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.

德国 FinBERT 是一种专门针对金融文本数据的新型预训练德语语言模型，通过综合的预训练过程进行训练，利用包括德国公司财务报告、临时公告和新闻在内的大量语料库。评估结果表明，在金融领域数据上，德国 FinBERT 在情绪预测、主题识别和问答等下游任务中表现出了改进的性能，表明其捕捉领域特定细微差别的效果，该研究认为德国 FinBERT 有望成为金融文本分析中有价值的工具，有潜在的应用于金融领域的多个应用场景。