1 00:00:10,960 --> 00:00:14,800 Speaker 1: Hello, and welcome to another episode of the Odd Lots podcast. 2 00:00:15,040 --> 00:00:19,080 Speaker 1: I'm Joe Wisenthal. My colleague and co host Tracy Elway 3 00:00:19,239 --> 00:00:22,160 Speaker 1: is out this week, so not going to be a 4 00:00:22,160 --> 00:00:25,880 Speaker 1: long introduction. But one of the big things that's going 5 00:00:25,920 --> 00:00:29,800 Speaker 1: to happen, at least expected to happen in markets in 6 00:00:29,920 --> 00:00:34,760 Speaker 1: t is the flotation of some pretty big, highly anticipated 7 00:00:34,880 --> 00:00:38,120 Speaker 1: I p O s, some of those Silicon Valley unicorns 8 00:00:38,159 --> 00:00:42,720 Speaker 1: that people have been talking about forever Uber lived Slack, 9 00:00:43,200 --> 00:00:46,760 Speaker 1: all of them plus more likely to go public uh 10 00:00:46,800 --> 00:00:49,600 Speaker 1: this year after a long wait. Of course, many of 11 00:00:49,640 --> 00:00:52,440 Speaker 1: them are huge, and they're going public at a stage 12 00:00:52,560 --> 00:00:55,560 Speaker 1: in life UH that is much later than many of 13 00:00:55,560 --> 00:00:58,840 Speaker 1: the big tech companies that are currently public when they 14 00:00:58,840 --> 00:01:00,720 Speaker 1: decided to go public or when they decided to do 15 00:01:00,800 --> 00:01:03,200 Speaker 1: their I p O. So I want to talk more 16 00:01:03,240 --> 00:01:06,240 Speaker 1: about the I p O market on today's episode, and 17 00:01:06,280 --> 00:01:09,080 Speaker 1: to discuss this I have with me Rhet Wallace. He 18 00:01:09,200 --> 00:01:13,160 Speaker 1: is the founder and CEO of triton Ai, a company 19 00:01:13,200 --> 00:01:17,399 Speaker 1: that analyzes I p O s for various proprietary measures, 20 00:01:17,600 --> 00:01:19,840 Speaker 1: and we're going to talk about the evolution of the 21 00:01:19,880 --> 00:01:23,399 Speaker 1: I p O market as well as how to analyze 22 00:01:23,400 --> 00:01:25,760 Speaker 1: an IPO because theoretically there might be people out there 23 00:01:25,760 --> 00:01:27,959 Speaker 1: looking at some of these that would like to have 24 00:01:28,080 --> 00:01:31,280 Speaker 1: some perspective on how to think about the value and 25 00:01:31,319 --> 00:01:34,440 Speaker 1: the investment appeal of these well known companies. So, Rhett, 26 00:01:34,480 --> 00:01:36,319 Speaker 1: thank you very much for joining us. Great to be here. 27 00:01:36,360 --> 00:01:40,800 Speaker 1: Thank you. Let's start with that question about why these 28 00:01:40,840 --> 00:01:46,319 Speaker 1: companies are so people say coming public much later in life. 29 00:01:46,520 --> 00:01:51,320 Speaker 1: So company is like Amazon and Microsoft and Apple. They 30 00:01:51,480 --> 00:01:54,440 Speaker 1: essentially became did their I p o s when they 31 00:01:54,440 --> 00:01:58,760 Speaker 1: were fairly tiny startups, at least by today's standards. What's 32 00:01:58,840 --> 00:02:03,760 Speaker 1: changed since then eighties to two thousand nineteen such that 33 00:02:04,280 --> 00:02:08,320 Speaker 1: these big companies are already produced billionaires and people have 34 00:02:08,360 --> 00:02:13,320 Speaker 1: gone on to great fortunes ever before floating a sheriff stock. Sure, well, 35 00:02:13,800 --> 00:02:15,919 Speaker 1: like most things, there are a couple of different narratives. 36 00:02:15,919 --> 00:02:19,280 Speaker 1: By way of explanation, what you will hear from people 37 00:02:19,280 --> 00:02:22,040 Speaker 1: in Silicon Valley is that founders don't like to take 38 00:02:22,080 --> 00:02:25,519 Speaker 1: their companies public because being a public company CEO is 39 00:02:25,560 --> 00:02:27,640 Speaker 1: kind of a pain in the neck. And so anybody 40 00:02:27,639 --> 00:02:31,040 Speaker 1: who's abut to comply with the sarbians Oxley Act and 41 00:02:31,080 --> 00:02:35,359 Speaker 1: other things that were imposed on publicly traded companies as 42 00:02:35,360 --> 00:02:39,120 Speaker 1: a protection against retail investors who buy their shares. You know, 43 00:02:39,200 --> 00:02:42,200 Speaker 1: if you have access to capital in the private markets, 44 00:02:42,680 --> 00:02:44,840 Speaker 1: it might be easier for you to stay private and 45 00:02:44,880 --> 00:02:48,240 Speaker 1: not expose your numbers, expose yourself to liability, and so forth. 46 00:02:48,600 --> 00:02:51,120 Speaker 1: So almost all of the reasons that you could think 47 00:02:51,160 --> 00:02:54,280 Speaker 1: of why a company wouldn't go public are regulatory there. 48 00:02:54,360 --> 00:02:57,359 Speaker 1: They stem from the changes in the regulations of the 49 00:02:57,400 --> 00:02:59,840 Speaker 1: securities business. And there's a long, geeky narrative that we 50 00:03:00,080 --> 00:03:04,320 Speaker 1: get into about that. When companies like Amazon and Netscape 51 00:03:04,360 --> 00:03:07,360 Speaker 1: and Yahoo went public on a couple of million dollars 52 00:03:07,360 --> 00:03:10,560 Speaker 1: of sales, and you know, earlier generations of companies like 53 00:03:10,720 --> 00:03:13,240 Speaker 1: Intel and so forth went public, you know, really as 54 00:03:13,240 --> 00:03:16,520 Speaker 1: soon as they got to revenue. That's because capital formation 55 00:03:16,600 --> 00:03:19,760 Speaker 1: happened in the public market. Let's actually, let's back up 56 00:03:19,800 --> 00:03:22,600 Speaker 1: for a second. Tell me about your firm and why 57 00:03:22,720 --> 00:03:25,480 Speaker 1: this is an area that you pursued. What is it 58 00:03:25,520 --> 00:03:28,040 Speaker 1: about I p O s that are interesting in general, 59 00:03:28,520 --> 00:03:31,400 Speaker 1: and what is your background that caused you or that 60 00:03:31,480 --> 00:03:34,400 Speaker 1: prompted you to go into analyzing them and providing the 61 00:03:34,600 --> 00:03:38,920 Speaker 1: service of breaking them down. Sure, well, it's I p 62 00:03:39,040 --> 00:03:41,880 Speaker 1: O s are a very good