| Data processing and analysis plays a very important role in the development of prediction theory. According to the forecast theory, we can use mathematical models to describe and analyze the information in order to reflect the trend of data. Tap the potential of natural law and other important information, so as to provide a reliable basis for decision makers. The theoretical prediction which is based on data processing and analysis plays a very important role in the financial markets. As is known to all, China’s stock market is still in the growth stage, so it has the inherent particularity. As the stock market is not standardized, there are all kinds of false information.The investors is difficult to judge the stock price, so as to lose a lot of interest. Therefore, it is crunch time to use the data processing and analsis to explore the internal rules in the financial markets. It will exist some potential inevitable rules behind the volatility of the stock price, and these rules will control the stock price. Therefore, the focus is how to find the potential rules.In this paper, we use numerical value analysis theory, statistical regression theory, and intelligent optimization theory to solve the problem of predicting in financial field. On the one hand, from the form, this paper gives three methods to solve the financial problems. On the other hand, understood from the essence, it is selected from three different perspectives as the breakthrough point to predict in the financial field. The numerical analysis theory is mainly to solve the computational complexity in prediction process, the statistical regression theory focused on eliminating the factor of multiplicity, and intelligent optimization theory focuses on the model parameters of optimization. With different types of financial data, the theory system is different. This paper has analyzed the importance of scientific predictions. Expound the scientific prediction of importance and necessity in the stock market. Based on the present study of financial prediction model at home and abroad and the existing problems, we appliy the grey prediction model, partial least squares regression model, time series prediction model and intelligent optimization prediction model to the financial field.In this paper, the research content and innovation are as follows:1. In the basis of traditional grey model, we use the strengthening and weakening buffer operator to preprocess the original data sequence. Then, we obtain a set of flat data sequence as the GM (1,1) prediction model of input values. We use a combination interpolation and three spline interpolations improve the traditional GM (1,1) model’s background value. Then, we can obtain a new prediction model. Finally, we use prediction method of simulation experiment in the Shanghai Composite Index daily return rate. The results show that, the method overcomes the disturbance problem, and has a high simulation and prediction accuracy.2. Based on the capital asset pricing model (CAPM), we propose a new method to solve the multicollinearity. The model is partial least squares and two polynomial regression methods. The method not only takes into account the effect of each factor on income, but also taking into account the effect of interaction between factors. It can comprehensive analysis of influencing factors of the return on equity. In addition, combined with partial least squares support regression theory and support vector regression theory, solve the multi factor optimization problem in Chinese stock market. The method can overcome the interference of Multicollinearity, so as to select the important factors of rate of return. Therefore, it is a reliable tool for the analysis of stock market.3. TheShanghai Composite Index Return and financial industry related tax revenue is non-linear and coupled, and is influenced by many factors. Therefore, traditional forecasting methods are not sufficient to predict the value of it. In this paper, disadvantages of the existing forecasting methods are analyzed. Then partial least square support vector regression (PLS-SVR) is used to construct a tax revenue prediction model. The Genetic algorithm with RBF networkis used to optimize the parameter set of (C,σ2) andε, which influences the performance of this model directly. By doing so, this model can deal with the nonlinearity and ensure stability and accuracy of support vector machine based regression. Experimental results show that, the algorithm has a good adaptability to the financial data which is highly nonlinear and coupling.4.In general, the transmission of volatility in the stock market is time-varying, nonlinear, and asymmetric with respect to both positive and negative results. Given this fact, we adopt the method of fuzzy logic systems to modify the threshold values for an EGARCH model. This study investigates the volatility forecasting for the SSEC stock index series and identifies the essential source of performance improvements between distributional assumption and volatility specification using distribution-type and asymmetry-typevolatility models through the superior predictive ability test. Such evidence strongly demonstrates that modeling asymmetric components which is the fuzzy EGARCH model is more important than specifying error distribution for improving volatility forecasts of financial returns in the presence of fat-tails, leptokurtosis, skewness, leverage effects and nonlinear effects in china stock market.In addition, we points out the deficiency of the existing methods. Based on them, the author renders WLS-SVR and the correlated mathematics modeling the errors of which are supposed as time series and the errors have auto-correlation between themselves.As a result, the paper supposed to use EGARCH model to mine the tendency information of the above errors. Then we use the result to modify predicted value of the volatility of stocks. Finally, we study a case with the satisfactory result by the SPA test which is showing that this model is more accurate than other models. |
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