Investor Knowledge Web for Portfolio Data Visualization
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Solution Overview
Problem
Financial advisors and clients face challenges in making timely and logical decisions due to the overwhelming amount of data and complex relationships presented in current financial portfolio management systems, which lack efficient tools for selecting trades or structuring portfolios.
Innovation Solution
The investor knowledge web system generates relevancy relationships between financial data, portfolio positions, and performance metrics, displaying these relationships in a graphical user interface as a knowledge web with interconnected nodes representing portfolios, sectors, topics, and news, allowing for quick assessment and visualization of impact on investment allocations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If data is presented in tables or graphs, then data can be organized and displayed, but it becomes challenging to make associations between the data and the instrument or portfolio
Solution Approach 1:
The patent transforms traditional two-dimensional tabular data into a three-dimensional network graph where nodes represent financial instruments, data points, and portfolios, and edges represent associations between them. This dimensional transformation enables users to visually trace relationships and make associations intuitively by following connections in the graph, resolving the difficulty of making associations in traditional tables or graphs.
2Quantity of substance
If extensive data is aggregated, then more associations between data and asset are created, but decision making becomes more time consuming
Solution Approach 1:
The patent segments the extensive financial data into discrete nodes (instruments, data points, portfolios) and their relationships into edges within a network graph. This segmentation allows users to focus on specific nodes and their direct connections rather than overwhelming aggregated data, enabling faster decision-making while preserving all data associations.
Solution Approach 2:
The network graph acts as an intermediary visualization layer between raw financial data and user decision-making. It translates complex aggregated data relationships into an intuitive visual format where associations are explicitly shown through graph edges, reducing the cognitive load and time required for decision-making while maintaining comprehensive data coverage.
3Quantity of substance
If current data presentation approaches are used, then all available data can be displayed, but efficient selection of trades or portfolio structure cannot be facilitated
Solution Approach 1:
The patent introduces a network graph dimension that overlays relationship information onto the data display. This additional dimensional layer shows connections between instruments, data points, and portfolios through graph edges, enabling users to efficiently identify trade opportunities and portfolio structures by visually tracing relationships rather than analyzing raw data tables, thus improving productivity while displaying all available data.
Data Source
AI summary
A system includes a topic subsystem module, a portfolio subsystem module, a performance subsystem module, a server, and a client device. The topic subsystem module acquires and stores artifact data relative to a first predetermined period having user-defined start and end. The topic subsystem module extracts topics related to preselected data. The portfolio subsystem module stores data regarding asset positions in a financial portfolio and the performance of each position over a second predetermined period having user-defined start and end. The performance subsystem module is configured to generate performance metrics for aggregate categories over a third predetermined period having user-defined start and end. The server generates relevancy relationships between the respective artifact data, asset positions, and performance metrics. The client receives the relevancy data and displays, on a graphical user interface, a knowledge web relating individual components of the relevancy data.