example of what came 63 00:03:42,080 --> 00:03:45,760 Speaker 1: after the Great Depression when the government decided to reform 64 00:03:45,840 --> 00:03:48,120 Speaker 1: the securities industry so that you didn't have a big 65 00:03:48,160 --> 00:03:51,840 Speaker 1: speculative bubble anymore of the kind that created the stock 66 00:03:51,880 --> 00:03:54,920 Speaker 1: market crash. And so the innovation at that moment was 67 00:03:55,000 --> 00:03:58,720 Speaker 1: that securities come with data stapling, the ten k's and 68 00:03:58,760 --> 00:04:02,920 Speaker 1: the ten queues, the regular are recurring reporting and disclosure 69 00:04:03,240 --> 00:04:05,680 Speaker 1: so investors would know what they are buying was the 70 00:04:05,720 --> 00:04:11,400 Speaker 1: great innovation after and that prompted a situation where companies, 71 00:04:11,440 --> 00:04:13,360 Speaker 1: if they wanted to trade stocks with each other or 72 00:04:13,400 --> 00:04:16,080 Speaker 1: have people by their shares, they had to be public. 73 00:04:16,400 --> 00:04:19,680 Speaker 1: And so companies went public much earlier. If you you 74 00:04:19,720 --> 00:04:23,600 Speaker 1: know again geekily read like the biography of Rockefeller, for example, 75 00:04:23,720 --> 00:04:25,640 Speaker 1: before the crash, one of the reasons he was so 76 00:04:25,760 --> 00:04:29,039 Speaker 1: successful in investing is that he had access to information 77 00:04:29,080 --> 00:04:33,360 Speaker 1: that wasn't broadly available. That always helps, right, So information 78 00:04:33,680 --> 00:04:37,160 Speaker 1: has always been a key component of being successful as 79 00:04:37,160 --> 00:04:40,799 Speaker 1: an investor. And so the origin story of our firm 80 00:04:41,440 --> 00:04:44,200 Speaker 1: is that we saw what was happening that fewer and 81 00:04:44,200 --> 00:04:46,640 Speaker 1: fewer companies were going public, and that meant that more 82 00:04:46,680 --> 00:04:49,479 Speaker 1: and more of the interesting companies were private. And these 83 00:04:49,520 --> 00:04:53,520 Speaker 1: companies operated outside of the information regime of the Securities 84 00:04:53,560 --> 00:04:56,800 Speaker 1: Acts of the United States. The other thing that we 85 00:04:56,920 --> 00:05:00,599 Speaker 1: noticed is that all of the information architecture that was 86 00:05:00,680 --> 00:05:04,719 Speaker 1: installed as the operating system of the securities trading institutions 87 00:05:04,839 --> 00:05:08,480 Speaker 1: was developed in the nineteen thirties, So, like generally accepted 88 00:05:08,520 --> 00:05:12,520 Speaker 1: accounting principles, some people will tell you it's like, you know, 89 00:05:12,960 --> 00:05:15,920 Speaker 1: the perfect information that you could have about a company, 90 00:05:15,960 --> 00:05:19,000 Speaker 1: but it never existed until like Moses did not come 91 00:05:19,040 --> 00:05:22,880 Speaker 1: down from the mountain with gap company categorization, this standard 92 00:05:22,920 --> 00:05:27,560 Speaker 1: industrial classification system again like the nineteen thirties, and so 93 00:05:27,640 --> 00:05:32,880 Speaker 1: these pieces of data architecture haven't iterated an advanced so 94 00:05:32,920 --> 00:05:35,040 Speaker 1: we're still sort of stuck in the thirties with the 95 00:05:35,040 --> 00:05:37,920 Speaker 1: way companies are analyzed. So the origin story of our 96 00:05:37,920 --> 00:05:40,680 Speaker 1: company was we were looking for ways to be smart 97 00:05:40,720 --> 00:05:44,280 Speaker 1: about investing in companies that were generally private companies, and 98 00:05:44,320 --> 00:05:46,960 Speaker 1: the architecture that people used to look at public companies 99 00:05:47,200 --> 00:05:49,720 Speaker 1: wasn't particularly serviceable to that end, so we had to 100 00:05:49,720 --> 00:05:52,400 Speaker 1: build a new one. So obviously, when a company files 101 00:05:52,440 --> 00:05:54,840 Speaker 1: to go public, and it files, it's s one to 102 00:05:54,960 --> 00:05:59,200 Speaker 1: the sec the company engauges in the practice of putting 103 00:05:59,240 --> 00:06:03,560 Speaker 1: its numbers into a type of a structure that's similar 104 00:06:03,600 --> 00:06:06,760 Speaker 1: to other public companies are identical. It then has to correct. 105 00:06:06,920 --> 00:06:09,599 Speaker 1: There's a template that everyone has to adhere to. But 106 00:06:10,200 --> 00:06:13,719 Speaker 1: there's still the problem of investors haven't really gotten to 107 00:06:13,720 --> 00:06:17,839 Speaker 1: know these companies, and even within generally accepted accounting principles, 108 00:06:18,000 --> 00:06:22,120 Speaker 1: there's all kinds of idiosyncrasies and opinions and different approaches. 109 00:06:22,440 --> 00:06:24,840 Speaker 1: And companies that have been public for a while, people 110 00:06:24,880 --> 00:06:27,680 Speaker 1: become familiar with aspects of their business model and they 111 00:06:27,760 --> 00:06:30,720 Speaker 1: understand the moving parts, and that just doesn't exist yet, 112 00:06:30,880 --> 00:06:33,960 Speaker 1: certainly at the time of the S one filing. So 113 00:06:34,640 --> 00:06:37,200 Speaker 1: when you look at an S one filing, besides the 114 00:06:37,240 --> 00:06:39,720 Speaker 1: obvious the balance sheet and the income statement and the 115 00:06:39,800 --> 00:06:43,159 Speaker 1: cash flow statement, what else are you looking for when 116 00:06:43,200 --> 00:06:46,640 Speaker 1: you start to break down what you know looking at 117 00:06:46,680 --> 00:06:49,440 Speaker 1: these companies from the perspective of an investor. So our 118 00:06:49,600 --> 00:06:52,120 Speaker 1: point of view on companies is that a company is 119 00:06:52,160 --> 00:06:56,080 Speaker 1: really just a receptacle for different product lines. So our 120 00:06:56,160 --> 00:07:00,560 Speaker 1: trope example is that uber x and uber Eats live 121 00:07:00,600 --> 00:07:03,960 Speaker 1: inside the same