@article {Apel65, author = {Matthias Apel and Andr{\'e} Betzer and Bernd Scherer}, title = {Point-in-Time Language Model for Geopolitical Risk Events}, volume = {5}, number = {1}, pages = {65--75}, year = {2023}, doi = {10.3905/jfds.2022.1.113}, publisher = {Institutional Investor Journals Umbrella}, abstract = {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.}, issn = {2640-3943}, URL = {https://jfds.pm-research.com/content/5/1/65}, eprint = {https://jfds.pm-research.com/content/5/1/65.full.pdf}, journal = {The Journal of Financial Data Science} }