00:05:28,800 --> 00:05:32,040 Speaker 2: who's at Citadel, who's at Viking, and now he is 107 00:05:32,040 --> 00:05:35,760 Speaker 2: the CEO and co founder of Arcana, which builds models 108 00:05:35,760 --> 00:05:39,600 Speaker 2: and software to help investors and hedge funds, et cetera 109 00:05:40,040 --> 00:05:43,159 Speaker 2: actually track all of this stuff and actually track what 110 00:05:43,240 --> 00:05:46,960 Speaker 2: kind of risks managers are taking and how they're actually 111 00:05:47,000 --> 00:05:50,960 Speaker 2: performing relative to their benchmark or expectations. So we're gonna 112 00:05:50,960 --> 00:05:53,600 Speaker 2: maybe understand a bit more of the technical aspects of 113 00:05:53,640 --> 00:05:55,880 Speaker 2: all this stuff. So Rich, thank you so much for 114 00:05:55,960 --> 00:05:56,360 Speaker 2: coming on. 115 00:05:56,279 --> 00:05:58,360 Speaker 4: As thanks so much for having me. I appreciate it. 116 00:05:58,880 --> 00:06:00,720 Speaker 2: Why don't we start with your best and obviously we're 117 00:06:00,720 --> 00:06:03,120 Speaker 2: going to talk about your software company or Kenna and 118 00:06:03,200 --> 00:06:06,520 Speaker 2: all that. But you were previously at a couple of 119 00:06:06,520 --> 00:06:08,720 Speaker 2: these big funds. What did you do? 120 00:06:09,000 --> 00:06:11,080 Speaker 4: Yeah, that's right. So I started my career on the 121 00:06:11,120 --> 00:06:14,120 Speaker 4: buy side, started originally in investment banking out of college 122 00:06:14,279 --> 00:06:16,560 Speaker 4: JP Morgan, and then worked at silver Point, which is 123 00:06:16,600 --> 00:06:19,720 Speaker 4: like a large credit distressed hedge fund, very value oriented, 124 00:06:20,279 --> 00:06:22,240 Speaker 4: none of that sort of risk model framework that gets 125 00:06:22,240 --> 00:06:24,279 Speaker 4: deployed at the pods. And then after that was at 126 00:06:24,320 --> 00:06:27,120 Speaker 4: Viking Global, which is I always describe the Tiger Cubs 127 00:06:27,120 --> 00:06:29,479 Speaker 4: in some ways as like a hybrid between the sort 128 00:06:29,520 --> 00:06:33,920 Speaker 4: of equity long short value orientation sort of philosophically and 129 00:06:33,960 --> 00:06:36,960 Speaker 4: the multi manager systems. And then finally, most recently, was 130 00:06:37,000 --> 00:06:41,159 Speaker 4: a portfolio manager at Citadel managed a global materials, natural 131 00:06:41,160 --> 00:06:42,920 Speaker 4: resources and materials portfolio. 132 00:06:43,360 --> 00:06:46,320 Speaker 3: I love this because when I think about silver Point, 133 00:06:46,440 --> 00:06:50,880 Speaker 3: I think more sort of traditional value investing, and then 134 00:06:50,960 --> 00:06:54,680 Speaker 3: you wind up doing metals at Citadel, which is a 135 00:06:54,760 --> 00:06:58,240 Speaker 3: hedge fund that's known for being very quantitatively driven to 136 00:06:58,320 --> 00:07:01,880 Speaker 3: better understand the pods. Now, talk to us about the 137 00:07:01,880 --> 00:07:06,000 Speaker 3: differences between what you were doing at silver Point versus Citadel. 138 00:07:06,279 --> 00:07:09,400 Speaker 4: Totally. Yeah, it's a great question. So the way that 139 00:07:09,560 --> 00:07:13,080 Speaker 4: any value, super deep value oriented kind of fund works 140 00:07:13,160 --> 00:07:15,520 Speaker 4: like a silver Point is that in the end, you 141 00:07:15,560 --> 00:07:18,160 Speaker 4: do a ton of very deep research on the company, 142 00:07:18,240 --> 00:07:21,800 Speaker 4: So you focus on what are the underlying fundamentals, what's 143 00:07:21,840 --> 00:07:25,240 Speaker 4: the contract structure out many years in the future, what 144 00:07:25,240 --> 00:07:27,360 Speaker 4: do the earnings look like, of course in the short term, 145 00:07:27,360 --> 00:07:30,920 Speaker 4: but also in the long term, what's structurally happening competitively? 146 00:07:31,360 --> 00:07:33,040 Speaker 4: You kind of go way down the rabbit hole. There's 147 00:07:33,040 --> 00:07:35,280 Speaker 4: a lot more and we can go into that. And 148 00:07:35,320 --> 00:07:37,720 Speaker 4: then as you kind of migrate sort of down the 149 00:07:38,040 --> 00:07:41,080 Speaker 4: time horizon spectrum, at least from what a thesis looks 150 00:07:41,120 --> 00:07:43,960 Speaker 4: like on a single stock, what you're kind of doing 151 00:07:44,080 --> 00:07:47,160 Speaker 4: is thinking about where are the catalysts that change the 152 00:07:47,160 --> 00:07:50,920 Speaker 4: market's perception of that long term. So, like I remember 153 00:07:50,920 --> 00:07:54,320 Speaker 4: when I joined Viking, I remember asking the question just generally, 154 00:07:54,320 --> 00:07:56,240 Speaker 4: like how much do you care about earnings. I think 155 00:07:56,240 --> 00:07:59,320 Speaker 4: for anybody who's very value oriented, you kind of are 156 00:07:59,520 --> 00:08:01,880 Speaker 4: concerned and about like, am I just going to be 157 00:08:01,880 --> 00:08:04,640 Speaker 4: focused on the next data point, the next earnings and 158 00:08:04,640 --> 00:08:06,760 Speaker 4: not sort of able to you know, see the forest 159 00:08:06,760 --> 00:08:08,480 Speaker 4: for the trees and sort of care about, you know, 160 00:08:08,520 --> 00:08:10,880 Speaker 4: what does this data mean for the long term? But 161 00:08:10,920 --> 00:08:14,160 Speaker 4: the answer I got back in general, not specifically there, 162 00:08:14,160 --> 00:08:17,000 Speaker 4: but is in that kind of framework it's or the 163 00:08:17,240 --> 00:08:19,960 Speaker 4: way the question was answered to me was, Hey, the 164 00:08:20,040 --> 00:08:22,960 Speaker 4: long term is a function a DCF is a function 165 00:08:23,000 --> 00:08:25,640 Speaker 4: of years. Years are a function of quarters, and so 166 00:08:25,640 --> 00:08:27,920 Speaker 4: therefore we care about the quarters. But what that tells 167 00:08:27,920 --> 00:08:30,680 Speaker 4: you is that, like the answer is what about the 168 00:08:30,720 --> 00:08:33,839 Speaker 4: short term, catalyst changes the perspective about the long term 169 00:08:33,920 --> 00:08:36,520 Speaker 4: valuation of the company. And so I think what people 170 00:08:36,800 --> 00:08:39,120 Speaker 4: sometimes looking from a far don't appreciate is the extent 171 00:08:39,160 --> 00:08:41,480 Speaker 4: to which there's actually a little bit more of a 172 00:08:41,520 --> 00:08:46,080 Speaker 4: convergence across styles from the underlying analyst workflow that like 173 00:08:46,200 --> 00:08:48,640 Speaker 4: even a very long term investor to some extent is saying, 174 00:08:49,000 --> 00:08:51,760 Speaker 4: even if I'm betting on the long term, the interim 175 00:08:51,840 --> 00:08:55,240 Speaker 4: proof points illustrate the view of that long term and 176 00:08:55,280 --> 00:08:57,760 Speaker 4: the short term guy says, well, I may get the 177 00:08:57,840 --> 00:08:59,599 Speaker 4: number right in the short term, but that only is 178 00:08:59,679 --> 00:09:02,320 Speaker 4: meaning to the change in the markets price if it 179 00:09:02,360 --> 00:09:04,120 Speaker 4: tells you something about that long term. And so there's 180 00:09:04,120 --> 00:09:05,880 Speaker 4: a little bit of like a yeah, and I think 181 00:09:05,920 --> 00:09:08,360 Speaker 4: that convergence is happening more and more where people are 182 00:09:08,400 --> 00:09:12,520 Speaker 4: kind of pushing towards that center actually, where everybody both 183 00:09:12,559 --> 00:09:15,560 Speaker 4: cares about the short term data point and is looking 184 00:09:15,600 --> 00:09:18,440 Speaker 4: to what that means about the long term. So but anyway, 185 00:09:18,480 --> 00:09:20,880 Speaker 4: at the beginning of that process, at the silver point 186 00:09:20,920 --> 00:09:24,240 Speaker 4: or any deep value type place, you're just really focused 187 00:09:24,240 --> 00:09:27,160 Speaker 4: on that longer term story. You're less focused on the 188 00:09:27,320 --> 00:09:30,200 Speaker 4: quarter or the catalyst than trying to understand sometimes things 189 00:09:30,200 --> 00:09:31,880 Speaker 4: that And I was a junior analyst when I kind 190 00:09:31,880 --> 00:09:34,240 Speaker 4: of started there. There was a first job out of banking, 191 00:09:34,440 --> 00:09:36,160 Speaker 4: and you know, but you can be looking at like 192 00:09:36,200 --> 00:09:38,560 Speaker 4: what does the rail contract look like in twenty twenty 193 00:09:38,559 --> 00:09:40,280 Speaker 4: four and how does that step up? And you're like, man, 194 00:09:40,280 --> 00:09:43,080 Speaker 4: does this matter to the stock. It's great training. It's 195 00:09:43,120 --> 00:09:44,719 Speaker 4: a perfect place to kind of get that. 196 00:09:44,920 --> 00:09:45,040 Speaker 3: You know. 197 00:09:45,040 --> 00:09:46,959 Speaker 4: It's almost like private equity like where you're just sort 198 00:09:47,000 --> 00:09:49,880 Speaker 4: of you know, looking through everything. But that's kind of 199 00:09:49,920 --> 00:09:51,319 Speaker 4: how that started, all right. 200 00:09:51,400 --> 00:09:56,319 Speaker 2: So then at Citadel you mentioned you covered materials, commodities, 201 00:09:56,320 --> 00:10:00,240 Speaker 2: stuff like that. I guess two questions. When you come 202 00:10:00,240 --> 00:10:02,480 Speaker 2: in the door there and you're told like, okay, this 203 00:10:02,520 --> 00:10:04,679 Speaker 2: is what you do. What are you're told as your 204 00:10:04,720 --> 00:10:09,199 Speaker 2: constraints and your specific remit and then also like how 205 00:10:09,200 --> 00:10:10,000 Speaker 2: do you pick a stock? 206 00:10:10,240 --> 00:10:13,959 Speaker 4: Yeah? Yeah, And I'll talk about this in a general sense, 207 00:10:14,000 --> 00:10:17,120 Speaker 4: not specific set it up, but to talk about multimanagers 208 00:10:17,120 --> 00:10:19,559 Speaker 4: in general, and our client base today at Arkana is 209 00:10:19,600 --> 00:10:21,920 Speaker 4: about fifty to fifty split. I would say between people 210 00:10:21,920 --> 00:10:23,920 Speaker 4: who I call like natives who come from the risk 211 00:10:23,960 --> 00:10:27,040 Speaker 4: model system either any of the major pods or related 212 00:10:27,240 --> 00:10:28,800 Speaker 4: and the other half can be like a deep value 213 00:10:28,800 --> 00:10:30,520 Speaker 4: fund that says, hey, I don't want to limit myself 214 00:10:30,559 --> 00:10:32,800 Speaker 4: to this stuff, but I see just like you guys 215 00:10:32,840 --> 00:10:35,680 Speaker 4: are saying, this is an increasingly important part of markets 216 00:10:35,880 --> 00:10:37,959 Speaker 4: and I want to be deep on it, educated whatever. 