计算机科学 ›› 2020, Vol. 47 ›› Issue (5): 79-83.doi: 10.11896/jsjkx.190400145

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于金融文本情感的股票波动预测

赵澄, 叶耀威, 姚明海   

  1. 浙江工业大学信息工程学院 杭州310014
  • 收稿日期:2019-04-26 出版日期:2020-05-15 发布日期:2020-05-19
  • 通讯作者: 姚明海(ymh@zjut.edu.cn)
  • 作者简介:zhaoc@zjut.edu.cn
  • 基金资助:
    国家自然科学基金项目(61902349)

Stock Volatility Forecast Based on Financial Text Emotion

ZHAO Cheng, YE Yao-wei, YAO Ming-hai   

  1. College of Information Engineering,Zhejiang University of Technology,Hangzhou 310014,China
  • Received:2019-04-26 Online:2020-05-15 Published:2020-05-19
  • About author:ZHAO Cheng,born in 1985,Ph.D,seni-or engineer.His main research intere-sts include quantitative financial and artificial intelligence.
    YAO Ming-hai,born in 1963,professor,Ph.D,doctoral tutor.Hismain research interests include pattern recognition and intelligent control,control theory and control engineering,and computer application.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61902349).

摘要: 股票市场的情绪可以在一定程度上反映投资者的行为并影响其投资决策。市场新闻作为一种非结构性数据,能够体现并引导市场的大环境情绪,与股票价格一同成为至关重要的市场参考数据,能够为投资者的投资决策提供有效帮助。文中提出了一种可以准确、快速地建立针对海量新闻数据的多维情绪特征向量化方法,利用支持向量机(Support Victor Machine,SVM)模型来预测金融新闻对股票市场的影响,并通过bootstrap来减轻过拟合问题。在沪深股指上进行实验的结果表明,相比于传统模型,所提方法能够将预测准确度提高约8%,并在3个月的回测实验中获得了6.52%的超额收益,证明了其有效性。

关键词: 股票市场预测, 金融情感驱动, 新闻, 文本特征, 交易信号, 人工智能

Abstract: Emotions in the stock market can reflect investor behavior to a certain extent and influence investors' investment decisions.As a kind of unstructured data,market news can reflect the advantages and disadvantages of the market environment,and become a vital market reference data with stock prices,which can provide effective help for investment decisions effectively.This paper proposes a multidimensional emotional feature vectorization method which can accurately and quickly establish a large amount of news data for massive news data.It uses the support victor machine (SVM) model to predict the impact of financial news on the stock market,and uses bootstrap to mitigate overfitting problems.The results on Shanghai and Shenzhen stock indexes show that compared with the traditional model,the proposed method can improve the prediction accuracy by about 8% and obtain an excess of 6.52% duringthree months,thus proving the effectiveness of the proposed method.

Key words: Stock market prediction, Financial emotion driven, News, Text feature, Trading signal, Artificial intelligence

中图分类号: 

