Speaker 1: Hello, and welcome to another episode of the Odd Lots podcast.
Speaker 1: I'm Joe Wisenthal. My colleague and co host Tracy Elway
Speaker 1: is out this week, so not going to be a
Speaker 1: long introduction. But one of the big things that's going
Speaker 1: to happen, at least expected to happen in markets in
Speaker 1: t is the flotation of some pretty big, highly anticipated
Speaker 1: I p O s, some of those Silicon Valley unicorns
Speaker 1: that people have been talking about forever Uber lived Slack,
Speaker 1: all of them plus more likely to go public uh
Speaker 1: this year after a long wait. Of course, many of
Speaker 1: them are huge, and they're going public at a stage
Speaker 1: in life UH that is much later than many of
Speaker 1: the big tech companies that are currently public when they
Speaker 1: decided to go public or when they decided to do
Speaker 1: their I p O. So I want to talk more
Speaker 1: about the I p O market on today's episode, and
Speaker 1: to discuss this I have with me Rhet Wallace. He
Speaker 1: is the founder and CEO of triton Ai, a company
Speaker 1: that analyzes I p O s for various proprietary measures,
Speaker 1: and we're going to talk about the evolution of the
Speaker 1: I p O market as well as how to analyze
Speaker 1: an IPO because theoretically there might be people out there
Speaker 1: looking at some of these that would like to have
Speaker 1: some perspective on how to think about the value and
Speaker 1: the investment appeal of these well known companies. So, Rhett,
Speaker 1: thank you very much for joining us. Great to be here.
Speaker 1: Thank you. Let's start with that question about why these
Speaker 1: companies are so people say coming public much later in life.
Speaker 1: So company is like Amazon and Microsoft and Apple. They
Speaker 1: essentially became did their I p o s when they
Speaker 1: were fairly tiny startups, at least by today's standards. What's
Speaker 1: changed since then eighties to two thousand nineteen such that
Speaker 1: these big companies are already produced billionaires and people have
Speaker 1: gone on to great fortunes ever before floating a sheriff stock. Sure, well,
Speaker 1: like most things, there are a couple of different narratives.
Speaker 1: By way of explanation, what you will hear from people
Speaker 1: in Silicon Valley is that founders don't like to take
Speaker 1: their companies public because being a public company CEO is
Speaker 1: kind of a pain in the neck. And so anybody
Speaker 1: who's abut to comply with the sarbians Oxley Act and
Speaker 1: other things that were imposed on publicly traded companies as
Speaker 1: a protection against retail investors who buy their shares. You know,
Speaker 1: if you have access to capital in the private markets,
Speaker 1: it might be easier for you to stay private and
Speaker 1: not expose your numbers, expose yourself to liability, and so forth.
Speaker 1: So almost all of the reasons that you could think
Speaker 1: of why a company wouldn't go public are regulatory there.
Speaker 1: They stem from the changes in the regulations of the
Speaker 1: securities business. And there's a long, geeky narrative that we
Speaker 1: get into about that. When companies like Amazon and Netscape
Speaker 1: and Yahoo went public on a couple of million dollars
Speaker 1: of sales, and you know, earlier generations of companies like
Speaker 1: Intel and so forth went public, you know, really as
Speaker 1: soon as they got to revenue. That's because capital formation
Speaker 1: happened in the public market. Let's actually, let's back up
Speaker 1: for a second. Tell me about your firm and why
Speaker 1: this is an area that you pursued. What is it
Speaker 1: about I p O s that are interesting in general,
Speaker 1: and what is your background that caused you or that
Speaker 1: prompted you to go into analyzing them and providing the
Speaker 1: service of breaking them down. Sure, well, it's I p
Speaker 1: O s are a very good example of what came
Speaker 1: after the Great Depression when the government decided to reform
Speaker 1: the securities industry so that you didn't have a big
Speaker 1: speculative bubble anymore of the kind that created the stock
Speaker 1: market crash. And so the innovation at that moment was
Speaker 1: that securities come with data stapling, the ten k's and
Speaker 1: the ten queues, the regular are recurring reporting and disclosure
Speaker 1: so investors would know what they are buying was the
Speaker 1: great innovation after and that prompted a situation where companies,
Speaker 1: if they wanted to trade stocks with each other or
Speaker 1: have people by their shares, they had to be public.
