A multiclass classification model for stock news based on structured data

Stock news plays an important role on market considering the timely dissemination of news will likely to affect a stock's price changes. Classifying stock news is very helpful for investors to make investment decisions. In this paper, we design and implement a structured data based classification model, which could be effectively applied to the field of stock news classification problem. The model firstly calculates the news correlation by using structured data, and then makes use of the parameter fitting method along with the output value of softmax regression model, after that, the final probability sequence is presented. The multi-label classification is realized by the method of selecting multi-labels. Finally, we compare the novel algorithm with a general algorithm, verifying the availability and accuracy of our classification method.