Research
Working papers and selected research projects.
Working papers
Abstract. Major AI announcements now rank among the key information events for equity markets, yet little is known about their firm-level transmission on the announcement day itself. This paper asks whether different AI-news types affect different subsets of AI-exposed firms, whether information transmission is centralized around Magnificent Seven (M7) stocks, and whether the main source node depends on the event type. Using high-frequency intraday data for M7 stocks and smaller AI-exposed firms, we estimate event-day networks with a DAG-based Directed R2 connectedness framework. The evidence shows that AI news does not follow a single market-wide transmission channel: the main source node changes across event types. M7 firms emerge as event-day hubs in compute and adoption-related events, whereas disruption-related information can shift the main source node to a non-M7 firm. The results indicate that AI-related information is transmitted through event-specific equity-market channels, creating spillover vulnerabilities when information transmission is organized around a few dominant source nodes.