Mining textual terms for stock market prediction analysis using financial news

Abstract

This study focuses on the use of machine learning algorithms to construct a model that can predict the movements of Bursa Malaysia stock prices. In this research, we concentrate on linguistics terms from financial news that can contribute movements of the prices. Our aim is to develop a prototype that can classify sentiments towards financial news for investment decision. We experimented with five blue-chip companies from different industries of the top market constituents in Bursa Malaysia KLCI. A total of 14,992 finance articles were crawled and used as the dataset. Support Vector Machine algorithm was employed and the accuracy recorded was at 56%. The findings of this research can be used to assist investors in investment decision making.