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FinBERT: A Pretrained Language Model for Financial Communications

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arxiv 2006.08097 v2 pith:Y7PE5P4C submitted 2020-06-15 cs.CL

Yi Yang , Mark Christopher Siy UY , Allen Huang This is my paper
classification cs.CL
keywords financialfinbertmodelspretrainedbertlanguagelargetasks
verification ladder T0 review T1 audit T2 compute T3 formal
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Contextual pretrained language models, such as BERT (Devlin et al., 2019), have made significant breakthrough in various NLP tasks by training on large scale of unlabeled text re-sources.Financial sector also accumulates large amount of financial communication text.However, there is no pretrained finance specific language models available. In this work,we address the need by pretraining a financial domain specific BERT models, FinBERT, using a large scale of financial communication corpora. Experiments on three financial sentiment classification tasks confirm the advantage of FinBERT over generic domain BERT model. The code and pretrained models are available at https://github.com/yya518/FinBERT. We hope this will be useful for practitioners and researchers working on financial NLP tasks.

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Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    FinGAIA is a 407-task Chinese financial agent benchmark where the best agent, ChatGPT DeepResearch, scores 48.9%, far below financial experts at 84.7%.

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    cs.CL 2026-07 conditional novelty 5.0 of 10

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  10. FinAI-BERT: A Transformer-Based Model for Sentence-Level Detection of AI Disclosures in Financial Reports

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  11. Enhancing Trading Performance Through Sentiment Analysis with Large Language Models: Evidence from the S&P 500

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    A backtest over May-August 2024 claims that adding GPT-2 and FinBERT news sentiment to technical indicators improves S&P 500 trading returns, with a best reported return of 5.77%.

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