1. A method of using AI for stock valuation, comprising: using natural language processing techniques to analyze news articles and social media posts related to the stock; combining the sentiment analysis with financial data analysis using machine learning algorithms; and generating a valuation report for the stock based on the combined analysis.

2. The method of claim 1, wherein the sentiment analysis is based on natural language processing techniques.

3. The method of claim 1, wherein the sentiment analysis is performed on social media posts from social media platform selected from Twitter, Facebook, LinkedIn, and Reddit.

4. The method of claim 1, wherein the sentiment analysis is performed on news articles from financial news outlets selected from financial newsletter, newspaper, Journal, interview and combination thereof.

5. The method of claim 1, wherein the financial data includes balance sheets, income statements, and cash flow statements.

6. The method of claim 1, wherein the machine learning algorithms used for financial data analysis include deep learning and regression analysis.

7. The method of claim 1, wherein the machine learning algorithms used for sentiment analysis include neural networks and decision trees.

8. The system of claim 1, further comprise generating a forecast for the future performance of the stock based on the combined analysis.

9. A method of using artificial intelligence for stock valuation, comprising: collecting financial data for a stock from a plurality of sources; analyzing the financial data using machine learning algorithms; performing sentiment analysis on news articles and social media posts related to the stock; combining the financial data analysis and sentiment analysis using machine learning algorithms; and generating a valuation report for the stock based on the combined analysis.

10. An AI-driven stock valuation system, comprising: a data collection module for collecting financial data related to a stock from a plurality of sources; a sentiment analysis module for analyzing news articles and social media posts related to the stock; a machine learning module for analyzing the financial data and sentiment analysis results; and a report generation module for generating a valuation report for the stock based on the analysis.   PROPOSING A NEW METHOD TO BUY/SELL THE STOCK THROUGH MOVING AVERAGES Abstract A method of applying artificial intelligence for stock valuation may be included in certain embodiments of the present disclosure. This method may involve using natural language processing techniques to evaluate news items and social media postings that are linked to the stock. In other embodiments, the analysis of sentiment may be combined with the analysis of financial data utilising machine learning techniques. The creation of a value report for the stock based on the combined study is another possible aspect of certain embodiments. Fig. XX , Claims:Claims :

1. A method of using AI for stock valuation, comprising: using natural language processing techniques to analyze news articles and social media posts related to the stock; combining the sentiment analysis with financial data analysis using machine learning algorithms; and generating a valuation report for the stock based on the combined analysis.

2. The method of claim 1, wherein the sentiment analysis is based on natural language processing techniques.

3. The method of claim 1, wherein the sentiment analysis is performed on social media posts from social media platform selected from Twitter, Facebook, LinkedIn, and Reddit.

4. The method of claim 1, wherein the sentiment analysis is performed on news articles from financial news outlets selected from financial newsletter, newspaper, Journal, interview and combination thereof.

5. The method of claim 1, wherein the financial data includes balance sheets, income statements, and cash flow statements.

6. The method of claim 1, wherein the machine learning algorithms used for financial data analysis include deep learning and regression analysis.

7. The method of claim 1, wherein the machine learning algorithms used for sentiment analysis include neural networks and decision trees.

8. The system of claim 1, further comprise generating a forecast for the future performance of the stock based on the combined analysis.

9. A method of using artificial intelligence for stock valuation, comprising: collecting financial data for a stock from a plurality of sources; analyzing the financial data using machine learning algorithms; performing sentiment analysis on news articles and social media posts related to the stock; combining the financial data analysis and sentiment analysis using machine learning algorithms; and generating a valuation report for the stock based on the combined analysis.

10. An AI-driven stock valuation system, comprising: a data collection module for collecting financial data related to a stock from a plurality of sources; a sentiment analysis module for analyzing news articles and social media posts related to the stock; a machine learning module for analyzing the financial data and sentiment analysis results; and a report generation module for generating a valuation report for the stock based on the analysis.

