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Vidrio Quarterly Review

New Features, Enhancements, and Advanced Analytics

 New Features

  • Enhanced Near-Real-Time Data - This new enhancement to our data refresh frequency allows portfolio and fund metrics in certain views to rapidly adjust to changes in fund data throughout the day - updating performance and related allocation, attribution and statistics, to provide clients with refreshed information throughout the day.
  • Enhanced Cash Management - This new development adds a series of improvements to Vidrio’s portfolio cash management capabilities, most notably the incorporation of Line of Credit and Margin accounts, Swaps, and automatic account transfers; as well as a more detailed breakdown of cash balances for enhanced visibility and planning.
  • Enhanced Investor Portal - This enhancement provides clients with greater ability to customize information shared with investors and other stakeholders via Vidrio’s Investor Portal feature. The new enhancements
    will provide users greater ability to manage and grant access to different products for specific clients and groups, as well as control access to other information such as specified documents, all via a user-friendly drag-and-drop feature.
  • Universal Product Architecture - As we continue to strengthen the flexibility of the platform to manage analytics across asset types, this newly introduced universal product architecture now allows users to view all products (Portfolios, Funds, Indices etc) from one location, allowing for easier cross-comparison and analysis across product types.

Advanced Analytics

(Product Clustering, Product Similarity, Style/Strategy/Sub-Strategy Auto-Classification)

This development represents the first phase in Vidrio’s significant investment in advanced analytics including machine learning tools. The objectives of this strategic project are to provide users with advanced value-added analytics to improve investment monitoring and risk management tools. The new functionalities and metrics allow users to create clusters of similar products; compute multi-dimensional similarity measures; and through a machine learning based algorithm, automatically define, assign and monitor strategies, sub-strategies and styles of investment products. The immediate value of the first release of this project, includes better insights and perspective which can provide:

    • The ability to efficiently and accurately construct more diversified or concentrated portfolios.
    • The ability to accurately monitor and manage concentration risk and style drift.
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