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Grey assessment and prediction of the financial agglomeration degree in central five cities

Grey assessment and prediction of the financial agglomeration degree in central five cities Purpose – At present, financial agglomeration tendency in domestic and foreign countries is increasingly evident. Therefore, from a comparative perspective, this paper aims to assess and predict the financial agglomeration degree in central five cities. Design/methodology/approach – According to the diversity of evaluating indexes and the uncertainty of financial agglomeration, this paper constructs a set of indexes of evaluating the financial agglomeration degree, comprehensively evaluates the financial agglomeration degree of the five cities – Wuhan, Changsha, Zhengzhou, Nanchang and Hefei – in China's middle region from 2001 to 2010 by using the multiple dimension grey fuzzy decision‐making model, and predicts their development tendency by using the GM (1, 1, β ) model. Findings – The results show that the multiple dimension grey fuzzy decision‐making pattern cannot only be used to determine the weights of evaluating indexes, but also get the fuzzy partition and ranking order of the financial agglomeration in central five cities. The grey prediction results can objectively reflect the development tendency of the financial agglomeration in central five cities. Practical implications – From the results, it is necessary for any competitive city to clarify their relative strengths and weaknesses in order for the accurate location and scientific development, and it also provides a reference for the government decision‐making. Originality/value – The paper succeeds in using the multiple dimension grey fuzzy decision‐making model to measure the financial agglomeration degree of the five central cities and the grey prediction model to predict future trends. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Grey Systems: Theory and Application Emerald Publishing

Grey assessment and prediction of the financial agglomeration degree in central five cities

Grey Systems: Theory and Application , Volume 4 (1): 9 – Jan 28, 2014

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Publisher
Emerald Publishing
Copyright
Copyright © 2014 Emerald Group Publishing Limited. All rights reserved.
ISSN
2043-9377
DOI
10.1108/GS-11-2013-0031
Publisher site
See Article on Publisher Site

Abstract

Purpose – At present, financial agglomeration tendency in domestic and foreign countries is increasingly evident. Therefore, from a comparative perspective, this paper aims to assess and predict the financial agglomeration degree in central five cities. Design/methodology/approach – According to the diversity of evaluating indexes and the uncertainty of financial agglomeration, this paper constructs a set of indexes of evaluating the financial agglomeration degree, comprehensively evaluates the financial agglomeration degree of the five cities – Wuhan, Changsha, Zhengzhou, Nanchang and Hefei – in China's middle region from 2001 to 2010 by using the multiple dimension grey fuzzy decision‐making model, and predicts their development tendency by using the GM (1, 1, β ) model. Findings – The results show that the multiple dimension grey fuzzy decision‐making pattern cannot only be used to determine the weights of evaluating indexes, but also get the fuzzy partition and ranking order of the financial agglomeration in central five cities. The grey prediction results can objectively reflect the development tendency of the financial agglomeration in central five cities. Practical implications – From the results, it is necessary for any competitive city to clarify their relative strengths and weaknesses in order for the accurate location and scientific development, and it also provides a reference for the government decision‐making. Originality/value – The paper succeeds in using the multiple dimension grey fuzzy decision‐making model to measure the financial agglomeration degree of the five central cities and the grey prediction model to predict future trends.

Journal

Grey Systems: Theory and ApplicationEmerald Publishing

Published: Jan 28, 2014

Keywords: Financial agglomeration; Fuzzy decision‐making; Grey assessment and prediction; Weight

References