用机器学习预测股市崩盘:回顾与方法论建议

Patience Okpeke Paul, Toluwalase Vanessa Iyelolu
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引用次数: 0

摘要

本综述论文探讨了利用机器学习技术预测股市崩盘的问题。它调查了现有方法,确定了共同趋势,并分析了优缺点。本文提出了一个新颖的方法框架,整合了集合学习、替代数据源和模型可解释性,以解决当前方法的局限性。所提出的框架旨在提高金融预测的准确性、透明度和可操作性。未来的研究方向包括经验验证、跨学科合作和新兴技术的整合。利用机器学习进行金融预测的持续研究,对于推进风险管理实践和培养弹性金融体系至关重要。
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Predicting stock market crashes with machine learning: A review and methodological proposal
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.
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