217 00:10:38,000 --> 00:10:41,520 Speaker 4: So to answer your question on how multimanagers kind of pickstocks, 218 00:10:41,559 --> 00:10:44,880 Speaker 4: run processes and think, not specific to any one place, 219 00:10:44,920 --> 00:10:48,480 Speaker 4: but that sort of natives group in general. So at 220 00:10:48,480 --> 00:10:51,080 Speaker 4: any of these places, the core contract is to say, 221 00:10:51,400 --> 00:10:53,160 Speaker 4: the core difference, I guess is the other way to 222 00:10:53,160 --> 00:10:56,600 Speaker 4: say it is versus a deep value place, it's about turnover. 223 00:10:56,960 --> 00:10:59,160 Speaker 4: In the end, it's two things. It's risk limits and 224 00:10:59,200 --> 00:11:02,720 Speaker 4: it's about how freak your book turns over. So at 225 00:11:02,800 --> 00:11:05,640 Speaker 4: a deep value fund, the goal might be in sort 226 00:11:05,679 --> 00:11:07,560 Speaker 4: of theory to have more than a year long average 227 00:11:07,559 --> 00:11:10,720 Speaker 4: hold period. In practice it'll be often shorter than that, 228 00:11:10,760 --> 00:11:13,280 Speaker 4: you know, nine months or whatever as bad idea cycloud 229 00:11:13,360 --> 00:11:16,000 Speaker 4: or whatever. But at a multi manager those numbers can 230 00:11:16,000 --> 00:11:18,400 Speaker 4: be anywhere from like ten to fifteen to even higher, 231 00:11:18,720 --> 00:11:21,360 Speaker 4: meaning the entire book turns over ten to fifteen times 232 00:11:21,400 --> 00:11:22,439 Speaker 4: in a year. 233 00:11:22,520 --> 00:11:22,720 Speaker 3: Wow. 234 00:11:22,920 --> 00:11:25,520 Speaker 4: Think about it simply like the average idea stays on 235 00:11:25,559 --> 00:11:27,120 Speaker 4: for a month is the way to put it in 236 00:11:27,160 --> 00:11:29,719 Speaker 4: the book. And so as you step into any of 237 00:11:29,760 --> 00:11:32,120 Speaker 4: these places to your point A, you have a you know, 238 00:11:32,120 --> 00:11:34,640 Speaker 4: there's a structure. There's an analyst, and then a portfolio manager. 239 00:11:34,880 --> 00:11:37,720 Speaker 4: And the analyst generally has a single industry focused so 240 00:11:37,720 --> 00:11:39,520 Speaker 4: it's like, hey, i am, as you said, chip stocks, 241 00:11:39,600 --> 00:11:42,559 Speaker 4: or you know, somebody else might have software, or it's 242 00:11:42,559 --> 00:11:45,240 Speaker 4: a sort of defined single universe. And then a portfolio 243 00:11:45,240 --> 00:11:47,600 Speaker 4: manager will have a set of analysts below them who 244 00:11:47,679 --> 00:11:50,960 Speaker 4: have typically very related coverage universes and will feed up 245 00:11:50,960 --> 00:11:53,400 Speaker 4: into the portfolio manager. So that's like kind of the structure. 246 00:11:53,960 --> 00:11:57,080 Speaker 4: Stockpicking kind of ends up being what we were talking 247 00:11:57,080 --> 00:11:59,719 Speaker 4: about earlier. In the end, it's the analyst job to 248 00:12:00,640 --> 00:12:03,960 Speaker 4: have a detailed model, of course, to have a view 249 00:12:04,000 --> 00:12:07,400 Speaker 4: on earnings across their coverage universe, and that coverage universe 250 00:12:07,400 --> 00:12:09,120 Speaker 4: for that analyst, by the way, can be and it 251 00:12:09,160 --> 00:12:11,040 Speaker 4: varies by a multi manager, but it can be anywhere 252 00:12:11,080 --> 00:12:13,320 Speaker 4: from like thirty names at the low end to like 253 00:12:13,480 --> 00:12:16,280 Speaker 4: eighty names at the high end buy analyst. So there's 254 00:12:16,280 --> 00:12:17,199 Speaker 4: a lot of process there. 255 00:12:32,480 --> 00:12:35,800 Speaker 3: There are many differences between a retail investor and a 256 00:12:35,920 --> 00:12:38,959 Speaker 3: multi strategy fund, but one of the key ones I 257 00:12:39,000 --> 00:12:43,760 Speaker 3: think is maybe position sizing. So if you're a retail 258 00:12:43,760 --> 00:12:47,160 Speaker 3: investor and you have a single stock thesis, I don't know, 259 00:12:47,200 --> 00:12:49,640 Speaker 3: you want to buy in video or something, you buy 260 00:12:49,640 --> 00:12:52,880 Speaker 3: in video, and you're probably making that decision based on 261 00:12:53,080 --> 00:12:56,520 Speaker 3: how much cash you have in your Robinhood account or 262 00:12:56,559 --> 00:12:59,760 Speaker 3: something like that. But if you're at a multi fund, 263 00:13:00,080 --> 00:13:03,560 Speaker 3: it seems like a much more sophisticated process. So I 264 00:13:03,559 --> 00:13:06,680 Speaker 3: guess I'm curious if you're at a podshop, how do 265 00:13:06,760 --> 00:13:09,760 Speaker 3: you know how much to buy? How do you know 266 00:13:09,880 --> 00:13:13,960 Speaker 3: how much to allocate to a single stock. And I 267 00:13:13,960 --> 00:13:17,280 Speaker 3: guess another way of saying it is you're looking at 268 00:13:17,280 --> 00:13:20,120 Speaker 3: that single stock on a risk adjusted basis, right, Like, 269 00:13:20,200 --> 00:13:23,079 Speaker 3: that's what you want to get, right, the risk adjusted performance, 270 00:13:23,200 --> 00:13:25,040 Speaker 3: not just the single stock performance. 271 00:13:25,120 --> 00:13:27,960 Speaker 4: That's right. That's right. So there are two or three 272 00:13:28,000 --> 00:13:31,880 Speaker 4: ways that gets implemented. So the first is constraints. So 273 00:13:32,280 --> 00:13:35,040 Speaker 4: step one is dollar neutrality. I'm long as many dollars 274 00:13:35,080 --> 00:13:37,280 Speaker 4: as i'm short. That's a simple limit, sort of. One 275 00:13:37,320 --> 00:13:40,000 Speaker 4: level higher is beta neutrality relative to the overall market? 276 00:13:40,040 --> 00:13:42,760 Speaker 4: Am I longer short? On a beta adjusted basis? Sort of? 277 00:13:42,760 --> 00:13:46,400 Speaker 4: The third level is factor neutrality. I'm balanced against all 278 00:13:46,400 --> 00:13:49,000 Speaker 4: of these sort of if you maybe simplified just slightly 279 00:13:49,040 --> 00:13:51,880 Speaker 4: the subcomponents of beta, so instead of like, hey, I 280 00:13:51,920 --> 00:13:53,560 Speaker 4: have a beta to the market, I actually have a 281 00:13:53,600 --> 00:13:56,599 Speaker 4: beta to the basket of size large companies. I have 282 00:13:56,640 --> 00:13:59,000 Speaker 4: a beta to the basket of companies with momentum. I 283 00:13:59,000 --> 00:14:00,640 Speaker 4: have a basket to the beta. 284 00:14:00,960 --> 00:14:01,240 Speaker 2: I see. 285 00:14:01,320 --> 00:14:03,000 Speaker 3: So you decompose beta. 286 00:14:02,880 --> 00:14:07,120 Speaker 4: Essentially, it's a decomposition. Essentially, when people talk about factors 287 00:14:07,120 --> 00:14:10,880 Speaker 4: and factor neutrality, it's a decomposition of beta into its 288 00:14:10,880 --> 00:14:13,920 Speaker 4: constituent parts. There's a lot of statistics that goes under 289 00:14:13,920 --> 00:14:17,199 Speaker 4: the hood to make that orthogonal and precise and. 290 00:14:18,559 --> 00:14:19,160 Speaker 3: Orthogonal. 291 00:14:19,720 --> 00:14:21,800 Speaker 4: But at the sort of functional level, at the level 292 00:14:21,800 --> 00:14:24,680 Speaker 4: that people at the stock picking level, at multi managers 293 00:14:24,720 --> 00:14:28,040 Speaker 4: interact with the model, it's essentially just a decomposition of 294 00:14:28,320 --> 00:14:32,480 Speaker 4: beta's and then you add up those exposures on each side, 295 00:14:32,560 --> 00:14:35,280 Speaker 4: and you are limited essentially by the percent of your 296 00:14:35,360 --> 00:14:39,119 Speaker 4: bets in a book in aggregate that are betting basically 297 00:14:39,280 --> 00:14:43,200 Speaker 4: on factor type bets as compared to the percentage of 298 00:14:43,200 --> 00:14:45,760 Speaker 4: your bets that are betting on the remainder term, the 299 00:14:45,840 --> 00:14:48,800 Speaker 4: non factor component of any stock. So as you look 300 00:14:48,840 --> 00:14:51,600 Speaker 4: at any stock, it fits within that broader portfolio that 301 00:14:51,640 --> 00:14:52,680 Speaker 4: you're putting together. 302 00:14:53,560 --> 00:14:56,720 Speaker 3: Okay, and then the second thing that you talked about 303 00:14:56,760 --> 00:15:01,600 Speaker 3: earlier is this idea of turnover. So just to press 304 00:15:01,600 --> 00:15:05,560 Speaker 3: on this point, how much do trading costs factor into 305 00:15:05,800 --> 00:15:10,440 Speaker 3: investment decisions? And also position sizing, because as you just stated, 306 00:15:10,880 --> 00:15:15,080 Speaker 3: you could theoretically size or arrange all of your positions 307 00:15:15,120 --> 00:15:19,080 Speaker 3: to be factor neutral or neutral in terms of systematic risk. 308 00:15:19,160 --> 00:15:22,840 Speaker 3: I guess, but I imagine in order to do that, you 309 00:15:22,880 --> 00:15:25,960 Speaker 3: would have to be trading pretty much like constantly right, 310 00:15:26,000 --> 00:15:30,280 Speaker 3: which would add to your execution costs. So does that 311 00:15:30,360 --> 00:15:31,400 Speaker 3: come into play as well? 312 00:15:31,520 --> 00:15:36,000 Speaker 4: Yeah, it does. In practice, the stock picker, portfolio manager 313 00:15:36,000 --> 00:15:40,239 Speaker 4: and analyst doesn't flow in a complicated set of formulas 314 00:15:40,280 --> 00:15:43,200 Speaker 4: to their decision around sort of how do I optimize 315 00:15:43,200 --> 00:15:47,040 Speaker 4: trading costs? The engines operating at the multi managers do 316 00:15:47,120 --> 00:15:50,320 Speaker 4: think a ton about how do I take the stock 317 00:15:50,360 --> 00:15:53,280 Speaker 4: picks that a single portfolio does and then execute them 318 00:15:53,720 --> 00:15:56,600 Speaker 4: in a optimal way a crossing some firms doing some 319 00:15:56,680 --> 00:15:59,240 Speaker 4: prints don't cross each other's orders within the pod level 320 00:16:00,080 --> 00:16:02,600 Speaker 4: about all of that, and then so the first level 321 00:16:02,640 --> 00:16:04,840 Speaker 4: of how do people get limited is the constraints on 322 00:16:04,840 --> 00:16:06,640 Speaker 4: what percent of my bets are in factor type bets 323 00:16:06,680 --> 00:16:09,160 Speaker 4: versus non factor type bets. There are all also a 324 00:16:09,160 --> 00:16:11,880 Speaker 4: bunch of like single position limits, So that's like one 325 00:16:12,080 --> 00:16:15,200 Speaker 4: version is basically limiting the portfolio manager to have to 326 00:16:15,240 --> 00:16:19,280 Speaker 4: sort of live pickstocks under this constraint. The other framework 327 00:16:19,320 --> 00:16:21,120 Speaker 4: of how do you size positions to your sort of 328 00:16:21,160 --> 00:16:23,440 Speaker 4: earlier question which comes around to this trading cost question 329 00:16:23,560 --> 00:16:26,600 Speaker 4: is there are tools that are called optimizers that basically 330 00:16:27,280 --> 00:16:30,120 Speaker 4: look at the expected return that each portfolio manager thinks 331 00:16:30,120 --> 00:16:32,400 Speaker 4: they have in their book of stocks and tries to 332 00:16:32,520 --> 00:16:36,400 Speaker 4: solve for the optimal balance of the expected return against 333 00:16:36,440 --> 