Do you have mountains of historical datasets wilting in silos across the organization? Do you have valuable home-grown content in imperfect structures over time? Do you find your current attempts at linking all these datasets not only challenging but also expensive and time-consuming? Wouldn't it be nice to be able to link these datasets using permanent asset identifiers generated algorithmically, using your own set of rules? You are at the right place.
Qualis® Historical Security Master delivers an algorithmic solution to the familiar historical security master challenge. The app generates permanent, vendor-agnostic identifiers—called QUID—by analyzing disparate datasets historically. The algorithm is designed to be tolerant of "ugly" data and can be customized to meet your specific needs. QUID then becomes the glue that ties all the vendor datasets together at any point in time historically. There are no limits to the depth of history.
The application is built on top of Qualis® Refinery, a sophisticated digital platform that eliminates the wasteful actions of expensive last-mile resources—informal, inconsistent, and repetitive "massaging" prior to analysis by data scientists in silos. A custom, maintenance-free version of the platform is bundled with the app.
There are no restrictions on the amount of history, or the number of data sources that can be on-boarded. A typical installation includes a dozen data sources each with 30+ years of history.
What's the challenge?
Lack of permanent identifier to track securities historically is at the root of all data challenges at any investment firm. Resolving this will deliver the most compelling cost savings across the organization, not to mention improved accuracy of analytics. Further, success of M&A actions at the firm are severely impacted by your ability to deploy a single, comprehensive, firm-wide solution to the problem.
This ubiquitous problem is further complicated by these hard realities.
- Ticker, CUSIP, SEDOL and other market symbols change (driven by corporate actions) over time. These symbols are also specific to the type of asset, with Equity sporting the longest history of standardized symbology. Most of these are designed for the sell-side, to support today-going-forward transactions with little regard for history. Symbols are even reused occasionally.
CUSIP, ISIN, SEDOL, and other symbols refer to completely different sets of attributes in the idealistic data model shown below.
And yet, data vendors routinely deliver a blend of these attributes against an identifier scheme that is most convenient for their database. For example, closing price is often delivered with a CUSIP which is accurate only for simplistic scenarios—like when it is a single asset-class, domestic, equity asset trading in a single US exchange. Increasingly, securities are multi-listed and tracked by several overseas analysts under different identifiers.
No vendor delivers content in such pristine structures depicted above.
Even if vendors transitioned to a more accurate data model in recent times, there's decades of historical data with valuable insights
that lack such sophistication. Even today, it is astonishing that so many major data vendors continue to lack a time-dimension in their
security master. Such models are accurate only as of now; neither yesterday nor tomorrow.
Additionally, similar challenges exist with home-grown content as well.
Given these challenges, it was evident to us that pursuing an idealistic data model will only result in yet another never-ending, boiling-the-ocean IT initiative that is deeply unpopular. That led to the development of Qualis® Historical Security Master which solves the problem using hierarchical waterfall models for symbology and source blended with a sophisticated matching algorithm that can be trained to deliver the best results for your datasets.
One of the lesser recognized dangers of not investing in a robust historical security master solution is that by ignoring hard to handle ugly content, you risk introducing survivorship bias unknowingly.
- Unlimited depth of history
Stop purging history just to process current security master!
Commercial EDM software packages tackle, at best, Ephemeral Security Master, often limiting history to 18 months or less. There are no such arbitrary limits in the Qualis® app. Master Security table is time-dimensioned so that point-in-time characteristics of assets can be captured historically.
Stop maintaining dozens of country, currency code tables!
Asset-level attributes, irrespective of data vendor, are stored internally in standardized relation structures with the content curated at intake. This means that not only coding schemes such as country and currency are standardized (ISO), but values such as percentages and FX rates are also made consistent. For example, one hundred percent will always be 100.0, and always blindly multiply from-currency value by the FX rate to convert to to-currency.
Making all assets look/feel the same no matter the source simply eliminates the extraordinary amount of time researchers spend working around nuances of individual vendor feeds.
- Easy access/integration
Stop injecting security master data into dozens of downstream systems, in proprietary formats!
QUID to vendor asset mapping information is published in easy-to-read, de-normalized SQL tables in a separate read-only database making it easy to access from your analytical tool of choice. Every research environment, from SAS®, MATLAB®, Python, and R, to Microsoft Excel® can read from SQL tables with ease.
The R/O database can also be surfaced inside existing database servers using storage engine based snapshot technologies, an integral component of Qualis® Refinery. This allows legacy back-end applications to also benefit from QUID assignments internally.
- Practical, not idealistic
Eliminate manual massaging of ugly content to make it perfect!
Qualis® solution is designed and built by researchers, for researchers. The algorithm is derived from practical lessons learned over 100+ years of collective sweat and tears dealing with data quality challenges in research.