Speaker 1: And so companies went public much earlier. If you you
Speaker 1: know again geekily read like the biography of Rockefeller, for example,
Speaker 1: before the crash, one of the reasons he was so
Speaker 1: successful in investing is that he had access to information
Speaker 1: that wasn't broadly available. That always helps, right, So information
Speaker 1: has always been a key component of being successful as
Speaker 1: an investor. And so the origin story of our firm
Speaker 1: is that we saw what was happening that fewer and
Speaker 1: fewer companies were going public, and that meant that more
Speaker 1: and more of the interesting companies were private. And these
Speaker 1: companies operated outside of the information regime of the Securities
Speaker 1: Acts of the United States. The other thing that we
Speaker 1: noticed is that all of the information architecture that was
Speaker 1: installed as the operating system of the securities trading institutions
Speaker 1: was developed in the nineteen thirties, So, like generally accepted
Speaker 1: accounting principles, some people will tell you it's like, you know,
Speaker 1: the perfect information that you could have about a company,
Speaker 1: but it never existed until like Moses did not come
Speaker 1: down from the mountain with gap company categorization, this standard
Speaker 1: industrial classification system again like the nineteen thirties, and so
Speaker 1: these pieces of data architecture haven't iterated an advanced so
Speaker 1: we're still sort of stuck in the thirties with the
Speaker 1: way companies are analyzed. So the origin story of our
Speaker 1: company was we were looking for ways to be smart
Speaker 1: about investing in companies that were generally private companies, and
Speaker 1: the architecture that people used to look at public companies
Speaker 1: wasn't particularly serviceable to that end, so we had to
Speaker 1: build a new one. So obviously, when a company files
Speaker 1: to go public, and it files, it's s one to
Speaker 1: the sec the company engauges in the practice of putting
Speaker 1: its numbers into a type of a structure that's similar
Speaker 1: to other public companies are identical. It then has to correct.
Speaker 1: There's a template that everyone has to adhere to. But
Speaker 1: there's still the problem of investors haven't really gotten to
Speaker 1: know these companies, and even within generally accepted accounting principles,
Speaker 1: there's all kinds of idiosyncrasies and opinions and different approaches.
Speaker 1: And companies that have been public for a while, people
Speaker 1: become familiar with aspects of their business model and they
Speaker 1: understand the moving parts, and that just doesn't exist yet,
Speaker 1: certainly at the time of the S one filing. So
Speaker 1: when you look at an S one filing, besides the
Speaker 1: obvious the balance sheet and the income statement and the
Speaker 1: cash flow statement, what else are you looking for when
Speaker 1: you start to break down what you know looking at
Speaker 1: these companies from the perspective of an investor. So our
Speaker 1: point of view on companies is that a company is
Speaker 1: really just a receptacle for different product lines. So our
Speaker 1: trope example is that uber x and uber Eats live
Speaker 1: inside the same company, but they're totally different businesses, completely
Speaker 1: different product lines. So as companies go public much later
Speaker 1: in their life, what it means is that the audit
Speaker 1: of the consolidated entity disguises all of the individual operations
Speaker 1: that are happening inside of a company that might have
Speaker 1: a bike sharing you know business, and a scooter sharing
Speaker 1: business and operates all over the world in different types
Speaker 1: of jurisdictions, and so the bigger it is, the harder
Speaker 1: it is to get your arms around it unless you
Speaker 1: can see the detail. So that's that's really interesting points.
Speaker 1: So if a company is just in the business of
Speaker 1: making widgets, then you can have some sense of like, okay,
Speaker 1: widgets cost the company this much to build, and raw
Speaker 1: materials cost as much, and labor costs as muge, and
Speaker 1: you sell the widgets for this much, and then you
Speaker 1: look at the gap between costs and the sale and
Speaker 1: you know something about the business. But with these big
Speaker 1: companies and with new businesses that people don't understand and
Speaker 1: sort of novel business models, simply so tracting costs from revenues,
Speaker 1: it just doesn't tell you that much about the company.
