Invest with AI
00:13:53 · Brett Caughran
[00:13:53] S1: Great uh minimize your wins, maximize your losses. Great advice for the primary research teams uh listening to this uh this pod. Yeah, you had a you had a really interesting front row seat because to some degree what you were doing in 06, 07 was bitter lessened away by alternative data, right? Even back in 08, I was calling Wendy's franchisees and trying to get in touch with 20 and and then sort of fast forward credit card panels. Can you sort of walk us through the sort of the early evolutions of the alternative data sets and how that changed what you were thinking?
00:14:26 · Kirk McKeown
[00:14:26] S0: Oh, yeah. I mean, it was wild, right? So I I was really lucky at Glenview, uh, you know, I had I had this middle office function. I had a great team of people, um, and and Larry was a huge proponent of primary research. So I got the opportunity to work with like early, early data providers that were coming on in like 2010, 2011. RS metrics. We were we were looking at parking lot data. Um, you know, that was the first, that was the first alternative data asset I think I ever looked at was RSmetrics. What year was that roughly? 2010, maybe 2010, yeah. 2011. Yeah. Um and um and then vertical knowledge. Uh and uh if you've ever met those guys, Matt Carpenter, um, they they were they were they had been working with like DOD and everybody else, um, but started to get on Wall Street in 2010, 2011. Um, started to that was more scraping. Um, but you know, those are early days of data. And what was really interesting was uh trying to figure out how to augment um you know some of those data assets into the process at the time. Um, and then sort of obviously saw over the years um, you know, sort of the the evolution of and the need for things like doing calls on comps, you know, understanding retail, right? You know, I I used to I used to do, you know, when I was at you know Glenview, we used to do monthly calls to understand where comps were going to shake out and how, you know, sort of wholesalers were seeing momentum and promotional cadence through the department stores and of the other big players. And then, you know, sort of credit card data started to prolip proliferate with some of the private providers that were going into the funds and then 1010 of the world, the 1010s of the world, and it completely changed how consumer uh due diligence uh and even consumer trading performed over the next decade, right? Like consumer is a completely different space than it used to be from a due diligence perspective and from a research perspective. And I think that's the place where we've seen the most transformation. Um, you know, I do think um channel checks have held up because of some of the reasons CarbonArc started, which is outside of card click, app and geo, um, which are the sort of the four horses of you know what I would call alternative data on Wall Street, they've they've hogged most of the CapEx from many firms.
00:16:32 · Kirk McKeown, Brett Caughran
Um so you largely still have a data richness on 150 tickers, and then there's a whole host of other tickers that still don't have good data. So you still need to do calls. Um, but it has it has evolved um, you know, sort of how people do channel checks in in consumer for sure. [00:16:47] S1: Yeah, yeah. Um yeah, and sort of, you know, like Tom Elning from Tegas always talked about, you know, selling alpha versus selling selling beta. What uh can you sort of walk us through that evolution? Because sort of felt like my rough prior was like those data sets actually were a ticket to alpha for a few years. Oh, absolutely. Like the early the early distribution was very tight. Very tight. Right. And as it broadened over time, they sort of the almost a sort of now casting dynamic happened where those data sets almost set set set an efficient first tick, sort of the day, the day they're released. Um how is how has sort of that use case evolved, do you think, of these data sets on these, specifically on these 150 tickers? Like how are funds using these data sets now versus 2012?
00:16:47 · Brett Caughran, Kirk McKeown
[00:16:47] S1: Yeah, yeah. Um yeah, and sort of, you know, like Tom Elning from Tegas always talked about, you know, selling alpha versus selling selling beta. What uh can you sort of walk us through that evolution? Because sort of felt like my rough prior was like those data sets actually were a ticket to alpha for a few years. Oh, absolutely. Like the early the early distribution was very tight. Very tight. Right. And as it broadened over time, they sort of the almost a sort of now casting dynamic happened where those data sets almost set set set an efficient first tick, sort of the day, the day they're released. Um how is how has sort of that use case evolved, do you think, of these data sets on these, specifically on these 150 tickers? Like how are funds using these data sets now versus 2012? [00:17:35] S0: Well, I think it's I think it's a great study in um alpha and how alpha moves around, right? Um, you know, alpha never goes away. It's alpha's competitive advantage, right? It's just like where does it reside and how does it get monetized? And it was a it was it's a fascinating study when you look back at it. So if you look back and and you think about sort of credit card panels, really started to proliferate in sort of the late the early teens. So call it 12, 13, 14. You know, at that time having that asset was a competitive advantage, right? And then when you turned it into a signal was a competitive advantage on top of that competitive advantage, yeah, right. [00:18:12] S1: Yeah.
00:18:12 · Kirk McKeown
[00:18:12] S0: And then, you know, sort of as it proliferated, call it 2017, 2018, you started to see setup become the competitive advantage, understanding where people were set up and all that fun stuff. And where we are today is for anybody that's trading sort of on a, I'd say, a 30 to you know 90 day setup, um, it's table stakes. You need to know what it's saying, um, but it's it's it's just another input in your process. Because it's a cartel and it's been really tight, um, the price points haven't moved as much as the alpha has. So you're you're really paying alpha prices for smart beta, um, which is which is one of the reasons CarbonArc, I formed CarbonArc. Yeah. Um I saw that I recognized that concept and realized that like something needed to change in the way data moved around. Um, because uh, you know, people were paying too much money uh for credit card data, um, that you know, sort of and crowding out all this other really cool data asset, all these other really cool data assets that if you could, if you could buy them piecemeal or you know, sort of they were at the right price, you could create a more mosaic-driven framework for understanding your names, right? Um But you know, sort of, you know, what's interesting today is what we see is um there's more vertical, just like there's vertical, like just like vertical AI is is something that we're seeing a lot of push on um in the in the in the AI movement and story, um vertical data is becoming more relevant, right? So like you know, we like we see a lot of action in TikTok data and Amazon data as people are starting to get deeper into that second derivative framework around how SKUs are moving in TikTok and how SKUs are moving in Amazon and what that means for the broader construct of you know, sort of consumer spending and also sort of brand momentum and brand heat in other parts of the market, right? And so that's a perfect example where you have your sort of market data-ish credit card panel, but then you have your sort of, you know, you like it's it's it's it's it's layering the inputs that becomes the competitive advantage.
00:20:06 · Brett Caughran, Kirk McKeown
[00:20:06] S1: Right. [00:20:07] S0: And if you think about, you know, sort of um, you know, you I know I I think you've had um, you know, sort of Rich from Arcana on, right? Like as factors have moved, you know, like what happened between 83 and 2026 in quant markets around stocks, then ETFs, then factors, um, you're gonna start seeing something similar occur in what I would call sort of alternative or external data, where you're gonna have continued uh diversification of inputs um to create sort of uh you know, sort of differentiated outcomes, right? [00:20:38] S1: Yeah. [00:20:39] S0: The alpha moves to how you consolidate your input portfolio, right? [00:20:43] S1: Yeah, yeah. [00:20:44] S0: Um I think that's a really interesting, but it has to be uh it has to be cost effective. [00:20:48] S1: Right, right. But but before we get to that, can I can I go deeper into the I think you you made a really important point on creating signal out of the underlying data? Yeah. A lot of these data firms started to basically create a research layer on top of their data asset. But a number of funds during the sort of mid-teens would ingest that raw data and actually generate superior signal. Can you just explain that concept of like the the creating the actual investable signal from the raw data asset? What are some of the the sort of processes that the best firms will do to turn that raw data into a better signal than may come out of a um one of the data vendors?