company, but they're totally different businesses, completely 122 00:07:03,960 --> 00:07:07,480 Speaker 1: different product lines. So as companies go public much later 123 00:07:07,520 --> 00:07:10,400 Speaker 1: in their life, what it means is that the audit 124 00:07:10,920 --> 00:07:15,640 Speaker 1: of the consolidated entity disguises all of the individual operations 125 00:07:15,680 --> 00:07:17,840 Speaker 1: that are happening inside of a company that might have 126 00:07:17,920 --> 00:07:20,800 Speaker 1: a bike sharing you know business, and a scooter sharing 127 00:07:20,800 --> 00:07:23,240 Speaker 1: business and operates all over the world in different types 128 00:07:23,280 --> 00:07:26,560 Speaker 1: of jurisdictions, and so the bigger it is, the harder 129 00:07:26,600 --> 00:07:28,400 Speaker 1: it is to get your arms around it unless you 130 00:07:28,400 --> 00:07:32,240 Speaker 1: can see the detail. So that's that's really interesting points. 131 00:07:32,320 --> 00:07:35,120 Speaker 1: So if a company is just in the business of 132 00:07:35,560 --> 00:07:39,000 Speaker 1: making widgets, then you can have some sense of like, okay, 133 00:07:39,080 --> 00:07:42,720 Speaker 1: widgets cost the company this much to build, and raw 134 00:07:42,800 --> 00:07:45,320 Speaker 1: materials cost as much, and labor costs as muge, and 135 00:07:45,360 --> 00:07:47,400 Speaker 1: you sell the widgets for this much, and then you 136 00:07:47,440 --> 00:07:50,000 Speaker 1: look at the gap between costs and the sale and 137 00:07:50,040 --> 00:07:53,800 Speaker 1: you know something about the business. But with these big 138 00:07:53,840 --> 00:07:57,040 Speaker 1: companies and with new businesses that people don't understand and 139 00:07:57,160 --> 00:08:01,920 Speaker 1: sort of novel business models, simply so tracting costs from revenues, 140 00:08:02,520 --> 00:08:04,440 Speaker 1: it just doesn't tell you that much about the company. 141 00:08:04,480 --> 00:08:08,200 Speaker 1: The architecture of a digital company is just completely different 142 00:08:08,360 --> 00:08:12,520 Speaker 1: than the architecture of a nineteen thirties railroad or metals 143 00:08:12,560 --> 00:08:15,120 Speaker 1: and mining company. One of the things that you know, 144 00:08:15,160 --> 00:08:16,960 Speaker 1: for again, geeks that have spent a lot of time 145 00:08:17,000 --> 00:08:20,280 Speaker 1: studying how gap works and have suffered through accounting class 146 00:08:20,640 --> 00:08:23,160 Speaker 1: inventory accounting is one of the things that's like really 147 00:08:23,200 --> 00:08:25,920 Speaker 1: painful and the fiefold life folk kind of stuff. How 148 00:08:25,960 --> 00:08:29,720 Speaker 1: do you track the inventory of Facebook? Well, so then 149 00:08:29,760 --> 00:08:32,280 Speaker 1: that gets to the question, Okay, going back to the 150 00:08:32,400 --> 00:08:37,080 Speaker 1: Uber example, Obviously it's still probably mostly a car sharing company, 151 00:08:37,080 --> 00:08:39,640 Speaker 1: but in many different businesses, and they do also now 152 00:08:39,679 --> 00:08:43,640 Speaker 1: have several different lines and in some places they have scooters. 153 00:08:43,720 --> 00:08:47,880 Speaker 1: So how do you go about essentially trying to disassemble 154 00:08:48,440 --> 00:08:52,840 Speaker 1: the business from this consolidated these consolidated financial states. So 155 00:08:52,920 --> 00:08:55,439 Speaker 1: when we started out, we were looking for ways to 156 00:08:55,480 --> 00:08:58,480 Speaker 1: be smart about how to tell which dog walking app 157 00:08:58,600 --> 00:09:00,520 Speaker 1: is going to be better than the other dog walking apps, 158 00:09:00,559 --> 00:09:03,400 Speaker 1: for example, because you listen to the young entrepreneurs come 159 00:09:03,400 --> 00:09:05,800 Speaker 1: and pitch you a company, and it always sounds good, 160 00:09:05,800 --> 00:09:08,320 Speaker 1: but you don't have a comparative base of data. And 161 00:09:08,360 --> 00:09:10,640 Speaker 1: so the s I C code system was no use 162 00:09:10,679 --> 00:09:14,240 Speaker 1: to us whatsoever in how to categorize companies into the 163 00:09:14,280 --> 00:09:16,800 Speaker 1: bucket of dog walking apps and then figure out which 164 00:09:16,800 --> 00:09:18,600 Speaker 1: one was going to be the best dog walking app. 165 00:09:18,920 --> 00:09:21,000 Speaker 1: So we had to design an architecture that you could 166 00:09:21,000 --> 00:09:23,600 Speaker 1: get the apples and apples in the same buckets and 167 00:09:23,640 --> 00:09:25,920 Speaker 1: separate them from the oranges and the grab apples and 168 00:09:25,960 --> 00:09:28,360 Speaker 1: the tangerines and everything else. And so one of the 169 00:09:28,400 --> 00:09:31,079 Speaker 1: things that was fairly funny about this is if you 170 00:09:31,200 --> 00:09:34,000 Speaker 1: use a sort of you know, a top down E. 171 00:09:34,280 --> 00:09:37,640 Speaker 1: S I. C. Level type categorization system, and you use 172 00:09:37,679 --> 00:09:42,199 Speaker 1: a word like transportation, what we found is that companies 173 00:09:42,240 --> 00:09:46,760 Speaker 1: like Uber bucketed into the same bucket as zip car, right. 174 00:09:47,000 --> 00:09:48,720 Speaker 1: But you look at it and you're like, okay, well, 175 00:09:49,080 --> 00:09:52,640 Speaker 1: Uber doesn't own any cars. Zip car owns thousands of 176 00:09:52,679 --> 00:09:56,560 Speaker 1: cars that they have to park, maintain, fuel, paint, all 177 00:09:56,559 --> 00:09:58,840 Speaker 1: that sort of stuff. So it's like, okay, even though 178 00:09:59,120 --> 00:10:02,480 Speaker 1: from a a sort of narrative perspective, these things look 179 00:10:02,559 --> 00:10:05,319 Speaker 1: the same, they're really not the same. So our response 180 00:10:05,360 --> 00:10:08,000 Speaker 1: to this was to flip everything upside down and to 181 00:10:08,080 --> 00:10:10,720 Speaker 1: look at how the thing works in terms of what 182 00:10:10,760 --> 00:10:13,520 Speaker 1: does the customer pay for and what does the customer 183 00:10:13,600 --> 00:10:16,600 Speaker 1: actually get. So in this example, if you're trying to 184 00:10:16,640 --> 00:10:20,720 Speaker 1: go to Brooklyn from Manhattan, you could rent a car 185 00:10:20,800 --> 00:10:23,280 Speaker 1: with zip car and drive it yourself, or you could 186 00:10:23,280 --> 00:10:25,800 Speaker 1: have Uber drive you there, and it just turns out 187 00:10:25,840 --> 00:10:28,840 Speaker 1: that the mechanics of the system that delivers a ride 188 00:10:29,040 --> 00:10:32,920 Speaker 1: versus the access to a car are totally different things. 