Accruals quality and share price of non-financial firms listed on the Nigerian exchange

Ogbaisi, Sebastine Abhus; Aronmwan, Edosa Joshua

doi:10.1504/ajaaf.2022.128417

Cited by 1 publication

(1 citation statement)

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“…Research identifies improvements needed in accrual models, highlighting their occasional distortion of economic realities, sensitivity to accounting choices and omission of cash flow data. These models also suffer from limited generalizability due to assumptions not reflecting real-world conditions and vulnerability to changes in variable selection or model specification, questioning their robustness (McNichols and Stubben, 2018; Ogbaisi and Aronmwan, 2022; Dbouk and Zaarour, 2017; Purba et al , 2022; Dechow and Dichev, 2002; Dechow et al , 2012; Peasnell et al , 2000). Despite these critiques, integrating ML presents opportunities to improve financial models’ accuracy and relevance, emphasizing the need for ongoing innovation and critical evaluation in financial analytics.…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 99%

Identifying new earnings management components: a machine learning approach

Almasarwah,

Aram,

Alhaj-Yaseen

2024

ARJ

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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.

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