Notice bibliographique
Résumé
Settlement efficiency has always been a concern for regulators and supervisors. This issue has recently gained the attention of all stakeholders in financial markets, however, due to: (1) the introduction of the Settlement Discipline Regime (SDR) in the European Union (EU) in February 2022; and (2) the migration to T+1 in the US, Mexico, Argentina and Canada, along with the consequent discussions in Europe about transitioning from T+2 to T+1. There is a perception in the market that the EU settlement efficiency rate is lower than that of other regions; however, there is no available failure rate for the US, and the methodologies used to assess these rates are neither uniform nor comparable. In fact, the EU methodology established by the Central Depositories Deposition Regulation (CSDR) is the most stringent. Furthermore, there are significant differences depending on asset classes, type of transactions and size of markets, etc. Equities markets usually have worse settlement ratios than bond markets, while exchange traded funds (ETFs) have the worst ratio, indicating possible structural problems. Larger markets generally tend to have worse ratios, likely due to higher levels of cross-border investment and more complex products, such as exchange traded products (ETPs) and ETFs. Settlement failures occur for a variety of reasons, encompassing numerous operational and technical issues along the custody value chain. Additionally, factors such as short selling activities and ‘strategic failures’ may contribute, although these are less frequently discussed by market participants. Determining the precise impact of each cause is challenging, as most are typically present during periods of increased settlement failures. These periods often align with high market volatility, elevated trading volumes, high borrowing costs and/or low interest rates. To improve settlement efficiency rates, a comprehensive set of actions should be undertaken by market participants, central securities depositories (CSDs) and regulators. A realistic and achievable target for settlement failures might be around 2 per cent in terms of value, which, while still ambitious, is more attainable than a 0 per cent target. This paper synthesises various analyses and personal experiences, rather than relying on a singular analytic study. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| 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 | 0,000 | 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 ».