Management Guidance and the Underpricing of Seasoned Equity Offerings*
Notice bibliographique
Résumé
investors is high, those who trade against an informed party will demand compensation by lowering (increasing) the price at which they are willing to buy (sell) assets, consequently affecting liquidity and asset prices (Amihud and Mendelson 1986).Similarly, when firms issue new shares in the presence of information asymmetry between managers and outside investors, rational investors will pay a lower price for the shares to compensate for their information disadvantage, therefore increasing the cost of raising external capital.Prior theoretical studies have argued that IPO or SEO underpricing is a result of information asymmetry.Most of the theories concerning IPOs also apply to SEOs (Loderer, Sheehan, and Kadlec 1991).Rock (1986) argues that uninformed investors face the winner's curse problem.To ensure that uninformed investors participate in IPOs and thus shares can be sold, the offer price has to be set below the intrinsic value of the shares so that uninformed investors at least break even.This causes stock prices to increase on the first day of trading and results in underpricing.Benveniste and Spindt (1989) argue that investment banks induce asymmetrically informed investors to reveal truthfully the information they hold about the value of IPOs.To reward these investors for truthful reporting, IPO offer prices are set below expected first-day closing prices.Parsons and Raviv (1985) use information asymmetry among investors to explain the underpricing of SEOs.In their model, an underwriter chooses a sufficiently low offer price to attract investors who attach a high value to the firm's new project.These investors realize that they can buy shares at the offer price only when the issue is undersubscribed.To avoid oversubscription, their demand drives the market price over the offer price, causing underpricing.While the above models are based on different assumptions and offer different explanations for underpricing, they all agree that information asymmetry is a source of underpricing.Many empirical studies on IPOs or SEOs have shown that various measures of information asymmetry are positively associated with underpricing.In the SEO setting, firm size and pre-SEO stock return volatility are often used to proxy for information asymmetry or uncertainty (Corwin 2003).Other proxies of information asymmetry include analyst forecast dispersion (Marquardt and Wiedman 1998), the bid-ask spread (Corwin 2003), and accruals quality (Lee and Masulis 2009).These studies find a positive association between information asymmetry proxies and SEO underpricing.Certain mechanisms can help reduce information asymmetry concerning firm value and thus the magnitude of underpricing.Titman and Trueman (1986) present a model in which the quality of auditors and ⁄ or investment bankers provides information useful to investors in valuing new issues.Beatty (1989) finds support for this model by documenting that the extent of IPO underpricing is lower for firms that hire more reputable auditors.Many IPO and SEO studies employ underwriter rank to measure the quality of investment banks and find that it is negatively associated with underpricing (Kim and Shin 2004;Mola and Loughran 2004).Information intermediaries such as financial analysts act as another uncertainty reduction mechanism.Analyst coverage can improve a firm's information environment and potentially reduce information asymmetry among investors, thereby reducing SEO underpricing.Mola and Loughran (2004) find that underwriters with toptier analysts are associated with a lower level of SEO underpricing.Bowen et al. (2008) show that analyst coverage, as well as certain attributes of analysts such as those working for the lead underwriters and those having a reputation for superior ability, lowers the magnitude of SEO underpricing. Management guidance and the underpricing of seasoned equity offeringsAs discussed above, auditor quality, underwriter reputation, and analyst coverage all represent information asymmetry reduction mechanisms provided by third parties: auditors, investment banks, and financial analysts, respectively.Firms can also reduce information asymmetry by voluntarily disclosing more information, including their own predictions of 712
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».