SEC’s proposed rule on machine-readable reporting could predict behaviour

Excerpts from speech by Scott Bauguess, Deputy Chief Economist and Deputy Director, Division of Economic and Risk Analysis, Securities and Exchange Commission, Financial Information Management (FIMA) Conference 2018

Technological advances are responsible for many recent changes in market methods and practices. Chief among them is the rise of machines in the automation of rote tasks. And also of many complicated tasks. Because human direction is not explicitly required, the analytical methods underlying the technology have given rise to the concept of machine learning. This has also fueled the notion that artificial intelligence has finally arrived.

The success of today’s new technology depends on the machine readability of decision-relevant information. And I don’t mean just for numerical data, but for all types of information. This includes narrative disclosures and analyses found in the written word. It also includes contextual information about the information, or data about the data, often referred to as “metadata.” Today’s advanced machine learning methods are able to draw incredibly valuable insights from these types of information, but only when it is made available in formats that allow for large-scale ingestion in a timely and efficient manner.

The key innovation of our developing disclosure technology is making machine accessibility invisible to the rendering of a document for human readability. This is illustrated well by a recently proposed rule that would require SEC reporting companies to file their periodic reports in Inline XBRL. Currently, filers separately report a human-readable html version of a periodic report and a machine-readable version in an eXtensible Business Reporting Language (XBRL) format. This proposed rule, if adopted, would combine the two requirements and create a single document designed to be read equally well by humans and machines.

From a machine-readability perspective, the financial statement data, footnotes, and other key information contained in an Inline-XBRL filing can be easily and automatically extracted, processed, and combined with similar data from other 10-K filings. This aggregation is possible because each of the extractable data elements or sections of textual information is tagged using definitions from a common taxonomy of reporting elements.

From a machine learning perspective, this standardized data can be combined with other relevant financial information and market participant actions to establish patterns that may warrant further inquiry. And that can ultimately lead to predictions about potential future registrant behavior. These are precisely the types of algorithms that staff in DERA are currently developing.

From a human perspective, you can see it for yourself. More than 100 companies are already voluntarily filing with the SEC using this technology. On SEC.gov these filers have an “iXBRL” label next to the html version of their 10-K filing. Click on one to see how a periodic report functions with interactive features not otherwise available from an html filing.

From an overall perspective, this is a good place to pause and remind everyone that the SEC is fundamentally committed to ensuring that all investors and market participants can access the information necessary to make informed financial decisions. But another aspect of the agency’s commitment to investor protection involves the use of sophisticated data analytics to ensure that we have insight into the market, particularly as we seek potential market misconduct.

Read the full speech

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