We introduce a predominantly German corpus comprising 12.5k PDF documents sourced from the financial domain. The corresponding extracted textual data encompasses more than 165 million tokens derived predominantly from German, and to a lesser extent, bilingual documents. We provide detailed information about the document types included in the corpus, such as final terms, base prospectuses, annual reports, information materials, law documents, international financial reporting standards, and monthly reports from the Bundesbank, accompanied by comprehensive statistical analysis. To our knowledge, it is the first non-email German financial corpus available, and we hope it will fill this gap and foster further research in the financial domain both in the German language and in multilingual contexts.

Recent advancements in self-supervised pre-training of Language Models (LMs) have significantly improved their performance across a wide range of Natural Language Processing (NLP) tasks. Yet, the adaptation of these models to specialized domains remains a critical endeavor, as it enables the models to grasp domain-specific nuances, terminology, and patterns more effectively, thereby enhancing their utility in specialized contexts. This paper presents an in-depth investigation into the training and fine-tuning of German language models specifically for the financial sector. We construct various datasets for training and fine-tuning to examine the impact of different data construction strategies on the models’ performance. Our study provides detailed insights into essential pre-processing steps, including text extraction from PDF documents and language identification, to evaluate their influence on the performance of the language models. Addressing the scarcity of resources in the German financial domain, we also introduce a German Text Classification benchmark dataset, aimed at fostering further research and development in this area. The performance of the trained models is evaluated on two domain-specific tasks, demonstrating that fine-tuning with domain-specific data improves model outcomes, even with limited amounts of domain-specific data.