IDEAS home Printed from https://ideas.repec.org/a/eee/finlet/v38y2021ics1544612319310943.html
   My bibliography  Save this article

The role of investor attention in predicting stock prices: The long short-term memory networks perspective

Author

Listed:
  • Zhang, Yongjie
  • Chu, Gang
  • Shen, Dehua

Abstract

In this paper, we use Long Short-Term Memory Networks (LSTM) to predict stock price movement. Compared with other Artificial Neural Networks (ANNs), LSTM is more suitable to process the non-linear, non-stationary, and complicated financial time series. To improve the prediction accuracy, we employ investor attention proxies as the supplements of market variables, e.g., price, volume, and other technique indexes. The empirical findings mainly show that the LSTM model employing online investor attention proxies outperforms other models with the best prediction accuracy and rational time cost. Our results should be noticeable to investors, who are interested in quantitative investment.

Suggested Citation

  • Zhang, Yongjie & Chu, Gang & Shen, Dehua, 2021. "The role of investor attention in predicting stock prices: The long short-term memory networks perspective," Finance Research Letters, Elsevier, vol. 38(C).
    • Handle: RePEc:eee:finlet:v:38:y:2021:i:c:s1544612319310943
    DOI: 10.1016/j.frl.2020.101484
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1544612319310943
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.frl.2020.101484?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhi-Qiang Jiang & Gang-Jin Wang & Askery Canabarro & Boris Podobnik & Chi Xie & H. Eugene Stanley & Wei-Xing Zhou, 2018. "Short term prediction of extreme returns based on the recurrence interval analysis," Quantitative Finance, Taylor & Francis Journals, vol. 18(3), pages 353-370, March.
    2. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    3. Alexandre Alles Rodrigues & Sébastien Lleo, 2018. "Combining standard and behavioral portfolio theories: a practical and intuitive approach," Quantitative Finance, Taylor & Francis Journals, vol. 18(5), pages 707-717, May.
    4. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    5. Zhang, Wei & Shen, Dehua & Zhang, Yongjie & Xiong, Xiong, 2013. "Open source information, investor attention, and asset pricing," Economic Modelling, Elsevier, vol. 33(C), pages 613-619.
    6. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    7. Tao Chen, 2017. "Investor Attention and Global Stock Returns," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 18(3), pages 358-372, July.
    8. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    9. Michael S. Drake & Darren T. Roulstone & Jacob R. Thornock, 2012. "Investor Information Demand: Evidence from Google Searches Around Earnings Announcements," Journal of Accounting Research, Wiley Blackwell, vol. 50(4), pages 1001-1040, September.
    10. Vozlyublennaia, Nadia, 2014. "Investor attention, index performance, and return predictability," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 17-35.
    11. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    12. Li, Jun & Yu, Jianfeng, 2012. "Investor attention, psychological anchors, and stock return predictability," Journal of Financial Economics, Elsevier, vol. 104(2), pages 401-419.
    13. Andreas Karatahansopoulos & Georgios Sermpinis & Jason Laws & Christian Dunis, 2014. "Modelling and Trading the Greek Stock Market with Gene Expression and Genetic Programing Algorithms," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(8), pages 596-610, December.
    14. Gustavo Grullon, 2004. "Advertising, Breadth of Ownership, and Liquidity," Review of Financial Studies, Society for Financial Studies, vol. 17(2), pages 439-461.
    15. Shun Chen & Lei Ge, 2019. "Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1507-1515, September.
    16. Arora, Siddharth & Taylor, James W., 2018. "Rule-based autoregressive moving average models for forecasting load on special days: A case study for France," European Journal of Operational Research, Elsevier, vol. 266(1), pages 259-268.
    17. Benjamin J. C. Kim & David Karemera, 2006. "Assessing the forecasting accuracy of alternative nominal exchange rate models: the case of long memory," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(5), pages 369-380.
    18. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    19. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
    20. Zahedi, Javad & Rounaghi, Mohammad Mahdi, 2015. "Application of artificial neural network models and principal component analysis method in predicting stock prices on Tehran Stock Exchange," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 178-187.
