Data management challenges in securities finance

This won’t be the first article to comment on data management challenges in securities finance and it won’t be the last, but we wanted to say our piece as regulators are looking at how to better monitor the market. The Fed’s piece on data in securities lending and repo was just the latest salvo (our review is here), and more are coming soon.

As the kind of people who work with securities finance data on a daily basis, we can attest that there are some real variances that make a straight-forward assessment of data difficult for lay practitioners. Securities lending data in particular is an art unto itself, and even data vendors readily admit that while they are the best offerings in the market, the data are not perfect. Would a central data repository make things better? Unlikely, since the data would need so many qualifiers as to make it almost meaningless once it got summed up.

In our work, we are constantly scrubbing data to ensure that we have one data set that is consistent for the task at hand. It feels a bit like Mr. Miyagi’s instructions in the Karate Kid (“wax on, wax off”) where you don’t quite know what the end result is, but ultimately you learn something really valuable and the puzzle pieces come together.

We are also the kind of people who look closely when big numbers get bandied about. For example, what is the true size of the Shadow Banking industry and how do we know ($10 trillion? $18 trillion? Finadium will present our estimates on this in a couple of months).

Data in the securities finance market aren’t perfect and they aren’t likely to get any better soon. The most important thing is to understand what data you are dealing with, how it was collected and what needs to happen to make it work for a user’s specific needs. Regulators should pay close heed to the subtleties of securities finance: major policy actions may depend on the outcome.

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