# Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis

arXiv: https://arxiv.org/abs/1705.02447 · HTML: https://mlsanity.com/paper/1705.02447/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2017.

## Built on by (most cited first, 2 of 2)

- [AMA-LSTM: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Prediction](https://mlsanity.com/paper/2407.18324/) (2024)
- [Using Twitter Attribute Information to Predict Stock Prices](https://mlsanity.com/paper/2105.01402/) (2021)

## Abstract (arXiv)

In this paper, we propose a model to analyze sentiment of online stock forum and use the information to predict the stock volatility in the Chinese market. We have labeled the sentiment of the online financial posts and make the dataset public available for research. By generating a sentimental dictionary based on financial terms, we develop a model to compute the sentimental score of each online post related to a particular stock. Such sentimental information is represented by two sentiment indicators, which are fused to market data for stock volatility prediction by using the Recurrent Neural Networks (RNNs). Empirical study shows that, comparing to using RNN only, the model performs significantly better with sentimental indicators.