Written by Bob Schneider     Posted on April 24, 2009

A few weeks ago Paul Wilkinson published a terrific post on Hernando de Soto's WSJ op-ed Toxic Assets Were Hidden Assets. To oversimplify his multifaceted blog, Paul argues that XBRL is part of the solution to the current inadequacies in recording, tracking, and analyzing toxic asset data that de Soto describes in his piece.

The Peruvian economist’s thinking had also figured prominently in a post I wrote a year ago on XBRL and microfinance institutions, which provide microloans to the poor in developing nations. In his masterwork The Mystery of Capital, de Soto described how, contrary to received opinion, the poor entrepreneurs in these countries possess real assets (in the form of land), business acumen, and the same "animal spirits" as small business people in advanced economies; what they lack is a system for legal ownership of their property, making it impossible for them to borrow. Microfinance institutions provide the capital they need, and their repayment rates have been excellent. The XBRL data standard has been adopted by the Microfinance Information Exchange (MIX) for the detailed financial and social performance information generated by leading microfinance institutions.

Paul's post made me wonder what it is about de Soto's arguments that they simultaneously speak so powerfully about two asset classes that couldn't be further apart on the economic hierarchy, namely, (1) the unregistered land holdings of the poor in countries like Peru, Haiti, and Indonesia, and (2) the quant-driven derivatives traded by the 21st century Masters of the Universe (well, until recently) in New York and London.  Why do de Soto’s writings, which so far as I know never mention interactive data, inspire calls for XBRL-based solutions?

The question demanded I read The Mystery of Capital. Reviewing the front and back matter first, I noted in the acknowledgments that the book's title was suggested by Bob Litan of the left-of-center Brookings Institution, while the text was worked by David Frum, a noted right-leaning pundit. Moreover, the book had received kind words from both liberal organs like the New York Review of Books and conservative journals like Commentary. Clearly we weren't in the balkanized states of Olbermann and Hannity anymore.

Read against the context of TARP bailouts and a bankrupt financial sector, de Soto provides a startling reminder of how parts of our economy have descended into Third World status. Here's the introduction to the chapter entitled The Mystery of Missing Information:

Imagine a country where nobody can identify who owns what, addresses cannot be easily verified, people cannot be made to pay their debts, resources cannot be conveniently turned into money, ownership cannot be divided into shares, descriptions of assets are not standardized and cannot be easily compared….You have just put yourself into the life of a developing country.

How distressingly similar that is to the toxic-asset sector described in de Soto's WSJ article:

These derivatives are the root of the credit crunch. Why? Unlike all other property paper, derivatives are not required by law to be recorded, continually tracked and tied to the assets they represent. Nobody knows precisely how many there are, where they are, and who is finally accountable for them. Thus, there is widespread fear that potential borrowers and recipients of capital with too many nonperforming derivatives will be unable to repay their loans.

Whether it's land in the poor neighborhoods of Port-au-Prince or securities cooked up by the Best and the Brightest on Wall Street, assets' value is crucially tied to the efficiency and efficacy of their recordkeeping systems. From de Soto's WSJ article:

Property is much more than a body of norms. It is also a huge information system that processes raw data until it is transformed into facts that can be tested for truth, and thereby destroys the main catalysts of recessions and panics — ambiguity and opacity.

De Soto spends a good part of his book describing how the legal system for real property we take for granted today developed over many decades in the United States. In the beginning:

Every farm or settlement recorded its assets and the rules governing them in rudimentary ledgers, symbols, or oral testimony. But the information was atomized, dispersed, and not available to any one agent at any given moment. As we know too well today, an abundance of facts is not necessarily an abundance of knowledge. For knowledge to be functional, advanced nations have to integrate into one comprehensive system all their loose and isolated data about property.

Unlike the recording of securities based on real property, recordkeeping for U.S. real property itself is excellent. But it is the end result of a complex historical process extending 200 years. In his book, de Soto pays particular attention to the abundance of squatters who attempted to establish ownership through extralegal bodies like claim associations and miners organizations. Only after decades-long battles about title in the 18th and 19th centuries were extralegal and legal systems of property combined and these conflicts ultimately resolved. The economic success the U.S. subsequently enjoyed depended vitally on that resolution. 

In contrast, recordkeeping for mortgage-backed securities (MBS) is a throwback to the chaos of earlier times. In a sense, it is a betrayal of our economic heritage. Here's how Philip Moyer of EDGAR Online describes MBS reporting in his January 2009 whitepaper Bringing Transparency to the Mortgage-backed Securities Market:

As a loan moves through the many participants in the MBS supply chain, each member of the supply chain — originators, retail banks, wholesale banks, issuers, servicers and ratings agencies — decides what to report publicly and when to report it. Additionally, all players use different report formats, different data labels, different ways of tracking the status of the collateral and even different models for tracking the identity of the individual loans. As a result, a loan can receive as many as five unique IDs between its origination and when it is bundled into an MBS. There is no centralized regulator that validates or collects all of this data. There is no central repository that can be queried… Therefore, it is difficult to track the status of a single loan in an MBS — even if it is in default — because every participant has completely different reporting models. The industry is awash in a sea of disconnected and incomparable data.

