Chinese stock market prediction based on multifeature fusion and TextCNN

Abstract

Stock trend forecasting plays a great role in maximizing the profit of stock investment. However, due to the high volatility and non-stationarity of the stock market, accurate trend prediction is very difficult. With the development of the Internet and deep learning technology, people can use deep learning methods to reveal market trends and volatility from the explosive information on the Internet. Unfortunately, there is a large amount of content related to the stock market, and a large part of it is useless information. As a result, how to extract the effective information and combine this information as different characteristics to effectively predict stock trends has become the biggest challenge. In order to cope with these challenges, we use TextCNN as the news text feature extractor for feature extraction of news information, and propose a prediction method based on multi-feature fusion: Bi-LSTNAA, to predict the Chinese stock market. Extensive experiments on actual stock market data show that the our method has a greater improvement in the accuracy of stock trend prediction.