IDEAS home Printed from https://ideas.repec.org/a/inm/orisre/v36y2025i2p828-846.html

Mobile Apps, Trading Behaviors, and Portfolio Performance: Evidence from a Quasi-Experiment in China

Author

Listed:
  • Che-Wei Liu

    (W. P. Carey School of Business, Arizona State University, Tempe, Arizona 85287)

  • Sunil Mithas

    (Muma College of Business, University of South Florida, Tampa, Florida 33620)

  • Yang Pan

    (A.B. Freeman School of Business, Tulane University, New Orleans, Louisiana 70118)

  • J. J. Po-An Hsieh

    (Robinson College of Business, Georgia State University, Atlanta, Georgia 30303)

Abstract

Mobile apps are among the most important and widely used financial technology (fintech) innovations in the brokerage industry. Surprisingly, despite their increasing economic importance and theoretical significance, few studies examine the effects of mobile app use on individual investors’ financial decisions and performance. This study seeks to understand how mobile apps influence investors’ trading behaviors and portfolio performance by using a proprietary longitudinal data set from December 2012 to November 2015 from a large securities company in China with a quasi-experimental setting to answer our research questions. We leverage the introduction of an app to identify the effect of mobile app adoption by using a sample of 20,665 investors. We use the generalized synthetic control method and find that mobile app adoption does not affect investors’ portfolio performance when one examines aggregate impacts using a binary indicator of mobile app use. Our analyses of the mechanisms indicate that adopting mobile apps results in a noticeable decrease in time constraints, a proxy for transaction friction, and a modest increase intrend-chasing bias, reflecting tendencies toward myopic decision making. Because the reduction in time constraints can benefit investors’ performance, the increase in trend-chasing can be detrimental to investors’ performance; our findings explain why mobile app adoption has no overall effect on portfolio performance. Further analyses of adopters’ postadoption behaviors provide interesting insights and show that the mobile app usage intensity has an inverted U–shaped relationship with portfolio performance. The results are robust to using different samples or excluding high market volatility periods and by using a variety of methods, such as propensity score matching, dynamic matching, stacked difference in differences, or an instrumental variable approach. We discuss the implications for research and practice.

Suggested Citation

  • Che-Wei Liu & Sunil Mithas & Yang Pan & J. J. Po-An Hsieh, 2025. "Mobile Apps, Trading Behaviors, and Portfolio Performance: Evidence from a Quasi-Experiment in China," Information Systems Research, INFORMS, vol. 36(2), pages 828-846, June.
  • Handle: RePEc:inm:orisre:v:36:y:2025:i:2:p:828-846
    DOI: 10.1287/isre.2020.0616
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/isre.2020.0616
    Download Restriction: no

