Summary

The mandate by the U.S. Securities and Exchange Commission (SEC) requiring filers to apply eXtensible Business Reporting Language (XBRL) tags to their financial statements has been in effect for more than three years. One of the most important ideas behind this requirement was to make it as simple as possible for investors to consume and analyze corporate financial data filed with the SEC. In the U.S., we are now past the initial hurdle of creating a workable taxonomy for XBRL tags. Corporations are moving from XBRL tagging as a project to making it just another step in the disclosure process. Yet most of the progress made to date is in capturing data; more progress still is needed to create usable tools, especially open source ones that are available to all investors. The closest we’ve come to this so far is a demonstration application that was a finalist in this year’s XBRL Challenge. Because there are still many holes on the data collection end – most companies’ earnings releases are not yet tagged – we advise companies to continue their data-tagging efforts, extending them to include earning releases and other crucial financial reports. Software providers should continue to develop open source tools, aiming for more robust, easily used applications.

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Over the past decade XBRL has advanced from a concept to a technology that underpins many day-to-day financial communications. There has been a tenfold increase in the number of companies doing in-house tagging, and they are using fewer extensions to the taxonomy, which means that there is greater comparability for investors in the reported data. One of the promises of XBRL was to enable individuals to arrange company data in any format of their choosing and include a broad array of analytic metrics for the income statement, statement of cash flows and balance sheet. This would represent a real improvement over manually rekeying data from financial statements or “scraping” information from electronic documents by copying and pasting. However, analytic applications are not yet sufficiently evolved for individuals to do this easily.

To address this issue, the consortium XBRL US established the XBRL Challenge to “encourage the development of more tools and build awareness among analysts about the wealth of data available to them.” The Challenge results are a good start, but they don’t go far enough. Despite its cost, free software should still be full-featured and user-friendly. Data aggregators such as Edgar Online and Thomson-Reuters provide readily consumable financial statement information for a fee and can do many of the things mentioned above. SavaNet offers XBRL-enabled publication and analysis software tools designed for institutional investors. It enables sell-side research departments (investment banks and brokers) to easily publish their analysts’ company or industry models so they can be integrated with their buy-side (those working for, say, investment advisors or mutual funds) portfolio managers and analyst counterparts’ models. These models incorporate analysts’ forecast earnings models, and they also have historical product line and industry data that is not available as tagged items in company reports. Nevertheless, data must still be manually entered or scraped into a spreadsheet to update a company model or create a table quantifying the differences between actual results and projections. This is time-consuming for both sell-side and buy-side analysts.

The XBRL taxonomy for US-GAAP is well-developed and comprehensive, but there is still a need for industry-specific measures (such as revenue per passenger kilometer for airlines or kilowatt hours for utilities). It would also be helpful if individuals could obtain company-specific product line data (such as specific drug revenues of a pharmaceutical company) as tagged items. Developing such industry-specific measures is probably a job best-suited for equity analysts and portfolio managers.

Assessment

Although the SEC’s XBRL mandate has made substantial strides over the past five years, much more should be done to make the millions of data elements from more than 9,000 reporting companies more easily consumable by ordinary investors and business analysts. The first two major hurdles – developing a workable taxonomy and assembling a substantive database – have been surmounted. With growing investor awareness of how tagged information can benefit them and tangible demonstrations of the many ways this information can be used, we expect increasing investments in products designed to unlock the value of this data trove. So that investors can make the best use of that data, companies in different industries should consider working closely with equity analysts and portfolio managers. These experts could help them maximize industry commonality in reporting and tagging results to achieve the highest degree of comparability among companies.