[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118456-en":3,"doc-seo-118456-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118456,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Analysis of Financial Reports in Companies Using Machine Learning","The article develops new algorithms for automated analysis of corporate financial reports using machine learning to improve the efficiency and accuracy of converting financial information into text. It focuses on deep learning and neural networks for generating and interpreting textual data from financial statements. The work systematizes technologies for text-data development, analyzes neural-network text-generation methods, and evaluates the prospects of machine learning for this task. It also details the creation of a Python-based module for automated report analysis, including its technical specification, structure, and functionality, and demonstrates results on Microsoft, Alphabet, and Apple.","Analysis of financial reports in companies using machine learning  \n[http://doi.org/10.61093/fmir.7](http://doi.org/10.61093/fmir.7)(4).135-154.2023  \nPiven Artem,  \nSumy State University, Sumy, Ukraine  \nResearch Supervisor: Tetiana Vasylieva, Dr., Prof., Director of Academic and Research Institute for Business, Economics and Management Sumy State University, Ukraine  \nAbstract. The article aims to develop new algorithms for the automated analysis of financial reports based on machine learning algorithms, which increase the efficiency and accuracy of converting financial information into a text form. In this context, special attention is paid to deep learning methods and neural networks that contribute to automating and analyzing financial reports and their further interpretation. The article examines the problems of generating text data from financial statements, describes the general characteristics of this process, and systematizes the technologies used to solve the task of developing text data and available methods of machine learning. Specific technologies of text generation using neural networks were analyzed, and the potential and prospects of machine learning in the creation of text data based on the analysis of financial reports were investigated. The process of developing a module intended for automated analysis of financial statements is described in detail, a technical task is created, which is necessary to solve the given task, and the structure and functionality of the developed module in the automated system are described. The result is a developed module for automated analysis of financial reports. Given that the module is created using Python, it can be easily integrated into different systems or function as an independent system, for example, a website or an application for a personal computer. The results of the developed automated module are demonstrated in the example of the analysis of financial reports of the companies Microsoft, Alphabet, and Apple.  \nJEL Classification: G2, G10, G20 .  \nReceived: 12.10.2023 Accepted: 10.12.2023 Published: 31.12.2023  \nFunding: There is no funding for this research.  \nPublisher: Academic Research and Publishing UG (i. G.) (Germany)  \nCite as: Piven, A. (2023) . Analysis of financial reports in companies using machine learning. Financial Markets, Institutions and Risks, 7(4), 135-154. [http://doi.org/10.61093/fmir.7](http://doi.org/10.61093/fmir.7)(4).135-154.2023  \nCopyright: © 2023 by the author. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)).  \nIntroduction  \nGrowing volumes of financial data and their complexity require more efficient tools for their processing and analysis. Traditional economic analysis methods often require significant time and resources, which can limit the speed of decision-making. Machine learning provides opportunities to automate and optimize analysis processes, increasing the accuracy and speed of information processing. It, in turn, contributes to a better understanding of the company's financial condition and increases the effectiveness of management decisions.  \nApplying modules developed based on machine learning to analyze financial reports opens up vast opportunities for identifying trends, anomalies and risks that traditional research cannot detect. In addition, these technologies allow the automation of routine analysis processes, which frees up resources to focus on more complex tasks and strategic planning.  \nMachine learning (ML) is a subfield of artificial intelligence (AI) [29] that focuses on the development of algorithms and statistical models that allow computer systems to improve their performance automatically through experience and the use of data. Machine learning models learn from data. It means that they analyze data sets and identify patter","cbCaid0gczUxdRMl","https://ap.wps.com/l/cbCaid0gczUxdRMl","pdf",761145,1,20,"English","en",105,"# Introduction\n## Machine learning overview and learning types\n## Deep learning and neural network architectures\n## Ensemble methods and automated analysis module","[{\"question\":\"What problem does the article address in analyzing financial reports?\",\"answer\":\"It addresses the need for more efficient tools to process growing volumes and complexity of financial data compared with traditional methods that can be slow and resource-intensive.\"},{\"question\":\"How does the research use machine learning in financial reporting analysis?\",\"answer\":\"It applies machine learning, especially deep learning with neural networks, to automate the conversion of financial statements into text data and support interpretation through learned patterns.\"},{\"question\":\"What is the developed system component and how is it demonstrated?\",\"answer\":\"The research describes a Python-based module for automated analysis of financial statements, specifying its technical task, structure, and functionality, and demonstrating results using Microsoft, Alphabet, and Apple reports.\"}]","Analysis of Financial Reports in Companies Using Machine Learning | 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