Description:PROPOSING A NEW METHOD TO BUY/SELL THE STOCK THROUGH MOVING AVERAGES

Field of the Invention
[0001] The present invention relates generally to stock valuation system. More particularly, the system and method for stock valuation using artificial intelligence (AI).
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Stock valuation is an essential aspect of investing and financial analysis. The value of a company's stock is an indication of its financial health and future growth prospects. Stock valuation is the process of determining the intrinsic value of a company's stock based on its financial performance, market trends, and other relevant factors. The goal of stock valuation is to estimate the fair value of a stock so that investors can make informed investment decisions. The background research on stock valuation is vast and multidisciplinary, including studies in finance, accounting, economics, and behavioural sciences. One of the most commonly used methods for stock valuation is fundamental analysis. This approach involves analyzing the company's financial statements, industry trends, and economic indicators to estimate the intrinsic value of a stock. Several financial ratios, such as price-to-earnings (P/E) ratio, price-to-book (P/B) ratio, and price-to-sales (P/S) ratio, are used to compare a company's stock price to its earnings, assets, and revenue. Other methods for stock valuation include technical analysis, which uses chart patterns and technical indicators to predict future price movements, and relative valuation, which compares the company's financial metrics to those of its peers and industry averages. Various manual stock valuation technique is disclosed such as Fundamental analysis, technical analysis, Relative valuation and many more.
[0004] Known manual stock valuation technique are time consuming and error-prone. To overcome manual analysis various technological solutions are disclosed in patent literature. Few of them are discussed here.
[0005] The TW200820115 (by PAN JIAN-TING) relates to a method of predicting overall stock market trend by utilizing buy sell strength is provided. "Buy strength" is defined by change of buying quantity of buyer's former and later price quotation on each stock plus transaction quantity in last period. "Sell strength" is defined by change of selling quantity of seller's former and later price quotation on each stock plus transaction quantity in last period. The "buy strength" and "sell strength" of all stocks are multiplied by the weighting factors of each stock respectively to define an "overall bull market strength" and an "overall bear market strength" which are then accumulated for a specific period of time. Therefore, the buy sell strength is rapidly calculated for index futures investor to see the overall stock market trend in very short time and then make advantageous judgments.
[0006] The KR20030071420 (by YOUFIRSTFN CO LTD) relates to a stock information service system and method is provided to allow a user to ask a server about a sale or a purchase on a specific stock item, to simultaneously transmit a corresponding question to plural experts, to receive answers on the sale or purchase of a specific stock item, and to transmit the answer analysis result to the user. CONSTITUTION: The system comprises a user terminal(10), plural expert terminals(20), and a stock information providing server(31). The user terminal(10) enables a user to access the stock information providing server(31), to select specific stock items, and to request an answer to a question about a sale or a purchase of the selected stock item. The expert terminals(20) receive a request of answering the question for a sale or a purchase of the selected stock item, and enable the experts to transmit the answers to the stock information providing server(31). The stock information providing server(31) receives the answers to the question for a sale or a purchase of specific stock items from the expert terminals, analyzes the answers, e.g. calculates an approval rate on a sale or a purchase of a specific stock item, and transmits the analysis result to the user terminal(10).
[0007] The US20100174665 (by LANNG SOREN) relates to a computerized trading system for trading a tradable product at stock exchanges having a user terminal connected to a data communication system being able to provide a graphical user interface (GUI) to the user terminal through which a user can define a number of criteria to be met in order for the system to allow/indicate a trade order of a selected Symbol or allow/indicate a pair trade of two selected Symbols. A method to format a data stream providing a novel result used for the calculation of technical indicators and the derived criteria and/or used for visual presentation of price movements of the tradable product in the computerized trading system by determining the time to close a bar. Product data having product prices is received at different times at the computer system, and the computer system for each closed bar holds corresponding bar data buffers with data representing time of close of bar and last received product price at close of bar.
[0008] While stock valuation techniques can be useful tools for investors and financial analysts, they are not without their limitations. Some of the limitations of stock valuation techniques include: Limited accuracy, Uncertainty, Lack of transparency etc. Thus, there is immense need to alternative technique which overcome limitation of existing technique.

