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Z/Yen is a commercial think-tank, consultancy and venture firm headquartered in the City of London. It works in the financial services, technology and voluntary sectors on a range of projects from research to performance review and strategic management.
The Global Financial Centres Index, September 2018 (GFCI 24)
The Global Financial Centres Index is a ranking of the competitiveness of financial centres based on over 29,000 financial centre assessments from an online questionnaire together with over 100 indices from organisations such as the World Bank, the Organisation for Economic Co-operation and Development (OECD) and the Economist Intelligence Unit. It is compiled and published twice a year by Z/Yen Group and sponsored by the Qatar Financial Centre Authority.
The aim of the GFCI is to examine the major financial centres globally in terms of competitiveness. The GFCI has been published every six months (although the index is actually produced every three months).
GFCI 23 provides profiles, rating and rankings for 96 financial centres, drawing on two separate sources of data - instrumental factors (external indices) and responses to an online survey.
Instrumental factors are objective evidence of competitiveness was sought from a wide variety of comparable sources. For example, evidence about the telecommunications infrastructure competitiveness of a financial centre is drawn from a global digital economy ranking (supplied by the Economist Intelligence Unit), a telecommunication infrastructure index (by the United Nations) and a Global Information Technology Index (by the World Economic Forum). Evidence about a business-friendly regulatory environment is drawn from an Ease of Doing Business Index (supplied by the World Bank) and an Institutional Effectiveness rating (from the EIU) amongst others. A total of 103 instrumental factors are used in GFCI 23 (of which 36 were updated since GFCI 16 and ten are new to the GFCI). Not all financial centres are represented in all the external sources, and the statistical model takes account of these gaps.
Financial centre assessments: by means of an online questionnaire, running continuously since 2007, 28,494 financial centre assessments drawn from 2,340 respondents have been used in GFCI 23.
The financial centre assessments and instrumental factors are used to build a predictive model of centre competitiveness using a support vector machine (SVM). SVMs are based upon statistical techniques that classify and model complex historic data in order to make predictions of new data. SVMs work well on discrete, categorical data but also handle continuous numerical or time series data. The SVM used for the GFCI provides information about the confidence with which each specific classification is made and the likelihood of other possible classifications.
A factor assessment model is built using the centre assessments from responses to the online questionnaire. Assessments from respondents’ home centres are excluded from the factor assessment model to remove home bias. The model then predicts how respondents would have assessed centres they are not familiar with, by answering questions such as: If an investment banker gives Singapore and Sydney certain assessments then, based on the relevant data for Singapore, Sydney and Paris, how would that person assess Paris? Or If a pension fund manager gives Edinburgh and Munich a certain assessment then, based on the relevant data for Edinburgh, Munich and Zurich, how would that person assess Zurich? Financial centre predictions from the SVM are re-combined with actual financial centre assessments (except those from the respondents’ home centres) to produce the GFCI – a set of financial centre ratings. The GFCI is dynamically updated either by updating and adding to the instrumental factors or through new financial centre assessments. These updates permit, for instance, a recently changed index of rental costs to affect the competitiveness rating of the centres.
Under Half yearly data:
H1 gives "March" Publication.
H2 gives "September" Publication.
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