Does Accrual Management Impair the Performance of Earnings-Based Valuation Models?
Bibliographic record
Abstract
This study examines empirically how the presence of accrual management may affect firm valuation. We compare the performance of earnings-based and non-earnings-based valuation models, represented by Residual Income Model (RIM) and Discounted Cash Flow (DCF), respectively, based on the absolute percentage pricing and valuation errors for two subsets of US firms: “Suspect” firms that are likely to have engaged in accrual management and “Normal” firms matched on industry, year and size. Results indicate that RIM enjoys an accuracy advantage over DCF when accrual management is not a serious concern. However, the presence of accrual management significantly narrows RIM’s accuracy advantage over DCF from the level observed for the matched Normal firms. These results are robust to the choice of model benchmark (i.e., current stock price vs. ex post intrinsic value), alternative definitions of Suspect (i.e., loss or earnings-decline avoidance vs. earnings-decline avoidance only vs. loss avoidance only) and of Normal firms (i.e., excluding vs. including real activity manipulators), and different assumptions about post-horizon growth (i.e., 2% vs. 4%). The overall conclusion that accrual management impairs RIM’s performance extends to settings where the regression model is expanded to include accrual components and when we focus on large, rather than small, earnings manipulators. Taken together, these results highlight the importance of considering earnings quality when assessing the performance of earnings-based valuation models.
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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 | 0.032 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| 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".