Information Spillover and Demand Shock Effect of the IPOs on the Stock Price of the Competitors: Evidence From the Korean Stock Market
Bibliographic record
Abstract
This paper examines the impact of IPOs on the stock prices of competing companies in the same industry in the Korean stock market. By observing the stock price responses of competitors at the time of IPO announcement and listing, this study attempts to separately examine the effect of IPO's information transfer and its impact on the stock demand of competitors. Before and after the IPO announcement, the stock prices of competitors did not change significantly. On the other hand, during the period surrounding the IPO stock listing, the stock price of competitors showed a significantly negative decline. This suggests that as the IPO stock related information was revealed through the public offering process, it negatively affected the stock price of competing companies. Also, the listing of IPO stocks seems to have adversely affected the stock demand for competing companies. In particular, among the effects of information transfer, the competitive effect is overwhelming, and the factors that influence relative competitiveness in the industry between competitors and an IPO company, such as operating profitability and R&D investment, are found to have a substantial influence on the share price of competitors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".