Summary
[0009] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00010] The following paragraphs provide additional support for the claims of the subject application.
[00011] The present invention relates generally to stock valuation system. More particularly, the system and method for stock valuation using artificial intelligence (AI).
[00012] Embodiments of the present disclosure may include a method of using AI for stock valuation, wherein the method including using natural language processing techniques to analyze news articles and social media posts related to the stock. Embodiments may also include combining the sentiment analysis with financial data analysis using machine learning algorithms. Embodiments may also include generating a valuation report for the stock based on the combined analysis.
[00013] In some embodiments, the sentiment analysis may be based on natural language processing techniques. In some embodiments, the sentiment analysis may be performed on social media posts from social media platform selected from Twitter, Facebook, LinkedIn, and Reddit. In some embodiments, the sentiment analysis may be performed on news articles from financial news outlets selected from financial newsletter, newspaper, Journal, interview and combination thereof.
[00014] In some embodiments, the financial data includes balance sheets, income statements, and cash flow statements. In some embodiments, the machine learning algorithms used for financial data analysis include deep learning and regression analysis. In some embodiments, the machine learning algorithms used for sentiment analysis include neural networks and decision trees. In some embodiments, the system, may include generating a forecast for the future performance of the stock based on the combined analysis.
[00015] Embodiments of the present disclosure may also include a method of using artificial intelligence for stock valuation, wherein the method including collecting financial data for a stock from a plurality of sources. Embodiments may also include analyzing the financial data using machine learning algorithms. Embodiments may also include performing sentiment analysis on news articles and social media posts related to the stock. Embodiments may also include combining the financial data analysis and sentiment analysis using machine learning algorithms. Embodiments may also include generating a valuation report for the stock based on the combined analysis.
[00016] Embodiments of the present disclosure may also include an AI-driven stock valuation system, including a data collection module for collecting financial data related to a stock from a plurality of sources. Embodiments may also include a sentiment analysis module for analyzing news articles and social media posts related to the stock. Embodiments may also include a machine learning module for analyzing the financial data and sentiment analysis results. Embodiments may also include a report generation module for generating a valuation report for the stock based on the analysis.
Brief Description of the Drawings
[00017] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00018] FIG. 1 is a flowchart illustrating a method of using AI for stock valuation, according to some embodiments of the present disclosure.
[00019] FIG. 2 is a flowchart illustrating a method of using artificial intelligence for stock valuation, according to some embodiments of the present disclosure.
[00020] FIG. 3 is a block diagram illustrating an AI-driven stock valuation system, according to some embodiments of the present disclosure.
Detailed Description
[00021] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00022] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00023] The present invention relates generally to stock valuation system. More particularly, the system and method for stock valuation using artificial intelligence (AI).
[00024] The application of artificial intelligence (AI) to the process of stock valuation, as shown in Figure 1, is one approach that conforms to certain embodiments of this current disclosure. The flowchart is shown in Figure 1, which may be seen here. The method may, in some implementations, at step 110 include completing an analysis of social media postings and news articles that are associated to the stock using natural language processing algorithms. At step 120 of the method, it is possible to combine the results of the sentiment analysis with those of the analysis of the financial data by utilising machine learning algorithms. Producing a valuation report for the stock based on the combined research may be included as an option at step 130 of the procedure in some implementations of the method. This action is performed at the very last stage of the procedure.
[00025] Methods derived from the study of natural language processing might, in some implementations, serve as the foundation for the sentiment analysis. In certain implementations, the analysis of sentiment can be performed on social media posts from a social media website selected from the group consisting of Twitter, Facebook, LinkedIn, and Reddit. The analysis of sentiment may, in particular implementations, be performed on news articles that have been sourced from a variety of different financial news suppliers. These venues may include financial news letters, newspapers, journals, interviews, or any combination of the aforementioned methods of communication.