Speaker 1: The architecture of a digital company is just completely different
Speaker 1: than the architecture of a nineteen thirties railroad or metals
Speaker 1: and mining company. One of the things that you know,
Speaker 1: for again, geeks that have spent a lot of time
Speaker 1: studying how gap works and have suffered through accounting class
Speaker 1: inventory accounting is one of the things that's like really
Speaker 1: painful and the fiefold life folk kind of stuff. How
Speaker 1: do you track the inventory of Facebook? Well, so then
Speaker 1: that gets to the question, Okay, going back to the
Speaker 1: Uber example, Obviously it's still probably mostly a car sharing company,
Speaker 1: but in many different businesses, and they do also now
Speaker 1: have several different lines and in some places they have scooters.
Speaker 1: So how do you go about essentially trying to disassemble
Speaker 1: the business from this consolidated these consolidated financial states. So
Speaker 1: when we started out, we were looking for ways to
Speaker 1: be smart about how to tell which dog walking app
Speaker 1: is going to be better than the other dog walking apps,
Speaker 1: for example, because you listen to the young entrepreneurs come
Speaker 1: and pitch you a company, and it always sounds good,
Speaker 1: but you don't have a comparative base of data. And
Speaker 1: so the s I C code system was no use
Speaker 1: to us whatsoever in how to categorize companies into the
Speaker 1: bucket of dog walking apps and then figure out which
Speaker 1: one was going to be the best dog walking app.
Speaker 1: So we had to design an architecture that you could
Speaker 1: get the apples and apples in the same buckets and
Speaker 1: separate them from the oranges and the grab apples and
Speaker 1: the tangerines and everything else. And so one of the
Speaker 1: things that was fairly funny about this is if you
Speaker 1: use a sort of you know, a top down E.
Speaker 1: S I. C. Level type categorization system, and you use
Speaker 1: a word like transportation, what we found is that companies
Speaker 1: like Uber bucketed into the same bucket as zip car, right.
Speaker 1: But you look at it and you're like, okay, well,
Speaker 1: Uber doesn't own any cars. Zip car owns thousands of
Speaker 1: cars that they have to park, maintain, fuel, paint, all
Speaker 1: that sort of stuff. So it's like, okay, even though
Speaker 1: from a a sort of narrative perspective, these things look
Speaker 1: the same, they're really not the same. So our response
Speaker 1: to this was to flip everything upside down and to
Speaker 1: look at how the thing works in terms of what
Speaker 1: does the customer pay for and what does the customer
Speaker 1: actually get. So in this example, if you're trying to
Speaker 1: go to Brooklyn from Manhattan, you could rent a car
Speaker 1: with zip car and drive it yourself, or you could
Speaker 1: have Uber drive you there, and it just turns out
Speaker 1: that the mechanics of the system that delivers a ride
Speaker 1: versus the access to a car are totally different things.
Speaker 1: So is there enough information straight from the s ones
Speaker 1: or I guess zip car has been public for a
Speaker 1: while right to actually perform that calculation, or do you
Speaker 1: need to go elsewhere? Well, so what's great about it
Speaker 1: is usually you don't need the s one to know
Speaker 1: like how a zip car works, because zip car tells
Speaker 1: you everything about how it works on their website. So
Speaker 1: if you flip the thing upside down and look at
Speaker 1: it like a user, it's actually not very difficult to
Speaker 1: figure out how these mousetraps work. Now, one of the
Speaker 1: things we've talked about, because we've talked on air on
Speaker 1: TV before is sort of non financial statement characteristics of companies.
Speaker 1: So people are interested in things like, you know, just
Speaker 1: the level of transparency period, structural things like voting control.
Speaker 1: What are the other things that you look at when
Speaker 1: you analyze a private company or assumed to be public
Speaker 1: company beyond just the numbers? Sure, well, one of the
Speaker 1: things about GAP is that GAP translates everything into dollars.
Speaker 1: So like the numbers, you see on a GAP pan
Speaker 1: L are all dollar denominated, but most of the numbers
Speaker 1: that are the most interesting about companies aren't dollar denominated,
Speaker 1: like how many customers and how much do they pay?