00:16:39,160 Speaker 4: the volatility of those stocks and the volatility the factor 334 00:16:39,200 --> 00:16:41,320 Speaker 4: bets in the book, and it'll spit out an answer 335 00:16:41,360 --> 00:16:43,040 Speaker 4: for you. That answer may not be exactly where you 336 00:16:43,080 --> 00:16:46,600 Speaker 4: want to land, but in the most sophisticated places, that 337 00:16:46,800 --> 00:16:50,160 Speaker 4: answer that the optimizer spitting out is including how much 338 00:16:50,280 --> 00:16:52,920 Speaker 4: trading cost impacts the book. So it's sort of flowing 339 00:16:52,960 --> 00:16:57,680 Speaker 4: that mathematically into a machine driven optimal book. But again 340 00:16:57,800 --> 00:16:59,720 Speaker 4: that's sort of in the more science bucket. Of course 341 00:16:59,760 --> 00:17:02,360 Speaker 4: there's art even underneath that statistics, but basically that's in 342 00:17:02,400 --> 00:17:05,200 Speaker 4: the more sort of science bucket. Then the PORTFOLI manaddressed 343 00:17:05,200 --> 00:17:07,439 Speaker 4: to say, Okay, the machine sort of took my expected returns, 344 00:17:07,480 --> 00:17:09,560 Speaker 4: took the variance of those pieces and the trading costs 345 00:17:09,600 --> 00:17:11,639 Speaker 4: into account. I gave me an answer. Does that actually 346 00:17:11,640 --> 00:17:14,400 Speaker 4: still fit with my you know, fundamental bottoms at work? 347 00:17:14,440 --> 00:17:16,560 Speaker 4: Back to hey, the contract of this company changes in 348 00:17:16,560 --> 00:17:18,760 Speaker 4: twenty twy six, the earnings going to be this. Here's 349 00:17:18,760 --> 00:17:21,040 Speaker 4: the positioning and set up and crowding of other pods, 350 00:17:21,040 --> 00:17:23,560 Speaker 4: you know, playing the game you mentioned earlier. So there's 351 00:17:23,600 --> 00:17:25,800 Speaker 4: then the sort of second level of art that goes 352 00:17:25,800 --> 00:17:26,439 Speaker 4: to the top of that. 353 00:17:26,600 --> 00:17:29,680 Speaker 2: Yeah, So I want to talk more about the speed 354 00:17:29,880 --> 00:17:33,440 Speaker 2: of turnover because Okay, let's say you're like bullish on 355 00:17:33,520 --> 00:17:36,320 Speaker 2: and video and videos had this big run and you're like, 356 00:17:36,359 --> 00:17:39,119 Speaker 2: all right, but I don't want to have size exposure 357 00:17:39,240 --> 00:17:41,359 Speaker 2: because it's going to be correlated to big caps. I 358 00:17:41,359 --> 00:17:44,760 Speaker 2: don't want to have general market beta because probably if 359 00:17:44,760 --> 00:17:47,040 Speaker 2: the market goes up and video is going to go up, 360 00:17:47,040 --> 00:17:49,040 Speaker 2: and I don't have chip beta and all this stuff. 361 00:17:49,200 --> 00:17:52,240 Speaker 2: So what you're trying to identify is just the Invidia 362 00:17:52,359 --> 00:17:56,199 Speaker 2: specific idiosyncredit. That's exactly right, right, But why does that 363 00:17:56,320 --> 00:18:00,440 Speaker 2: inherently lend itself when you're thinking about I mean, I 364 00:18:00,440 --> 00:18:02,520 Speaker 2: feel like there must be some connection, But you're trying 365 00:18:02,560 --> 00:18:04,760 Speaker 2: to strip out all of these different factors that you 366 00:18:04,800 --> 00:18:06,760 Speaker 2: don't want to have exposure to. You're trying to find 367 00:18:06,800 --> 00:18:10,560 Speaker 2: the idiosyncratic drivers of a specific name. What is it 368 00:18:10,640 --> 00:18:14,520 Speaker 2: about that process that sort of inherently lends itself to 369 00:18:14,640 --> 00:18:15,640 Speaker 2: shorthold periods. 370 00:18:15,720 --> 00:18:17,720 Speaker 4: That's a great question, and as sort of deep one, 371 00:18:18,000 --> 00:18:19,840 Speaker 4: and you might get different answers to that question from 372 00:18:19,840 --> 00:18:23,000 Speaker 4: a few different people. I'll give you mine. The essential 373 00:18:23,240 --> 00:18:27,000 Speaker 4: reality is that In order for this entire model to work, 374 00:18:27,560 --> 00:18:31,560 Speaker 4: you have to have a great deal of diversification across 375 00:18:31,640 --> 00:18:36,080 Speaker 4: idiosyncratic bets non factor bets. And the way to think 376 00:18:36,119 --> 00:18:38,720 Speaker 4: about that is the core reason a lot of these 377 00:18:38,720 --> 00:18:41,840 Speaker 4: models work, is that the residual return or idiosyncratic return 378 00:18:42,520 --> 00:18:47,360 Speaker 4: is approximately normally distributed across a certain window. Meaning it's 379 00:18:47,400 --> 00:18:50,199 Speaker 4: sort of, you know, like flipping a coin basically, And 380 00:18:50,240 --> 00:18:53,119 Speaker 4: the intuition is, if you flip a thousand coins, obviously 381 00:18:53,280 --> 00:18:56,760 Speaker 4: you'll center around whatever your hit rate is on that coin. 382 00:18:56,800 --> 00:18:59,280 Speaker 4: If the coin is loaded fifty two percent, yeah, versus 383 00:18:59,320 --> 00:19:01,879 Speaker 4: fifty As you flip three coins, it could be you know, 384 00:19:01,920 --> 00:19:04,080 Speaker 4: the mean, the expected value of that is going to 385 00:19:04,080 --> 00:19:06,000 Speaker 4: be you know, who knows, right, But as you flip 386 00:19:06,040 --> 00:19:08,919 Speaker 4: a thousand coins or ten thousand coins, you will center 387 00:19:08,960 --> 00:19:12,640 Speaker 4: around that mean. And so and that variance is effectively, 388 00:19:13,080 --> 00:19:15,200 Speaker 4: if you think about things from a return standpoint, the 389 00:19:15,240 --> 00:19:17,200 Speaker 4: sharp ratio, right is they're returned about it about the 390 00:19:17,280 --> 00:19:20,880 Speaker 4: variance of the volatility of that return. And so as 391 00:19:20,920 --> 00:19:23,639 Speaker 4: you have more and more bets, you shrink the variants 392 00:19:23,800 --> 00:19:25,800 Speaker 4: relative to the return you're generating. And the more and 393 00:19:25,800 --> 00:19:29,359 Speaker 4: more your bets are in idiosyncratic bets which are normally 394 00:19:29,359 --> 00:19:32,239 Speaker 4: distributed unlike market bets you know, which are you know 395 00:19:32,240 --> 00:19:32,919 Speaker 4: can be wild. 396 00:19:33,040 --> 00:19:35,760 Speaker 2: Right, Wait, can you actually just explain that point, because 397 00:19:35,800 --> 00:19:39,360 Speaker 2: that's a great answer. You're basically you have some assumption 398 00:19:39,520 --> 00:19:42,120 Speaker 2: about returns, but there's going to be a lot of variants, 399 00:19:42,280 --> 00:19:44,080 Speaker 2: so you want to make a lot of bets. That's 400 00:19:44,160 --> 00:19:47,800 Speaker 2: exactly in order to achieve that. Why is it that 401 00:19:47,960 --> 00:19:53,719 Speaker 2: idiosyncretic returns are normally distributed and such as you described totally? 402 00:19:53,800 --> 00:19:55,720 Speaker 4: Yeah, so what you're actually solving for is you go 403 00:19:55,800 --> 00:19:59,960 Speaker 4: down the factor model building a rabbit hole is cross sectional, 404 00:20:00,240 --> 00:20:03,199 Speaker 4: normally distributed, meaning across the universe of stocks within a 405 00:20:03,200 --> 00:20:05,960 Speaker 4: period of time. Okay, so that's kind of also what 406 00:20:06,000 --> 00:20:08,240 Speaker 4: the model solves for, and it sort of solves for 407 00:20:08,320 --> 00:20:12,960 Speaker 4: a combination effectively of what's the highest R squared meaning 408 00:20:12,960 --> 00:20:15,680 Speaker 4: how much of the model explains what's happening across stock 409 00:20:15,720 --> 00:20:19,000 Speaker 4: movements across different stocks in the market, and then the 410 00:20:19,040 --> 00:20:22,000 Speaker 4: output of any regression within its period is going to 411 00:20:22,040 --> 00:20:25,840 Speaker 4: produce that result of a normally distributed kind of residual term. 412 00:20:26,080 --> 00:20:28,199 Speaker 4: But the key way that this model works is that 413 00:20:28,200 --> 00:20:32,160 Speaker 4: it's normally distributed, not across time but across stocks within 414 00:20:32,200 --> 00:20:35,000 Speaker 4: a given period. And so what that means is you're 415 00:20:35,000 --> 00:20:37,720 Speaker 4: going to have you know, as many stocks that are 416 00:20:37,760 --> 00:20:40,520 Speaker 4: on the residual basis that are outperforming in a period 417 00:20:40,520 --> 00:20:43,440 Speaker 4: as that are underperforming on this residual basis. Whereas, of course, 418 00:20:43,440 --> 00:20:45,440 Speaker 4: if you just bet on semis right in a month, 419 00:20:45,480 --> 00:20:48,480 Speaker 4: and you just were long Semis within a period within 420 00:20:48,480 --> 00:20:50,320 Speaker 4: a month, right, that's not going to be normally distributed, 421 00:20:50,359 --> 00:20:52,879 Speaker 4: of course, Right, It's just if you're managing to a 422 00:20:52,920 --> 00:20:56,520 Speaker 4: model that is cross sectionally approximately normally distributed within a month, 423 00:20:56,600 --> 00:20:58,840 Speaker 4: Let's say you're going to get winners and losers, and 424 00:20:58,880 --> 00:21:01,200 Speaker 4: you're going to center around that hit rate basically. 425 00:21:01,359 --> 00:21:04,399 Speaker 3: Okay, I get that, you keep mentioning a month. What 426 00:21:04,560 --> 00:21:09,399 Speaker 3: is like a normal or a reasonable time horizon that 427 00:21:09,480 --> 00:21:11,320 Speaker 3: these models like typically operate. 428 00:21:11,520 --> 00:21:14,720 Speaker 4: Yeah, they're sort of calibrated to so technically the model, 429 00:21:14,960 --> 00:21:18,480 Speaker 4: the regression runs daily. Actually, but when you are building 430 00:21:18,640 --> 00:21:21,240 Speaker 4: any of these models, people calibrate them to sort of 431 00:21:21,280 --> 00:21:24,320 Speaker 4: optimize for like, the average whole period of a discretionary 432 00:21:24,400 --> 00:21:27,040 Speaker 4: stock picker is not a day obviously, and so you 433 00:21:27,040 --> 00:21:29,479 Speaker 4: try to calibrate the bias of these models to say, 434 00:21:29,600 --> 00:21:32,040 Speaker 4: and people actually you can run multiple models, say hey, 435 00:21:32,040 --> 00:21:33,960 Speaker 4: we're going to run one that's calibrated for a one 436 00:21:33,960 --> 00:21:37,040 Speaker 4: month horizon or a six month horizon or whatever. And 437 00:21:37,080 --> 00:21:40,399 Speaker 4: so you're trying to pick the calibration horizon that matches 438 00:21:40,400 --> 00:21:42,520 Speaker 4: the investor that we're talking about. So I mentioned a 439 00:21:42,520 --> 00:21:44,840 Speaker 4: month because a lot of the multi managers, let's say 440 00:21:44,880 --> 00:21:47,320 Speaker 4: the average hole period ends up around a month, you know, 441 00:21:47,320 --> 00:21:50,000 Speaker 4: twelve that's twelve times turns a year, but it could 442 00:21:50,040 --> 00:21:51,760 Speaker 4: be high. It could be seventeen turns, it could be 443 00:21:51,920 --> 00:21:54,240 Speaker 4: eight turns. They are managers who are in that range. 