189 00:10:33,000 --> 00:10:37,600 Speaker 1: So is there enough information straight from the s ones 190 00:10:37,800 --> 00:10:39,520 Speaker 1: or I guess zip car has been public for a 191 00:10:39,520 --> 00:10:44,280 Speaker 1: while right to actually perform that calculation, or do you 192 00:10:44,280 --> 00:10:46,360 Speaker 1: need to go elsewhere? Well, so what's great about it 193 00:10:46,400 --> 00:10:48,720 Speaker 1: is usually you don't need the s one to know 194 00:10:48,880 --> 00:10:51,000 Speaker 1: like how a zip car works, because zip car tells 195 00:10:51,000 --> 00:10:53,600 Speaker 1: you everything about how it works on their website. So 196 00:10:53,640 --> 00:10:55,400 Speaker 1: if you flip the thing upside down and look at 197 00:10:55,400 --> 00:10:58,160 Speaker 1: it like a user, it's actually not very difficult to 198 00:10:58,200 --> 00:11:16,839 Speaker 1: figure out how these mousetraps work. Now, one of the 199 00:11:16,920 --> 00:11:19,720 Speaker 1: things we've talked about, because we've talked on air on 200 00:11:19,760 --> 00:11:26,439 Speaker 1: TV before is sort of non financial statement characteristics of companies. 201 00:11:26,480 --> 00:11:29,120 Speaker 1: So people are interested in things like, you know, just 202 00:11:29,200 --> 00:11:33,480 Speaker 1: the level of transparency period, structural things like voting control. 203 00:11:33,600 --> 00:11:36,200 Speaker 1: What are the other things that you look at when 204 00:11:36,240 --> 00:11:39,360 Speaker 1: you analyze a private company or assumed to be public 205 00:11:39,400 --> 00:11:44,240 Speaker 1: company beyond just the numbers? Sure, well, one of the 206 00:11:44,280 --> 00:11:48,240 Speaker 1: things about GAP is that GAP translates everything into dollars. 207 00:11:48,600 --> 00:11:50,840 Speaker 1: So like the numbers, you see on a GAP pan 208 00:11:50,920 --> 00:11:54,240 Speaker 1: L are all dollar denominated, but most of the numbers 209 00:11:54,280 --> 00:11:57,400 Speaker 1: that are the most interesting about companies aren't dollar denominated, 210 00:11:57,559 --> 00:11:59,559 Speaker 1: like how many customers and how much do they pay? 211 00:11:59,559 --> 00:12:01,480 Speaker 1: And how long do they stick around? And where do 212 00:12:01,520 --> 00:12:03,520 Speaker 1: I get them from? And things just how many cars 213 00:12:03,520 --> 00:12:07,000 Speaker 1: they might have an inventory, for example, right, And so 214 00:12:07,040 --> 00:12:09,360 Speaker 1: there's a big debate that you could read about. Matt 215 00:12:09,400 --> 00:12:12,079 Speaker 1: Levin here is very articulate on the subject about non 216 00:12:12,160 --> 00:12:14,959 Speaker 1: gap reporting, and some people get kind of religious about 217 00:12:14,960 --> 00:12:17,199 Speaker 1: this and say that you shouldn't report things that aren't 218 00:12:17,200 --> 00:12:20,520 Speaker 1: gap because then companies aren't comparable anymore. But the problem 219 00:12:20,600 --> 00:12:22,600 Speaker 1: is that if you only have the P and L, 220 00:12:23,200 --> 00:12:25,000 Speaker 1: like for example, if you were looking at the Snap 221 00:12:25,000 --> 00:12:27,400 Speaker 1: I PO and you saw that Snap lost a billion 222 00:12:27,440 --> 00:12:30,160 Speaker 1: dollars in the trailing year, you don't know very much 223 00:12:30,160 --> 00:12:33,240 Speaker 1: about Snap. But the intuition that people have about that 224 00:12:33,280 --> 00:12:35,680 Speaker 1: company as well, I know my teenager can't put it down, 225 00:12:36,240 --> 00:12:38,800 Speaker 1: But you don't have the statement about how many teenagers 226 00:12:38,960 --> 00:12:40,880 Speaker 1: and how long they stick around. And what you definitely 227 00:12:40,960 --> 00:12:43,920 Speaker 1: don't have is the statement of how many advertisers and 228 00:12:43,960 --> 00:12:46,160 Speaker 1: how long they stick around, and how many salespeople it 229 00:12:46,160 --> 00:12:48,320 Speaker 1: takes to go get those advertisers to pay you and 230 00:12:48,360 --> 00:12:51,800 Speaker 1: so forth. So to us. Again, the numbers that matter 231 00:12:52,200 --> 00:12:55,680 Speaker 1: are the numbers that help you calculate the mechanics of 232 00:12:55,720 --> 00:12:58,800 Speaker 1: how the masse trap works, and those things are often 233 00:12:58,880 --> 00:13:00,880 Speaker 1: not disclosed in an US one at all, and you 234 00:13:00,960 --> 00:13:03,000 Speaker 1: need other ways to go get them. What do you 235 00:13:03,040 --> 00:13:06,439 Speaker 1: think you know? It's interesting you mentioned snap And maybe 236 00:13:06,440 --> 00:13:09,120 Speaker 1: this is a slight tangent or maybe not, but it 237 00:13:09,200 --> 00:13:12,400 Speaker 1: feels like there have been efforts with a lot of 238 00:13:12,400 --> 00:13:16,000 Speaker 1: these Internet companies to essentially standardize some of these non 239 00:13:16,000 --> 00:13:19,480 Speaker 1: financial metrics. So m a use monthly average users is 240 00:13:19,480 --> 00:13:22,200 Speaker 1: a popular way to compare them, but it feels like 241 00:13:22,280 --> 00:13:25,520 Speaker 1: the companies are really pushing back against that or like 242 00:13:25,640 --> 00:13:27,559 Speaker 1: to and they want to create their own bespoke ones 243 00:13:27,559 --> 00:13:29,760 Speaker 1: and they say, no, no, no, you can't compare our 244 00:13:29,920 --> 00:13:32,360 Speaker 1: m a US to facebooks or our d a used. 245 00:13:35,920 --> 00:13:39,240 Speaker 1: Twitter recently announced that they were going to for the 246 00:13:39,320 --> 00:13:43,440 Speaker 1: first time start revealing d a use daily average users. 