The US GAAP XBRL Taxonomy is more than just something used for creating XBRL-based financial reports for submission to the SEC. Both public and private companies can benefit from the US GAAP XBRL Taxonomy.
There is a difference between alternatives and ambiguity. I look at this difference in the resource Differentiating Alternatives from Ambiguity. I also point out how the structured nature of XBRL-based financial reports helps machines assist humans in increasing clarity, logical coherence, consistency, and reducing ambiguity.
In the financial reporting world we can live with clear, known alternatives or options. Professional accountants use their judgment to pick and choose amongst those known alternatives or options; applying what they consider the best alternative given all available alternatives or options. Exercising professional judgment is and should be part of financial reporting.
What financial reporting cannot live with are diverse interpretations which result in different results based on the exact same facts due to standard definitions and principles that are vague, inconsistent, logically incoherent, and ambiguous. A different understanding of the exact same facts is not judgement; it is lack of clarity, lack of consistency, lack of coherence, and ambiguity. You can have different interpretations of facts, that is judgment.
The vagueness, inconsistencies, logically incoherent, and ambiguities in the definitions and principles used in financial reporting standards are not alternatives or options; they are unintended errors in the standards.
Working with and creating XBRL-based financial reports of public companies revealed the following:
- ASC inconsistencies and conflicts in segmentation of an economic entity.
- Lots of variability in where the line item Income (loss) from equity method investments is reporting on the income statement.
- Two different ways to compute net cash flow.
Are these conscious alternatives designed into US GAAP or the result of errors in financial reporting standards? Decide for yourself.
Machine readable ontologies such as the US GAAP XBRL Taxonomy will highly likely result in higher quality financial reporting standards because the machines can help humans create clear, consistent, logically coherent, and unambiguous standards.