Comment by defrost | Hacker News Reader

Comment by defrost | Hacker News Reader

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EDGAR filings (structured text) is an area unto itself, I see you've limited yourself to quarterlies.

Across any market area (eg: mineral resources) there are thousands of documents released daily across multiple exchanges (via EDGAR, SEDAR, etc) ranging from two line advisories, to 4,000 page technical reports on projects | acquisitions, alongside the usual quarterly | yearly annual reports, etc.

There's plenty to do parsing common forms for generic changes (board members, board member share changes, etc) and market regime specifics (exploration property aquisition) and trends (series of related aquisitions) for those that like the weeds.

Some might argue that 'understanding' these patterns lead the changes in stock price movements, and give insight wrt weathering short term changes for longer term returns.

This is a very insightful remark, thank you.

I focused on 10-Qs for the EDGAR filings module as you rightly pointed out - it seemed to be a good balance between implicit information and usefulness of the data. TBH I didn't actually investigate the other (many) patterns.

Having said that, I have really enjoyed Kai Wu's research from Sparkline Capital (https://www.sparklinecapital.com/), especially his extraction of the innovation factor from EDGAR filing texts. He's appeared in numerous podcasts, and they have all been super useful to listen to. Maybe someday when I re-investigate EDGAR filings and go further, I might target these signals you talk about here.

You're welcome.

14+ years back a small group of West Australians put together what became

https://www.spglobal.com/marketintelligence/en/campaigns/met...

which was based upon integrating (GIS and regular DB) every daily mineral lease record across the accessable globe together with every publicly filed document across the relevant stock markets (AU, TSX, South Africa, London, etc) using (and updating|refing) templated patterns that appear in various classes of forms .. I would assume that territory has been revisited with better ML techniques.

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