混合CNN-LSTM-GNN神经网络用于A股股票预测
1Department of Mechanical Engineering, University of Hong Kong, Hong Kong.
Entropy (Basel, Switzerland)
|August 28, 2025
概括
一个新的深度学习模型,CNN-LSTM-GNN (CLGNN),通过分析复杂的数据关系来增强股票选择. 与现有方法相比,这种方法提高了预测准确性和投资回报率.
科学领域:
- 计算金融
- 机器学习
- 金融计量学
背景情况:
- 深度学习模型在股票预测方面比传统方法提供了更高的性能.
- 现有的模型往往缺乏特征选择的透明度和直接适用于库存选择.
- 预测股票指数趋势或个人股票在实际应用中具有挑战性.
研究的目的:
- 提出一种新的多变量混合神经网络,CNN-LSTM-GNN (CLGNN),用于股票预测和选择.
- 为财务时间序列数据开发有效的特征选择方法.
- 创建一个直接的股票选择策略,输出回报和股票代码.
主要方法:
- 开发了一个混合神经网络模型 (CLGNN),集成卷积神经网络 (CNN),长期短期记忆 (LSTM) 和图形神经网络 (GNN).
- 采用混合过方法,使用和皮尔森相关性来选择特征.
- 利用GNN模块中的图形学习,图形卷积和时间卷积层来分析数据之间的关系.
主要成果:
- 与TCN和Transformer等其他模型相比,CLGNN模型在库存预测和选择方面表现出卓越的表现.
- 使用每日回报率,营业额,相对强度指数,数量和前期调整的收盘价格的特征选择产生了最高回报.
- 提出的混合特征选择方法在确定库存预测的关键输入变量方面被证明是有效的.
结论:
- 通过捕捉复杂的数据依赖性,CLGNN模型提供了一个可靠和可解释的库存选择方法.
- 混合特征选择方法提高了金融领域深度学习模型的预测准确性和实用性.
- 这项研究为开发先进的,数据驱动的库存选择策略提供了有价值的框架.
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