Stock trend prediction method coupled with multilevel indicators  

耦合多层次指标的股票走势预测方法

作　　者：Liu Yu Pan Yuting Liu Xiaoxing 刘宇;潘宇庭;刘晓星(东南大学网络空间安全学院,南京211196;东南大学经济管理学院,南京211196)

机构地区：[1]School of Cyber Science and Engineering,Southeast University,Nanjing 211189,China [2]School of Economics and Management,Southeast University,Nanjing 211189,China

出　　处：《Journal of Southeast University(English Edition)》2024年第4期425-431,共7页东南大学学报（英文版）

基　　金：The National Natural Science Foundation of China(No.72173018).

摘　　要：To systematically incorporate multiple influencing factors,the coupled-state frequency memory(Co-SFM)network is proposed.This model integrates Copula estimation with neural networks,fusing multilevel data information,which is then fed into downstream learning modules.Co-SFM employs an upstream fusion module to incorporate multilevel data,thereby constructing a macro-plate-micro data structure.This configuration helps identify and integrate characteristics from different data levels,facilitating a deeper understanding of the internal links within the financial system.In the downstream model,Co-SFM uses a state-frequency memory network to mine hidden frequency information within stock prices,and the multifrequency patterns of sequential data are modeled.Empirical results show that Co-SFM s prediction accuracy for stock price trends is significantly better than that of other models.This is especially evident in multistep medium and long-term trend predictions,where integrating multilevel data results in notably improved accuracy.为了系统地将多种指标的影响纳入到股票走势研究中,设计了一个耦合多层次数据的状态频率记忆网络(Co-SFM)模型.该模型将Copula估计整合到神经网络中,将各个层次的数据信息进行融合并输入到下游的学习模块.Co-SFM使用上游的融合模块来纳入多个层次的数据,构建了宏观板块微观的数据结构.这种结构通过多层次的数据指标识别出金融系统中不同层级数据的特征并进行融合,可以更好地挖掘出金融系统中的内在联系.而在下游的模型中,Co-SFM模型使用状态频率记忆网络来挖掘价格数据中隐含的频率信息,对价格数据的多频交易模式进行建模.实证结果表明,Co-SFM模型在融入多层次数据后对股价走势的预测效果明显优于其他模型,尤其在多步长的中长期走势预测中,其预测的精度得到明显提升.

关 键 词：stock trend prediction multilevel indicators COPULA state-frequency memory network 

分 类 号：TP391.42[自动化与计算机技术—计算机应用技术]