444 00:21:54,600 --> 00:21:57,680 Speaker 2: Just to go back to the question of idea generation, 445 00:21:58,560 --> 00:22:00,840 Speaker 2: you're going to hold a stock from month, maybe maybe 446 00:22:00,880 --> 00:22:04,720 Speaker 2: a few weeks, maybe a little longer. Some analysts who's 447 00:22:04,760 --> 00:22:07,280 Speaker 2: like monitoring all this stuff, what goes into it? Someone 448 00:22:07,320 --> 00:22:12,200 Speaker 2: says to you, okay, like you're doing materials and or commodities. Yeah, 449 00:22:12,320 --> 00:22:15,040 Speaker 2: and they say, suddenly you have a bullish view on 450 00:22:15,160 --> 00:22:20,040 Speaker 2: exony something or some small shale player. What happened before that? 451 00:22:20,240 --> 00:22:23,320 Speaker 2: Totally that led to that idea? Well, not just that 452 00:22:23,359 --> 00:22:25,439 Speaker 2: they like the stock, but that they like the stock 453 00:22:25,640 --> 00:22:27,200 Speaker 2: in a very short period of time. 454 00:22:27,600 --> 00:22:29,800 Speaker 4: Yeah. So, and this is not always true, but as 455 00:22:29,840 --> 00:22:32,639 Speaker 4: a sort of simplified rule of thumb. Typically the winners 456 00:22:32,640 --> 00:22:34,479 Speaker 4: are going to be on longer than that month, okay, 457 00:22:34,520 --> 00:22:36,480 Speaker 4: and you know, you realize you were wrong about something, 458 00:22:36,680 --> 00:22:39,440 Speaker 4: then you cut that. And there's trading turnover as well. 459 00:22:39,480 --> 00:22:42,240 Speaker 4: That's not pure idea turnover, if that makes sense, which 460 00:22:42,280 --> 00:22:43,800 Speaker 4: is idea generation. So that's going to be a little 461 00:22:43,800 --> 00:22:47,600 Speaker 4: slower too. But anyway, with with those caveats to your question, yeah, 462 00:22:47,600 --> 00:22:50,199 Speaker 4: so there's in an ideal world you do a you 463 00:22:50,240 --> 00:22:53,200 Speaker 4: sort of separate the idea generation process into two steps. 464 00:22:53,240 --> 00:22:53,560 Speaker 1: Okay. 465 00:22:53,600 --> 00:22:56,680 Speaker 4: The first is initiation, where you sort of learn about 466 00:22:56,680 --> 00:22:59,040 Speaker 4: the stock, if that makes sense, And in that process 467 00:22:59,080 --> 00:23:00,639 Speaker 4: you basically do all the things I mentioned that a 468 00:23:00,720 --> 00:23:03,800 Speaker 4: core value oriented fund does in terms of thinking about, Okay, 469 00:23:03,800 --> 00:23:06,080 Speaker 4: what's the long term of this, what's the secular trend 470 00:23:06,480 --> 00:23:09,040 Speaker 4: within companies, who's gaining who's losing share. In order to 471 00:23:09,040 --> 00:23:12,119 Speaker 4: do that, you do all the classic Warren Buffett stuff. 472 00:23:12,200 --> 00:23:15,000 Speaker 4: I mean, you understand, you look at industry earning earnings 473 00:23:15,080 --> 00:23:17,640 Speaker 4: and earned industry reports and filings and all of those 474 00:23:17,720 --> 00:23:20,199 Speaker 4: kinds of things. You talk to experts also as part 475 00:23:20,240 --> 00:23:22,479 Speaker 4: of that process. That could be any of the expert 476 00:23:22,480 --> 00:23:25,400 Speaker 4: network calls somebody who were people who were executives, and 477 00:23:25,440 --> 00:23:27,760 Speaker 4: that can inform that initiation and understanding of the industry 478 00:23:27,880 --> 00:23:30,479 Speaker 4: as well. And some people spend you know, some analysts 479 00:23:30,520 --> 00:23:32,840 Speaker 4: spend the majority of their time doing sort of initiation 480 00:23:33,000 --> 00:23:36,560 Speaker 4: type work that sort of build a deep financial model 481 00:23:36,600 --> 00:23:38,560 Speaker 4: that tries to build not just from like the high 482 00:23:38,640 --> 00:23:41,159 Speaker 4: level revenue but to unit economics like okay, And by 483 00:23:41,200 --> 00:23:42,919 Speaker 4: that I mean like, you know, if you're looking at 484 00:23:42,960 --> 00:23:45,280 Speaker 4: a coffee shop, like, okay, how many cups of coffee 485 00:23:45,280 --> 00:23:46,760 Speaker 4: do this? Hell, what's the price? How much is that 486 00:23:46,800 --> 00:23:48,640 Speaker 4: going to change? What are the inputs to a cup 487 00:23:48,640 --> 00:23:50,480 Speaker 4: of coffee? And just trying to get to that level 488 00:23:50,520 --> 00:23:53,640 Speaker 4: of granularity on unit economics. Yes, and so that's kind 489 00:23:53,640 --> 00:23:57,680 Speaker 4: of like the initiation process, and then ongoing coverage is 490 00:23:57,720 --> 00:23:59,479 Speaker 4: a little bit more of Hey, I have a view 491 00:23:59,600 --> 00:24:03,000 Speaker 4: from that initiation work on sort of long term relative 492 00:24:03,000 --> 00:24:04,919 Speaker 4: winners and losers in a space. I have an understanding 493 00:24:04,960 --> 00:24:06,919 Speaker 4: of uniit economics of each player and how each of 494 00:24:06,960 --> 00:24:09,800 Speaker 4: those is kind of heading. And then the ongoing maintenance 495 00:24:09,840 --> 00:24:12,919 Speaker 4: process is a lot to do with what data sets, 496 00:24:13,280 --> 00:24:16,600 Speaker 4: what data points, what conversations from an industry conference standpoint 497 00:24:16,680 --> 00:24:20,280 Speaker 4: or whatever can I do to understand more granularly how 498 00:24:20,359 --> 00:24:23,600 Speaker 4: each of those unit economics points is changing. And then 499 00:24:23,720 --> 00:24:26,880 Speaker 4: finally also like there's this question of crowding and positioning 500 00:24:26,880 --> 00:24:29,720 Speaker 4: and understanding what everybody else thinks, that sort of weighing 501 00:24:29,760 --> 00:24:34,080 Speaker 4: machine versus voting machine ben Gram classic analogy, but you 502 00:24:34,119 --> 00:24:36,399 Speaker 4: sort of separate that process, have that secular view, and 503 00:24:36,440 --> 00:24:38,399 Speaker 4: then you're trying to understand what data sets. So that 504 00:24:38,400 --> 00:24:41,400 Speaker 4: could be like all data sets, It could be industry conferences, 505 00:24:41,400 --> 00:24:43,400 Speaker 4: it could be talking to people in the industry through 506 00:24:43,400 --> 00:24:46,800 Speaker 4: the supply chain. It could be you know, people always 507 00:24:46,840 --> 00:24:48,800 Speaker 4: should be doing this but don't always actually in practice 508 00:24:48,840 --> 00:24:50,840 Speaker 4: doing it. But your analyst should understand if they're covering 509 00:24:51,320 --> 00:24:53,439 Speaker 4: an auto company, they should understand auto suppliers, and they 510 00:24:53,480 --> 00:24:56,440 Speaker 4: should understand the downstream of that. So each of those 511 00:24:56,680 --> 00:24:58,639 Speaker 4: sort of up and down the value chain. That's like 512 00:24:58,680 --> 00:25:01,960 Speaker 4: a big I'd say in reality, a differentiator among analysts 513 00:25:02,000 --> 00:25:04,760 Speaker 4: is how deep into the value chains you're seeing what's 514 00:25:04,800 --> 00:25:08,160 Speaker 4: happening to inform you about the changing trends in those 515 00:25:08,240 --> 00:25:10,840 Speaker 4: unit economics that you had a baseline view about at 516 00:25:10,840 --> 00:25:14,119 Speaker 4: the beginning of the sort of initiation you understanding the industry. 517 00:25:15,040 --> 00:25:17,639 Speaker 3: Convince me or you don't have to convince me. You 518 00:25:17,640 --> 00:25:20,640 Speaker 3: could try to try to convince me, or you could 519 00:25:20,680 --> 00:25:22,639 Speaker 3: agree with me. I don't know, convince me that this 520 00:25:22,760 --> 00:25:27,080 Speaker 3: isn't just momentum trading with some added maths and maybe 521 00:25:27,119 --> 00:25:32,200 Speaker 3: efficiencies coming from like centralized risk management and capital management systems. 522 00:25:32,640 --> 00:25:35,560 Speaker 4: Okay, so on the convincing part. So momentum itself is 523 00:25:35,600 --> 00:25:39,199 Speaker 4: a factor in every essentially commercial factor model, and so 524 00:25:39,240 --> 00:25:42,720 Speaker 4: you're actually therefore, because you were limited constrained on your 525 00:25:42,800 --> 00:25:46,160 Speaker 4: factor bets, you're constrained on how much like just momentum 526 00:25:46,160 --> 00:25:48,919 Speaker 4: you can be long. Ever, so you're limited in your 527 00:25:48,960 --> 00:25:51,399 Speaker 4: ability to be long, and momentum can have nuance like 528 00:25:51,400 --> 00:25:53,760 Speaker 4: do you calculate momentum over a six month window a 529 00:25:53,840 --> 00:25:55,760 Speaker 4: nine month window and what are the inputs to that, 530 00:25:55,880 --> 00:25:58,760 Speaker 4: But in aggregate, you're actually limited in your ability to 531 00:25:58,800 --> 00:26:01,440 Speaker 4: be long or short momentum at all. It's actually one 532 00:26:01,440 --> 00:26:04,280 Speaker 4: of the most focused on factors within commercial factor models 533 00:26:04,320 --> 00:26:07,119 Speaker 4: that everybody asks about all the time. So that's like 534 00:26:07,160 --> 00:26:10,040 Speaker 4: point one to mention on momentum. The other is the 535 00:26:10,080 --> 00:26:12,280 Speaker 4: way you described at the beginning was interesting too, because 536 00:26:12,359 --> 00:26:15,000 Speaker 4: there's a concept of factor investing where you're betting on 537 00:26:15,119 --> 00:26:17,560 Speaker 4: the factor, meaning you're finding cheap ways to be long 538 00:26:17,600 --> 00:26:20,000 Speaker 4: momentum or cheap ways to be long the value factor 539 00:26:20,119 --> 00:26:22,800 Speaker 4: or other pieces. And that's the kind of growing and 540 00:26:22,840 --> 00:26:24,639 Speaker 4: that ties into the whole sort of growth of passive 541 00:26:24,640 --> 00:26:26,800 Speaker 4: and all those things. What these risk models actually do 542 00:26:26,800 --> 00:26:30,040 Speaker 4: in the multi managers essentially are the elimination of factor 543 00:26:30,080 --> 00:26:31,960 Speaker 4: bet meaning it's the opposite. It's kind of a mirror 544 00:26:32,000 --> 00:26:34,600 Speaker 4: image of that, where you're sort of eliminating the factor 545 00:26:34,640 --> 00:26:37,440 Speaker 4: bets entirely and trying to find just the performance in 546 00:26:37,480 --> 00:26:39,399 Speaker 4: the residual. That then leads to this question of like 547 00:26:39,600 --> 00:26:42,960 Speaker 4: what factors exist inside the residual term that are not 548 00:26:43,040 --> 00:26:45,159 Speaker 4: momentum And that's where you get the concept that you 549 00:26:45,160 --> 00:26:47,520 Speaker 4: mentioned earlier, like a pods blowing up and what's positioning 550 00:26:47,520 --> 00:26:50,119 Speaker 4: and crowding and nuances there, which something we spent a 551 00:26:50,160 --> 00:26:51,800 Speaker 4: lot of time thinking about, Okay, like how do we 552 00:26:52,359 --> 00:26:55,560 Speaker 4: mathematize how do we characterize that? And what gives information 553 00:26:55,640 --> 00:26:59,440 Speaker 4: incrementally beyond? Okay, you've eliminated this sort of straightforward momentum topics. 