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Stock Price Prediction based on Stock-Specific and Sub-Industry-Specific News Articles

Shynkevich, Yauheniya, McGinnity, TM, Coleman, SA and Belatreche, Ammar (2015) Stock Price Prediction based on Stock-Specific and Sub-Industry-Specific News Articles. In: 2015 International Joint Conference on Neural Networks (IJCNN),, Killarney. IEEE. 8 pp. [Conference contribution]

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DOI: 10.1109/IJCNN.2015.7280517

Abstract

Accurate forecasting of upcoming trends in the capital markets is extremely important for algorithmic trading and investment management. Before making a trading decision, investors estimate the probability that a certain news item will influence the market based on the available information. Speculation among traders is often caused by the release of a breaking news article and results in price movements. Publications of news articles influence the market state that makes them a powerful source of data in financial forecasting. Recently, researchers have developed trend and price prediction models based on information extracted from news articles. However, to date no previous research that investigates the advantages of using news articles with different levels of relevance to the target stock has been conducted. This research study uses the multiple kernel learning technique to effectively combine information extracted from stock-specific and sub-industry-specific news articles for prediction of an upcoming price movement. News articles are divided into these two categories based on their relevance to a targeted stock and analyzed by separate kernels. The experimental results show that utilizing two categories of news improves the prediction accuracy in comparison with methods based on a single news category.

Item Type:Conference contribution (Paper)
Keywords:multiple kernel learning; stock price prediction; text mining; financial news
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:32525
Deposited By: Dr Sonya Coleman
Deposited On:04 Nov 2015 11:38
Last Modified:04 Nov 2015 11:38

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