Essays on How Cultural Factors Affect the Sentiment and Behavior of Financial Market Participants
Notice bibliographique
Résumé
The aim of this dissertation is to explore how cultural factors influence the sentiment and behavior of financial market participants. This dissertation consists of three chapters that encompass sports events, cultural dimensions, institutional investors, financial analysts, and earnings announcements. Chapter 1 is the introduction, Chapter 2 to 4 are the main content, and Chapter 5 concludes. In Chapter 2, I construct the Sports Mood Index (SMI) of 49 metropolitan areas in USA and Canada based on the performance of Big 4 professional sports teams and build the firm-level SMI based on institutional investors’ holdings as a proxy for investors’ mood. Under sports-induced bad mood settings, earnings announcement premium becomes higher because of increased uncertainty avoidance premium under pessimism, and post-earnings-announcement drift (PEAD) becomes lower because of the reversal effect. A standard deviation increase in SMI leads to a 22 bps increase of earnings announcement premium and a 16 bps decrease of PEAD in the following week. Whereas sports-induced good mood has no significant impact on the trading behavior of institutional investors, sports-induced bad mood leads to inattention. Institutional investors with sports-induced bad mood underreact to Standardized Unexpected Earnings (SUE) facing both positive and negative news, as evidenced by lower abnormal trading volume around earnings announcement days. The results remain valid after controls for SMI in the metropolitan areas of firm headquarters and are more pronounced if institutional investors located in NYC metropolitan area are excluded or if the market is facing high illiquidity. The SMI calculated based on dedicated institutional investors’ holdings has a greater impact on earnings announcement premium and abnormal trading volume than the SMI calculated based on quasi-indexers and transitory institutional investors. In Chapter 3, I explore how sports-induced bad mood affects the sentiment and behavior of sell-side financial analysts. Under sports-induced bad mood settings, sell-side analysts tend to issue more pessimistic forecasts in both earnings forecasts and price targets. Sports-induced bad mood also leads to inattention. Analysts under sports-induced bad mood have larger forecast errors and are slower or less likely to respond to earnings announcements. The results are robust to various measurements of pessimism, forecast errors and activity levels, and samples without analysts located in NYC. In Chapter 4, I examine whether Hofstede’s cultural dimensions influence the forecasting behavior of financial analysts, and how cultural diversity affects the quality of consensus forecasts. Combining earnings forecast, price target and recommendation samples, I find that individualism has a positive effect on boldness, whereas uncertainty avoidance has a negative effect; long-term oriented analysts are likely to have lower forecasting errors; and indulgent analysts tend to respond to earnings announcements slower. The quality of consensus forecasts would be better, if firms are covered by more culturally diversified analysts, which is associated with improved individual forecasting results. A standard deviation increase in diversity leads to a 45 bps decrease of the consensus forecast error. The effect of diversity is non-linear, that the benefits of diversity decline as the levels of diversity increase.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».