How can XBRL help? On pages 17-18 of his paper, Moyer describes eight benefits of using XBRL in a centralized MBS reporting system instead of current document/spreadsheet formats:

• Data Quality   Consistent labels and content, a structure for organizing data, and, most important, "a model to validate that the structure and the content of a report adheres to appropriate standards."
• Security and Privacy   Traceability and detailed control of the information reported.
• Historical Comparability   Versioning and an enduring structure allow comparisons regardless of changes in reporting requirements over time.
• Platform Compatibility   XBRL is platform agnostic.
• Multicurrency and Multilingual   Investors can view data in their own language and multiple currencies.
• Reduced Reporting Costs   Sunk costs for existing regulatory and banking XBRL reporting can be leveraged.
• Reduced Data Processing Costs   Receiving better data from upstream business partners offsets the costs incurred by each member of the supply chain.
• International Industry Standard   Leveraging the existing body of knowledge for XBRL saves resources that would otherwise go to resolving technical and financial reporting problems.

XBRL is not a cure-all, of course. To the extent that the data continues to be plagued by incompleteness and both intentional and unintentional error, MBS reporting still will have significant problems.

Nevertheless, introducing XBRL into the process will go a long way to providing the consistent, comparable, and accessible information that de Soto recognizes as an essential element in asset value, no matter what the asset class.
 

Chie Mitsui is a Data Analyst at Nomura Research Institute, where she is helping design NRI's new XBRL-enhanced services. She previously worked as system designer for information service products at Jiji Press, a Japanese financial news and data distribution company. Ms. Mitsui holds a masters in physics from the Tokyo University of Science and is pursuing her MBA.

1. In February you conducted a well attended workshop at the Tokyo Stock Exchange that was sponsored by  XBRL Japan. Could you tell us its purpose and who signed up for it?

XBRL has been implemented at both the Tokyo Stock Exchange’s TDnet and the Financial Services Agency’s EDINET. Naturally, interest in using XBRL for investment analysis has increased; analysts and investors want to know “What can XBRL do for me?” These end-users represent the final and key link in the financial reporting supply chain, yet their views on XBRLized disclosures haven’t been given great weight in the implementation process.

Given that TDnet’s mission is to deliver timely financial information to the investment community, there was a strong sense that such workshops are essential for helping end-users utilize the system’s XBRLized data.  The attendees were (primarily) analysts, data intermediaries, and representatives of securities firms.

2. Were they receptive to the need for XBRLized data?

I think many end-users are not satisfied with their current choices for retrieving financial data, namely, using the services of a data vendor or inputting the data themselves from a PDF into, say, Excel.  But that doesn't necessarily mean they are completely convinced of the need for XBRLized data.

Let me give you an example from our workshop. The XBRL tool we used has dialogs to choose elements on the calculation sheets and provides easy viewing for beginners. Once you construct an initial sheet with an opened XBRL file, you can use the same sheet for other files. When attendees made the calculations for the target company we selected, some users felt it would indeed be easier to simply stick with copying data from a PDF.

But what I emphasized to these users was that, while that might be true for a single company’s data, it’s not the case when you want to compare all companies in the same industry. That’s when XBRL really shows its colors.

We used XBRLized data for 3Q 2008 to calculate the ratio of accounts receivable to sales for both the target and a broad group of companies; this would help identify the risk in reported profits and potential cash flow problems. When we compared the results, the target company’s ratio was a little higher than average. The attendees did realize the usefulness of XBRL in this situation. Nevertheless, we did have some hurdles to overcome.

3. Which were?       

For the first company we selected, the actual element was notes and accounts receivable-trade, and for most of the companies we selected this was the right choice. But for one company the element was just accounts receivable-trade, which didn’t treat notes receivable, and its inclusion generated an error message. The experience highlighted for attendees the special nature of using XBRL data.

When analysts and analysts use data from data intermediaries, the data is normalized so that items are combined for better comparability. Having the original data allows for more specificity and better analysis, but it also raises issues like the one I just cited. If you want to compare a financial ratio for dozens of companies, you do not have time to check each instance for errors. You need to choose the right element to verify your hypothesis.

4. So in your view using TDnet’s XBRL data for comparing financial ratios of many Japanese companies simultaneously can prove difficult?

Well, it’s not a problem of the XBRL – it’s the nature of Japanese financial reporting. The taxonomies used in the US reflect US GAAP accounting and have much more structure. The software enables users to identify the parent element, and they can use tree-like navigation to select the elements they need. Japanese taxonomies, in contrast, are relatively flat, and it is more difficult to know the relationships between elements. That is the real issue: how to make taxonomies.

There are other significant differences in US and Japanese taxonomies. For example, attendees were surprised that US GAAP taxonomies had contexts that identify the specific time period. In contrast, Japanese taxonomies have contexts like CurrentYearConsolidatedDuration. That doesn’t mean you’re likely to confuse 2007 and 2008 data for Japanese companies -- the taxonomy provides for that distinction. But it does require that you adjust your software settings accordingly.

5. So you see consequences for international comparisons as well?

I do. Of course, institutional investors, as well as some individual investors, trade equities on an international basis. Differing accounting standards among nations  naturally result in differing taxonomies. But beyond the dissimilarities that result from accounting standards, the nature of XBRL taxonomies, the naming of elements, and so forth can vary among countries in important ways. So for now, sector analysts who want to analyze their industries on a worldwide basis will encounter comparability issues directly related to the XBRL.

At the same time, I see opportunities for improved financial reporting through XBRL. Items that have been traditionally combined for paper-based reporting – such as notes and accounts receivable --  can be disaggregated and reported separately with XBRL. More granularity will mean more accurate reporting.   

6. What was the main conclusion you drew from the workshop?

The most important thing I came away with is the need for end-users to be part of the XBRL conversation.  After all, all of the work of XBRL implementation is ultimately aimed at them. This meeting was a good opportunity for their voices to be heard, and I hope there will be more of them in the future.