IDEAS home Printed from https://ideas.repec.org/a/spr/jecfin/v46y2022i2d10.1007_s12197-021-09565-5.html
   My bibliography  Save this article

Learning to trade on sentiment

Author

Listed:
  • Cuiyuan Wang

    (CUNY Graduate Center)

  • Tao Wang

    (Queens College and CUNY Graduate Center)

  • Changhe Yuan

    (Queens College and CUNY Graduate Center)

  • Jane Yihua Rong

    (CUNY Queens College)

Abstract

The increasing availability of big data has made it possible to research the sentiment influence to the individual company. We use investment social media data to extract the sentiment expressed in the financial news articles by applying deep learning model, Long Short-Term Memory (LSTM) neural network. The textual sentiment (bullish or bearish idea) can be classified by all the machine learning classifiers and deep learning models and even some traditional dictionary approaches. Based on our experiments, we have found that the Long Short-Term Memory (LSTM) neural network performs best with the accuracy at 94%. Based on the sentiment related with individual company, we build a market-neutral trading strategy called majority votes strategy to perform a comprehensive study on how the sentiment of the individual company influence the financial returns. In this paper, we demonstrate how financial sentiment analysis can be utilized to build trading strategy by incorporating the sentiment factor.

Suggested Citation

  • Cuiyuan Wang & Tao Wang & Changhe Yuan & Jane Yihua Rong, 2022. "Learning to trade on sentiment," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 46(2), pages 308-323, April.
  • Handle: RePEc:spr:jecfin:v:46:y:2022:i:2:d:10.1007_s12197-021-09565-5
    DOI: 10.1007/s12197-021-09565-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12197-021-09565-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12197-021-09565-5?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. 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.
    2. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    3. 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.
    4. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    5. 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.
    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. Gianluca Anese & Marco Corazza & Michele Costola & Loriana Pelizzon, 2023. "Impact of public news sentiment on stock market index return and volatility," Computational Management Science, Springer, vol. 20(1), pages 1-36, December.

    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. Ahmad, Khurshid & Han, JingGuang & Hutson, Elaine & Kearney, Colm & Liu, Sha, 2016. "Media-expressed negative tone and firm-level stock returns," Journal of Corporate Finance, Elsevier, vol. 37(C), pages 152-172.
    2. Santi, Caterina, 2023. "Investor climate sentiment and financial markets," International Review of Financial Analysis, Elsevier, vol. 86(C).
    3. Manuel Ammann & Nic Schaub, 2021. "Do Individual Investors Trade on Investment-Related Internet Postings?," Management Science, INFORMS, vol. 67(9), pages 5679-5702, September.
    4. Daniele Ballinari & Simon Behrendt, 2021. "How to gauge investor behavior? A comparison of online investor sentiment measures," Digital Finance, Springer, vol. 3(2), pages 169-204, June.
    5. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2022. "Media-expressed tone, option characteristics, and stock return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    6. Ferdinand Graf, 2011. "Mechanically Extracted Company Signals and their Impact on Stock and Credit Markets," Working Paper Series of the Department of Economics, University of Konstanz 2011-18, Department of Economics, University of Konstanz.
    7. Domonkos F. Vamossy, 2020. "Investor Emotions and Earnings Announcements," Papers 2006.13934, arXiv.org, revised Jun 2020.
    8. Obaid, Khaled & Pukthuanthong, Kuntara, 2022. "A picture is worth a thousand words: Measuring investor sentiment by combining machine learning and photos from news," Journal of Financial Economics, Elsevier, vol. 144(1), pages 273-297.
    9. John Garcia, 2021. "Analyst herding and firm-level investor sentiment," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 35(4), pages 461-494, December.
    10. Steven Heston & Nitish R. Sinha, 2016. "News versus Sentiment : Predicting Stock Returns from News Stories," Finance and Economics Discussion Series 2016-048, Board of Governors of the Federal Reserve System (U.S.).
    11. Jiao, Peiran & Veiga, André & Walther, Ansgar, 2020. "Social media, news media and the stock market," Journal of Economic Behavior & Organization, Elsevier, vol. 176(C), pages 63-90.
    12. Chong, Terence Tai Leung & Wu, Zhang & Liu, Yuchen, 2019. "Market Reaction to iPhone Rumors," MPRA Paper 92014, University Library of Munich, Germany.
    13. Vegard Høghaug Larsen & Leif Anders Thorsrud, 2022. "Asset returns, news topics, and media effects," Scandinavian Journal of Economics, Wiley Blackwell, vol. 124(3), pages 838-868, July.
    14. Sun, Licheng & Najand, Mohammad & Shen, Jiancheng, 2016. "Stock return predictability and investor sentiment: A high-frequency perspective," Journal of Banking & Finance, Elsevier, vol. 73(C), pages 147-164.
    15. David F. Larcker & Anastasia A. Zakolyukina, 2012. "Detecting Deceptive Discussions in Conference Calls," Journal of Accounting Research, Wiley Blackwell, vol. 50(2), pages 495-540, May.
    16. Ingrid E. Fisher & Margaret R. Garnsey & Mark E. Hughes, 2016. "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 157-214, July.
    17. Qingbin Meng & Congyi Ju & Qinghua Huang & Song Wang, 2023. "The informativeness of investor communication with corporate insiders: Evidence from China," International Finance, Wiley Blackwell, vol. 26(2), pages 189-207, August.
    18. Loughran, Tim & McDonald, Bill & Pragidis, Ioannis, 2019. "Assimilation of oil news into prices," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 105-118.
    19. 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.
    20. Anand, Abhinav & Basu, Sankarshan & Pathak, Jalaj & Thampy, Ashok, 2021. "The impact of sentiment on emerging stock markets," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 161-177.

    More about this item

    Keywords

    Deep learning; Long short term memory neural network; Trading strategy; Sentiment analysis;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

    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:spr:jecfin:v:46:y:2022:i:2:d:10.1007_s12197-021-09565-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.