IDEAS home Printed from https://ideas.repec.org/a/eee/pacfin/v85y2024ics0927538x24000684.html
   My bibliography  Save this article

Mobile device use and the ranking effect on trading behavior: Evidence from natural experiments

Author

Listed:
  • Wu, Haibo
  • Wu, Chongfeng

Abstract

This paper investigates how the trading behavior of investors using mobile devices differs from that of investors using PCs in the context of limited attention. We hypothesize that mobile device-based trading is associated with a stronger “ranking effect” that investors disproportionally trade top-ranked stocks. By exploiting the exogeneity of the return-based ranking of price limit events, we examine the ranking effect on retail investors' buying behavior and find supportive evidence that (1) based on the absolute returns of price limit events, top-ranked stocks are more heavily bought than the lower ranks by investors using mobile devices, while there is no significant ranking effect for investors using PCs; and (2) the ranking effect is enhanced with the increase of the number of contemporaneous price limit events.

Suggested Citation

  • Wu, Haibo & Wu, Chongfeng, 2024. "Mobile device use and the ranking effect on trading behavior: Evidence from natural experiments," Pacific-Basin Finance Journal, Elsevier, vol. 85(C).
    • Handle: RePEc:eee:pacfin:v:85:y:2024:i:c:s0927538x24000684
    DOI: 10.1016/j.pacfin.2024.102317
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0927538X24000684
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.pacfin.2024.102317?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. Anindya Ghose & Avi Goldfarb & Sang Pil Han, 2013. "How Is the Mobile Internet Different? Search Costs and Local Activities," Information Systems Research, INFORMS, vol. 24(3), pages 613-631, September.
    2. Chen, Yong & Kelly, Bryan & Wu, Wei, 2020. "Sophisticated investors and market efficiency: Evidence from a natural experiment," Journal of Financial Economics, Elsevier, vol. 138(2), pages 316-341.
    3. Nicholas Barberis & Ming Huang, 2008. "Stocks as Lotteries: The Implications of Probability Weighting for Security Prices," American Economic Review, American Economic Association, vol. 98(5), pages 2066-2100, December.
    4. Li Liao & Zhengwei Wang & Jia Xiang & Hongjun Yan & Jun Yang & LaurenCohen, 2021. "User Interface and Firsthand Experience in Retail Investing," The Review of Financial Studies, Society for Financial Studies, vol. 34(9), pages 4486-4523.
    5. Brown, T. & Grant, Stephanie M. & Winn, Amanda M., 2020. "The effect of mobile device use and headline focus on investor judgments," Accounting, Organizations and Society, Elsevier, vol. 83(C).
    6. Heiko Jacobs & Alexander Hillert, 2016. "Alphabetic Bias, Investor Recognition, and Trading Behavior," Review of Finance, European Finance Association, vol. 20(2), pages 693-723.
    7. Peng, Lin & Xiong, Wei, 2006. "Investor attention, overconfidence and category learning," Journal of Financial Economics, Elsevier, vol. 80(3), pages 563-602, June.
    8. 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.
    9. Stephanie M. Grant, 2020. "How Does Using a Mobile Device Change Investors’ Reactions to Firm Disclosures?," Journal of Accounting Research, Wiley Blackwell, vol. 58(3), pages 741-775, June.
