Event-Driven Abnormal Return Modeling for Portfolio Events
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Solution Overview
Problem
Existing methods fail to provide easy ways to determine how extreme events impact a portfolio in the short term, leaving users unprotected against both predictable and unpredictable events.
Innovation Solution
An event modeling system that utilizes a processor and memory to analyze historical performance and event databases, calculating abnormal returns for financial instruments based on event types and dates, allowing users to anticipate and mitigate potential impacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional portfolio analysis methods are used, then long term gains and losses can be estimated, but short term event impacts cannot be determined
Solution Approach 1:
The patent segments the analysis into distinct event types (earnings announcements, product launches, mergers, etc.) and analyzes each separately. By dividing the continuous time series data into event-specific segments, the system can precisely measure short-term impacts of individual events without being overwhelmed by the complexity of overall portfolio analysis.
Solution Approach 2:
The system performs preliminary actions by pre-processing historical data, pre-identifying event dates and types, and pre-calculating baseline performance metrics. This preparation work is done before actual event analysis, enabling rapid and precise measurement when events occur without requiring complex real-time computations.
2Reliability
If no event analysis system is implemented, then users cannot protect against events, but implementing such a system increases complexity
Solution Approach 1:
The patent creates a universal event analysis system that handles multiple event types (earnings, product launches, mergers, acquisitions, bankruptcies, etc.) through a single integrated platform. The system performs multiple functions including data collection, event identification, impact measurement, and portfolio adjustment recommendations, all within one unified structure that serves various analysis needs without requiring separate specialized systems.
Solution Approach 2:
The system introduces an intermediary event detection and analysis layer between raw market data and portfolio management decisions. This intermediary component processes historical data, identifies events, measures impacts, and translates findings into actionable insights, shielding users from the complexity of underlying data processing while providing reliable portfolio protection capabilities.
3Measurement precision
If detailed historical data analysis is performed for each event, then accurate abnormal returns can be calculated, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing historical time series data, pre-identifying event dates and types, and pre-calculating baseline performance metrics during off-peak periods. This preparation work stores processed information in readily accessible formats, enabling rapid retrieval and accurate abnormal return calculation when events occur without requiring time-consuming real-time data processing.
Solution Approach 2:
The patent uses copying by creating standardized templates and models for different event types based on historical patterns. Once event structures and analysis methodologies are established for one event type, they can be copied and adapted for similar events, eliminating the need to reprocess and reanalyze raw data from scratch for each new event while maintaining calculation accuracy.
Data Source
AI summary
A system stores instructions including, in response to receiving user input, identifying a first event type and a first security identifier and obtaining a first set of event dates from the event database and, for each event date of the first set of event dates, obtaining a corresponding event value on the corresponding event date of the first security identifier. The instructions include, for a first day related to each event date of the first set of event dates: obtaining a corresponding value on the first day of the first security identifier, determining a corresponding difference value between the corresponding event value and the corresponding value, and storing the corresponding difference value in a set of difference values. The instructions include calculating an average difference on the first day using the set of difference values and displaying the average difference and an event indicator corresponding to the first event type.