IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0144945.html
   My bibliography  Save this article

Do Emotions Expressed Online Correlate with Actual Changes in Decision-Making?: The Case of Stock Day Traders

Author

Listed:
  • Bin Liu
  • Ramesh Govindan
  • Brian Uzzi

Abstract

Emotions are increasingly inferred linguistically from online data with a goal of predicting off-line behavior. Yet, it is unknown whether emotions inferred linguistically from online communications correlate with actual changes in off-line activity. We analyzed all 886,000 trading decisions and 1,234,822 instant messages of 30 professional day traders over a continuous 2 year period. Linguistically inferring the traders’ emotional states from instant messages, we find that emotions expressed in online communications reflect the same distributions of emotions found in controlled experiments done on traders. Further, we find that expressed online emotions predict the profitability of actual trading behavior. Relative to their baselines, traders who expressed little emotion or traders that expressed high levels of emotion made relatively unprofitable trades. Conversely, traders expressing moderate levels of emotional activation made relatively profitable trades.

Suggested Citation

  • Bin Liu & Ramesh Govindan & Brian Uzzi, 2016. "Do Emotions Expressed Online Correlate with Actual Changes in Decision-Making?: The Case of Stock Day Traders," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-11, January.
    • Handle: RePEc:plo:pone00:0144945
    DOI: 10.1371/journal.pone.0144945
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0144945
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0144945&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0144945?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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Karim S Kassam & Amanda R Markey & Vladimir L Cherkassky & George Loewenstein & Marcel Adam Just, 2013. "Identifying Emotions on the Basis of Neural Activation," PLOS ONE, Public Library of Science, vol. 8(6), pages 1-12, June.
    3. Wilcox, Nathaniel T, 1993. "Lottery Choice: Incentives, Complexity and Decision Time," Economic Journal, Royal Economic Society, vol. 103(421), pages 1397-1417, November.
    4. 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.
    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. Manda, Vijaya Kittu & Sana, Alekhya, 2021. "Impact Of Mental Health And Well-Being Of Indian Stock Market Traders," MPRA Paper 109941, University Library of Munich, Germany.
    2. Khalid A. Bin Abdulrahman & Abdulaziz Yahya Alsharif & Abdulrahman Bandar Alotaibi & Abdulrahman Ali Alajaji & Abdullah Ali Alhubaysh & Abdulrahman Ibrahim Alsubaihi & Nahaa Eid Alsubaie, 2022. "Anxiety and Stress among Day Traders in Saudi Arabia," IJERPH, MDPI, vol. 19(18), pages 1-10, September.

    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, Wenzhao & Duxbury, Darren, 2021. "Institutional investor sentiment and the mean-variance relationship: Global evidence," Journal of Economic Behavior & Organization, Elsevier, vol. 191(C), pages 415-441.
    2. 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.
    3. Hailiang Huang & Yanhong Li & Yingying Zhang, 2018. "Investors’ attention and overpricing of IPO: an empirical study on China’s growth enterprise market," Information Systems and e-Business Management, Springer, vol. 16(4), pages 761-774, November.
    4. Baker, Malcolm & Wurgler, Jeffrey & Yuan, Yu, 2012. "Global, local, and contagious investor sentiment," Journal of Financial Economics, Elsevier, vol. 104(2), pages 272-287.
    5. Duygu Ider & Stefan Lessmann, 2022. "Forecasting Cryptocurrency Returns from Sentiment Signals: An Analysis of BERT Classifiers and Weak Supervision," Papers 2204.05781, arXiv.org, revised Mar 2023.
    6. Vojtěch Fiala & Svatopluk Kapounek & Ondřej Veselý, 2015. "Impact of Social Media on the Stock Market: Evidence from Tweets," European Journal of Business Science and Technology, Mendel University in Brno, Faculty of Business and Economics, vol. 1(1), pages 24-35.
    7. Philipp Stephan & Rüdiger Nitzsch, 2013. "Do individual investors’ stock recommendations in online communities contain investment value?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 27(2), pages 149-186, June.
    8. Jonathan Readshaw & Stefano Giani, 2020. "Using Company Specific Headlines and Convolutional Neural Networks to Predict Stock Fluctuations," Papers 2006.12426, arXiv.org.
    9. Goodell, John W. & Kumar, Satish & Lim, Weng Marc & Pattnaik, Debidutta, 2021. "Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 32(C).
    10. Caporale, Guglielmo Maria & Spagnolo, Fabio & Spagnolo, Nicola, 2018. "Exchange rates and macro news in emerging markets," Research in International Business and Finance, Elsevier, vol. 46(C), pages 516-527.
    11. Abdi, Farshid & Kormanyos, Emily & Pelizzon, Loriana & Getmansky, Mila & Simon, Zorka, 2021. "Market impact of government communication: The case of presidential tweets," SAFE Working Paper Series 314, Leibniz Institute for Financial Research SAFE, revised 2021.
    12. Blankespoor, Elizabeth & deHaan, Ed & Marinovic, Iván, 2020. "Disclosure processing costs, investors’ information choice, and equity market outcomes: A review," Journal of Accounting and Economics, Elsevier, vol. 70(2).
    13. Mao, Huina & Counts, Scott & Bollen, Johan, 2015. "Quantifying the effects of online bullishness on international financial markets," Statistics Paper Series 09, European Central Bank.
    14. Boubaker, Sabri & Liu, Zhenya & Zhai, Ling, 2021. "Big data, news diversity and financial market crash," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    15. Kiran Thapa, 2013. "Stock Message Board Recommendations and Share Trading Activity," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 10, July-Dece.
    16. Bousselmi, Wael & Sentis, Patrick & Willinger, Marc, 2019. "How do markets react to (un)expected fundamental value shocks? An experimental analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 23(C), pages 90-113.
    17. Jingyi Gu & Sarvesh Shukla & Junyi Ye & Ajim Uddin & Guiling Wang, 2023. "Deep learning model with sentiment score and weekend effect in stock price prediction," SN Business & Economics, Springer, vol. 3(7), pages 1-20, July.
    18. Frank, Murray Z. & Sanati, Ali, 2018. "How does the stock market absorb shocks?," Journal of Financial Economics, Elsevier, vol. 129(1), pages 136-153.
    19. Michael Nofer & Oliver Hinz, 2015. "Using Twitter to Predict the Stock Market," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 57(4), pages 229-242, August.
    20. 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).

    More about this item

    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:plo:pone00:0144945. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.