IDEAS home Printed from https://ideas.repec.org/a/eee/ecolet/v204y2021ics0165176521001944.html
   My bibliography  Save this article

Predicting stock prices based on informed traders’ activities using deep neural networks

Author

Listed:
  • Na, Haejung
  • Kim, Soonho

Abstract

This study investigates the predictive power of informed traders’ activities in stock price movements by employing neural networks. Specifically, we examine whether informed investors’ trading activities can predict drastic changes in stock prices in the subsequent 5-day period. Our empirical results show that the probability of the model being correct can be as high as 74%. In addition, the simulated trading strategies based on our trained model lead to significantly positive risk-adjusted returns and show strong performance measures. Overall, we find that informed traders’ activities contain informational content and may provide actual investors with information that is useful for stock price prediction.

Suggested Citation

  • Na, Haejung & Kim, Soonho, 2021. "Predicting stock prices based on informed traders’ activities using deep neural networks," Economics Letters, Elsevier, vol. 204(C).
    • Handle: RePEc:eee:ecolet:v:204:y:2021:i:c:s0165176521001944
    DOI: 10.1016/j.econlet.2021.109917
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0165176521001944
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.econlet.2021.109917?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. John M. Griffin & Jeffrey H. Harris & Selim Topaloglu, 2003. "The Dynamics of Institutional and Individual Trading," Journal of Finance, American Finance Association, vol. 58(6), pages 2285-2320, December.
    2. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," Review of Finance, European Finance Association, vol. 33(5), pages 2223-2273.
    3. Chen, Hsiu-Lang & Jegadeesh, Narasimhan & Wermers, Russ, 2000. "The Value of Active Mutual Fund Management: An Examination of the Stockholdings and Trades of Fund Managers," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(3), pages 343-368, September.
    4. Kewei Hou & Tobias J. Moskowitz, 2005. "Market Frictions, Price Delay, and the Cross-Section of Expected Returns," The Review of Financial Studies, Society for Financial Studies, vol. 18(3), pages 981-1020.
    5. Basu, Sanjoy, 1983. "The relationship between earnings' yield, market value and return for NYSE common stocks : Further evidence," Journal of Financial Economics, Elsevier, vol. 12(1), pages 129-156, June.
    6. Cheng, Ching-Hsue & Wei, Liang-Ying, 2014. "A novel time-series model based on empirical mode decomposition for forecasting TAIEX," Economic Modelling, Elsevier, vol. 36(C), pages 136-141.
    7. Cheol‐Ho Park & Scott H. Irwin, 2007. "What Do We Know About The Profitability Of Technical Analysis?," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 786-826, September.
    8. Vanstone, Bruce & Finnie, Gavin & Hahn, Tobias, 2012. "Creating trading systems with fundamental variables and neural networks: The Aby case study," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 86(C), pages 78-91.
    9. Baruch, Shmuel & Panayides, Marios & Venkataraman, Kumar, 2017. "Informed trading and price discovery before corporate events," Journal of Financial Economics, Elsevier, vol. 125(3), pages 561-588.
    10. Renault, Thomas, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 25-40.
    11. Mondria, Jordi & Wu, Thomas & Zhang, Yi, 2010. "The determinants of international investment and attention allocation: Using internet search query data," Journal of International Economics, Elsevier, vol. 82(1), pages 85-95, September.
    12. David E. Allen & Michael McAleer & Abhay K. Singh, 2019. "Daily market news sentiment and stock prices," Applied Economics, Taylor & Francis Journals, vol. 51(30), pages 3212-3235, June.
    13. Zhang, Wei & Li, Xiao & Shen, Dehua & Teglio, Andrea, 2016. "Daily happiness and stock returns: Some international evidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 201-209.
    14. Daifeng Li & Yintian Wang & Andrew Madden & Ying Ding & Jie Tang & Gordon Guozheng Sun & Ning Zhang & Enguo Zhou, 2019. "Analyzing stock market trends using social media user moods and social influence," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(9), pages 1000-1013, September.
    15. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    16. Lee, Charles M C & Shleifer, Andrei & Thaler, Richard H, 1991. "Investor Sentiment and the Closed-End Fund Puzzle," Journal of Finance, American Finance Association, vol. 46(1), pages 75-109, March.