Speaker 1: And how long do they stick around? And where do
Speaker 1: I get them from? And things just how many cars
Speaker 1: they might have an inventory, for example, right, And so
Speaker 1: there's a big debate that you could read about. Matt
Speaker 1: Levin here is very articulate on the subject about non
Speaker 1: gap reporting, and some people get kind of religious about
Speaker 1: this and say that you shouldn't report things that aren't
Speaker 1: gap because then companies aren't comparable anymore. But the problem
Speaker 1: is that if you only have the P and L,
Speaker 1: like for example, if you were looking at the Snap
Speaker 1: I PO and you saw that Snap lost a billion
Speaker 1: dollars in the trailing year, you don't know very much
Speaker 1: about Snap. But the intuition that people have about that
Speaker 1: company as well, I know my teenager can't put it down,
Speaker 1: But you don't have the statement about how many teenagers
Speaker 1: and how long they stick around. And what you definitely
Speaker 1: don't have is the statement of how many advertisers and
Speaker 1: how long they stick around, and how many salespeople it
Speaker 1: takes to go get those advertisers to pay you and
Speaker 1: so forth. So to us. Again, the numbers that matter
Speaker 1: are the numbers that help you calculate the mechanics of
Speaker 1: how the masse trap works, and those things are often
Speaker 1: not disclosed in an US one at all, and you
Speaker 1: need other ways to go get them. What do you
Speaker 1: think you know? It's interesting you mentioned snap And maybe
Speaker 1: this is a slight tangent or maybe not, but it
Speaker 1: feels like there have been efforts with a lot of
Speaker 1: these Internet companies to essentially standardize some of these non
Speaker 1: financial metrics. So m a use monthly average users is
Speaker 1: a popular way to compare them, but it feels like
Speaker 1: the companies are really pushing back against that or like
Speaker 1: to and they want to create their own bespoke ones
Speaker 1: and they say, no, no, no, you can't compare our
Speaker 1: m a US to facebooks or our d a used.
Speaker 1: Twitter recently announced that they were going to for the
Speaker 1: first time start revealing d a use daily average users.
Speaker 1: They're no longer going to report monthly average users, but
Speaker 1: even their d A numbers, they're calling them m d
Speaker 1: a U s monetize herble daily average users to distinguish
Speaker 1: from users who they probably are gonna make any money
Speaker 1: from so their m d a us are going up anyway.
Speaker 1: The point is, what is your view on this. Do
Speaker 1: companies have an incentive to sort of try to break
Speaker 1: out of the standardized comparable numbers and come up with
Speaker 1: their own sort of vanity metrics that are always going
Speaker 1: up into the right? Yeah? I think you know, the
Speaker 1: world doesn't divide on this, Like people don't like accountability, right,
Speaker 1: so if you don't have to be accountable to particular metrics,
Speaker 1: you'd rather not. One of the things that's interesting about
Speaker 1: what's happened in capital formation right now is that private
Speaker 1: company investors have access to all this information, all the
Speaker 1: real information, not the fake you know, monetize herbal daily
Speaker 1: average users that you know. They can see all of
Speaker 1: that sort of stuff, and they have a real sense
Speaker 1: of how those mechanics work. Once you arrive in public
Speaker 1: company lands, many of those numbers are not disclosed anymore.
Speaker 1: So you find a situation where as capital is forming
Speaker 1: around these companies, the investors that put up the money
Speaker 1: have much better access to information, so more transparent situation,
Speaker 1: but an a liquid situation, and then you trade liquidity
Speaker 1: for transparency in the sense that the look. Investors don't
Speaker 1: really get to learn any of the you know, the
Speaker 1: way that the mouse trap works, but at least they
Speaker 1: can sell the stock. And so that's the trade. As
Speaker 1: far as your question about the standardization, and sorry to
Speaker 1: go on so long, using like an ad supported companies
Speaker 1: metrics to analyze the subscription business is just not very helpful.
