Earnings management visualization and prediction using machine learning methods

IF 6 3区管理学 Q2 BUSINESS

International Journal of Accounting Information Systems Pub Date : 2025-03-10 DOI:10.1016/j.accinf.2025.100743

David Veganzones , Eric Séverin

{"title":"Earnings management visualization and prediction using machine learning methods","authors":"David Veganzones , Eric Séverin","doi":"10.1016/j.accinf.2025.100743","DOIUrl":null,"url":null,"abstract":"<div><div>To create new insights and understanding of earnings management, this study attempts to diagnose firms’ financial profiles using machine learning methods and thereby provide a visual representation of the financial profiles that characterize earnings management strategies (upward and downward) and tools (accruals and real activities). By applying a novel machine learning method to detect signs of earnings management, this research reveals diverse financial profiles related to earnings management. Firms that conduct downward manipulation (accruals and real activities) share a sound financial profile. For firms that manipulate earnings upward, different types of financial distress influence the earnings management tool they use: Companies with liquidity constraints undertake accruals earnings management; companies with solvency difficulties are prone to real activities management. Notably, the proposed machine learning method outperforms traditional prediction methods in detecting signals of earnings management.</div></div>","PeriodicalId":47170,"journal":{"name":"International Journal of Accounting Information Systems","volume":"56 ","pages":"Article 100743"},"PeriodicalIF":6.0000,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Accounting Information Systems","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1467089525000193","RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}

引用次数: 0

Abstract

To create new insights and understanding of earnings management, this study attempts to diagnose firms’ financial profiles using machine learning methods and thereby provide a visual representation of the financial profiles that characterize earnings management strategies (upward and downward) and tools (accruals and real activities). By applying a novel machine learning method to detect signs of earnings management, this research reveals diverse financial profiles related to earnings management. Firms that conduct downward manipulation (accruals and real activities) share a sound financial profile. For firms that manipulate earnings upward, different types of financial distress influence the earnings management tool they use: Companies with liquidity constraints undertake accruals earnings management; companies with solvency difficulties are prone to real activities management. Notably, the proposed machine learning method outperforms traditional prediction methods in detecting signals of earnings management.

查看原文本刊更多论文

使用机器学习方法的盈余管理可视化和预测

为了创造对盈余管理的新见解和理解，本研究试图使用机器学习方法诊断公司的财务概况，从而提供表征盈余管理策略（向上和向下）和工具（应计项目和实际活动）的财务概况的可视化表示。通过应用一种新的机器学习方法来检测盈余管理的迹象，本研究揭示了与盈余管理相关的各种财务概况。进行向下操纵（应计项目和实际活动）的公司都有良好的财务状况。对于向上操纵盈余的公司，不同类型的财务困境影响其使用的盈余管理工具：流动性受限的公司采用应计盈余管理；有偿付能力困难的公司容易进行实际活动管理。值得注意的是，本文提出的机器学习方法在检测盈余管理信号方面优于传统的预测方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

International Journal of Accounting Information Systems Multiple-

CiteScore

9.00

自引率

6.50%

发文量

期刊介绍： The International Journal of Accounting Information Systems will publish thoughtful, well developed articles that examine the rapidly evolving relationship between accounting and information technology. Articles may range from empirical to analytical, from practice-based to the development of new techniques, but must be related to problems facing the integration of accounting and information technology. The journal will address (but will not limit itself to) the following specific issues: control and auditability of information systems; management of information technology; artificial intelligence research in accounting; development issues in accounting and information systems; human factors issues related to information technology; development of theories related to information technology; methodological issues in information technology research; information systems validation; human–computer interaction research in accounting information systems. The journal welcomes and encourages articles from both practitioners and academicians.