Zum Hauptinhalt springen Skip to page footer
sofia logo sofia logo

Algorithm Aversion als Hindernis bei der Etablierung von Robo Advisors

Ibrahim Filiz, Jan René Judek, Marco Lorenz und Markus Spiwoks

sofia Diskussionsbeiträge 2022, No. 2 https://doi.org/10.46850/sofia.9783947850006

Wir untersuchen im Rahmen eines ökonomischen Laborexperimentes, wie hinderlich sich die Algorithm Aversion bei der Etablierung von Robo Advisors auswirkt. Die Probanden müssen Diversifikationsaufgaben bewältigen. Sie können dies selbst tun oder sie können einen Robo Advisor mit dieser Aufgabe betrauen. Der Robo Advisor wertet alle relevanten Daten aus und trifft stets die Entscheidung, die zum höchsten Erwartungswert der Vergütung für den Probanden führt. Obwohl die hohe Leistungsfähigkeit des Robo Advisors offensichtlich ist, vertrauen die Probanden nur in rund 40% aller Entscheidungen dem Robo Advisor. Damit reduzieren sie ihren Erfolg und ihre Vergütung. Viele Probanden orientieren sich an der 1/n-Heuristik, was zu ihren suboptimalen Entscheidungen beiträgt. Sofern die Probanden für andere entscheiden müssen, geben sie sich erkennbar mehr Mühe und sind auch erfolgreicher, als wenn sie für sich selbst entscheiden. Dies wirkt sich jedoch nicht auf die Akzeptanz des Robo Advisors aus. Auch bei Stellvertreter-Entscheidungen wird der Robo Advisor nur in rund 40% der Fälle in Anspruch genommen. Die Neigung der Wirtschaftssubjekte zur Algorithm Aversion steht einer breiten Etablierung von Robo Advisors im Weg.

