Application of stock index analysis based on multivariate linear regression macroeconomic variables

Based on the traditional basis of multiple linear regression, factor analysis model is optimized to eliminate multicollinearity model to achieve better fitting effect. Compared to the existing stock prediction method for selecting data collection simple and no specific data requirements, predictions fit high for most stocks. In this paper, two Western Mining Guangzhou Pharmaceutical and historical stock prices as an example, to the day open, high, low, close, turnover, turnover, and the next day opening price as the independent variable, predicted the stock times closing price, by comparing the two models eliminating collinearity results before and after the closing price for the prediction, the predicted effect of the regression equation to verify the elimination of the use of co-factor analysis model better linearity.