Invest with AI
00:25:52 · Kirk McKeown
[00:25:52] S0: Setup is is is what are all the key uh you know sort of uh points that are you know sort of leading into a catalyst or an event and a name, right? And so, you know, one of the things that I learned um in so I was really lucky because Glenview was a longer-term shop, right? Top 10 positions represented 50% of committed capital. Larry was an incredible Garp investor, super talented, um, and unbelievable at identifying core themes, market structure, and business model trades, right? Hospitals in 09, pills in 06, very thematic oriented, by the forest, and make the bet on the secular trade. And it was amazing. So I got a really great education at around understanding that when I went to Glenview and then I and then I got to work at Point72, which is a very different model. Uh catalyst-driven, 30, 60 day setup, high turnover businesses, um, you know, and and and sort of uh incredibly sort of diligent analysts and PMs um that you know sweat every sort of every change in the business. And there, it's really about understanding event path. And so the setup is really about understanding event path. And event path is usually around a quarter, but it could be around a conference, um, it could be around Black Friday or a you know a three-day weekend. Um, there are different types of event classes, and having a really good understanding of the opportunity set going into them and coming out of them is the setup.
00:27:21 · Brett Caughran
[00:27:21] S1: Yeah, yeah. And these 150 tickers where the data assets have become very deep, sort of distorted. You've talked to most consumer investors, like deeply distorted um price discovery in many ways made those stocks more efficient, in some ways have made sort of increased setup and positioning, and you get these sort of overshoots uh more often. Like what's what's been your observation of market price discovery, trading path of stocks that are deeply covered by these data assets?
00:27:51 · Kirk McKeown
[00:27:51] S0: You know, it's I'm I'm I'm I haven't been as close to it um since I started CarbonArc for a bunch of reasons, but you know, sort of uh the the net the natural is, you know, sort of what especially with the proliferation of card data, you know, sort of the number of folks that were trading names for the data rather than for the business increased pretty pretty meaningfully, um, which meant they were only there for the event and not necessarily for the turnaround of a business or what have you. And and and and I think I think one of the things that became most apparent is especially with like factor frameworks as they emerged as well, it was like the rise of factors and then the rise of data created this like really weird weird volatility cycle in a lot of names where mistakes or misses were way outsized, uh, you know, sort of uh impacted to the and then it would blow up people's portfolios and create all sorts of uh dislocations that just frankly, um, you know, sort of weren't fundamental. They were technical, but technical still hurts and technical still puts you in a really weird place. And so it just, you know, it it's gotten to the point where I think, you know, sort of one of the one of the big parts of the data business is either increasing conviction so that you can buy more on those dislocations, or um, you know, sort of competing, you know, in by figuring out what the data is going to uh you know, predicting the credit card data, being in front of those types of data assets to have more visibility into what's going on, right? Um it's those, you know, that's largely consumer, but I would say soft consumer and food. Um, you know, sort of uh, you know, um like you know, a lot of the big ticket stuff uh isn't well covered, um, largely because uh buying a mattress or uh buying something that is sort of big ticket is is not as episodic as going to Chipotle. Um so it's not as strong a card name. Um and then, you know, soft uh what I would call soft tech, you know what I mean? Um and online, uh internet and online, right? Um your hard tech is still, you know, sort of uh, you know, a more uh hotly contested data space. Um people are trying to find it. It's it's very hard. Um you can see it. Um but you know, sort of, and then industrials is actually really hard to build because it's like building a cottage industry in every vertical, right?
00:30:00 · Kirk McKeown, Brett Caughran
Um it's very hard to find horizontal data assets in in industrials. [00:30:04] S1: Yeah. Yeah, and sort of like you wrap it all together. I was talking to a friend who runs a consumer pad at a multi and he spends about three million dollars a year on data. And I just asked him, like, is that how does that show up in PL? He's like, he doesn't. It's just table stakes. I need to sort of understand why these stocks are priced at these levels. And I'm like, that's quite a big, that's quite a big nut to just under understand expectations. Um but sort of walk me through that dynamic, sort of how alternative data has become more beta, has become this now casting dynamic. And then I'd sort of love to understand how that evolution led to the sort of conviction to to launch CarbonArc.
00:30:46 · Kirk McKeown
[00:30:46] S0: Yeah. So, you know, it was one of those things where like when you started to look at, you know, how data was bought and sold um in the markets in the mid-teens, um, you had long lead times for legal and compliance and contracts, um, and then portability, usability, and flexibility of the data was also really hard. You needed a huge team, like we talked about, and you also sort of needed a balance sheet. And so that kept prices artificially high, right? And so it kept it up in the ether so that the people could buy were buying, and the people could sell, couldn't sell down markets, they had to keep the prices up to validate why they were selling the data in the first place and maximize their opportunity because they only had 500 qualified buyers, right? Uh the the the total customer base, potential customer base was really small. Um the view we took was um if you could smash down the cost of insight, move the insight instead of the asset, insight being an aggregation or what have you, a cohort, um, you could expand your TAM meaningfully to folks that just wanted to buy single tickers or um you know single zip codes, you know, um, and increase density and velocity of consumption um and and sort of uh build into that. You know, those ideas, you know, are are you know sort of a have aged well. Um you know, sort of uh, you know, we were early in the way we were thinking about it. Um but you know, sort of fundamentally the whole my whole the whole core thesis is when you when you when if you were pitching a name to a PM, you never wanted to go in and pitch Chipotle because the credit card data is better. You wanted to have three, four, or five inputs to sort of say credit card data is better, foot traffic's better, especially in the Northeast, where they're they've a they have a denser denser store store store base. Um we're seeing sort of that they increased ad spending throughout the quarter, and the reviews on Reddit are positive, right? Now you have surface area and a portfolio of inputs that give you sort of higher conviction into an event, i.e., you know, sort of in sort of how they're talking about sort of how they're gonna talk about it on the call and what they're gonna guide to, you feel good that there's good momentum going into the call. And so that was sort of that's always been my view, but like to do that costs millions of dollars.
00:32:51 · Kirk McKeown, Brett Caughran
Yeah. And so, like, how do you do that for hundreds of dollars? Right. And so that's why we started CarbonArc, um, which was because like everybody wants to, everybody still wants to make a decision, trade a stock, whatever they want to do. Um, you know, we just wanted to make it so that anybody could do it, and then you could start pushing it down market into, you know, sort of uh corporates and other folks that are trying to figure out a lot of the same problems that Wall Street are from the other side. [00:33:18] S1: To to some it makes a ton of sense and like this is how the ecosystem should operate. Also, if you have 500 core buyers who have to spend 300K for a data set on the potential that they use that three or four times a year, that's actually very good for the data incumbents. It's almost sort of a supercharge dynamic of the gym business model where you know 80% of your people never show up or once once once or twice. Can you just talk talk to us about the the recommercialization frictions, particularly in the really high caliber data sets? Like how do you get those, like if those data sets all played ball with you, that's actually leads to dilution of their revenue model.