Using XBRL Data For Analysis
The XBRL standard has been used by the US SEC for over a decade. A vast pool of corporate data has thus been available to analysts, investors, and other stakeholders for quite some time. However, as the quantum of data being generated by companies has grown exponentially in recent years, tools and solutions that facilitate its analysis have become very critical. XBRL’s standardized open data structure lends itself well to comprehensive and granular analysis of corporate data, allowing users to combine and query facts in meaningful ways.
There is no doubt about the fact that better data leads to better decisions. The financial statements and footnotes of all public companies can be directly obtained from them in XBRL, entirely bypassing third-party data providers. XBRL, therefore, helps facilitate analysis by not just investors and analysts, but also regulators and credit rating agencies. It even helps management run the business. It becomes quicker and more economical to pull out, sort, and compare financial data across companies for investors analyzing large amounts of data.
The year 2021 saw over 814,000 EDGAR filings with the US SEC. While every filing is different, some can be over 100 pages long. As you may have guessed, not all that information is available in a structured format like XBRL. Navigating through and evaluating this unstructured data can be an extremely laborious and expensive exercise for investors. Therefore, we have begun to see a regulatory push toward structured XBRL data for more sections of corporate reports that can provide investors and other relevant stakeholders with data in a usable format.
The versatility of XBRL allows it to be an excellent tool for analytics enabling analysts to examine the implications of effective tax rates, assess the real impact of changes in the accounting treatment of intangible assets or even scrutinize the tax implications of the Coronavirus Aid, Relief, and Economic Security (CARES) Act and its effect on corporate liquidity. To facilitate these analyses, XBRL US hosts a vast pool of resources that can be utilized by anyone. Let’s take a quick look at these in our next section.
Analysis Tools From XBRL US
XBRL US has a very vibrant data community that aids analysts, developers, and BI professionals use a wide range of assets that include:
1) The XBRL Application Programming Interface (API)
2) XULE, an open-source XBRL processor, and
3) Other staff and community-based resources (Google Sheets and Office 365 templates)
The XBRL US Database is available for anyone to explore using their API, SQL or AWS database snapshots. It contains over 10 years of as-reported data from public companies filing with the US SEC and the FERC and is continually kept up-to-date by the respective regulators. XBRL data from other reporting domains are set to be added at a later date.
XULE makes it possible to query XBRL reports and taxonomies using a XULE processor. It can be used to automate data collection and create proprietary, normalized frameworks. This can give users timely, comparative time series for precise analysis. It is also used to generate business rules and has been used to validate SEC filings using DQC rules, which are published in a XULE format.
Additional resources include Google Sheet templates that facilitate comparison of financials of US GAAP and IFRS filers featuring a customizable Taxonomy Map, extraction of margin, revenue, and income elements in table and graphical display, and even a classroom learning activity on building and using simple API queries for data extraction.
Excel add-ons are available for filtering a single report to review a specific SEC statement, disclosure, or FERC schedule and even facilitating the comparison of financial schedules, statements, or disclosures for 3 companies at a time.
In addition to this, codebases for launching scripts using R or Python are also available to use for gathering all available data points for a query. A wide gamut of additional tools and resources is available to use here.
With so many resources on hand, what is the future of analytics with XBRL? Let’s take a brief look at this in our last and final section.
The Future Of XBRL Data Analysis
While structured data for corporate financial disclosures has been around for over a decade, it is only in the last few years that we have seen alternative data gain more currency with investors and analysts alike. Alternative data comprises data that is extracted from non-traditional sources and generally lends an added advantage to those perusing it to make informed investment decisions. It may include ESG data, non-GAAP financial measures and other types of KPIs. Such data is generally unstructured and fairly new in the sense that it is only now being reported in a meaningful way by companies. Therefore, it is tough to find good quality alternative data and currently involves a lot of manual effort for those people who want to access and evaluate it.
The exponential pace at which both traditional and alternative data continue to grow gives us all the more reason to standardize, store and distribute it equitably to those who want it. The democratization of such data can produce positive outcomes for all of society. The XBRL standard is very well placed to facilitate this with the US SEC inviting comments on climate change disclosures to bring them under the ambit of structured data. Information about proxy voting by investment funds is also seeing a move to structured data via amendments to form N-PX. Even just block tagging narrative disclosures can enable investors and analysts to extract and compare non-structured disclosures like earnings reports and executive compensation.
At IRIS CARBON®, we are steadily moving towards this future by leveraging our very own Data Consumption Platform (DCP) that will facilitate smart decisions through smart analytics. In addition to having the ability to compare companies across sectors and geographies, it provides users with the ability to create custom dashboards and visualize information for added clarity.
Image 1.1 gives an example of a custom dashboard that enables the comparison of the financial indicators of two companies in different parts of the world using different reporting taxonomies. Exxon Mobil is an American multinational oil and gas company while Nuclearelectrica is a Romanian nuclear energy company.
Another advantage of advanced analytics is the ability to create custom data models where users can pick companies of their choice using a simple Excel add-in, and perform peer benchmarking and cross-industry analysis using normalized and reported data. Image 1.2 illustrates this below.
Image 1.2.: Build your own data models with a simple Excel add-in
Gradually, what is alternative data today, can become traditional data tomorrow. With XBRL, it is possible to bring a lot more transparency and comparability to corporate disclosures not just across different sectors, but across different geographies as well. As a global standard, XBRL has the power to unify and standardize all of this information and make it available to end-users
Its inclusion in alternative data such as ESG, non-financial GAAP measures, and various other KPIs is transforming the landscape of corporate data analytics and empowering stakeholders to make critical investment and divestment decisions using more nuance and foresight.
