IDEAS home Printed from https://ideas.repec.org/a/spr/fininn/v3y2017i1d10.1186_s40854-017-0056-y.html
   My bibliography  Save this article

Internet big data and capital markets: a literature review

Author

Listed:
  • Minjian Ye

    (Sun Yat-Sen University)

  • Guangzhong Li

    (Sun Yat-Sen University)

Abstract

Background Research in various academic disciplines has undergone tremendous changes in the era of big data. Everyone is talking about big data nowadays, but how exactly is it being applied in research on financial studies? Results This study summarizes the sources of Internet big data for research related to capital markets and the analytical methods that have been used in the literature. In addition, it presents a review of the research findings based on Internet big data in the field of capital markets and proposes suggestions for future studies in which big data can be applied to examine issues related to capital markets. Conclusion (1) Internet big data sources related to present capital market research can be categorized into forum-type data, microblog-type data and search class data. (2) As for research about investors’ sentiments on the basis of Internet big data, the main methods of sentiment analysis include building an inventory of lexical categories, using dictionaries for analysis of lexical categories, and machine learning. (3) Many studies address whether Internet big data can predict capital markets. However, they reach no consistent conclusions, which could be due to limitations of sample and analysis method used. (4) Data collection technique and analysis methods require further improvements.

Suggested Citation

  • Minjian Ye & Guangzhong Li, 2017. "Internet big data and capital markets: a literature review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 3(1), pages 1-18, December.
    • Handle: RePEc:spr:fininn:v:3:y:2017:i:1:d:10.1186_s40854-017-0056-y
    DOI: 10.1186/s40854-017-0056-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1186/s40854-017-0056-y
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1186/s40854-017-0056-y?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. Ying Zhang & Peggy Swanson, 2010. "Are day traders bias free?—evidence from internet stock message boards," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 34(1), pages 96-112, January.
    2. 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.
    3. Zhi Da & Joseph Engelberg & Pengjie Gao, 2015. "Editor's Choice The Sum of All FEARS Investor Sentiment and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 28(1), pages 1-32.
    4. Siganos, Antonios, 2013. "Google attention and target price run ups," International Review of Financial Analysis, Elsevier, vol. 29(C), pages 219-226.
    5. Huang, Yuqin & Qiu, Huiyan & Wu, Zhiguo, 2016. "Local bias in investor attention: Evidence from China's Internet stock message boards," Journal of Empirical Finance, Elsevier, vol. 38(PA), pages 338-354.
    6. Michela Nardo & Marco Petracco-Giudici & Minás Naltsidis, 2016. "Walking Down Wall Street With A Tablet: A Survey Of Stock Market Predictions Using The Web," Journal of Economic Surveys, Wiley Blackwell, vol. 30(2), pages 356-369, April.
    7. Sanjiv Das & Asís Martínez-Jerez & Peter Tufano, 2005. "eInformation: A Clinical Study of Investor Discussion and Sentiment," Financial Management, Financial Management Association, vol. 34(3), Fall.
    8. Ackert, Lucy F. & Jiang, Lei & Lee, Hoan Soo & Liu, Jie, 2016. "Influential investors in online stock forums," International Review of Financial Analysis, Elsevier, vol. 45(C), pages 39-46.
    9. 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.
    10. Leung, Henry & Ton, Thai, 2015. "The impact of internet stock message boards on cross-sectional returns of small-capitalization stocks," Journal of Banking & Finance, Elsevier, vol. 55(C), pages 37-55.
    11. 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.
    12. Kim, Soon-Ho & Kim, Dongcheol, 2014. "Investor sentiment from internet message postings and the predictability of stock returns," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 708-729.
    13. Blankespoor, Elizabeth & Miller, Gregory S. & White, Hal D., 2013. "The Role of Dissemination in Market Liquidity: Evidence from Firms' Use of Twitter," Research Papers 2106r, Stanford University, Graduate School of Business.
    14. Joseph, Kissan & Babajide Wintoki, M. & Zhang, Zelin, 2011. "Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1116-1127, October.
    15. Brad M. Barber & Terrance Odean, 2001. "The Internet and the Investor," Journal of Economic Perspectives, American Economic Association, vol. 15(1), pages 41-54, Winter.
    16. 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.
    17. Sanjiv Sabherwal & Salil K. Sarkar & Ying Zhang, 2011. "Do Internet Stock Message Boards Influence Trading? Evidence from Heavily Discussed Stocks with No Fundamental News," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 38(9-10), pages 1209-1237, November.
    18. repec:bla:jfinan:v:59:y:2004:i:3:p:1259-1294 is not listed on IDEAS
    19. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
    20. 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.
    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. Muhammad Ateeq ur REHMAN & Furman ALI & Shang XIE, 2022. "Impact of Foreign Investment News on the Return, Cost of Equity and Cash Flow Activities," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 112-127, December.
    2. Jihong Xiao & Xuehong Zhu & Chuangxia Huang & Xiaoguang Yang & Fenghua Wen & Meirui Zhong, 2019. "A New Approach for Stock Price Analysis and Prediction Based on SSA and SVM," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 287-310, January.
    3. Mu-En Wu & Wei-Ho Chung, 2019. "Empirical Evaluations on Momentum Effects of Taiwan Index Futures via Stop-Loss and Stop-Profit Mechanisms," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 629-648, March.
    4. Darabseh Mohammad & Martins João Poças, 2023. "Protecting the intellectual property of built environment designs using blockchain technology," Organization, Technology and Management in Construction, Sciendo, vol. 15(1), pages 157-168, January.
    5. Ning Wang & Shanhui Ke & Yibo Chen & Tao Yan & Andrew Lim, 2019. "Textual Sentiment of Chinese Microblog Toward the Stock Market," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 649-671, March.
    6. Manik Sharma & Samriti Sharma & Gurvinder Singh, 2018. "Performance Analysis of Statistical and Supervised Learning Techniques in Stock Data Mining," Data, MDPI, vol. 3(4), pages 1-16, November.

