Cutting-edge applications: Clients explore new ways to utilize BQuant Enterprise’s advanced data and textual analytics capabilities

Bloomberg’s BQuant Enterprise team recently hosted a gathering of forward-thinking clients in London. The event brought together groups from across EMEA who have used BQuant Enterprise to build powerful, data-driven BQuant Published Applications for colleagues across each respective firm to use on their Terminal Launchpad. This fully managed, cloud-based analytics platform enables financial institutions to scale investment workflows with advanced compute power, broad access to Bloomberg data, streamlined integration with their own internal systems, and seamless collaboration. The Code Crunch event on June 19, 2025, which included Chloe Vuong, EMEA Head of BQuant Product Specialists, and Bruno Dupire, Head of Quantitative Research in the Office of the CTO, honored three teams with awards for exceptional breakthroughs, following presentations that showcased their innovative use of BQuant Enterprise to develop advanced quantitative finance applications for their colleagues. 

T. Rowe Price was honored as a Data Innovator. Julien Fabre, Fixed Income Systematic Research Lead at the firm, provided an overview of his firm’s work linking tariff announcements to market sentiment and investment performance. T. Rowe Price had initially used the textual analytics tools offered by BQuant Enterprise to develop a nuanced, granular view of the media’s impact on market sentiment. T. Rowe Price’s quantitative group developed a model that assessed the effect of Bloomberg News stories covering inflation, growth, or politics on market sentiment. But, as fast-changing trade policy began to drive market performance in 2025, the group opted to apply this technique more specifically to Bloomberg News stories about tariffs. 

Atlantic House received the Model Master award. Mark Greenwood, Deputy Chief Investment Officer at the asset manager, described his firm’s efforts to use BQuant Enterprise’s data-driven tools to analyze economists’ inflation estimates. Using an array of statistical tools, including L-estimators, bootstrapping and random forest analysis, Atlantic House’s team was able to generate inflation estimates based on forecasts from economists with the best record for accurate inflation predictions. Greenwood calculates that for every one basis point improvement in the accuracy of economists’ mean inflation estimates, his firm can boost returns on its UK macro strategy by approximately 0.1 Sharpe points. 

J.P. Morgan was named the Visual Insighter. Daniel Thiel, Executive Director, Quantitative Research, Equities, described how his firm built an application using  BQuant Enterprise that employs textual analytics on a dataset based on Bloomberg News to flag earnings warnings, or changes in corporate profit forecasts for J.P. Morgan’s equity traders and salespeople. These announcements can move markets sharply, like when retailers reported a spate of downward revisions set off by new tariffs this spring. Thiel’s team automated this process using the Python data science tools available within BQuant Enterprise. The application automatically pulls the latest, most accurate Bloomberg News headlines regarding profit warnings, providing a summary and text preview on the application user’s Terminal Launchpad. It can be used to develop both company-specific or industry-wide perspectives, or to highlight earnings trends at related companies. In addition, it offers month-on-month data to highlight longer-term trends. 

The event concluded a four-month intensive development process in which teams from all over the world used BQuant Enterprise to explore new use cases for the programmatic analytics solution, with the support of the BQuant Enterprise team. “When we first started planning Code Crunch, we set out to do something ambitious. We want to create a space for experimentation, shared learning and best practices across the BQuant community,” said Vuong. 

Enhancing factor models with machine learning techniques

In addition to highlighting client work using BQuant Enterprise, the event showcased the BQuant Enterprise team’s own capabilities. Bloomberg’s own Anish Popat, a market specialist, and Ricard Radomski, a Desktop Build Group analyst, explained how an example framework they built incorporates machine-learning-based signals into a multifaceted investment analysis. 

“These signals can capture complex and nonlinear relationships that standard models often miss,” Popat explained. “By blending the traditional insights with modern techniques, our goal is to refine the signal, and ultimately, a better, more refined signal should lead to better decisions, whether you’re building a portfolio or screening for stocks.” 

Code Crunch is just the beginning

On their own, the projects created through Code Crunch are impressive. Even more important is the process of collaborative innovation generated by this event. 

“Whether the client teams were alpha innovators, model masters, or data storytellers, every project brought unique value to the table,” said Dupire. “Code Crunch was about pushing the limits of BQuant Enterprise, and these are the kinds of investment analyses that we want to empower our clients to do every day.”