00:00:00 · Ying Hua, Brett Caughran
[00:00:00] S0: I actually personally think public market, specific stocks are one of the hardest domain knowledge to grasp. [00:00:06] S1: To Kay's point, many of our clients are sort of building dashboards with cloud code, but this sort of topic of agent debt and this topic of token... Tokenomics sort of growing their own budgets. [00:00:17] S0: Yeah. When you read the surface level reports, they will have one interpretation. But when you have more domain knowledge, like depth of domain knowledge, you're gonna reach a different conclusion. [00:00:32] S1: Hello, and welcome to Invest with AI, where we explore the intersection of fundamental investment research and artificial intelligence. We're really excited to have Ying Hua today from Implied on the podcast. Hello, and welcome, Ying. [00:00:48] S0: Hi. Thank you so much for having me. Excited to be here. [00:00:51] S1: Thanks for being here. Can you just start sort of giving us the Ying Genesis story of your quick encapsulation of your career and what you're doing now?
00:00:51 · Brett Caughran, Ying Hua
[00:00:51] S1: Thanks for being here. Can you just start sort of giving us the Ying Genesis story of your quick encapsulation of your career and what you're doing now? [00:01:01] S0: Yeah. My background is in fundamental investing. Started my career at Goldman on the equity research side, then moved to Citadel, was there for about five years, and then went to Ballyasne for about three years. And then I started applied in 2023. I was a Financials and Fintech PM at Ballyasne, But I also ran a fundamental book. So I think I had some experience with fundamental investing as well as sort of quantitative, not quite AI, but quantitative kind of tools. One last interesting tidbit is between Citadel and BaliASNI, I had a very long non compete, end up learning Python and got a mastery data science and played it with language model back then before it became popular. So before LLMs became the mainstream, this is when like BERT was still the state of art. So I actually had a chance to play around with it when I was at BAM too. [00:01:58] S1: And even back in sort of more in style in 2026 to leave the buy side for an AI startup. But in 2023, was more of a contrarian position. Gave you the conviction? Sort of what did you see out of that, your experience back then when, frankly, the foundation models were pretty not very good for my use case? What sort of gave you the conviction to plant a flag and start an AI business?
00:02:26 · Ying Hua
[00:02:26] S0: Yeah, I think there are two catalysts. Number one was, I believe, GPT-four was, I remember, the distinct turning point. I think when I was initially playing around with things, a lot of things I wanted to do it couldn't really get there. By GPT-four I feel like with enough prompting and enough chaining, can get it mostly there and to kind of edit things a bit. That's number one is I definitely saw the potential of making it work and knowing a full well that things are going improve and stuff like that right. So that's number one. Number two is at the time there were a few options out there, AI tools for investing. A lot of them are companies that kind of started before AI and then with the TryGPT release kind of try to pivot. And I actually had a chance to play around with a lot of them. I was sitting next to our AI team so I got a chance to see a lot of things. And I didn't really quite find a tool that really helped me. I think the gap that I saw, I thought it required someone with a domain knowledge to solve and hence I thought my background is interesting that it's kind of intersection right I know fundamental very well I know enough about quantitative stuff I'm not a best programmer I know enough about quant sci to really fill in those gaps. So that was the reason.
00:03:51 · Brett Caughran
[00:03:51] S1: That sounds great. And one of the, part I of the reason Kay and I started this podcast, we don't really have the answers, but we know the questions. And one of the key questions we're sort of planning to explore is the sort of competition of the horizontal foundation lab versus vertical finance agents. How would you frame that competitive tension sort of today? And how do you see that tension playing out over the next nine to eighteen to thirty six months?
00:04:18 · Ying Hua
[00:04:18] S0: Yeah, I think especially with the recent agendic improvement, I think a lot of people are in the sense that, oh, model company will eat the world. Obviously I'm starting a company so my answer is going to be biased. I don't think that's going to be the case longer term. So I'll give you like an analogy I usually use with our clients and I'll give you another kind of more in-depth reasoning for that. Number one is I think of language model very much like universities. They produce intelligence that are capable of reasoning, basic level reasoning. They produce intelligence that are capable of using tools produces like kind of basic level that you can do a lot of it. It's very generalist, you can do a lot of things. When you dig deeper it's kind of similar to when you hire someone very smart from college, when you bring them to your buy side. There are a lot of training you need to do on your own team. How does your PM think about things? How are things beyond even just like the stock level, just process wise? There's a lot of things to learn. And then on the stock level, that's even harder, right? I actually personally think public market, specific stocks are one of the hardest domain knowledge to grasp because unlike some other field think of medical law. A law is a law is a law. It rarely changes right. And a medical disease is a disease. It really changes. Fundamental investing is tough. One is it's always changing. Whatever you knew as of yesterday might change based on one single news. That's number one. Number two is it's an interconnectivity. I used to say like if you can figure out the knowledge of a whole market, you can really figure out the knowledge of the whole world. Because any concept in this world has some sort of representation in the market. So that interconnectivity thing I think it's very unique to this industry as well. And lastly is it requires an understanding that flows between qualitative and quantitative. It's not just qualitative, you need to think about quantitative stuff. So I think those three characteristics makes it really hard to get domain knowledge. And personally I think domain knowledge is a piece that the horizontal model companies are not going do. Do you think they can actually figure out a way to ingest ever changing knowledge for finance, for the market? So that's number one. I think that's tough.
00:06:41 · Ying Hua
It's mostly just as a dirty work too. I just don't think they are interested in doing that dirty work. But the second thing is I think from first principle, one needs to understand limitation of language model, right? Language model, at least the bark architecture today, you know, it's next token prediction. It means, language is really a coding too. It's a coding language. It's like we put information in our language and based on that information you try to predict what is next piece of information that can likely come through. It is by definition backward looking, it's by definition pattern matching, it's by definition not true understanding, meaning it's just kind of filling the pattern. The analogy I will do is think about quant investing. All right, quant investing it's kind of like similar to that, it's just like information coded in numbers. You're trying to predict what's the next number that comes up. In this case, it's information encoded in language, you figure out what's next word that comes up. Right, so when you have that kind of market model architecture, by definition you're going to run into a few things. Number one is it's not true understanding. It's not true reasoning either. It's more just how do you give it enough information in your language in that context window, so it can do that prediction more accurately. That's all it is, right? That's number one. Number two is can never figure out things that hasn't happened before. It's a pattern matching. Quants backward looking, right? It's always backward looking. When regime change happen, when pattern changes, it cannot identify it. Same thing with LLM. If information you give up till that point, if something changes, it's not going to be able to predict that. So those are limitations. Think based on that, I think there's still a lot of holes to fill from each vertical. And I think that's kind of where we come in.