    17. Siganos, Antonios & Vagenas-Nanos, Evangelos & Verwijmeren, Patrick, 2014. "Facebook's daily sentiment and international stock markets," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 730-743.
    18. Mark Grinblatt & Matti Keloharju, 2001. "What Makes Investors Trade?," Journal of Finance, American Finance Association, vol. 56(2), pages 589-616, April.
    19. Brad M. Barber & Terrance Odean, 2000. "Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors," Journal of Finance, American Finance Association, vol. 55(2), pages 773-806, April.
    20. Thomas Renault, 2017. "Intraday online investor sentiment and return patterns in the U.S. stock market," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-03205113, HAL.
    21. Shihao Gu & Bryan Kelly & Dacheng Xiu, 2020. "Empirical Asset Pricing via Machine Learning," The Review of Financial Studies, Society for Financial Studies, vol. 33(5), pages 2223-2273.
    22. Michael J. Cooper & Huseyin Gulen & Michael J. Schill, 2008. "Asset Growth and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 63(4), pages 1609-1651, August.
    23. You, Wanhai & Guo, Yawei & Peng, Cheng, 2017. "Twitter's daily happiness sentiment and the predictability of stock returns," Finance Research Letters, Elsevier, vol. 23(C), pages 58-64.
    24. Jegadeesh, Narasimhan & Livnat, Joshua, 2006. "Revenue surprises and stock returns," Journal of Accounting and Economics, Elsevier, vol. 41(1-2), pages 147-171, April.
    25. De Bondt, Werner F M & Thaler, Richard H, 1987. "Further Evidence on Investor Overreaction and Stock Market Seasonalit y," Journal of Finance, American Finance Association, vol. 42(3), pages 557-581, July.
    26. Cohen, Randolph B. & Gompers, Paul A. & Vuolteenaho, Tuomo, 2002. "Who underreacts to cash-flow news? evidence from trading between individuals and institutions," Journal of Financial Economics, Elsevier, vol. 66(2-3), pages 409-462.
    27. Litzenberger, Robert H. & Ramaswamy, Krishna, 1979. "The effect of personal taxes and dividends on capital asset prices : Theory and empirical evidence," Journal of Financial Economics, Elsevier, vol. 7(2), pages 163-195, June.
    28. Timm O. Sprenger & Andranik Tumasjan & Philipp G. Sandner & Isabell M. Welpe, 2014. "Tweets and Trades: the Information Content of Stock Microblogs," European Financial Management, European Financial Management Association, vol. 20(5), pages 926-957, November.
    29. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    30. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    31. Sun, Andrew & Lachanski, Michael & Fabozzi, Frank J., 2016. "Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction," International Review of Financial Analysis, Elsevier, vol. 48(C), pages 272-281.
    32. Li, Xiao & Shen, Dehua & Xue, Mei & Zhang, Wei, 2017. "Daily happiness and stock returns: The case of Chinese company listed in the United States," Economic Modelling, Elsevier, vol. 64(C), pages 496-501.
    33. Kim, Soon-Ho & Kim, Dongcheol, 2014. "Investor sentiment from internet message postings and the predictability of stock returns," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 708-729.
    34. 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.
    35. Cai, Jinghan & He, Jia & He, Jibao, 2010. "How better informed are the institutional investors?," Economics Letters, Elsevier, vol. 106(3), pages 234-237, March.
    36. Xiong Xiong & Chunchun Luo & Ye Zhang & Shen Lin, 2019. "Do stock bulletin board systems (BBS) contain useful information? A viewpoint of interaction between BBS quality and predicting ability," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(5), pages 1385-1411, March.
    37. Terrance Odean, 1999. "Do Investors Trade Too Much?," American Economic Review, American Economic Association, vol. 89(5), pages 1279-1298, December.
    38. Hirshleifer, David & Subrahmanyam, Avanidhar & Titman, Sheridan, 1994. "Security Analysis and Trading Patterns When Some Investors Receive Information before Others," Journal of Finance, American Finance Association, vol. 49(5), pages 1665-1698, December.
    39. Hailiang Chen & Prabuddha De & Yu (Jeffrey) Hu & Byoung-Hyoun Hwang, 2014. "Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media," The Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1367-1403.
    40. Jegadeesh, Narasimhan & Titman, Sheridan, 1993. "Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency," Journal of Finance, American Finance Association, vol. 48(1), pages 65-91, March.
    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. Moitra, Agnij, 2024. "Directional Stock Price Forecasting Based on Quantitative Value Investing Principles for Loss Averted Bogle-Head Investing using Various Machine Learning Algorithms," OSF Preprints y3mr6, Center for Open Science.

