Financial sentiment analysis using FinBERT with application in predicting stock movement

ArXiv ID: 2306.02136 “View on arXiv”

Authors: Unknown

Abstract

In this study, we integrate sentiment analysis within a financial framework by leveraging FinBERT, a fine-tuned BERT model specialized for financial text, to construct an advanced deep learning model based on Long Short-Term Memory (LSTM) networks. Our objective is to forecast financial market trends with greater accuracy. To evaluate our model’s predictive capabilities, we apply it to a comprehensive dataset of stock market news and perform a comparative analysis against standard BERT, standalone LSTM, and the traditional ARIMA models. Our findings indicate that incorporating sentiment analysis significantly enhances the model’s ability to anticipate market fluctuations. Furthermore, we propose a suite of optimization techniques aimed at refining the model’s performance, paving the way for more robust and reliable market prediction tools in the field of AI-driven finance.

Keywords: Sentiment Analysis, FinBERT, LSTM, Deep Learning, Market Forecasting

Complexity vs Empirical Score

• Math Complexity: 4.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Street Traders
• Why: The paper employs a relatively straightforward application of established deep learning architectures (FinBERT and LSTM) without advanced theoretical derivations, but it is heavily implementation-focused with a comprehensive dataset, detailed preprocessing steps, and performance comparisons against baselines, making it more practical and empirical.

  flowchart TD
    Start["Research Goal: Forecast stock market trends<br/>using financial sentiment analysis"] --> Data["Dataset: Comprehensive stock market news"]
    Data --> Process1["Text Preprocessing &<br/>Sentiment Extraction (FinBERT)"]
    Process1 --> Process2["Feature Integration &<br/>Model Construction (LSTM)"]
    Process2 --> Eval["Comparative Evaluation<br/>vs. BERT, LSTM, ARIMA"]
    Eval --> Outcome["Key Findings:<br/>1. Sentiment integration enhances accuracy<br/>2. FinBERT-LSTM outperforms baselines<br/>3. Proposed optimization techniques<br/>achieved robust predictions"]