Financial Forecasting Using Disruption Indexes and Relevancy Scoring
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Solution Overview
Problem
Traditional financial forecasting techniques struggle to deliver timely and accurate predictions during volatile market conditions, primarily relying on delayed macro-level sources and failing to account for intricate supply chains and shifting economic factors.
Innovation Solution
A method and system for financial forecasting that categorizes content documents, computes relevancy scores based on expert input, determines significant content documents, generates disruption indexes, and integrates time-series data and knowledge bases to train a forecasting model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional forecasting techniques rely on macro-level sources like government and industry reports, then the forecasting model maintains stability, but the timeliness and accuracy of predictions deteriorate during volatile market conditions
Solution Approach 1:
The patent segments the forecasting data sources into multiple categories: macro-level sources (government reports, industry reports) and micro-level sources (news articles, social media, company-specific data). This segmentation allows the system to process different types of data through specialized processing pipelines, enabling timely capture of volatile market signals while maintaining the stability provided by macro-level sources.
Solution Approach 2:
The system performs preliminary action by continuously collecting and pre-processing data from multiple sources before volatile conditions occur. It maintains pre-processed datasets and established processing pipelines ready to quickly analyze new information when market volatility detected, reducing the time lag in generating accurate predictions during critical periods.
2Ease of manufacture
If traditional forecasting techniques use historical financial data and macro-level reports, then the data collection process is simplified, but the ability to capture intricate supply chain factors and shifting economic conditions deteriorates
Solution Approach 1:
The patent implements a universal data collection framework that handles multiple data types and sources through a single integrated system. The platform can process structured financial data, unstructured news articles, social media posts, and company-specific information using common processing pipelines and AI models, making the system adaptable to various market factors without requiring separate collection processes for each data type.
Solution Approach 2:
The system introduces an intermediary layer of AI-powered natural language processing and data normalization that bridges diverse data sources from different formats and structures. This intermediary automatically extracts relevant features from news articles, social media, and reports, converting them into a standardized format that can be integrated with traditional financial data, thus capturing intricate market factors without complicating the overall data collection process.
3Reliability
If the forecasting system processes multiple content sources and computes relevancy scores, then the forecasting accuracy improves, but the system complexity increases
Solution Approach 1:
The patent applies partial action by computing relevancy scores for only the most important features and attributes extracted from content documents, rather than analyzing every possible attribute. The system identifies and focuses computational resources on the top-k most relevant features based on preliminary analysis, thereby improving forecasting accuracy while avoiding the exponential complexity increase that would result from exhaustive feature analysis.
Solution Approach 2:
The system dynamically adjusts parameters such as the number of features to analyze, the depth of content processing, and the threshold for relevancy scores based on market conditions and data availability. During periods of high volatility, the system increases processing depth and analyzes more features; during stable periods, it reduces complexity. This adaptive parameter adjustment maintains high forecasting accuracy while preventing system complexity from becoming unmanageable.
Data Source
AI summary
A method for financial forecasting is disclosed. The method includes categorizing first content documents into a plurality of categories. Further, the includes computing a first relevancy score for each document based on expert input from real-world. Furthermore, the method includes determining second content documents based on correlating the first relevancy score with a predefined threshold score. The method is followed by determining time-series data based on computing an impact factor associated with each of a set of attributes associated with the second content documents. The impact factor indicates a significant quantification of a subsequent impact corresponding to the one or more entities in response to the second content documents. Moreover, the method includes generating disruption indexes based on integrating the time-series data and knowledge bases. The method further includes generating a forecast of the one or more entities based on the generated disruption indexes.