Window dressing in mutual fund portfolios: fact or fiction?
Notice bibliographique
Résumé
Purpose Congress and the Securities and Exchange Commission (SEC) have mandated mutual fund disclosure regimes to help investors make better investment decisions to strike an optimal balance between the investors' interest in more timely and accurate portfolio holdings disclosure and the cost associated with making and disclosing the holdings information available to investors. Many academics and practitioners point out that, despite all the regulations on portfolio disclosure, fund managers can still engage in practices that go against the spirit of the rules without violating the letter of the law. The purpose of this paper is to address the empirical question of whether the practice exists, using holdings data for more than 3,000 equity mutual funds during the time period from 1995 to 2004. Design/methodology/approach In this paper, the authors examine window dressing by mutual fund portfolio managers, using holdings data covering more than 3,000 equity mutual funds from 1995 to 2004. The authors first investigate whether the fund holdings are materially different from universe holdings across performance quintiles based on holdings in the month of disclosure and in the following month. The second part of the analysis examines funds' patterns of buying and selling. Finally, the measure of “Buying Intensity” and “Selling Intensity” is examined, with a specific focus on the holdings data for the fourth quarter. Findings An examination of fund holdings finds no statistically significant evidence of systematic window dressing, either at the aggregate level or within subsamples of funds based on size or past performance. Rather, it was found that fund managers tend to chase momentum. A combination of investor sophistication and market oversight may serve to be effective in dissuading fund managers from engaging in the practice. Originality/value The authors' data are at the individual fund level, based on equity mutual funds holdings data provided to Morningstar on a quarterly or monthly basis (according to Elton et al. , the Morningstar database provides timely and accurate mutual fund holdings information). These data allow us to infer better the investment manager intent vis‐à‐vis using 13F data, which is aggregate data across various fund families and separate accounts, or aggregate pension fund equity holdings data that includes aggregate holdings of multiple portfolio managers. In addition, the authors comment on the significance of the regulatory checks and balances that are designed to restrict fund managers' ability to window‐dress their portfolios. In summary, the combination of quantitative evidence from empirical tests and an examination of the legal framework under which mutual fund portfolio managers operate, lead to the conclusion that window dressing is not prevalent in the industry.
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 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) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».