Bibliographic record
Abstract
Purpose The purpose of the study is to investigate the possible role of annual report readability in accrual anomaly, shedding light on why investors fail to incorporate accruals information in a timely and unbiased manner beyond the original naive investor fixation explanation. Design/methodology/approach Using five proxies of annual report readability and available data over 1993–2017, we investigate whether accrual overpricing is more severe when annual reports are less readable. Findings We find little (substantive) evidence of accrual overpricing among high (low) readability firms. The readability effects are contingent on the level of business complexity and earnings management. Research limitations/implications This study extends the original naive investor fixation explanation and documents annual report complexity as a market friction in explaining the accrual anomaly, contributing to the mispricing vs risk debate and supporting the efficient market hypothesis. Practical implications Low readability of annual reports is a red flag to investors. Social implications This study provides support for regulatory initiatives aimed at enhancing readability of corporate disclosures to address market frictions and improve market efficiency. Originality/value Accrual anomaly has posed a challenge to the efficient market hypothesis. This study draws on and adds to the line of research indicating that annual report complexity is a friction erecting a barrier to transparency, hindering market efficiency. This study contributes to our understanding of the enigmatic accrual anomaly.
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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 | 0.012 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".