IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v510y2018icp802-811.html
   My bibliography  Save this article

Internet attention and information asymmetry: Evidence from Qihoo 360 search data on the Chinese stock market

Author

Listed:
  • Gao, Yang
  • Wang, Yaojun
  • Wang, Chao
  • Liu, Chao

Abstract

Investor and media attention play a significant role in stock markets, and internet attention has been used as a proxy for investor and media attention to explore their influence on fluctuations in stock prices. This study relied on the Qihoo 360 search index to investigate the effects of investor and media attention in the Chinese stock market. We used three major information asymmetry measures to explore whether investor and media attention were relevant for stock market information asymmetry. Our empirical results reveal that investor attention accelerates information dissemination into stock prices and reduces information asymmetry significantly. However, after investor attention and other control variables are controlled for, the effect of media attention is trivial. The results confirm that internet attention facilitates forecasting market performance in the Chinese stock market.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:510:y:2018:i:c:p:802-811
    DOI: 10.1016/j.physa.2018.07.016
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711830880X
    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.2018.07.016?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. Hasbrouck, Joel, 1991. "Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
    2. Goddard, John & Kita, Arben & Wang, Qingwei, 2015. "Investor attention and FX market volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 38(C), pages 79-96.
    3. Dumitrescu, Elena-Ivona & Hurlin, Christophe, 2012. "Testing for Granger non-causality in heterogeneous panels," Economic Modelling, Elsevier, vol. 29(4), pages 1450-1460.
    4. 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.
    5. Mitchell, Mark L & Mulherin, J Harold, 1994. "The Impact of Public Information on the Stock Market," Journal of Finance, American Finance Association, vol. 49(3), pages 923-950, July.
    6. Ya-Chun Gao & Yong Zeng & Shi-Min Cai, 2015. "Influence network in Chinese stock market," Papers 1503.00823, arXiv.org.
    7. Bijl, Laurens & Kringhaug, Glenn & Molnár, Peter & Sandvik, Eirik, 2016. "Google searches and stock returns," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 150-156.
    8. Chan, Yue-cheong & Chui, Andy C. W. & Kwok, Chuck C. Y., 2001. "The impact of salient political and economic news on the trading activity," Pacific-Basin Finance Journal, Elsevier, vol. 9(3), pages 195-217, June.
    9. Vozlyublennaia, Nadia, 2014. "Investor attention, index performance, and return predictability," Journal of Banking & Finance, Elsevier, vol. 41(C), pages 17-35.
    10. Easley, David & Hvidkjaer, Soeren & O’Hara, Maureen, 2010. "Factoring Information into Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 45(2), pages 293-309, April.
    11. Guo, Kun & Sun, Yi & Qian, Xin, 2017. "Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 390-396.
    12. Huang, Roger D & Stoll, Hans R, 1997. "The Components of the Bid-Ask Spread: A General Approach," Review of Financial Studies, Society for Financial Studies, vol. 10(4), pages 995-1034.
    13. Zhao, Longfeng & Wang, Gang-Jin & Wang, Mingang & Bao, Weiqi & Li, Wei & Stanley, H. Eugene, 2018. "Stock market as temporal network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 506(C), pages 1104-1112.
    14. Gao, Ya-Chun & Cai, Shi-Min & Lü, Linyuan & Wang, Bing-Hong, 2013. "Evolutionary model on market ecology of investors and investments," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(16), pages 3385-3391.
    15. 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.
    16. 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.
    17. Matthias Bank & Martin Larch & Georg Peter, 2011. "Google search volume and its influence on liquidity and returns of German stocks," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 25(3), pages 239-264, September.
    18. Hasbrouck, Joel, 1991. "The Summary Informativeness of Stock Trades: An Econometric Analysis," Review of Financial Studies, Society for Financial Studies, vol. 4(3), pages 571-595.
    19. Jian Yang, 2003. "Market Segmentation and Information Asymmetry in Chinese Stock Markets: A VAR Analysis," The Financial Review, Eastern Finance Association, vol. 38(4), pages 591-609, November.
    20. 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.
