The Global Financial Centres Index - 1 - Z/Yen

The Global Financial Centres Index3. The GFCIThe GFCI provides ratings for financial centres calculated by a‘factor assessment model’ built using two distinct sets of input:■ instrumental factors – drawn from external sources. For example,infrastructure competitiveness for a financial centre is indicatedby ‘instrumental factors’ including a cost of property survey andan occupancy costs index; a fair and just business environmentis indicated by ratings such as a corruption perception indexand an opacity index. Objective evidence of competitivefactors has been sought in instrumental factors drawn from awide variety of comparative sources – 47 instrumental factorswere used to construct this first set of GFCI ratings. These include,for example, Mercer’s Quality of Living Survey, UBS’s WageComparison Index, Transparency International’s CorruptionPerceptions Index, and Anholt’s City Brands Index. Not allcentres have data for all instrumental factors and the statisticalmodel takes account of these gaps;■ financial centre assessments – to construct the first set of GFCIratings we used 491 responses to two online surveys (detailed inSection 6 of this report). Each respondent assessed the financialcentres they knew. We received 3,992 individual financialcentre assessments. The second online survey is runningcontinuously to keep the GFCI up-to-date with people’schanging assessments.Financial centres are assessed in terms of five key competitivenessareas: people, business environment, market access, infrastructureand general competitiveness.At the outset of this project, a number of guidelines were set out toensure that financial centre assessments and instrumental factorswere selected and used in a reliable and consistent manner. Forexample, indices used as instrumental factors should, whereverpossible, be readily available, regularly updated, provided by areputable body and derived using a sound methodology.The financial centre assessments and instrumental factors werecombined using statistical techniques to build a predictive modelof financial centre competitiveness using support vector machinemathematics. The predictive model was used to answer questionssuch as “If an investment banker gives Singapore a certainassessment, then, based on the instrumental factors for Singaporeand Paris, how would that person assess Paris?” This predictivemodel produced competitiveness ratings for 46 financial centres.Full details of the methodology behind the GFCI can be found inAppendix A. The results are shown in Table 2:12