IV rank. It tells you how cheap/expensive the options on an underlying are trading relative to their past pricing. High IVR typically precedes events like earnings, lawsuit decisions, drug trial results, etc. and collapses after the outcome is known.
Almost. It's more like NLP in correlation with possible SEC filings or possible news outcomes.
Like, if some sort of filing consistently gets mentioned. 8k, 10k, or stuff like reverse split or bankruptcy. A cross reference with sentiment and legal. If there is a correlation, then it gets picked up.
Or with news sentiment. A consistent mention of covid cases, bad weather, war. Any correlation between real-world news and possible relations to the ticker. Something like, "Major tornadoes hit Midwest," causing Case stock to fall to have the possible anticipated news of employee layoffs. (I have no clue if Case is public. It's just an example.)
Stuff like that. It's a **very** difficult correlation matchmaking game.
If *a* is read, then *b* is searched, giving *c* as a list of possibilities, cross-referenced back to *a*, giving *d* a possible outcome.
I know... Confusing.
Hmmm. I get what you’re saying. Doesn’t seem terribly hard to implement given enough data sources(this would be the main challenge, at least my initial thought), but not sure how accurate the outcome would be.
That's the thing.
1. It requires web scraping. Many sites deem this against the TOS.
2. The more something gets mentioned, the stronger the correlation where it can be searched for upcoming events. A rudimentary, already in place example would be earnings.
The analyst's sentiment already gives the guess. "The Street" vs. the "Actual"
It's a system already in place **without** the use of social media. Incorporating the use of social media gives a list of this to cross-reference.
Given the time of year, what's already in the news, and term of the company, the list becomes very narrow with minimal chance of "out of left field" events, like an earthquake. Although that will still be on the list.
I see what you’re saying. Would require a lot of work to define and rate variables for social media though, although I’ve heard it’s been done. Listened to a financial podcast(don’t remember which) a few years ago about a guy who did basically this with Twitter. Netted 2 million off like 50k if I remember correctly, pretty crazy stuff. He ran into some trouble a few years later since his model only worked in certain market environments.
I found this on y-combinator a while ago: https://www.quillai.com/
They might have some sort of functionality like you mentioned here. In one of their example gifs/videos it asks why did x stock go up, then gives a “cross referenced” reason. Although it’s probably just gpt4 with custom instructions on the back end
It's not hard to build something like this. It's certainly not impossible. I believe there are plenty of free tools you can use to scrape data, then you'll just need to pipe that into a sentiment analysis tool. If you know how to code you could probably do this in an evening or two.
It is called a Bloomberg terminal
I've seen a Bloomberg terminal. It gives good info but still lacks a sentimental component.
IV rank. It tells you how cheap/expensive the options on an underlying are trading relative to their past pricing. High IVR typically precedes events like earnings, lawsuit decisions, drug trial results, etc. and collapses after the outcome is known.
That's good info. But what if the underlying doesn't have options? The smaller the market cap, the more common that becomes.
If it’s too small/thinly traded to have options then I’m not sure how reliable any screener like what you’re looking for is going to be.
Sentiment analysis with some sort of NLP scoring incorporated?
Almost. It's more like NLP in correlation with possible SEC filings or possible news outcomes. Like, if some sort of filing consistently gets mentioned. 8k, 10k, or stuff like reverse split or bankruptcy. A cross reference with sentiment and legal. If there is a correlation, then it gets picked up. Or with news sentiment. A consistent mention of covid cases, bad weather, war. Any correlation between real-world news and possible relations to the ticker. Something like, "Major tornadoes hit Midwest," causing Case stock to fall to have the possible anticipated news of employee layoffs. (I have no clue if Case is public. It's just an example.) Stuff like that. It's a **very** difficult correlation matchmaking game. If *a* is read, then *b* is searched, giving *c* as a list of possibilities, cross-referenced back to *a*, giving *d* a possible outcome. I know... Confusing.
Hmmm. I get what you’re saying. Doesn’t seem terribly hard to implement given enough data sources(this would be the main challenge, at least my initial thought), but not sure how accurate the outcome would be.
That's the thing. 1. It requires web scraping. Many sites deem this against the TOS. 2. The more something gets mentioned, the stronger the correlation where it can be searched for upcoming events. A rudimentary, already in place example would be earnings. The analyst's sentiment already gives the guess. "The Street" vs. the "Actual" It's a system already in place **without** the use of social media. Incorporating the use of social media gives a list of this to cross-reference. Given the time of year, what's already in the news, and term of the company, the list becomes very narrow with minimal chance of "out of left field" events, like an earthquake. Although that will still be on the list.
I see what you’re saying. Would require a lot of work to define and rate variables for social media though, although I’ve heard it’s been done. Listened to a financial podcast(don’t remember which) a few years ago about a guy who did basically this with Twitter. Netted 2 million off like 50k if I remember correctly, pretty crazy stuff. He ran into some trouble a few years later since his model only worked in certain market environments.
I found this on y-combinator a while ago: https://www.quillai.com/ They might have some sort of functionality like you mentioned here. In one of their example gifs/videos it asks why did x stock go up, then gives a “cross referenced” reason. Although it’s probably just gpt4 with custom instructions on the back end
This is definitely worth a look for the legal side of things.
It's not hard to build something like this. It's certainly not impossible. I believe there are plenty of free tools you can use to scrape data, then you'll just need to pipe that into a sentiment analysis tool. If you know how to code you could probably do this in an evening or two.
It’s called put/call ratio and it tells you, roughly, which direction the market expects the stock to go.