Invest with AI
00:42:20 · Brett Caughran, Khe Hy
[00:42:20] S1: It almost sort of like investing or sort of the craft of creating a thesis almost is more like writing than math, obviously, where it's hard to sort of verify ex ante. There's been more sort of watching the narrative around the latest set of models that actually like writing is number of people observed like writing is actually deteriorated. The writing quality is deteriorated. Yeah. So the reinforcement learning verifiable rewards is sort of one hypothesis I've seen. So it's interesting. Okay, you had a question [00:42:49] S2: On that point, Thomas, none, am I hearing you correctly that a lot of knowledge work is not verifiable and like discreet with like discreet metrics? Does that, how does that impact how does that what's the long term impact of this unverifiability for knowledge work? Like, do we you know, coding went exponential because it is verifiable. You know, whether Amazon is a good buy is hard to verify. Does that create a natural, like, ceiling for LLM usefulness in the long term?
00:43:27 · Thomas Li
[00:43:27] S0: No, it does not. I don't think so. I think what it does is it creates a ceiling for trying to post train a model to solve that problem, but doesn't mean you cannot break that problem down, right? So we know we cannot determine if one pitch is better than the other just by reading it. Right? I mean, past a certain point, right? We know we can't really discern, it becomes a judgment call. But what we do know is that within a certain pitch, there are things that are quantifiable and verifiable. Like your numbers should be right, your historical numbers should be right, right? If you're calculating gross margins, like your math should be right, right? If you're telling me that there is some historical fact that went through the business, right, Jeff Bezos retired as CEO, like those are facts that shouldn't be questionable. So those you can verify, right? It's the judgment piece that you probably can't, but it doesn't mean that there is nothing that you can verify on. And that's where you can add a lot of value. Right? Say differently, when I think about historically what a human is doing, right? A human does three things at an investment institution, you're either gathering facts, you're analyzing the facts, or you're passing judgment on the facts. And for the most part, anything you're doing that's not part of these three is not a good use of your time. There's a lot of like random stuff that comes up, but it's probably not a good use of your time. The analysis piece, you can very easily quantify a lot of it today. Is this the right analysis, right calculation or not? If you build a three step model, did it balance or did it not balance? It's verifiable, right? But the judgment piece is hard to verify. If someone pitched an Amazon long and you bought Amazon and it's down tomorrow, like, was he wrong? Like we know probably not. But why is it that we know that? What is the right time horizon? Right? So that becomes a human judgment piece that I would say is very hard to do post training on. But on the analysis piece, it's much easier today.
00:45:27 · Brett Caughran
[00:45:27] S1: Push back a little bit on that and sort of one world that's sort of grown meaningfully over the last ten or fifteen years is world of factor investing, which sort of tries to capture that judgment. Like there is sort of an ex ante table odds improvement of a company with high free cash flow margins versus low free cash flow margins. So this is sort of a demonstrated sort of tilting of the table odds. I think a number of funds are starting to think about this concept of pattern recognition. How do you codify the patterns that have been sixtyforty coins versus fortysixty coins? Obviously, this realm of AI has a much higher sort of value quotient than update this model. What you see out there? Say it is a more nuanced ability to verify than, hey, update this model or the numbers right. What are your observations on sort of that side of the world? This thinking machine's Bridgewater report got a lot of despite being sort of just a very simple analysis, got a lot of discussion about can AI actually start to pass better judgment and investment tasks?
00:46:36 · Thomas Li
[00:46:36] S0: Yeah, that's good question. Because the question you're really asking is what is judgment, right? Can you actually take this whole concept of judgment and break it down? And factors does a great job of that. So I think in short, yes, the answer is yes, like AI can get to some piece of judgment, assuming that we can break down what judgment is. So let's think about factors, right? Very simply, when I think about what makes a stock move, there's only one thing that ever makes a stock move, which is demand and supply. Right? Every stock moves because they're buyers and they're sellers, and there's like a higher willingness to buy, then sell prices go up, whatever demand supply curves. What we're trying to figure out is why someone is buying. And the reason different people buy are very, very different. The reason the index is buying, or like a big mutual fund is buying, is very different from the reason, you know, a long only hedge fund is buying is very different from like a Qantas buy, right? But in the market, they all get expressed as buying signals. Obviously, if a big mutual fund is buying, let's say, the market, the S and P 500, they're probably not just buying Amazon, they're buying, you know, 500 different companies at the same time. So that creates a market factor move that might be different from, you know, whatever, a single stock investor buying Amazon, right? So what factors try to do is, given any single period of time, break down the reasons for why someone is buying by looking at what other things are moving. So instead of saying, hey, whatever Amazon is, the only factor is the S and P 500, or you know, the Qs or whatever, you can basically say, I have a much more intelligent way of thinking about it, right? I can run PCA and I can say, hey, Amazon is, you know, X amount, correlation with the first factor in the PCA, and Y amount in the second factor, and so on and so forth. And that allows you to, without naming what the PCA is, like what a first order is, to say that there is a first order, right? There is something. You can make a guess, maybe it's momentum, like I don't know, but you can make a guess, right? But we know that you can statistically prove that.
00:48:47 · Thomas Li, Brett Caughran
The beauty of this is not that the AI can pass judgment, it's that we have taken judgment of what makes a stock move, and we have actually broken it down into a language that we all agree on, right, which is like sort of like this PCA language. And therefore, it's quantified, and then you can bring it to an AI. What I would actually argue is that the stuff that is not quantifiable today is the new set of judgment. [00:49:13] S1: Yeah, so let me frame it this way, Abhir, like the factor world has grown up around observations of the present, right? So it's sort of by definition, looking in the rearview mirror of what's happened, this company sort of accumulated a high momentum loading. The sort of intriguing element, at least to me, is started thinking about actually the look ahead. If I had two companies to sort of give a simple example, one's going to raise revenue guidance by 5%, one's going to cut revenue guidance by 5%. That's like a sort of most like not it's not going be 100% batting average, but that will be a good batting average on the trajectory of those two stocks. Sort of classic PCA analysis hasn't been able to sort of find correlations to that. But these sort of agent, the agentic digital analyst approach fed with the right data is getting close to having the ability to do that in some sort of reliable. And that's sort of a more verifiable framework. Like you can sort of look at batting averages of revisions.
00:50:18 · Thomas Li
[00:50:18] S0: Well, thing that is really hard for a human to do, and if you look at most funds, you don't even have the same people solving this problem, is that the person who's trained in fundamental analysis really only thinks in one factor, which is fundamentals, right? So a person, you might be really good at predicting whether a company is going to raise guidance because of all the work that you've done. But you might not actually know, or most of the time, people aren't in tune with the factor environment to say, sometimes raising 10% guidance in some environments create 10% moves for your stock, sometimes it creates 2% moves to your stock, right? So that people who know that tends to be PMs risk, they tend to not be close enough to the fundamentals to be able to marry them together. And that's always been like a problem for a lot of funds, right? Which is people who understand factors don't understand fundamentals and vice versa. But the stock doesn't care. The stock is a combination of both. Right? The move of the stock is a combination of every demand and supply in the market at that moment. It doesn't say, today I live the momentum factor, and tomorrow I live in the fundamental factor. Like everything is influencing it at the same time. We just don't have people that can capture that. And the question is, can an AI do that? Yeah, probably.