Research on New Energy and Traditional Energy Based on SPSS

In China, the main profit of the energy industry is traditional energy sources, the proportion of traditional energy companies take on a high number. However, China has been putting forward green economy, with strongly support of national policy, the new energy enterprises emerge in an endlessly stream, the businesses involved in new energy economy profit a lot and that everyone is better off, which leads to a relatively strong upward tendency for new energy stocks. Therefore, based on such a fierce competition in the energy industry, it is necessary to know if the relevance of the new energy stock and traditional energy stock is positive or negative. This thesis is based on a combination of correlation analysis and regression analysis, analyze the correlation of new energy stock and traditional energy stock, and the sub-sectors of new energy, do research on stock investment strategy through the analysis of convergence. We firstly use SPSS to carry out correlation analysis on stock price, quantitatively illustrate the relationship between the two kinds of stocks, and then calculate the correlation coefficient, determine its correlation strength, at last linear regression analysis by SPSS, and summarize a functional relationship for the stock.

nuclear and other new energy enterprises is different, so the market competition circumstance is different, and the company's stock performance in the market is also not the same.For the purpose of the practical significance of research and conclusion, it is necessary to select some representative company from various kinds of new energy, comparatively analyze according to the company's stock prices so as to drew the conclusion of that there exists the correlativity between the sectors of new energy.

Concepts of Correlation and Regression Analysis
Stock market is a barometer of the economic development in China, the stock market has become the main factors for us to get a reference of economic development, so the research of the stock market is a hot topic in the field of economics.On the other hand, the recent researches of many scholars focus on the overall stock market price prediction and empirical study, which are lack of systematic analysis of individual industry and lack of an inaccurate understanding of the industry characteristics and development trend.This thesis is on the base of the stock quotations tendency of some representative companies in new energy industry and traditional energy industry, we proceed to do the correlation analysis and regression analysis by SPSS, then carry out the data analysis on the stock price fluctuation trend of new energy and traditional energy, finally we sum up a function relation and positive and negative correlation between these two, which give the answer to stock investment strategy.
The correlation analysis is to use a certain amount of sample data, get the variables from two kinds of the sample data, and then conduct the analysis of the degree of linear correlation between variables, at last manifest the linear correlation as converging relation of two variables.The correlation analysis generally has three steps: first, put forward the hypothetical judgment of linear correlation between two variables; secondly, draw a simple scatter diagram using software, roughly present the converging relation through the variable distribution situation of each point; Thirdly, accurately calculate the coefficient of the linear relationship between the two variables through software.The most common factor in the correlation analysis is the Pearson Correlation Coefficient, the formula is shown as the following: According to the number of the Pearson correlation coefficient r, the linear correlation between variables can be divided into four levels: 0.8< r ≤1 means highly relevant; 0.5< r ≤0.8 means middling relevant; 0.3< r ≤0.5 means low grade relevant; 0< r ≤0.3 means weakly relevant.
Regression analysis is based on a certain amount of sample data, choose the independent variable and dependent variable from the sample data, and then come to a conclusion of the regression relations between the independent variable and dependent variable by means of a series of mathematical statistics methods.In general regression analysis can be divided into four steps: first step is to determine the independent variable and dependent variable; secondly, make sure of the mathematical relationship based on the samples; the third step is to determine the parameters of the regression equation, and then verify that the checksum matches; the final step is to estimate and forecast by using regression equation.

Stock Investment Algorithm Basic Thoughts
This thesis is to realize the stock investment analysis mainly by means of the statistical analysis of stock price, which is known as the method of correlation and regression analysis.The correlation between two stock prices often manifest as the fluctuation of one stock can have obvious influence on the price of another kind of stock, if the influence does exist, it indicate there are convergence and association between these two.The association between the two stocks can be expressed by concrete numerical value.From Pearson coefficient formula, we can get the correlation coefficient between the variable is r, the stock price of the two variables is X, Y, the function relational expression is X Y , in which β 1 is constant, β 1 stands for the regression coefficients of X and Y.

