System and method for carrying out market summary based on AI analysis financial data
Through the automatic analysis of financial data by AI-based systems, the problems of inefficient and low accuracy of traditional market summary methods are solved, and more efficient and accurate market summary is achieved, and market changes can be understood in a timely manner.
Patent Information
- Application Number
- CN202411730943.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-29
- Publication Date
- 2025-03-04
AI Technical Summary
Traditional market summary methods are inefficient and have low accuracy, making it difficult to quickly and accurately understand market conditions, which affects corporate decision-making.
Design an AI-based system, including data collection, data preprocessing, feature extraction, model training and market summary modules, use AI to automatically analyze financial data and generate market summary reports.
Automatic analysis of financial data through AI technology improves the efficiency and accuracy of market summary, can analyze new financial data in real time, timely understand changes in market conditions, and generate a more comprehensive market summary report.
Smart Images
Figure CN119558889A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of financial data analysis, and specifically to a system and method for market summarization based on AI analysis of financial data. Background Art
[0002] In today's complex and ever-changing market environment, enterprises need to understand the market situation in a timely and accurate manner in order to make wise decisions. As an important indicator of enterprise operations, financial data contains rich market information. Market summary of financial data is an important part of enterprise management. It involves in-depth analysis of financial data to understand the current situation of the market, predict future trends, and provide a basis for the strategic decision-making of enterprises. Through the calculation and analysis of financial data, various financial ratios can be obtained, such as liquidity ratio, asset management ratio, debt ratio, profitability ratio, etc. These ratios reflect the financial status and operating efficiency of the enterprise and are important indicators for evaluating the market performance of the enterprise.
[0003] However, traditional market summary methods often rely on manual analysis, which is not only inefficient but also easily affected by subjective factors, resulting in inaccurate and incomplete analysis results. With the development of artificial intelligence technology, using AI to analyze financial data for market summary has become a possible solution. To this end, this application proposes a system and method for market summary based on AI analysis of financial data to solve the above problems. Summary of the invention
[0004] 1. Technical issues to be resolved
[0005] In view of the shortcomings of the existing technology, the present invention provides a system and method for market summary based on AI analysis of financial data, so as to solve the problems of low efficiency and accuracy of traditional market summary methods, and help enterprises understand market conditions more quickly and accurately and make more informed decisions.
[0006] (II) Technical solution
[0007] To achieve the above objectives, the technical solution adopted by the present invention is: a system for market summary based on AI analysis of financial data, including a data collection module, a data preprocessing module, a feature extraction module, a model training module and a market summary module;
[0008] The data collection module is responsible for collecting the financial data of the enterprise;
[0009] The data preprocessing module is used to clean, convert and standardize the collected financial data;
[0010] The feature extraction module is used to extract relevant features from the preprocessed financial data;
[0011] The model training module is responsible for training the AI model using the extracted features;
[0012] The market summary module uses the trained AI model to analyze new financial data and generate a market summary report.
[0013] Preferably, when the data collection module collects the financial data of the enterprise, it collects the data through the enterprise's financial system, database or other data sources, and stores it locally or in the cloud.
[0014] Preferably, the data preprocessing module removes duplicate data, missing values and outliers when cleaning the collected financial data, and converts and standardizes the data to meet the requirements of model training.
[0015] Preferably, the relevant features extracted by the feature extraction module are specifically such as operating income, net profit, gross profit margin, and debt-to-asset ratio.
[0016] Preferably, the AI models trained by the model training module include neural networks, decision trees, support vector machines, etc.
[0017] Preferably, the market summary report generated by the market summary module specifically includes market trends, competitor analysis, and industry prospects.
[0018] A method for using a system for market summarization based on AI analysis of financial data, comprising the following steps:
[0019] Step 1: Data collection: Collect financial data through the company's financial system, database or other data sources and store it locally or in the cloud;
[0020] Step 2: Data preprocessing: Clean the collected financial data, remove duplicate data, missing values, and outliers, and then transform and standardize the data to meet the requirements of model training;
[0021] Step 3: Feature extraction: Extract relevant features from the preprocessed financial data, such as operating income, net profit, gross profit margin, debt-to-asset ratio, etc.;
[0022] Step 4: Model training: Use the extracted features to train AI models, such as neural networks, decision trees, support vector machines, etc.
[0023] Step 5: Market summary: Use the trained AI model to analyze the new financial data and generate a market summary report that includes market trends, competitor analysis, industry prospects, etc.
[0024] Preferably, the data preprocessing in step 2 also requires integrating data from different sources to form a unified data set to facilitate subsequent analysis.
[0025] Preferably, the data preprocessing in step 2 also specifies the scope of financial data to be analyzed, including time span (such as quarter, year), industry field, and company type (such as listed companies, unlisted companies).