554 00:26:59,480 --> 00:27:02,840 Speaker 4: You've eliminated did value What within that residual can give 555 00:27:02,840 --> 00:27:04,840 Speaker 4: you more and more insight beyond just like the core 556 00:27:05,000 --> 00:27:06,480 Speaker 4: research work can we talked about earlier. 557 00:27:22,080 --> 00:27:25,040 Speaker 3: I'm glad you mentioned the sort of off the shelf 558 00:27:25,080 --> 00:27:27,720 Speaker 3: commercial factor models because this is something that came up 559 00:27:27,840 --> 00:27:31,520 Speaker 3: in our conversation with Gappy as well. So in order 560 00:27:31,600 --> 00:27:35,199 Speaker 3: to be factor neutral, you have to be able to 561 00:27:35,480 --> 00:27:38,520 Speaker 3: identify the factors in the first place. And my understanding 562 00:27:38,600 --> 00:27:41,520 Speaker 3: is that most of the pods will just purchase those 563 00:27:41,600 --> 00:27:43,840 Speaker 3: models from a company like yours. 564 00:27:44,880 --> 00:27:47,199 Speaker 4: Yeah, So what people do is is kind of a 565 00:27:47,240 --> 00:27:50,720 Speaker 4: full spectrum of the way people implement a factor awareness 566 00:27:50,800 --> 00:27:55,679 Speaker 4: or factor neutrality strategy. Some will buy a single model 567 00:27:55,880 --> 00:27:57,800 Speaker 4: and sort of view that and then integrate that in 568 00:27:57,800 --> 00:27:59,639 Speaker 4: whatever way they do. And at the other end of 569 00:27:59,640 --> 00:28:01,720 Speaker 4: the spectrum, and there are funds, sort of the most 570 00:28:01,720 --> 00:28:05,560 Speaker 4: heavily infrastructured funds, it'll buy several factor models and pick 571 00:28:05,600 --> 00:28:08,600 Speaker 4: and choose different Hey, I think this factor is constructed appropriately. 572 00:28:08,640 --> 00:28:11,679 Speaker 4: Here this factor is less well constructed by this model, 573 00:28:11,760 --> 00:28:14,480 Speaker 4: and kind of put them together. And then there's sort 574 00:28:14,480 --> 00:28:17,240 Speaker 4: of also a spectrum in terms of people software tooling 575 00:28:17,320 --> 00:28:20,480 Speaker 4: that they how far down they hand into the organization 576 00:28:20,600 --> 00:28:23,639 Speaker 4: a sort of sophisticated tool to let portfolio managers see 577 00:28:23,760 --> 00:28:26,920 Speaker 4: what are my factor exposures. So like some places there's 578 00:28:26,960 --> 00:28:29,520 Speaker 4: a total separation almost of church and state of you know, 579 00:28:29,640 --> 00:28:33,359 Speaker 4: stock picking and risk management, and that is partly a function. 580 00:28:33,400 --> 00:28:35,000 Speaker 4: There could be a philosophy element to that, and there 581 00:28:35,040 --> 00:28:37,080 Speaker 4: could also just be a constraint. I mean, it takes 582 00:28:37,200 --> 00:28:39,800 Speaker 4: engineers and time and money and focus to build all 583 00:28:39,840 --> 00:28:42,240 Speaker 4: this stuff. So some places will have nothing in terms 584 00:28:42,280 --> 00:28:43,920 Speaker 4: of tooling, and they'll just have a risk team that 585 00:28:44,000 --> 00:28:46,600 Speaker 4: kind of looks at books and helps people understand their 586 00:28:46,680 --> 00:28:49,560 Speaker 4: risks on a sort of shorter cycle meaning longer cycle, 587 00:28:49,640 --> 00:28:52,680 Speaker 4: like it'll take oh, once a week, once a month, whatever, 588 00:28:52,680 --> 00:28:54,760 Speaker 4: they'll get a report on their risks, or they'll check 589 00:28:54,800 --> 00:28:57,160 Speaker 4: in et cetera, et cetera. And then at the far 590 00:28:57,280 --> 00:28:59,680 Speaker 4: end you have funds that have like full software platforms 591 00:28:59,720 --> 00:29:02,120 Speaker 4: that hand and to it portfolio manager like okay, if 592 00:29:02,120 --> 00:29:04,920 Speaker 4: you change this, what happens to that? If you want 593 00:29:04,920 --> 00:29:07,640 Speaker 4: to sort of see what the optimization math does for 594 00:29:07,680 --> 00:29:10,560 Speaker 4: you instantly, can you see that? And so that's kind 595 00:29:10,600 --> 00:29:13,000 Speaker 4: of the spectrum of what things do. And we sort 596 00:29:13,000 --> 00:29:16,240 Speaker 4: of provide that software toolkit everything from the risk model 597 00:29:16,240 --> 00:29:19,080 Speaker 4: as you mentioned, like the core underlying factors all the 598 00:29:19,080 --> 00:29:21,840 Speaker 4: way up to the software infrastructure that lets you just 599 00:29:22,360 --> 00:29:24,040 Speaker 4: play with it. Okay, if I had a billion dollars 600 00:29:24,040 --> 00:29:26,040 Speaker 4: in video, what does this do to my risk numbers, 601 00:29:26,040 --> 00:29:27,520 Speaker 4: that idio number of fact number? What is it do 602 00:29:27,600 --> 00:29:29,560 Speaker 4: to each of my factor exposures? And then how does 603 00:29:29,600 --> 00:29:31,960 Speaker 4: that change dynamically? And it'll also sort of like find 604 00:29:32,000 --> 00:29:34,960 Speaker 4: hedges for you, Like what single stocks would optimally hedge 605 00:29:35,000 --> 00:29:37,280 Speaker 4: this book in this way. Now, of course it's still 606 00:29:37,280 --> 00:29:40,880 Speaker 4: on you to pick stocks, but it it'll source. Okay, 607 00:29:40,920 --> 00:29:43,240 Speaker 4: I've got a whole universe of stocks. What single stocks 608 00:29:43,240 --> 00:29:45,600 Speaker 4: would offset this in video? Or these five single stocks 609 00:29:45,640 --> 00:29:46,480 Speaker 4: would offset that? 610 00:29:46,880 --> 00:29:48,440 Speaker 2: Just to go back, and then I want to talk 611 00:29:48,520 --> 00:29:50,600 Speaker 2: more about the software and what you sell, et cetera. 612 00:29:50,640 --> 00:29:52,920 Speaker 2: But just to go back, one last question on the 613 00:29:53,000 --> 00:29:55,280 Speaker 2: idea of like actually selecting a stock. You know, you 614 00:29:55,400 --> 00:29:58,560 Speaker 2: mentioned maintenance, and the analyst really builds out a coverage 615 00:29:58,640 --> 00:30:01,560 Speaker 2: universe and then they really to know the unit economics 616 00:30:01,600 --> 00:30:04,959 Speaker 2: of the coffee shop or the company that makes you know, 617 00:30:05,040 --> 00:30:07,920 Speaker 2: something for a car or whatever. But then what do 618 00:30:08,000 --> 00:30:11,280 Speaker 2: they see to say and now we should buy it, 619 00:30:11,400 --> 00:30:14,040 Speaker 2: Like what would be the signal that they're looking for 620 00:30:14,680 --> 00:30:17,080 Speaker 2: in the market that say, you know, again on some 621 00:30:17,120 --> 00:30:20,400 Speaker 2: short term period. This is really I've gotten to really 622 00:30:20,480 --> 00:30:22,920 Speaker 2: know this company, but there's something about X right now 623 00:30:22,960 --> 00:30:25,360 Speaker 2: that makes it a compelling buy for a short term period. 624 00:30:25,480 --> 00:30:29,680 Speaker 4: Totally. The core idea is that you're looking for differential insight, 625 00:30:29,800 --> 00:30:32,840 Speaker 4: meaning something that changes the perception of everybody else about 626 00:30:32,880 --> 00:30:35,080 Speaker 4: the value of this company in a long term sense. 627 00:30:35,120 --> 00:30:38,880 Speaker 4: So meaning I see if the market's perception is pick 628 00:30:38,920 --> 00:30:41,560 Speaker 4: a coffee shop is going to grow, and people will 629 00:30:41,760 --> 00:30:43,920 Speaker 4: the market it's, you know, whatever the market means. But 630 00:30:43,960 --> 00:30:46,200 Speaker 4: typically the market is who is the marginal price cetter 631 00:30:46,280 --> 00:30:49,800 Speaker 4: of a stock basically, and there's a perception there implicit 632 00:30:49,920 --> 00:30:52,720 Speaker 4: in the price at a minimum about okay, how many 633 00:30:52,840 --> 00:30:55,080 Speaker 4: units of coffee and what's the price of those coffee 634 00:30:55,080 --> 00:30:57,120 Speaker 4: cup's going to be, and what's the underlying cost. 635 00:30:57,280 --> 00:31:02,120 Speaker 2: You're waiting for moments in which you believe something is 636 00:31:02,160 --> 00:31:06,360 Speaker 2: going to emerge. Yes, that will change the long term expectation. 637 00:31:06,560 --> 00:31:09,040 Speaker 4: That will exactly, that will change the you know, And 638 00:31:09,080 --> 00:31:11,600 Speaker 4: there are other situations like tactical things where hey it's 639 00:31:11,600 --> 00:31:14,720 Speaker 4: so heavily shorted, Yeah that'll change slightly, And I'm really 640 00:31:14,720 --> 00:31:17,400 Speaker 4: looking for a short term catalyst, or hey, look everybody's 641 00:31:17,440 --> 00:31:21,280 Speaker 4: expecting this next all data print to mean something specific, 642 00:31:21,320 --> 00:31:23,760 Speaker 4: and they're all positioned on one side. That's where crowding 643 00:31:23,840 --> 00:31:26,360 Speaker 4: positioning comes into the equation. And everybody's position this way, 644 00:31:26,360 --> 00:31:27,400 Speaker 4: and I think it's going to go the other way, 645 00:31:27,440 --> 00:31:29,640 Speaker 4: and I've got a very tactical thing that is a 646 00:31:29,680 --> 00:31:32,320 Speaker 4: part of the equation, but a much larger part of 647 00:31:32,360 --> 00:31:35,480 Speaker 4: the equation are still catalyst driven, Like Okay, there's a 648 00:31:35,560 --> 00:31:37,600 Speaker 4: data point that comes out, but it's a data point 649 00:31:37,600 --> 00:31:40,760 Speaker 4: that indicates something about the overall perception of where this 650 00:31:40,800 --> 00:31:43,320 Speaker 4: company is headed. And so like classic ones and software 651 00:31:43,360 --> 00:31:46,960 Speaker 4: can be changes in churn direction and like where people 652 00:31:47,040 --> 00:31:48,880 Speaker 4: can get smart on that is often like okay, there's 653 00:31:48,880 --> 00:31:51,520 Speaker 4: an overall headline churn number, but then there's like like 654 00:31:51,520 --> 00:31:53,400 Speaker 4: if it's an internet company or something like that, or 655 00:31:53,440 --> 00:31:55,960 Speaker 4: subscriber company, and then you can go down the line 656 00:31:55,960 --> 00:31:58,160 Speaker 4: like okay, if somebody's looking at churned by region and 657 00:31:58,240 --> 00:32:00,920 Speaker 4: has some forward look on something that gives them insight 658 00:32:00,960 --> 00:32:03,320 Speaker 4: to like, okay, churin is changing in this region, and 659 00:32:03,360 --> 00:32:05,280 Speaker 4: this reading is small today, so it actually doesn't hit 660 00:32:05,320 --> 00:32:08,200 Speaker 4: the headline churn number. Yeah, but that's actually structurally growing 661 00:32:08,320 --> 00:32:11,560 Speaker 4: faster than every other region, and so the underlying churn 662 00:32:11,640 --> 00:32:14,160 Speaker 4: rate that looks like it's this level is going to 663 00:32:14,160 --> 00:32:16,640 Speaker 4: step up structurally because this smaller region is going to 664 00:32:16,640 --> 00:32:18,520 Speaker 4: be a bigger part of the overall path. That's the 665 00:32:18,600 --> 00:32:19,080 Speaker 4: kind of thing. 