skip to main content
10.1145/3461778.3462008acmconferencesArticle/Chapter ViewAbstractPublication PagesdisConference Proceedingsconference-collections
research-article
Open Access

Design Patterns of Investing Apps and Their Effects on Investing Behaviors

Authors Info & Claims
Published:28 June 2021Publication History

ABSTRACT

Smartphone apps such as Robinhood and Public that promise to “democratize investing” have risen in popularity over the past few years. These apps allow retail investors, who often possess little prior investing experience, to trade stocks, options, and other securities easily and inexpensively, often commission-free. It seems plausible that the interaction patterns of these new apps may significantly influence trading behaviors of their users. But so far, there is little formal design guidance on how such apps should be designed. This paper introduces a set of design guidelines for encouraging healthy investing behaviors by drawing on three bodies of related work: 1) findings from finance and economics literature on healthy investment practices, 2) the dual process theory from behavioral sciences, and 3) design metaphors used in interfaces with uncertain rewards. Using these guidelines, we qualitatively analyze the user interfaces of some popular investment platforms. Our analysis reveal that, unfortunately, popular trading apps generally do not follow design patterns that encourage healthier trading behaviors. We discuss design implications and opportunities for future design.

References

  1. [n.d.]. Our Principles. https://ai.google/principles/Google ScholarGoogle Scholar
  2. [n.d.]. Tenets. https://www.partnershiponai.org/tenets/Google ScholarGoogle Scholar
  3. Maqsood Ahmad, Syed Shah, and Faisal Mahmood. 2018. Heuristic biases in investment decision-making and perceived market efficiency: A survey at the Pakistan stock exchange. Qualitative Research in Financial Markets 10 (01 2018), 00–00. https://doi.org/10.1108/QRFM-04-2017-0033Google ScholarGoogle Scholar
  4. Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N Bennett, Kori Inkpen, 2019. Guidelines for human-AI interaction. In Proceedings of the 2019 chi conference on human factors in computing systems. 1–13.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Ioannis Arapakis, Xiao Bai, and B. Barla Cambazoglu. 2014. Impact of Response Latency on User Behavior in Web Search. In Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval(Gold Coast, Queensland, Australia) (SIGIR ’14). Association for Computing Machinery, New York, NY, USA, 103–112. https://doi.org/10.1145/2600428.2609627Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Chris Argyris. 1990. The dilemma of implementing controls: the case of managerial accounting. In Readings in accounting for management control. Springer, 669–680.Google ScholarGoogle Scholar
  7. Waqar Badshah, Shoaib Irshad, and Usman Hakam. 2016. Effect of Representativeness Bias on Investment Decision Making. (01 2016).Google ScholarGoogle Scholar
  8. Brad M Barber and Terrance Odean. 2013. The behavior of individual investors. In Handbook of the Economics of Finance. Vol. 2. Elsevier, 1533–1570.Google ScholarGoogle Scholar
  9. Brad M. Barber and Terrance Odean. 2015. Online Investors: Do the Slow Die First?The Review of Financial Studies 15, 2 (06 2015), 455–488. https://doi.org/10.1093/rfs/15.2.455 arXiv:https://academic.oup.com/rfs/article-pdf/15/2/455/24432450/150455.pdfGoogle ScholarGoogle Scholar
