Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
11-2021
Abstract
Stock return prediction has been a hot topic in both research and industry given its potential for large financial gain. The return signal, apart from its inherent volatility and complexity, is often accompanied by a multitude of noises, such as other stocks’ performance, macroeconomic factors and financial news, etc. To better characterize these factors, we propose a new model that consists of two levels of sequence: an NLP-based module to capture the sequential nature of words and sentences in the financial news, and a time-series-based module to exploit the sequential nature of adjacent observations in the stock price. In this proposed framework, we employ Hierarchical Attention Networks (HAN) in the text mining module, which could effectively model the financial news and extract important signals at both word and sentence level. For the time series module, the established Long-Short Term Memory (LSTM) network is used to model the complex serial dependence in the time series data. We compare with benchmark models using either module alone, as well as other alternatives using the traditional Bag of Words (BOW) approach, based on the Dow Jones Industrial Average (DJIA) dataset. Experiment results show that our proposal method performs better in several classification metrics for both positive and negative stock returns.
Keywords
stock price prediction, text classification, natural language processing, hierarchical attention networks (HAN), long short-term memory (LSTM)
Discipline
Finance | Finance and Financial Management
Research Areas
Finance
Publication
Proceedings of the 2021 International Conference on Signal Processing and Machine Learning (CONF-SPML), Stanford, California, November 14
First Page
133
Last Page
138
ISBN
9781665417341
Identifier
10.1109/CONF-SPML54095.2021.00034
Publisher
Elsevier
City or Country
Stanford, CA
Citation
CHEN, Haoling and LIU, Peng.
Stock return prediction using financial news: A unified sequence model based on hierarchical attention and long-short term memory networks. (2021). Proceedings of the 2021 International Conference on Signal Processing and Machine Learning (CONF-SPML), Stanford, California, November 14. 133-138.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/7045
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
External URL
https://doi.org/10.1109/CONF-SPML54095.2021.00034