    21. Michael H. Breitner & Christian Dunis & Hans-Jörg Mettenheim & Christopher Neely & Georgios Sermpinis & Rafael Rosillo & Javier Giner & David De la Fuente, 2014. "Stock Market Simulation Using Support Vector Machines," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 33(6), pages 488-500, September.
    22. Wu, Yu-Xi & Wu, Qing-Biao & Zhu, Jia-Qi, 2019. "Improved EEMD-based crude oil price forecasting using LSTM networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 114-124.
    23. Lean Yu & Yang Zhao & Ling Tang, 2017. "Ensemble Forecasting for Complex Time Series Using Sparse Representation and Neural Networks," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 122-138, March.
    24. Petra Kynčlová & Peter Filzmoser & Karel Hron, 2015. "Modeling Compositional Time Series with Vector Autoregressive Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(4), pages 303-314, July.
    25. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    26. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    27. Xian Li & James A. Hendler & John L. Teall, 2016. "Investor Attention on the Social Web," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 17(1), pages 45-59, January.
    28. Ding, Rong & Hou, Wenxuan, 2015. "Retail investor attention and stock liquidity," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 12-26.
    29. František Čech & Jozef Baruník, 2017. "On the Modelling and Forecasting of Multivariate Realized Volatility: Generalized Heterogeneous Autoregressive (GHAR) Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(2), pages 181-206, March.
    30. Jong†Min Kim & Hojin Jung, 2018. "Time series forecasting using functional partial least square regression with stochastic volatility, GARCH, and exponential smoothing," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(3), pages 269-280, April.
    31. Xianming Fang & Yu Jiang & Zhijun Qian, 2014. "The Effects of Individual Investors' Attention on Stock Returns: Evidence from the ChiNext Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(03), pages 158-168, May.
    32. Xianming Fang & Yu Jiang & Zhijun Qian, 2014. "The Effects of Individual Investors' Attention on Stock Returns: Evidence from the ChiNext Market," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 50(S3), pages 158-168.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Goutte, Stéphane & Le, Hoang-Viet & Liu, Fei & von Mettenheim, Hans-Jörg, 2023. "Deep learning and technical analysis in cryptocurrency market," Finance Research Letters, Elsevier, vol. 54(C).
    2. Kanzari, Dalel & Nakhli, Mohamed Sahbi & Gaies, Brahim & Sahut, Jean-Michel, 2023. "Predicting macro-financial instability – How relevant is sentiment? Evidence from long short-term memory networks," Research in International Business and Finance, Elsevier, vol. 65(C).
    3. Jingjian, Si & Xiangyun, Gao & Jinsheng, Zhou & Anjian, Wang & Xiaotian, Sun & Yiran, Zhao & Hongyu, Wei, 2023. "The impact of oil price shocks on energy stocks from the perspective of investor attention," Energy, Elsevier, vol. 278(PB).
    4. Ito, Katsuki & Iima, Hitoshi & Kitamura, Yoshihiro, 2022. "LSTM forecasting foreign exchange rates using limit order book," Finance Research Letters, Elsevier, vol. 47(PA).
    5. Kyungsub Lee, 2023. "Recurrent neural network based parameter estimation of Hawkes model on high-frequency financial data," Papers 2304.11883, arXiv.org.
    6. Lee, Kyungsub, 2023. "Recurrent neural network based parameter estimation of Hawkes model on high-frequency financial data," Finance Research Letters, Elsevier, vol. 55(PA).
    7. Liu, Qingfu & Tao, Zhenyi & Tse, Yiuman & Wang, Chuanjie, 2022. "Stock market prediction with deep learning: The case of China," Finance Research Letters, Elsevier, vol. 46(PA).
    8. Wenwen Liu & Jinyu Yang & Jingrui Chen & Lei Xu, 2023. "How Social-Network Attention and Sentiment of Investors Affect Commodity Futures Market Returns: New Evidence From China," SAGE Open, , vol. 13(1), pages 21582440231, January.
    9. Oliveira, Alexandre Silva de & Ceretta, Paulo Sergio & Albrecht, Peter, 2023. "Performance comparison of multifractal techniques and artificial neural networks in the construction of investment portfolios," Finance Research Letters, Elsevier, vol. 55(PA).
    10. Deng, Chao & Zhou, Xiaoying & Peng, Cheng & Zhu, Huiming, 2022. "Going green: Insight from asymmetric risk spillover between investor attention and pro-environmental investment," Finance Research Letters, Elsevier, vol. 47(PA).
    11. Paweł Jakubowski & Robert Ślepaczuk & Franciszek Windorbski, 2023. "REnsembling ARIMAX Model in Algorithmic Investment Strategies on Commodities Market," Working Papers 2023-20, Faculty of Economic Sciences, University of Warsaw.
