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Minimal spanning tree problem in stock networks analysis: An efficient algorithm

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  • Djauhari, Maman Abdurachman
  • Gan, Siew Lee

Abstract

Since the last decade, minimal spanning trees (MSTs) have become one of the main streams in econophysics to filter the important information contained, for example, in stock networks. The standard practice to find an MST is by using Kruskal’s algorithm. However, it becomes slower and slower when the number of stocks gets larger and larger. In this paper we propose an algorithm to find an MST which has considerably promising performance. It is significantly faster than Kruskal’s algorithm and far faster if there is only one unique MST in the network. Our approach is based on the combination of fuzzy relation theory and graph theoretical properties of the forest of all MSTs. A comparison study based on real data from four stock markets and four types of simulated data will be presented to illustrate the significant advantages of the proposed algorithm.

Suggested Citation

  • Djauhari, Maman Abdurachman & Gan, Siew Lee, 2013. "Minimal spanning tree problem in stock networks analysis: An efficient algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(9), pages 2226-2234.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:9:p:2226-2234
    DOI: 10.1016/j.physa.2012.12.032
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    Cited by:

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    7. Kalyagin, V.A. & Koldanov, A.P. & Koldanov, P.A. & Pardalos, P.M. & Zamaraev, V.A., 2014. "Measures of uncertainty in market network analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 59-70.
    8. Kazemilari, Mansooreh & Djauhari, Maman Abdurachman, 2015. "Correlation network analysis for multi-dimensional data in stocks market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 429(C), pages 62-75.

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