Forecast Analysis of Shanghai Composite Index Based on Machine Learning Method

Content Provider	Semantic Scholar
Author	Wu, Rengkang Yunnan, Kunming
Copyright Year	2016
Abstract	The Shanghai composite index is an important index that general investors pay close attention to. Shanghai composite index, which not only reflects the basic situation of the stock market in our country, but also takes an important guiding role to our economy. Prediction of Shanghai composite index and trend analysis plays an important role to stabilize market and guide investors. And stock market data are a typical nonlinear system; traditional statistical forecasting methods predict a low accuracy. In this paper, we use R software comprehensively and combine with the latest six kinds of methods in machine learning field, decision tree, boosting, bagging, random forests, support vector machine (SVM), neural network to train the training set, respectively, get the corresponding model. And set up the corresponding ten-fold cross validation to calculate the prediction mean square error of each method for comparison. Select the model with better effect, and make a visualized comparison between prediction data and real data. Analysis shows that the results of random forests, SVM are more fitting, and have high precision.
File Format	PDF HTM / HTML
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Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article