    File URL: https://libkey.io/10.1287/isre.2020.0616?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. Arthur Lewbel, 2012. "Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 67-80.
    2. Jiban Khuntia & Raveesh Mayya & Sunil Mithas & Ritu Agarwal, 2021. "Managing Cellphone Services for Customer Satisfaction: Evidence from the Base‐of‐the‐Pyramid Markets," Production and Operations Management, Production and Operations Management Society, vol. 30(2), pages 438-450, February.
    3. Cai, Wenwu & Lu, Jing, 2019. "Investors’ financial attention frequency and trading activity," Pacific-Basin Finance Journal, Elsevier, vol. 58(C).
    4. Ramon P. DeGennaro & Cesare Robotti, 2007. "Financial market frictions," Economic Review, Federal Reserve Bank of Atlanta, vol. 92(Q 3), pages 1-16.
    5. Dumitrescu, Ariadna & Gil-Bazo, Javier, 2018. "Market frictions, investor sophistication, and persistence in mutual fund performance," Journal of Financial Markets, Elsevier, vol. 40(C), pages 40-59.
    6. Daniel Dorn & Gur Huberman & Paul Sengmueller, 2008. "Correlated Trading and Returns," Journal of Finance, American Finance Association, vol. 63(2), pages 885-920, April.
    7. 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.
    8. Shu He & Jing Peng & Jianbin Li & Liping Xu, 2020. "Impact of Platform Owner’s Entry on Third-Party Stores," Information Systems Research, INFORMS, vol. 31(4), pages 1467-1484, December.
    9. Gervais, Simon & Odean, Terrance, 2001. "Learning to be Overconfident," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 1-27.
    10. Ruyi Ge & Zhiqiang (Eric) Zheng & Xuan Tian & Li Liao, 2021. "Human–Robot Interaction: When Investors Adjust the Usage of Robo-Advisors in Peer-to-Peer Lending," Information Systems Research, INFORMS, vol. 32(3), pages 774-785, September.
    11. Francesco D’Acunto & Nagpurnanand Prabhala & Alberto G Rossi, 2019. "The Promises and Pitfalls of Robo-Advising," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 1983-2020.
    12. 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.
    13. Xinshu Zhao & John G. Lynch & Qimei Chen, 2010. "Reconsidering Baron and Kenny: Myths and Truths about Mediation Analysis," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 37(2), pages 197-206, August.
    14. Baker, Andrew C. & Larcker, David F. & Wang, Charles C.Y., 2022. "How much should we trust staggered difference-in-differences estimates?," Journal of Financial Economics, Elsevier, vol. 144(2), pages 370-395.
    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. Doruk Cengiz & Arindrajit Dube & Attila Lindner & Ben Zipperer, 2019. "The Effect of Minimum Wages on Low-Wage Jobs," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(3), pages 1405-1454.
    17. Adrian F. Ward & Kristen Duke & Ayelet Gneezy & Maarten W. Bos, 2017. "Brain Drain: The Mere Presence of One’s Own Smartphone Reduces Available Cognitive Capacity," Journal of the Association for Consumer Research, University of Chicago Press, vol. 2(2), pages 140-154.
    18. Jo Thori Lind & Halvor Mehlum, 2010. "With or Without U? The Appropriate Test for a U‐Shaped Relationship," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 72(1), pages 109-118, February.
    19. Alexander Kurov, 2008. "Investor Sentiment, Trading Behavior and Informational Efficiency in Index Futures Markets," The Financial Review, Eastern Finance Association, vol. 43(1), pages 107-127, February.
    20. Xu, Yiqing, 2017. "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models," Political Analysis, Cambridge University Press, vol. 25(1), pages 57-76, January.
    21. Hirshleifer, David & Teoh, Siew Hong, 2003. "Limited attention, information disclosure, and financial reporting," Journal of Accounting and Economics, Elsevier, vol. 36(1-3), pages 337-386, December.
    22. Peter O. Dietz, 1968. "Components Of A Measurement Model: Rate Of Return, Risk, And Timing," Journal of Finance, American Finance Association, vol. 23(2), pages 267-275, May.
    23. Shlomo Benartzi & Richard H. Thaler, 1995. "Myopic Loss Aversion and the Equity Premium Puzzle," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 73-92.
    24. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    25. Anders Anderson, 2013. "Trading and Under-Diversification," Review of Finance, European Finance Association, vol. 17(5), pages 1699-1741.
    26. Dongwon Lee & Anandasivam Gopal & Sung-Hyuk Park, 2020. "Different but Equal? A Field Experiment on the Impact of Recommendation Systems on Mobile and Personal Computer Channels in Retail," Information Systems Research, INFORMS, vol. 31(3), pages 892-912, September.
