IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v482y2017icp621-626.html
   My bibliography  Save this article

Does microblogging convey firm-specific information? Evidence from China

Author

Listed:
  • Shen, Dehua
  • Li, Xiao
  • Xue, Mei
  • Zhang, Wei

Abstract

This paper investigates the impact of opening microblogging account in Sina Weibo on the diffusion of firm-specific information in Chinese stock market. With the unique sample of firms opening their official accounts, the empirical results show that this newly emerged information diffusion channel, i.e., Sina Weibo, plays an important role in conveying firm-specific information to the market. Generally speaking, these empirical findings have practical implications to securities regulators who have interest in monitoring the diffused information via social media.

Suggested Citation

  • Shen, Dehua & Li, Xiao & Xue, Mei & Zhang, Wei, 2017. "Does microblogging convey firm-specific information? Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 621-626.
    • Handle: RePEc:eee:phsmap:v:482:y:2017:i:c:p:621-626
    DOI: 10.1016/j.physa.2017.04.058
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117303618
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2017.04.058?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. Jin, Li & Myers, Stewart C., 2006. "R2 around the world: New theory and new tests," Journal of Financial Economics, Elsevier, vol. 79(2), pages 257-292, February.
    2. Zhang, Wei & Shen, Dehua & Zhang, Yongjie & Xiong, Xiong, 2013. "Open source information, investor attention, and asset pricing," Economic Modelling, Elsevier, vol. 33(C), pages 613-619.
    3. 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.
    4. Vakrman, Tomas & Kristoufek, Ladislav, 2015. "Underpricing, underperformance and overreaction in initial pubic offerings: Evidence from investor attention using online searches," FinMaP-Working Papers 35, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    5. Thomas Dimpfl & Stephan Jank, 2016. "Can Internet Search Queries Help to Predict Stock Market Volatility?," European Financial Management, European Financial Management Association, vol. 22(2), pages 171-192, March.
    6. Jin, Xi & Shen, Dehua & Zhang, Wei, 2016. "Has microblogging changed stock market behavior? Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 151-156.
    7. Zhang, Yongjie & Song, Weixin & Shen, Dehua & Zhang, Wei, 2016. "Market reaction to internet news: Information diffusion and price pressure," Economic Modelling, Elsevier, vol. 56(C), pages 43-49.
    8. Zhang, Yongjie & Feng, Lina & Jin, Xi & Shen, Dehua & Xiong, Xiong & Zhang, Wei, 2014. "Internet information arrival and volatility of SME PRICE INDEX," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 399(C), pages 70-74.
    9. 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.
    10. Zhang, Wei & Li, Xiao & Shen, Dehua & Teglio, Andrea, 2016. "R2 and idiosyncratic volatility: Which captures the firm-specific return variation?," Economic Modelling, Elsevier, vol. 55(C), pages 298-304.
    11. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    12. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    13. Lee, Dong Wook & Liu, Mark H., 2011. "Does more information in stock price lead to greater or smaller idiosyncratic return volatility?," Journal of Banking & Finance, Elsevier, vol. 35(6), pages 1563-1580, June.
    14. Zhang, Yongjie & Zhang, Yuzhao & Shen, Dehua & Zhang, Wei, 2017. "Investor sentiment and stock returns: Evidence from provincial TV audience rating in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 288-294.
    15. Shen, Dehua & Zhang, Wei & Xiong, Xiong & Li, Xiao & Zhang, Yongjie, 2016. "Trading and non-trading period Internet information flow and intraday return volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 451(C), pages 519-524.
    16. Kee-Hong Bae & Jin-Mo Kim & Yang Ni, 2013. "Is Firm-specific Return Variation a Measure of Information Efficiency?," International Review of Finance, International Review of Finance Ltd., vol. 13(4), pages 407-445, December.
    17. 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.
    18. Black, Fischer, 1986. "Noise," Journal of Finance, American Finance Association, vol. 41(3), pages 529-543, July.
    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. Zu, Xu & Diao, Xinyi & Meng, Zhiyi, 2019. "The impact of social media input intensity on firm performance: Evidence from Sina Weibo," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    2. Yan, Hanhuan & Han, Liyan, 2019. "Empirical distributions of stock returns: Mixed normal or kernel density?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 473-486.
    3. Zhang, Tonghui & Yuan, Ying & Wu, Xi, 2020. "Is microblogging data reflected in stock market volatility? Evidence from Sina Weibo," Finance Research Letters, Elsevier, vol. 32(C).
    4. Xiong, Xiong & Bian, Yuxiang & Shen, Dehua, 2018. "The time-varying correlation between policy uncertainty and stock returns: Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 413-419.
    5. Shen, Dehua & Liu, Lanbiao & Zhang, Yongjie, 2018. "Quantifying the cross-sectional relationship between online sentiment and the skewness of stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 928-934.
    6. Zuochao Zhang & Yongjie Zhang & Dehua Shen & Wei Zhang, 2018. "The Dynamic Cross-Correlations between Mass Media News, New Media News, and Stock Returns," Complexity, Hindawi, vol. 2018, pages 1-11, February.
    7. Zhao, Ruwei & Xiong, Xiong & Shen, Dehua, 2018. "Investor attention and performance of IPO firms: Evidence from online searches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 342-348.

