基于ATLG混合模型的股票价格预测  

Stock Price Prediction Based on ATLG Hybrid Model

作　　者：王德广[1] 马恒锐 梁叶 WANG De-Guang;MA Heng-Rui;LIANG Ye(Software Technology Institute,Dalian Jiaotong University,Dalian 116052,China;School of Computer and Communication Engineering,Dalian Jiaotong University,Dalian 116052,China)

机构地区：[1]大连交通大学软件学院,大连116052 [2]大连交通大学计算机与通信工程学院,大连116052

出　　处：《计算机系统应用》2023年第3期171-179,共9页Computer Systems & Applications

摘　　要：股市是金融市场的重要组成部分,对股票价格预测有着重要的意义.同时,深度学习具有强大的数据处理能力,可以解决金融时间序列的复杂性所带来的问题.对此,本文提出一种结合自注意力机制的混合神经网络模型(ATLG).该模型由长短期记忆网络(LSTM)、门控递归单元(GRU)、自注意力机制构建而成,用于对股票价格的预测.实验结果表明:(1)与LSTM、GRU、RNN-LSTM、RNN-GRU等模型相比, ATLG模型的准确率更高;(2)引入自注意力机制使模型更能聚焦于重要时间点的股票特征信息;(3)通过对比,双层神经网络起到的效果更为明显.(4)通过MACD (moving average convergence and divergence)指标进行回测检验,获得了53%的收益,高于同期沪深300的收益.结果证明了该模型在股票价格预测中的有效性和实用性.The stock market is an important part of the financial market, and it is of great importance for stock price prediction. Meanwhile, deep learning has powerful data processing capability to solve the problems caused by the complexity of financial time series. In this regard, this study proposes a hybrid neural network model(ATLG) that combines a self-attention mechanism, a long short-term memory(LSTM) network, and a gated recurrent unit(GRU) for stock price prediction. The experimental results show the followings:(1) The ATLG model has higher accuracy than LSTM, GRU, RNN-LSTM, and RNN-GRU models.(2) The introduction of the self-attention mechanism makes the model more focused on the information of stock characteristics at important time points.(3) Comparison reveals that the two-layer neural network plays a more distinct role.(4) The backtesting with the moving average convergence and divergence(MACD) indicator achieves a 53% return, which is higher than the return of CSI 300 in the same period. The results prove the effectiveness and practicality of the model in stock price prediction.

关 键 词：股票预测 长短期记忆网络 门控递归单元 自注意力机制 MACD指标 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] F832.51[自动化与计算机技术—控制科学与工程]