arbiter
JournalsPricingAccount

Abnormal Accrual Estimation: an Automation Data Analysis Technique

Francesca Rossignoli & Nicola Tommasi

International Journal of Data Analysis Techniques and Strategies2025https://doi.org/10.1504/ijdats.2025.10069523article
AJG 1
Weight
0.50

What the paper says

Accounting studies rely on predictive analytics to estimate abnormal accruals as indicators of managerial opportunism.Abnormal accruals are estimated by running predictive models and manually imposing a combination of conditions to select the control sample.This process is executed using loops where the estimation is repeated over the control observations meeting the combined conditions.The recursive estimation generates several inefficiencies.We provide a technique to estimate abnormal measures by automatising: i) the estimation of the predictive model; and ii) the selection of the control sample according to multiple procedures.The command offers a unique information set about the estimation results and process.We illustrate the use of abnormalest through empirical applications.We compare the accuracy of predictions under different approaches and models.The command abnormalest allows to overcome the inefficiencies, provides a unique set of information about the estimation, and is extendible to every social science.

Open paper page →

Cite this paper

https://doi.org/https://doi.org/10.1504/ijdats.2025.10069523

Or copy a formatted citation

@article{francesca2025,
  title        = {{Abnormal Accrual Estimation: an Automation Data Analysis Technique}},
  author       = {Francesca Rossignoli & Nicola Tommasi},
  journal      = {International Journal of Data Analysis Techniques and Strategies},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijdats.2025.10069523},
}

Paste directly into BibTeX, Zotero, or your reference manager.

Flag this paper

Abnormal Accrual Estimation: an Automation Data Analysis Technique

Flags are reviewed by the Arbiter methodology team within 5 business days.

Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.