Neural Network Financial Prediction Accuracy via Data Weighting
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Solution Overview
Problem
Conventional financial information systems rely solely on retrieving and reiterating financial data without analysis, leading to inaccurate predictions and recommendations, and are limited by their reliance on subjective professional opinions and incomplete information sources.
Innovation Solution
The implementation of neural networks that process various types of data, including user and financial data, to provide interactive financial predictions and recommendations, weighting historically accurate information more heavily and analyzing large datasets for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems retrieve and reiterate financial information from sources, then the system operation is simple and fast, but the prediction accuracy is low due to lack of analysis
Solution Approach 1:
A neural network model is introduced as an intermediary between financial information sources and prediction outputs. The model processes and analyzes multiple types of input data (news articles, financial reports, market data) to generate predictions, acting as a mediator that transforms raw information into actionable insights without requiring direct human intervention in the analysis process.
Solution Approach 2:
The patent replaces manual financial analysis by professionals with an automated neural network system. The mechanical process of human experts reading and interpreting financial information is substituted with an electronic system that automatically processes data through trained models, eliminating subjectivity and availability constraints while maintaining continuous operation.
2Measurement precision
If financial professionals analyze markets and news to predict investment movements, then prediction accuracy improves compared to lay persons, but errors still occur due to subjective feelings and reliance on potentially incorrect sources
Solution Approach 1:
The neural network system performs self-service by automatically collecting, processing, and analyzing financial data without requiring continuous human supervision. The model trains itself on historical data and continuously updates its predictions based on new information, eliminating the need for human experts to manually monitor multiple news sources and maintain consistent analysis standards.
Solution Approach 2:
The system incorporates feedback mechanisms where prediction outcomes are continuously evaluated against actual market movements. This feedback loop allows the neural network to learn from past performance, adjust its parameters, and improve future predictions, creating a self-correcting system that continuously refines its accuracy based on real-world results.
3Loss of information
If systems rely on financial news and publicist information, then the information source is readily available, but the information may be incomplete or biased toward promotional content
Solution Approach 1:
The neural network system is designed to process multiple types of financial information simultaneously through a unified architecture. It can ingest news articles, financial reports, market data, and other information sources through the same processing pipeline, making the system versatile in handling diverse data formats and reducing information loss by comprehensively analyzing all available sources.
Data Source
AI summary
In various examples, interactive systems that use neural networks to determine financial investment predictions or recommendations are presented. Systems and methods are disclosed that determine financial predictions or recommendations associated with one or more investments using a neural network(s). The financial predictions may include a predicted movement of an investment (e.g., extremely down, down, preserved, up, extremely up, etc.), a predicted price of an investment (e.g., a future stock price, etc.), a specific investment for a user to buy/sell/trade, and/or so forth. In some examples, the systems and methods may include an interactive system(s), such as a dialogue system(s), that interacts with users to provide the financial predictions.