[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121561-en":3,"doc-seo-121561-105":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":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},121561,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Predicting Financial Distress Using Supervised Machine Learning Algorithms - An Application on Borsa Istanbul","The study aims to identify the most significant variables for earlier detection of financial distress and to determine the most effective supervised machine learning model. Support Vector Machine, Logistic Regression, Random Forest, and K-nearest neighbors are applied to Turkish firms listed on Borsa Istanbul from 2012 to 2021. Random Forest achieves the highest precision, accuracy, and recall, and five key financial indicators are highlighted. Cash ratios, profitability ratios, and sales growth are found to be most important one-year ahead.","PREDICTING FINANCIAL DISTRESS USING SUPERVISED MACHINE LEARNING ALGORITHMS: AN APPLICATION ON BORSA ISTANBUL  \nDOI: 10.17261/Pressacademia.2023.1828  \nJEFA-V.10-ISS.4-2023(3)-p.217-223  \nSeyfullah Selimefendigil  \nTurkish-German University, Department of Business Administration Istanbul, Turkiye.  \n[selimefendigil@tau.edu.tr](selimefendigil@tau.edu.tr), ORCID: 0000-0001-7017-9673  \nDate Received: October, 29, 2023 Date Accepted: December 21, 2023    \n\n| To cite this document\u003Cbr>Selimefendigil, S., (2023) . Predicting financial distress using supervised machine learning algorithms: an application on Borsa Istanbul. Journal of Economics, Finance and Accounting (JEFA), 10(4), 217-223.\u003Cbr>Permanent link to this document: [http://doi.org/10.17261/Pressacademia.2023.1828](http://doi.org/10.17261/Pressacademia.2023.1828)\u003Cbr>[Copyright:](Copyright: Published by PressAcademia and limited licensed re-use rights only.)[ Published by PressAcademia and limited licensed re-use rights only.](Copyright: Published by PressAcademia and limited licensed re-use rights only.) |\n| --- |\n| ABSTRACT\u003Cbr>Purpose-The main purpose of this study is to identify the most significant variables to detect financial distress earlier and to find the best machine learning algorithm model.\u003Cbr>Methodology-This study has used Support Vector Machine, Logistic Regression, Random Forest and K-nearest neighbors method techniques to predict the financial distress prediction for the companies of Turkey between 2012 and 2021.\u003Cbr>Findings-As a result of the study, it has been determined that Random Forest provides the best results in terms of precision, accuracy, and recall. Further, this study has found the most important five independent variables to determine the financial distress status of the firms. In this way, it has been found that Current Assets/ Current Liabilities, Working Capital / Total Assets, Gross profit / Revenue, Retained Earnings / Total Assets and Sales growth rate are the most useful variables to determine financial distress status of Turkish firms earlier.\u003Cbr>Conclusion-This study has concluded that cash ratios and profitability ratios and sales growth are the most important independent variables to determine financial distress one-year ahead. Furthermore, it has been found that random forest is the best machine learning method among other supervised machine learning methods used in this study.\u003Cbr>Keywords: Financial distress, support vector machine, logistic regression, random forest, k-nearest neighbors\u003Cbr>JEL Codes: G32, G33, C52 . |\n\n1. INTRODUCTION  \nThe prediction of bankruptcy is one of the most pressing issues in finance. As a result, financial distress (i.e. bankruptcy likelihood) prediction continues to be a hot topic in finance research (Elhoseny et al., 2022) . Studies on predicting financial distress have been in progress for more than a half century. To identify the corporate solvency the financial distress prediction is a key issue. The primary objective of the financial distress prediction is to distinguish the stabilize companies from firms at the risk of financial distress. Financial risk is important for the investors as they decide to invest with their risk preferences. Regulators also benefit from the rapid identification of risk of each firm and are able to perform well in terms of supervision and management. The result of this has been a growing interest in the accurate prediction of business risks both in academia and in the business community (Qian et al., 2022) .  \nWhile a consensus definition of financial distress remains elusive, it is acknowledged that varying degrees of financial distress exist. In its mildest form, financial distress may manifest as a shortage of cash. On the other hand, the most severe cases may involve a liquidity crisis or even bankruptcy (Özparlak and Özdemir Dilidüzgün, 2022) . Although bankruptcy and financial failure are used interchangeably, bankruptcy is defined as the last resort to recover fro","cbCaihsiAjPz4NMA","https://ap.wps.com/l/cbCaihsiAjPz4NMA","pdf",386666,1,7,"English","en",105,"# Introduction\n## Background and motivation\n## Machine learning and supervised learning\n## Research purpose and dataset overview","[{\"question\":\"Which supervised machine learning algorithms are used to predict financial distress?\",\"answer\":\"The study uses Support Vector Machine, Logistic Regression, Random Forest, and K-nearest neighbors to predict financial distress for firms listed on Borsa Istanbul.\"},{\"question\":\"What result is achieved by Random Forest compared with other models?\",\"answer\":\"Random Forest delivers the best performance in precision, accuracy, and recall among the tested supervised algorithms.\"},{\"question\":\"Which variables are most useful for determining financial distress status and one-year ahead risk?\",\"answer\":\"Five key independent variables are identified for distress status: Current Assets/Current Liabilities, Working Capital/Total Assets, Gross profit/Revenue, Retained Earnings/Total Assets, and Sales growth rate. Cash ratios and profitability ratios alongside sales growth are most important one-year ahead.\"}]","Predicting Financial Distress Using Supervised Machine Learning Algorithms - An Application on Borsa Istanbul | PDF",1785736251,18,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"predicting-financial-distress-using-supervised-machine-learning-algorithms-an-application-on-borsa-istanbul","",{"@graph":36,"@context":85},[37,54,68],{"@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/predicting-financial-distress-using-supervised-machine-learning-algorithms-an-application-on-borsa-istanbul/121561/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which supervised machine learning algorithms are used to predict financial distress?","Question",{"text":75,"@type":76},"The study uses Support Vector Machine, Logistic Regression, Random Forest, and K-nearest neighbors to predict financial distress for firms listed on Borsa Istanbul.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What result is achieved by Random Forest compared with other models?",{"text":80,"@type":76},"Random Forest delivers the best performance in precision, accuracy, and recall among the tested supervised algorithms.",{"name":82,"@type":73,"acceptedAnswer":83},"Which variables are most useful for determining financial distress status and one-year ahead risk?",{"text":84,"@type":76},"Five key independent variables are identified for distress status: Current Assets/Current Liabilities, Working Capital/Total Assets, Gross profit/Revenue, Retained Earnings/Total Assets, and Sales growth rate. 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