Research
Research
This paper examines what the structure of economic narratives reveals about how risks are perceived to connect across firms, sectors, and countries. Using sentence-level causal claims extracted from international news publishers between 2016 and 2025, we construct daily directed networks and summarise them with a large set of network statistics. Narrative concentration, the extent to which causal explanations and negative attention converge on a small set of central topics, rises during major disruptions, when economic developments become organised around common risks. Innovations in this structure are followed by contractions in the aggregate economy, even after accounting for established measures of uncertainty and market volatility. Focusing on US financial institutions, we describe changes in the sector’s position within the network during market stress and show that its structure distinguishes the role of financial institutions over time. These findings show that the architecture of economic narratives reveals how risks are perceived to connect and propagate information that cannot be recovered from scalar risk measures.
This work leverages a unique database that identifies written causal statements between economic indicators and events with daily precision, from a total of 128 million documents processed since 2014, covering over 40,000 firms across 187 exchanges worldwide. This database offers a detailed view of causal structures within written media content, allowing a detailed analysis of its impact on the world’s economy. Our study models these interactions as an evolving dynamic network, represented by daily weighted directed graphs. We use and adapt the preferential attachment (PA) model to estimate the probability that new nodes connect to existing ones. PA is an appealing edge-generating mechanism for capturing the growth dynamics of evolving networks and for modelling the tail-heaviness of the distributions of node degrees and strengths. Model parameters are estimated using both parametric and nonparametric methods, which are then used to efficiently generate future day-ahead weighted and directed PA networks. The predictive accuracy of the proposed approach is assessed by examining how well the generated networks reproduce the daily characteristics and empirical linking probabilities observed in the data. This methodology enhances our understanding of media’s influence on the global economy and evaluates the model’s suitability for complex dynamic network analysis.