Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning

Shynkevich, Y, McGinnity, TM ORCID: https://orcid.org/0000-0002-9897-4748, Coleman, SA and Belatreche, A, 2016. Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning. Decision Support Systems, 85 (May), pp. 74-83. ISSN 0167-9236

Preview

Text PubSub5698_McGinnity.pdf - Post-print Download (724kB) | Preview

Abstract

—The market state changes when a new piece of information arrives. It affects decisions made by investors and is considered to be an important data source that can be used for financial forecasting. Recently information derived from news articles has become a part of financial predictive systems. The usage of news articles and their forecasting potential have been extensively researched.
However, so far no attempts have been made to utilise different categories of news articles simultaneously. This paper studies how the concurrent, and appropriately weighted, usage of news articles, having different degrees of relevance to the target stock, can improve the performance of financial forecasting and support the decision-making process of investors and traders. Stock price movements are predicted using the multiple kernel learning technique which integrates information extracted from multiple news categories while separate kernels are utilised to analyse each category. News articles are partitioned according to their relevance to the target stock, its sub industry, industry, group industry and sector. The experiments are run on stocks from the Health Care sector and show that increasing the number of relevant news categories used as data sources for financial forecasting improves the performance of the predictive system in comparison with approaches based on a lower number of categories.

Item Type:	Journal article
Publication Title:	Decision Support Systems
Creators:	Shynkevich, Y., McGinnity, T.M., Coleman, S.A. and Belatreche, A.
Publisher:	Elsevier
Date:	May 2016
Volume:	85
Number:	May
ISSN:	0167-9236
Identifiers:	Number Type 10.1016/j.dss.2016.03.001 DOI S0167923616300252 Publisher Item Identifier
Divisions:	Schools > School of Science and Technology
Record created by:	Jill Tomkinson
Date Added:	19 Jul 2016 13:52
Last Modified:	10 Sep 2017 03:00
URI:	https://irep.ntu.ac.uk/id/eprint/28153

Actions (login required)

Edit View

Statistics

Views

Downloads