Information uncertainty of fiscal year end quarter earnings
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate whether within the same firm, earnings risk is exacerbated in the fiscal year end (FYE) quarters relative to that of other quarters, more importantly, if this type of earnings risk is unique. Further, the authors discuss solutions to mitigate this type of information risk. Design/methodology/approach This study provides evidence that the information risk associated with FYE quarter earnings cannot be explained by other identified risk factors. Solutions to mitigate this risk include strong corporate governance and a more streamlined financial reporting structure. Findings The paper shows that there is significantly lower earnings response coefficient for FYE quarters than for non-FYE quarters (1984–2015). Furthermore, strong corporate governance and a more streamlined financial reporting structure, either by firms willingly reducing the usage of extraordinary item reporting or by FASB codification changes such as FASB 145, can help mitigate this type of information uncertainty. Research limitations/implications This study explains that the causes of the exacerbated information risk associated with FYE quarter earnings identified in prior literature, namely, the “integral explanation” and “manipulation explanation,” are not mutually exclusive. Therefore, the authors deem it futile to disentangle the two. Instead, the authors offer two possible solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".