111–116 of 116 posts
Rather than finding patterns in historical numbers, LLM can help quantify the current world in ways not possible before. This opens up a new world of finding new secrets.
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And there is your arbitrage opportunity. If you can model how analysts will react to a particular timeseries, even if it was random until that point, you have some information about the future. It'd be a good question to figure out if there is a consensus or majority about how to interpret patterns among the people making decisions or writing quant algos, that's something one could use.
That's an interesting take! You show them meaningless data, in order to extract their overall market sentiment, and use _that_ to inform your investing strategy?
We are working on a project for a client which functions as an analysis tool for stocks using LLMs. Ingesting 10ks, presentations, news, etc. and doing comparative analysis and other reports. It works great, but one of the things we have learned (and it makes sense) is that traceability of the information for financial professionals is very important - where did the facts and information come from in what the AI is p…
We build multimodal search engine on day-to-day basis. We recently launched video documents search engine. I made a Show HN [0] post about ingesting Mutual Fund Risk/Return summary data (485BPOS, 497) and searching it with AI search. We are able to pinpoint to exact term on given page. It is fairly easy for us to ingest 10K, 10Q, 8K and other forms.
You can try out demo for finance-application at https://finance-demo.joyspace.ai.
Our search engine can be used to build RAG pipelines that further minimizes hallucinations for your LLM model.
Happy to answer any questions around this and around search engine.
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You can tell a stock time series by certain characteristics: 1) There are more jumps down than up. (Maybe not in Pharma, but in general). If there's a gap up, chances are it's on earnings day. 2) Upward movements tend to be accompanied by lower volatility, and downwards by higher. 3) There's a lot of nothing-happened days, and a lot more large jumps than you'd expect in a random walk. I've also spent a bunch of time…
Perhaps it would be easy to code a pseudo trading sequence given a model of the psychological state of the agents in the trading system
A lot of words for not bringing much new content to the discussion. I think the most interesting application of LLMs in Finance are (1) synthetic data models for data cleansing, (2) journal management, (3) anomaly tracking, (4) critiquing investments All of this should be done by professionals and nothing is "retail" ready.
One other area which I think is potentially quite interesting is using LLMs to help in deciphering "Fed-speak". Eg JP Morgan built an LLM to try to predict the impact on interest rate markets of speeches by various central bank policymakers.
Although my simple test didn't prove anything, I'm 100% sure there is value here and if I had more time I would attempt to exploit it. I collect data from financial social platforms that assign bearish/neutral/bullish ratings and there are highly correlated markers of impending market movements when certain conditions are met. I'm sure fed speeches can be used in the same way for indicators.