[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127233-en":3,"doc-seo-127233-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127233,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Financial Accounting Research - Explaining the Earnings Management Prediction Model Using the Hybrid of Machine Learning Methods","Knowledge of earnings management is essential for users of accounting information because it affects performance evaluation, profitability forecasting, and the determination of a company’s true value. The research develops a diagnostic model for both accrual-based and real earnings management using machine learning methods—decision tree, support vector machine, k-nearest neighbor, deep learning—and combines them with feature selection via relief and principal component analysis. Using 180 Tehran Stock Exchange firms from 2010–2021, it evaluates accuracy and type I/II errors.","Financial Accounting Research E-ISSN: 2322-3405  \nVol. 16, Issue 2, No.60, Summer 2024, P:53-88 Received: 14.07.2024 Accepted: 30.11.2024  \nFinancial Accounting Research  \nExplaining the Earnings Management Prediction Model Using the Hybrid of Machine Learning Methods  \nHassan Hassani : Ph.D. Student in Accounting, Faculty of Economics and Administrative Sciences,  \nUniversity of Mazandaran, Babolsar, Iran.  \n[h.hassani13@yahoo.com](h.hassani13@yahoo.com)  \nEsfandiar Malekian Kallehbasti *: Professor, Department of Accounting, Faculty of Economics and Administrative Sciences, University of Mazandaran, Babolsar, Iran.  \n[e.malekian@umz.ac.ir](e.malekian@umz.ac.ir)  \nYahya Kamyabi : Professor, Department of Accounting, Faculty of Economics and Administrative  \nSciences, University of Mazandaran, Babolsar, Iran.  \n[kamyabi@umz.ac.ir](kamyabi@umz.ac.ir)  \nAbstract  \nKnowledge of earnings management is essential for users of accounting information due to performance evaluation, profitability forecasting, and determining the true value of the company. The purpose of this research is to provide a model to diagnose accrual-based earnings management and real earnings management through performance evaluation of machine learning methods including decision tree, support vector machine, k-nearest neighbor, deep learning, and combining them with feature selection methods based on relief and principal component analysis. To achieve this goal, 180 companies admitted to the Tehran Stock Exchange were selected as a statistical sample from 2010 to 2021. Also, to test the hypotheses, the criteria of average accuracy and type I and type ΙΙ errors were used. The results show that the performance of accrual-based earnings management forecasting methods based on the relief-based feature selection model is better than the feature selection model based on principal component analysis. This result was confirmed in all prediction methods. However, the results did not show the superiority of the relief-based feature selection model over the principal component analysis-based feature selection model in predicting real earnings management. Also, the findings showed that accrual earnings management can be more accurately predicted than real earnings management. The research results can be of interest to investors, creditors, financial analysts, and auditors. Incorporating machine learning methods can help identify potential earnings management activities.  \nKeywords: Accruals Earnings Management, Real Earnings Management, Earnings Management Forecast, Machine Learning, Feature Selection.  \nIntroduction  \nEarnings management can be described as the discretion utilized by managers to provide generally accepted accounting principles (GAAP)-based financial reports that can affect the relevance and reliability of the presented accounting information. EM can be performed either (1) through deviations from normal business practices to purposefully manipulate earnings; this is called real earnings management (Roychowdhury, 2006), and it affects cash flow from operating activities; or (b) by manipulating reported earnings through accruals, that is accrual-based earnings management, to achieve a suitable earnings figure. As a corporation's earnings are used  \nby different financial statement users (such as shareholders, creditors, and financial analysts) to gauge its performance, detection of earnings management can be interesting and crucial for them. In this context, this study attempts to present prediction tools that aid in detecting earnings management activities. For this purpose, six machine learning methods have been discussed to predict earnings management.  \nMethods & Material  \nA sample of 180 companies listed on the Tehran Stock Exchange during the period 2010-2021 was selected for testing hypotheses. The performance of each machine learning method at predicting accrual-based earnings management and real earnings management was evaluated based on three cr","cbCaicTp8SKcqfUC","https://ap.wps.com/l/cbCaicTp8SKcqfUC","pdf",2088361,2,1,36,"English","en",105,"# Abstract\n# Introduction\n# Methods & Material\n# Finding\n# Conclusion & Results","[{\"question\":\"What is the main goal of the research on earnings management prediction?\",\"answer\":\"To diagnose both accrual-based earnings management and real earnings management using hybrid machine learning methods combined with feature selection.\"},{\"question\":\"Which feature selection approach performed better for predicting accrual-based earnings management?\",\"answer\":\"The relief-based feature selection model showed better forecasting performance than the principal component analysis-based feature selection model across prediction methods.\"},{\"question\":\"How did the model’s predictions differ between accrual-based and real earnings management?\",\"answer\":\"Accrual earnings management could be predicted more accurately than real earnings management, and relief-based feature selection did not prove superior for predicting real earnings management.\"}]","Financial Accounting Research - Explaining the Earnings Management Prediction Model Using the Hybrid of Machine Learning Methods | PDF",1785937661,91,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"financial-accounting-research-explaining-the-earnings-management-prediction-model-using-the-hybrid-of-machine-learning-methods","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/financial-accounting-research-explaining-the-earnings-management-prediction-model-using-the-hybrid-of-machine-learning-methods/127233/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",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},"What is the main goal of the research on earnings management prediction?","Question",{"text":76,"@type":77},"To diagnose both accrual-based earnings management and real earnings management using hybrid machine learning methods combined with feature selection.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which feature selection approach performed better for predicting accrual-based earnings management?",{"text":81,"@type":77},"The relief-based feature selection model showed better forecasting performance than the principal component analysis-based feature selection model across prediction methods.",{"name":83,"@type":74,"acceptedAnswer":84},"How did the model’s predictions differ between accrual-based and real earnings management?",{"text":85,"@type":77},"Accrual earnings management could be predicted more accurately than real earnings management, and relief-based feature selection did not prove superior for predicting real earnings management.","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]