Following the signing of a joint venture with the London Stock Exchange (LSE) in February to add its entity data to the UnaVista platform, data vendor FactSet has been focused on adding another string to its entity data bow by building a single name exposure offering, which it plans to launch before the end of the year. According to Ken Zockoll, senior vice president and director of content development, and Chris Ellis, senior vice president and director of analytical products at the vendor, the solution brings together four key pieces of FactSet’s data offering to enable firms to track granular parent/child hierarchy data for individual securities.
While the LSE deal brought the vendor another route to market and access to the exchange provider’s middle and back office client base, the single name exposure solution will sit very much in the traditional stable of FactSet’s risk analytics offerings. “Our value proposition has always been to be a productivity tool and we want to help our clients do more analysis in less time so they can spend less time compiling and more time performing analysis,” explains Ellis of the drivers behind the development.
He indicates that FactSet set off down the development path as a result of client requirements around the assessment of single name exposure: “We were startled by how many very sophisticated clients were doing this in an incredibly manual, non-systematic way.” Accordingly, the biggest competition for the soon to be launched solution is likely to be internal build, where clients have taken entity data feeds into an internal data warehouse to be able to create reports for single exposure.
The vendor is now in the latter stages of development and hopes to have the solution out on the market before the year end. Ellis has spent the last two months visiting around 20 of FactSet’s largest clients in the US to demonstrate where the vendor is headed with this and getting their feedback on it. “We don’t expect huge differences between a US and non-US need, but we will do the same sort of spin through in Europe, Australia and Japan,” he adds.
Ellis indicates that there are a few loose ends to tie up over the next few months and some functionality to put in, such as how to integrate and update the history of the data for time series historical trend analysis. This needs to be done in such a way that it is not negatively impacting calculation time, he explains.
The solution also represents the gradual evolution of the vendor’s solution set away from being primarily equities focused. Ellis elaborates: “Five or six years ago FactSet was predominantly an equities-based tool but that is clearly not the case any more and we have become much more multi-asset class. The securities exposure analysis by definition cannot be equity centric.”
To this end the solution brings together four key pieces of the vendor’s overall market offering, the first of which is its client portfolio holdings data. “We have over 700 investment managers and plan sponsors who use our analytics tool for performance attribution, characteristics analysis and ex ante predictive risk,” explains Ellis. “In order to facilitate those portfolio analytics those 700 clients are loading over a million portfolios onto our system every night, which means we have a lot of client data at hand that is integrated from whole lot of accounting systems, custodians and prime brokers. We have more than 50 custodian and prime broker position feeds that come into the system every night.”
All of that portfolio data is then combined with the vendor’s entity data to enable clients to view, for example, American depositary receipts (ADRs) or global depositary receipts (GDRs) with their parents. Firms are also able to combine A shares with B shares, equity with corporate debt or credit default swaps (CDSs) and options, according to Ellis. “Those are four key security types that get to the heart of issuer exposure.”
He continues: “By having all of that rich entity data about parents and children and securities, you can allow a user to look at exposure to an individual security and all related equity securities or all the securities related to the issuer.”
The third piece of the puzzle is pulling in the vendor’s Universal Screening solution, which allows clients to identify the universe of companies using a set number of criteria. Ellis explains: “When you are doing exposure analysis sometimes you don’t want to look at an individual security but you want to look at the securities that fit a certain theme. That could be securities that have been downgraded by Moody’s or S&P in the last six months that are in a particular sector and region. Our application is an incredibly powerful tool for building a list of companies that meet a certain set of criteria.”
The fourth key piece, according to Ellis, is that the vendor has wired all of those results into the same reporting and charting tool that it provides for attribution analysis. “This allows users to slice and dice the data and turn it into reports and charts that make it easier to digest and act upon the data,” he says.
All of this also builds on the work that has been done with the LSE to establish links between Sedols and FactSet’s entity identifiers. Zockoll explains the extent of the work thus far: “We have around 2.6 million securities linked to around 181,000 issuing entities and are maintaining the relationships between the entities themselves via 250,000 unique parent/child hierarchies. With the LSE, we have established links between the securities identifiers, or Sedols, and the issuing entities and ultimate parents. That adds another dimension to the UnaVista offering where they can now provide back office risk and compliance groups links between securities identifiers and ultimate parents.”
The focus on making risk management an easier process by providing more intuitive tools has also been a common theme in the industry of late. In fact, at last month’s A-Team Insight Exchange event in London, a number of speakers explained the need for graphical dashboards and easy to use risk tools in order to be able to make quicker decisions based on risk data.
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