Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis
Abstract
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.
Keywords
Cite
@article{arxiv.1705.02447,
title = {Stock Volatility Prediction Using Recurrent Neural Networks with Sentiment Analysis},
author = {Yifan Liu and Zengchang Qin and Pengyu Li and Tao Wan},
journal= {arXiv preprint arXiv:1705.02447},
year = {2017}
}
Comments
10 pages, 5 figures and it is an extended vision of our conference paper in IEA/AIE 2017