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Financial sentiment analysis using FinBERT with application in predicting stock movement

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  • Tingsong Jiang
  • Andy Zeng

Abstract

We apply sentiment analysis in financial context using FinBERT, and build a deep neural network model based on LSTM to predict the movement of financial market movement. We apply this model on stock news dataset, and compare its effectiveness to BERT, LSTM and classical ARIMA model. We find that sentiment is an effective factor in predicting market movement. We also propose several method to improve the model.

Suggested Citation

  • Tingsong Jiang & Andy Zeng, 2023. "Financial sentiment analysis using FinBERT with application in predicting stock movement," Papers 2306.02136, arXiv.org.
    • Handle: RePEc:arx:papers:2306.02136
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    References listed on IDEAS

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    1. Carmina Fjellstrom, 2022. "Long Short-Term Memory Neural Network for Financial Time Series," Papers 2201.08218, arXiv.org.
    2. 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.
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    Cited by:

    1. Valentina Aparicio & Daniel Gordon & Sebastian G. Huayamares & Yuhuai Luo, 2024. "BioFinBERT: Finetuning Large Language Models (LLMs) to Analyze Sentiment of Press Releases and Financial Text Around Inflection Points of Biotech Stocks," Papers 2401.11011, arXiv.org.

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