  10. Belén Barros Pena, Bailey Kursar, Rachel E. Clarke, Katie Alpin, Merlyn Holkar, and John Vines. 2021. ”Pick Someone Who Can Kick Your Ass” - Moneywork in Financial Third Party Access. Proc. ACM Hum.-Comput. Interact. 4, CSCW3, Article 218 (Jan. 2021), 28 pages. https://doi.org/10.1145/3432917Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Jamil Baz, Josh Davis, Cristian Fuenzalida, and Jerry Tsai. 2020. Method in the Madness: Bubbles, Trading, and Incentives. The Journal of Portfolio Management 46, 8 (2020), 27–33.Google ScholarGoogle ScholarCross RefCross Ref
  12. Christoph Bösch, Benjamin Erb, Frank Kargl, Henning Kopp, and Stefan Pfattheicher. 2016. Tales from the dark side: Privacy dark strategies and privacy dark patterns. Proceedings on Privacy Enhancing Technologies 2016, 4(2016), 237–254.Google ScholarGoogle ScholarCross RefCross Ref
  13. Michael Bossetta. 2018. The Digital Architectures of Social Media: Comparing Political Campaigning on Facebook, Twitter, Instagram, and Snapchat in the 2016 U.S. Election. Journalism & Mass Communication Quarterly 95, 2 (2018), 471–496. https://doi.org/10.1177/1077699018763307 arXiv:https://doi.org/10.1177/107769901876330Google ScholarGoogle ScholarCross RefCross Ref
  14. Ramzi Boussaidi. 2013. Representativeness Heuristic, Investor Sentiment and Overreaction to Accounting Earnings: The Case of the Tunisian Stock Market. Procedia - Social and Behavioral Sciences 81 (2013), 9 – 21. https://doi.org/10.1016/j.sbspro.2013.06.380 World Congress on Administrative and Political Sciences.Google ScholarGoogle ScholarCross RefCross Ref
  15. Joel Brockner, Jeff Greenberg, Audrey Brockner, Jenny Bortz, Jeanette Davy, and Carolyn Carter. 1986. Layoffs, equity theory, and work performance: Further evidence of the impact of survivor guilt. Academy of Management journal 29, 2 (1986), 373–384.Google ScholarGoogle ScholarCross RefCross Ref
  16. C.M. Brown. 1999. Human-computer Interface Design Guidelines. Intellect. https://books.google.com/books?id=b2FphAxqIYICGoogle ScholarGoogle Scholar
  17. Roger Burkhardt, Nicolas Hohn, and Chris Wigley. 2019. Leading your organization to responsible AI. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/leading-your-organization-to-responsible-aiGoogle ScholarGoogle Scholar
  18. Ryan Calo. 2013. Digital market manipulation. Geo. Wash. L. Rev. 82(2013), 995.Google ScholarGoogle Scholar
  19. Shelly Chaiken. 1980. Heuristic versus systematic information processing and the use of source versus message cues in persuasion.Journal of personality and social psychology 39, 5(1980), 752.Google ScholarGoogle Scholar
  20. Edward Chancellor. 1999. Devil take the hindmost: A history of financial speculation. (1999).Google ScholarGoogle Scholar
  21. Robert B Cialdini. 2009. Influence: Science and practice. Vol. 4. Pearson education Boston, MA.Google ScholarGoogle Scholar
  22. Michael Dickard. 2020. Cognitive Biases in Retail Investor Decision Making and HCI: A Research Agenda. (2020).Google ScholarGoogle Scholar
  23. Chris Elsden, Tom Feltwell, Shaun Lawson, and John Vines. 2019. Recipes for Programmable Money. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3290605.3300481Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Charles B Ferster and Burrhus Frederic Skinner. 1957. Schedules of reinforcement.(1957).Google ScholarGoogle Scholar
  25. William E Fruhan 1992. Diversification, the Capital Asset Pricing Model, and the Cost of Equity Capital. Case Problems in Finance(1992), 407–20.Google ScholarGoogle Scholar
  26. Markus Glaser and Torsten Walther. 2013. Run, Walk, or Buy? Financial Literacy, Dual-Process Theory, and Investment Behavior. SSRN Electronic Journal (03 2013). https://doi.org/10.2139/ssrn.2167270Google ScholarGoogle Scholar
  27. William N Goetzmann and Alok Kumar. 2008. Equity portfolio diversification. Review of Finance 12, 3 (2008), 433–463.Google ScholarGoogle ScholarCross RefCross Ref
