Threat of Exit by Non‐Blockholders and Income Smoothing: Evidence from Foreign Institutional Investors in Japan*
Bibliographic record
Abstract
ABSTRACT We examine how the threat of exit by non‐blockholders (investors with ownership <5%) relates to firms' income smoothing. Unlike informed blockholders, non‐blockholders lack private information and therefore rely more on reported accounting numbers to evaluate firm performance. To isolate the exit threat, we use the unique setting in Japan where strong firm‐centric social norms and lack of insider access lead non‐blockholding foreign institutions to influence management primarily through the threat of exit. We predict and find that foreign non‐blockholders' exit threat is positively associated with the extent of income smoothing. This effect is more pronounced for firms less embedded in Japan's stakeholder‐based system, firms with greater stock liquidity, and firms with higher US institutional ownership. In addition, smoothing associated with such an exit threat, on average, is informative. Our findings suggest that Japanese firms under non‐blockholders' exit threat increase income smoothing to reduce perceived uncertainty and that such smoothing generally meets non‐blockholders' information needs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".