Adel Almasarwah, Khalid Y. Aram, Yaseen S. Alhaj-Yaseen
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引用次数: 0
Abstract
Purpose
This study aims to apply machine learning (ML) to identify new financial elements managers might use for earnings management (EM), assessing their impact on the Standard Jones Model and modified Jones model for EM detection and examining managerial motives for using these components.
Design/methodology/approach
Using eXtreme gradient boosting on 23,310 the US firm-year observations from 2012 to2021, the study pinpoints nine financial variables potentially used for earnings manipulation, not covered by traditional accruals models.
Findings
Cost of goods sold and earnings before interest, taxes, depreciation and amortization are identified as the most significant for EM, with relative importances of 40.2% and 11.5%, respectively.
Research limitations/implications
The study’s scope, limited to a specific data set and timeframe, and the exclusion of some financial variables may impact the findings’ broader applicability.
Practical implications
The results are crucial for researchers, practitioners, regulators and investors, offering strategies for detecting and addressing EM.
Social implications
Insights from the study advocate for greater financial transparency and integrity in businesses.
Originality/value
By incorporating ML in EM detection and spotlighting overlooked financial variables, the research brings fresh perspectives and opens new avenues for further exploration in the field.
目的本研究旨在应用机器学习(ML)识别管理者可能用于收益管理(EM)的新财务要素,评估其对标准琼斯模型(Standard Jones Model)和修正琼斯模型(Modified Jones Model)的影响,并研究管理者使用这些要素的动机。研究结果销售成本和未计利息、税项、折旧及摊销前的利润被认定为对收益管理最重要的因素,其相对重要性分别为 40.2% 和 11.5%。研究的局限性/影响由于研究范围仅限于特定的数据集和时间范围,且排除了一些财务变量,因此可能会影响研究结果的广泛适用性。社会意义本研究的观点倡导提高企业的财务透明度和诚信度。原创性/价值通过将 ML 纳入 EM 检测并强调被忽视的财务变量,本研究带来了全新的视角,并为该领域的进一步探索开辟了新的途径。