BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights

ArXiv ID: 2404.02053 “View on arXiv”

Authors: Unknown

Abstract

This paper explores the intersection of Natural Language Processing (NLP) and financial analysis, focusing on the impact of sentiment analysis in stock price prediction. We employ BERTopic, an advanced NLP technique, to analyze the sentiment of topics derived from stock market comments. Our methodology integrates this sentiment analysis with various deep learning models, renowned for their effectiveness in time series and stock prediction tasks. Through comprehensive experiments, we demonstrate that incorporating topic sentiment notably enhances the performance of these models. The results indicate that topics in stock market comments provide implicit, valuable insights into stock market volatility and price trends. This study contributes to the field by showcasing the potential of NLP in enriching financial analysis and opens up avenues for further research into real-time sentiment analysis and the exploration of emotional and contextual aspects of market sentiment. The integration of advanced NLP techniques like BERTopic with traditional financial analysis methods marks a step forward in developing more sophisticated tools for understanding and predicting market behaviors.

Keywords: Natural Language Processing (NLP), BERTopic, Sentiment Analysis, Stock Prediction, Deep Learning, Equities

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders
• Why: The paper demonstrates strong empirical rigor with a comprehensive backtesting methodology using deep learning models (LSTM, CNN) and real-world stock market data, but the math complexity is modest, focusing on applied NLP techniques rather than novel theoretical derivations.

  flowchart TD
    A["Research Goal<br/>Predict Stock Prices using<br/>Sentiment from Market Comments"] --> B["Data Collection<br/>Stock Market Comments"]
    B --> C["Topic Modeling<br/>BERTopic Analysis"]
    C --> D["Sentiment Extraction<br/>Per Topic Analysis"]
    D --> E{"Model Integration<br/>Deep Learning Models"}
    E --> F["Prediction Output<br/>Stock Price Trends"]
    F --> G["Key Findings<br/>Enhanced Accuracy<br/>NLP Improves Financial Analysis"]
    G --> H["Future Work<br/>Real-time Analysis &<br/>Contextual Insights"]