  28. Benjamin Graham, David L. Dodd, and Warren Buffett. 2009. Security analysis: principles and technique. McGraw-Hill.Google ScholarGoogle Scholar
  29. Colin M Gray, Yubo Kou, Bryan Battles, Joseph Hoggatt, and Austin L Toombs. 2018. The dark (patterns) side of UX design. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. 1–14.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Mark Griffiths and Richard Wood. 2008. The psychology of lottery gambling. International Gambling Studies 1 (02 2008), 27–45. https://doi.org/10.1080/14459800108732286Google ScholarGoogle Scholar
  31. Mark Grinblatt and Matti Keloharju. 2009. Sensation seeking, overconfidence, and trading activity. The Journal of Finance 64, 2 (2009), 549–578.Google ScholarGoogle ScholarCross RefCross Ref
  32. Jon D Hanson and Douglas A Kysar. 1999. Taking behavioralism seriously: The problem of market manipulation. NYUL Rev. 74(1999), 630.Google ScholarGoogle Scholar
  33. Joseph Henrich, Steven J Heine, and Ara Norenzayan. 2010. The weirdest people in the world?Behavioral and brain sciences 33, 2-3 (2010), 61–83.Google ScholarGoogle Scholar
  34. Tory Hobson. 2018. Gamification in the Most Delightful Way. https://medium.com/pinch-pull-press/gamification-in-the-most-delightful-way-504caf72c1bcGoogle ScholarGoogle Scholar
  35. K. Höök. 2000. Steps to take before intelligent user interfaces become real. Interact. Comput. 12(2000), 409–426.Google ScholarGoogle ScholarCross RefCross Ref
  36. Eric Horvitz. 1999. Principles of mixed-initiative user interfaces. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems. 159–166.Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Jürgen Huber, Michael Kirchler, and Thomas Stöckl. 2010. The hot hand belief and the gambler’s fallacy in investment decisions under risk. Theory and decision 68, 4 (2010), 445–462.Google ScholarGoogle Scholar
  38. Lilly C Irani and M Six Silberman. 2013. Turkopticon: Interrupting worker invisibility in amazon mechanical turk. In Proceedings of the SIGCHI conference on human factors in computing systems. 611–620.Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Anthony Jameson, Bettina Berendt, Silvia Gabrielli, Federica Cena, Cristina Gena, Fabiana Vernero, and Katharina Reinecke. 2014. Choice architecture for human-computer interaction. Foundations and Trends in Human-Computer Interaction 7, 1–2(2014), 1–235.Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Paul Kiel Justin Elliott. 2019. Inside TurboTax’s 20-Year Fight to Stop Americans From Filing Their Taxes for Free. https://www.propublica.org/article/inside-turbotax-20-year-fight-to-stop-americans-from-filing-their-taxes-for-freeGoogle ScholarGoogle Scholar
  41. Daniel Kahneman and Amos Tversky. 1979. Prospect Theory: An Analysis of Decision under Risk. Econometrica 47, 2 (1979), 263–291. http://www.jstor.org/stable/1914185Google ScholarGoogle ScholarCross RefCross Ref
  42. Gabriel Katz, R Michael Alvarez, Ernesto Calvo, Marcelo Escolar, and Julia Pomares. 2011. Assessing the impact of alternative voting technologies on multi-party elections: Design features, heuristic processing and voter choice. Political Behavior 33, 2 (2011), 247–270.Google ScholarGoogle ScholarCross RefCross Ref
  43. Joseph Jofish Kaye, Mary McCuistion, Rebecca Gulotta, and David A. Shamma. 2014. Money Talks: Tracking Personal Finances. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Toronto, Ontario, Canada) (CHI ’14). Association for Computing Machinery, New York, NY, USA, 521–530. https://doi.org/10.1145/2556288.2556975Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Doron Kliger and Andrey Kudryavtsev. 2010. The Availability Heuristic and Investors’ Reaction to Company-Specific Events. Journal of Behavioral Finance 11, 1 (2010), 50–65. https://doi.org/10.1080/15427561003591116 arXiv:https://doi.org/10.1080/15427561003591116Google ScholarGoogle ScholarCross RefCross Ref
