A Wavelet-Based Method to Measure Stock Market Development

Adel Al Sharkasi, Heather J. Ruskin, Martin Crane, José Matos, Sílvio M. A. Gama

CMUP & Departamento de Matemática Aplicada, Faculdade de Ciencias, Universidade do Porto, Porto, Portugal.
CMUP & Grupo de Matemática Informática, Faculdade de Econmia, Universidade do Porto, Porto, Portugal.
Department of Statistics, Faculty of Science, University of Benghazi, Benghazi, Libya.
School of Computing, Dublin City University, Dublin, Ireland.

DOI: 10.4236/ojs.2014.41009                

Abstract

In this paper, we introduce a novel algorithm, based on the wavelet transform, to measure stock market development. This algorithm is applied to the return series of fourteen worldwide market indices from 1996 to 2005. We find that a comparison of the return series in terms of the quantity of fractional Gaussian noise (fGn), for different values of Hurst exponent (H), facilitates the classification of stock markets according to their degree of development. We also observe that the simple classification of stock markets into “emerging” or “developing” and “mature” or “developed” is no longer sufficient. However, stock markets can be grouped into three categories that we named emerging, intermediate and mature.

Keywords

Wavelet Transform; Hurst Exponent (H); Stock Market Classifications

Conflicts of Interest

The authors declare no conflicts of interest.

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