247 00:13:43,480 --> 00:13:45,880 Speaker 1: They're no longer going to report monthly average users, but 248 00:13:45,960 --> 00:13:49,120 Speaker 1: even their d A numbers, they're calling them m d 249 00:13:49,280 --> 00:13:53,080 Speaker 1: a U s monetize herble daily average users to distinguish 250 00:13:53,160 --> 00:13:55,240 Speaker 1: from users who they probably are gonna make any money 251 00:13:55,240 --> 00:13:58,040 Speaker 1: from so their m d a us are going up anyway. 252 00:13:58,080 --> 00:14:00,360 Speaker 1: The point is, what is your view on this. Do 253 00:14:00,440 --> 00:14:03,880 Speaker 1: companies have an incentive to sort of try to break 254 00:14:03,880 --> 00:14:06,760 Speaker 1: out of the standardized comparable numbers and come up with 255 00:14:06,800 --> 00:14:09,040 Speaker 1: their own sort of vanity metrics that are always going 256 00:14:09,120 --> 00:14:11,280 Speaker 1: up into the right? Yeah? I think you know, the 257 00:14:11,320 --> 00:14:15,480 Speaker 1: world doesn't divide on this, Like people don't like accountability, right, 258 00:14:15,520 --> 00:14:18,520 Speaker 1: so if you don't have to be accountable to particular metrics, 259 00:14:19,000 --> 00:14:21,800 Speaker 1: you'd rather not. One of the things that's interesting about 260 00:14:21,840 --> 00:14:25,400 Speaker 1: what's happened in capital formation right now is that private 261 00:14:25,400 --> 00:14:29,040 Speaker 1: company investors have access to all this information, all the 262 00:14:29,080 --> 00:14:33,160 Speaker 1: real information, not the fake you know, monetize herbal daily 263 00:14:33,200 --> 00:14:35,480 Speaker 1: average users that you know. They can see all of 264 00:14:35,480 --> 00:14:37,520 Speaker 1: that sort of stuff, and they have a real sense 265 00:14:37,560 --> 00:14:40,560 Speaker 1: of how those mechanics work. Once you arrive in public 266 00:14:40,560 --> 00:14:44,240 Speaker 1: company lands, many of those numbers are not disclosed anymore. 267 00:14:44,280 --> 00:14:47,600 Speaker 1: So you find a situation where as capital is forming 268 00:14:47,600 --> 00:14:50,000 Speaker 1: around these companies, the investors that put up the money 269 00:14:50,240 --> 00:14:54,440 Speaker 1: have much better access to information, so more transparent situation, 270 00:14:54,920 --> 00:14:58,280 Speaker 1: but an a liquid situation, and then you trade liquidity 271 00:14:58,320 --> 00:15:00,960 Speaker 1: for transparency in the sense that the look. Investors don't 272 00:15:01,000 --> 00:15:03,040 Speaker 1: really get to learn any of the you know, the 273 00:15:03,080 --> 00:15:05,560 Speaker 1: way that the mouse trap works, but at least they 274 00:15:05,560 --> 00:15:08,160 Speaker 1: can sell the stock. And so that's the trade. As 275 00:15:08,160 --> 00:15:10,600 Speaker 1: far as your question about the standardization, and sorry to 276 00:15:10,600 --> 00:15:15,680 Speaker 1: go on so long, using like an ad supported companies 277 00:15:15,720 --> 00:15:20,560 Speaker 1: metrics to analyze the subscription business is just not very helpful. 278 00:15:21,160 --> 00:15:24,080 Speaker 1: So like engagement metrics, for example, people ask us about 279 00:15:24,080 --> 00:15:26,600 Speaker 1: our engagement metrics, which I always laugh because I think 280 00:15:26,680 --> 00:15:29,600 Speaker 1: engagement is bad. We want our users to figure out 281 00:15:29,640 --> 00:15:32,160 Speaker 1: the answer in as little time as possible because I'm 282 00:15:32,200 --> 00:15:34,640 Speaker 1: not trying to serve. And add to that, right, so 283 00:15:34,760 --> 00:15:37,000 Speaker 1: each company is different. This is what we spent years 284 00:15:37,040 --> 00:15:39,600 Speaker 1: doing is developing an architecture so that you can understand 285 00:15:39,600 --> 00:15:41,320 Speaker 1: what kind of company you're looking at and look at 286 00:15:41,320 --> 00:15:44,040 Speaker 1: the appropriate metrics to do So, going back to what 287 00:15:44,080 --> 00:15:48,480 Speaker 1: you were saying about the trade between liquidity and transparency, 288 00:15:48,520 --> 00:15:51,600 Speaker 1: we had a recent episode a few months ago. We 289 00:15:51,600 --> 00:15:55,040 Speaker 1: were talking to um a VC and he was arguing 290 00:15:55,320 --> 00:15:58,720 Speaker 1: that one of the things that made this period in 291 00:15:58,800 --> 00:16:03,760 Speaker 1: market unique is that whereas in the past, uh illiquidity 292 00:16:03,840 --> 00:16:07,080 Speaker 1: was a penalty for a company and a private company 293 00:16:07,200 --> 00:16:10,240 Speaker 1: had to offer a bigger premium to get um private 294 00:16:10,280 --> 00:16:13,480 Speaker 1: capital because that was more locked in. These days, people 295 00:16:13,480 --> 00:16:16,880 Speaker 1: are paying a premium for access, in his view, to 296 00:16:17,240 --> 00:16:20,120 Speaker 1: a liquid companies. Maybe they didn't want to have to 297 00:16:20,200 --> 00:16:22,760 Speaker 1: mark their books day to day, or maybe there was 298 00:16:22,880 --> 00:16:26,040 Speaker 1: some sort of prestige value of being in a lift 299 00:16:26,200 --> 00:16:29,240 Speaker 1: or an uber that caused people to overpay. Do you 300 00:16:29,320 --> 00:16:32,600 Speaker 1: see that that the sort of traditional discount that would 301 00:16:32,640 --> 00:16:35,640 Speaker 1: have in the past come along with private equity stock 302 00:16:36,080 --> 00:16:38,520 Speaker 1: has flipped. So I'm gonna give you two answers to 303 00:16:38,600 --> 00:16:41,800 Speaker 1: that question. One, just to verify with data the claim 304 00:16:41,920 --> 00:16:45,080 Speaker 1: that people pay a premium. Over the last five years, 305 00:16:45,080 --> 00:16:47,480 Speaker 1: the I p O s that we've looked at tend 306 00:16:47,560 --> 00:16:51,040 Speaker 1: to trade up in the first half of the first 307 00:16:51,080 --> 00:16:54,080 Speaker 1: year that they're public and then in general trade down 308 00:16:54,120 --> 00:16:57,440 Speaker 1: again below the ip O price. Right, so public public 309 00:16:57,480 --> 00:16:59,880 Speaker 1: market investors. And that's not just that that's over the 310 00:17:00,040 --> 00:17:02,360 Speaker 1: US five years. So it's not just we're not just 311 00:17:02,400 --> 00:17:05,960 Speaker 1: looking at effect and it's not just DOLLI Bober or whatever. 