Stock market liberalization and earnings management: Evidence from a quasi‐natural experiment in China
Bibliographic record
Abstract
Abstract Exploiting a quasi‐natural experiment in China in which some firms become investible to foreign investors across different times (i.e., pilot firms), we explore the role that stock market liberalization plays in shaping firms' earnings management activities. In one direction, the national‐level liberalization reform may elicit public attention from various stakeholders, piling pressure on managers to refrain from distorting their firms' earnings. In the other direction, the various restrictions that the government imposes on foreign investors cast doubt on whether China's capital control reform will materially affect pilot firms' incentives and scope to manipulate their earnings. To gauge which force is more dominant, we rely on a staggered difference‐in‐differences research design and find that pilot firms significantly reduce the magnitude of their discretionary accruals and the incidence of financial reporting irregularities from the pre‐ to the post‐liberalization period, compared to non‐pilot firms during the same time frame. Additional analysis implies that externalities in the form of stricter external monitoring from the media, institutional investors, and auditors is the major mechanism that helps market liberalization curb firms' earnings management. Our research provides insight on the importance of financial global integration to firms' earnings management practices.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".