Browse our growing collection of case studies, white papers and resources documenting the significance of the business reporting data standard, or check out one of our Infographics to get a clear picture of the hows and whys of the financial data standard.
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Case Studies & White Papers
|Review case studies, white papers and research from XBRL US and others. Here’s a sample:
BDO USA, LLP, the US member firm of BDO International, a global accounting network, was asked to evaluate the fair value of the intangible assets acquired as part of a business combination. A primary factor in this type of evaluation is determining the reasonableness of the underlying projections used to support the intangible asset values. These projections include various forward-looking and highly subjective components such as future revenue growth, profitability and capital expenditure requirements. To evaluate the reasonableness of these projections, BDO often looks at data from similar companies within the same industry. For example, if the client is projecting 8% revenue growth and the range from peer companies is 2% to 10%, BDO could reach a level of comfort that the projections are within a reasonable range.
Before the availability of structured data, analysis was performed either by purchasing expensive commercial datasets of corporate financials for peer companies, or a more likely scenario, by pulling the pertinent SEC filings for each peer company and manually extracting the data needed to calculate comparable growth rates and ratios. The process of extracting about 10 data points for 40-50 companies was not only time consuming, it often was prone to errors due to its manual nature.
The traditional method required the following steps: 1) finding all ticker symbols for certain SIC codes, 2) performing an individual lookup of each ticker symbol on www.sec.gov, 3) locating and converting amounts needed into the same scale, 4) rekeying the data into Excel, and 5) performing the analysis, determining values for peer companies.
Under the traditional method, substantially all the effort was in gathering the data. Once the data was available, the actual analysis could be performed relatively quickly.
The ability to extract XBRL data using an online tool dramatically reduced the time spent completing the analysis from a few hours to less than one. BDO used an online XBRL tool that extracted normalized XBRL data directly into Excel. The tool generated the data in a manner that allowed for immediate analysis and easier comparisons, for example converting units (e.g., thousands versus millions) and standardizing taxonomy concepts. Commercial, non-XBRL datasets also provide this kind of standardization but can be significantly more expensive, use proprietary taxonomies and can be substantially less timely. With “ready to use” data from the XBRL tool, no further standardization or editing of the raw data was required of BDO. Furthermore, BDO was able to quickly verify the data using automatically generated hyperlinks linked directing to the sec.gov filings.
XBRL data helped BDO get the big picture faster, reduced the work involved in data collection and gave BDO greater confidence in the accuracy of the data generated. In addition, audit quality is improved as XBRL allows for larger data sets with more companies than would normally be utilized in a traditional manually generated analysis.
|Get the big picture on selected topics with one of our standardized financial data infographics.
Establishing a framework to standardize, and automate the processing of, corporate sustainability data is imperative to provide needed information to investors, researchers and policy setters who need access to timely, consistent, accurate data for decision-making.
Auditors evaluate the reasonableness of client projections such as future revenue growth, profitability and capital expenditure requirements. To evaluate the reasonableness of these projections, BDO USA, LLP often looks at data from similar companies within the same industry. XBRL allowed them to perform a more robust analysis with more companies over a significantly shorter time period.