Analyst Data Peer Security Identification System
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing financial information systems struggle to accurately identify peer securities for investment research due to manual errors, inconsistencies in classification schemes, and inclusion of non-relevant securities, leading to inadequate comparative analysis.
Innovation Solution
A system and method that uses analyst data to identify peer securities based on degrees of overlap in analyst coverage, specializing in specific areas, thereby providing a more accurate and relevant set of comparable securities for investment decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual peer identification is used, then user control over peer selection is improved, but accuracy and completeness of peer identification deteriorates due to human error and limited knowledge
Solution Approach 1:
The patent introduces an automated peer identification system that acts as an intermediary between user intent and peer selection. The system uses natural language processing to interpret user queries and automatically identifies peer securities based on multiple data sources including fundamental data, price data, and analyst data, thereby maintaining user control while eliminating manual errors.
Solution Approach 2:
The system enables self-service peer identification by automatically processing user queries without requiring manual security selection. The automated system retrieves relevant peer securities based on the primary security and user-specified time periods, allowing users to obtain accurate peer comparisons without manual intervention or specialized knowledge.
2Productivity
If third-party classification schemes are used, then large numbers of securities can be sorted into categories, but consistency and relevance of peer classification deteriorates due to hierarchical inconsistencies and facially similar industries
Solution Approach 1:
The patent changes the classification parameters from static hierarchical categories to dynamic, multi-dimensional analysis incorporating fundamental data, price data, and analyst data. This allows securities to be classified based on actual peer relationships rather than fixed industry categories, improving consistency while maintaining sorting efficiency through automated processing.
Solution Approach 2:
The system creates a composite classification approach that combines multiple data sources (fundamental data, price data, analyst coverage) to form a more robust and consistent peer identification framework. This composite methodology overcomes the limitations of single-source classification schemes by integrating multiple dimensions of security characteristics.
3Productivity
If automated peer identification systems are implemented, then efficiency and consistency are improved, but accuracy deteriorates due to inclusion of non-relevant securities
Solution Approach 1:
The system initially identifies a broad set of candidate peer securities using multiple data sources, then applies filtering criteria to refine the list to the most relevant peers. This partial action approach ensures comprehensive coverage while maintaining accuracy by selectively including only the most relevant securities in the final peer set.
Solution Approach 2:
The system incorporates feedback mechanisms where user interactions, selections, and corrections are used to refine and improve peer identification accuracy over time. The system learns from user feedback to better distinguish relevant from non-relevant securities, thereby improving accuracy while maintaining automated efficiency.
Data Source
AI summary
A system and method is provided for identifying peer securities relative to a primary security based, at least in part, on analyst coverage. Peer securities may be identified for the primary security by identifying one or more analysts associated with the primary security, and generating a set of candidate peer securities, each of which are associated with at least one of the primary security's analysts. A set of peer securities may be generated based on a degree of analyst overlap among the candidate peer securities. In some implementations, the set of peer securities may be customizable. Thus, investors, analysts, or other users may compare data for comparable securities based on degrees of overlapping analyst coverage, or the comparable securities may be analyzed in other ways, such as creating a classification system based on degrees of overlapping analyst coverage.