    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. Daniel, Kent & Hirshleifer, David & Teoh, Siew Hong, 2002. "Investor psychology in capital markets: evidence and policy implications," Journal of Monetary Economics, Elsevier, vol. 49(1), pages 139-209, January.
    2. Adam Zaremba & Jacob Koby Shemer, 2018. "Price-Based Investment Strategies," Springer Books, Springer, number 978-3-319-91530-2, December.
    3. David Hirshleifer, 2001. "Investor Psychology and Asset Pricing," Journal of Finance, American Finance Association, vol. 56(4), pages 1533-1597, August.
    4. Szymon Lis, 2022. "Investor Sentiment in Asset Pricing Models: A Review," Working Papers 2022-14, Faculty of Economic Sciences, University of Warsaw.
    5. Zhaobo Zhu & Licheng Sun, 2024. "When Buffett Meets Bollinger: An Integrated Approach to Fundamental and Technical Analysis," Post-Print hal-04703041, HAL.
    6. Schwert, G. William, 2003. "Anomalies and market efficiency," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, edition 1, volume 1, chapter 15, pages 939-974, Elsevier.
    7. Eierle, Brigitte & Klamer, Sebastian & Muck, Matthias, 2022. "Does it really pay off for investors to consider information from social media?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    8. Agarwal, Shweta & Kumar, Shailendra & Goel, Utkarsh, 2019. "Stock market response to information diffusion through internet sources: A literature review," International Journal of Information Management, Elsevier, vol. 45(C), pages 118-131.
    9. Stephen Foerster, 2011. "Double then Nothing: Why Stock Investments Relying on Simple Heuristics May Disappoint," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 3(2), pages 115-140, September.
    10. Amit Goyal, 2012. "Empirical cross-sectional asset pricing: a survey," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 26(1), pages 3-38, March.
    11. Naeem, Muhammad Abubakr & Farid, Saqib & Faruk, Balli & Shahzad, Syed Jawad Hussain, 2020. "Can happiness predict future volatility in stock markets?," Research in International Business and Finance, Elsevier, vol. 54(C).
    12. Hou, Kewei & Xue, Chen & Zhang, Lu, 2017. "Replicating Anomalies," Working Paper Series 2017-10, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    13. Doron Avramov & Guy Kaplanski & Avanidhar Subrahmanyam, 2022. "Postfundamentals Price Drift in Capital Markets: A Regression Regularization Perspective," Management Science, INFORMS, vol. 68(10), pages 7658-7681, October.
    14. Mahmoudi, Nader & Docherty, Paul & Melia, Adrian, 2022. "Firm-level investor sentiment and corporate announcement returns," Journal of Banking & Finance, Elsevier, vol. 144(C).
    15. Svetlana Bryzgalova & Jiantao Huang & Christian Julliard, 2023. "Bayesian Solutions for the Factor Zoo: We Just Ran Two Quadrillion Models," Journal of Finance, American Finance Association, vol. 78(1), pages 487-557, February.
    16. Fotini Economou & Konstantinos Gavriilidis & Bartosz Gebka & Vasileios Kallinterakis, 2022. "Feedback trading: a review of theory and empirical evidence," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 15(4), pages 429-476, February.
    17. Jiaju Miao & Pawel Polak, 2023. "Online Ensemble of Models for Optimal Predictive Performance with Applications to Sector Rotation Strategy," Papers 2304.09947, arXiv.org.
    18. Rui Fan & Oleksandr Talavera & Vu Tran, 2023. "Social media and price discovery: The case of cross‐listed firms," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 46(1), pages 151-167, February.
    19. Santi, Caterina, 2023. "Investor climate sentiment and financial markets," International Review of Financial Analysis, Elsevier, vol. 86(C).
    20. Xiong, Xiong & Meng, Yongqiang & Joseph, Nathan Lael & Shen, Dehua, 2020. "Stock mispricing, hard-to-value stocks and the influence of internet stock message boards," International Review of Financial Analysis, Elsevier, vol. 72(C).

    More about this item

    Keywords

    Artificial neural network; Informed investors; Stock price prediction; Market failure;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G4 - Financial Economics - - Behavioral Finance

    Statistics

    Access and download statistics

    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:ecolet:v:204:y:2021:i:c:s0165176521001944. 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/ecolet .

    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.