666 00:32:19,680 --> 00:32:21,959 Speaker 2: At some point, by the way, we really need to 667 00:32:22,040 --> 00:32:24,840 Speaker 2: do another I'm sure we've done on the past a 668 00:32:24,880 --> 00:32:28,680 Speaker 2: deep episode on all data, because yeah, Walmart satellites of 669 00:32:28,720 --> 00:32:31,880 Speaker 2: Walmart parking lots and like credit cards, I've heard about it, 670 00:32:31,920 --> 00:32:33,840 Speaker 2: but it's like, I know, there's more to it, and 671 00:32:33,920 --> 00:32:37,840 Speaker 2: there's you know, it's important. You mentioned the different shops 672 00:32:38,240 --> 00:32:42,480 Speaker 2: have different software infrastructure, and the level at which it's 673 00:32:42,520 --> 00:32:46,040 Speaker 2: on the managers different sometimes and the different which it's 674 00:32:46,080 --> 00:32:50,040 Speaker 2: at the umbrella level, So like does that mean that, 675 00:32:50,160 --> 00:32:54,480 Speaker 2: like does it happen where the at the very high 676 00:32:54,560 --> 00:32:57,840 Speaker 2: end of risk management they look across and they say, wow, 677 00:32:57,880 --> 00:33:03,840 Speaker 2: you know, in aggregate, our portfolio managers, maybe perhaps unintentionally 678 00:33:03,960 --> 00:33:06,280 Speaker 2: or even within their remit, have built up a lot 679 00:33:06,320 --> 00:33:11,680 Speaker 2: of implied exposure to momentum or implied exposure to rates, 680 00:33:11,760 --> 00:33:15,120 Speaker 2: or implied exposure to value. And then what do they do, 681 00:33:15,200 --> 00:33:17,120 Speaker 2: like tap people on the shoulder and say, like, how 682 00:33:17,160 --> 00:33:17,720 Speaker 2: what happens? 683 00:33:17,760 --> 00:33:17,880 Speaker 3: Then? 684 00:33:17,960 --> 00:33:20,680 Speaker 4: Yeah, absolutely, so again this sort of a spectrum of 685 00:33:20,720 --> 00:33:24,880 Speaker 4: people's technology and factor awareness risk systems, but at the 686 00:33:24,920 --> 00:33:27,640 Speaker 4: sort of platonic ideal of that that you know exists 687 00:33:27,680 --> 00:33:30,600 Speaker 4: in various forms. There's sort of a CIO level, there's 688 00:33:30,600 --> 00:33:32,560 Speaker 4: a you know, COO and risk team level, there's the 689 00:33:32,560 --> 00:33:35,280 Speaker 4: PM level. There's even an analyst level that sort of 690 00:33:35,320 --> 00:33:38,840 Speaker 4: is monitoring each level of that. So like you'll put limits, 691 00:33:38,840 --> 00:33:42,120 Speaker 4: as we talked about on the portfolio level, right on 692 00:33:42,200 --> 00:33:44,960 Speaker 4: an aggregate risk basis, and then on an individual factor 693 00:33:45,000 --> 00:33:47,200 Speaker 4: you'll say, okay, you can have more than blank percent 694 00:33:47,240 --> 00:33:49,320 Speaker 4: of your variants in your book in a specific in 695 00:33:49,360 --> 00:33:52,400 Speaker 4: any specific factor, So put those limits individually, and then 696 00:33:52,720 --> 00:33:54,560 Speaker 4: exactly as you said, they roll it up just like 697 00:33:54,600 --> 00:33:57,400 Speaker 4: you know, you just add up the line items. Essentially, 698 00:33:57,400 --> 00:34:00,720 Speaker 4: all these models are structurally linear decomposit so they add 699 00:34:00,800 --> 00:34:05,440 Speaker 4: up actually linearly, So like John's momentum exposure in dollar terms, 700 00:34:05,440 --> 00:34:08,680 Speaker 4: here Jaill's exposure is there, and they add up. So 701 00:34:08,880 --> 00:34:11,680 Speaker 4: you do see aggregate level CIO level kind of hey, 702 00:34:11,680 --> 00:34:14,239 Speaker 4: we're net lung blank or whatever at that level, and 703 00:34:14,320 --> 00:34:17,000 Speaker 4: it depends how teams structure their limits and how tightly 704 00:34:17,040 --> 00:34:18,839 Speaker 4: they limit exposures at the portfolio level. But you will 705 00:34:18,840 --> 00:34:21,399 Speaker 4: see aggriate exposures, and then there are ways to take 706 00:34:21,480 --> 00:34:23,720 Speaker 4: like an ETF or a basket or a custom basket 707 00:34:23,719 --> 00:34:26,200 Speaker 4: that will just limit out We'll just literally hedge that basket. 708 00:34:26,480 --> 00:34:28,480 Speaker 4: And there's nuance even there, like, hey do I can 709 00:34:28,520 --> 00:34:31,440 Speaker 4: I build a basket that hedges out that exposure but 710 00:34:31,480 --> 00:34:33,759 Speaker 4: doesn't actually basically end up being short at the same 711 00:34:33,800 --> 00:34:35,640 Speaker 4: stocks I'm long underlying the book, you can see how 712 00:34:35,680 --> 00:34:37,359 Speaker 4: that can get into a whole rabbit hole of like 713 00:34:37,400 --> 00:34:40,239 Speaker 4: sort of technical behind the scenes execution detail. But at 714 00:34:40,280 --> 00:34:42,200 Speaker 4: the high level, you sort of roll up the exposures, 715 00:34:42,200 --> 00:34:43,839 Speaker 4: you add them up, and you say, am I long 716 00:34:43,920 --> 00:34:45,840 Speaker 4: or short one or two or three or all the factors, 717 00:34:46,000 --> 00:34:48,120 Speaker 4: and let me balance those out at an aggurate level. 718 00:34:49,120 --> 00:34:52,359 Speaker 3: So one thing that often comes up in discussions of 719 00:34:52,680 --> 00:34:57,319 Speaker 3: risk management software that's been popularized on Wall Street, and 720 00:34:57,360 --> 00:35:01,360 Speaker 3: I'm thinking especially you hear this a lot about and Aladdin, 721 00:35:01,640 --> 00:35:05,239 Speaker 3: but this idea that if everyone's using the same risk 722 00:35:05,320 --> 00:35:09,160 Speaker 3: management software, then is there a risk that you could 723 00:35:09,400 --> 00:35:12,480 Speaker 3: get everyone like doing the same thing at the same time, so, 724 00:35:12,520 --> 00:35:17,040 Speaker 3: for instance, a mass deleveraging event because everyone software is 725 00:35:17,120 --> 00:35:20,200 Speaker 3: like based on a particular model and one thing happens 726 00:35:20,239 --> 00:35:22,560 Speaker 3: and the model spits out and says, everyone needs to 727 00:35:22,600 --> 00:35:25,920 Speaker 3: sell right now. Is that a risk? Is that like 728 00:35:25,960 --> 00:35:29,040 Speaker 3: a realistic risk or is it the case that all 729 00:35:29,080 --> 00:35:33,400 Speaker 3: of this off the shelf risk management software is so customizable, 730 00:35:33,520 --> 00:35:36,799 Speaker 3: I guess, and there's still that discretionary factor for the 731 00:35:36,840 --> 00:35:40,960 Speaker 3: PMS that you don't really get that hurting behavior. 732 00:35:40,640 --> 00:35:43,640 Speaker 4: M So I'd say yes and no. I think in 733 00:35:43,800 --> 00:35:46,960 Speaker 4: the no camp, the fact is that you're kind of 734 00:35:46,960 --> 00:35:52,399 Speaker 4: eliminating those sources of exposure that are common. So you're 735 00:35:52,480 --> 00:35:56,600 Speaker 4: kind of trying to focus people on residual bets. And 736 00:35:56,680 --> 00:35:59,160 Speaker 4: you know, for example, that could be oversimplifying, but that 737 00:35:59,200 --> 00:36:01,600 Speaker 4: could be long and short pepsi, or long pepsi and 738 00:36:01,600 --> 00:36:05,200 Speaker 4: short coke, and that would equivalently neutralized factors. Let's assume 739 00:36:05,239 --> 00:36:07,799 Speaker 4: they're kind of proxies for each other. And so kind 740 00:36:07,800 --> 00:36:09,600 Speaker 4: of what the model lets you do is kind of, 741 00:36:09,680 --> 00:36:12,120 Speaker 4: instead of having to be perfect pairs in the Alfred 742 00:36:12,160 --> 00:36:15,440 Speaker 4: Sloan original hedge fund concept, where you have to in 743 00:36:15,520 --> 00:36:17,439 Speaker 4: order to be factor neutral, you just have to find 744 00:36:17,440 --> 00:36:20,200 Speaker 4: perfect comps, it kind of lets you pick non perfect 745 00:36:20,200 --> 00:36:22,640 Speaker 4: comps but end up in a risk place that is 746 00:36:22,760 --> 00:36:24,719 Speaker 4: similar to that where your only bet is on a 747 00:36:24,760 --> 00:36:27,120 Speaker 4: single stock. But anyway, so like what the model is 748 00:36:27,160 --> 00:36:29,879 Speaker 4: pushing you to is not any specific stock, right, It's 749 00:36:29,920 --> 00:36:32,279 Speaker 4: telling you to pick which one of the stocks that 750 00:36:32,320 --> 00:36:36,439 Speaker 4: don't have comparable factor exposures is more attractive. So that's 751 00:36:36,480 --> 00:36:39,120 Speaker 4: one level. The second is there is leverage, and the 752 00:36:39,239 --> 00:36:42,480 Speaker 4: leverage you're putting on is not leverage against beta. That's 753 00:36:42,520 --> 00:36:45,239 Speaker 4: the distinction that I think people often allide is that 754 00:36:45,280 --> 00:36:49,360 Speaker 4: when you think of like LTCM or maybe forgetting even LTCM, 755 00:36:49,400 --> 00:36:51,880 Speaker 4: but any fund that takes very high leverage on a 756 00:36:51,920 --> 00:36:56,040 Speaker 4: beta a directional bet that's beta on a factor, and 757 00:36:56,080 --> 00:36:58,560 Speaker 4: the issue with that is many issues with that. If 758 00:36:58,560 --> 00:37:00,239 Speaker 4: you're taking lots of leverage on a beta is there's 759 00:37:00,239 --> 00:37:01,680 Speaker 4: just sort of that risk that it has a big 760 00:37:01,800 --> 00:37:04,600 Speaker 4: draw down. The hope, I guess, or the sort of 761 00:37:04,680 --> 00:37:07,360 Speaker 4: mathematical reality as you kind of pointed out that's actually 762 00:37:07,360 --> 00:37:11,120 Speaker 4: been executed on is that when you're levering alpha, it's 763 00:37:11,160 --> 00:37:14,120 Speaker 4: again it's sort of the quant fund world works this 764 00:37:14,200 --> 00:37:16,880 Speaker 4: way too, is that what you're levering is just that 765 00:37:16,960 --> 00:37:19,719 Speaker 4: residual term you're getting back to that coin flipping and 766 00:37:19,719 --> 00:37:23,160 Speaker 4: you're finding a source of return that is normally distributed 767 00:37:23,200 --> 00:37:26,319 Speaker 4: across stocks, and therefore if there is a big blow 768 00:37:26,360 --> 00:37:30,080 Speaker 4: up in markets, actually typically the factors become more and 769 00:37:30,080 --> 00:37:33,400 Speaker 4: more statistically significant, and so if you're neutral against those factors, 770 00:37:33,760 --> 00:37:37,040 Speaker 4: the residual return remains a cross sexually normally distributed. So 771 00:37:37,160 --> 00:37:39,400 Speaker 4: there's obviously a lot of detail under the hood, but 772 00:37:39,480 --> 00:37:41,640 Speaker 4: the basic answer is that you're trying to find a 773 00:37:41,760 --> 00:37:44,320 Speaker 4: type of return, and a diversified source type of return 774 00:37:44,760 --> 00:37:47,640 Speaker 4: that doesn't have that risk in a blow up, So 775 00:37:47,840 --> 00:37:49,960 Speaker 4: you're kind of levering alpha. That's the key kind of 776 00:37:49,960 --> 00:37:52,799 Speaker 4: point versus beta. And the final yes answer to your 777 00:37:52,880 --> 00:37:56,960 Speaker 4: question is that you are still levered. So notwithstanding everything 778 00:37:57,000 --> 00:38:00,560 Speaker 4: you can do to sort of solve the mathematic piece 779 00:38:00,640 --> 00:38:03,840 Speaker 4: of this equation, you still have some risk that the 780 00:38:03,840 --> 00:38:06,239 Speaker 4: person providing you the leverage has a business problem or 781 00:38:06,280 --> 00:38:08,840 Speaker 4: somebody who like whoever is providing that leverage to you, 782 00:38:08,880 --> 00:38:11,960 Speaker 4: which is typically the banks. Basically that person for whatever 783 00:38:12,000 --> 00:38:14,360 Speaker 4: reason needs to pull that leverage or whatever, and that 784 00:38:14,400 --> 00:38:16,440 Speaker 4: it's almost a little bit even in the category of 785 00:38:16,440 --> 00:38:20,680 Speaker 4: business risk that exists intrinsically with leverage. So that's kind 786 00:38:20,680 --> 00:38:22,080 Speaker 4: of the the yes portion of the answer. 