Researches On Security Market Price System Complexity And Simulation

The study of the complexity of security market price system has been the research direction. With the frame of system theory and complexity science, the dissertation presents a new pattern to analysis security market price system. This dissertation shows the complexity of price behavior of Chinese Security Market Composite Index from a macro-level by using methods such as chaos, fractal and Zipf-type analysis method. And a virtual security market system has been built by mapping the features of real market. Virtual security market system has been proved effective by analyze of the price time sequence produced by the virtual system. The micro-mechanisms of real market price behaviors were detected by mapping the virtual security market features to real market. The innovative points of the dissertation include four aspects:Firstly, this dissertation observed the chaos characteristic quantities of Shanghai and Shenzhen Security Market Composite Index time sequence by applying the Phase Space Reconstruction Technique, G-P algorithm and Wolf algorithm. Non-integer correlation dimensions, largest Lyapunov exponents and Kolmogorov entropies of the two markets were obtained. According to the rules of judgment, the existence of chaos and fractal in above two systems was identified.Secondly, the Rescaled Range analysis method was applied to investigate the fractal behaviors from the prices of Shanghai and Shenzhen Security Market Composite Index time sequence. The Hurst exponents of the two systems were estimated with the different time scales. The results have implied that the systems have positive persistency and fractal features. Furthermore, long-term memory effects and a serials period of non-period cycles were discovered by using V statistics.Thirdly, based on Zipf-type analysis method, the time sequences of Shanghai and Shenzhen Security Market Composite Index were mapped and converted into 3-charactered sequences, which containing the fundamental information of price fluctuations (up, down and no-change). According to the statistic results of 3 characters, the rates of absolute and relative price changes of two markets were applied to build a link between price fluctuations and investors' trading behaviors.Fourthly, based on Multi-Agent skills, a simulation stock market system was built with the simplest consumptions abstracted from real security market. The complex behaviors and phenomena were found from the data produced by the simulation stock market system. It means that the simulation system were effective to simulate the price mechanisms of real market. According to two phases of mapping mechanism of Multi-Agent based Computational Economics, those consumptions were mapped into real market for detecting the micro-mechanisms of real market complex price behaviors.In summary, with the help of new complex analyze pattern; this dissertation has observed the complex features of security market. Multi-Agent method not only supports and verifies the traditional conclusions, but also manages to deal with tough problems that traditional methods can't analyze and even might able to penetrate into untouched areas.