Impact of Charismatic Leadership and Market Shares on IPO First-Day Returns: The Case of Technology Firms
Bibliographic record
Abstract
First-day returns of initial public offerings (IPOs) have always been an important topic in academic research. Previous literature generally attributes the first-day return of an IPO to the underpricing of the stock, and most studies emphasize on the market-level factors such as the hot market influence and people’s pursuit over IPOs based on the pre-selling market return data. Firm-level variations, on the other hand, are generally under investigated. This research investigates the variations across companies by focusing on two factors that previous studies have not fully articulated: charismatic leadership and market shares. Using logistic regression analysis and a sample of 92 firms in technology industries that went public in the USA during the period from 1 January 2012 to 31 December 2014, we find that there is a statistically significant and positive relationship between charismatic leadership and first-day returns of IPOs, as well as between market shares and first-day returns of IPOs. Our study contributes to the IPO performance literature, and it provides practical implications on IPO management and investment.
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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 | 0.013 |
| 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".