With the return of deep learning methods to the public eye, more and more scholars and industry researchers have tried to start exploring the possibility of neural networks to solve the problem, and some progress has been made. However, although neural networks have powerful function fitting ability, they are often criticized for their lack of explanatory power. Due to the large number of parameters and complex structure of neural network models, academics are unable to explain the predictive logic of most neural networks, test the significance of model parameters, and summarize the laws that humans can understand and use. Inspired by the technical analysis theory in the field of stock investment, this paper selects neural network models with different characteristics and extracts effective feature combinations from short-term stock price fluctuation data. In addition, on the basis of ensuring that the prediction effect of the model is not lower than that of the mainstream models, this paper uses the attention mechanism to further explore the predictive -line patterns, which summarizes usable judgment experience for human researchers on the one hand and explains the prediction logic of the hybrid neural network on the other. Experiments show that the classification effect is better using this model, and the investor sentiment is obtained more accurately, and the accuracy rate can reach 85%, which lays the foundation for the establishment of the whole stock trend prediction model. In terms of explaining the prediction logic of the model, it is experimentally demonstrated that the -line patterns mined using the attention mechanism have more significant predictive power than the general -line patterns, and this result explains the prediction basis of the hybrid neural network.

中文翻译：

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基于深度循环神经网络的股票趋势预测算法

随着深度学习方法回归大众视野，越来越多的学者和行业研究人员开始尝试探索神经网络解决问题的可能性，并取得了一些进展。然而，尽管神经网络具有强大的函数拟合能力，但也经常因缺乏解释力而受到批评。由于神经网络模型参数众多、结构复杂，学术界无法解释大多数神经网络的预测逻辑，无法检验模型参数的意义，也无法总结出人类可以理解和使用的规律。受股票投资领域技术分析理论的启发，本文选取具有不同特征的神经网络模型，从短期股价波动数据中提取有效的特征组合。另外，本文在保证模型预测效果不低于主流模型的基础上，利用attention机制进一步探索预测效果-线条模式，一方面总结了人类研究人员可用的判断经验，另一方面解释了混合神经网络的预测逻辑。实验表明，使用该模型分类效果更好，获得的投资者情绪更准确，准确率可达85%，为建立整个股票趋势预测模型奠定了基础。在解释模型的预测逻辑方面，实验证明了使用注意力机制挖掘的-线模式比一般的-线模式具有更显着的预测能力，这一结果解释了混合神经网络的预测基础。