[00026] There are other sorts of financial statements, such as balance sheets, income statements, and cash flow statements, all of which are examples of the types of statements that might be included in the data in some implementations. Deep learning and regression analysis are two instances of the different kinds of machine learning algorithms that are two examples of the kinds of machine learning algorithms that may be utilised in various implementations of the process of analysing financial data. Many applications of sentiment analysis may make use of a wide variety of machine learning methods, some examples of which include neural networks and decision trees. A forecast for the future performance of the stock based on the combined study is also included in certain implementations of the system. This estimate is generated based on the combined research. This is one of the many ways that the system may be put into place.
[00027] FIG. 2 is a flowchart that illustrates the technique of using artificial intelligence for stock valuation that is disclosed in some embodiments of the present disclosure. This method can be viewed below and can be found in certain embodiments. In some applications of the method, step 210 may entail the step of collecting financial data for a stock from a variety of various sources. This may be the case in some implementations of the method. During the 220th step of the process, the procedure may include utilising machine learning algorithms in order to conduct an analysis of the financial data. At step 230, the procedure might comprise doing sentiment research on postings and articles relating to the stock that are found in the news as well as on social media platforms. To merge the earlier processes of analysing financial data and sentiment, the method may, at the 240step, add the use of machine learning algorithms to do so. It is possible that you may be requested to produce a valuation report for the stock based on the combined research when you reach step 250 of the method.
[00028] A block diagram representation of an AI-driven stock valuation system 300 is shown in Figure 3. This representation of the system is in line with some features of the current disclosure and represents the system. The AI-driven stock valuation system 300 may, in some implementations, includes a data collection module 310 for collecting financial data related to a stock from a plurality of sources; a sentiment analysis module 320 for analysing news articles and social media posts related to the stock; a machine learning module 330 for analysing the financial data and sentiment analysis results; and a report generation module 340 for generating a valuation report.
[00029] A method of applying artificial intelligence for stock valuation may be included in certain embodiments of the present disclosure. This method may involve using natural language processing techniques to evaluate news items and social media postings that are linked to the stock. In other embodiments, the analysis of sentiment may be combined with the analysis of financial data utilising machine learning techniques. The creation of a value report for the stock based on the combined study is another possible aspect of certain embodiments.
[00030] Techniques from the field of natural language processing may, in some implementations, form the basis for the sentiment analysis. The analysis of sentiment may be carried out on social media posts from a social media site chosen from the group consisting of Twitter, Facebook, LinkedIn, and Reddit in certain embodiments. In certain implementations, the sentiment analysis may be carried out on news pieces sourced from various financial news providers. These outlets may include financial news letters, newspapers, journals, interviews, or any combination of the aforementioned.
[00031] The balance sheets, income statements, and cash flow statements are examples of the types of financial statements that can be included in the data. Deep learning and regression analysis are two examples of the machine learning algorithms that may be utilised in various configurations for the purpose of analysing financial data. Neural networks and decision trees are two examples of the types of machine learning algorithms that can be utilised in various embodiments of sentiment analysis. The system may, in certain implementations, include the generation of a forecast for the future performance of the stock based on the combined analysis.
[00032] A method of applying artificial intelligence for stock valuation may also be included in embodiments of the current disclosure. This technique may comprise gathering financial data for a stock from a variety of sources. Analyzing financial data with machine learning algorithms is another possibility that could be included in embodiments. Conducting sentiment analysis on posts and articles in social media and the news that are related to the stock is another possible aspect of embodiments. In some embodiments, the analysis of financial data and sentiment are combined using machine learning algorithms. The creation of a value report for the stock based on the combined study is another possible aspect of certain embodiments.
[00033] An AI-driven stock valuation system may also be included in some embodiments of the current disclosure. Such a system would consist of a data collection module, which would be used to collect financial data relating to a stock from a variety of different sources. A sentiment analysis module may also be included in embodiments. This module is used to analyse social media posts and news articles that are related to the stock. In some embodiments, there may also be a machine learning module included for the purpose of analysing the results of sentiment analysis and the financial data. A report creation module that can generate a valuation report for the stock based on the analysis is another component that might be included in embodiments.
[00034] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00035] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00036] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00037] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00038] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

