Tuesday, May 21st

Last update11:17:24 PM GMT

Industries > Capital Markets > Research & Sales

Capital Markets

Research & Sales

  • PDF

Watch free online webinars explaining use cases for Panopticon data visualization software in real-time capital markets applications.

Improve Effectiveness for Research & Sales Operations

Buy-side and sell-side firms use Panopticon to analyze and monitor financial markets data from many different sources. They monitor real-time streaming market feeds and also analyze databases of time series historical information. They use our visual analytics software to understand historic returns and correlations as well as to analyze fundamentals, including actuals and forecasts driving returns.Treemapping software tools for interactive equity research applications.Treemap data visualizations combined with traditional Scatter Plots enable extremely fast understanding of even the most complex data in research applications. Panopticon's filtering controls enable fast, efficient isolation of outliers and anomalies.

Application scenarios include:

  • Macro analysis
  • Market, industry, region, portfolio, and instrument screening
  • Issuer and maturity screening
  • Market monitoring with a focus on identifying unusual market activity

Interactive Dashboards with the tools you need for truly efficient research and sales analysis

Panopticon is an excellent fit with research and sales requirements, including:

  • Subscribe to streaming data from Microsoft Excel — clients usually route Bloomberg or Reuters streaming feeds into Excel, which lists their own interest universe — connect Panopticon directly to Excel to visualize this real-time streaming data
  • Subscribe to full and parameterized streams from CEP engines and message queuesMany capital markets firms have contracts in place for trading & execution analysis and other applications for Panopticon.
  • Federate static and real-time streaming data through joins and unions
  • Display and interact with instrument hierarchies
  • Dynamically change additive hierarchies and aggregate values
  • Dynamically calculate aggregated performance based on the weighted mean of the individual constituents
  • Drill and jump between data dimensions — for example, book to product to client to geography and so on
  • Aggregation and netting
  • Interpolation between known values
  • Time window and time period analysis to identify deltas and changes between time periods
  • Visualize trends, clustering, correlations, and outliers

Share