It could be news parsing, where it can turn news article text into data points that an investment model could imply market signals from.

Something similar could happen with transcripts of company investor calls, or any other text/audio companies put out. With the right prompt you can turn text into signals, the trick would be getting that prompt right (and prompt engineering to limit hallucinations as much as possible).

all those data sources are trailing though. Most intentionally try to (over simplistically) explain; few to none predict.

Sentiment analysis is AI now?

This is a classic example of "AI is whatever hasn't been done yet." https://en.wikipedia.org/wiki/AI_effect

Sentiment analysis has always been done with ML models. It's one of those problems that can really only be solved with data.

Always was

AI is bullish now

> the trick would be getting that prompt right (and prompt engineering to limit hallucinations as much as possible).

Why do you have to get it right? Don't you just have to get it the same as everyone else, slightly faster?

That is the definition of ‘right’ in a trading context.

Or you can just not use ai and build your own sentiment analysis metric that is actually going to work. E.g. maybe you make a word cloud of a bunch of recent news articles, you identify certain candidate words as positive sentiment or negative sentiment. Now you can score any new article by counting pattern matches with either list. Not really much initial work to set up, and you probably get much higher quality results than what hallucinating ai would get you, if you happened to find that magic prompt that is.

Could see it being used for data analysis when doing sourcing, plenty of AI-powered datascience notebooks out there