    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. 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.
    2. Xiong, Xiong & Meng, Yongqiang & Joseph, Nathan Lael & Shen, Dehua, 2020. "Stock mispricing, hard-to-value stocks and the influence of internet stock message boards," International Review of Financial Analysis, Elsevier, vol. 72(C).
    3. 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.
    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. 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).
    6. Desagre, Christophe & D’Hondt, Catherine, 2021. "Googlization and retail trading activity," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    7. Takayuki Morimoto & Yoshinori Kawasaki, 2017. "Forecasting Financial Market Volatility Using a Dynamic Topic Model," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 24(3), pages 149-167, September.
    8. Yongqiang Meng & Dehua Shen & Xiong Xiong & Jorgen Vitting Andersen, 2020. "A Socio-Finance Model: The Case of Bitcoin," Documents de travail du Centre d'Economie de la Sorbonne 20031, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    9. Christophe Desagre & Catherine D'Hondt, 2020. "Googlization and retail investors' trading activity," LIDAM Discussion Papers LFIN 2020004, Université catholique de Louvain, Louvain Finance (LFIN).
    10. Reyes, Tomas, 2018. "Limited attention and M&A announcements," Journal of Empirical Finance, Elsevier, vol. 49(C), pages 201-222.
    11. 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).
    12. 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.
    13. 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.
    14. Eierle, Brigitte & Klamer, Sebastian & Muck, Matthias, 2022. "Does it really pay off for investors to consider information from social media?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    15. Qing Liu & Hosung Son, 2024. "Data selection and collection for constructing investor sentiment from social media," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    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. Moussa, Faten & BenOuda, Olfa & Delhoumi, Ezzeddine, 2017. "The use of open source internet to analysis and predict stock market trading volume," Research in International Business and Finance, Elsevier, vol. 41(C), pages 399-411.
    19. Nattapong Laksomya & John G. Powell & Suparatana Tanthanongsakkun & Sirimon Treepongkaruna, 2018. "Are Internet message boards used to facilitate stock price manipulation? Evidence from an emerging market, Thailand," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 58(S1), pages 275-309, November.
    20. González-Fernández, Marcos & González-Velasco, Carmen, 2020. "A sentiment index to measure sovereign risk using Google data," International Review of Economics & Finance, Elsevier, vol. 69(C), pages 406-418.

    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:fininn:v:3:y:2017:i:1:d:10.1186_s40854-017-0056-y. 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.