计算机科学 ›› 2023, Vol. 50 ›› Issue (5): 128-136.doi: 10.11896/jsjkx.220400089
程海阳1, 张建新2, 孙启森1, 张强1,3, 魏小鹏1,3
CHENG Haiyang1, ZHANG Jianxin2, SUN Qisen1, ZHANG Qiang1,3, WEI Xiaopeng1,3
摘要： 股票走势预测是经典且具有挑战性的任务,可帮助交易者做出获得更大收益的交易决策。近年来,基于深度学习的股票走势预测方法的性能得到明显提升,但现有方法大多仅依托于股票价格的历史数据来完成走势预测,无法捕捉价格指标之外的市场动态规律,在一定程度上限制了方法的性能。为此,将社交媒体文本与股票历史价格信息相结合,提出了一种基于深度跨模态信息融合网络(DCIFNet)的股票走势预测新方法。DCIFNet首先采用时间卷积操作对股票价格和推特文本进行编码,使得每个元素对其邻域元素都有足够的了解;然后,将结果输入到基于transformer的跨模态融合结构中,以更有效地融合股票价格和推特文本中的重要信息;最后,引入多图卷积注意力网络从不同角度描述不同股票之间的相互关系,能够更有效地捕获关联股票间的行业、维基和相关关系,从而提升股票走势预测的精度。在9个不同行业的高频交易数据集上实施走势预测和模拟交易实验。消融实验及所提方法与用于股票预测的多管齐下的注意力网络(MAN-SF)方法的比较结果验证了DCIFNet方法的有效性,准确率达到了 0.6309,明显优于领域内代表性方法。
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| [1]DEVI B U,SUNDAR D,ALLI P.An effective time series analysis for stock trend prediction using ARIMA model for nifty midcap-50[J].International Journal of Data Mining Knowledge Management Process,2013,3(1):65-78. [2]DENG S M,ZHANG N Y,ZHANG W,et al.Knowledge-driven stock trend prediction and explanation via temporal convolu-tional network[C]//Companion Proceedings of The 2019 World Wide Web Conference.San Francisco,New York,USA:ACM,2019:678-685. [3]HU Z N,LIU W Q,BIAN J,et al.Listening to chaotic whispers:A deep learning framework for news-oriented stock trend prediction[C]//Proceedings of the 18th ACM International Conference on Web Search and Data Mining.Marina Del Rey,New York,USA:ACM,2018:261-269. [4]WENG B,AHMED M A,MEGAHED F M.Stock market one-day ahead movement prediction using disparate data sources[J].Expert Systems with Applications,2017,79(2017):153-163. [5]HUANG J Y,ZHANG Y J,ZHANG J L,et al.A tensor-based sub-mode coordinate algorithm for stock prediction[C]//IEEE 3rd International Conference on Data Science in Cyberspace(DSC).Guangzhou,China.Piscataway:IEEE,2018:716-721. [6]SAWHNEY R,AGARWAL S,WADHWA A,et al.Deep attentive learning for stock movement prediction from social media text and company correlations[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Proces-sing(EMNLP).Online.New York:ACM,2020:8415-8426. [7]TSAI Y H H,BAI S J,LIANG P P,et al.Multimodal transformer for unaligned multimodal language sequences[C]//Proceedings of the Conference Association for Computational Linguistics Meeting.Bethesda:NIH Public Access,2019:6558-6569. [8]CHEN L,CHI Y G,GUAN Y Y,et al.A Hybrid Attention-Based EMD-LSTM Model for Financial Time Series Prediction[C]//2nd International Conference on Artificial Intelligence and Big Data(ICAIBD).Chengdu,China.Piscataway:IEEE,2019:113-118. [9]LIU M,SHAN Y Y.Predicition of Closing Price of Stock Index Based on EMD-LSTM Model[J].Journal of Chongqing University of Technology(Natural Science),2021,35(12):269-276. [10]YANG B,GONG Z,YANG W.Stock market index predictionusing deep neural network ensemble[C]//2017 36th Chinese Control Conference(CCC).Dalian,China.Piscataway:IEEE,2017:3882-3887. [11]CHENG L C,HUANG Y H,WU M E.Applied attention-based LSTM neural networks in stock prediction[C]//IEEE International Conference on Big Data(Big Data).Seattle,WA,USA.Piscataway:IEEE,2018 :4716-4718. [12]BOLLERSLEV T.Generalized autoregressive conditional he-teroskedasticity[J].Journal of Econometrics,1986,31(3):307-327. [13]ETHEM A.Introduction to machine learning[M].Cambridge:MIT press,2020. [14]SANBOON T,KEATRUANGKAMALA K,JAIYEN S.ADeep Learning Model for Predicting Buy and Sell Recommendations in Stock Exchange of Thailand using Long Short-Term Memory[C]//2019 IEEE 4th International Conference on Computer and Communication Systems(ICCCS).Singapore.Piscataway:IEEE,2019:757-760. [15]WANG D,WANG X P,YANG C D.A Study of Stock Forecasting Based on LSTM Model of Principal Component Analysis[J].Journal of Chongqing University of Technology(Natural Science),2021,35(2):282-288. [16]ALEXIEI D,KARL F.Financial time series forecasting-a deep learning approach[J].International Journal of