Predicting earnings management through machine learning ensemble classifiers
Bibliographic record
Abstract
Abstract In this paper, we utilize six novel ensemble classifiers to predict earnings management (EM) in both its forms, accrual‐based earnings management (AEM) and real earnings management (REM), and then compare the EM prediction accuracy of wrapper feature selection (FS) and filtering FS techniques in the context of EM. Specifically, we integrate three well‐known filtering FS techniques (information gain [IG], principal component analysis [PCA], and relief [Re]) and three popular wrapper FS techniques (particle swarm optimization [PSO], genetic algorithm [GA], and artificial bee colony [ABC]) with the support vector machine (SVM) to generate our ensemble classifiers. We then assess the performance of each of the six ensemble classifiers to predict AEM and REM based on three criteria: type Ι error, type ΙΙ error, and average accuracy. The results show that the ABC‐SVM ensemble classifier outperforms the others in predicting both AEM and REM. We also find that, overall, wrapper FS ensemble classifiers outperform filtering FS ensemble classifiers in predicting AEM and REM and that it is more difficult for our ensemble classifiers to predict REM than to predict AEM. This paper contributes to the literature on EM prediction by introducing six new ensemble classifiers. It is also the first work (to the best of our knowledge) in the domain of ensemble classifiers' applications (a) to consider both REM and AEM in one context and to show that REM is more difficult to predict than AEM and (b) to compare the performance of wrapper and filtering FS techniques in the EM prediction setting.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".