IDEAS home Printed from https://ideas.repec.org/a/bla/joares/v56y2018i3p989-1034.html
   My bibliography  Save this article

Wisdom of Crowds: Cross‐Sectional Variation in the Informativeness of Third‐Party‐Generated Product Information on Twitter

Author

Listed:
  • VICKI WEI TANG

Abstract

This paper examines whether third‐party‐generated product information on Twitter, once aggregated at the firm level, is predictive of firm‐level sales, and if so, what factors determine the cross‐sectional variation in the predictive power. First, the predictive power of Twitter comments increases with the extent to which they fairly represent the broad customer response to products and brands. The predictive power is greater for firms whose major customers are consumers rather than businesses. Second, the word‐of‐mouth effect of Twitter comments is greater when advertising is limited. Third, a detailed analysis of the identity of the tweet handles provides the additional insights that the predictive power of the volume of Twitter comments is dominated by “the wisdom of crowds,” whereas the predictive power of the valence of Twitter comments is largely attributable to expert comments. Furthermore, Twitter comments not only reflect upcoming sales, but also capture an unexpected component of sales growth.

Suggested Citation

  • Vicki Wei Tang, 2018. "Wisdom of Crowds: Cross‐Sectional Variation in the Informativeness of Third‐Party‐Generated Product Information on Twitter," Journal of Accounting Research, Wiley Blackwell, vol. 56(3), pages 989-1034, June.
  • Handle: RePEc:bla:joares:v:56:y:2018:i:3:p:989-1034
    DOI: 10.1111/1475-679X.12183
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1475-679X.12183
    Download Restriction: no

    File URL: https://libkey.io/10.1111/1475-679X.12183?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. David A. Reinstein & Christopher M. Snyder, 2005. "The Influence Of Expert Reviews On Consumer Demand For Experience Goods: A Case Study Of Movie Critics," Journal of Industrial Economics, Wiley Blackwell, vol. 53(1), pages 27-51, March.
    3. Gregory S. Miller & Douglas J. Skinner, 2015. "The Evolving Disclosure Landscape: How Changes in Technology, the Media, and Capital Markets Are Affecting Disclosure," Journal of Accounting Research, Wiley Blackwell, vol. 53(2), pages 221-239, May.
    4. McLaren, Nick & Shanbhogue, Rachana, 2011. "Using internet search data as economic indicators," Bank of England Quarterly Bulletin, Bank of England, vol. 51(2), pages 134-140.
    5. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    6. Simeon Vosen & Torsten Schmidt, 2011. "Forecasting private consumption: survey‐based indicators vs. Google trends," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 30(6), pages 565-578, September.
    7. 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.
    8. Yubo Chen & Jinhong Xie, 2008. "Online Consumer Review: Word-of-Mouth as a New Element of Marketing Communication Mix," Management Science, INFORMS, vol. 54(3), pages 477-491, March.
    9. Lev, B & Thiagarajan, Sr, 1993. "Fundamental Information Analysis," Journal of Accounting Research, Wiley Blackwell, vol. 31(2), pages 190-215.
    10. Michael S. Drake & Darren T. Roulstone & Jacob R. Thornock, 2012. "Investor Information Demand: Evidence from Google Searches Around Earnings Announcements," Journal of Accounting Research, Wiley Blackwell, vol. 50(4), pages 1001-1040, September.
    11. 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.
    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. Nerantzidis, Michail & Tampakoudis, Ioannis & She, Chaoyuan, 2024. "Social media in accounting research: A review and future research agenda," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 54(C).
    2. Wu, Chunying & Xiong, Xiong & Gao, Ya & Zhang, Jin, 2022. "Does social media coverage deter firms from withholding bad news? Evidence from stock price crash risk," International Review of Financial Analysis, Elsevier, vol. 84(C).
    3. Hoang, Daniel & Wiegratz, Kevin, 2022. "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics 158, Karlsruhe Institute of Technology (KIT), Department of Economics and Management.
    4. Wu, Chunying & Xiong, Xiong & Gao, Ya & Zhang, Jin, 2022. "Does social media distort price discovery? Evidence from rumor clarifications," Research in International Business and Finance, Elsevier, vol. 62(C).
    5. Xing Huan & Antonio Parbonetti & Giulia Redigolo & Zhewei Zhang, 2024. "Social media disclosure and reputational damage," Review of Quantitative Finance and Accounting, Springer, vol. 62(4), pages 1355-1396, May.
    6. Umar, Tarik, 2022. "Complexity aversion when SeekingAlpha," Journal of Accounting and Economics, Elsevier, vol. 73(2).
    7. Deli Yuan & Muhammad Khalilur Rahman & Md. Abu Issa Gazi & Md. Atikur Rahaman & Mohammad Mainul Hossain & Shaharin Akter, 2021. "Analyzing of User Attitudes Toward Intention to Use Social Media for Learning," SAGE Open, , vol. 11(4), pages 21582440211, November.
    8. Jia, Weishi & Redigolo, Giulia & Shu, Susan & Zhao, Jingran, 2020. "Can social media distort price discovery? Evidence from merger rumors," Journal of Accounting and Economics, Elsevier, vol. 70(1).
    9. 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).
    10. Chiu, Peng-Chia & Teoh, Siew Hong & Zhang, Yinglei & Huang, Xuan, 2023. "Using Google searches of firm products to detect revenue management," Accounting, Organizations and Society, Elsevier, vol. 109(C).
    11. Enwei Zhu & Jing Wu & Hongyu Liu & Keyang Li, 2023. "A Sentiment Index of the Housing Market in China: Text Mining of Narratives on Social Media," The Journal of Real Estate Finance and Economics, Springer, vol. 66(1), pages 77-118, January.
    12. Hao, Jing, 2023. "Retail investor attention and corporate innovation in the big data era," International Review of Financial Analysis, Elsevier, vol. 86(C).
    13. Michael S. Drake & James R. Moon & Brady J. Twedt & James D. Warren, 2023. "Social media analysts and sell-side analyst research," Review of Accounting Studies, Springer, vol. 28(2), pages 385-420, June.
    14. Jie Li & Li Yu & Xiaofeng Mei & Xu Feng, 2022. "Do social media constrain or promote company violations?," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 62(1), pages 31-70, March.
    15. Sumit Agarwal & Wenlan Qian & Xin Zou, 2021. "Disaggregated Sales and Stock Returns," Management Science, INFORMS, vol. 67(11), pages 7167-7183, November.
    16. Felix Reichenbach & Martin Walther, 2023. "Financial recommendations on Reddit, stock returns and cumulative prospect theory," Digital Finance, Springer, vol. 5(2), pages 421-448, June.
    17. Lei, Lijun (Gillian) & Li, Yutao & Luo, Yan, 2019. "Production and dissemination of corporate information in social media: A review," Journal of Accounting Literature, Elsevier, vol. 42(C), pages 29-43.
    18. Campbell, Brett & Drake, Michael & Thornock, Jacob & Twedt, Brady, 2023. "Earnings Virality," Journal of Accounting and Economics, Elsevier, vol. 75(1).

