Invest with AI
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.
00:51:40 · Brett Caughran
[00:51:40] S1: Yeah. Yeah, this sort of promise of, you know, QuantiMental, which has been this sort of, you know, one plus one equals three concept for decades, really, which has been really hard to like people have sort of, you know, made money on QuantiMental, but it's more of like an overlay strategy rather than a true merging of the best of both practices. I don't think there's really any scaled manager that has brought the best of both into. But that's sort of my hypothesis is like agent or the agent architecture allows a new way of investing almost like systematizing the fundamental investor.
00:52:16 · Thomas Li
[00:52:16] S0: I mean, what we're seeing a lot is like there is this race to the medium right now, which is the quant funds are basically saying, our challenge historically has never been on understanding factors like we got this stuff down pretty good, but we couldn't get our hands on fundamental data and make sense of it in a way we systematically could do so with stock price, with security prices. And with fundamental guys, it's we've always been able to build models and talk to companies that understand that, but we couldn't systematically think about factors and overlay that onto our process the way the quants could. So you almost see this race into the middle where the quants are saying AI has unlocked the ability for me to get fundamental data, right? How do I marry that into my process and to become a quant fundamental shop? And the fundamental guys are saying, now ClockCode has changed the way I look at the world. I can now be a software engineer, I can analyze security pricing, right? How do I go from fundamental to fundamental quant? And you almost have like this like race to the middle.
00:53:15 · Brett Caughran
[00:53:15] S1: 100%. Thomas, one other question I had, I spent a couple of weeks head down a couple of months ago with some of the Excel integrations, including the DeLuca MCP and DeCloud Excel. And that was actually quite like the simple update a quarter worked. Beyond that, I was actually quite disappointed in the ability to do things I was hopeful would be possible, like asking Claude XL to rebuild my revenue, build for a resegmentation or handle more complicated issues, which across 300 models, like 100 are going to be simple, 200 are going to have some sort of messiness to them. Where do you think we're at in terms of LLM fluency with Excel, the ability to just speak the into Excel LLM and have it do more complicated tasks like that?
00:54:18 · Thomas Li
[00:54:18] S0: Yeah, so I would have given you a different answer a year ago than what I'm going to give you now. So if you asked me that a year ago, I would have said the Dilipid philosophy is we build the best data and we pipe it into whatever platform to go solve that problem, because the platform is going to take care of that problem. And so we were hopeful that Cloud Excel with IMCP will be able to solve, you know, all the Excel problems. But as you just pointed out, it's been a year and it's not there. And so we looked into why. And very simply put, the Excel problem, it's about a set of specific business use cases in Excel that require a very, very fine tuned understanding of what's going on. So let's say someone comes into a model and says something very simple. I want to understand the operating leverage of the business. The amount of analysis that needs to happen is actually very detailed, right? You need to first get cash numbers, you need to get the non GAAP numbers out, you need to understand everything from how CapEx changes changes your revenue, how much of that is because of OpEx versus CapEx. Can you actually spend on? Can you actually cut back on CapEx? Or is that like, you know, fake CapEx, right? So there are a lot of little, little steps that needs to happen that doesn't get captured by the simple question, help me understand operating leverage for this business. And then every business is different, right? Understanding operating leverage for an industrial company and a software company, despite the fact that they have different amounts of operating leverage, the way you would approach your question is also different. So once we realized that there are multiple layers of depth to this problem, we started asking ourselves, is there an ultimate solution of basically saying, hey, Cloud with my MCP can solve, like if the model became better, can we get there? And in the near term, I think our answer is no, it cannot get there. And so what we've then decided to do is, we've then decided to build our own version of an Excel add in. It's not launched yet. But the idea is, in order to solve the Excel problem, it's not an Excel problem, it's a very specific problem. It's the operating leverage problem. It's the hey, can I get the KPIs broken down problem? Is the hey, can I understand what each KPI represents?
00:56:42 · Thomas Li, Brett Caughran
Can I understand if this company needs to be forecasted using FX neutral same store sales or non FX neutral same store sales? And how do I think about that? So each of these set of problems require very specific harnesses. And it is a very, very large set of harnessing that needs to get produced. So my view, at least as of right now, is that because the labs won't get to the level of detail in the harnessing to be able to solve this problem, this is actually down to the vendor's guide, like guys like me who are willing to build into workflow to go solve. [00:57:17] S1: And the labs, you know, have really built their modeling corpus toward like tuned to investment banking, right, which is a very different which a very different modeling than build a hedge fund model to forecast revenues, EBITDA, EPS.