  45. Todd Kulesza, Margaret Burnett, Weng-Keen Wong, and Simone Stumpf. 2015. Principles of Explanatory Debugging to Personalize Interactive Machine Learning. In Proceedings of the 20th International Conference on Intelligent User Interfaces (Atlanta, Georgia, USA) (IUI ’15). Association for Computing Machinery, New York, NY, USA, 126–137. https://doi.org/10.1145/2678025.2701399Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Alok Kumar and Ravi Dhar. 2001. A Non-Random Walk Down the Main Street: Impact of Price Trends on Trading Decisions of Individual Investors. (07 2001).Google ScholarGoogle Scholar
  47. Ellen J Langer and Jane Roth. 1975. Heads I win, tails it’s chance: The illusion of control as a function of the sequence of outcomes in a purely chance task.Journal of personality and social psychology 32, 6(1975), 951.Google ScholarGoogle Scholar
  48. Josh Lerner, Antoinette Schoar, and Jialan Wang. 2008. Secrets of the academy: The drivers of university endowment success. Journal of Economic Perspectives 22, 3 (2008), 207–22.Google ScholarGoogle ScholarCross RefCross Ref
  49. Brian Y. Lim and Anind K. Dey. 2009. Assessing Demand for Intelligibility in Context-Aware Applications. In Proceedings of the 11th International Conference on Ubiquitous Computing (Orlando, Florida, USA) (UbiComp ’09). Association for Computing Machinery, New York, NY, USA, 195–204. https://doi.org/10.1145/1620545.1620576Google ScholarGoogle ScholarDigital LibraryDigital Library
  50. Dan Lockton. 2012. Cognitive biases, heuristics and decision-making in design for behaviour change. Heuristics and Decision-Making in Design for Behaviour Change (August 5, 2012) (2012).Google ScholarGoogle Scholar
  51. Sheng Luo, Bin Gu, Xingbiao Wang, and Zhaoquan Zhou. 2018. Online Compulsive Buying Behavior: The Mediating Role of Self-control and Negative Emotions. In Proceedings of the 2018 International Conference on Internet and e-Business. 65–69.Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Xueming Luo. 2005. How Does Shopping With Others Influence Impulsive Purchasing?Journal of Consumer Psychology 15, 4 (2005), 288–294. https://doi.org/10.1207/s15327663jcp1504_3Google ScholarGoogle Scholar
  53. Michael A. Madaio, Luke Stark, Jennifer Wortman Vaughan, and Hanna Wallach. 2020. Co-Designing Checklists to Understand Organizational Challenges and Opportunities around Fairness in AI. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI ’20). Association for Computing Machinery, New York, NY, USA, 1–14. https://doi.org/10.1145/3313831.3376445Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Reza S. Mahani and Allen M. Poteshman. 2008. Overreaction to stock market news and misevaluation of stock prices by unsophisticated investors: Evidence from the option market. Journal of Empirical Finance 15, 4 (2008), 635 – 655. https://doi.org/10.1016/j.jempfin.2007.11.001Google ScholarGoogle ScholarCross RefCross Ref
  55. Annie Massa and Sarah Ponczek. 2020. Robinhood’s Addictive App Made Trading a Pandemic Pastime. https://www.bloomberg.com/news/features/2020-10-22/how-robinhood-s-addictive-app-made-trading-a-covid-pandemic-pastimeGoogle ScholarGoogle Scholar
  56. Arunesh Mathur, Gunes Acar, Michael J Friedman, Elena Lucherini, Jonathan Mayer, Marshini Chetty, and Arvind Narayanan. 2019. Dark patterns at scale: Findings from a crawl of 11K shopping websites. Proceedings of the ACM on Human-Computer Interaction 3, CSCW(2019), 1–32.Google ScholarGoogle ScholarDigital LibraryDigital Library
