Financial analysts across the world are married into their spreadsheet models, lovingly crafted over the years and zealously guarded. These Excel sheets normally carry loads of information arranged uniquely, though a closer examination would often reveal that a large number of data points are derived from a smaller set of financial disclosures and market data variables. Every time these primary variables are disclosed afresh, the analyst or her assistants would painstakingly capture data once again for a new time context, make those fine adjustments and proceed to tweak the financial forecasts and valuation models. This is a time-honored process and has been pretty much undisturbed for decades.
This is going to change in the next few years. Let’s see how.
The Empowered Spreadsheet
The advent of structured data (financial disclosures available in the XBRL format) delivered through APIs(App Interfaces) is set to empower the analyst spreadsheet in multiple ways. Availability of these data APIs would increase the depth and quality of information resulting in substantially higher speeds of research response, superior analyst efficiency, and higher quality and depth of information leading to better analysis.
For example, in the United States, 10 Q and 10 K statements of listed companies filed with the SEC are available in XBRL format. This structured data is much more comprehensive than that provided by conventional data aggregators. And by using data APIs, this structured data can be delivered directly to analysts’ spreadsheets.
The dilemma of as reported and normalized
While companies are required to file according to the standard taxonomy, many companies file their returns in the XBRL format with modifications to the data dictionary (taxonomy), the so-called extensions. Extensions, were previously used to severely limit the comparability of financial data across companies. But not anymore as vendors are now beginning to make available normalized data sets based on SEC XBRL filings across companies. Normalized data is nothing but similarly reported variables across companies grouped under an all-encompassing head.
The availability of both as-reported and normalized data sets is a big boon to the analyst community. With these data sets, the existing spreadsheet can be linked to data APIs for the as-reported and normalized data sets based on the analyst’s requirement. For instance, for single company analysis, you can directly use reported data elements and carry out appropriate adjustments already in the model. However, for multi-company comparisons, you may use vendor normalization logic or perform custom normalizations if required. It is also possible to carry out a one-time setup for the linking process using rule-based software that does most of the basic mapping.
Advantages of plugging spreadsheets with data APIs
Transforming the spreadsheet by plugging in data APIs can lead to a big shift in the research process, both for the buy and sell sides.
Firstly, there is the advantage of speed in updating the model. Imagine a situation where the entire financial model is updated minutes after the 10 K filings hit the SEC. Usually, large research houses employ a set of data analysts based in a different time zone to plug in the data to reap the time advantage. With data API-linked models, small and big research teams can be on a level playing ground as much as data access and processing is concerned. Better productivity can also help research teams to look at more companies for coverage.
Secondly, the availability of information is much deeper (no footnote information is left uncovered) and contextual too. Analysts can explore each data element in the context of the whole filing from the spreadsheet itself or carry out drill-downs to the final data element. This is better than working with data APIs such as that available from a terminal providing limited and normalized information.
Finally, there is the question of whether faster and deeper data sets would improve the quality of the research itself. This is difficult to comment on since a research analyst is essentially gazing at a crystal ball and offering an informed judgment. However, better information access through open, structured data formats would certainly democratize the research process which should lead to efficiencies in the overall price discovery process as well.
The journey of digitally transforming the analyst spreadsheet has only just begun. Key data streams such as earning releases, investor presentations, and conference call transcripts are still not available in a structured data format. A spreadsheet model captures key information, most of which is repeated in the subsequent detailed filings as well, from these sources too. It is, however, quite conceivable that such data too would soon wend its way to the comfortable environs of an analyst spreadsheet sooner than later.
