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

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  • 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.

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  • 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
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    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," 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," 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. Werner Antweiler & Murray Z. Frank, 2004. "Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards," Journal of Finance, American Finance Association, vol. 59(3), pages 1259-1294, June.
    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," 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.
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    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

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