  57. Stewart Mayhew. 1995. Implied Volatility. Financial Analysts Journal 51, 4 (1995), 8–20. https://doi.org/10.2469/faj.v51.n4.1916 arXiv:https://doi.org/10.2469/faj.v51.n4.1916Google ScholarGoogle ScholarCross RefCross Ref
  58. Margaret Mitchell, Simone Wu, Andrew Zaldivar, Parker Barnes, Lucy Vasserman, Ben Hutchinson, Elena Spitzer, Inioluwa Deborah Raji, and Timnit Gebru. 2019. Model Cards for Model Reporting. Proceedings of the Conference on Fairness, Accountability, and Transparency (Jan 2019). https://doi.org/10.1145/3287560.3287596Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. Janet Morrissey. 2017. With No Frills and No Commissions, Robinhood App Takes On Big Brokerages. https://www.nytimes.com/2017/02/18/business/robinhood-stock-trading-app.htmlGoogle ScholarGoogle Scholar
  60. David W Mullins. 1982. Does the Capital Asset Pricing Model Work?.Harvard Business Review.Google ScholarGoogle Scholar
  61. Zain Naqvi, Omer Farooq, Naheed Sultana, and Mariam Farooq. 2017. The impact of heuristics on investment decision and performance: Exploring multiple mediation mechanisms. Research in International Business and Finance 42 (07 2017). https://doi.org/10.1016/j.ribaf.2017.07.010Google ScholarGoogle Scholar
  62. Jakob Nielsen and Rolf Molich. 1990. Heuristic evaluation of user interfaces. In Proceedings of the SIGCHI conference on Human factors in computing systems. 249–256.Google ScholarGoogle ScholarDigital LibraryDigital Library
  63. Don Norman. 2004. Affordances and design. Unpublished article, available online at: http://www. jnd. org/dn. mss/affordances-and-design. html(2004).Google ScholarGoogle Scholar
  64. Don Norman. 2013. The design of everyday things: Revised and expanded edition. Basic books.Google ScholarGoogle Scholar
  65. Terrance Odean. 1998. Are investors reluctant to realize their losses?The Journal of finance 53, 5 (1998), 1775–1798.Google ScholarGoogle Scholar
  66. William Odom, Mark Selby, Abigail Sellen, David Kirk, Richard Banks, and Tim Regan. 2012. Photobox: On the Design of a Slow Technology. In Proceedings of the Designing Interactive Systems Conference (Newcastle Upon Tyne, United Kingdom) (DIS ’12). Association for Computing Machinery, New York, NY, USA, 665–668. https://doi.org/10.1145/2317956.2318055Google ScholarGoogle ScholarDigital LibraryDigital Library
  67. William Odom, Ron Wakkary, Jeroen Hol, Bram Naus, Pepijn Verburg, Tal Amram, and Amy Yo Sue Chen. 2019. Investigating slowness as a frame to design longer-term experiences with personal data: A field study of olly. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. 1–16.Google ScholarGoogle ScholarDigital LibraryDigital Library
  68. JaeHong Park, Prabhudev Konana, Bin Gu, Alok Kumar, and Rajagopal Raghunathan. 2010. Confirmation bias, overconfidence, and investment performance: Evidence from stock message boards. McCombs Research Paper Series No. IROM-07-10 (2010).Google ScholarGoogle Scholar
  69. Richard E Petty and John T Cacioppo. 2012. Communication and persuasion: Central and peripheral routes to attitude change. Springer Science & Business Media.Google ScholarGoogle Scholar
  70. Alberto Peña and Alina Gómez-Mejía. 2019. Effect of the anchoring and adjustment heuristic and optimism bias in stock market forecasts. Revista Finanzas y Politica Economica 11, 2 (Jul 2019), 383–405. https://search.proquest.com/scholarly-journals/effect-anchoring-adjustment-heuristic-optimism/docview/2438995769/se-2? accountid = 9902Google ScholarGoogle Scholar