312 00:17:06,000 --> 00:17:08,720 Speaker 1: It's you know, a hundred and fifty odd transactions. And 313 00:17:08,760 --> 00:17:11,360 Speaker 1: so what happens is as a capital markets matter, these 314 00:17:11,400 --> 00:17:14,200 Speaker 1: things come out. They you know, the I p O prices, 315 00:17:14,280 --> 00:17:16,400 Speaker 1: it begins to trade. You get the famous pop which 316 00:17:16,400 --> 00:17:18,959 Speaker 1: some people love and some people hate, and in general, 317 00:17:19,040 --> 00:17:21,840 Speaker 1: these things trade up for a while and then large 318 00:17:21,840 --> 00:17:24,760 Speaker 1: amounts of shares are unlocked and people stop paying attention 319 00:17:24,800 --> 00:17:26,640 Speaker 1: and they change the channel and they look at something else, 320 00:17:26,640 --> 00:17:29,879 Speaker 1: and then they trade down. And so it's definitely true 321 00:17:30,440 --> 00:17:33,320 Speaker 1: that private market investors have paid a premium. Like the 322 00:17:33,359 --> 00:17:36,800 Speaker 1: guys who bought the last round of those deals could 323 00:17:36,840 --> 00:17:38,960 Speaker 1: end up underwater if they didn't sell, but they could sell, 324 00:17:39,600 --> 00:17:43,000 Speaker 1: so it's unclear if they've been penalized for paying that 325 00:17:43,040 --> 00:17:45,400 Speaker 1: premium because they had a moment where they probably could 326 00:17:45,400 --> 00:17:48,040 Speaker 1: have made made a profit on the trade. But in 327 00:17:48,080 --> 00:17:50,200 Speaker 1: general it's not a good trade for the broad base 328 00:17:50,240 --> 00:17:52,560 Speaker 1: of shareholders. So that's out of number one. But item 329 00:17:52,640 --> 00:17:55,800 Speaker 1: number two, why do people pay a premium for this? 330 00:17:56,320 --> 00:17:59,160 Speaker 1: And the answer we think is because if you want 331 00:17:59,200 --> 00:18:02,760 Speaker 1: to invest in growth companies, you have to pay the price. 332 00:18:03,200 --> 00:18:06,200 Speaker 1: And so if you are a long only manager who's 333 00:18:06,240 --> 00:18:08,399 Speaker 1: managing a growth fund, who has a carve out that 334 00:18:08,440 --> 00:18:12,280 Speaker 1: allows you to invest in Uber Lift, whatever, your ability 335 00:18:12,320 --> 00:18:15,399 Speaker 1: to set prices very limited, but you want to participate 336 00:18:15,440 --> 00:18:18,359 Speaker 1: in those deals, and as more and more capital has 337 00:18:18,400 --> 00:18:22,159 Speaker 1: flowed into this place. What happens when there's more demanded supply, 338 00:18:22,280 --> 00:18:24,800 Speaker 1: prices go up. So it's kind of like it's a 339 00:18:24,880 --> 00:18:27,320 Speaker 1: function of the fact that, even if we're just looking 340 00:18:27,320 --> 00:18:30,440 Speaker 1: in public markets, we know that the growth factor has 341 00:18:30,440 --> 00:18:33,680 Speaker 1: done extremely well in recent years, and that's just even 342 00:18:33,720 --> 00:18:37,760 Speaker 1: more exacerbated in the ultra high growth private area. So 343 00:18:37,800 --> 00:18:40,520 Speaker 1: that could explain at least part of this premium. Sure. 344 00:18:40,600 --> 00:18:43,280 Speaker 1: I mean, if you were a growth investor in you know, 345 00:18:43,320 --> 00:18:47,200 Speaker 1: the ninety nineties, you would be investing in companies publicly 346 00:18:47,600 --> 00:18:50,320 Speaker 1: that we're young, and you'd be buying you know, Amazon 347 00:18:50,680 --> 00:18:54,120 Speaker 1: or you know Yahoo or the globe dot Com, right, 348 00:18:54,400 --> 00:18:56,040 Speaker 1: you know, you buy the good and the bad. But 349 00:18:56,080 --> 00:18:58,040 Speaker 1: you get to do all of that in the public market. Now, 350 00:18:58,080 --> 00:19:01,119 Speaker 1: all of that capital formation and all of that value 351 00:19:01,160 --> 00:19:04,439 Speaker 1: appreciation happens in the private market, and the guys with 352 00:19:04,520 --> 00:19:07,479 Speaker 1: large pools of capital want to participate in that. But 353 00:19:07,520 --> 00:19:10,040 Speaker 1: what it's also done is created a situation where larger 354 00:19:10,080 --> 00:19:14,440 Speaker 1: pools of capital, the vision funds for example, have formed 355 00:19:14,800 --> 00:19:18,960 Speaker 1: to participate in that trade. I'm glad you mentioned the 356 00:19:19,000 --> 00:19:22,520 Speaker 1: I p o s prior to the bubble, because obviously 357 00:19:22,640 --> 00:19:25,440 Speaker 1: everyone knows you'd be rich if you had bought into 358 00:19:25,480 --> 00:19:27,520 Speaker 1: that Amazon i p O, but you would have lost 359 00:19:27,520 --> 00:19:29,360 Speaker 1: all your money if you bought into the Globe dot 360 00:19:29,400 --> 00:19:33,560 Speaker 1: Com i p O. People bemoan the decline of I 361 00:19:33,720 --> 00:19:36,760 Speaker 1: p o s for precisely because they have memories of 362 00:19:36,840 --> 00:19:39,680 Speaker 1: Amazon and Microsoft in their mind, and they say, well, 363 00:19:39,920 --> 00:19:42,119 Speaker 1: the stock market used to be this avenue where people 364 00:19:42,160 --> 00:19:44,760 Speaker 1: could make a lot of money investing in these companies. 