Notice bibliographique
Résumé
The twenty-seventh edition of the Global Financial Centres Index (GFCI 27) was published on 26 March 2020. GFCI 27 provides evaluations of future competitiveness and rankings for 108 major financial centres around the world. The GFCI serves as a valuable reference for policy and investment decision-makers. China Development Institute (CDI) in Shenzhen and Z/Yen Partners in London collaborate in producing the GFCI. The GFCI is updated and published every March and September, and receives considerable attention from the global financial community. 120 financial centres were researched for GFCI 27 of which 108 are now in the main index. The GFCI is compiled using 138 instrumental factors. These quantitative measures are provided by third parties including the World Bank, the Economist Intelligence Unit, the OECD and the United Nations. The instrumental factors are combined with financial centre assessments provided by respondents to the GFCI online questionnaire. GFCI 27 uses 37,695 assessments from 5,064 respondents. The Results Of GFCI 27 Include: GFCI 27 showed a high level of volatility, with 26 centres rising ten or more places in the rankings and 23 falling ten or more places. This may reflect the uncertainty around international trade and the impact of geopolitical and local unrest with a flight to stability; and also reflects the importance of sustainable finance, with Western European centres benefitting, and centres with a legacy of brown finance losing ground. Nine of the top ten centres in the index had lower ratings (eight of these centres fell by 12 points or more). Of the next 40 centres, 24 improved their rating while 16 fell. Eastern Europe & Central Asia showed the strongest regional improvement with twelve centres increasing their rating, while only two centres received lower ratings. Leading Centres New York retains its first place in the index, further extending its lead over London from 17 to 27 points (although the ratings for both centres dropped by more than 20 points). Tokyo moved up three places to rank third in the index. Hong Kong fell from third place to sixth. Five Asian centres are now within ten points of London. Geneva, Los Angeles, and San Francisco entered the top 10, easing out Dubai, Shenzhen, and Sydney. Within the top 30 centres, Amsterdam, Edinburgh, Geneva, Hamburg, and Stockholm all rose by more than ten places. Western Europe After a mixed performance in GFCI 26, this region had a strong performance in GFCI 27, with 23 centres rising in the rankings and five falling. Fourteen centres increased their ranking by ten places or more, including Geneva which is now in the top 10. Asia/Pacific Asia/Pacific Centres had a somewhat downbeat performance with fifteen centres falling in the rankings and ten rising. This appears to reflect levels of confidence in the stability of Asian centres and in their approach to sustainable finance, which appears to be growing in its effect on the overall rating of centres. Tokyo and Shanghai improved their ranking in the top 10, whilst Singapore and Hong Kong fell. North America North American centres showed little change from GFCI 26, with the exceptions of Calgary, which climbed 17 places, and Toronto which fell 12 places. Four out of the eleven North American centres are in the top 20. Eastern Europe & Central Asia All bar two centres in this region improved their rating (the exceptions being Nur-Sultan and Istanbul). Nur-Sultan may rise rapidly as people become more familiar with the new name and residence of the Astana International Financial Centre. Nine of the centres improved their ranking (moving mainly from the bottom of the index to its middle), with four falling and one (Moscow) remaining in the same position. Middle East & Africa Centres in the Middle East and Africa performed poorly with ten of the 13 centres falling in ranking. Only Nairobi and Riyadh improved their position. Tehran entered the index for the first time. Latin America & The Caribbean Centres in Latin America & The Caribbean also performed poorly, with only the British Virgin Islands increasing its ranking (by 15 places). Barbados is a new entry. Island Centres The British Crown Dependencies’ performances bounced back with the Isle of Man up 12 places in the rankings, Jersey up 10, and Guernsey rising 19 places. FinTech For the second time, we include within the GFCI a separate index ranking financial centres as competitive places for FinTech. New York leads the FinTech rankings, followed by Beijing, Shanghai, London, and Singapore. Seven of the top ten centres for FinTech are Chinese. Vilnius, on its first entry in the GFCI, ranks 13th in the FinTech ranking.
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 | 0,000 |
| 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 | 0,002 |
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 ».