The relationship between loss, macroeconomic condition and conservatism
Bibliographic record
Abstract
This study is motivated by the high frequency of loss occurrence since late 1990s among Malaysian public listed firms, and the conflicting findings of the impact of the macroeconomic conditions and firm-specific attributes on different measures of earnings quality.In addition, this study examines the impact of firms' specific attributes on earnings quality using a better established theory, known as the life-cycle hypothesis.The objectives of this study are; (1) to examine the relationship between firms' loss condition on conservatism as an earnings quality measure as well as the moderation of macroeconomic condition on the relationship and (2) to examine the relationship between life-cycle stages and conditional conservatism.Samples for the study are companies listed on Bursa Malaysia from 1995 to 2010.Using the C_Score measure of conservatism as the dependent variable, firms with loss condition, have been found to be significantly more conservative than profit firms.In addition, macroeconomic condition, strengthen the relationship between loss and conservatism when the results indicate that loss firms undergoing economic crisis are significantly more conservative than loss firms under normal economic condition.Incorporating the firms' life-cycle stages, the study found that growth firms signal fewer losses than mature firms, thus accepting the set hypothesis.Implication of this study is that ignorance of these issues could lead to significantly misleading interpretation of earnings quality.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".