Speaker 1: So like engagement metrics, for example, people ask us about
Speaker 1: our engagement metrics, which I always laugh because I think
Speaker 1: engagement is bad. We want our users to figure out
Speaker 1: the answer in as little time as possible because I'm
Speaker 1: not trying to serve. And add to that, right, so
Speaker 1: each company is different. This is what we spent years
Speaker 1: doing is developing an architecture so that you can understand
Speaker 1: what kind of company you're looking at and look at
Speaker 1: the appropriate metrics to do So, going back to what
Speaker 1: you were saying about the trade between liquidity and transparency,
Speaker 1: we had a recent episode a few months ago. We
Speaker 1: were talking to um a VC and he was arguing
Speaker 1: that one of the things that made this period in
Speaker 1: market unique is that whereas in the past, uh illiquidity
Speaker 1: was a penalty for a company and a private company
Speaker 1: had to offer a bigger premium to get um private
Speaker 1: capital because that was more locked in. These days, people
Speaker 1: are paying a premium for access, in his view, to
Speaker 1: a liquid companies. Maybe they didn't want to have to
Speaker 1: mark their books day to day, or maybe there was
Speaker 1: some sort of prestige value of being in a lift
Speaker 1: or an uber that caused people to overpay. Do you
Speaker 1: see that that the sort of traditional discount that would
Speaker 1: have in the past come along with private equity stock
Speaker 1: has flipped. So I'm gonna give you two answers to
Speaker 1: that question. One, just to verify with data the claim
Speaker 1: that people pay a premium. Over the last five years,
Speaker 1: the I p O s that we've looked at tend
Speaker 1: to trade up in the first half of the first
Speaker 1: year that they're public and then in general trade down
Speaker 1: again below the ip O price. Right, so public public
Speaker 1: market investors. And that's not just that that's over the
Speaker 1: US five years. So it's not just we're not just
Speaker 1: looking at effect and it's not just DOLLI Bober or whatever.
Speaker 1: It's you know, a hundred and fifty odd transactions. And
Speaker 1: so what happens is as a capital markets matter, these
Speaker 1: things come out. They you know, the I p O prices,
Speaker 1: it begins to trade. You get the famous pop which
Speaker 1: some people love and some people hate, and in general,
Speaker 1: these things trade up for a while and then large
Speaker 1: amounts of shares are unlocked and people stop paying attention
Speaker 1: and they change the channel and they look at something else,
Speaker 1: and then they trade down. And so it's definitely true
Speaker 1: that private market investors have paid a premium. Like the
Speaker 1: guys who bought the last round of those deals could
Speaker 1: end up underwater if they didn't sell, but they could sell,
Speaker 1: so it's unclear if they've been penalized for paying that
Speaker 1: premium because they had a moment where they probably could
Speaker 1: have made made a profit on the trade. But in
Speaker 1: general it's not a good trade for the broad base
Speaker 1: of shareholders. So that's out of number one. But item
Speaker 1: number two, why do people pay a premium for this?
Speaker 1: And the answer we think is because if you want
Speaker 1: to invest in growth companies, you have to pay the price.
Speaker 1: And so if you are a long only manager who's
Speaker 1: managing a growth fund, who has a carve out that
Speaker 1: allows you to invest in Uber Lift, whatever, your ability
Speaker 1: to set prices very limited, but you want to participate
Speaker 1: in those deals, and as more and more capital has
Speaker 1: flowed into this place. What happens when there's more demanded supply,
Speaker 1: prices go up. So it's kind of like it's a
Speaker 1: function of the fact that, even if we're just looking
Speaker 1: in public markets, we know that the growth factor has
Speaker 1: done extremely well in recent years, and that's just even
Speaker 1: more exacerbated in the ultra high growth private area. So
Speaker 1: that could explain at least part of this premium. Sure.
