Stock Market Liberalization, Investment Banks, and Analyst Forecast Quality: Evidence From a Quasi-Natural Experiment in China
Bibliographic record
Abstract
Capitalizing on a quasi-natural experiment in China where certain investment banks become investible to the global market across different periods, we explore the role that stock market liberalization plays in shaping local analysts’ incentives to provide high quality forecasts. In a staggered difference-in-differences research design to improve identification, we find that analysts affiliated with liberalized banks (i.e., pilot analysts) significantly reduce the errors and bias in their earnings forecasts from the pre-liberalization period to the post-liberalization period, relative to non-pilot analysts whose employers remain under strict capital controls during the same timeframe. Consistent with expectations, this result is concentrated among local investment banks that are smaller, have higher existing institutional ownership, and have stronger tournament incentives. Additionally, we identify three mechanisms through which market liberalization affects the quality of analysts’ forecasts: pilot analysts (i) become more focused by reducing the size of their coverage portfolios; (ii) devote more effort to forecasting; and (iii) become subject to harsher career punishments for making deficient forecasts. Our analysis provides insight on the importance of financial globalization to the institutional environment of a country’s capital market.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".