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A two-step machine learning approach to predict S&P 500 bubbles.

Fatma Başoğlu Kabran1, Kamil Demirberk Ünlü2

  • 1Department of Finance, Banking and Insurance, Izmir Kavram Vocational School, Izmir, Turkey.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary

This study introduces a two-step machine learning method to predict S&P 500 stock market bubbles. The approach combines a real-time bubble detection test with Support Vector Machines (SVM) for early crisis warnings.

Keywords:
Bubblesearly warningmachine learningmacroeconomic indicatorssupport vector machines

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Area of Science:

  • * Financial econometrics
  • * Computational finance
  • * Machine learning applications in finance

Background:

  • * Stock market bubbles, defined as asset prices exceeding fundamental value, pose significant risks to financial stability.
  • * Early detection and prediction of market bubbles are crucial for policymakers and regulators to implement preemptive measures against financial crises.
  • * Existing research focuses on understanding bubble formation factors and developing predictive models for timely intervention.

Purpose of the Study:

  • * To develop and evaluate a novel two-step machine learning approach for predicting bubbles in the S&P 500 stock market.
  • * To assess the effectiveness of Support Vector Machines (SVM) in conjunction with a real-time bubble detection test for forecasting market bubbles.
  • * To compare the predictive performance of SVM against other supervised learning algorithms using k-fold cross-validation.

Main Methods:

  • * A two-step methodology was employed, beginning with a right-tailed unit root test for real-time bubble identification in the S&P 500 index.
  • * Support Vector Machines (SVM), a non-parametric binary classification technique, were utilized to predict identified bubbles based on macroeconomic indicators.
  • * The performance of the SVM model was rigorously evaluated and compared with alternative supervised learning algorithms through k-fold cross-validation.

Main Results:

  • * The proposed two-step machine learning approach demonstrated high predictive power in identifying and forecasting stock market bubbles.
  • * SVM, when integrated with the bubble detection test and macroeconomic indicators, proved effective in predicting bubble formation.
  • * Comparative analysis using k-fold cross-validation indicated that the SVM-based method is a favorable alternative for bubble prediction.

Conclusions:

  • * The developed two-step machine learning framework offers a robust and effective solution for predicting S&P 500 stock market bubbles.
  • * The integration of a real-time bubble detection test with SVM provides a valuable early warning system for financial market participants and regulators.
  • * The findings suggest that this approach can significantly contribute to mitigating risks associated with financial crises by enabling timely preemptive actions.