A three-step machine learning approach to predict market bubbles with financial news

ArXiv ID: 2510.16636 “View on arXiv”

Authors: Abraham Atsiwo

Abstract

This study presents a three-step machine learning framework to predict bubbles in the S&P 500 stock market by combining financial news sentiment with macroeconomic indicators. Building on traditional econometric approaches, the proposed approach predicts bubble formation by integrating textual and quantitative data sources. In the first step, bubble periods in the S&P 500 index are identified using a right-tailed unit root test, a widely recognized real-time bubble detection method. The second step extracts sentiment features from large-scale financial news articles using natural language processing (NLP) techniques, which capture investors’ expectations and behavioral patterns. In the final step, ensemble learning methods are applied to predict bubble occurrences based on high sentiment-based and macroeconomic predictors. Model performance is evaluated through k-fold cross-validation and compared against benchmark machine learning algorithms. Empirical results indicate that the proposed three-step ensemble approach significantly improves predictive accuracy and robustness, providing valuable early warning insights for investors, regulators, and policymakers in mitigating systemic financial risks.

Keywords: S&P 500, Bubble Prediction, Sentiment Analysis, NLP, Ensemble Learning, Equities

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Lab Rats
  • Why: The paper employs advanced econometric models like the PSY/SADF test and integrates NLP with ensemble learning, indicating solid mathematical complexity. However, the evaluation relies on k-fold cross-validation without mention of robust out-of-sample backtesting or real-world implementation details, suggesting lower empirical rigor.
  flowchart TD
    A["Research Goal<br>Predict market bubbles<br>in S&P 500"] --> B["Step 1: Bubble Identification<br>Right-tailed unit root test"]
    A --> C["Data Sources<br>Financial News +<br>Macroeconomic Indicators"]
    
    C --> D["Step 2: Feature Extraction<br>NLP for Sentiment Analysis"]
    B --> E["Step 3: Prediction Model<br>Ensemble Learning"]
    D --> E
    
    E --> F["Model Evaluation<br>K-fold Cross-Validation"]
    F --> G["Key Findings<br>High predictive accuracy<br>Robust early warning system<br>Risk mitigation insights"]