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Computational Economics

, Volume 50, Issue 3, pp 373–392 | Cite as

A New Method For Dynamic Stock Clustering Based On Spectral Analysis

  • Zhaoyuan Li
  • Maozai TianEmail author
Article
  • 414 Downloads

Abstract

In this paper, we propose a new method to classify the stock cluster based on the motions of stock returns. Specifically, there are three criteria: (1) The positive or negative signs of elements in the eigenvector of correlation matrix indicate the response direction of individual stocks. (2) The components are included based on the sequence of corresponding eigenvalue magnitudes from large to small. (3) All the elements in the cluster representing individual stocks should have same signs across the components included in the classification process. With the number of vectors included in the process increasing, a speed-up process for cluster number is identified. We interpret this phenomenon as a phase transition from a state dominated by meaningful information to one dominated by trivial information. And a critical point exists in this process. The sizes of clusters near this critical point can be regarded as a power law distribution. The critical exponent evolvement indicates structure of the market. The vector number at this point can be adopted to classify the stock clusters. We analyze the cross-correlation matrices of stock logarithm returns of both China and US stock market for the period from January 4, 2005 to December 31, 2010. The period includes the anomalies time of financial crisis. The number of clusters in financial and technology sectors is further examined to reveal the varying feather of traditional industries. Distinct patterns of industrial differentiation between China and US have been found according to our study.

Keywords

Stock return Cross-correlation Stock cluster Phase transition Spectral Analysis 

Notes

Acknowledgments

The work was partially supported by the major research projects of philosophy and social science of the Chinese Ministry of Education (No. 15JZD015), National Natural Science Foundation of China (No. 11271368), Project supported by the Major Program of Beijing Philosophy and Social Science Foundation of China (No. 15ZDA17), Project of Ministry of Education supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20130004110007) , the Key Program of National Philosophy and Social Science Foundation Grant (No. 13AZD064), the Major Project of Humanities Social Science Foundation of Ministry of Education (No. 15JJD910001), the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China (No. 15XNL008), the Project of Flying Apsaras Scholar of Lanzhou University of Finance & Economics, and the Project of Tianshan Mountain Scholar of Xinjiang University of Finance & Economics.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Center for Applied Statistics, School of StatisticsRenmin University of ChinaBeijingChina
  2. 2.Department of Statistics and Actuarial ScienceThe University of Hong KongPokfulamHong Kong
  3. 3.School of StatisticsLanzhou University of Finance and EconomicsLanzhouChina
  4. 4.School of Statistics and InformationXinjiang University of Finance and EconomicsÜrümqiChina

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