On the Importance of Text Analysis for Stock Price Prediction
By Heeyoung Lee, Mihai Surdeanu, Bill MacCartney, Dan Jurafsky. 2014
107 citations recorded by the source corpus.
We investigate the importance of text analysis for stock price prediction. In particular, we introduce a system that forecasts companies stock price changes (UP, DOWN, STAY) in response to financial events reported in 8-K documents. Our results indicate that using text boosts prediction accuracy over 10% (relative) over a strong baseline that incorporates many financially-rooted features. This impact is most important in the short term (i.e., the next day after the financial event) but persists for up to five days.
Subfields: Artificial Intelligence, Management Science and Operations Research
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Jure Leskovec
- William L. Hamilton
- Dan Jurafsky
- Percy Liang
- Luke S. Zettlemoyer
- Mohit Bansal
- Yoshua Bengio
- Jianfeng Gao
In Management Science and Operations Research
- Yang Peng
- Hui Jiang
- Kevin Clark
- Jure Leskovec
- Dan Jurafsky
- Bin Ke
- William L. Hamilton
- M. Ugur Gudelek
Citations in the corpus
- Leverage Financial News to Predict Stock Price Movements Using Word Embeddings and Deep Neural Networks (2015, methodology): “…natural language processing, many methods have been proposed to explore additional information (mainly online text data) for stock forecasting, such as financial news (Xie et al., 2013; Ding et al., 2014), twitter sentiments (Si et al., 2013; Si et al., 2014), financial reports (Lee et al., 2014).”
- Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora (2016, methodology): “To test against this lexicon, we constructed embeddings using a dataset of ∼ 2 × 10 7 tokens from financial 8K documents (Lee et al., 2014).”
- Measuring the Information Content of Financial News (2016, background): “In terms of linguistic features, existing work can be classified into tree major categories: bag-of-words (Kogan et al., 2009; Lee et al., 2014; Qiu et al., 2014), sentiment-based (Das and Chen, 2007; Li, 2010; Bollen et al.”