Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009, Revised Papers, Part 2

Research Article

Observing Stock Market Fluctuation in Networks of Stocks

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  • @INPROCEEDINGS{10.1007/978-3-642-02469-6_86,
        author={C. Tse and J. Liu and F. Lau and K. He},
        title={Observing Stock Market Fluctuation in Networks of Stocks},
        proceedings={Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009, Revised Papers, Part 2},
        proceedings_a={COMPLEX PART 2},
        year={2012},
        month={5},
        keywords={},
        doi={10.1007/978-3-642-02469-6_86}
    }
    
  • C. Tse
    J. Liu
    F. Lau
    K. He
    Year: 2012
    Observing Stock Market Fluctuation in Networks of Stocks
    COMPLEX PART 2
    Springer
    DOI: 10.1007/978-3-642-02469-6_86
C. Tse1,*, J. Liu2,*, F. Lau1,*, K. He2,*
  • 1: The Hong Kong Polytechnic University
  • 2: Wuhan University
*Contact email: encktse@polyu.edu.hk, j_liu@whu.edu.cn, encmlau@polyu.edu.hk, hekeqing@public.wh.hb.cn

Abstract

In this paper we study the structural variation of the network formed by connecting Standard & Poor’s 500 (S&P500) stocks whose closing prices (or price returns) are highly correlated. Specifically we consider S&P500 stocks that were traded from January 1, 2000 to December 31, 2004, and construct complex networks based on cross correlation between the time series of the closing prices (or price returns) over a fixed period of time. A simple threshold approach is used for establishing connections between stocks. The period over which the network is constructed is 20 trading days, which should be long enough to produce meaningful cross correlation values, but sufficiently short in order to avoid averaging effects that smooth off the salient fluctuations. A network is constructed for each 20-trading-day window in the entire trading period under study. The window moves at a 1-trading-day step. The power-law exponent is determined for each window, along with the corresponding mean error of the power law approximation which reflects how closely the degree distribution resembles a scalefree-like distribution. The key finding is that the scalefreeness of the degree distribution is disrupted when the market experiences fluctuation. Thus, the mean error of the power-law approximation becomes an effective indicative parameter of the volatility of the stock market.