365 00:19:44,800 --> 00:19:48,120 Speaker 1: Now that's closed off to anyone who doesn't have access, 366 00:19:48,160 --> 00:19:50,040 Speaker 1: but of course there is. It does seem like there's 367 00:19:50,080 --> 00:19:53,480 Speaker 1: a lot of hindsight bias because most of them, most 368 00:19:53,520 --> 00:19:55,880 Speaker 1: companies are more like the Globe, right. Sure, well, Mary 369 00:19:55,880 --> 00:19:58,240 Speaker 1: Meeker has a great statistic that was in her deck 370 00:19:58,280 --> 00:20:01,399 Speaker 1: for a long time after the bust that two of 371 00:20:01,440 --> 00:20:05,080 Speaker 1: the companies that went public during that moment in our 372 00:20:05,119 --> 00:20:09,000 Speaker 1: culture created more than the returns. So it's just so 373 00:20:09,119 --> 00:20:12,879 Speaker 1: the vast majority of them were total flow totally adds. 374 00:20:12,920 --> 00:20:15,679 Speaker 1: You know, well, if you were in the two that 375 00:20:15,800 --> 00:20:19,520 Speaker 1: compensated for you know, the negative jacurb. Right, So it's 376 00:20:20,240 --> 00:20:22,280 Speaker 1: the vast majority were a bust and more money was 377 00:20:22,359 --> 00:20:25,119 Speaker 1: lost than made in aggregate. Right, So you had to 378 00:20:25,119 --> 00:20:27,040 Speaker 1: be very very picky to not be one of the 379 00:20:27,080 --> 00:20:29,119 Speaker 1: losers is the decline of the I p O a 380 00:20:29,200 --> 00:20:31,840 Speaker 1: bad thing. It's bens. I mean, if you were a 381 00:20:31,840 --> 00:20:35,359 Speaker 1: retail investor and in hindsight you're totally convinced that you 382 00:20:35,359 --> 00:20:38,199 Speaker 1: would have absolutely put your life savings into Uber if 383 00:20:38,200 --> 00:20:40,359 Speaker 1: you'd been able to buy it, you know, five years ago, 384 00:20:40,920 --> 00:20:44,159 Speaker 1: then it's a bad thing. But one of the reasons 385 00:20:44,200 --> 00:20:46,360 Speaker 1: that the bar has been raised so much for companies 386 00:20:46,400 --> 00:20:50,399 Speaker 1: to go public is to protect retail investors from themselves. Right, 387 00:20:50,480 --> 00:20:53,320 Speaker 1: Retail investors fueled a lot of the bubble that happened. 388 00:20:53,320 --> 00:20:55,840 Speaker 1: There are other structural reasons why the Internet bubble happened, 389 00:20:56,160 --> 00:20:58,399 Speaker 1: but there was a huge amount of demands in the 390 00:20:58,440 --> 00:21:01,320 Speaker 1: same way that people now spect laid in cryptocurrencies and 391 00:21:01,359 --> 00:21:03,680 Speaker 1: other things like that. Because it was perceived to be 392 00:21:03,760 --> 00:21:07,359 Speaker 1: an easy buck, people are always gonna look for actions. 393 00:21:08,160 --> 00:21:11,919 Speaker 1: Let's uh talk about Okay, So as of this moment, 394 00:21:12,040 --> 00:21:14,480 Speaker 1: when we're recording and we don't know any day now, 395 00:21:14,520 --> 00:21:17,000 Speaker 1: we could get s one filings from some of these 396 00:21:17,000 --> 00:21:22,800 Speaker 1: companies that we mentioned, so Uber and Lived and Slack 397 00:21:22,840 --> 00:21:25,199 Speaker 1: and a bunch of others that we could get for 398 00:21:25,240 --> 00:21:29,680 Speaker 1: the first time public data on these companies. So when 399 00:21:29,720 --> 00:21:31,720 Speaker 1: these come out. What are going to be the first 400 00:21:31,720 --> 00:21:34,679 Speaker 1: things that you look at and what should people listening 401 00:21:34,680 --> 00:21:38,520 Speaker 1: at home what should they start to look at specifically? Um, well, 402 00:21:38,640 --> 00:21:41,879 Speaker 1: us first, our our system is just to take it 403 00:21:41,920 --> 00:21:44,720 Speaker 1: apart and do the sort of fifteen point inspection on 404 00:21:44,800 --> 00:21:48,480 Speaker 1: these things. So does the math makes sense? Like does 405 00:21:48,520 --> 00:21:51,159 Speaker 1: this company make money? Is one of the things that 406 00:21:51,200 --> 00:21:53,479 Speaker 1: we've talked about, you know, on TV before. There are 407 00:21:53,520 --> 00:21:57,719 Speaker 1: times where will put companies numbers into our model machine 408 00:21:57,840 --> 00:21:59,880 Speaker 1: and we'll look at it and see like, jeez, there's 409 00:21:59,880 --> 00:22:03,680 Speaker 1: no setting of the model that produces a profit ever. Right, 410 00:22:03,720 --> 00:22:06,399 Speaker 1: So that's a really low low score as far as 411 00:22:06,440 --> 00:22:08,240 Speaker 1: the earnings power of the company. But we also look 412 00:22:08,240 --> 00:22:11,760 Speaker 1: at the management team. We look at the founder. When 413 00:22:11,760 --> 00:22:15,160 Speaker 1: you say look at the management team, were okay? Uh 414 00:22:15,400 --> 00:22:17,360 Speaker 1: paused there for a second. So how do you score 415 00:22:18,040 --> 00:22:20,440 Speaker 1: in theory a manage the quality of a management team. 416 00:22:20,600 --> 00:22:23,120 Speaker 1: They're the things that you think that you would think 417 00:22:23,160 --> 00:22:25,639 Speaker 1: to do if you wrote out a rigorous system, like 418 00:22:25,800 --> 00:22:28,040 Speaker 1: have they done it before? How long have they worked together? 419 00:22:28,200 --> 00:22:30,160 Speaker 1: Have they worked in places that you've heard of before? 420 00:22:30,200 --> 00:22:32,280 Speaker 1: Were they successful there? Did they go to schools that 421 00:22:32,320 --> 00:22:35,280 Speaker 1: you've heard of before? Right? Do they have advanced degrees, 422 00:22:36,119 --> 00:22:39,240 Speaker 1: you know. And then when you toggle to the founder 423 00:22:39,320 --> 00:22:43,360 Speaker 1: aspect of the management team, sometimes you see total control 424 00:22:43,400 --> 00:22:45,600 Speaker 1: of the founders, which tends to be great because they're 425 00:22:45,640 --> 00:22:48,040 Speaker 1: highly invested and have a lot of skin in the game. Sometimes, 426 00:22:48,040 --> 00:22:50,840 Speaker 1: you know, like for example a Twitter, you see like 427 00:22:50,880 --> 00:22:53,760 Speaker 1: the company is totally post founder, and that means that 428 00:22:53,800 --> 00:22:57,280 Speaker 1: the management team has economics that are heavily weighted towards 429 00:22:57,320 --> 00:22:59,600 Speaker 1: the upside, but doesn't have a lot of pain associated 430 00:22:59,600 --> 00:23:02,640 Speaker 1: with the Dow Todd So founder power is very important. 