  71. Jon Picoult. 2020. The Dark Side Of Customer Experience. https://www.forbes.com/sites/jonpicoult/2020/11/18/the-dark-side-of-customer-experience/?sh=2086717b59e2Google ScholarGoogle Scholar
  72. Nathaniel Popper. 2020. Robinhood Has Lured Young Traders, Sometimes With Devastating Results. https://www.nytimes.com/2020/07/08/technology/robinhood-risky-trading.htmlGoogle ScholarGoogle Scholar
  73. Emilee Rader, Kelley Cotter, and Janghee Cho. 2018. Explanations as Mechanisms for Supporting Algorithmic Transparency. Association for Computing Machinery, New York, NY, USA, 1–13. https://doi.org/10.1145/3173574.3173677Google ScholarGoogle ScholarDigital LibraryDigital Library
  74. Kate Rooney. 2020. Robinhood reports more monthly trades than rivals Charles Schwab, E-Trade combined. https://www.cnbc.com/2020/08/10/robinhood-reports-more-monthly-trades-than-rivals-charles-schwab-e-trade-combined.htmlGoogle ScholarGoogle Scholar
  75. Peter Rudegeair. 2021. Robinhood Faces Wrongful-Death Lawsuit Over Young Trader’s Suicide. https://www.wsj.com/articles/robinhood-faces-wrongful-death-lawsuit-over-young-traders-suicide-11612813320?mod=lead_feature_below_a_pos1Google ScholarGoogle Scholar
  76. Andrew Ross Sorkin, Jason Karaian, Michael J. De La Merced, Lauren Hirsch, and Ephrat Livni. 2021. Can Anything Stop GameStop?https://www.nytimes.com/2021/01/27/business/dealbook/reddit-wallstreetbets-gamestop.htmlGoogle ScholarGoogle Scholar
  77. Keith E. Stanovich and Richard F. West. 2000. Individual differences in reasoning: Implications for the rationality debate?Behavioral and Brain Sciences 23, 5 (2000), 645–665. https://doi.org/10.1017/S0140525X00003435Google ScholarGoogle Scholar
  78. Meir Statman. 1987. How Many Stocks Make a Diversified Portfolio?The Journal of Financial and Quantitative Analysis 22, 3 (1987), 353–363. http://www.jstor.org/stable/2330969Google ScholarGoogle Scholar
  79. Barry M Staw and Jerry Ross. 1987. Behavior in escalation situations: Antecedents, prototypes, and solutions.Research in organizational behavior(1987).Google ScholarGoogle Scholar
  80. Christian Sturm, Alice Oh, Sebastian Linxen, Jose Abdelnour Nocera, Susan Dray, and Katharina Reinecke. 2015. How WEIRD is HCI? Extending HCI principles to other countries and cultures. In Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems. 2425–2428.Google ScholarGoogle ScholarDigital LibraryDigital Library
  81. Takumi Tanaka and Hideaki Kawabata. 2020. Interface predictability changes betting behavior in computerized gambling. Computers in Human Behavior 110 (2020), 106387. https://doi.org/10.1016/j.chb.2020.106387Google ScholarGoogle ScholarCross RefCross Ref
  82. Amos Tversky and Daniel Kahneman. 1974. Judgment under uncertainty: Heuristics and biases. science 185, 4157 (1974), 1124–1131.Google ScholarGoogle Scholar
  83. Amos Tversky and Daniel Kahneman. 1981. The framing of decisions and the psychology of choice. science 211, 4481 (1981), 453–458.Google ScholarGoogle Scholar
  84. Amos Tversky and Daniel Kahneman. 1989. Rational choice and the framing of decisions. In Multiple criteria decision making and risk analysis using microcomputers. Springer, 81–126.Google ScholarGoogle Scholar
  85. Danding Wang, Qian Yang, Ashraf Abdul, and Brian Y. Lim. 2019. Designing Theory-Driven User-Centric Explainable AI. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI ’19). Association for Computing Machinery, New York, NY, USA, 1–15. https://doi.org/10.1145/3290605.3300831Google ScholarGoogle ScholarDigital LibraryDigital Library
  86. Ivo Welch. 2020. The Wisdom of the Robinhood Crowd. Working Paper 27866. National Bureau of Economic Research. https://doi.org/10.3386/w27866Google ScholarGoogle Scholar
