Performance under pressure
The impending enforcement of the revised Markets in Financial Instruments Directive (MiFID II) means that accurate asset performance data will become an essential deliverable for players across the financial services space.
For many businesses, compliance is the immediate concern—but as time-weighted return insights become a standard part of the financial services offering, there will also be opportunities to harness this data to drive client satisfaction, foster loyalty and capture market share.
In this paper, Graz explores the key challenges of architecting and deploying a performance calculation engine, and the best practices for developing a solution that satisfies both the short-term compliance objectives and the long-term client-experience goals.
As the revised Markets in Financial Instruments Directive (MiFID II) comes into force, financial services organisations across Europe are facing the urgent requirement to deliver accurate insights into the performance of their clients’ investments. For smaller firms, calculating time-weighted returns (TWRs) is a relatively straightforward proposition—but complying with the directive at enterprise scale presents far more significant challenges.
The ability for firms to deliver accurate, timely performance data will also become increasingly important from a competitive perspective. As TWR data becomes a standard feature of financial services offerings, future-facing businesses will seize the opportunity to turn performance insights into a source of competitive differentiation. By offering a view of asset performance that is instant, accurate and easy to consume via mobile devices, these businesses will be in a strong position to capture incremental market share and nurture long-term client loyalty.
Businesses have essentially two options for delivering TWR insights: buying a pre-configured solution, or building a solution in-house. In this paper, we explore the challenges of building a solution, and recommend best practices to help overcome the challenges Graz identified while developing our own solution.
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Why choosing the optimal approach to delivering time-weighted returns data will be crucial for long-term success