ESAN: Efficient Sentiment Analysis Network of A-Shares Research Reports for Stock Price Prediction
This addresses stock prediction for investors, but appears incremental as it combines existing NLP and time-series methods.
The paper tackles stock price prediction by developing ESAN, a model that combines sentiment analysis of A-shares research reports with time-series forecasting, resulting in predictions of stock earnings yield.
In this paper, we are going to develop a natural language processing model to help us to predict stocks in the long term. The whole network includes two modules. The first module is a natural language processing model which seeks out reliable factors from input reports. While the other is a time-series forecasting model which takes the factors as input and aims to predict stocks earnings yield. To indicate the efficiency of our model to combine the sentiment analysis module and the time-series forecasting module, we name our method ESAN.