Extracting value from data is a priority for financial institutions as the business looks to increase efficiency, reduce costs, identify new opportunities and gain competitive advantage. Some source in-house tools to improve the quality and accessibility of internal and external data, others look to third-parties for solutions.
A new tool from GoldenSource, Quant Workbench, brings the vendor’s reference data, traditionally used in back-office data management, into play in the front office. The workbench is designed to help firms make better use of the data by running sophisticated analytics and quantitative research directly on available reference and pricing data.
On the buy side, with interest rates at rock bottom, a streamlined approach to analytics has become important to asset managers looking to improve their yield. On the sell side, quants and analysts working off approved centralised data sources can cut data costs, reduce terminal use, and leverage data that is common across trading desks.
Quant Workbench acknowledges increasing interest in bridging market and reference data from the back office to the front office. It is the company’s first packaged tool for the front office and designed specifically for quants and analysts.
Charlie Browne, head of market data and risk solutions at GoldenSource, says: “We have spent the past few years getting reference data, market data, time series data, curves, volatility, risk and quant type data into our enterprise data management (EDM) system. Our customers are interested in taking advantage of the data, and now quants want to get involved and have direct access to centrally stored data rather than working with spreadsheets and downloads.”
The Quant Benchmark complements the company’s back-office packages dedicated to generating financial reports for a variety of regulations. It provides access to cleansed, normalised, validated and standardised reference and pricing data in the GoldenSource EDM system, as well as investment factors and curves, surfaces, risk factor and historical time-series data.
Its key ingredient is an integrated development environment including the Jupyter notebook that allows quants and analysts direct access to the data, the ability to code in the development environment, and then use change control processes to bring code into their own development environments. Sample calculations are included covering both buy-side and sell-side use cases.
Browne explains: “Quant Workbench offers a standardised development framework for quants and analysts. The capability sits around the data to provide a direct interface to quant libraries and the ability to drill down for data.” Benefits of the solution, he adds, include efficiency of development, a user-friendly standardised approach to development, and a potential reduction in costs.
