A Test of Income Smoothing Using Pseudo Fiscal Years
Bibliographic record
Abstract
The purpose of our study is to further understand managerial incentives that affect the volatility of reported earnings. Prior research suggests that the volatility of fourth-quarter earnings may be affected by the integral approach to accounting (i.e., “settling up” of accrual estimation errors in the first three quarters of the fiscal year) or earnings management to meet certain reporting objectives (e.g., analyst forecasts). We suggest that another factor affecting fourth-quarter earnings is managers’ intentional smoothing of fiscal-year earnings. For each firm, we create pseudo-year earnings using four consecutive quarters other than the four quarters of the reported fiscal year. We then compare the earnings volatility of pseudo years to the earnings volatility of the firm’s own reported fiscal year. We find evidence consistent with fourth-quarter accruals reflecting managerial incentives to smooth fiscal-year earnings. This conclusion is validated by several cross-sectional tests, the pattern in quarterly cash flows and accruals, and several robustness tests. Overall, we contribute to the literature exploring alternative explanations for the differential volatility of fiscal-year and fourth-quarter earnings. This paper was accepted by Brian Bushee, accounting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".