The Random Forest Model for analyzing and Forecasting the US Stock Market under the background of smart finance
Jiajian Zheng, Duan Xin, Qishuo Cheng, Miao Tian, Le Yang
Abstract
As an important part of the financial market, The stock market plays a crucial role in wealth accumulation for investors, financing costs for listed companies, and the stable development of the national macroeconomy.Consequently, significant fluctuations in the stock market will not only damage the interests of stock investors, but also cause the imbalance of the industrial structure, which will interfere with the development of the national economy on the macro level.As a result, the prediction of stock price trend has become a hot research topic in the academic circles.Therefore, the prediction of three movement trends of stock price trend, namely, rising, sideways and falling, is more helpful for stock investors to make choices among all decision-making behaviors, namely, buying, holding and selling stocks.Given this context, establishing an effective forecasting model for these three stock price trends is of substantial practical importance to establish an effective forecasting model for the prediction of the three movement trends of stock prices.In this paper, the stock price trend of the financial market under the background of smart finance is predicted by model, and the stock price trend of the United States is predicted by random forest model through the combination of artificial intelligence, deep learning and other fields.[1]Moreover, the test set of three stocks is used to test the prediction effect of the model under the optimal parameters of the random forest models combined with artificial intelligence.Based on the modeling and forecasting process, the corresponding time consumption is recorded.Therefore, the prediction performance of the model is evaluated comprehensively by using the prediction effect and time consuming of the model.