http://dx.doi.org/10.1016/j.knosys.2014.04.022">
 

Document Type

Journal Article

Department/Unit

Department of Computer Science

Title

News impact on stock price return via sentiment analysis

Language

English

Abstract

© 2014 Elsevier B.V. All rights reserved.Financial news articles are believed to have impacts on stock price return. Previous works model news pieces in bag-of-words space, which analyzes the latent relationship between word statistical patterns and stock price movements. However, news sentiment, which is an important ring on the chain of mapping from the word patterns to the price movements, is rarely touched. In this paper, we first implement a generic stock price prediction framework, and plug in six different models with different analyzing approaches. To take one step further, we use Harvard psychological dictionary and Loughran-McDonald financial sentiment dictionary to construct a sentiment space. Textual news articles are then quantitatively measured and projected onto the sentiment space. Instance labeling method is rigorously discussed and tested. We evaluate the models' prediction accuracy and empirically compare their performance at different market classification levels. Experiments are conducted on five years historical Hong Kong Stock Exchange prices and news articles. Results show that (1) at individual stock, sector and index levels, the models with sentiment analysis outperform the bag-of-words model in both validation set and independent testing set; (2) the models which use sentiment polarity cannot provide useful predictions; (3) there is a minor difference between the models using two different sentiment dictionaries.

Keywords

Experiment, News impact, Prediction, Sentiment, Stock price return

Publication Date

2014

Source Publication Title

Knowledge-Based Systems

Volume

69

Start Page

14

End Page

23

Publisher

Elsevier

ISSN (print)

09507051

ISSN (electronic)

18727409

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