    27. Marc M. Kramer, 2012. "Financial Advice and Individual Investor Portfolio Performance," Financial Management, Financial Management Association International, vol. 41(2), pages 395-428, June.
    28. Perraudin, William R. M. & Sorensen, Bent E., 2000. "The demand for risky assets: Sample selection and household portfolios," Journal of Econometrics, Elsevier, vol. 97(1), pages 117-144, July.
    29. 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.
    30. Lorin M. Hitt & Frances X. Frei, 2002. "Do Better Customers Utilize Electronic Distribution Channels? The Case of PC Banking," Management Science, INFORMS, vol. 48(6), pages 732-748, June.
    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. Dhammika Dharmapala & Marvin Suesse, 2025. "Decolonization, Legitimacy and Fiscal Capacity: Event Study Evidence from Africa," CESifo Working Paper Series 12059, CESifo.
    2. David Hirshleife, 2015. "Behavioral Finance," Annual Review of Financial Economics, Annual Reviews, vol. 7(1), pages 133-159, December.
    3. Che‐Wei Liu & Mochen Yang & Ming‐Hui Wen, 2023. "Judge me on my losers: Do robo‐advisors outperform human investors during the COVID‐19 financial market crash?," Production and Operations Management, Production and Operations Management Society, vol. 32(10), pages 3174-3192, October.
    4. Hoffmann, Arvid O.I. & Shefrin, Hersh, 2014. "Technical analysis and individual investors," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PB), pages 487-511.
    5. Wang, Yimin, 2025. "Links between COVID-19 lockdowns and drug overdose deaths, evidence from panel data," Economics & Human Biology, Elsevier, vol. 58(C).
    6. Bellofatto, Anthony & Broihanne, Marie-Hélène & D'Hondt, Catherine, 2019. "Appetite for information and trading behavior," LIDAM Discussion Papers LFIN 2019002, Université catholique de Louvain, Louvain Finance (LFIN).
    7. Benjamin Hansen & Drew McNichols, 2020. "Information and the Persistence of the Gender Wage Gap: Early Evidence from California's Salary History Ban," NBER Working Papers 27054, National Bureau of Economic Research, Inc.
    8. Kim, Sehoon & Kumar, Nitish & Lee, Jongsub & Oh, Junho, 2025. "ESG lending," Journal of Financial Economics, Elsevier, vol. 173(C).
    9. Luo, Shiyue & Lu, Mingyue & Ye, Jinhui & Guo, Yuying & Hao, Yu, 2025. "Nurturing finance and harvesting intelligence: The green growth of urban industrial intelligence fueled by green finance," Research in International Business and Finance, Elsevier, vol. 80(C).
    10. Yidi Liu & Xin Li & Zhiqiang (Eric) Zheng, 2024. "Consequences of China’s 2018 Online Lending Regulation and the Promise of PolicyTech," Information Systems Research, INFORMS, vol. 35(3), pages 1235-1256, September.
    11. Wei Li, 2025. "Investor-Paid Credit Ratings and Managerial Information Disclosure," Management Science, INFORMS, vol. 71(3), pages 2142-2169, March.
    12. 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.
    13. Cai, Wenwu & Lu, Jing, 2019. "Investors’ financial attention frequency and trading activity," Pacific-Basin Finance Journal, Elsevier, vol. 58(C).
    14. Lü, Yiqing & Zhao, Bin & Zhu, Ning, 2024. "Unveiling investors' substitution behavior: Stock trading decisions in response to housing market dynamics," Journal of Corporate Finance, Elsevier, vol. 86(C).
    15. Guo, Xue & Cheng, Aaron & Pavlou, Paul A., 2025. "Skill-biased technical change, again? Online gig platforms and local employment," LSE Research Online Documents on Economics 124538, London School of Economics and Political Science, LSE Library.
    16. Zabinski, Zenon & Black, Bernard S., 2022. "The deterrent effect of tort law: Evidence from medical malpractice reform," Journal of Health Economics, Elsevier, vol. 84(C).
    17. Rafaty, Ryan & Dolphin, Geoffroy & Pretis, Felix, 2025. "Carbon pricing and the elasticity of CO2 emissions," Energy Economics, Elsevier, vol. 144(C).
    18. Duong, Huu Nhan & Goyal, Abhinav & Zolotoy, Leon, 2024. "Anti-collusion leniency legislations and IPO activity: Worldwide evidence," Journal of Corporate Finance, Elsevier, vol. 89(C).
    19. Ryan Rafaty & Geoffroy Dolphin & Felix Pretis, 2020. "Carbon pricing and the elasticity of CO2 emissions," Working Papers EPRG2035, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    20. Roth, Jonathan & Sant’Anna, Pedro H.C. & Bilinski, Alyssa & Poe, John, 2023. "What’s trending in difference-in-differences? A synthesis of the recent econometrics literature," Journal of Econometrics, Elsevier, vol. 235(2), pages 2218-2244.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:inm:orisre:v:36:y:2025:i:2:p:828-846. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    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.