Study On Inherent Complexity And Forecasting Of HS300 Stock Index

In an open complex system, there are intricate relationships among the economic variables in the financial system, and both linear and nonlinear relationships exist in it. The financial theory system within the linear framework propels the financial market forward, however, more and more evidence demonstrates that the fluctuation of financial market cannot be explained clearly only by linear method. Since the 1980's, many scholars have been exploring and looking for some nonlinear methods to explain financial phenomenon and forecast the price evolvement in financial market. Yet the conclusions are different due to the different methods and utilities. In this paper, time series of HS300 stock index is analyzed and forecasting based on the theory of chaos and neural network. The paper mainly includes the following integrations:1. The representative methods of detecting chaos in financial time series and are introduced and the relevant data about HS300 stock index are calculated via the methods of power spectrum, RPS, Cao, surrogate-data and so on. The obtained results are used to detect chaos in the time series.2. The characters of RP and CRP of various time series are studied, and on that basis, the complexity and the state classification are analyzed. The maximal Lyapunov index of the data of some HS300 stock index are calculated via the method of Wolf and the meaning of the results are also presented.3. The nonlinear autocorrelated model for the stock data of No. 510050 of SSE is established and the parameters are identified. Various prices of Vanke A of SH300 are forecasted via the local prediction method based on RBF neural network.4. Elliott wave theory is introduced and used to analyze the response of market sentiment and behavior. Also, the method of identifying the market position according to characters of the wave level is provided and is used to forecasting the macro trend of the stock market.Considering the nonlinear characteristics of financial market, the theories of chaos, forecasting method based on neural network and Elliott wave theory are applied to analyze HS300 stock index time series. The results show these theories and methods can explain and forecast the fluctuation of financial market and provide important theoretical and empirical basis for nonlinear modeling and forecasting of financial time series.