Import the Stock History Data
We use the "Tong Hua Shun" software to derive the stocks history data of the two representatives in the new energy industry and traditional energy industry.In this thesis, we take "TOPRAYSOLAR" and "Shanxi Coal and Chemical Industry" as examples, the two stocks are selected the quarterly closing price from 1th January 2014 to 31th March 2016, and then consolidate it to the form of Excel to import into SPSS, in which the date, closing price and the residual error must be included.We use SPSS software to carry on the "TOPRAYSOLAR" and "Shanxi Coal and Chemical Industry" correlation analysis.But before jumping ahead into correlation analysis, first of all we need to make a scatter plot between the two variables, judging whether it has linear relation between the two from the subjective, as shown in Figure 1.
Figure 1.Scatter plot between the two variables The Figure 1 shows there is a certain relationship between the two variables,In particular with the increase of "Shanxi Coal and Chemical Industry" stock price, the price of "TOPRAYSOLAR" is also increasing, however, as to how relevant one stock is to the other, judging from subjective can not be absolute correct, so we need to take things to a next level to calculate the correlation coefficient between "Shanxi Coal and Chemical Industry" and "TOPRAYSOLAR", to determine the correlation degree between the two variables.
The calculated correlation coefficient and inspection is as shown in Table 2. From Table 2, we can get that the Pearson correlation coefficient between "Shanxi Coal and Chemical Industry" and "TOPRAYSOLAR" is 0.774, which means a positive correlation between the two stock, as one stock price growing, the other one grows with it.As we can see from the P value of the significance test results, P value is 0.014, clearly less than 0.05, under the circumstances of 5% significance level, the two variables pass the test of significance,therefore, it proves a positive linear relationship between the two stock, with a highly related degree.On the basis of this, we continue to use the regression analysis to move forward a single step.

The Regression Analysis
To reinforce the stock price change of "TOPRAYSOLAR", to find out how it affected by other factors, "TOPRAYSOLAR" is defined as the dependent variable (y), while "Shanxi Coal and Chemical Industry" is defined as the independent variable (x), establishing the linear regression model of one-variable between the two.The Table 3 shows that R square value is 0.599, the adjusted R square is 0.542, meaning the degree of fitting for the model is 59.9%, the adjusted degree of fitting for the model is 54.2%, illustrating the model fitting is relatively good to a certain extent .Table 5 is the parameter estimation result of the linear regression of one-variable model, the result shows that constant is 5.822, regression coefficient is 0.829, the corresponding t statistic value is 3.236, the corresponding P value is 0.014, less than 0.05, under the circumstance of 5% significance level,the regression coefficient of the model apparently pass the significance test.In conclusion, the model has ideal effect.In the end, model expression can be written as: 5.822 0.829 The stock price of the new energy and traditional energy presents obvious convergence relationship.Similarly, Asymmetric BEKK and Asymmetric DCC model also provides a evidence that new energy stock market in China and the international crude oil market exist volatility spillover and asset price fluctuations asymmetrically, for example, two asset price fluctuations is asymmetry on its variance,that is with respect to the positive impact on its earnings, the negative impact is much more bigger on its earnings volatility.The short-term impact of new energy company stock prices has obvious volatility spillover effect on WTI crude oil futures market, which depend on the symbols of two kinds of asset price shocks.From the point of long-term volatility of asset prices, there are bidirectional volatility spillover effect between two markets, and the changes of covariance and correlation coefficient between two markets is also associated with two kinds of asset price shocks symbols.

Import the Stock History Data
We use the "Tong Hua Shun" software to derive the stocks history data of the two representatives in the new energy industry's sub-professions.In this thesis, we take "TOPRAYSOLAR" and "Goldwind Science&Technology" as examples, the two stocks are selected the quarterly closing price from 1th January 2014 to 31th March 2016, and then consolidate it to the form of Excel to import into SPSS, in which the date, closing price and the residual error must be included.

The correlation analysis
We use SPSS software to carry on the "TOPRAYSOLAR" and "Goldwind Science & Technology" correlation analysis.But before jumping ahead into correlation analysis, first of all we need to make a scatter plot between the two variables, judging whether it has linear relation between the two from the subjective, as shown in Figure 2. The Figure 2 shows there is a certain relationship between the two variables,In particular with the increase of "Goldwind Science & Technology" stock price, the price of "TOPRAYSOLAR" is also increasing, however, as to how relevant one stock is to the other, judging from subjective can not be absolute correct, so we need to take things to a next level to calculate the correlation coefficient between "Goldwind Science & Technology" and "TOPRAYSOLAR", to determine the correlation degree between the two variables.
The calculated correlation coefficient and inspection is as shown in Table 7.

Figure 2 .
Figure 2. Scatter plot between the two variables

Table 1 .
The stocks history data of "TOPRAYSOLAR" and "Shanxi coal and chemical industry"

Table 3 .
Model summary

Table 4 .
Analysis of variance b. Independent variable:Shanxi Coal and Chemical Industry.

Table 4
is the test results of variance analysis for the model significance, what can be seen from the table is that the F statistic value is 10.470, the corresponding P value is 0.014, which is less than 0.05, under the circumstance of 5% significance level, the model passes the significance test.

Table 5 .
Estimation on varying coeffcient models

Table 6 .
The stocks history data of "TOPRAYSOLAR" and "Goldwind Science & Technology"