Overview

 

forward look's research focuses on the interplay between portfolio implementation shortfalls and the management of transactional data. By tracking how data is assembled by various counterparties to structure the transactions that flow between them, we determine what dynamically enables efficient information transfer in support of STP and ultimately, alpha retention.

 

We focus on how differing terminologies (eg, variants of XML or in-house data dictionaries) and varied application contexts (eg, front vs back office processes) result in syntactic and semantic breaks. More importantly, we try to ascertain how these differences can be programatically bridged by spanning the implicit ontological gaps. As Michael Stonebraker noted, the challenge is enabling 'integration on demand' to supplant the prevailing but more brittle approach of 'integration in advance'.

 

We actively explore how evolving schema and data mapping techniques can be used to automatically harmonize information contexts. We regularly examine how semantics (data meanings and usage) and ontologies (contextual maps between related terms across the industry) capture the industry's processes and workflows, with particular attention to the differing information requirements of various trading counterparties across the life cycles of cross-border transactions.

 

We continually discover interesting facets about transactions and data that act as catalysts for our client's implementation strategies. For example, we observed how reference data depth (e.g, the number of codified data values for "Settlement Location") exhibits significant correlations with the costs of trade fails - but not necessarily with STP rates. On the other hand, low STP rates exhibit strong associations with reference data breadth (e.g, the number of codified data categories within a transaction such as traded market, counterparty identifiers, security types, and so on) - highlighting that complexity begets portfolio implementation shortfalls.