- First is to actually evaluate whether these LLMs have any intelligence around investing. If you actually give them all the data, can they do well? Can they beat the market? I'm not sure, we're testing that.
- My thesis is that they will actually beat the market (I know a lot of you will disagree). If that's the case, how can we invest a lot of resources in building the best harness, tool calling, etc to enable these models to invest.
- We've built a local vector database with every SEC filing over the last few years. And we've built a tool call on top of that to allow these LLMs to read and query sec filings.
- Have done the same for a lot of other data sources, just giving the LLM access to them and allowing it to spend some time to actually research.
I actually think it's doing better now. It was just too stubborn to exit its position for the first few months. It did that, and put some money into MSFT/JPM recently.
This is an experiment to see how well can LLMs invest in the market through a lot of research. We give them tool calls to access every financial dataset that exists online, and also some money to manage. And we then see how well they do.
ML driven is. LLM driven is still nascent, especially the idea that as large language models get more advanced, can they research and invest like a fund manager.
Founder here: YC actually had an "AI hedge fund" idea in one of their recent "request for startups" post. We've been working on evaluating the capabilities of frontier models in investing money in the stock market. Results are encouraging and we're not doubling down on it.
Working on building an investment assistant backed by real time data. ChatGPT and Perplexity finance are amazing, but all of them are based on web search data only, which is a big limitation in finance since realtime data is important.
We have an agent that has access to almost every data point you can think of in the stock market (as much as we can get), which gets leveraged before answering.
And we also figured out ways to build amazing charts in between answer snippets, which looks very cool. Investors are usually very visual.
I work at the intersection of AI and investing, and I'm really amazed at the ability of this model to build spreadsheets.
I gave it a few tools to access sec filings (and a small local vector database), and it's generating full fledged spreadsheets with valid, real time data. Analysts in wallstreet are going to get really empowered, but for the first time, I'm really glad that retail investors are also getting these models.