Machine Learning Computing,2017,7(5):118-122. [17]LIU Q K,CHENG X,SU S,et al.Hierarchical Complementary Attention Network for Predicting Stock Price Movements with News[C]//Proceedings of the 27th ACM International Confe-rence on Information and Knowledge Management.Torino,Italy; ACM.2018:1603-1606. [18]DING X,ZHANG Y,LIU T,et al.Deep learning for event-dri-ven stock prediction[C]//Proceedings of the 24th International Conference on Artificial Intelligence.Buenos Aires,Argentina:AAAI.2015:2327-2333. [19]NIKFARJAM A,EMADZADEH E,MUTHAIYAH S.Textmining approaches for stock market prediction[C]//Interna-tional Conference on Computer and Automation Engineering.Singapore.Piscataway:IEEE,2010:256-260. [20]WU H Z,ZHANG W,SHEN W W,et al.Hybrid deep sequential modeling for social text-driven stock prediction[C]//Proceedings of the 27th ACM International Conference on Information and Knowledge Management.Torino Italy.New York:ACM,2018:1627-1630. [21]FENG F L,HE X N,WANG X,et al.Temporal relational ran-king for stock prediction[J].ACM Transactions on Information Systems,2019,37(2):1-30. [22]CHEN Y M,WEI Z Y,HUANG X J.Incorporating corporation relationship via graph convolutional neural networks for stock price prediction[C]//ACM International Conference on Information and Knowledge Management.New York,United States.New York:ACM,2018:1655-1658. [23]MATSUNAGA D,SUZUMURA T,TAKAHASHI T.Exploring graph neural networks for stock market predictions with rolling window analysis[J].arXiv:1909.10660,2019. [24]KIM R,SO C H,JEONG M,et al.Hats:A hierarchical graph attention network for stock movement prediction[J].arXiv:1908.07999,2019. [25]XU W T,LIU W Q,XU C,et al.REST:Relational Event-driven Stock Trend Forecasting[C]//Proceedings of the Web Confe-rence.Ljubljana,Slovenia.New York:ACM,2021:1-10. [26]SERGEY L,CHRISTIAN S.Batch normalization:Accelerating deep network training by reducing internal covariate shift[C]//International Conference on Machine Learning.Lille,France.New York:PMLR,2015:448-456. [27]XU Y,COHEN S B.Stock movement prediction from tweets and historical prices[C]//Proceedings of the 56th Annual Mee-ting of the Association for Computational Linguistics(Volume 1:Long Papers)Melbourne,Australia.Melbourne:ACL,2018:1970-1979. [28]LEI B J,RYAN K J,E H G.Layer normalization[J].arXiv:1607.06450,2016. [29]LIU G,WANG X J,LI R F.Multi-scale RCNN model for financial time-series classification[J].arXiv:1911.09359,2019. [30]CHEN Q K,ROBERT C Y.Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data[J].ar-Xiv:2107.10941,2021. [31]YE J X,ZHAO J J,YE K J,et al.Multi-graph convolutional network for relationship-driven stock movement prediction[C]//2020 25th International Conference on Pattern Recognition(ICPR).Milan,Italy.Piscataway:IEEE,2021:6702-6709. [32]BENESTY J,CHEN J D,HUANG Y T,et al.Pearson correlation coefficient[M]//Noise Reduction in Speech Processing.Berlin,Heidelberg:Springer.2009:1-4. [33]KIPF T N,WELLINGS M.Semi-supervised classification with graph convolutional networks[J].arXiv:1609.02907,2016. [34]GLOROT X,BORDES A,BENGIO Y.Deep sparse rectifierneural networks[C]//Proceedings of the 14th International Conference on Artificial Intelligence and Statistics.Microtome Publishing:JMLR Workshop and Conference Proceedings,2011:315-323. [35]SHARPE W F.The sharpe ratio[J].Journal of portfolio ma-nagement,1998,21(1):169-185. [36]CHICCO D,JURMAN G.The advantages of the Matthews correlation coefficient(MCC) over F1 score and accuracy in binary classification evaluation[J].BMC Genomics,2020,21(1):1-13. [37]NICOLAS H.Pairs selection and outranking:An application to the S&P 100 index[J].European Journal of Operational Research,2009,196(2):819-825. [38]NICOLAS H.Pairs trading and outranking:The multi-step-ahead forecasting case[J].European Journal of Operational Research,2010,207(3):1702-1716. |
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