IPO Underpricing and Prospectus Readability: A Machine Learning Approach
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
IPO prospectus is the crucial document available to investors, allowing investors to understand the company and the IPO. IPO underpricing occurs when the closing price of the initial public offer (IPO) is higher than the offering price on its first trading day. If investors know whether the IPO is likely to be underpriced, they can earn a significant return by subscribing to those underpriced IPOs and selling the shares on the first trading day. In this study, the relationships between the readability of the IPO prospectus and IPO underpricing of firms listed in the Hong Kong Stock Exchange are analyzed using the gradient boost decision tree approach. This study shows that the readability scores of the chapters in the IPO prospectus are relevant to identify underpriced IPOs. Additionally, several indicators are more crucial to identify underpriced IPOs.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it