[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119070-en":3,"doc-seo-119070-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},119070,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Machine Learning and Data Balancing Methods for Bankruptcy Prediction - Research Report","The paper examines the use of various machine learning algorithms to forecast corporate bankruptcy using financial indicators. Competing data-preparation strategies are compared, with a focus on dataset balancing. Nine machine learning algorithms are trained, while five balancing methods—random oversampling, SMOTE, ADASYN, random undersampling, and near miss—are applied for classification. Results indicate the strongest performance for small, imbalanced samples with bagging and random forest combined with Near-Miss and random undersampling, and for large samples with artificial neural networks and decision trees combined with SMOTE and random resampling.","Contents lists available at Vilnius University Press  \nEkonomika ISSN 1392-1258 eISSN 2424-6166  \n2023, vol. 102(2), pp. 28–46 DOI: [https://doi.org/10.15388/Ekon.2023.102.2.2](https://doi.org/10.15388/Ekon.2023.102.2.2)  \n\n| Machine Learning and Data Balancing Methods for Bankruptcy Prediction\u003Cbr>Prof. Olena Liashenko\u003Cbr>Taras Shevchenko National University of Kyiv, Ukraine Email: [olenalyashenko@knu.ua](olenalyashenko@knu.ua)\u003Cbr>[Assoc. prof. Tetyana Kravets](Assoc. prof. Tetyana Kravets)\u003Cbr>Taras Shevchenko National University of Kyiv, Ukraine Email: [tetiana.kravets@knu.ua](tetiana.kravets@knu.ua)\u003Cbr>Yevhenii Kostovetskyi\u003Cbr>Taras Shevchenko National University of Kyiv, Ukraine Email: [yevheniikostovetskyi@knu.ua](yevheniikostovetskyi@knu.ua) |\n| --- |\n| Abstract. The paper examines the use of various machine learning algorithms for the task of forecasting the company’s bankruptcy based on financial indicators. Different approaches to the formation of the data set on which the models are trained are compared, in particular, data balancing methods. Nine machine learning algorithms are implemented, in addition five data balancing methods (random oversampling, SMOTE, ADASYN, random undersampling, and near miss) were applied to classification tasks. It was found that bagging and random forest together with Near-Miss and Random under-sampling showed the best results in terms of the possibility of identifying bankrupt companies in small samples, while artificial neural networks and decision tree methods, together with SMOTE and random resampling, worked better on large samples. With highly unbalanced data accumulation, both small and large training samples can be used to distinguish between bankrupt companies. Keywords: bankruptcy, bankruptcy forecasting, machine learning, data balancing, binary classification. |\n\n1. Introduction  \nBankruptcy is the final stage of the crisis state of an enterprise, which is characterized by the fixation of negative results of financial and economic activity, ranging from a temporary inability to fulfill monetary obligations to a full-fledged stable inability to pay debts.  \nThe crisis of the enterprise can be caused by external and internal factors. External factors are objective to the bankrupt enterprise. They do not directly depend on the actions of the enterprise and cannot be prevented or controlled. The internal causes of bankruptcy are determined by problems within the company itself, they are subjective to the enterprise, and if they are detected in a timely manner, can be eliminated in order to avoid a crisis (Brent, 2017) .  \nReceived: 23/12/2022 . Revised: 13/04/2023 . Accepted: 01/06/2023  \nCopyright © 2023 Olena Liashenko, Tetyana Kravets, Yevhenii Kostovetskyi. Published by Vilnius University Press  \nThis is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nOlena Liashenko et al. Machine Learning and Data Balancing Methods for Bankruptcy Prediction  \nIn the conditions of growing economic instability in the world, the urgency of the problem of the financial crisis is increasing both at the global and national levels, and atthe level of a particular enterprise. Companies, especially small ones, always have a hard time in crisis situations and often cannot remain solvent after a crisis occurs in a company, as a result of which most of them are forced to declare themselves bankrupt.  \nTo combat bankruptcy, it is customary to use three approaches: bankruptcy prevention, which involves the use of methods for predicting and determining the probability of bankruptcy of enterprises and timely informing management about potential threats and risks; bankruptcy warning, which consists in the introduction of anti-crisis management, controlling and economic security systems at the enterprise in order to prepare a strat","cbCaiiDbZjacd1qm","https://ap.wps.com/l/cbCaiiDbZjacd1qm","pdf",1307598,1,19,"English","en",105,"# Introduction\n## Approaches to bankruptcy management\n## Bankruptcy forecasting with modern models\n# Methods and data preparation\n## Data set balancing strategies\n## Classification experiments\n# Results and discussion\n## Performance on small vs. large samples\n## Best-performing model combinations","[{\"question\":\"Which data balancing methods are evaluated in the study?\",\"answer\":\"The study applies random oversampling, SMOTE, ADASYN, random undersampling, and near miss to classification tasks.\"},{\"question\":\"How do results differ between small and large training samples?\",\"answer\":\"Small samples perform best with bagging and random forest combined with Near-Miss and random undersampling, while large samples perform better with artificial neural networks and decision trees combined with SMOTE and random resampling.\"},{\"question\":\"What forecasting task is the paper focused on?\",\"answer\":\"The paper focuses on predicting whether a company will become bankrupt using financial indicators, treating it as a binary classification problem.\"}]","Machine Learning and Data Balancing Methods for Bankruptcy Prediction - Research Report | PDF",1785722174,48,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-and-data-balancing-methods-for-bankruptcy-prediction-research-report","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-and-data-balancing-methods-for-bankruptcy-prediction-research-report/119070/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Which data balancing methods are evaluated in the study?","Question",{"text":76,"@type":77},"The study applies random oversampling, SMOTE, ADASYN, random undersampling, and near miss to classification tasks.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How do results differ between small and large training samples?",{"text":81,"@type":77},"Small samples perform best with bagging and random forest combined with Near-Miss and random undersampling, while large samples perform better with artificial neural networks and decision trees combined with SMOTE and random resampling.",{"name":83,"@type":74,"acceptedAnswer":84},"What forecasting task is the paper focused on?",{"text":85,"@type":77},"The paper focuses on predicting whether a company will become bankrupt using financial indicators, treating it as a binary classification problem.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},"General","general"]