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

arXiv: https://arxiv.org/abs/2112.11444 · HTML: https://mlsanity.com/paper/2112.11444/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 3 Dec 2021.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition](https://mlsanity.com/paper/1402.1128/) (2014)
- [A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing](https://mlsanity.com/paper/1912.07700/) (2019)
- [Stock price prediction using BERT and GAN](https://mlsanity.com/paper/2107.09055/) (2021)
- [Time Series Prediction : Predicting Stock Price](https://mlsanity.com/paper/1710.05751/) (2017)

## Abstract (arXiv)

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.