COVID-19对卡拉奇证券交易所的影响:用于预测预测的比较机器学习算法研究
Tahir Munir1, Rabia Emhamed Al Mamlook2,3, Abdu R Rahman4
1Department of Anaesthesiology, The Aga Khan University, Karachi, 74800, Pakistan.
Heliyon
|July 22, 2024
概括
随机森林机器学习模型在COVID-19大流行期间有效预测了卡拉奇证券交易所 (KSE) 的表现. 本研究确定了在全球卫生危机中预测股票市场趋势的最佳模型.
科学领域:
- 经济学 经济学 经济学
- 数据科学数据科学数据科学
- 金融市场 金融市场
背景情况:
- COVID-19 疫情对全球经济和金融市场产生了重大影响.
- 了解健康危机期间的股票市场行为对于经济稳定至关重要.
研究的目的:
- 确定最有效的机器学习 (ML) 模型来预测卡拉奇证券交易所 (KSE) 在COVID-19大流行期间的表现.
- 分析COVID-19指标与巴基斯坦股票市场波动之间的相互联系.
主要方法:
- 研究期间从2020年3月1日到2021年11月26日,涵盖了COVID-19高峰期.
- 五种机器学习模型 (线性回归,K-最近邻居,随机森林,回归树,支持矢量机) 应用于KSE 100指数数据和COVID-19变量.
- 模型性能使用平均绝对百分比误差 (MAPE),平均平方误差 (MSE),平均绝对误差 (MAE) 和R平方 (R2) 来评估.
主要成果:
- 随机森林 (RF) 模型表现出卓越的预测准确性,达到0.91.9的R平方值.
- 这一发现与之前的研究相反,该研究表明COVID-19对主要股票市场的负面影响.
- 该研究强调了ML模型在在流行病期间导航市场波动方面的潜力.
结论:
- 随机森林模型被推用于预测受流行病相关因素影响的股票市场波动.
- 洞察力可以指导投资者在战略决策和政策制定者减轻经济影响.
- 进一步的研究可能会探索其他金融市场和机器学习技术.
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