Why Verified and Adjusted XBRL is the Best Choice for Company Analysis

If you read my last post Do Extensions make XBRL Unusable out of the Box?,  you will know that using XBRL without adjusting the tagging could well be calamitous.

So what are your options? Well in the post before that Which Source of Company Financial Data should I use?, I set out the options for finding the best source for comparative analysis. Lets re-iterate them here but I'm also gonna add one further choice.

1. Lifting values straight from the Financial Reports filed by a company
2. Data Vendor - Bloomberg, Thomson Reuters, Capital IQ, FactSet etc
3. XBRL (unadjusted)
4. Verified and Adjusted XBRL (VAXBRL)

The first choice is only an option if you have ridiculous amounts of time. The second if you have ridiculous amounts of cash and you are willing to take the risk the data is completely error free and has been interpreted correctly. Vendor data by its nature has been handled by a third party so really needs to be verified if it is to form the basis of an expensive transaction. This is why it is never used as the primary source for in-depth company analysis in critical departments such as M&A.

My previous post explains the danger of using neat XBRL. Over half the Dow Jones 30 companies had used extensions in a way that could lead to significant errors in your analysis if you just plugged the raw XBRL into your model. Only 20% of companies had no extensions on the face of their Primary Financial Statements. And if the trend over the last five years is anything to go by (which I also examined in my analysis - I was interested to see what that fifth year of data might look like compared to the first), it not going to get any better.

There is a viable alternative. And it is not expensive. And by expensive I mean in terms of time, that most precious of commodities. VAXBRL. Just spend a little time verifying and adjusting the XBRL. This way you'll always know the provenance of the source (direct from the company). And with the right tools, it can become an easy and inherent part of your financial modelling process over which you will have complete control.

What you'll end of with is a set of financials better than any vendors at a fraction of the cost. Now that I think is what the XBRL revolution was supposed to be about.

Of course I wouldn't be telling you this if I didn't have a set of tools up my sleeve that might be just the job. Take a look at our totaliZd product. Download a totaliZd X Sheet at work (ready for adjustments). Yo can now also see a fully adjusted version of an X Sheet and video which I talk about in my next post.

Which source of Company Financial Data should I use for my model?

If we want to create a model of a company's future performance, we need a starting point, a set of inputs which we can examine, adjust and extrapolate our best estimate of how we anticipate the company will perform in the future.

So what are our choices?

1. Lifting values straight from the Financial Reports filed by a company
2. Data Vendor - Bloomberg, Thomson Reuters, Capital IQ, FactSet etc
3. XBRL

I believe XBRL provides the best possible place to start. Why?

Well, as my old Physics teacher was never short of telling us, lets go back to first principles.

Our ideal starting point would be a perfectly accurate picture of how a company is performing right now. We can’t get this however for two reasons:

1. We don’t have real time access to companies accounting systems so we are constrained behind the curve by the reporting calendar.
2. We can only see the published external numbers, the numbers that the company allows us to see, subject of course to any legal disclosure requirements or the opinion of their auditors.

So even this, our best source, is inherently flawed, so we must be ready at all times to make adjustments/correct the figures put in front of us. Despite these caveats, the company still has to be our first port of call because only they have the closest and best view of the current operating performance.

So why would we use a data vendor?

Well their starting point is exactly the same but what they do is prepare the accounts for financial analysis. If you start from the Financial Reports filed by a company, preparing every single company this way is expensive, which is why they will charge you thousands of dollars for the privilege. Criticisms levelled at data vendors in the past have been that they don’t always get the figures right and that its not always clear how and what adjustments have been made.

Also a standardised approach can be problematic as the Corporate Finance Institute note:

“Companies such as Bloomberg, Capital IQ, and Thompson Reuters provide powerful databases of financial data. However, financial statements retrieved from these databases tend to be in a standardized format. Thus, if the company uses an accounting value unique to its business operations, you will not grasp it from data retrieved and it will affect your analysis.”

This is why in critical decision making, when an investment decision or M&A deal could be worth millions of dollars, vendor data would never be used as a starting point for a single entity centric model. Of course this data has value in screening and modelling whole markets or sectors but even here, for the reasons stated, they are potentially flawed inputs.

So what does XBRL bring to the party?

XBRL takes the effort out of lifting the financial values from the report and provides a first pass at standardization. Not normalization mind, but a first step in that process if your goal is vertical analysis against a company’s peers. As I discuss in this article, unfettered XBRL, as filed with the SEC and made available through Edgar or alternatively for a fee via the XBRL.US API, does not, nor indeed intend to, provide a perfect set of standardized values.

By harnessing the XBRL tagging, your models can be automatically derived from genuinely as reported values, the closest view of past operating performance direct from the company and untampered by any third parties. The hard graft of lifting values, monotonous, error prone and time consuming, is removed. But as I underlined above, this is not quite the finishing point. Most of the hard work is done but we must always be ready and prepared to make a few adjustments*. They will undoubtedly be required.

*How you can easily make these adjustments is discussed here and in the following video. If you want to read more about the need for adjustments in XBRL, then check out the next post in this series.

You can also read about totaliZd from Fundamental X here, our complete solution for preparing XBRL derived inputs to financial models in Excel.

How to screen for the very latest filings in Excel

Now EDGAR is very good at showing you all the latest filings. But I wanted to take it a step further in my analysis of XBRL filings for the 2017 10-K reporting season. I also wanted to do it in Excel. By the way, this is all part of the Has the dream come true? series of posts and videos which you can follow starting from here.

If you have read my previous posts, you will know I have set up a control group of companies so we can see for real in this reporting season what we can and can't do with XBRL. Now I've needed to be able to monitor those filers, just as if they were my portfolio of potential investments. So we've added a watchlist feature to Xbrl sheet. Technologically, it's just a variation on the existing queries you can run in Xbrl sheet. It uses the same query file but you set different parameters to do some very powerful screening of the latest flings at the SEC.

Great thing about Excel data queries is that you can set them up to run whenever you want automatically, so when you open your workbook or indeed at regular intervals, say every hour.

You won't always know which companies your wish to watch so we've covered that by adding the ability to screen by filing date. Say for example, show my every company that filed yesterday (whisper it but you can actually search down to the latest minute so you could set it to show you all those that filed in the last hour for example. We update our database from the SEC in real time so as soon as it's available on EDGAR, it's available in Xbrl sheet). Not only that, you can filter by industry to build a peer group of real time filings. You can specify a particular SIC code or a wider range to pick a bigger industry grouping or search by filer (i.e. find filers in the same industry as your chosen target).

Anyway this video shows you how to do all that. And once you've identified a filing, you bring down all it's XBRL tagged data using the same mechanism in Xbrl sheet.

And there's an example sheet to play with here.