431 00:23:02,960 --> 00:23:05,760 Speaker 1: The quality of the board, the quality investors is interesting. 432 00:23:05,760 --> 00:23:08,159 Speaker 1: How famous is it? Like faym and buzz is one 433 00:23:08,160 --> 00:23:10,119 Speaker 1: of the things that we score. Companies that nobody has 434 00:23:10,119 --> 00:23:12,520 Speaker 1: ever heard of, you know, do do less well than 435 00:23:12,560 --> 00:23:14,640 Speaker 1: companies that are well known. Okay, so you have all 436 00:23:14,680 --> 00:23:19,600 Speaker 1: these factors fifteen different We have fifteen different scores that 437 00:23:19,680 --> 00:23:23,160 Speaker 1: will roll up into the summary score. Fifteen different scores, 438 00:23:23,359 --> 00:23:26,720 Speaker 1: and so in your experience the aggregate, higher scoring companies 439 00:23:26,760 --> 00:23:28,679 Speaker 1: do better than the lower one way better. Otherwise you 440 00:23:28,680 --> 00:23:31,920 Speaker 1: wouldn't have a business or totally right. But shockingly, because 441 00:23:31,920 --> 00:23:34,800 Speaker 1: there are times where we get the score because you know, 442 00:23:34,880 --> 00:23:36,359 Speaker 1: we see it when it comes out of the machine. 443 00:23:36,520 --> 00:23:38,120 Speaker 1: We look at it and we're like, man, that can't 444 00:23:38,119 --> 00:23:41,119 Speaker 1: be right. So it's always interesting to us, like maybe 445 00:23:41,119 --> 00:23:43,040 Speaker 1: this will be the one that we we you know, 446 00:23:43,080 --> 00:23:45,440 Speaker 1: have to rebuild the whole system one. I think here's 447 00:23:45,480 --> 00:23:48,760 Speaker 1: sort of my final question, or the key question I 448 00:23:48,840 --> 00:23:52,800 Speaker 1: have is do the do high scores say you should 449 00:23:52,840 --> 00:23:56,640 Speaker 1: invest in this company? Or is it if you invested 450 00:23:56,720 --> 00:24:01,359 Speaker 1: in every company with high scores and shorted or avoided 451 00:24:01,359 --> 00:24:03,800 Speaker 1: all the companies and low scores, would that be a 452 00:24:03,840 --> 00:24:06,040 Speaker 1: superior strategy? You know? Do you see what I'm Do 453 00:24:06,040 --> 00:24:08,200 Speaker 1: you see? Like? Yeah, so the aggregate trade is always 454 00:24:08,200 --> 00:24:11,720 Speaker 1: better unless you are so good that you can sniper 455 00:24:11,720 --> 00:24:15,239 Speaker 1: shot the singular winner. But that's incredibly hard to do. 456 00:24:15,480 --> 00:24:18,359 Speaker 1: But the premise of the scoring system is essentially that 457 00:24:18,760 --> 00:24:21,520 Speaker 1: on aggregate you'll strip out a lot of noise and 458 00:24:21,600 --> 00:24:24,560 Speaker 1: be much more likely to have a winning portfolio of 459 00:24:24,600 --> 00:24:27,280 Speaker 1: I p O s with the higher scoring companies. Not 460 00:24:27,400 --> 00:24:30,400 Speaker 1: only that, so that's certainly true if you're an institutional investorent. 461 00:24:30,400 --> 00:24:33,439 Speaker 1: Most of our customers are institutions that buy at the 462 00:24:33,480 --> 00:24:36,879 Speaker 1: I p O price, and so the returns are you know, 463 00:24:36,960 --> 00:24:39,160 Speaker 1: three times better if you buy the high scores than 464 00:24:39,200 --> 00:24:41,760 Speaker 1: the low scores. But if you buy the first trade, 465 00:24:41,960 --> 00:24:44,800 Speaker 1: if you're a retail investor buying high scores versus low scores, 466 00:24:44,840 --> 00:24:46,920 Speaker 1: this is the difference between making money and losing money. 467 00:24:47,160 --> 00:24:50,480 Speaker 1: Got it. Well, it should be a very interesting year 468 00:24:50,640 --> 00:24:54,200 Speaker 1: for I p o s as mentioned, and looking forward 469 00:24:54,200 --> 00:24:56,280 Speaker 1: to seeing over the coming years how your scores do. 470 00:24:56,520 --> 00:24:58,840 Speaker 1: I think we have a week or two before it 471 00:24:58,880 --> 00:25:02,600 Speaker 1: comes and then well I'm on vacation next week. It's 472 00:25:02,600 --> 00:25:05,040 Speaker 1: a good time for it. Hopefully, I'm really hoping I 473 00:25:05,119 --> 00:25:07,080 Speaker 1: don't miss all these, but then I'll be back in hopefully. 474 00:25:07,119 --> 00:25:09,400 Speaker 1: I think you're good six weeks. All right? Great Rott 475 00:25:09,440 --> 00:25:11,760 Speaker 1: Wallace of Tried and Ai, thank you very much for 476 00:25:11,880 --> 00:25:27,720 Speaker 1: coming out Odd Lot. Thanks for having me here. Well, 477 00:25:27,800 --> 00:25:30,560 Speaker 1: normally I would do a little outro with Tracy here 478 00:25:30,560 --> 00:25:33,240 Speaker 1: and we would talk about what a great conversation that was. 479 00:25:33,560 --> 00:25:36,359 Speaker 1: But I actually think that was a great conversation and 480 00:25:36,400 --> 00:25:38,840 Speaker 1: I love this topic and I'm looking forward to all 481 00:25:38,920 --> 00:25:40,520 Speaker 1: the I p o s this year and seeing how 482 00:25:40,640 --> 00:25:43,840 Speaker 1: they do. So this has been another episode of the 483 00:25:43,840 --> 00:25:47,080 Speaker 1: Odd Lots podcast. I'm Joe Wisenthal. You can follow me 484 00:25:47,200 --> 00:25:50,080 Speaker 1: on Twitter at the Stalwart, and you should follow our 485 00:25:50,119 --> 00:25:52,760 Speaker 1: co host on Twitter even though she wasn't here, Tracy 486 00:25:52,800 --> 00:25:56,080 Speaker 1: Alloway at Tracy Alloway and you should follow our producer 487 00:25:56,240 --> 00:25:59,560 Speaker 1: on Twitter tow for four Heads. He's at four Heads 488 00:25:59,560 --> 00:26:02,760 Speaker 1: t as well as the Bloomberg head of podcast, Francesco 489 00:26:02,840 --> 00:26:06,000 Speaker 1: Levy at Francesca Today. Thanks for listening.