基于 HAR 模型和 LSTM 模型预测教育公司的波动性,同时考虑情绪和教育政策
Xuefan Li1, Donghua Li2, Yuxiang Cheng3
1Ontario Institute for Studies in Education, University of Toronto, Toronto, Ontario, Canada.
Heliyon
|October 18, 2024
概括
投资者情绪和教育政策在不同时间段对教育股价波动产生重大影响. 像LSTM这样的先进模型证实了它们对市场波动的预测能力.
科学领域:
- 金融经济学 金融经济学
- 量化金融 量化金融
- 教育股市分析教育股市分析
背景情况:
- 教育股票市场受到各种外部因素的影响.
- 了解波动的驱动因素对于投资者和政策制定者来说至关重要.
- 现有的研究往往忽视了情绪和政策对这一特定行业的综合影响.
研究的目的:
- 调查情绪和政策对教育股票价格波动的影响.
- 分析异质自回归 (HAR) 和长短期记忆 (LSTM) 模型的预测能力.
- 探索情绪和教育政策对市场动态的交叉影响.
主要方法:
- 从九家上市公司构建一个加权的教育指数波动率.
- 应用普通最小平方 (OLS) 回归和LSTM预测模型.
- 分析不同时期的情绪和政策指数.
主要成果:
- 情绪和政策指数都显示了对教育股价波动的重大影响.
- 该LSTM模型有效地结合了情绪和政策,以提高波动性预测.
- 经验证据支持这些因素对市场行为的影响.
结论:
- 情绪和政策是教育股价波动的关键决定因素.
- 将HAR和LSTM模型结合起来,为波动性预测提供了一个强大的方法.
- 调查结果为教育领域的投资者,公司和政策制定者提供了宝贵的见解.
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