Financial sentiment analysis using FinBERT with application in predicting stock movement

Tingsong Jiang and Qingyun Zeng

Papers from arXiv.org

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.

Date: 2023-06, Revised 2025-03
New Economics Papers: this item is included in nep-big, nep-cmp and nep-mfd
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