  87. Alex Wilhelm. 2020. As investing apps boom, Public doubles down on its social focus. https://techcrunch.com/2020/05/07/as-investing-apps-boom-public-doubles-down-on-its-social-focus/Google ScholarGoogle Scholar
  88. Alex Wilhelm. 2020. Robinhood raises $200M more at $11.2B valuation as its revenue scales. https://techcrunch.com/2020/08/17/robinhood-raises-200m-more-at-11-2b-valuation-as-its-revenue-scales/Google ScholarGoogle Scholar
  89. Alex Wilhelm. 2021. Trading app Public drops payment for order flow in favor of tips. https://techcrunch.com/2021/02/01/trading-app-public-drops-payment-for-order-flow-in-favor-of-tips/Google ScholarGoogle Scholar
  90. Richard MS Wilson and Qing Zhang. 1997. Entrapment and escalating commitment in investment decision making: A review. The British Accounting Review 29, 3 (1997), 277–305.Google ScholarGoogle ScholarCross RefCross Ref
  91. Ethan Wolff-Mann. 2021. 28% of Americans bought GameStop or other viral stocks in January: Yahoo Finance-Harris Poll. https://finance.yahoo.com/news/gamestop-amc-reddit-investing-213609595.htmlGoogle ScholarGoogle Scholar
  92. Kristin Wong. 2020. How Financial Apps Get You to Spend More and Question Less. https://www.wired.com/story/financial-apps-investing-dark-patterns/Google ScholarGoogle Scholar
  93. Michael Wursthorn and Euirim Choi. 2020. Does Robinhood Make It Too Easy to Trade? From Free Stocks to Confetti. https://www.wsj.com/articles/confetti-free-stocks-does-robinhoods-design-make-trading-too-easy-11597915801Google ScholarGoogle Scholar
  94. Diyi Yang and Robert E. Kraut. 2017. Persuading Teammates to Give: Systematic versus Heuristic Cues for Soliciting Loans. Proc. ACM Hum.-Comput. Interact. 1, CSCW, Article 114 (Dec. 2017), 21 pages. https://doi.org/10.1145/3134749Google ScholarGoogle ScholarDigital LibraryDigital Library
  95. Viviana A Rotman Zelizer. 1997. The Social Meaning of Money. Princeton University Press.Google ScholarGoogle Scholar
  96. Fuzheng Zhang, Nicholas Jing Yuan, Kai Zheng, Defu Lian, Xing Xie, and Yong Rui. 2015. Mining consumer impulsivity from offline and online behavior. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 1281–1292.Google ScholarGoogle ScholarDigital LibraryDigital Library
  97. John Zimmerman, Jodi Forlizzi, Justin Finkenaur, Sarah Amick, Ji Young Ahn, Nanako Era, and Owen Tong. 2016. Teens, parents, and financial literacy. In Proceedings of the 2016 ACM Conference on Designing Interactive Systems. 312–322.Google ScholarGoogle ScholarDigital LibraryDigital Library
  98. Jason Zweig. 2015. Lessons of May Day 1975 Ring True Today: The Intelligent Investor. https://www.wsj.com/articles/lessons-of-may-day-1975-ring-true-today-the-intelligent-investor-1430450405Google ScholarGoogle Scholar

Recommendations

Comments

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Sign in

Full Access

  • Published in

    cover image ACM Conferences
    DIS '21: Proceedings of the 2021 ACM Designing Interactive Systems Conference
    June 2021
    2082 pages
    ISBN:9781450384766
    DOI:10.1145/3461778

    Copyright © 2021 Owner/Author

    This work is licensed under a Creative Commons Attribution International 4.0 License.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 28 June 2021

    Check for updates

    Qualifiers

    • research-article
    • Research
    • Refereed limited

    Acceptance Rates

    Overall Acceptance Rate1,158of4,684submissions,25%

    Upcoming Conference

    DIS '24
    Designing Interactive Systems Conference
    July 1 - 5, 2024
    IT University of Copenhagen , Denmark

PDF Format

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format .

View HTML Format