    10. Sims, Christopher A., 2003. "Implications of rational inattention," Journal of Monetary Economics, Elsevier, vol. 50(3), pages 665-690, April.
    11. Song Yao & Carl F. Mela, 2011. "A Dynamic Model of Sponsored Search Advertising," Marketing Science, INFORMS, vol. 30(3), pages 447-468, 05-06.
    12. John M. Griffin & Federico Nardari & René M. Stulz, 2007. "Do Investors Trade More When Stocks Have Performed Well? Evidence from 46 Countries," The Review of Financial Studies, Society for Financial Studies, vol. 20(3), pages 905-951.
    13. Barrot, Jean-Noel & Kaniel, Ron & Sraer, David, 2016. "Are retail traders compensated for providing liquidity?," Journal of Financial Economics, Elsevier, vol. 120(1), pages 146-168.
    14. Li, Xindan & Geng, Ziyang & Subrahmanyam, Avanidhar & Yu, Honghai, 2017. "Do wealthy investors have an informational advantage? Evidence based on account classifications of individual investors," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 1-18.
    15. Chen, Ting & Gao, Zhenyu & He, Jibao & Jiang, Wenxi & Xiong, Wei, 2019. "Daily price limits and destructive market behavior," Journal of Econometrics, Elsevier, vol. 208(1), pages 249-264.
    16. Samuel M. Hartzmark, 2015. "The Worst, the Best, Ignoring All the Rest: The Rank Effect and Trading Behavior," The Review of Financial Studies, Society for Financial Studies, vol. 28(4), pages 1024-1059.
    17. Nerissa C. Brown & Han Stice & Roger M. White, 2015. "Mobile Communication and Local Information Flow: Evidence from Distracted Driving Laws," Journal of Accounting Research, Wiley Blackwell, vol. 53(2), pages 275-329, May.
    18. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," The Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    19. Bogan, Vicki, 2008. "Stock Market Participation and the Internet," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 43(1), pages 191-211, March.
    20. Brad M. Barber & Terrance Odean, 2002. "Online Investors: Do the Slow Die First?," The Review of Financial Studies, Society for Financial Studies, vol. 15(2), pages 455-488, March.
    21. Li, Zhuolei & Diao, Xundi & Wu, Chongfeng, 2022. "The influence of mobile trading on return dispersion and herding behavior," Pacific-Basin Finance Journal, Elsevier, vol. 73(C).
    22. Seasholes, Mark S. & Wu, Guojun, 2007. "Predictable behavior, profits, and attention," Journal of Empirical Finance, Elsevier, vol. 14(5), pages 590-610, December.
    23. An, Li & Lou, Dong & Shi, Donghui, 2022. "Wealth redistribution in bubbles and crashes," LSE Research Online Documents on Economics 113766, London School of Economics and Political Science, LSE Library.
    24. Eric K. Kelley & Paul C. Tetlock, 2013. "How Wise Are Crowds? Insights from Retail Orders and Stock Returns," Journal of Finance, American Finance Association, vol. 68(3), pages 1229-1265, June.
    25. Terrance Odean, 1999. "Do Investors Trade Too Much?," American Economic Review, American Economic Association, vol. 89(5), pages 1279-1298, December.
    26. Deb, Saikat Sovan & Kalev, Petko S. & Marisetty, Vijaya B., 2010. "Are price limits really bad for equity markets?," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2462-2471, October.
    27. Anindya Ghose & Sha Yang, 2009. "An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets," Management Science, INFORMS, vol. 55(10), pages 1605-1622, October.
    Full references (including those not matched with items on IDEAS)

