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作为未来股票市场预测的组成部分,使用基于RNN的模型的异构粘性效应
Aida Nabilah Sadon1, Shuhaida Ismail1, Azme Khamis1
1Universiti Tun Hussein Onn Malaysia, Johor, Malaysia.
PloS one
|May 24, 2024
概括
预测股票波动性通过新的混合GARCH-LSTM模型得到了改进. 这种模型有效地使用异种复杂性来更准确地预测股票回报率,帮助投资者做出决策.
科学领域:
- 量化金融 量化金融
- 金融中的机器学习
- 计量经济学 计量经济学
背景情况:
- 股票波动性预测对投资者和交易者至关重要,提供商业洞察力.
- 预测股票波动是金融市场中具有挑战性但至关重要的任务.
- 现有的模型可能无法完全捕捉股票价格波动的关键因素异构复杂性.
研究的目的:
- 提出一种混合模型,GARCH-LSTM,用于增强股票波动性预测.
- 为了提高预测准确性,利用股票价格的异质性.
- 评估GARCH-LSTM模型与传统方法的性能.
主要方法:
- 这是一种混合方法,它结合了通用自回归条件异种硬性 (GARCH) 和长期短期记忆 (LSTM) 循环神经网络 (RNN).
- GARCH模型估计,然后进行ARCH效应测试,以确定残留物中的剩余异种粘性.
- 使用LSTM算法建模异构数据集.
主要成果:
- 与单个LSTM相比,GARCH-LSTM模型表现出较低的MAE,RMSE,MAPE和HMAE值的优异性能.
- 混合GARCH-LSTM模型的预测准确性比单个LSTMs提高了15%和13%.
- GARCH-LSTM模型有效地利用了异性,单独表现优于GARCH模型.
结论:
- 混合GARCH-LSTM模型是预测股票价格变动的有效工具.
- 拟议的模型通过仔细预测和模仿观察到的股票价格模式和趋势来帮助投资者.
- 整合异质复杂性显著提高了股票波动性预测的准确性.
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