Digital Financial Reporting

This information is taken from the TopQuadrant web site, Ontology Development page. I am still trying to understand some of this information.

More people understand CSV files (comma separated values), relational databases, Microsoft Excel spreadsheets, and perhaps even XML for approaches to storing and using information better than they might understand an ontology. (See this blog post if you are trying to understand what an ontology is.)

Ontologies are powering the next generation of software applications. Ontologies are active models of information that are like and unlike other information modeling approaches you may be more familiar with:

• Like databases, ontologies are used by software applications at run time to provide information to the users of the software applications. HOWEVER, unlike databases, relationships in the ontologies are ‘first-class’ constructs, have rich explicit semantics and are used locally or globally because the nature of the information and the syntax of the information can be exchanged between business systems (as opposed to use being restricted to your internal database because the syntax is proprietary or the semantics are hard coded into the software)
• Like object models, ontologies describe classes and attributes (properties). HOWEVER, unlike object models, ontologies are set-based. (I don't understand what this means, I will try and figure this out...watch for a subsequent post.)
• Like business rules, ontologies encode business rules which are basically some formal and implementable expression of a user requirement; HOWEVER, unlike business rules, ontologies organize rules using class structures (they are formally grouped), they are written using a global standard format, which means that the business rules can be effectively exchange between business systems.
• Like XML schemas, ontologies are native to the web and can be serialized in XML; HOWEVER, unlike XML schemas, ontologies are graphs (not trees), can be used for reasoning, an infinite number of explicit relations can be expressed (rather than the one implicit relation of an XML schema).

XBRL is closer to something like RDF than it is to XML Schema. In fact, that is why the elements within an XBRL taxonomy are "flat".  XBRL tried to overcome the limitations of XML Schema by using only parts of it.  This is why tuples in XBRL are so bad for extensibility; they basically go back to using XML Schema.

The XBRL definition relation is similar to RDF-type expressions of information.  Definition relations are somewhat standard in that they use XLink.  But, it does not seem that XLink is catching on.

This webcast is an excellent introduction to SKOS, Simple Knowledge Organization System. This is part of resource made available by the W3C for SKOS, which you can get to here.

This is a SKOS System Organization Primer.

The most important thing which I got out of this webcast was a better understanding exactly what SKOS brings to the table and the difference between a controlled vocabulary, a thesaurus, a taxonomy, an ontology:

• Controlled vocabulary. Basically a set of standard terms.  For example, "Yes" and "No" is a controlled vocabulary.  May seem odd; but one could also use "yes" and "no"; "true" and "false"; "yeah" and "nay"; you get the point.  A listing of postal codes for each of the states in the United States is a controlled vocabulary.
• Taxonomy. A taxonomy adds the notion of a hierarchy between the members of a controlled vocabulary.  For example, the terms "horse" and "cat" and "dog" are all types of "mammals".
• Thesaurus.  A thesaurus provides a specific type of relationship, a similar term, a broader term, or a narrower term.
• Ontology. An ontology allows you to define your own types of relationships; specific, explicit types of relationships rather than general "parent-child" type relationships.

So SKOS is the global standard way for defining controlled vocabularies, taxonomies, thesauri.  SKOS is also a standard way to build other ontologies, it seems.  SKOS leverages the standards RDF and OWL.  So basically, SKOS is a standard way of using the RDF and OWL standard to extend the SKOS framework.  So, SKOS is extensible in a controlled manner.

Why is this important for XBRL?  Two reasons.  First, this clearly shows the weakness of things like the "parent-child" relationship in the XBRL presentation relations.  "Parent-child" has no real meaning.  But, people building taxonomies (a) imply meaning, (b) imply meaning differently thus creating inconsistencies, (c) don't communicate that meaning to others using the XBRL taxonomy.  This is true of both the IFRS and US GAAP financial reporting taxonomies.

Second, the XBRL definition relations DO provide a mechanism for communicating meaning of relations.  For example, the XBRL Dimensions specification does exactly this, defining "all", "not-all", "hypercube-dimension", "dimension-domain", "domain-member" relations.  Now, these are more technically oriented-type relations; but they are explicit.  Other explicit type relations can be defined.  However, is XBRL the best mechanism for articulating more relationships?  Is SKOS better? Or OWL?

Project10X's Semantic Wave Report: Industry Roadmap to Web 3.0 & Multibillion Dollar Market Opportunities (this is a 34 page executive summary) lays out a vision for what "the web" will become/is becoming.

Things like XBRL, the US GAAP and IFRS taxonomies, and the move by the U.S. Securities and Exchange Commission and others to XBRL-based digital financial reports contributes to moving toward the Project10X vision. However, we are only getting started.

