  This study presents German FinBERT, a novel pre-trained German language model
tailored for financial textual data. The model is trained through a
comprehensive pre-training process, leveraging a substantial corpus comprising
financial reports, ad-hoc announcements and news related to German companies.
The corpus size is comparable to the data sets commonly used for training
standard BERT models. I evaluate the performance of German FinBERT on
downstream tasks, specifically sentiment prediction, topic recognition and
question answering against generic German language models. My results
demonstrate improved performance on finance-specific data, indicating the
efficacy of German FinBERT in capturing domain-specific nuances. The presented
findings suggest that German FinBERT holds promise as a valuable tool for
financial text analysis, potentially benefiting various applications in the
financial domain.