787 00:38:22,120 --> 00:38:26,040 Speaker 2: I'd say this type of software, these models. They exist, 788 00:38:26,040 --> 00:38:27,240 Speaker 2: they've existed for a while. 789 00:38:27,400 --> 00:38:27,839 Speaker 4: That's right. 790 00:38:28,400 --> 00:38:32,680 Speaker 2: When you started your company, are kinda what was the 791 00:38:32,680 --> 00:38:35,479 Speaker 2: theory that there was need for more? 792 00:38:35,640 --> 00:38:37,759 Speaker 4: Yeah, and it's what you mentioned at the beginning too, 793 00:38:37,840 --> 00:38:41,120 Speaker 4: which is the sort of the theoretical beauty of these 794 00:38:41,160 --> 00:38:43,920 Speaker 4: models and how it all works, and the normally distributed 795 00:38:43,960 --> 00:38:47,040 Speaker 4: residuals and the sort of diversification of alpha and the 796 00:38:47,120 --> 00:38:50,360 Speaker 4: levering of alpha. But what really has happened over you know, 797 00:38:50,440 --> 00:38:53,040 Speaker 4: decades now is that that model has been proven to 798 00:38:53,120 --> 00:38:54,640 Speaker 4: be at least have something. It may not be the 799 00:38:54,680 --> 00:38:57,719 Speaker 4: only model that's viable to make attractive returns for investors, 800 00:38:57,960 --> 00:39:00,400 Speaker 4: but at least that sort of you know, result has 801 00:39:00,440 --> 00:39:04,239 Speaker 4: jumped from the sort of academic theory to realized practice. 802 00:39:04,719 --> 00:39:07,680 Speaker 2: And yeah, and it also explains why we're seeing some 803 00:39:07,680 --> 00:39:08,640 Speaker 2: pretty big launches in. 804 00:39:08,640 --> 00:39:11,600 Speaker 4: This absolutely absolutely, Yeah, it certainly has jumped that gulf. 805 00:39:11,680 --> 00:39:14,279 Speaker 4: And you know, look, in the quantitative world, it made 806 00:39:14,280 --> 00:39:17,360 Speaker 4: that jump long ago. It's in the fundamental stock picking 807 00:39:17,360 --> 00:39:20,320 Speaker 4: world that it made that and again, a few firms 808 00:39:20,320 --> 00:39:21,800 Speaker 4: had been doing it for a long time, but it 809 00:39:21,920 --> 00:39:24,160 Speaker 4: sort of made the most convincing leap over the last 810 00:39:24,200 --> 00:39:26,480 Speaker 4: whatever five to ten years, where it just sort of 811 00:39:26,520 --> 00:39:30,960 Speaker 4: decisively generated very attractive risk adjusted returns for investors and 812 00:39:31,000 --> 00:39:33,359 Speaker 4: kind of proved that sort of synthesis, which is really 813 00:39:33,400 --> 00:39:35,600 Speaker 4: what's happening between the sort of quant view of the 814 00:39:35,600 --> 00:39:40,760 Speaker 4: world of factorization and finding idiosyncratic or residual performance within 815 00:39:41,200 --> 00:39:44,919 Speaker 4: inside what's left over after the factors synthesized that risk 816 00:39:45,000 --> 00:39:48,080 Speaker 4: and sort of quant perspective with that Warren Buffett's style 817 00:39:48,200 --> 00:39:51,880 Speaker 4: fundamental type research and analysis and work, that synthesis was 818 00:39:52,239 --> 00:39:54,560 Speaker 4: implemented by a few firms and now it's sort of 819 00:39:54,560 --> 00:39:56,319 Speaker 4: proven itself to work in a lot of different ways. 820 00:39:56,360 --> 00:39:58,759 Speaker 4: So that's what's happening. So from our angle, like what 821 00:39:58,800 --> 00:40:01,279 Speaker 4: we have seen is just that wide range as I 822 00:40:01,360 --> 00:40:04,400 Speaker 4: kind of mentioned earlier of execution of that, like how 823 00:40:04,480 --> 00:40:07,120 Speaker 4: easy is it to actually have a system in place 824 00:40:07,120 --> 00:40:11,200 Speaker 4: for a portfolio manager or analyst or cio. How sort 825 00:40:11,239 --> 00:40:13,560 Speaker 4: of not only user friendly but sort of functional. How 826 00:40:13,600 --> 00:40:17,000 Speaker 4: efficiently can it source new hedge ideas that balance out 827 00:40:17,000 --> 00:40:20,560 Speaker 4: a specific factor exposure? How efficiently does it connect that 828 00:40:20,680 --> 00:40:23,400 Speaker 4: risk perspective of where I'm long and short? To a 829 00:40:23,440 --> 00:40:25,799 Speaker 4: topic you mentioned earlier, performance attribution, like where are my 830 00:40:26,400 --> 00:40:28,400 Speaker 4: generating returns? What are my hit rates, What are my 831 00:40:28,440 --> 00:40:30,920 Speaker 4: hit rates on residual versus on factor? What are my 832 00:40:31,000 --> 00:40:34,120 Speaker 4: hit rates on earning season versus outside of earning season 833 00:40:34,400 --> 00:40:36,360 Speaker 4: and on a residual basis, And how does that connect 834 00:40:36,400 --> 00:40:39,400 Speaker 4: to my risk and my portfolio construction? And all of 835 00:40:39,400 --> 00:40:40,960 Speaker 4: that is a lot of work, you know, it's a 836 00:40:41,000 --> 00:40:43,239 Speaker 4: lot of painful kind of putting together the software and 837 00:40:43,239 --> 00:40:46,040 Speaker 4: the risk and all the different elements together. And as 838 00:40:46,040 --> 00:40:47,840 Speaker 4: I mentioned, what we see is some funds have done this, 839 00:40:48,280 --> 00:40:50,480 Speaker 4: you know, at a level that is really excellent, and 840 00:40:50,520 --> 00:40:52,239 Speaker 4: some funds, most funds, because they have to do the 841 00:40:52,400 --> 00:40:55,160 Speaker 4: very hard work of stock picking. It's a very challenging job. 842 00:40:55,160 --> 00:40:57,760 Speaker 4: You have to have incredible IQ allocated to that problem 843 00:40:57,920 --> 00:41:00,680 Speaker 4: and effort. They have, Okay, systems of a sheet that 844 00:41:00,920 --> 00:41:02,600 Speaker 4: gives some risk insight, but it doesn't have the sort 845 00:41:02,640 --> 00:41:06,000 Speaker 4: of detailed input output experience of let me tweak this, 846 00:41:06,080 --> 00:41:07,839 Speaker 4: let me see what happens there, let me understand how 847 00:41:07,840 --> 00:41:10,160 Speaker 4: it connects. And so putting all those elements together is 848 00:41:10,160 --> 00:41:11,080 Speaker 4: what we kind of hope to do. 849 00:41:11,719 --> 00:41:13,479 Speaker 3: When did you actually found our. 850 00:41:13,440 --> 00:41:14,799 Speaker 4: Camera a little over two years ago? 851 00:41:14,840 --> 00:41:15,439 Speaker 2: Two years ago? 852 00:41:15,480 --> 00:41:20,320 Speaker 3: Okay, so what's the difference between what clients ask for now? 853 00:41:20,480 --> 00:41:23,120 Speaker 3: Versus what they were asking for two years ago. Because 854 00:41:23,120 --> 00:41:25,759 Speaker 3: this is a rapidly evolving space. 855 00:41:26,000 --> 00:41:27,640 Speaker 4: You know. I think in that sort of split that 856 00:41:27,680 --> 00:41:29,440 Speaker 4: I mentioned of our client base that is kind of 857 00:41:29,520 --> 00:41:32,360 Speaker 4: native to that risk world and the group that is 858 00:41:32,400 --> 00:41:34,680 Speaker 4: sort of newer to it, I think the native group 859 00:41:34,719 --> 00:41:37,520 Speaker 4: has this constant sort of question set of how do 860 00:41:37,600 --> 00:41:41,560 Speaker 4: I make this again more functional? See more analysis more quickly, 861 00:41:42,040 --> 00:41:45,040 Speaker 4: how everything relates to each piece? Can I see insights 862 00:41:45,040 --> 00:41:47,080 Speaker 4: on crowding and how that relates to my book? And 863 00:41:47,120 --> 00:41:49,480 Speaker 4: can I see all those different pieces. So that's kind 864 00:41:49,480 --> 00:41:53,279 Speaker 4: of like a steady escalation in thoughtfulness. I would say, 865 00:41:53,600 --> 00:41:57,400 Speaker 4: as you hand the portfolio manager tools on this factor, 866 00:41:57,560 --> 00:42:00,080 Speaker 4: and you also sort of empower them because again and 867 00:42:00,080 --> 00:42:02,000 Speaker 4: a lot of these organizations are set up where there's 868 00:42:02,000 --> 00:42:05,600 Speaker 4: a risk side and a portfolio management function, and you know, 869 00:42:05,680 --> 00:42:07,719 Speaker 4: the portfolio manager isn't necessarily the client of the risk 870 00:42:07,800 --> 00:42:10,640 Speaker 4: in house at these places. But as there becomes this 871 00:42:10,760 --> 00:42:13,840 Speaker 4: industry of people like us who are providing these tools, 872 00:42:13,920 --> 00:42:15,080 Speaker 4: in a way we have to be a little more 873 00:42:15,120 --> 00:42:17,719 Speaker 4: responsive to the portfolio manager says Okay, I see how 874 00:42:17,719 --> 00:42:18,959 Speaker 4: that was built. Can I double click? 875 00:42:19,000 --> 00:42:21,560 Speaker 2: I mean the portfolio manager isn't necessarily a client. 876 00:42:22,000 --> 00:42:24,160 Speaker 4: So at a big multi manager, you have a risk 877 00:42:24,200 --> 00:42:26,920 Speaker 4: division which kind of sits under the CIO almost, and 878 00:42:26,960 --> 00:42:30,000 Speaker 4: you have the portfolio managers, and the portfolio managers aren't 879 00:42:30,040 --> 00:42:31,640 Speaker 4: the client of the risk people. The risk people kind 880 00:42:31,640 --> 00:42:32,839 Speaker 4: of work, if you want to put it that way, 881 00:42:32,840 --> 00:42:35,600 Speaker 4: for the CIO, who says, you know, sort of it's 882 00:42:35,640 --> 00:42:37,239 Speaker 4: kind of a limitter in some rice. It's kind of 883 00:42:37,239 --> 00:42:39,839 Speaker 4: a constraint in a lot of cases. And the best 884 00:42:39,840 --> 00:42:43,200 Speaker 4: places are doing it where it's completely synergistic, where you're 885 00:42:43,280 --> 00:42:45,920 Speaker 4: using the risk tools and this factor awareness and all 886 00:42:45,960 --> 00:42:48,240 Speaker 4: of the things you can do with that on offense, 887 00:42:48,360 --> 00:42:50,760 Speaker 4: not just defense. So that is happening at a few places, 888 00:42:50,880 --> 00:42:52,640 Speaker 4: but there's a whole other group of places where it's 889 00:42:52,719 --> 00:42:55,400 Speaker 4: kind of, hey, this limits me, This isn't working with 890 00:42:55,600 --> 00:42:58,200 Speaker 4: or for me. And so as this becomes a little 891 00:42:58,200 --> 00:43:00,839 Speaker 4: bit more, you know of an industry or like, we 892 00:43:00,880 --> 00:43:03,080 Speaker 4: do work for them, right, so we're hey, I want 893 00:43:03,080 --> 00:43:05,920 Speaker 4: this additional feature that you know they're the client. Right. 