XBRL
Instead of filing financial statements as plain text or PDFs, companies use XBRL to label each number and concept. Revenue, assets, and liabilities all get specific tags that explain what the data represents.
These tags follow shared rules, which means the same type of data is labeled the same way across companies. That consistency makes automated analysis possible at scale.
XBRL does not change the numbers themselves. It changes how the information is structured, making filings easier to search, extract, and analyze.
Why It Matters
XBRL turns SEC filings into usable data instead of static documents. It saves time, reduces errors, and allows investors to compare companies more easily.
How does XBRL improve financial data analysis?
XBRL allows software to pull specific financial values directly from filings. Analysts no longer need to manually copy numbers from reports. This speeds up comparisons across companies and reporting periods. It also lowers the risk of misreading or mislabeling data.
Why does the SEC require companies to use XBRL?
The SEC uses XBRL to improve transparency and oversight. Structured data makes it easier to monitor trends, spot inconsistencies, and enforce reporting standards. It also helps make public financial data more accessible. This benefits both regulators and market participants.
What types of filings use XBRL formatting?
Most financial statements in 10-K and 10-Q filings are submitted in XBRL format. This includes balance sheets, income statements, and cash flow statements. Notes and detailed disclosures are increasingly tagged as well. Over time, the scope of XBRL reporting has expanded.
Real-World Example
An analyst wants to compare revenue growth across hundreds of companies. Instead of opening each 10-K, they use XBRL data to pull the revenue tag from every filing and analyze it in one dataset.
XBRL and FinFeedAPI
FinFeedAPI’s SEC API provides access to XBRL-tagged financial data directly from SEC filings. This allows developers and analysts to work with structured numbers instead of raw documents. It makes large-scale financial analysis faster and more consistent.