 

Claims
I/We Claim:
1. A method of using AI for stock valuation, comprising:
using natural language processing techniques to analyze news articles and social media posts related to the stock;
combining the sentiment analysis with financial data analysis using machine learning algorithms; and
generating a valuation report for the stock based on the combined analysis.

2. The method of claim 1, wherein the sentiment analysis is based on natural language processing techniques.

3. The method of claim 1, wherein the sentiment analysis is performed on social media posts from social media platform selected from Twitter, Facebook, LinkedIn, and Reddit.

4. The method of claim 1, wherein the sentiment analysis is performed on news articles from financial news outlets selected from financial newsletter, newspaper, Journal, interview and combination thereof.

5. The method of claim 1, wherein the financial data includes balance sheets, income statements, and cash flow statements.

6. The method of claim 1, wherein the machine learning algorithms used for financial data analysis include deep learning and regression analysis.

7. The method of claim 1, wherein the machine learning algorithms used for sentiment analysis include neural networks and decision trees.

8. The system of claim 1, further comprise generating a forecast for the future performance of the stock based on the combined analysis.

9. A method of using artificial intelligence for stock valuation, comprising:
collecting financial data for a stock from a plurality of sources;
analyzing the financial data using machine learning algorithms;
performing sentiment analysis on news articles and social media posts related to the stock;
combining the financial data analysis and sentiment analysis using machine learning algorithms; and
generating a valuation report for the stock based on the combined analysis.

10. An AI-driven stock valuation system, comprising:
a data collection module for collecting financial data related to a stock from a plurality of sources;
a sentiment analysis module for analyzing news articles and social media posts related to the stock;
a machine learning module for analyzing the financial data and sentiment analysis results; and
a report generation module for generating a valuation report for the stock based on the analysis.
 

PROPOSING A NEW METHOD TO BUY/SELL THE STOCK THROUGH MOVING AVERAGES

Abstract
A method of applying artificial intelligence for stock valuation may be included in certain embodiments of the present disclosure. This method may involve using natural language processing techniques to evaluate news items and social media postings that are linked to the stock. In other embodiments, the analysis of sentiment may be combined with the analysis of financial data utilising machine learning techniques. The creation of a value report for the stock based on the combined study is another possible aspect of certain embodiments.

Fig. XX , Claims:Claims
I/We Claim:
1. A method of using AI for stock valuation, comprising:
using natural language processing techniques to analyze news articles and social media posts related to the stock;
combining the sentiment analysis with financial data analysis using machine learning algorithms; and
generating a valuation report for the stock based on the combined analysis.

2. The method of claim 1, wherein the sentiment analysis is based on natural language processing techniques.

3. The method of claim 1, wherein the sentiment analysis is performed on social media posts from social media platform selected from Twitter, Facebook, LinkedIn, and Reddit.

4. The method of claim 1, wherein the sentiment analysis is performed on news articles from financial news outlets selected from financial newsletter, newspaper, Journal, interview and combination thereof.

5. The method of claim 1, wherein the financial data includes balance sheets, income statements, and cash flow statements.

6. The method of claim 1, wherein the machine learning algorithms used for financial data analysis include deep learning and regression analysis.

7. The method of claim 1, wherein the machine learning algorithms used for sentiment analysis include neural networks and decision trees.

8. The system of claim 1, further comprise generating a forecast for the future performance of the stock based on the combined analysis.

9. A method of using artificial intelligence for stock valuation, comprising:
collecting financial data for a stock from a plurality of sources;
analyzing the financial data using machine learning algorithms;
performing sentiment analysis on news articles and social media posts related to the stock;
combining the financial data analysis and sentiment analysis using machine learning algorithms; and
generating a valuation report for the stock based on the combined analysis.

10. An AI-driven stock valuation system, comprising:
a data collection module for collecting financial data related to a stock from a plurality of sources;
a sentiment analysis module for analyzing news articles and social media posts related to the stock;
a machine learning module for analyzing the financial data and sentiment analysis results; and
a report generation module for generating a valuation report for the stock based on the analysis.