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Study on the Non-linear Characteristics and Trend of the China Stock Market

Author: MaoZhanBin
Tutor: RanMaoSheng
School: Chongqing University
Course: Finance
Keywords: Stock market Nonlinear Analysis Chaos Forecast
CLC: F832.51
Type: Master's thesis
Year: 2008
Downloads: 163
Quote: 1
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Abstract


The current stock market is built on top of a modern capital market theory , the efficient market hypothesis is the basis of modern capital market theory , efficient market premise random walk random walk implies a normal distribution . However , after the examination of the domestic and foreign scholars , the capital markets are not subject to normal distribution , there is no doubt , this is the huge impact of the modern capital market theory . China's stock market data for the study , more comprehensive empirical study of the use of nonlinear time series analysis methods to examine China's stock market is whether there is a chaotic phenomenon . Based on the Shanghai stock composite index and Shenzhen stock market component index data range for December 16, 1996 to 2006 , the log-linear detrended time series smoothing treatment ultimately be used to nonlinear analysis of time-series data for a smooth sequence . First qualitative index time series analysis , frequency distribution , it is found that there is a greater difference between the stock market data and the normal distribution ; through power spectrum analysis , principal component analysis showed that the Shanghai Composite Index and Shenzhen Component Index time series are non- line sexual nature. On the basis of qualitative analysis , two time series of nonlinear geometric invariants calculated . Using the average mutual information obtained by the delay time and Cao obtained optimal embedding dimension , using the GP algorithm based on the calculation of correlation dimension and Kolmogorov entropy Wolf method to calculate the largest Lyapunov exponent . The Shanghai Composite Index and Shenzhen Component Index calculated correlation dimension were 2.4151 and 2.5171 ; Kolmogorov entropy were 0.011 and 0.0075 , and the largest Lyapunov exponents were 0.0096 and 0.0059 . Common movement patterns characteristic quantities analysis, the Shanghai Composite Index and Shenzhen Component Index time series of chaotic phenomena . Study on the basis of the above analysis , the development trend of the stock market , short-term prediction of chaotic prediction method - Volterra series adaptive prediction model index time series , and with the local prediction method and the linear prediction method forecast results compared to that Volterra adaptive prediction predict results better prediction accuracy .

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CLC: > Economic > Fiscal, monetary > Finance, banking > China's financial,banking > Financial market
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