Understanding financial news with multi-layer network analysis

Abstract

What is in the news? We address this question by constructing and comparing multi-layer networks from different sources. The layers consist of the same nodes (hence multiplex networks), but links are constructed from textual news on one hand, and empirical data on the other hand. Nodes represent entities of interest, recognized in the news. From the news, links are extracted from significant co-occurrences of entities, and from strong positive and negative sentiment associated with the co-occurrences. In a case study, the observed entities are 50 countries, extracted from more than 1.3 million financial news acquired over a period of 2 years. The empirical network layers are constructed from the geographical proximity, the trade connections, and from correlations between financial indicators of the same countries. Different network comparison metrics are used to explore the similarity between the news and the empirical networks. We examine the overlap of the most important links in the constructed networks, and compare their structural similarity by node centrality and main k-cores. The comparative analysis reveals that the co-occurrences of countries in the news most closely match their geographical proximity, while positive sentiment links most closely match the trade connections between the countries. Correlations between financial indicators have the lowest similarity to financial news.