When Does Earnings Management Matter? Evidence across the Corporate Life Cycle for Non-Financial Chinese Listed Companies
Bibliographic record
Abstract
Information availability, firm performance, idiosyncratic volatility and bankruptcy-risk vary across the Corporate Life Cycle (CLC) stages. The purpose of this paper is to examine whether CLC stages explain firm’s propensity to engage in both accrual base and real earning management practices in the context of China. Panel data of 3250 non-financial Chinese listed firms spanning from 2009 to 2018 is used to investigate the proposed relationship. CLC stages were captured through Dickinson’s model, while earnings management is measured by employing both techniques, i.e., accruals-base earnings management and real earnings management. The data were analyzed through Panel data fixed-effects and random-effects techniques. Results reveal that, when compared to shakeout phase, managers’ response to use both earnings management practices is significantly higher during introduction and decline phases, and lower during growth and mature stages of CLC. It suggests that introductory and later-staged firms distort their factual financial information from creditors to obtain loans without strict debt covenants. Our results are robust to alternate measures and specifications. The core contribution of this research is to add a fresh perspective to the CLC research by uncovering its imperative role in influencing the earning management behavior of corporate managers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".