Stock forecast model based on text news by random forest

摘要

#$%^&*AU2018101531A420181115.pdf#####ABSTRACT: The main purpose of this project is to use random forest (RF) algorithm to analyze the correlation between the stock news on the historical days and the ups and downs of the stocks the next day. We then find out the information hidden behind these data by sorting and screening stocks, that is, the key words or key events related to the rise and fall of the stocks to predict the rise and fall of the stocks in the near future. By using this method, after entering the news data of the stock market on that day, we can predict the rise and fall of one stock the next day. This relatively accurate method can help shareholders get rid of the risk of stock investment and even can guarantee stable investment returns. This model uses random forest algorithm to carry out excavating and classifying the information of text news. Random forest is a combinatorial classifier, which can be used for the classification and screening of the stocks. The essence of it is a set of tree classifiers. Among them, the base classifier H (x, beta k) is a classification decision tree constructed by CART algorithm without pruning. x is the input vector and beta k is an independent and identically distributed random vector which determine the growth process of the single tree (base classifier). The output is determined by a simple majority voting method.START READ ALL THE CONTENTS INPUT OF THE NEWS DATA DATA TRAIN THE CLEANING W2V MODEL READ THE CONTENTS ACCORDING TO THE DATES MATCH EACH WORD WITH A VECTOR TAKE T HE AVERAGE OF THE WORD VECTORS TEXT VECTORISATION INPUT(x) INPUT(y) RANDOM 1 FOREST MODEL Fig.1 General Flow-Chart