Earnings benchmarks, earnings management and future stock performance of Chinese listed companies reporting under ASBE-IFRS
Bibliographic record
Abstract
Purpose Prior literature has revealed three key earnings benchmarks: earnings level; earnings change; and analysts’ expectations. The purpose of this paper is twofold. First, the authors seek to establish which earnings benchmark induces the largest extent of earnings management. Second, the authors explore the implications of earnings management on firm future performance. Both of these purposes are investigated for Chinese listed companies during China’s IFRS/ISA reporting era. Design/methodology/approach The authors rely upon the unique regulations and incentives for Chinese listed companies in order to develop four testable hypotheses. Next, the authors employ both logistic and ordinary least squares regressions to test the hypotheses. Findings The results suggest that Chinese listed firms have the highest level of income increasing discretionary accruals around the earnings level benchmark, followed by the earnings change benchmark. The authors do not find any evidence of earnings management to beat analysts’ expectation. In addition, the authors find evidence that Chinese listed firms with relatively high level of earnings management and low earnings exhibit relatively weak future stock performance. Originality/value The findings are the first to document an earnings management benchmark hierarchy with respect to the extent of income increasing discretionary accruals, while simultaneously establishing a link between earnings management and firm future stock performance, for Chinese listed companies. The findings are valuable for regulators and investors by suggesting that management intervention in the reporting process during China’s IFRS/ISA reporting era may act to circumvent delisting regulations and cloud earnings signal for firms that beat certain earnings benchmarks.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".