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Comment by csandstedt | Hacker News Reader

"the lack of consistency in the format (in some cases, there's at least several hundred 'tags' for the same or similar financial line item) makes any large scale data processing from EDGAR a massive undertaking."

Izyda, you are absolutely correct on the issues with XBRL. I'm the co-founder of a company called TagniFi that is working on a solution. We have a standardized dataset that makes comparing this data across companies, industries and sectors possible. We are in beta so you are welcome to check out for API for free: http://www.tagnifi.com

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This looks very cool - I will certainly sign up for the beta. I think there is real potential in this space.

The only concern - and maybe you have a solution - I have is that to my knowledge, XBRL has only been required on 10K's and 10Q's since around 2009 (I may be incorrect, 2010?). As someone involved in research with financial data, there's really no avoiding using pre 2009 data. Parsing that from raw text files is even harder than XBRL - is TagniFi able to do that as well? (I've perused your website but haven't found the answer). Unfortunately, without that capability, I'm forced to rely on services like Fact Set at least partially.

Thanks for the feedback. You are correct that the lack of history is an issue for us but we have to start somewhere and the cost to collect non-XBRL data is really high. The timing of the available history in XBRL depends on the size of the company. XBRL was phased in starting in 2009 with the largest 500 companies filing their 2010 10-Ks in XBRL. These 10-Ks generally had income statement data back to 2007 so that is where most of our data starts on the larger companies. The next largest 1,500 companies started filing in 2010 so their data generally starts in 2008. The remaining 9,000 companies started filing XBRL in 2011 so their data generally starts in 2009. This means we have 7 years of data for the largest 500 companies, 6 years of data on the next largest 1,500 companies and 5 years of data on the remaining 9,000 companies. Since the cost to go back and collect this data manually is really high we are going to focus on building the deepest datasets (footnotes and industry-specific) to offset the lack of history.

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