Accounting Conservatism and Information Asymmetry: Evidence from Taiwan
Bibliographic record
Abstract
This paper adopts information neutrality perspective to examine the role of earnings conservatism to explore the effect of accounting conservatism on information asymmetry in Taiwan. Results reveal that when corporate earnings are increasingly conservative, information asymmetry is more severe generally. In addition, when earnings conservatism is excessive or insufficient, varying effects are produced between earnings conservatism levels and information asymmetry. Specifically, when corporate conservatism is insufficient, the relationship between earnings conservatism and information asymmetry is significantly negative. Conversely, when accounting earnings is much more conservative, the influence on information asymmetry is positive. The empirical results of this study support the current development of financial standards. The empirical results also show that when the corporation performance demonstrates good news or bad news separately, investors’ perception regarding the informativeness of accounting conservatism is different. Investors are more likely to identify with relevant results despite an overly conservative accounting recognition if the current period of a corporation provides good news.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".