AI Trading Forecasting via Knowledge Graphs
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Solution Overview
Problem
Current methods for forecasting trading behavior and thematic concepts for trade baskets involving derivatives and other financial instruments are manual and non-systematic, lacking an efficient mechanism to leverage artificial intelligence techniques.
Innovation Solution
The implementation of a method and system that uses artificial intelligence techniques, specifically involving a processor that retrieves government filing information, generates knowledge graphs, and applies AI algorithms to forecast proposed future transactions related to financial instruments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used to analyze trading behavior and thematic concepts, then flexibility in understanding client portfolios is maintained, but productivity and timeliness of transaction predictions deteriorate
Solution Approach 1:
The system segments the complex task of forecasting trading behavior into multiple specialized API modules, each handling specific aspects such as entity identification, trading history analysis, and thematic concept detection. This segmentation enables parallel processing and improves overall productivity while managing system complexity through modular design.
Solution Approach 2:
The patent introduces an intermediary AI processing layer that mediates between raw government filing data and the forecasting output. This intermediary layer uses NLP and knowledge graphs to transform unstructured data into actionable insights, significantly improving forecasting efficiency without requiring direct manual analysis.
2Measurement precision
If artificial intelligence techniques are implemented to forecast trading behavior, then accuracy and timeliness of predictions improve, but device complexity increases
Solution Approach 1:
The patent replaces manual mechanical analysis methods with automated AI algorithms including NLP, knowledge graphs, and machine learning models. This substitution significantly improves prediction accuracy by systematically analyzing large volumes of government filing data, trading histories, and news articles that would be impossible to process manually.
Solution Approach 2:
The system implements universal AI processing capabilities that can handle multiple types of financial instruments (equities, bonds, derivatives) and various data sources (government filings, trading histories, news articles) through a single integrated platform. This multi-functionality improves prediction accuracy across different asset classes while avoiding the need for separate specialized systems.
3Measurement precision
If comprehensive data analysis is performed to understand client portfolios and risk appetites, then forecasting accuracy improves, but loss of time in data processing increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing key information from government filings, trading histories, and news articles in structured formats before forecasting is needed. Knowledge graphs are built in advance to capture entity relationships, and trading patterns are pre-analyzed, enabling rapid retrieval and processing during actual forecasting operations without sacrificing accuracy.
Solution Approach 2:
The patent implements continuous data collection and analysis processes that operate in the background, continuously updating knowledge graphs and trading behavior models as new data becomes available. This continuous processing eliminates batch processing delays and ensures that forecasting always uses the most current information without requiring dedicated processing time.
Data Source
AI summary
A method for using an artificial intelligence (AI) technique to forecast trading behavior and thematic concepts for trade baskets with respect to derivatives and other specific types of financial instruments is provided. The method includes: retrieving, from an internet website, information that relates to at least one form that corresponds to a government filing; using the retrieved information to generate a knowledge graph that relates to a particular entity; generating at least one application programming interface (API) that is configured to analyze the retrieved information and the knowledge graph in order to provide insight into at least one financial instrument that relates to the particular entity; and forecasting, based on an output of the API(s) and by applying an AI algorithm to the knowledge graph, at least one proposed future transaction to be executed with respect to the financial instrument(s) that relate to the particular entity.