RT Journal Article
SR Electronic
T1 Point-in-Time Language Model for Geopolitical Risk Events
JF The Journal of Financial Data Science
FD Institutional Investor Journals
SP 65
OP 75
DO 10.3905/jfds.2022.1.113
VO 5
IS 1
A1 Matthias Apel
A1 André Betzer
A1 Bernd Scherer
YR 2023
UL https://pm-research.com/content/5/1/65.abstract
AB 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.