    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. Dierick, Nicolas & Heyman, Dries & Inghelbrecht, Koen & Stieperaere, Hannes, 2019. "Financial attention and the disposition effect," Journal of Economic Behavior & Organization, Elsevier, vol. 163(C), pages 190-217.
    2. Cai, Wenwu & Lu, Jing, 2019. "Investors’ financial attention frequency and trading activity," Pacific-Basin Finance Journal, Elsevier, vol. 58(C).
    3. Francisco Gomes & Michael Haliassos & Tarun Ramadorai, 2021. "Household Finance," Journal of Economic Literature, American Economic Association, vol. 59(3), pages 919-1000, September.
    4. Fernando Chague & Bruno Giovannetti & Bernardo Guimaraes, 2021. "The Contrarian Put," Discussion Papers 2106, Centre for Macroeconomics (CFM).
    5. David Hirshleife, 2015. "Behavioral Finance," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 133-159, December.
    6. Brad M. Barber & Xing Huang & Terrance Odean & Christopher Schwarz, 2022. "Attention‐Induced Trading and Returns: Evidence from Robinhood Users," Journal of Finance, American Finance Association, vol. 77(6), pages 3141-3190, December.
    7. Brown, Nerissa C. & Elliott, W. Brooke & Wermers, Russ & White, Roger M., 2021. "News or noise: Mobile internet technology and stock market activity," CFR Working Papers 21-10, University of Cologne, Centre for Financial Research (CFR).
    8. Cai, Haidong & Jiang, Ying & Liu, Xiaoquan, 2022. "Investor attention, aggregate limit-hits, and stock returns," International Review of Financial Analysis, Elsevier, vol. 83(C).
    9. Huang, Shiyang & Huang, Yulin & Lin, Tse-Chun, 2019. "Attention allocation and return co-movement: Evidence from repeated natural experiments," Journal of Financial Economics, Elsevier, vol. 132(2), pages 369-383.
    10. Turan G. Bali & Robert F. Engle & Yi Tang, 2017. "Dynamic Conditional Beta Is Alive and Well in the Cross Section of Daily Stock Returns," Management Science, INFORMS, vol. 63(11), pages 3760-3779, November.
    11. Pagano, Michael S. & Sedunov, John & Velthuis, Raisa, 2021. "How did retail investors respond to the COVID-19 pandemic? The effect of Robinhood brokerage customers on market quality," Finance Research Letters, Elsevier, vol. 43(C).
    12. Wang, Albert Y. & Young, Michael, 2023. "Mood, attention, and household trading: Evidence from terrorist attacks," Journal of Financial Markets, Elsevier, vol. 66(C).
    13. Jakusch, Sven Thorsten & Meyer, Steffen & Hackethal, Andreas, 2019. "Taming models of prospect theory in the wild? Estimation of Vlcek and Hens (2011)," SAFE Working Paper Series 146, Leibniz Institute for Financial Research SAFE, revised 2019.
    14. Andreas Oehler & Julian Schneider, 2023. "Social trading: do signal providers trigger gambling?," Review of Managerial Science, Springer, vol. 17(4), pages 1269-1331, May.
    15. repec:acb:cbeeco:2023-698 is not listed on IDEAS
    16. Guomei Tang & Xueyong Zhang, 2021. "Media attention to locations and the cross‐section of stock returns," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 61(S1), pages 2301-2336, April.
    17. Zhibing Li & Jie Liu & Xiaoyu Liu & Chonglin Wu, 2024. "Investor attention and stock price efficiency: Evidence from quasi‐natural experiments in China," Financial Management, Financial Management Association International, vol. 53(1), pages 175-225, March.
    18. Guiso, Luigi & Sodini, Paolo, 2013. "Household Finance: An Emerging Field," Handbook of the Economics of Finance, in: G.M. Constantinides & M. Harris & R. M. Stulz (ed.), Handbook of the Economics of Finance, volume 2, chapter 0, pages 1397-1532, Elsevier.
    19. Chen, Xing & Wu, Chongfeng, 2022. "Retail investor attention and information asymmetry: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 75(C).
    20. Hwang, Yoontae & Park, Junpyo & Kim, Jang Ho & Lee, Yongjae & Fabozzi, Frank J., 2024. "Heterogeneous trading behaviors of individual investors: A deep clustering approach," Finance Research Letters, Elsevier, vol. 65(C).
    21. Kent Daniel & David Hirshleifer, 2015. "Overconfident Investors, Predictable Returns, and Excessive Trading," Journal of Economic Perspectives, American Economic Association, vol. 29(4), pages 61-88, Fall.

    More about this item

    Keywords

    Mobile technology; Screen size; Ranking effect; Limited attention; Price limit events;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

    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:eee:pacfin:v:85:y:2024:i:c:s0927538x24000684. 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.elsevier.com/locate/pacfin .

    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.