Invest with AI
00:23:34 · Kirk McKeown
And if it doesn't, because of something like a cash pay mix, it might be a good read of the consumer or the low-end consumer anyway. So you might use it, but again, it comes back to waiting. These are mosaic decisions, semi-systematic decisions that require sort of like a massive amount of domain expertise to go in. And that's what I think the largest places have figured out with their businesses, which is the more you can inject into the into the data structure and into how you're thinking about your model, geographic bias, demographic bias, um, key drivers in the business, the second derivative of segment margins. So think about it, like tractor supply, right? I've used this example a few times. Tractor supply, I think 20% of their stores are in Texas and 20% of their business is animal feed. If weather in Texas is 2x colder than usual, means the cows and horses are eating more because they eat three times their weight in the in the cold. So you could see a comp boost in cold weather markets for you know, sort of animal feed, but it's it's like it's like it's like flu traffic for uh hospitals. You know, it's it's it's good traffic, bad margin, right? And so like knowing it's like lumber at at Home Depot, it's a comp boost, but not a margin boost, right?
00:24:42 · Brett Caughran, Kirk McKeown
[00:24:42] S1: Like I covered managed care, like you'd see the sort of a trend spike, but then it wouldn't show up in Q1 MLR because the sort of claims from the providers are on a 60 to 90 day leg, so it doesn't show up until Q2. And so there's all these sort of great nuances in all of these businesses. Like you actually sort of need to understand the business model to like, okay, we want to actually leg this by a quarter because there's actual uh a sort of a causal logic behind doing that. [00:25:08] S0: And and and and getting that right inside of a large organization is really tricky and requires a ton of collaboration and trust throughout the org, right? Because, you know, sort of especially at the big shops, you know, sort of um, you know, it and so it's important to embed as much of that into the centralized research function as possible because it's a bona fide's credibility thing, right? It's like you show up and talk like that with a PM, they feel more comfort taking data from you, taking research from you because they're like, okay, might not get it right all the time, but they're thinking about it the right way. [00:25:40] S1: Yeah. And then you talked about setups, and they're like, we're we're two hedge fund guys, so we know what setup setups mean, but maybe not all of our viewers uh viewers understand that. Like what what's a setup?
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.