Valuing IPOs Using Price-Earnings Multiples Disclosed by IPO Firms in an Emerging Capital Market
Bibliographic record
Abstract
Existing studies show that markets use comparable firm multiples to price IPOs. This study explores IPO valuations in an emerging market where reliable comparable price multiples may not be readily available, or cannot be reliably identified. In particular, we examine the value relevance of price-earnings multiples disclosed by managers in IPO prospectuses in China. Using a sample of IPOs from 1992 to 2002, we find that price-earnings multiples disclosed by IPO firms provide significant power in explaining price formation in this emerging market. We also find that price-earnings multiples disclosed by IPO firms after 1999, when the China Securities and Regulatory Commission relaxed its internal guideline for approving IPO applications, are more informative. The results are robust to a variety of empirical model specifications. This study contributes to the existing IPO literature by showing that the disclosure of price-earnings multiples provides a mechanism for IPO firms to convey information about IPO firm quality when reliable comparable firm multiples may not exist.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".