TY - JOUR T1 - Point-in-Time Language Model for Geopolitical Risk Events JF - The Journal of Financial Data Science DO - 10.3905/jfds.2022.1.113 SP - jfds.2022.1.113 AU - Matthias Apel AU - André Betzer AU - Bernd Scherer Y1 - 2022/12/14 UR - https://pm-research.com/content/early/2022/12/14/jfds.2022.1.113.abstract N2 - In this article, the authors show how to build a real-time geopolitical risk index from news data using textual analysis. The presented method defines a point-in-time dictionary of terms related to political tension. It does not rely on the in-sample definition of a set of n-grams that are likely chosen and updated with hindsight bias. The proposed model can be applied to any topic and is language agnostic. Only a few topic-related words are required to initialize the buildup of a dynamically self-adjusting dictionary. The authors show that their approach can resemble the results of other more supervised methods. The findings indicate how topic identification and news index construction may benefit from a time-dependent dictionary generation. ER -