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Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models

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  • Boyu Zhang
  • Hongyang Yang
  • Xiao-Yang Liu

Abstract

Sentiment analysis is a vital tool for uncovering insights from financial articles, news, and social media, shaping our understanding of market movements. Despite the impressive capabilities of large language models (LLMs) in financial natural language processing (NLP), they still struggle with accurately interpreting numerical values and grasping financial context, limiting their effectiveness in predicting financial sentiment. In this paper, we introduce a simple yet effective instruction tuning approach to address these issues. By transforming a small portion of supervised financial sentiment analysis data into instruction data and fine-tuning a general-purpose LLM with this method, we achieve remarkable advancements in financial sentiment analysis. In the experiment, our approach outperforms state-of-the-art supervised sentiment analysis models, as well as widely used LLMs like ChatGPT and LLaMAs, particularly in scenarios where numerical understanding and contextual comprehension are vital.

Suggested Citation

  • Boyu Zhang & Hongyang Yang & Xiao-Yang Liu, 2023. "Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models," Papers 2306.12659, arXiv.org.
    • Handle: RePEc:arx:papers:2306.12659
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    References listed on IDEAS

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    1. Pekka Malo & Ankur Sinha & Pekka Korhonen & Jyrki Wallenius & Pyry Takala, 2014. "Good debt or bad debt: Detecting semantic orientations in economic texts," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 782-796, April.
    2. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
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    Cited by:

    1. Yinheng Li & Shaofei Wang & Han Ding & Hang Chen, 2023. "Large Language Models in Finance: A Survey," Papers 2311.10723, arXiv.org, revised Jul 2024.
    2. Zijie Zhao & Roy E. Welsch, 2024. "Hierarchical Reinforced Trader (HRT): A Bi-Level Approach for Optimizing Stock Selection and Execution," Papers 2410.14927, arXiv.org.
    3. Masanori Hirano & Kentaro Imajo, 2024. "Construction of Domain-specified Japanese Large Language Model for Finance through Continual Pre-training," Papers 2404.10555, arXiv.org.
    4. Masanori Hirano & Kentaro Imajo, 2024. "The Construction of Instruction-tuned LLMs for Finance without Instruction Data Using Continual Pretraining and Model Merging," Papers 2409.19854, arXiv.org.
    5. Xiao-Yang Liu & Guoxuan Wang & Hongyang Yang & Daochen Zha, 2023. "FinGPT: Democratizing Internet-scale Data for Financial Large Language Models," Papers 2307.10485, arXiv.org, revised Nov 2023.
    6. Alonso-Robisco, Andres & Carbó, José Manuel, 2023. "Analysis of CBDC narrative by central banks using large language models," Finance Research Letters, Elsevier, vol. 58(PC).
    7. Thanos Konstantinidis & Giorgos Iacovides & Mingxue Xu & Tony G. Constantinides & Danilo Mandic, 2024. "FinLlama: Financial Sentiment Classification for Algorithmic Trading Applications," Papers 2403.12285, arXiv.org.
    8. Yuqi Nie & Yaxuan Kong & Xiaowen Dong & John M. Mulvey & H. Vincent Poor & Qingsong Wen & Stefan Zohren, 2024. "A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges," Papers 2406.11903, arXiv.org.
    9. Zihan Dong & Xinyu Fan & Zhiyuan Peng, 2024. "FNSPID: A Comprehensive Financial News Dataset in Time Series," Papers 2402.06698, arXiv.org.

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