    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. Zhang, Yongjie & Zhang, Yuzhao & Shen, Dehua & Zhang, Wei, 2017. "Investor sentiment and stock returns: Evidence from provincial TV audience rating in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 288-294.
    2. Zhang, Yongjie & Zhang, Zuochao & Liu, Lanbiao & Shen, Dehua, 2017. "The interaction of financial news between mass media and new media: Evidence from news on Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 535-541.
    3. Shen, Dehua & Liu, Lanbiao & Zhang, Yongjie, 2018. "Quantifying the cross-sectional relationship between online sentiment and the skewness of stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 928-934.
    4. Zhang, Yongjie & Song, Weixin & Shen, Dehua & Zhang, Wei, 2016. "Market reaction to internet news: Information diffusion and price pressure," Economic Modelling, Elsevier, vol. 56(C), pages 43-49.
    5. Agarwal, Shweta & Kumar, Shailendra & Goel, Utkarsh, 2019. "Stock market response to information diffusion through internet sources: A literature review," International Journal of Information Management, Elsevier, vol. 45(C), pages 118-131.
    6. Zuochao Zhang & Yongjie Zhang & Dehua Shen & Wei Zhang, 2018. "The Dynamic Cross-Correlations between Mass Media News, New Media News, and Stock Returns," Complexity, Hindawi, vol. 2018, pages 1-11, February.
    7. Zhang, Wei & Bi, Zhengzheng & Shen, Dehua, 2017. "Investor structure and the price–volume relationship in a continuous double auction market: An agent-based modeling perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 467(C), pages 345-355.
    8. Xiong, Xiong & Bian, Yuxiang & Shen, Dehua, 2018. "The time-varying correlation between policy uncertainty and stock returns: Evidence from China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 413-419.
    9. Zhang, Wei & Li, Xiao & Shen, Dehua & Teglio, Andrea, 2016. "R2 and idiosyncratic volatility: Which captures the firm-specific return variation?," Economic Modelling, Elsevier, vol. 55(C), pages 298-304.
    10. Zhao, Ruwei & Xiong, Xiong & Shen, Dehua, 2018. "Investor attention and performance of IPO firms: Evidence from online searches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 342-348.
    11. 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.
    12. Chu, Gang & Li, Xiao & Zhang, Yongjie, 2022. "Information demand and net selling around earnings announcement," Research in International Business and Finance, Elsevier, vol. 59(C).
    13. Zhang, Yuzhao & Liu, Haifei, 2021. "Stock market reactions to social media: Evidence from WeChat recommendations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    14. Gao, Yang & Wang, Yaojun & Wang, Chao & Liu, Chao, 2018. "Internet attention and information asymmetry: Evidence from Qihoo 360 search data on the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 802-811.
    15. Zhen-Hua Yang & Jian-Guo Liu & Chang-Rui Yu & Jing-Ti Han, 2017. "Quantifying the effect of investors’ attention on stock market," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-16, May.
    16. 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.
    17. Li, Xiao & Shen, Dehua & Zhang, Wei, 2018. "Do Chinese internet stock message boards convey firm-specific information?," Pacific-Basin Finance Journal, Elsevier, vol. 49(C), pages 1-14.
    18. Dehua Shen & Yongjie Zhang & Xiong Xiong & Wei Zhang, 2017. "Baidu index and predictability of Chinese stock returns," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-8, December.
    19. Patrick Houlihan & Germán G. Creamer, 2017. "Can Sentiment Analysis and Options Volume Anticipate Future Returns?," Computational Economics, Springer;Society for Computational Economics, vol. 50(4), pages 669-685, December.
    20. Shen, Dehua & Li, Xiao & Zhang, Wei, 2018. "Baidu news information flow and return volatility: Evidence for the Sequential Information Arrival Hypothesis," Economic Modelling, Elsevier, vol. 69(C), pages 127-133.

    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:eee:phsmap:v:482:y:2017:i:c:p:621-626. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.