1 " " Forecasting Movements of Health-Care Stock Prices Based on Different Categories of News Articles using Multiple Kernel Learning

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. Keywords—stock price prediction; financial news; text mining; multiple kernel learning; decision support systems """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""" * Corresponding author. E-mail address: shynkevich-y@email.ulster. ac.uk Abbreviations: SS (stock-specific), SIS (sub-industry-specific), IS (industry-specific), GIS (group-indusrtyspecific), SeS (sector-specific) 2" " 1. INTRODUCTION Investors make investment decisions based on the information available to market participants. News articles bring new information to the market. They contain news about a company, the activities in which it is involved, its fundamentals and what is expected by market participants about its future price changes [1], [2]: stock prices are driven by these publications. With the development of the internet, finance-related websites and applications constantly provide a large amount of textual data containing new information. A system capable of efficiently utilising this new data to predict future changes in prices is required to support the decision making of investors and traders. Researchers have been studying the influence of news articles and developed several automated frameworks that consider large amounts of financial news. These frameworks extract relevant information and employ it to forecast prices and their changes [3]. As has been shown in previous research [4], there is a strong relationship between stock prices fluctuations and publications of relevant news. The effect that news items have on stock prices has been studied using existing data mining techniques [5], [6], [7]. According to the related literature, researchers usually employ a predefined criterion for selecting news articles from a large collection of textual information. Generally, only news articles highly relevant to an analysed stock are selected. After that, equal importance is given to all articles so that every article is treated as impacting the stock price to the same extent. So far no previous studies employ articles that are divided into different news categories and analysed simultaneously yet differently based on their relevance to the analysed stock, which is the focus of this paper. This paper investigates whether financial news articles that have different degrees of relevance to the target stock can provide an advantage in financial news-based forecasting when used simultaneously and appropriately. Toward this end, the considered stocks are assigned to the corresponding sub industries, industries, group industries and sectors according to the Global Industry Classification Standard (GICS) as in [8]. Then news published about these stocks are allocated to different news categories. We consider five news categories; these are stock-specific (SS), sub-industry-specific (SIS), industry-specific (IS), group-industry-specific (GIS) and sector-specific (SeS) news items. The experiments are performed on stocks from the S&P 500 index belonging the Health Care sector. News