When to Buy and Sell the Shares of the Publicly Traded Rivals of a Firm Making an Initial Public Offering: A New Model
Bibliographic record
Abstract
A review of the literature regarding the supply shock effects of a firm's initial public offering on its publicly traded rivals leads to a redefinition of the competitive and contagion effects.Owing to the persistence of the competitive effect over time and in different markets, it is identified as an anomaly.Therefore, we develop a 3D graphic tool capable of measuring systematically, from a continuous perspective, how information leaks into a stock market and how its effect on the returns of publicly traded companies spreads, in depth and length.The tool can be applied to any event study.In this study, it was used to visualize the short-term effects produced by a firm's initial public offering on its traded rivals in the Spanish stock market, using data over a 30-year period.A competitive effect, similar in size and extent to the ones detected by the state-of-the-art studies, was demonstrated.These results are comparable to the projections of the main asset pricing models.This demonstrates the similarity between the above stated competitive effect and the substitution effect related to the supply and demand theory regarding substitutive products.Based on this, a theory capable of explaining the competitive effect is proposed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 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".