The asset management arm of one of the Top 10 Swiss Banks uses GAIN Quant DB to industrialize the data preparation activities of its “quants” research team, by automating the aggregation of index and market data from multiple sources (using the Thomson Reuters QA offering that includes DataStream, IBES, Worldscope as well as MSCI indices) to feed MatLab, the portfolio modelling application.
Quantitative Analysts – also called “quants” – design and implement complex models to create new investment strategies. The process involves the capture and preparation of large volumes of time-series from various sources to research and back-test. The goal is to validate the models. The data preparation is a crucial step to guarantee the best possible accuracy of the data underlying of new quantitative models before new funds are launched.
In many Asset Management companies , each quant is developing its own tools, scripts, databases, spreadsheets, and this data preparation work has historically been manually intensive, time-consuming, error-prone and not fully documented.
In worst cases, the know-how on the datasets and tools leaves upon individual’s departure, leading to a high operational risk for the institution. Successful Quants are valuable and can be headhunted by the competition – together with the know-how, the tools and the data they have been using to develop their models on their desks. Institutional investors increasingly demand for more professionalism in quantitative investment, still perceived as a lucrative but secretive area of asset management.
The client needed to automate the data preparation activities. “We needed a powerful solution enabling us to easily aggregate index and market data from multiple sources including Thomson Reuters QA Direct as well as MSCI and Bloomberg to feed MatLab. AIM Software’s GAIN Quant DB was the perfect match” says the Head of Investment Management of the firm.