Three-dimensional Data
Paul Reynolds

Three-dimensional Data

At TS we have no problem taking a blank sheet of paper and developing a concept for our customers. We are not bound by any preconceptions and encourage our colleagues to think freely and imaginatively.

Eighteen months ago, we initiated some conversations with dealers based on some discussions we had with our buy-side clients. The buy-side were very eager to consume and understand Fixed Income data to drive improvement in their execution activity. Although they were not entirely sure precisely what kind of data they needed, they came to TS to explore what we could provide.

It quickly became clear how polarised the market is around evaluated prices and how other very useful data is completely ignored. We expanded our data range beyond dealer prices, axes, IOIs and reported trades to a more pre-trade focus of watch lists, actual live orders and the extraordinarily huge number of failed to trade RFQs.

At this point we realised our concept required us to collect both dealer and buy-side activity data and combine it with TS analysis. These three dimensions would be broader in scope, much higher integrity and most importantly, directly serve the different objectives of dealers and buy-sides.

The core issue to solve however was how to aggregate all the data generated by the individual buy-side user with the dealer data they wanted to see. Our concept was to create a value proposition for both buy-sides and dealers by combining all their inputs into a system that was highly targeted, insulated from leakage and aggregated to create exceptional market insight.

It also required a commercial model that did not prejudice data provider and consumer. In this case though, buy-sides and dealers are both data provider and consumer. Buy-sides own watch lists, live orders, failed to trade and successful RFQs and dealers’ own prices, axes and IOIs. Both own traded volumes. Our task was to provide the third dimension of connectivity, aggregation and analysis.

Eighteen months on and we find ourselves at the epicentre of connecting the top five dealers to a growing list of buy-side clients via TS. The interesting thing is how varied those clients are. The systematic credit investors are intense users of matchable data to generate reactive prices. Some of the insurance/pension clients want to consume vast quantities of dealer data and run their own quantative analysis. The private wealth managers need historic data to auto-route and execute huge numbers of low-touch orders.

It is normally quite difficult to be aware of reaching a tipping point until some time afterwards when there is enough evidence to support the claim. I do feel now however that momentum is building between buy-sides and dealers to consume each other’s data in a highly symbiotic manner that will contribute significantly to a more efficient market structure.

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