Notice bibliographique
Résumé
In the first chapter, we study how innovation networks among firms cause interdependencies in their stock returns. Using patent grants and citation data from the United States Patent and Trademark Office, we find that patents generate positive abnormal returns not only for the innovating firm but also for firms that have previously cited them in their own patents. The magnitude of this financial spillover is directly proportional to the quality of the granted patents and the intensity of a firm’s reliance on its upstream firms in the innovation network. The spillover effect is diminished when firms compete in the product market, but it is larger when the firms are also interconnected within the supply chain. Additionally, we find that the financial spillovers of innovation are restricted to firms that are directly connected in the innovation network. We quantify the spillovers and find that innovations generate large positive financial externalities on other firms. The second chapter investigates the strategic behavior of chief executive officers (CEOs) in disclosing discretionary corporate news close to their contract renewal dates. Analyzing 296 fixed-term employment contracts of Standard & Poor’s (S&P) 1500 firms covering the period from 2000 to 2009 and using contract length as an instrumental variable (IV), we show that, compared to the baseline, the number of discretionary news items is higher by 1.4 in the quarter preceding the renewal date and by 1.44 in the quarter following it. The sentiment of news items is higher by 0.45 in the quarter before the renewal date and lower by 0.35 in the subsequent quarter. This provides evidence of strategic disclosure of good news before, and clustering of bad news after, the contract renewal dates. This behavior is stronger among CEOs with a history of poor performance and weaker among those who also hold the position of chairman. In the third chapter, I study the effect of mutual fund managers’ learning and experience on their portfolio decisions and performance, with a particular focus on stock-specific experience. Using the total number of quarters a specific stock has been held in a manager’s portfolio as a proxy for the manager’s stock-specific experience, the findings show that managers consistently generate superior abnormal returns when dealing with stocks on which they have more experience. The results show that, on average, each additional quarter of experience a manager has with a particular stock in their portfolio predicts a 2.7 basis point higher abnormal return for the stock in the following quarter. Additionally, this experience leads to a 2.06% ex-post increase in the proportionate value that the stock adds to the portfolio in the following quarter. The results not only highlight the effect of stock-specific experience on performance but also underscore the significant impact of industry-specific experience. Specifically, when fund managers with more experience in a given industry add a stock from the same industry that has not previously been in their portfolio, they achieve better performance with that stock in the next quarter compared to their peers.
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 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,029 | 0,008 |
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 ».