[0026] (III) Beneficial effects
[0027] The beneficial effects of the present invention are:
[0028] This system and method for market summary based on AI analysis of financial data uses AI technology to automatically analyze financial data, greatly improving the efficiency of market summary through financial data. The AI model can learn a large amount of financial data, thereby improving the accuracy of market summary through financial data. At the same time, it can analyze financial data in multiple dimensions to generate a more comprehensive market summary report. It can also analyze new financial data in real time to keep abreast of changes in market conditions. BRIEF DESCRIPTION OF THE DRAWINGS
[0029] Figure 1 A flow chart is provided for use with the system of the present invention. DETAILED DESCRIPTION
[0030] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0031] The present invention provides a technical solution: a system for market summarization based on AI analysis of financial data, including a data collection module, a data preprocessing module, a feature extraction module, a model training module and a market summary module;
[0032] The data collection module is responsible for collecting the financial data of the enterprise. When the data collection module collects the financial data of the enterprise, it collects the data through the financial system, database or other data sources of the enterprise and stores it locally or in the cloud;
[0033] The data preprocessing module is used to clean, convert and standardize the collected financial data. When cleaning the collected financial data, the data preprocessing module removes duplicate data, missing values and outliers, and converts and standardizes the data to make it meet the requirements of model training;
[0034] The feature extraction module is used to extract relevant features from the preprocessed financial data. The relevant features extracted by the feature extraction module are specifically such as operating income, net profit, gross profit margin, and debt-to-asset ratio;
[0035] The model training module is responsible for training the AI model using the extracted features. The AI models trained by the model training module include neural networks, decision trees, support vector machines, etc. The AI model can learn a large amount of financial data, thereby improving the accuracy of market summarization through financial data;
[0036] The market summary module uses the trained AI model to analyze new financial data and generate a market summary report. The market summary report generated by the market summary module specifically includes market trends, competitor analysis, and industry prospects. It uses AI technology to automatically analyze financial data, greatly improving the efficiency of market summarization through financial data. It analyzes financial data in multiple dimensions to generate a more comprehensive market summary report. It can also analyze new financial data in real time to keep abreast of changes in market conditions.
[0037] Combination Figure 1 , a method for using a system to analyze financial data based on AI to perform market summarization, comprising the following steps:
[0038] Step 1: Data collection: Collect financial data through the company's financial system, database or other data sources and store it locally or in the cloud;
[0039] Step 2: Data preprocessing: clarify the scope of financial data to be analyzed, including time span (such as quarter, year), industry field, company type (such as listed companies, unlisted companies), etc., clean the collected financial data, remove duplicate data, missing values and outliers, and then convert and standardize the data to meet the requirements of model training, and integrate data from different sources to form a unified data set for subsequent analysis;
[0040] Step 3: Feature extraction: Extract relevant features from the preprocessed financial data, such as operating income, net profit, gross profit margin, debt-to-asset ratio, etc.;
[0041] Step 4: Model training: Use the extracted features to train AI models, such as neural networks, decision trees, support vector machines, etc.
[0042] Step 5: Market summary: Use the trained AI model to analyze the new financial data and generate a market summary report that includes market trends, competitor analysis, industry prospects, etc.
[0043] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.
Claims
1. A system for market summarization based on AI analysis of financial data, characterized by: It includes data collection module, data preprocessing module, feature extraction module, model training module and market summary module; The data collection module is responsible for collecting the financial data of the enterprise; The data preprocessing module is used to clean, convert and standardize the collected financial data; The feature extraction module is used to extract relevant features from the preprocessed financial data; The model training module is responsible for training the AI model using the extracted features; The market summary module uses the trained AI model to analyze new financial data and generate a market summary report.
2. The system for market summarization based on AI analysis of financial data according to claim 1, characterized in that: When the data collection module collects the financial data of an enterprise, it collects the data through the financial system, database or other data sources of the enterprise, and stores the data locally or in the cloud.
3. The system for market summarization based on AI analysis of financial data according to claim 1, characterized in that: The data preprocessing module removes duplicate data, missing values and outliers when cleaning the collected financial data, and converts and standardizes the data to make it meet the requirements of model training.
4. The system for market summarization based on AI analysis of financial data according to claim 1, characterized in that: The relevant features extracted by the feature extraction module are specifically such as operating income, net profit, gross profit margin, and debt-to-asset ratio.
5. The system for market summarization based on AI analysis of financial data according to claim 1, characterized in that: The AI models trained by the model training module include neural networks, decision trees, support vector machines, etc.
6. The system for market summarization based on AI analysis of financial data according to claim 1, characterized in that: The market summary report generated by the market summary module specifically includes market trends, competitor analysis, and industry prospects.
7. A method for using a system to analyze financial data based on AI to conduct market summarization, characterized in that: The following steps are involved: Step 1: Data collection: Collect financial data through the company's financial system, database or other data sources and store it locally or in the cloud; Step 2: Data preprocessing: Clean the collected financial data, remove duplicate data, missing values, and outliers, and then transform and standardize the data to meet the requirements of model training; Step 3: Feature extraction: Extract relevant features from the preprocessed financial data, such as operating income, net profit, gross profit margin, debt-to-asset ratio, etc.; Step 4: Model training: Use the extracted features to train AI models, such as neural networks, decision trees, support vector machines, etc. Step 5: Market summary: Use the trained AI model to analyze the new financial data and generate a market summary report that includes market trends, competitor analysis, industry prospects, etc.
8. The system for market summarization based on AI analysis of financial data according to claim 7, characterized in that: The data preprocessing in step 2 also requires integrating data from different sources to form a unified data set to facilitate subsequent analysis.
9. The system for market summarization based on AI analysis of financial data according to claim 7, characterized in that: The data preprocessing in step 2 also specifies the scope of financial data that needs to be analyzed, including time span, industry field, and company type.