Theory of Blocks

Years ago I posed a hypothesis that financial statements could be broken down into blocks of information.  After significant testing, between 2015 and about 2018, I have reached the conclusion that my hypothesis was correct and now I have my theory of blocks.

Here are many of the best details related to my testing, poking, and prodding and prototypes that I constructed to test my hypothesis:

When interacting with a financial report, sometimes you want to interact with the lowest level of detail witch is the "fact".  A fact is a single piece of information.  Other times you want to interact with larger pieces of a report such as the "balance sheet" and you want all the facts on the balance sheet to work with.

Facts are not "free floating in space".  Every fact exists within some sort of structure which is an assembly of several facts.  For example, a balance sheet is a structure which contains an assembly of facts.

But XBRL-based reports don't have the notion of "balance sheet".  XBRL has technical oriented artifacts called a "network" and it also has artifacts called a "hypercube".  You use technical artifacts, networks and hypercubes, to build the a representation of a balance sheet.  But the problem with networks and hypercubes is that you can organize the logical artifacts differently to create the same logical artifact.  Basically, reports don't always use the same technical artifacts in the same ways to build logical pieces.  Because of this, it is hard to use the technical artifacts to find the logical pieces.

This is where the BLOCK comes in.  The block is a logical artifact.  A block is a "chunk" of information.  That chuck of information has logical characteristics.  You can use those logical characteristics to identify every block of information within a financial report.

Why is this important?  Well, sometimes a "fact" is to small a piece to work with and "networks" or "hypercubes" are too big to work with.  A block is "just right".

I pointed a software application to a list of 6,023 XBRL-based 10-K financial reports and this is what I found:
  • Total reports: 6,023
  • Total facts reported in all those reports: 8,532,275
  • Average facts per report: 1,416
  • Total networks in all reports: 462,786
  • Average networks per report: 77
  • Total blocks in all reports: 754,430 
  • Average number of blocks per report: 125
  • Average number of blocks per network: 1.6
  • Average number of facts per network: 18
  • Average number of facts per block: 11
  • Of the 754,430 blocks there were: (Here is what a set of blocks in a network looks like; that is 6 blocks, the first is a Set and the other 5 are Roll Ups)
    • Text blocks: 407,392 (54%) are text blocks (Level 1 Notes, Level 2 Policies, Level 3 Disclosures)
    • Sets: 181,063 (24%) are sets (or hierarchies, no mathematical computations)
    • Roll ups: 120,708 (16%) are roll ups
    • Roll forwards: 37,721 (5%) are roll forwards
    • All other logical patterns of blocks: 7,546 (1%) are some other pattern
Why could I reliably grab all of the above information?  Because the information is represented using the global standard XBRL technical syntax and all 6,023 reports were prepared consistent with the XBRL technical specification.  Software can read that XBRL technical syntax and identify all those pieces. The blocks follow specific XBRL technical syntax patterns. If you can identify those patterns, you can identify the block and the logical pattern of that block.

Now, you don't know what the block is disclosing; but see the Theory of Disclosures and Disclosure Mechanics which is how you do that.

The graphic below is an example of a block of information.


Imagine what you can do with a rock solid mechanism for creating, maintaining, and extracting information from a logical information block.  Think Lego Blocks.

Additional Information:

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