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

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  • 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
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    1. 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.
    2. 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.
    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. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. 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.
    6. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    7. 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.
    8. 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.
    9. 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.
    10. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    11. Tao Chen, 2017. "Investor Attention and Global Stock Returns," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 18(3), pages 358-372, July.
    12. 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.
    13. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    14. 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.
    15. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    16. 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.
    17. 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.
    18. Michael S. Drake & Darren T. Roulstone & Jacob R. Thornock, 2012. "Investor Information Demand: Evidence from Google Searches Around Earnings Announcements," Journal of Accounting Research, John Wiley & Sons, Ltd., vol. 50(4), pages 1001-1040, September.
    19. Vozlyublennaia, Nadia, 2014. "Investor attention, index performance, and return predictability," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 17-35.
    20. 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.
    21. 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.
    22. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    23. Li, Jun & Yu, Jianfeng, 2012. "Investor attention, psychological anchors, and stock return predictability," Journal of Financial Economics, Elsevier, vol. 104(2), pages 401-419.
    24. 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.
    25. Gustavo Grullon, 2004. "Advertising, Breadth of Ownership, and Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 17(2), pages 439-461.
    26. 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.
    27. 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.
    28. 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.
    29. 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.
    30. 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.
    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(S3), pages 158-168.
    32. 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.
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