  • TP391
[1] OLIVEIRA N,CORTEZ P,AREAL N.Stock market sentiment lexicon acquisition using microblogging data and statistical measures[J].Decision Support Systems,2016,85:62-73.
[2] LONG W,TANG Y,TIAN Y.Investor sentiment identification based on the universum SVM[J].Neural Computing and Applications,2018,30(2):661-670.
[3] PERIKOS I,HATZILYGEROUDIS I.Recognizing emotions in text using ensemble of classifiers[J].Engineering Applications of Artificial Intelligence,2016,51:191-201.
[4] WU B,ZHOU X,JIN Q,et al.Analyzing Social Roles Based on a Hierarchical Model and Data Mining for Collective Decision-Making Support[J].IEEE Systems Journal,2015:1-10.
[5] JIANG F,LEE J,MARTIN X,et al.Manager sentiment andstock returns[J].Journal of Financial Economics,2019,132(1):126-149.
[6] MIWA K.Investor sentiment,stock mispricing,and long-termgrowth expectations[J].Research in International Business and Finance,2016,36:414-423.
[7] BOLLEN J,MAO H,ZENG X.Twitter mood predicts the stock market[J].Journal of Computational Science,2011,2(1):1-8.
[8] SUL H K,DENNIS A R,YUAN L.Trading on twitter:Using social media sentiment to predict stock returns[J].DecisionScie-nces,2017,48(3):454-488.
[9] OLIVEIRA N,CORTEZ P,AREAL N.On the predictability of stock market behavior using stocktwits sentiment and posting volume[C]//Portuguese Conference on Artificial Intelligence.Berlin,Heidelberg:Springer,2013:355-365.
[10] MAKREHCHI M,SHAH S,LIAO W.Stock prediction usingevent-based sentiment analysis[C]//Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)-Vo-lume 01.IEEE Computer Society,2013:337-342.
[11] CHECKLEY M S,HIGÓN D A,ALLES H.The hasty wisdom of the mob:How market sentiment predicts stock market beha-vior[J].Expert Systems with Applications,2017,77:256-263.
[12] NIKKINEN J,SAHLSTRÖM P.Impact of Scheduled US Macroeconomic News on Stock Market Uncertainty:A Multinational Perspecive[J].Multinational Finance Journal,2011,5(2):129-148.
[13] REN R,WU D D,LIU T.Forecasting stock market movement direction using sentiment analysis and support vector machine[J].IEEE Systems Journal,2018,13(1):760-770.
[14] CHEN Y,HAO Y.A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction[J].Expert Systems with Applications,2017,80:340-355.
[15] HUANG W,NAKAMORI Y,WANG S Y.Forecasting stockmarket movement direction with support vector machine[J].Computers & Operations Research,2005,32(10):2513-2522.
[16] CHEN W,ZHANG Y,YEO C K,et al.Stock market prediction using neural network through news on online social networks[C]//2017 International Smart Cities Conference (ISC2).IEEE,2017:1-6.
[17] HÁJEK P.Combining bag-of-words and sentiment features ofannual reports to predict abnormal stock returns[J].Neural Computing and Applications,2018,29(7):343-358.
[18] SCHUMAKER R P,CHEN H.A discrete stock price prediction engine based on financial news[J].COMPUTER,2010,43(1):51-56.
[19] CI Y X,ZHAO S L,LUO Y,et al.Text data preprocessingmethod based on word frequency statistics[J].Computer Scie-nce,2017,44(10):276-282,288.
[20] LI L,ZHANG G Y,LI Z W,et al.Research on topic crawlertechnology based on SVM[J].Computer Science,2015,42(2):118-122.
[21] LI X,XIE H,WANG R,et al.Empirical analysis:stock market prediction via extreme learning machine[J].Neural Computing and Applications,2016,27(1):67-78.
[22] YAO W D,WANG R J.An Empirical Study of the Relationship between Stock Market Volatility and Policy Events from the Perspective of Structural Decomposition-Based on EEMD Algorithm [J].Shanghai Economic Research,2016(1):71-80.
[1] 袁禄, 朱郑州, 任庭玉. 虚假评论识别研究综述[J]. 计算机科学, 2021, 48(1): 111-118.
[2] 仝鑫, 王斌君, 王润正, 潘孝勤. 面向自然语言处理的深度学习对抗样本综述[J]. 计算机科学, 2021, 48(1): 258-267.
[3] 周蔚, 罗旭东. 一种替代性纠纷在线仲裁系统[J]. 计算机科学, 2020, 47(6A): 583-590.
[4] 任仪. 基于区块链与人工智能的网络多服务器SIP信息加密系统设计[J]. 计算机科学, 2020, 47(6A): 634-638.
[5] 吴小坤, 赵甜芳. 自然语言处理技术在社会传播学中的应用研究和前景展望[J]. 计算机科学, 2020, 47(6): 184-193.
[6] 王国胤, 瞿中, 赵显莲. 交叉融合的“人工智能+”学科建设探索与实践[J]. 计算机科学, 2020, 47(4): 1-5.
[7] 康雁,崔国荣,李浩,杨其越,李晋源,王沛尧. 融合自注意力机制和多路金字塔卷积的软件需求聚类算法[J]. 计算机科学, 2020, 47(3): 48-53.
[8] 王晓明,赵歆波. 阅读眼动追踪语料库的构建与应用研究综述[J]. 计算机科学, 2020, 47(3): 174-181.
[9] 杨惟轶,白辰甲,蔡超,赵英男,刘鹏. 深度强化学习中稀疏奖励问题研究综述[J]. 计算机科学, 2020, 47(3): 182-191.
[10] 曹锋,徐扬,钟建,宁欣然. 基于目标演绎距离的一阶逻辑子句集预处理方法[J]. 计算机科学, 2020, 47(3): 217-221.
[11] 董超颖, 续欣, 刘爱军, 苌敬辉. 低轨卫星星座网络路由新方法[J]. 计算机科学, 2020, 47(12): 285-290.
[12] 王海涛, 宋丽华, 向婷婷, 刘力军. 人工智能发展的新方向——人机物三元融合智能[J]. 计算机科学, 2020, 47(11A): 1-5.
[13] 张永安, 颜斌斌. 一种股票市场的深度学习复合预测模型[J]. 计算机科学, 2020, 47(11): 255-267.
[14] 张雨倩,顾冬云. 帕金森震颤与原发性震颤的计算机辅助诊断方法综述[J]. 计算机科学, 2019, 46(7): 22-29.
[15] 刘胜娃, 孙俊明, 高翔, 王敏. 基于人工神经网络的钻井机械钻速预测模型的分析与建立[J]. 计算机科学, 2019, 46(6A): 605-608.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 雷丽晖,王静. 可能性测度下的LTL模型检测并行化研究[J]. 计算机科学, 2018, 45(4): 71 -75 .
[2] 孙启,金燕,何琨,徐凌轩. 用于求解混合车辆路径问题的混合进化算法[J]. 计算机科学, 2018, 45(4): 76 -82 .
[3] 张佳男,肖鸣宇. 带权混合支配问题的近似算法研究[J]. 计算机科学, 2018, 45(4): 83 -88 .
[4] 伍建辉,黄中祥,李武,吴健辉,彭鑫,张生. 城市道路建设时序决策的鲁棒优化[J]. 计算机科学, 2018, 45(4): 89 -93 .
[5] 史雯隽,武继刚,罗裕春. 针对移动云计算任务迁移的快速高效调度算法[J]. 计算机科学, 2018, 45(4): 94 -99 .
[6] 周燕萍,业巧林. 基于L1-范数距离的最小二乘对支持向量机[J]. 计算机科学, 2018, 45(4): 100 -105 .
[7] 刘博艺,唐湘滟,程杰仁. 基于多生长时期模板匹配的玉米螟识别方法[J]. 计算机科学, 2018, 45(4): 106 -111 .
[8] 耿海军,施新刚,王之梁,尹霞,尹少平. 基于有向无环图的互联网域内节能路由算法[J]. 计算机科学, 2018, 45(4): 112 -116 .
[9] 崔琼,李建华,王宏,南明莉. 基于节点修复的网络化指挥信息系统弹性分析模型[J]. 计算机科学, 2018, 45(4): 117 -121 .
[10] 王振朝,侯欢欢,连蕊. 抑制CMT中乱序程度的路径优化方案[J]. 计算机科学, 2018, 45(4): 122 -125 .