FinBERT: A Large Language Model for Extracting Information from Financial Text*

ABSTRACT We develop FinBERT, a state‐of‐the‐art large language model that adapts to the finance domain. Last, we show that other approaches underestimate the textual informativeness of earnings conference calls by at least 18% compared to FinBERT.

⚡ This is an original paraphrased summary — not copied from the abstract. Full paper available at the source link below.

• 1 We show that FinBERT incorporates finance knowledge and can better summarize contextual information in financial texts.
• 2 Using a sample of researcher‐labeled sentences from analyst reports, we document that FinBERT substantially outperforms the Loughran and McDonald dictionary and other machine learning algorithms, including naïve Bayes, support vector machine, random forest, convolutional neural network, and long short‐term memory, in sentiment classification.
• 3 Our results show that FinBERT excels in identifying the positive or negative sentiment of sentences that other algorithms mislabel as neutral, likely because it uses contextual information in financial text.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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