IDEAS home Printed from https://ideas.repec.org/a/wly/isacfm/v27y2020i1p22-37.html
   My bibliography  Save this article

Tehran stock exchange prediction using sentiment analysis of online textual opinions

Author

Listed:
  • Arezoo Hatefi Ghahfarrokhi
  • Mehrnoush Shamsfard

Abstract

We investigate the impact of social media data in predicting the Tehran Stock Exchange variables for the first time. We consider the closing price and daily return of three different stocks for this investigation. We collected our social media data from Sahamyab.com/stocktwits for about 3 months. To extract information from online comments, we propose a hybrid sentiment analysis approach that combines lexicon‐based and learning‐based methods. Since lexicons that are available for the Persian language are not practical for sentiment analysis in the stock market domain, we built a particular sentiment lexicon for this domain. After designing and calculating daily sentiment indices using the sentiment of the comments, we examine their impact on the baseline models that only use historical market data and propose new predictor models using multi‐regression analysis. In addition to the sentiments, we also examine the comments volume and the users' reliabilities. We conclude that the predictability of various stocks in the Tehran Stock Exchange is different depending on their attributes. Moreover, we indicate that only comments volume could be useful for predicting the closing price, and both the volume and the sentiment of the comments could be useful for predicting the daily return. We demonstrate that users' trust coefficients have different behaviours toward the three stocks.

Suggested Citation

  • Arezoo Hatefi Ghahfarrokhi & Mehrnoush Shamsfard, 2020. "Tehran stock exchange prediction using sentiment analysis of online textual opinions," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 27(1), pages 22-37, January.
  • Handle: RePEc:wly:isacfm:v:27:y:2020:i:1:p:22-37
    DOI: 10.1002/isaf.1465
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/isaf.1465
    Download Restriction: no

    File URL: https://libkey.io/10.1002/isaf.1465?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. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    2. Huina Mao & Scott Counts & Johan Bollen, 2011. "Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data," Papers 1112.1051, arXiv.org.
    3. Pingyang Gao, 2008. "Keynesian Beauty Contest, Accounting Disclosure, and Market Efficiency," Journal of Accounting Research, Wiley Blackwell, vol. 46(4), pages 785-807, September.
    4. Ilaria Bordino & Stefano Battiston & Guido Caldarelli & Matthieu Cristelli & Antti Ukkonen & Ingmar Weber, 2012. "Web Search Queries Can Predict Stock Market Volumes," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-17, July.
    5. Shleifer, Andrei, 2000. "Inefficient Markets: An Introduction to Behavioral Finance," OUP Catalogue, Oxford University Press, number 9780198292272, Decembrie.
    6. K. Coussement & D. Van Den Poel, 2008. "Improving Customer Attrition Prediction by Integrating Emotions from Client/Company Interaction Emails and Evaluating Multiple Classifiers," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 08/527, Ghent University, Faculty of Economics and Business Administration.
    7. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816.
    8. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    9. Greig, A.C., 1992. "Fundamental Analysis and Subsequent Stock Returns," Papers 51, Rochester, Business - Ph.D.,.
    10. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    11. Ying Zhang, 2009. "Determinants of Poster Reputation on Internet Stock Message Boards," American Journal of Economics and Business Administration, Science Publications, vol. 1(2), pages 114-121, June.
    12. 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.
    13. 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.
    14. Greig, Anthony C., 1992. "Fundamental analysis and subsequent stock returns," Journal of Accounting and Economics, Elsevier, vol. 15(2-3), pages 413-442, August.
    15. 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.
    16. 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. Elfadil A. Mohamed & Ibrahim Elsiddig Ahmed & Riyadh Mehdi & Hanan Hussain, 2021. "Impact of corporate performance on stock price predictions in the UAE markets: Neuro‐fuzzy model," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(1), pages 52-71, January.

    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. Arezoo Hatefi Ghahfarrokhi & Mehrnoush Shamsfard, 2019. "Tehran Stock Exchange Prediction Using Sentiment Analysis of Online Textual Opinions," Papers 1909.03792, arXiv.org, revised Sep 2019.
    2. 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.
    3. Rui Fan & Oleksandr Talavera & Vu Tran, 2020. "Social media bots and stock markets," European Financial Management, European Financial Management Association, vol. 26(3), pages 753-777, June.
    4. Al-Nasseri, Alya & Menla Ali, Faek, 2018. "What does investors' online divergence of opinion tell us about stock returns and trading volume?," Journal of Business Research, Elsevier, vol. 86(C), pages 166-178.
    5. 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.
    6. Thomas Renault, 2020. "Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages," Digital Finance, Springer, vol. 2(1), pages 1-13, September.
    7. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    8. Eric. W. K. See-To & Yang Yang, 2017. "Market sentiment dispersion and its effects on stock return and volatility," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 283-296, August.
    9. Miwa, Kotaro, 2023. "Divergent opinions on social media," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 182-196.
    10. Szymon Lis, 2022. "Investor Sentiment in Asset Pricing Models: A Review," Working Papers 2022-14, Faculty of Economic Sciences, University of Warsaw.
    11. Yanjian Zhu & Zhaoying Wu & Hua Zhang & Jing Yu, 2017. "Media sentiment, institutional investors and probability of stock price crash: evidence from Chinese stock markets," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 57(5), pages 1635-1670, December.
    12. Junni L. Zhang & Wolfgang Karl Hardle & Cathy Y. Chen & Elisabeth Bommes, 2020. "Distillation of News Flow into Analysis of Stock Reactions," Papers 2009.10392, arXiv.org.
    13. Fang, Hao & Chung, Chien-Ping & Lu, Yang-Cheng & Lee, Yen-Hsien & Wang, Wen-Hao, 2021. "The impacts of investors' sentiments on stock returns using fintech approaches," International Review of Financial Analysis, Elsevier, vol. 77(C).
    14. 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.
    15. Chong, Terence Tai Leung & Wu, Zhang & Liu, Yuchen, 2019. "Market Reaction to iPhone Rumors," MPRA Paper 92014, University Library of Munich, Germany.
    16. Ahelegbey, Daniel Felix & Cerchiello, Paola & Scaramozzino, Roberta, 2022. "Network based evidence of the financial impact of Covid-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 81(C).
    17. Lars Steinert & Christian Herff, 2018. "Predicting altcoin returns using social media," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-12, December.
    18. Arcuri, Maria Cristina & Gandolfi, Gino & Russo, Ivan, 2023. "Does fake news impact stock returns? Evidence from US and EU stock markets," Journal of Economics and Business, Elsevier, vol. 125.
    19. Piñeiro-Chousa, Juan Ramón & López-Cabarcos, M. Ángeles & Pérez-Pico, Ada María, 2016. "Examining the influence of stock market variables on microblogging sentiment," Journal of Business Research, Elsevier, vol. 69(6), pages 2087-2092.
    20. Junni L. Zhang & Wolfgang K. Härdle & Cathy Y. Chen & Elisabeth Bommes, 2015. "Distillation of News Flow into Analysis of Stock Reactions," SFB 649 Discussion Papers SFB649DP2015-005, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

    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:wly:isacfm:v:27:y:2020:i:1:p:22-37. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.interscience.wiley.com/jpages/1099-1174/ .

    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.