00:34:51 · Brett Caughran
[00:34:51] S1: I don't put much weight in any of the benchmarks personally for we could have a long conversation on that. But I agree with you is sort of and I've heard this from a number of real people to the sort of estimate accuracy is something that is becoming real now, just sort of sharpening estimates. Now, getting the numbers right doesn't necessarily mean you get the stock right, any multi manager analyst knows. I'd say it's very common in earning scorecards for human investors to have high 50s, low 60s batting average on estimate accuracy and even 48% accuracy on stock price movement on print day. So I would sort of submit to you that that almost becomes like a new consensus. Like we have sort of a new concept emerge of AI generated consensus, which by itself could be an interesting data stream or interesting like, hey, streets at 45¢ on Nike, AI, clouds at 41¢, right? Like the real number is 41¢. Then we're shooting against that, right? So it's sort of just this adaptive, always adaptive, adaptive bar.
00:36:05 · Alistair Smallwood
[00:36:05] S0: Exactly. That's exactly our view, to be honest. It's really a kind of additional data input to understand where non human participants in the markets are expecting things. And crucially, when we publish these numbers, we publish full models, including the reasoning as to why we think that numbers could be higher or lower on every line item within that model. One of the really cool things in that you talk about getting numbers right on current quarter and that not necessarily helping with on the day share price move. One of the things that obviously the next extension is getting not only this quarter right, but getting quantitative guidance right for next quarter, because that ultimately does drive some level of future expectations. And within those statistics for last earnings season, we were closer to the midpoint of the new guidance than consensus was 70% of the time, which again, we're super proud of. But that number needs to be higher in order for this to be a really credible, valuable data set. Yeah,
00:37:07 · Brett Caughran
[00:37:07] S1: I yeah. Think the concept is really critical. And you mentioned too, Alistair, how much alpha really is there in the one to two quarters? Like today, there has been, but I think that becomes more compressed. Like the example I use is a few years ago, there was this company eHealth, which was a Medicare Advantage broker, and everyone was tracking all of the sort of Medicare Advantage volumes quarter by quarter. But you could take a giant step back and you could have a conversation with Humana and United and they would tell you, this is a short term fix to work with these brokers. We want to bring more of this in house. It's like, all right, I don't care what happens over the next quarters. There will be more of a competitive threat to the business. And that only happens if you have good relationships with the management teams, you can ask the questions the right way. They're not out there releasing press releases like, hey, we're gonna move volumes off of eHealth because that would create complications for them by virtue of a close relationship. And then you could talk to other brokers and sort of build this mosaic around what's happening over the next twenty four months. Then part of the trigger for me was going to a Credit Suisse breakout where it's like you had 24 year old hedge fund analyst just hammering the CFO on how the next quarter's going to shake out. He started being defensive. I'm like, Something's wrong here. You could sort of see everything come together from a different perch. You're like, I don't know how AI eliminates that. And it was a great trade. I'll pat myself on the back for that one. I've had plenty of bad ones as well, too. That's a sort of like alpha. I'm like, how does that become systematized? In fact, opportunities like that will probably be more sort of acute in markets because you have more crowding around these short term dynamics.
00:38:58 · Alistair Smallwood
[00:38:58] S0: Yeah, I think it's a really great question. And maybe back to your point around consumer PMs and having the alternative data, and that doesn't necessarily lead you to calling the print correct every time. I think what really happens is that when you become alternative data focused, the people that do it really, really well use it as a guide and explanatory factor on an existing quarter. But the really great calls are really calls around multiple expansion or contraction, which are much more fundamental, bottom up, great understanding of the business, whether it be structurally supported, structural decline, and really understanding that re rating, de rating potential. Because ultimately, if company company X beats, because you've seen the alternative data in quarter one, in order for you to genuinely profit from that trade, you have to understand what's next. I yes, they've beaten this trade, but do you really understand quality of products, quality of management team, and ultimately, where could this get to? And there was still a huge human overlay of how well do people understand this business, I. E, is this a recovery play that people don't believe yet, and therefore there are seven or eight turns of the multiple to still come back here as this business reproves itself? And that type of work is still fundamentally human. That real nuanced understanding of what's going on, where could this business get to, what does terminal value actually look like, And is there a genuinely credible path to get there? And crucially, as we know, financial markets are a behavioural land, and therefore to what extent and what ultimately would it take for the market to believe this story? And it is all about stories. Ultimately, we believe that share prices are the discounted factor of its future expected cash flows. But in reality, those can be distorted in the short term by the function of the story that gets told and believed accordingly. And that is still fundamentally human that sits on top of that.
00:40:56 · Brett Caughran
[00:40:56] S1: As poker players will say, you have to play the cards, you have to play the player almost more importantly. And that's why I sort of like go through this doomer cycle of like, oh, we're in trouble. And then I sort of actually look at a Bloomberg terminal. I'm like, oh, we're fine. Because just the distortive impacts of market price discovery in these markets where fundamental investing has been gutted over the last fifteen years as sort of a price setter. And the incremental price setter are indexers, quants and constrained pods. And so actually like investing with a fundamental thesis, you have these sort of opportunities, multi quarter multi year opportunities emerge that almost like you never did before. So some of the old alphas have been compressed, but some of these new alphas feel sort of, I don't know, I have to like check my numbers often when I go through these. Mean, is this like really how this thing is priced? Because you see these distortive effects. So I think just market structure now that it requires sort of old dogs learning new tricks, sort of adapt alpha capture. But yeah, I don't see, like the picture of efficient markets would be sort of a, you know, volatility in equity price. Do you see signs of that? Don't see signs of that.
00:42:23 · Alistair Smallwood
[00:42:23] S0: I don't because I think we're introducing new bias. And I think the only thing that is your friend is duration. I think that historically, there's obviously that age old adage of can you stay liquid long enough to be right? And I think that that is something that will become increasingly true. So you talk about old dogs learning new tricks over the last ten to fifteen years as investment horizons have shortened and the technicalities around crowding and understanding that have compressed. Ultimately, I think that it will now be old dogs who learn new tricks, relearn old tricks and shift back to a duration thinking, will be great. I think the implications of market structure are fascinating. We have seen a mass exodus of capital out of duration long only funds, generally speaking, and directional long short funds globally, particularly in Europe. And all of those flows have flowed into low net, low vol multi manager strategies. Ultimately, the capturable alpha within those is compressing and we've seen Ken Griffin talk about the commoditization of data in the short term and ultimately what that might mean for the long term It'd be fascinating to see how the multi manager constructs think about volatility tolerance and duration on an ongoing basis if that shorter term alpha becomes more difficult to capture. And ultimately, what that then means for the fee pass through model and fundamentally leverage at the group level. Ultimately, if returns are higher volatility, does that mean that gross leverage at the topco then has to come down? And what does that then mean to capital formation in the rest of equity markets? And I think that my original background was in UK smaller mid cap who have seen deathly outflows for the best part of seven or eight years. And there was definitely some mild excitement that exists within that of people reallocating to longer duration, slightly low, slightly higher volatility, because ultimately, potentially, that's where the reliable alpha capture might start to shift to. But that is a view that I hold now, and I hold it very loosely. But without a conviction right now, that might change significantly as we see this continuous exponential improvement in models and technology.