Predicting stock market crashes with machine learning: A review and methodological proposal
1 Henry Jackson Foundation Medical Research International Ltd/Gte, Nigeria.
2 Financial analyst, Texas USA.
Review
Open Access Research Journal of Science and Technology, 2024, 11(02), 074–081.
Article DOI: 10.53022/oarjst.2024.11.2.0095
Publication history:
Received on 06 June 2024; revised on 13 July 2024; accepted on 16 July 2024
Abstract:
This review paper examines the utilisation of machine learning techniques for predicting stock market crashes. It surveys existing methodologies, identifies common trends, and analyses strengths and weaknesses. A novel methodological framework is proposed, integrating ensemble learning, alternative data sources, and model interpretability to address limitations in current approaches. The proposed framework aims to enhance predictive accuracy, transparency, and actionable insights in financial forecasting. Future research directions include empirical validation, interdisciplinary collaboration, and the integration of emerging technologies. Continued research in leveraging machine learning for financial forecasting is vital for advancing risk management practices and fostering resilient financial systems.
Keywords:
Machine learning; Stock market crashes; Predictive modeling; Ensemble learning; Alternative data sources; Model interpretability
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0