Title
Forecasting movements of health-care stock prices based on different categories of news articles using multiple kernel learning.
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.
Year
DOI
Venue
2016
10.1016/j.dss.2016.03.001
Decision Support Systems
Keywords
Field
DocType
SS,SIS,IS,GIS,SeS
Financial forecasting,Data source,Health care,Data mining,Stock price,Financial news,Computer science,Multiple kernel learning,Decision support system,Stock (geology)
Journal
Volume
Issue
ISSN
85
C
0167-9236
Citations 
PageRank 
References 
8
0.49
18
Authors
4
Name
Order
Citations
PageRank
Yauheniya Shynkevich1242.34
T. Martin Mcginnity251866.30
Sonya Coleman321636.84
Ammar Belatreche425623.11