ESAN: Efficient Sentiment Analysis Network of A-Shares Research Reports for Stock Price Prediction

TL;DR

This paper introduces ESAN, a model combining sentiment analysis of research reports and time-series forecasting to predict stock earnings yield, aiming for long-term stock prediction accuracy.

Contribution

The paper presents a novel integrated model that combines NLP-based sentiment analysis with time-series forecasting for stock prediction.

Findings

Effective integration of sentiment analysis and forecasting modules

Improved long-term stock prediction accuracy

Demonstrated efficiency of the combined approach

Abstract

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.