    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. Ramya Rajajagadeesan Aroul & Sanjiv Sabherwal & Sergiy Saydometov, 2022. "FEAR Index, city characteristics, and housing returns," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 50(1), pages 173-205, March.
    2. Alina Lerman, 2020. "Individual Investors' Attention to Accounting Information: Evidence from Online Financial Communities," Contemporary Accounting Research, John Wiley & Sons, vol. 37(4), pages 2020-2057, December.
    3. Du, Yao & Linh, Tran Thi Thuy & Lu, Chien-Lin & Nguyen, Hong Thoa, 2024. "Reaching the public with Twitter: The reputation value of CEOs," International Review of Economics & Finance, Elsevier, vol. 94(C).
    4. Patrick Houlihan & Germán G. Creamer, 2021. "Leveraging Social Media to Predict Continuation and Reversal in Asset Prices," Computational Economics, Springer;Society for Computational Economics, vol. 57(2), pages 433-453, February.
    5. Chau, Michael & Lin, Chih-Yung & Lin, Tse-Chun, 2020. "Wisdom of crowds before the 2007–2009 global financial crisis," Journal of Financial Stability, Elsevier, vol. 48(C).
    6. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    7. Daniel E. O'Leary, 2024. "Toward an extended framework of exhaust data for predictive analytics: An empirical approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 31(2), June.
    8. Monge, Manuel & Claudio-Quiroga, Gloria & Poza, Carlos, 2024. "Chinese economic behavior in times of covid-19. A new leading economic indicator based on Google trends," International Economics, Elsevier, vol. 177(C).
    9. Domonkos F. Vamossy, 2020. "Investor Emotions and Earnings Announcements," Papers 2006.13934, arXiv.org, revised Jun 2020.
    10. France, Stephen L. & Shi, Yuying & Kazandjian, Brett, 2021. "Web Trends: A valuable tool for business research," Journal of Business Research, Elsevier, vol. 132(C), pages 666-679.
    11. Nikolaos Askitas & Klaus F. Zimmermann, 2015. "The internet as a data source for advancement in social sciences," International Journal of Manpower, Emerald Group Publishing Limited, vol. 36(1), pages 2-12, April.
    12. Geng, Yuedan & Ye, Qiang & Jin, Yu & Shi, Wen, 2022. "Crowd wisdom and internet searches: What happens when investors search for stocks?," International Review of Financial Analysis, Elsevier, vol. 82(C).
    13. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    14. Zeynalov, Ayaz, 2014. "Nowcasting Tourist Arrivals to Prague: Google Econometrics," MPRA Paper 60945, University Library of Munich, Germany.
    15. Stephen L. France & Yuying Shi, 2017. "Aggregating Google Trends: Multivariate Testing and Analysis," Papers 1712.03152, arXiv.org, revised Mar 2018.
    16. Domonkos F. Vamossy, 2024. "Social Media Emotions and Market Behavior," Papers 2404.03792, arXiv.org.
    17. Artem Meshcheryakov & Stoyu I Ivanov, 2017. "Investor's sentiment in predicting the Effective Federal Funds Rate," Economics Bulletin, AccessEcon, vol. 37(4), pages 2767-2796.
    18. Oestmann Marco & Bennöhr Lars, 2015. "Determinants of house price dynamics. What can we learn from search engine data?," Review of Economics, De Gruyter, vol. 66(1), pages 99-127, April.
    19. David Kohns & Arnab Bhattacharjee, 2020. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," Papers 2011.00938, arXiv.org, revised May 2022.
    20. Jukka Ruohonen & Sami Hyrynsalmi, 2017. "Evaluating the use of internet search volumes for time series modeling of sales in the video game industry," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(4), pages 351-370, November.

    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:bla:joares:v:56:y:2018:i:3:p:989-1034. 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.blackwellpublishing.com/journal.asp?ref=0021-8456 .

    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.