Does Investors’ Information‐acquisition Ability Affect <scp>IPO</scp> Underpricing? Evidence from a Quasi‐natural Experiment
Bibliographic record
Abstract
Initial public offering (IPO) underpricing, driven by information asymmetry, is a prevalent and serious global phenomenon. In addition to the influence of information providers such as IPO firms, investors’ ability to acquire information may also significantly affect IPO underpricing. We investigate the impact of investors’ information‐acquisition ability on IPO underpricing, using Google's withdrawal as an exogenous shock. On 23 March 2010, Google unexpectedly announced the withdrawal of its search business from mainland China. As Google was the primary search engine used by investors in mainland China to access foreign information, its withdrawal significantly impaired investors’ ability to search for and acquire such information. Our findings show that firms engaged in foreign trade experience a significant increase in IPO underpricing following Google's withdrawal, compared to firms not engaged in foreign trade. This effect is more pronounced for firms with lower information‐disclosure quality in their IPO prospectuses, those exhibiting higher complexity, those hiring lower‐reputation intermediaries, those with lower institutional ownership, those with a greater need for investors to acquire foreign information, and those where investors have less alternative access to foreign information. Our findings suggest that investors’ information‐acquisition ability significantly affects IPO underpricing, thus contributing to the literature on the determinants of IPO underpricing and extending the literature on the economic consequences of internet information acquisition to the IPO field.
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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 | 0.005 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".