Crisis Detection Model Using Momentum and CIUR
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Solution Overview
Problem
Current methods for predicting industrial crises are limited by their reliance on leading economic indices and individual company indices, which fail to provide integrated information about overall industry trends, leading to inaccurate and incomplete crisis predictions.
Innovation Solution
A method and device using the momentum theory and crisis index up-side risk (CIUR) to redefine crisis detection, analyzing literature, and creating a crisis detection model that predicts crises by averaging normalized momentum values and CIUR values, allowing for more accurate and detailed predictions across industries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If leading economic indices and individual company indices are used for crisis prediction, then the prediction process is simple, but the prediction accuracy and integrated industry information are insufficient
Solution Approach 1:
The patent combines multiple data sources including leading economic indices, coincident economic indices, and individual company indices into a unified crisis detection model. This integration allows the system to capture both macroeconomic trends and micro-level company-specific information, thereby improving prediction accuracy while maintaining a manageable model structure through systematic data aggregation.
Solution Approach 2:
The crisis detection model functions as a composite information system that integrates diverse data types (economic indices, company performance data, industry-specific indicators) into a unified predictive framework. This composite approach allows the model to leverage the strengths of each data source while compensating for individual limitations, resulting in more accurate and comprehensive crisis predictions.
2Loss of information
If individual company indices are used, then company-specific information is available, but integrated information about the overall industry is lacking
Solution Approach 1:
The model segments industry analysis into multiple hierarchical levels: macroeconomic indicators (leading and coincident indices), industry-specific indicators, and individual company indices. This segmentation allows the system to process and integrate information at different granularities, ensuring that both aggregate industry trends and specific company performances are captured without creating overwhelming data complexity.
Solution Approach 2:
The crisis detection model serves multiple functions simultaneously: it tracks macroeconomic conditions, monitors industry-specific trends, evaluates individual company performance, and integrates all these dimensions into a unified crisis prediction. This multi-functional design enables comprehensive industry information gathering while maintaining a cohesive analytical framework.
Data Source
AI summary
The present disclosure discloses a method of detecting crises for each industry and a device therefor. A crisis detection device includes a definition setting unit that redefines the meanings of crisis and crisis detection for each industry; a document analysis unit that analyzes literature on the crisis and the crisis detection that have been redefined; a model creation unit that creates a crisis detection model based on the momentum theory reflecting the trend of a crisis index and the crisis index up-side risk (CIUR) reflecting volatility, considering the analysis results; and a crisis prediction unit that predicts crises for each industry using the crisis detection model.