    21. Takeda, Fumiko & Wakao, Takumi, 2014. "Google search intensity and its relationship with returns and trading volume of Japanese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 1-18.
    22. Sankaraguruswamy, Srinivasan & Shen, Jianfeng & Yamada, Takeshi, 2013. "The relationship between the frequency of news release and the information asymmetry: The role of uninformed trading," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4134-4143.
    23. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
    24. Lily Fang & Joel Peress, 2009. "Media Coverage and the Cross‐section of Stock Returns," Journal of Finance, American Finance Association, vol. 64(5), pages 2023-2052, October.
    25. Vega, Clara, 2006. "Stock price reaction to public and private information," Journal of Financial Economics, Elsevier, vol. 82(1), pages 103-133, October.
    26. Ding, Rong & Hou, Wenxuan, 2015. "Retail investor attention and stock liquidity," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 12-26.
    27. Duarte, Jefferson & Young, Lance, 2009. "Why is PIN priced?," Journal of Financial Economics, Elsevier, vol. 91(2), pages 119-138, February.
    28. Zhi Da & Joseph Engelberg & Pengjie Gao, 2011. "In Search of Attention," Journal of Finance, American Finance Association, vol. 66(5), pages 1461-1499, October.
    29. Wang, Minggang & Tian, Lixin, 2016. "From time series to complex networks: The phase space coarse graining," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 456-468.
    30. Easley, David & O'Hara, Maureen & Saar, Gideon, 2001. "How Stock Splits Affect Trading: A Microstructure Approach," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 36(1), pages 25-51, March.
    31. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    32. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2011. "When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 321-340, March.
    33. Gustavo Grullon, 2004. "Advertising, Breadth of Ownership, and Liquidity," Review of Financial Studies, Society for Financial Studies, vol. 17(2), pages 439-461.
    34. Aktas, Nihat & de Bodt, Eric & Declerck, Fany & Van Oppens, Herve, 2007. "The PIN anomaly around M&A announcements," Journal of Financial Markets, Elsevier, vol. 10(2), pages 169-191, May.
    35. AKTAS, Nihat & DE BODT, Eric & DECLERCK, Fany & VAN OPPENS, Hervé, 2007. "The PIN anomaly around M&A announcements," LIDAM Reprints CORE 1965, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    36. 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.
    37. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    38. Easley, David, et al, 1996. "Liquidity, Information, and Infrequently Traded Stocks," Journal of Finance, American Finance Association, vol. 51(4), pages 1405-1436, September.
    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. Wen, Fenghua & Xu, Longhao & Ouyang, Guangda & Kou, Gang, 2019. "Retail investor attention and stock price crash risk: Evidence from China," International Review of Financial Analysis, Elsevier, vol. 65(C).
    2. Yueyang Zhao & Jinzhou Mao, 2023. "Mixed ownership reforms and the transparency of nonstate‐owned enterprises: Evidence from China," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(1), pages 271-284, January.
    3. Cheng, Feiyang & Wang, Chunfeng & Chiao, Chaoshin & Yao, Shouyu & Fang, Zhenming, 2021. "Retail attention, retail trades, and stock price crash risk," Emerging Markets Review, Elsevier, vol. 49(C).
    4. Gao, Yang & Li, Yunhai & Wang, Yaojun & Wang, Chao & Liu, Chao, 2019. "Asymptotic comparison of three spread estimators based on Roll’s model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 420-432.
    5. Gao, Yang & Zhao, Kun & Wang, Chao & Liu, Chao, 2020. "The dynamic relationship between internet attention and stock market liquidity: A thermal optimal path method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).
    6. Cui, Xin & Sensoy, Ahmet & Nguyen, Duc Khuong & Yao, Shouyu & Wu, Yiyao, 2022. "Positive information shocks, investor behavior and stock price crash risk," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 493-518.
    7. He, Feng & Yan, Yulin & Hao, Jing & Wu, Ji (George), 2022. "Retail investor attention and corporate green innovation: Evidence from China," Energy Economics, Elsevier, vol. 115(C).
    8. Chen, Xing & Wu, Chongfeng, 2022. "Retail investor attention and information asymmetry: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 75(C).
    9. Fu, Junhui & Wu, Xiang & Liu, Yufang & Chen, Rongda, 2021. "Firm-specific investor sentiment and stock price crash risk," Finance Research Letters, Elsevier, vol. 38(C).