Speaker 1: I mean, if you were a growth investor in you know,
Speaker 1: the ninety nineties, you would be investing in companies publicly
Speaker 1: that we're young, and you'd be buying you know, Amazon
Speaker 1: or you know Yahoo or the globe dot Com, right,
Speaker 1: you know, you buy the good and the bad. But
Speaker 1: you get to do all of that in the public market. Now,
Speaker 1: all of that capital formation and all of that value
Speaker 1: appreciation happens in the private market, and the guys with
Speaker 1: large pools of capital want to participate in that. But
Speaker 1: what it's also done is created a situation where larger
Speaker 1: pools of capital, the vision funds for example, have formed
Speaker 1: to participate in that trade. I'm glad you mentioned the
Speaker 1: I p o s prior to the bubble, because obviously
Speaker 1: everyone knows you'd be rich if you had bought into
Speaker 1: that Amazon i p O, but you would have lost
Speaker 1: all your money if you bought into the Globe dot
Speaker 1: Com i p O. People bemoan the decline of I
Speaker 1: p o s for precisely because they have memories of
Speaker 1: Amazon and Microsoft in their mind, and they say, well,
Speaker 1: the stock market used to be this avenue where people
Speaker 1: could make a lot of money investing in these companies.
Speaker 1: Now that's closed off to anyone who doesn't have access,
Speaker 1: but of course there is. It does seem like there's
Speaker 1: a lot of hindsight bias because most of them, most
Speaker 1: companies are more like the Globe, right. Sure, well, Mary
Speaker 1: Meeker has a great statistic that was in her deck
Speaker 1: for a long time after the bust that two of
Speaker 1: the companies that went public during that moment in our
Speaker 1: culture created more than the returns. So it's just so
Speaker 1: the vast majority of them were total flow totally adds.
Speaker 1: You know, well, if you were in the two that
Speaker 1: compensated for you know, the negative jacurb. Right, So it's
Speaker 1: the vast majority were a bust and more money was
Speaker 1: lost than made in aggregate. Right, So you had to
Speaker 1: be very very picky to not be one of the
Speaker 1: losers is the decline of the I p O a
Speaker 1: bad thing. It's bens. I mean, if you were a
Speaker 1: retail investor and in hindsight you're totally convinced that you
Speaker 1: would have absolutely put your life savings into Uber if
Speaker 1: you'd been able to buy it, you know, five years ago,
Speaker 1: then it's a bad thing. But one of the reasons
Speaker 1: that the bar has been raised so much for companies
Speaker 1: to go public is to protect retail investors from themselves. Right,
Speaker 1: Retail investors fueled a lot of the bubble that happened.
Speaker 1: There are other structural reasons why the Internet bubble happened,
Speaker 1: but there was a huge amount of demands in the
Speaker 1: same way that people now spect laid in cryptocurrencies and
Speaker 1: other things like that. Because it was perceived to be
Speaker 1: an easy buck, people are always gonna look for actions.
Speaker 1: Let's uh talk about Okay, So as of this moment,
Speaker 1: when we're recording and we don't know any day now,
Speaker 1: we could get s one filings from some of these
Speaker 1: companies that we mentioned, so Uber and Lived and Slack
Speaker 1: and a bunch of others that we could get for
Speaker 1: the first time public data on these companies. So when
Speaker 1: these come out. What are going to be the first
Speaker 1: things that you look at and what should people listening
Speaker 1: at home what should they start to look at specifically? Um, well,
Speaker 1: us first, our our system is just to take it
Speaker 1: apart and do the sort of fifteen point inspection on
Speaker 1: these things. So does the math makes sense? Like does
Speaker 1: this company make money? Is one of the things that
Speaker 1: we've talked about, you know, on TV before. There are
Speaker 1: times where will put companies numbers into our model machine
Speaker 1: and we'll look at it and see like, jeez, there's
Speaker 1: no setting of the model that produces a profit ever. Right,
Speaker 1: So that's a really low low score as far as
Speaker 1: the earnings power of the company. But we also look
Speaker 1: at the management team. We look at the founder. When
Speaker 1: you say look at the management team, were okay? Uh
Speaker 1: paused there for a second. So how do you score
Speaker 1: in theory a manage the quality of a management team.
Speaker 1: They're the things that you think that you would think
Speaker 1: to do if you wrote out a rigorous system, like
Speaker 1: have they done it before? How long have they worked together?
Speaker 1: Have they worked in places that you've heard of before?
