CASE STUDY : PERFORMANCE ATTRIBUTION
Our client, a leading global financial data and analytics provider, was facing major scalability issues with its existing performance attribution platform. The firm wanted to completely revamp this platform, to meet the demands of the market. Specifically, they wanted to make the platform highly scalable, to meet the performance goals with increasing load. The firm also wanted to build a market leading visualization capability, to slice and dice performance attribution results and analyze sources of portfolio risk and returns. They needed features to compare the portfolio performance against the benchmarks, across asset and factor groups and over different time ranges. The firm wanted functionality to allow drill-down and filtering into different performance attribution factors and to easily identify performance outliers.
Another critical requirement that the firm had was to add new performance attribution models to the platform and support multi-asset class portfolios with different attribution models.
Wissen was chosen to take up this project and it deployed a highly talented team to analyse the root cause of the scalability issues. It found that the existing performance attribution platform recomputed the results for the client jobs every day, even though most client jobs only extended the attribution period by a single day. Based on this analysis, the team came up with a brilliant solution that would store previous results, perform incremental calculations, and then link with previous day results to generate full period results. A result storage capability was developed to store partial results. The results were stored in a generic fashion in an Oracle database to support results from all attribution models. A fast linking engine was built to link partial results on the fly to generate full period results.
Spotfire was selected as the visualization platform after a thorough analysis of various BI tools. A custom extension was built in Spotfire to dynamically query the new linking engine for full period results. Thus, a new visualization capability was built leveraging partial stored results. Various custom designed dashboards were built to show performance attribution results. An intuitive graphical selection tool was built to allow drill down into groups and factors and to enable sophisticated filtering on various numerical criterion.
The new visualization capability allowed quick and robust comparison between current and historical factors affecting performance for both portfolios and benchmarks. The access to these advanced visualization and analysis tools was available through a web application. This rich functionality received significant industry attention and became a primary selling point for the product. Also, the introduction of storage capabilities led to the reduction of job run time by 80-90% and thus resolved the scalability issues with the older platform.