What a relief: How do firms respond to competitors' listing delays?
Bibliographic record
Abstract
Abstract We examine the effect of product market competitors' listing delays on incumbent firms' defensive strategies, including efforts in customer retention and acquisition as well as merger and acquisition (M&A) activities. To establish causality, we use four regulation‐induced IPO suspensions in China that expose firms already approved for an IPO to indeterminate listing delays. Using a difference‐in‐differences design, we find that incumbent firms reduce efforts in customer retention and acquisition, as manifested in an increase in accounts receivable turnover and a decrease in selling expenses. Incumbent firms also reduce M&A activities, including high‐premium and horizontal ones. The effects are stronger for incumbent firms that are subject to more intensive competition from the suspended firm, face larger competitive pressure from existing public firms, and are more financially constrained. Additionally, incumbent firms' managers reduce competition‐related disclosures, and the firms' financial performance improves after competitors' listing delays. Consistent with the findings based on listing delays, we find that incumbent firms increase efforts in customer retention and acquisition and M&As surrounding competitors' IPO application and approval. Our paper sheds new light on the IPO peer effect, especially on how incumbent firms respond to the product market competitor's capital market entry.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".