Speaker 1: Were they successful there? Did they go to schools that
Speaker 1: you've heard of before? Right? Do they have advanced degrees,
Speaker 1: you know. And then when you toggle to the founder
Speaker 1: aspect of the management team, sometimes you see total control
Speaker 1: of the founders, which tends to be great because they're
Speaker 1: highly invested and have a lot of skin in the game. Sometimes,
Speaker 1: you know, like for example a Twitter, you see like
Speaker 1: the company is totally post founder, and that means that
Speaker 1: the management team has economics that are heavily weighted towards
Speaker 1: the upside, but doesn't have a lot of pain associated
Speaker 1: with the Dow Todd So founder power is very important.
Speaker 1: The quality of the board, the quality investors is interesting.
Speaker 1: How famous is it? Like faym and buzz is one
Speaker 1: of the things that we score. Companies that nobody has
Speaker 1: ever heard of, you know, do do less well than
Speaker 1: companies that are well known. Okay, so you have all
Speaker 1: these factors fifteen different We have fifteen different scores that
Speaker 1: will roll up into the summary score. Fifteen different scores,
Speaker 1: and so in your experience the aggregate, higher scoring companies
Speaker 1: do better than the lower one way better. Otherwise you
Speaker 1: wouldn't have a business or totally right. But shockingly, because
Speaker 1: there are times where we get the score because you know,
Speaker 1: we see it when it comes out of the machine.
Speaker 1: We look at it and we're like, man, that can't
Speaker 1: be right. So it's always interesting to us, like maybe
Speaker 1: this will be the one that we we you know,
Speaker 1: have to rebuild the whole system one. I think here's
Speaker 1: sort of my final question, or the key question I
Speaker 1: have is do the do high scores say you should
Speaker 1: invest in this company? Or is it if you invested
Speaker 1: in every company with high scores and shorted or avoided
Speaker 1: all the companies and low scores, would that be a
Speaker 1: superior strategy? You know? Do you see what I'm Do
Speaker 1: you see? Like? Yeah, so the aggregate trade is always
Speaker 1: better unless you are so good that you can sniper
Speaker 1: shot the singular winner. But that's incredibly hard to do.
Speaker 1: But the premise of the scoring system is essentially that
Speaker 1: on aggregate you'll strip out a lot of noise and
Speaker 1: be much more likely to have a winning portfolio of
Speaker 1: I p O s with the higher scoring companies. Not
Speaker 1: only that, so that's certainly true if you're an institutional investorent.
Speaker 1: Most of our customers are institutions that buy at the
Speaker 1: I p O price, and so the returns are you know,
Speaker 1: three times better if you buy the high scores than
Speaker 1: the low scores. But if you buy the first trade,
Speaker 1: if you're a retail investor buying high scores versus low scores,
Speaker 1: this is the difference between making money and losing money.
Speaker 1: Got it. Well, it should be a very interesting year
Speaker 1: for I p o s as mentioned, and looking forward
Speaker 1: to seeing over the coming years how your scores do.
Speaker 1: I think we have a week or two before it
Speaker 1: comes and then well I'm on vacation next week. It's
Speaker 1: a good time for it. Hopefully, I'm really hoping I
Speaker 1: don't miss all these, but then I'll be back in hopefully.
Speaker 1: I think you're good six weeks. All right? Great Rott
Speaker 1: Wallace of Tried and Ai, thank you very much for
Speaker 1: coming out Odd Lot. Thanks for having me here. Well,
Speaker 1: normally I would do a little outro with Tracy here
Speaker 1: and we would talk about what a great conversation that was.
Speaker 1: But I actually think that was a great conversation and
Speaker 1: I love this topic and I'm looking forward to all
Speaker 1: the I p o s this year and seeing how
Speaker 1: they do. So this has been another episode of the
Speaker 1: Odd Lots podcast. I'm Joe Wisenthal. You can follow me
Speaker 1: on Twitter at the Stalwart, and you should follow our
Speaker 1: co host on Twitter even though she wasn't here, Tracy
Speaker 1: Alloway at Tracy Alloway and you should follow our producer
Speaker 1: on Twitter tow for four Heads. He's at four Heads
Speaker 1: t as well as the Bloomberg head of podcast, Francesco
Speaker 1: Levy at Francesca Today. Thanks for listening.