    12. Zheng, Yan & Wen, Fenghua & Deng, Hanshi & Zeng, Aiqing, 2022. "The relationship between carbon market attention and the EU CET market: Evidence from different market conditions," Finance Research Letters, Elsevier, vol. 50(C).
    13. Wang, Chen & Shen, Dehua & Li, Youwei, 2022. "Aggregate Investor Attention and Bitcoin Return: The Long Short-term Memory Networks Perspective," Finance Research Letters, Elsevier, vol. 49(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Chen & Shen, Dehua & Li, Youwei, 2022. "Aggregate Investor Attention and Bitcoin Return: The Long Short-term Memory Networks Perspective," Finance Research Letters, Elsevier, vol. 49(C).
    2. Christophe Desagre & Catherine D'Hondt, 2020. "Googlization and retail investors' trading activity," LIDAM Discussion Papers LFIN 2020004, Université catholique de Louvain, Louvain Finance (LFIN).
    3. Goodell, John W. & Kumar, Satish & Li, Xiao & Pattnaik, Debidutta & Sharma, Anuj, 2022. "Foundations and research clusters in investor attention: Evidence from bibliometric and topic modelling analysis," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 511-529.
    4. Aouadi, Amal & Arouri, Mohamed & Roubaud, David, 2018. "Information demand and stock market liquidity: International evidence," Economic Modelling, Elsevier, vol. 70(C), pages 194-202.
    5. Tihana Škrinjarić, 2019. "Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets," IJFS, MDPI, vol. 7(4), pages 1-30, October.
    6. Smales, L.A., 2021. "Investor attention and global market returns during the COVID-19 crisis," International Review of Financial Analysis, Elsevier, vol. 73(C).
    7. Chen, Zhongdong & Craig, Karen Ann, 2023. "Active attention, retail investor base, and stock returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    8. Gang Chu & John W. Goodell & Dehua Shen & Yongjie Zhang, 2022. "Machine learning to establish proxies for investor attention: evidence of improved stock-return prediction," Annals of Operations Research, Springer, vol. 318(1), pages 103-128, November.
    9. Desagre, Christophe & D’Hondt, Catherine, 2021. "Googlization and retail trading activity," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    10. Peltomäki, Jarkko & Graham, Michael & Hasselgren, Anton, 2018. "Investor attention to market categories and market volatility: The case of emerging markets," Research in International Business and Finance, Elsevier, vol. 44(C), pages 532-546.
    11. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    12. repec:men:wpaper:57_2015 is not listed on IDEAS
    13. Aouadi, Amal & Arouri, Mohamed & Teulon, Frédéric, 2013. "Investor attention and stock market activity: Evidence from France," Economic Modelling, Elsevier, vol. 35(C), pages 674-681.
    14. Papadamou, Stephanos & Fassas, Athanasios & Kenourgios, Dimitris & Dimitriou, Dimitrios, 2020. "Direct and Indirect Effects of COVID-19 Pandemic on Implied Stock Market Volatility: Evidence from Panel Data Analysis," MPRA Paper 100020, University Library of Munich, Germany.
    15. Gao, Yang & Wang, Yaojun & Wang, Chao & Liu, Chao, 2018. "Internet attention and information asymmetry: Evidence from Qihoo 360 search data on the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 802-811.
    16. Dong, Dayong & Wu, Keke & Fang, Jianchun & Gozgor, Giray & Yan, Cheng, 2022. "Investor attention factors and stock returns: Evidence from China," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    17. Tantaopas, Parkpoom & Padungsaksawasdi, Chaiyuth & Treepongkaruna, Sirimon, 2016. "Attention effect via internet search intensity in Asia-Pacific stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 38(C), pages 107-124.
    18. Jaroslav Bukovina, 2016. "Social Media and Capital Markets – an Overview," MENDELU Working Papers in Business and Economics 2016-57, Mendel University in Brno, Faculty of Business and Economics.
    19. Chen, Zhongdong & Schmidt, Adam & Wang, Jin’ai, 2021. "Retail investor risk-seeking, attention, and the January effect," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    20. Hsieh, Shu-Fan & Chan, Chia-Ying & Wang, Ming-Chun, 2020. "Retail investor attention and herding behavior," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 109-132.
    21. Afkhami, Mohamad & Cormack, Lindsey & Ghoddusi, Hamed, 2017. "Google search keywords that best predict energy price volatility," Energy Economics, Elsevier, vol. 67(C), pages 17-27.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:finlet:v:38:y:2021:i:c:s1544612319310943. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/frl .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.