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Topological Properties of the Minimal Spanning Tree in Korean and American Stock Markets

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  • Cheoljun Eom
  • Gabjin Oh
  • Seunghwan Kim

Abstract

We investigate a factor that can affect the number of links of a specific stock in a network between stocks created by the minimal spanning tree (MST) method, by using individual stock data listed on the S&P500 and KOSPI. Among the common factors mentioned in the arbitrage pricing model (APM), widely acknowledged in the financial field, a representative market index is established as a possible factor. We found that the correlation distribution, $\rho_{ij}$, of 400 stocks taken from the S&P500 index shows a very similar with that of the Korean stock market and those deviate from the correlation distribution of time series removed a nonlinearity by the surrogate method. We also shows that the degree distribution of the MSTs for both stock markets follows a power-law distribution with the exponent $\zeta \sim$ 2.1, while the degree distribution of the time series eliminated a nonlinearity follows an exponential distribution with the exponent, $\delta \sim 0.77$. Furthermore the correlation, $\rho_{iM}$, between the degree k of individual stock, $i$, and the market index, $M$, follows a power-law distribution, $ \sim k^{\gamma}$, with the exponent $\gamma_{\textrm{S&P500}} \approx 0.16$ and $\gamma_{\textrm{KOSPI}} \approx 0.14$, respectively. Thus, regardless of the markets, the indivisual stocks closely related to the common factor in the market, the market index, are likely to be located around the center of the network between stocks, while those weakly related to the market index are likely to be placed in the outside.

Suggested Citation

  • Cheoljun Eom & Gabjin Oh & Seunghwan Kim, 2006. "Topological Properties of the Minimal Spanning Tree in Korean and American Stock Markets," Papers physics/0612068, arXiv.org, revised Jan 2007.
  • Handle: RePEc:arx:papers:physics/0612068
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    Cited by:

    1. Djauhari, Maman A., 2012. "A robust filter in stock networks analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 5049-5057.
    2. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    3. 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.
    4. Eom, Cheoljun, 2017. "Two-faced property of a market factor in asset pricing and diversification effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 471(C), pages 190-199.
    5. Eom, Cheoljun & Kwon, Okyu & Jung, Woo-Sung & Kim, Seunghwan, 2010. "The effect of a market factor on information flow between stocks using the minimal spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1643-1652.
    6. Song, Dong-Ming & Jiang, Zhi-Qiang & Zhou, Wei-Xing, 2009. "Statistical properties of world investment networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(12), pages 2450-2460.
    7. Zhu, Jia & Wei, Daijun, 2021. "Analysis of stock market based on visibility graph and structure entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 576(C).

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