894 00:43:06,239 --> 00:43:08,240 Speaker 4: So there's that piece, but there's sort of this constantly 895 00:43:08,320 --> 00:43:12,640 Speaker 4: escalating sort of demand for tooling and incremental insight and okay, 896 00:43:12,760 --> 00:43:14,680 Speaker 4: let me click this, let me understand this across my 897 00:43:14,760 --> 00:43:17,120 Speaker 4: whole universe, across the entire universe stocks, I could cover 898 00:43:17,719 --> 00:43:19,799 Speaker 4: all those kinds of tools on the sort of the 899 00:43:19,800 --> 00:43:22,040 Speaker 4: people who don't come necessarily out of the POD systems. 900 00:43:22,640 --> 00:43:24,600 Speaker 4: The interesting thing is the extent to which people want 901 00:43:24,640 --> 00:43:27,000 Speaker 4: to focus on. Okay, let me, how do I frame 902 00:43:27,120 --> 00:43:30,120 Speaker 4: that system that factor awareness instead of in a market 903 00:43:30,160 --> 00:43:32,960 Speaker 4: neutral context. But hey, I'm a long only or I'm 904 00:43:33,000 --> 00:43:36,920 Speaker 4: a sort of directionally oriented value fund or whatever. How 905 00:43:36,960 --> 00:43:40,120 Speaker 4: do I reorient the model shift it to be sort 906 00:43:40,120 --> 00:43:43,200 Speaker 4: of true comparative to my benchmark? And so that's been 907 00:43:43,200 --> 00:43:45,759 Speaker 4: an interesting evolution is the types of investors who are 908 00:43:45,800 --> 00:43:48,840 Speaker 4: not structurally market neutral but still want all the insights 909 00:43:48,840 --> 00:43:52,280 Speaker 4: from this where you can recalibrate the entire model against 910 00:43:52,320 --> 00:43:54,080 Speaker 4: a benchmark. And so that's been one example. 911 00:43:55,000 --> 00:43:57,439 Speaker 2: Rich Folk Wallace, thank you so much for coming on 912 00:43:57,440 --> 00:44:00,880 Speaker 2: od lotch. That was a fantastic learned and now have 913 00:44:01,040 --> 00:44:04,359 Speaker 2: like ten ideas for further episodes we have to do, 914 00:44:04,400 --> 00:44:06,680 Speaker 2: which is always, as we say, the test of whether 915 00:44:06,840 --> 00:44:07,239 Speaker 2: we had a. 916 00:44:07,160 --> 00:44:09,719 Speaker 4: Good conversation or absolutely absolutely Thanks so much for having me. 917 00:44:09,800 --> 00:44:24,239 Speaker 2: Really appreciate it, Tracy, I thought that was great. I 918 00:44:24,280 --> 00:44:27,279 Speaker 2: really do have like there's like ten more episodes that 919 00:44:27,400 --> 00:44:30,600 Speaker 2: we have to do now. But that was very illuminating 920 00:44:30,719 --> 00:44:34,920 Speaker 2: on multiple levels, particularly about like what the job of 921 00:44:34,960 --> 00:44:39,000 Speaker 2: the PM or the analyst actually is in these contexts. Yeah. 922 00:44:39,040 --> 00:44:43,200 Speaker 3: Absolutely, And also I was thinking it kind of dovetailed, 923 00:44:43,320 --> 00:44:47,560 Speaker 3: interestingly enough with the conversation we had recently about thematic investing, Yes, 924 00:44:47,680 --> 00:44:51,160 Speaker 3: James Fankiland, where he was talking about like, okay, price 925 00:44:51,280 --> 00:44:54,120 Speaker 3: is obviously a factor, but also you kind of want 926 00:44:54,120 --> 00:44:57,920 Speaker 3: to identify the story that everyone's going to latch onto. 927 00:44:58,520 --> 00:45:01,120 Speaker 3: And then Rich was talking about how when you're coming 928 00:45:01,200 --> 00:45:03,719 Speaker 3: up with investment ideas, you're sort of trying to identify 929 00:45:03,800 --> 00:45:08,120 Speaker 3: something that will change everyone's perception of the trajectory of 930 00:45:08,160 --> 00:45:10,680 Speaker 3: a particular stock or investment totally. 931 00:45:10,719 --> 00:45:13,000 Speaker 2: And I thought it was just like really interesting this 932 00:45:13,080 --> 00:45:16,120 Speaker 2: idea that like, okay, like no one knows what's going 933 00:45:16,200 --> 00:45:19,560 Speaker 2: to happen tomorrow, some major event could take place that 934 00:45:19,719 --> 00:45:22,759 Speaker 2: causes the you know, the whole market to crash. I 935 00:45:22,760 --> 00:45:26,000 Speaker 2: guess big events don't usually happen to cause the whole 936 00:45:26,040 --> 00:45:29,000 Speaker 2: market to surge, Unfortunately, it's always the other way around, 937 00:45:29,080 --> 00:45:31,520 Speaker 2: Like nobody knows what interest rates are going to do, 938 00:45:31,600 --> 00:45:33,920 Speaker 2: and we know you know, a lot of stocks are 939 00:45:33,960 --> 00:45:36,200 Speaker 2: tied to interest rates, and no one knows maybe some 940 00:45:36,560 --> 00:45:40,080 Speaker 2: chip company will come out tomorrow that beats and video whatever. 941 00:45:40,160 --> 00:45:42,360 Speaker 2: No one knows any of that stuff. And then this 942 00:45:42,520 --> 00:45:44,600 Speaker 2: idea that if you can then strip out all of 943 00:45:44,640 --> 00:45:49,000 Speaker 2: this and then identify the idiosyncratic drivers of a stock, 944 00:45:49,200 --> 00:45:53,000 Speaker 2: and then those idiosyncratic drivers of stock almost inherently, some 945 00:45:53,080 --> 00:45:55,319 Speaker 2: will be winners and some will be losers. Yeah, I 946 00:45:55,360 --> 00:45:59,840 Speaker 2: could see why. Then the game is lots of bets 947 00:46:00,480 --> 00:46:04,920 Speaker 2: over relatively short time periods. Like that that like really 948 00:46:04,960 --> 00:46:06,279 Speaker 2: clicked to me in this conversation. 949 00:46:06,400 --> 00:46:08,200 Speaker 3: Yes, that's the other thing that stood out to me, 950 00:46:08,320 --> 00:46:14,000 Speaker 3: like the idea of diversification across those different bets. Like, yeah, 951 00:46:14,080 --> 00:46:17,120 Speaker 3: I hadn't really, I guess, like when you think about 952 00:46:17,160 --> 00:46:20,839 Speaker 3: hedge funds still, even though multi strategy funds are sort 953 00:46:20,840 --> 00:46:23,880 Speaker 3: of where it's at SAE, Yeah, I still think about 954 00:46:23,920 --> 00:46:27,520 Speaker 3: like that classic I don't know, Bill Ackman type thing 955 00:46:27,719 --> 00:46:31,080 Speaker 3: where you make one big bet on something and that's 956 00:46:31,120 --> 00:46:34,160 Speaker 3: your source of alpha. But again, the thing that's coming 957 00:46:34,200 --> 00:46:37,480 Speaker 3: through in this conversation is really like the diversification aspect, 958 00:46:38,040 --> 00:46:42,680 Speaker 3: the desire to be factor neutral and to lever the 959 00:46:42,719 --> 00:46:44,279 Speaker 3: alpha instead of the beta. 960 00:46:44,360 --> 00:46:46,440 Speaker 2: All kinds of interesting stuff there. I want to do 961 00:46:46,520 --> 00:46:49,640 Speaker 2: more on what do I do more? I well, I 962 00:46:49,680 --> 00:46:51,960 Speaker 2: definitely want to do more on al data because I 963 00:46:51,960 --> 00:46:54,480 Speaker 2: feel like usually when that gets discussed, it's like this 964 00:46:54,680 --> 00:46:58,319 Speaker 2: like very like sort of tired cliche ways, like I 965 00:46:58,360 --> 00:47:01,520 Speaker 2: know everyone has a credit card day, but I want 966 00:47:01,520 --> 00:47:04,319 Speaker 2: to understand more about that. I don't know, there's a 967 00:47:04,360 --> 00:47:08,000 Speaker 2: lot more that we can also, just like the different models, 968 00:47:08,000 --> 00:47:10,120 Speaker 2: Like I'm sort of fascinated that, like there's all of 969 00:47:10,160 --> 00:47:13,440 Speaker 2: these different multistrad funds that exist, and the fact that 970 00:47:13,480 --> 00:47:16,279 Speaker 2: they're not all the same is interesting to me. And 971 00:47:16,320 --> 00:47:19,640 Speaker 2: the fact that like where the risk manager sits in 972 00:47:19,760 --> 00:47:22,120 Speaker 2: the amount of tools and what they build in house 973 00:47:22,160 --> 00:47:25,040 Speaker 2: and what they don't, and the degree of flexibility that 974 00:47:25,160 --> 00:47:28,719 Speaker 2: pods get and you know, what analysts actually do and 975 00:47:28,719 --> 00:47:30,200 Speaker 2: stuff like that. There's much more to do. 976 00:47:30,400 --> 00:47:33,160 Speaker 3: I really want to do an episode on differences in 977 00:47:33,239 --> 00:47:34,360 Speaker 3: compensation models. 978 00:47:34,400 --> 00:47:35,680 Speaker 2: Oh yeah, at the pod shops. 979 00:47:35,719 --> 00:47:37,719 Speaker 3: I think that would be really interesting because that would 980 00:47:37,760 --> 00:47:43,160 Speaker 3: also feed into I assume investor behavior. Yeah all right, Well, 981 00:47:43,320 --> 00:47:45,719 Speaker 3: now that we've come out of that with like ideas 982 00:47:45,760 --> 00:47:47,719 Speaker 3: for ten more episodes, shall we leave it there. 983 00:47:47,840 --> 00:47:48,520 Speaker 2: Let's leave it there. 984 00:47:48,680 --> 00:47:51,640 Speaker 3: This has been another episode of the All Thoughts podcast. 985 00:47:51,719 --> 00:47:54,520 Speaker 3: I'm Tracy Alloway. You can follow me at Tracy Alloway. 986 00:47:54,680 --> 00:47:57,640 Speaker 2: And I'm Joe Wisenthal. You can follow me at the Stalwart. 987 00:47:57,840 --> 00:48:01,320 Speaker 2: Follow our guest rich FULK Wallace. He's rich Folk Wallace. 988 00:48:01,440 --> 00:48:05,240 Speaker 2: Follow our producers Carmen Rodriguez at Carman Arman, Dashel Bennett 989 00:48:05,280 --> 00:48:08,799 Speaker 2: at dashbot in Kilbrooks at Kilbrooks. And thank you to 990 00:48:08,880 --> 00:48:11,960 Speaker 2: our producer Moses One. For more odd Lots content, go 991 00:48:12,040 --> 00:48:15,279 Speaker 2: to Bloomberg dot com slash odd Lots, where we have transcripts, 992 00:48:15,320 --> 00:48:17,960 Speaker 2: a blog and a newsletter. And if you want to 993 00:48:17,960 --> 00:48:21,440 Speaker 2: talk about all of these topics, including investing and markets, 994 00:48:21,680 --> 00:48:23,799 Speaker 2: you can do that twenty four to seven in the 995 00:48:23,920 --> 00:48:28,240 Speaker 2: odd Lots Discord Discord dot gg slash odd lots. 996 00:48:28,520 --> 00:48:32,359 Speaker 3: And if you enjoy Adlots, if you like our ongoing 997 00:48:32,560 --> 00:48:36,200 Speaker 3: attempt to understand multi strategy funds, then please leave us 998 00:48:36,239 --> 00:48:40,080 Speaker 3: a positive review on your favorite podcast platform. 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