    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. Christophe Desagre & Catherine D'Hondt, 2020. "Googlization and retail investors' trading activity," LIDAM Discussion Papers LFIN 2020004, Université catholique de Louvain, Louvain Finance (LFIN).
    2. Tihana Škrinjarić, 2019. "Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets," IJFS, MDPI, vol. 7(4), pages 1-30, October.
    3. Desagre, Christophe & D’Hondt, Catherine, 2021. "Googlization and retail trading activity," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    4. 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.
    5. Goodell, John W. & Kumar, Satish & Li, Xiao & Pattnaik, Debidutta & Sharma, Anuj, 2022. "Foundations and research clusters in investor attention: Evidence from bibliometric and topic modelling analysis," International Review of Economics & Finance, Elsevier, vol. 82(C), pages 511-529.
    6. Chen, Zhongdong & Craig, Karen Ann, 2023. "Active attention, retail investor base, and stock returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    7. Lof, Matthijs & van Bommel, Jos, 2023. "Asymmetric information and the distribution of trading volume," Journal of Corporate Finance, Elsevier, vol. 82(C).
    8. Chaiyuth Padungsaksawasdi & Sirimon Treepongkaruna & Robert Brooks, 2019. "Investor Attention and Stock Market Activities: New Evidence from Panel Data," IJFS, MDPI, vol. 7(2), pages 1-19, June.
    9. Hsieh, Shu-Fan & Chan, Chia-Ying & Wang, Ming-Chun, 2020. "Retail investor attention and herding behavior," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 109-132.
    10. Gang Chu & Xiao Li & Dehua Shen & Yongjie Zhang, 2021. "Stock Crashes and Jumps Reactions to Information Demand and Supply: An Intraday Analysis," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(3), pages 397-427, September.
    11. Agudelo, Diego A. & Giraldo, Santiago & Villarraga, Edwin, 2015. "Does PIN measure information? Informed trading effects on returns and liquidity in six emerging markets," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 149-161.
    12. Tantaopas, Parkpoom & Padungsaksawasdi, Chaiyuth & Treepongkaruna, Sirimon, 2016. "Attention effect via internet search intensity in Asia-Pacific stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 38(C), pages 107-124.
    13. Thomas Pöppe & Michael Aitken & Dirk Schiereck & Ingo Wiegand, 2016. "A PIN per day shows what news convey: the intraday probability of informed trading," Review of Quantitative Finance and Accounting, Springer, vol. 47(4), pages 1187-1220, November.
    14. Lamoureux, Christopher G. & Wang, Qin, 2015. "Measuring private information in a specialist market," Journal of Empirical Finance, Elsevier, vol. 30(C), pages 92-119.
    15. Lof, Matthijs & Bommel, Jos van, 2018. "Asymmetric information and the distribution of trading volume," Research Discussion Papers 1, Bank of Finland.
    16. repec:zbw:bofrdp:001 is not listed on IDEAS
    17. Fan, Xiaoqian & Yuan, Ying & Zhuang, Xintian & Jin, Xiu, 2017. "Long memory of abnormal investor attention and the cross-correlations between abnormal investor attention and trading volume, volatility respectively," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 323-333.
    18. Aouadi, Amal & Arouri, Mohamed & Roubaud, David, 2018. "Information demand and stock market liquidity: International evidence," Economic Modelling, Elsevier, vol. 70(C), pages 194-202.
    19. Papadamou, Stephanos & Fassas, Athanasios & Kenourgios, Dimitris & Dimitriou, Dimitrios, 2020. "Direct and Indirect Effects of COVID-19 Pandemic on Implied Stock Market Volatility: Evidence from Panel Data Analysis," MPRA Paper 100020, University Library of Munich, Germany.
    20. Kim, Neri & Lučivjanská, Katarína & Molnár, Peter & Villa, Roviel, 2019. "Google searches and stock market activity: Evidence from Norway," Finance Research Letters, Elsevier, vol. 28(C), pages 208-220.
    21. Gao, Yang & Zhao, Kun & Wang, Chao & Liu, Chao, 2020. "The dynamic relationship between internet attention and stock market liquidity: A thermal optimal path method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 550(C).

    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:510:y:2018:i:c:p:802-811. 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.