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Financial news-based stock movement prediction using causality analysis of influence in the Korean stock market
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2019 | Decision Support Systems | Citations: 0
Authors: Nam, KiHwan; Seong, NohYoon

Abstract: With the advent of the Big Data era and the development of machine learning tech ... Expand

Abstract: With the advent of the Big Data era and the development of machine learning technologies, predicting stock movements by analyzing news articles, which are unstructured data, has been studied actively. However, so far no attempts have been made to utilize the asymmetric relationship of firms. Thus far, most papers focus on only the target firm, and few papers focus on the target firm and relevant firms together. In this article, we propose a novel machine learning model to forecast stock price movement based on the financial news considering causality. Specifically, our method analyzes the causal relationship between companies, and it accounts for the directional impact within the Global Industry Classification Standard sectors. In our proposed method, transfer entropy is used to find causality, and multiple kernel learning is used to combine features of target firm and causal firms. Based on a Korean market dataset and out-of-sample test, our experimental results reveal that the proposed causal analytic-based framework outperforms two traditional state-of-the-art algorithms. Furthermore, the experimental results show that the proposed method can predict the stock price directional movements even when there is no financial news on the target firm, but financial news is published on causal firms. Our findings reveal that identifying causal relationship is important in prediction problems, and we suggest that it is important to develop machine learning algorithms and it is also important to find connections with well-established theories such as the complex system theory. Collapse

Semantic filters: physics theory

Topics: Facebook Twitter computer hardware social network information technology infrastructure

Methods: computational algorithm machine learning term frequency–inverse document frequency experiment natural language processing

Theories: information theory economic theory general systems theory physics theory
How to select measures for decision support systems - An optimization approach integrating informational and economic objectives

2009 | European Conference On Information Systems | Citations: 0
Authors: Capra, Eugenio; Merlo, Francesco

Abstract: In this position paper we discuss the importance of Green IT as a new research f ... Expand

Abstract: In this position paper we discuss the importance of Green IT as a new research field that investigates all the environmental and energy issues related to IT and information systems in general. In particular we focus on the energy consumption of software applications, which is amplified by all the above IT layers in a data center and thus is worth a greater attention. By adopting a top-down approach, we address the problem from a logical perspective and try to identify the original cause that leads to energy consumption, i.e. the elaboration of information. We propose a research roadmap to identify a set of software complexity and quality metrics that can be used to estimate energy consumption and to compare specific software applications. Collapse

Semantic filters: physics theory

Topics: computer hardware data center open source IT manager software developer

Methods: experiment computational algorithm

Theories: quantum theory physics theory

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Financial news-based stock movement prediction using causality analysis of influence in the Korean stock market

How to select measures for decision support systems - An optimization approach integrating informational and economic objectives