Graph-Based Attribution Analysis for Investment Portfolios
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Solution Overview
Problem
Current attribution analysis methods for investment portfolios are inefficient due to their reliance on matrix-based approaches that are inflexible, computationally costly, and require separate calculators for each investment engine, limiting scalability and flexibility in handling various asset classes and dimensional aspects.
Innovation Solution
A graph-based attribution analysis method that identifies and calculates attribution results for individual attributes independently, allowing for the generation of graphs representing decision points and enabling parallel computation across multiple portfolios, thereby facilitating flexible and scalable attribution analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a matrix-based approach with predefined asset class scheme is used, then calculation paths can be defined in advance, but the system lacks flexibility to handle different investment engines and requires separate calculators for each team
Solution Approach 1:
The patent implements a universal attribution calculator that can handle multiple investment engines and asset class schemes through a common graph-based framework. The system allows different teams to define their own asset class hierarchies and attribution rules within the same platform, eliminating the need for separate calculators while maintaining flexibility for each investment engine's specific requirements
Solution Approach 2:
The system transitions from static predefined matrix paths to dynamic graph-based calculation paths that can be adjusted based on different investment engines and asset class schemes. The graph structure allows flexible configuration of decision points and attributes that can be modified without recoding, enabling the system to adapt to different teams' requirements while using the same underlying calculator
2Ease of manufacture
If a matrix-based approach with fixed calculation paths is used, then calculation can be performed sequentially, but the computation is inefficient and cannot be parallelized
Solution Approach 1:
The patent segments the attribution calculation into independent parallel components by representing the calculation as a graph with multiple decision points and attributes. Each attribute calculation can be performed independently and in parallel, allowing the system to utilize multi-threading and distributed computing to significantly improve computation efficiency while maintaining the logical structure of sequential matrix-based approaches
3Adaptability or versatility
If separate attribution calculators are created for each investment engine, then each team has customized attribution analysis, but the system lacks scalability and requires high data maintenance
Solution Approach 1:
The patent creates a single universal attribution calculator that serves multiple investment engines and teams through configuration rather than separate implementations. The system maintains customized attribution analysis for each team by allowing them to define their own asset class hierarchies, decision points, and attribution rules within the unified platform, thereby reducing system complexity while preserving team-specific customization capabilities
Solution Approach 2:
Instead of creating separate calculators for each investment engine, the system uses configuration files and parameter settings to copy and adapt the same underlying calculation engine to different teams' requirements. This allows rapid deployment of customized attribution analysis without duplicating the entire calculator infrastructure, significantly reducing data maintenance requirements
4Reliability
If large amounts of historical data are retained for version auditing, then historical results can be accessed, but system performance deteriorates due to high data maintenance requirements
Solution Approach 1:
The patent extracts historical attribution results from the active calculation system and stores them in a separate archival storage mechanism. The system maintains version auditing capabilities by storing historical results externally, allowing access to past performance data without loading it into the active computational environment, thereby preserving system performance while ensuring reliability through historical data accessibility
Data Source
AI summary
A method for performing attribution analysis with respect to an investment portfolio is provided. The method includes: identifying a set of attributes, such as asset class, time period, risk profile, liquidity profile, and/or geographic region, of the investment portfolio; generating a graph that indicates respective relationships among attribute-related decision points that impact the investment portfolio; and calculating, based on the respective relationships among decision points, a respective attribution result for each individual attribute, in order to obtain a set of attribution results that is associated with the investment portfolio. The calculations of the attribution results are independently performable and may be performed in parallel.