Negated XBRL Labels
In HData, importing the Excel Upload form automatically tags the datapoint with the correct XBRL fact, based on the coordinates of the value in the Excel Upload form. Further, the import process produces the necessary files for submitting data to FERC. The imported values are validated against the FERC taxonomy validation rules. These files are called instance documents and are ultimately submitted to FERC. The instance documents also act as the basis for rendering the FERC Form in its human-readable form.
Why does this matter?
- There are differences between filing vendors and how they choose to manage these pieces of data.
- HData does not flip signs; instead we allow the FERC XBRL taxonomy negation to dictate which values will be negated, as intended.
More Details
HData’s XBRL tagging follows the FERC’s taxonomy rules and takes into account the proper “weight” or “sign” of each account included in the calculation as defined by the taxonomy. We do not adjust the sign of any of your values. XBRL's negation mechanism allows for an accurate representation of negative values in financial reports by using special attributes or indicators to differentiate them from positive values. The use of negation ensures that data consumers, such as financial analysts or regulatory bodies, can accurately interpret the reported financial information, distinguishing between positive and negative values. This means the user must review the true value being reported as opposed to the way the value is presented in the rendering. This enables consistent and machine-readable communication of financial data, which is crucial for transparency, analysis, and compliance in the business and financial reporting landscape.
—->In this example you can see how the negation is used to “flip” the sign of a rendered amount.
Additional Questions? Let Us Know
Hopefully this article provided the answers you were looking for on Negated XBRL Labels within the HData platform. If you still need help, please reach out to us at support@hdata.us.