Do Suppliers Care About Analyst Forecasts When Extending Trade Credit? A Quasi‐Natural Experiment
Bibliographic record
Abstract
ABSTRACT We examine whether suppliers care about analyst forecasts for their customers when making trade credit decisions. Using the suspension of the 2018 New Fortune Star Analyst Contest in China as an exogenous shock and employing difference‐in‐differences analyses, we find that after the suspension of this contest, there is a significant improvement in the information environment of firms followed mainly by analysts signing up for the 2018 contest, as evidenced by more accurate analyst earnings forecasts and lower bid‐ask spreads, and that suppliers extend more trade credit to these firms. Further analyses reveal that the effect of the suspension of this contest on trade credit is more pronounced for firms with higher information asymmetry, for firms whose future earnings are more challenging to forecast, for firms whose suppliers have higher information acquisition costs, and for firms followed by more competent analysts. These findings support the view that the star analyst contest distracts analysts and shed light on the benefits of suspending this contest.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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 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".