TY - GEN

T1 - Observing stock market fluctuation in networks of stocks

AU - Tse, Chi Kong

AU - Liu, J.

AU - Lau, Chung Ming

AU - He, K.

PY - 2009/12/1

Y1 - 2009/12/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=78649431531&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-02469-6_86

DO - 10.1007/978-3-642-02469-6_86

M3 - Conference article published in proceeding or book

SN - 3642024688

SN - 9783642024689

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering

SP - 2099

EP - 2108

BT - Complex Sciences - First International Conference, Complex 2009, Revised Papers

T2 - 1st International Conference on Complex Sciences: Theory and Applications, Complex 2009

Y2 - 23 February 2009 through 25 February 2009

ER -