Do Analysts Cater to Investor Beliefs? Evidence from Dual-Listed Chinese Firms
Bibliographic record
Abstract
We take advantage of a unique setting in China to provide novel evidence on a catering theory for analyst optimism. Our study utilizes the Stock Connect programs that allowed foreign investors to invest in Chinese stocks as an exogenous shock to investor beliefs. We further focus our study on a subset of Chinese firms with both "A shares" (listed in mainland China) and "H shares" (listed in Hong Kong) to provide a clean test of our hypotheses. We find that A share analysts become less optimistic in their recommendations following the introduction of less optimistic investors through the Stock Connect programs. In addition, catering theory predicts that when investors hold heterogeneous beliefs, analysts tend to segment the market and slant toward extreme positions in order to attract target investors. Consistent with this prediction, we find that A share analysts with buy or strong buy (sell or underperform) recommendations of a given firm become more optimistic (pessimistic) in their forecasts and research report tone after the Stock Connect programs. Finally, we show that in updating their earnings forecasts, analysts are more (less) responsive to earnings surprises that are consistent (inconsistent) with their stock recommendations. Overall, the results suggest that analysts cater to investors' opinions.
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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 | 0.001 | 0.008 |
| 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 | 0.001 | 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".