The Project10X executive summary, which I strongly encourage you to read, talks about things like "semantic user experience" and "semantic applications" and "semantic infrastructure". That summary and this Prezi presentation Semantics Overview provides graphic "From Searching to Knowing - Spectrum of Knowledge Representation and Reasoning Capabilities".  The graphic is shown below (click on it to see a larger version).  On the graphic I have made some annotations specific to XBRL, the US GAAP and IFRS taxonomies, and how those taxonomies will change, moving from "weak semantics" towards "strong semantics":

If you look at the graphic and notice terms such as semantics, model, ER model, topic map, RDF, UML, OWL, conceptual model, syntactic interoperability, semantic interoperability, and such that I have been mentioning on this blog for a number of years.

I could not have told you the difference between syntax and semantics seven years ago.  But I learned. Most people have a vision of the US GAAP and IFRS taxonomy as little more than a glossary or list of terms.  That will change over time.

Back to terms like "semantic applications". The Prezi uses the term "Smart Applications" and "Smart Data".  They describe this as:

• Knowledge is baked into the application
• New knowledge can be inferred
• Agility to adapt to ever-changing conditions
• Semi-automated data integration
• Machine intelligence

Maybe these are just buzz words, people trying to communicate ideas which are hard to explain.  But, the disclosure management systems being built will work this way.

There will be no magic involved here. The key to this semantic technology is the representation of knowledge in forms which both people and computers can understand.  Knowledge about how to do things, knowledge about something, etc.  Again, no magic.

In that chart above, my knowledge and imagination can only get me about half way up that graphic, about to the area of "First Order Logic".  But, I am learning more and more by going down the right path, realizing that we are building models rather than "tagging financial information".  Others are also realizing that "tagging" is not what should be going on.

Web 3.0 is not only inevitable, it is imminent. Are you doing the right things to prepare?

Integrated reporting seems to be gaining more and more traction, particularly in Europe. One major proponent of integrated reporting is the International Integrated Reporting Council (IIRC). The IIRC states their purpose on their web site as:

The International Integrated Reporting Council (IIRC) is a global coalition of regulators, investors, companies, standard setters, the accounting profession and NGOs. Together, this coalition shares the view that communication about businesses’ value creation should be the next step in the evolution of corporate reporting.

As I understand it, the IIRC is in the process of getting an XBRL taxonomy acknowledged by XBRL International. It seems that the XBRL taxonomy has to do with the "integrated scorecard".

Here is additional information related to integrated reporting:

• Executive overview.  This overview which is about 18 pages long explains integrated reporting very well.
• Integrated Reporting: The Integrated Scoreboard (IS-FESG) and its XBRL Taxonomy. This 128 page document provides additional details and explains the XBRL taxonomy being created.
• Integrated Reporting Taxonomy: Extending IFRS & COREP.  This is a presentation made at XBRL Europe recently.

In a prior post I covered my analysis of 8289 SEC XBRL financial filings for core financial integrity.

This post is an analysis of the Dow 30 and Fortune 100 for the same core financial integrity.  You can download the raw data here in Excel.

The summary of the analysis is this: (Note that the core financial integrity relates to the existence of concepts for: assets, liabilities and equity, equity, net cash flow, and net income (loss); I also checked to see if the balance sheets balance).

• Dow.  Of the 150 total possible data points (30 entities x 5 data points); 149 were discernible, only 1 was not. All the balance sheets balanced. The one data point for which an extension concept was created was net cash flow for General Electric.  It is likely the case that General Electric chose to create an extension concept because of an issue related to discontinued operations in the definition of net cash flow; so they created an extension concept.  (This issue was corrected in the 2013 taxonomy).  However, there were two other DOW companies who were in the same situation as General electric (i.e. had cash flows from discontinued operations) and they made the choice to use the existing concept to express net cash flow.  Further, of the total of 8289 reporting entities analyzed (the complete set), 724 (or 96%) of a total of 760 filers where also in the same situation and chose NOT to create an extension concept.  The remaining 36  (which is 4%) chose to create an extension concept as General Electric did. So General Electric is not incorrect in what they did, but it seems that they would not have been incorrect to follow the path of the 2 other DOW companies and the 724 who chose to use the existing US GAAP Taxonomy concept.
• Fortune 100.  Of the 500 possible data points (100 entities x 5 data points); 494 were discernible, only 6 were not. All the balance sheets balanced. Like the DOW, there were similar issues with net cash flow extension concepts (4 filers created extensions).  Another issue related to extending "Liabilities and Equity" and an issue related to extending Equity.  You can take a look at the Excel comments I provided.  See if you think the modeling of these concepts was appropriate.

Per what I understand about the 2013 US GAAP Taxonomy (which was just released today by the FASB); there is now no reason for any of the issues related to the DOW or Fortune 100 to be issues in the future. The issues relating to the lack of clarity as to how equity and liabilities and equity when a filer has member equity are now clear in the 2013 taxonomy.  Same with the definition of net cash flow, that has been cleaned up.  As such, these 5 data points for the DOW and Fortune 100 should all be resolved and therefore all the DOW and Fortune 100 filings should have zero issues with regard to these five core financial integrity checks which I do.

We shall see what happens in February.