Access full article

References

  1. Agnew, J., Balduzzi, P. & Sundén, A. (2003), Portfolio Choice and Trading in a Large 401(k) Plan, The American Economic Review, 93(1), 193-215, https://doi.org/10.1257/000282803321455223.
  2. Anderson, A. (2013), Trading and Under-Diversification, Review of Finance, 17(5), 1699–1741, https://doi.org/10.1093/rof/rfs044.
  3. Andersson, O., Holm, H. J. & Tyran, J.-R. & Wengström, E. R. (2022), Deciding for Others Reduces Loss Aversion, Management Science, 62(1), 29-36, https://doi.org/10.1287/mnsc.2014.2085.
  4. Baltussen, G. & Post, G. T. (2011), Irrational Diversification: an Examination of Individual Portfolio Choice, Journal of Financial and Quantitative Analysis, 46(5), 1463-1491, https://doi.org/10.2139/ssrn.625942.
  5. Barber, B. M. &, Odean, T. (2000), Trading is Hazardous to your Wealth: The Common Stock Investment Performance of Individual Investors, Journal of Finance, 55(2), 773-806, https://doi.org/10.2139/ssrn.219228.
  6. Beketov, M., Lehmann, K. & Wittke, M. (2018), Robo Advisors: quantitative methods inside the robots, Journal of Asset Management, 19, 363–370, https://doi.org/10.1057/s41260-018-0092-9.
  7. Benartzi, S. (2001), Excessive Extrapolation and the Allocation of 401(k) Accounts to Company Stock, The Journal of Finance, 56(5), 1747-1764, https://doi.org/10.1111/0022-1082.00388.
  8. Benartzi, S. & Thaler, R. H. (2001), Naïve Diversification Strategies in Defined Contribution Saving Plans, American Economic Review, 91(1), 79-98, https://doi.org/10.1257/aer.91.1.79.
  9. Bhatia, A., Chandani, A. & Chhateja, J. (2020), Robo advisory and its potential in addressing the behavioral biases of investors — A qualitative study in Indian context, Journal of Behavioral and Experimental Finance, 25, https://doi.org/10.1016/j.jbef.2020.100281.
  10. Blume, M. E. & Friend, I. (1975), The Asset Structure of Individual Portfolios and Some Implications for Utility Functions, The Journal of Finance, 30(2), 585-603, https://doi.org/10.1111/j.1540-6261.1975.tb01833.x.
  11. Bode, M., van Echelpoel, A. & Sievi, C. R. (1994), Multinationale Diversifikation: Viel zitiert, kaum befolgt, Die Bank, 94(4), 202-206.
  12. Bolton, G. E., Ockenfels, A. & Stauf, J. (2015), Social responsibility promotes conservative risk behavior, European Economic Review, 74(C), 109-127, https://doi.org/10.1016/j.euroecorev.2014.10.002.
  13. Burton, J., Stein, M. & Jensen, T. (2020), A systematic review of algorithm aversion in augmented decision making, Journal of Behavioral Decision Making, 33(2), 220-239, https://doi.org/10.1002/bdm.2155.
  14. Burns, B. D. (2001), The hot hand in basketball: Fallacy or adaptive thinking, Proceedings of the Annual Meeting of the Cognitive Science Society, 23(23), 152-157.
  15. Castelo, N., Bos, M. W. & Lehmann, D. R. (2019), Task-dependent algorithm aversion, Journal of Marketing Research, 56(5), 809-825, https://doi.org/10.1177/0022243719851788.
  16. Chakravarty, S., Harrison, G. W., Haruvy, E., Rutstrom, E. (2011), Are You Risk Averse over Other People's Money? Southern Economic Journal, 77(4), 901-913, https://doi.org/10.4284/0038-4038-77.4.901.
  17. Charness, G. & Jackson, M. O. (2009), The role of responsibility in strategic risktaking, Journal of Economic Behavior & Organization, 69(3), 241–247, https://doi.org/10.1016/j.jebo.2008.10.006.
  18. Chu, Z., Wang, Z., Xiao, J. J., & Zhang, W. (2017), Financial literacy, portfolio choice and financial well-being, Social Indicators Research, 132(2), 799-820, https://doi.org/10.1007/s11205-016-1309-2.
  19. Cornil, Y., Hardisty, D. J., & Bart, Y. (2019), Easy, breezy, risky: Lay investors fail to diversify because correlated assets feel more fluent and less risky, Organizational Behavior and Human Decision Processes, 153, 103-117, https://doi.org/10.1016/j.obhdp.2019.06.001.
  20. D’Acunto, F., Prabhala, N. & Rossi, A. G. (2019), The Promises and Pitfalls of Robo-Advising, The Review of Financial Studies, 32(5), 1983–2020, https://doi.org/10.1093/rfs/hhz014.
  21. Dietvorst, B. J., Simmons, J. P. & Massey, C. (2018), Overcoming algorithm aversion: People will use imperfect algorithms if they can (even slightly) modify them, Management Science, 64(3), 1155-1170, https://doi.org/10.1287/mnsc.2016.2643.
  22. Dietvorst, B. J., Simmons, J. P. & Massey, C. (2015), Algorithm aversion: People erroneously avoid algorithms after seeing them err, Journal of Experimental Psychology: General, 144(1), 114-126, https://doi.org/10.1037/xge0000033.
  23. Dimmock, S. G., Kouwenberg, R., Mitchell, O. S. & Peijnenburg, K. (2016), Ambiguity Aversion and Household Portfolio Choice Puzzles: Empirical Evidence, Journal of Financial Economics, 119, 559-577, https://doi.org/10.1016/j.jfineco.2016.01.003.
  24. Enke, B., & Zimmermann, F. (2019), Correlation neglect in belief formation, The Review of Economic Studies, 86(1), 313-332.
  25. Eriksen, K. W., Kvaløy, O. & Luzuriaga, M. (2020), Risk-taking on behalf of others, Journal of Behavioral and Experimental Finance, 26(C), 1-13, https://doi.org/10.1016/j.jbef.2020.100283.
  26. Eyster, E. & Weizsäcker, G. (2011), Correlation Neglect in Financial Decision Making, DIW Discussion Papers, No. 1104, Berlin, https://doi.org/10.2139/ssrn.1735339.
  27. Fernandes, D. (2013), The 1/N Rule Revisited: Heterogeneity in the Naïve Diversification Bias, International Journal of Research in Marketing, 30(3), 310- 313, https://doi.org/10.1016/j.ijresmar.2013.04.001.
  28. Filiz, I., Judek, J. R., Lorenz, M. & Spiwoks, M. (2021a), Reducing algorithm aversion through experience, in: Journal of Behavioral and Experimental Finance, 31, 1-8, https://doi.org/10.1016/j.jbef.2021.100524.
  29. Filiz, I., Judek, J. R., Lorenz, M. & Spiwoks, M. (2021b), The Tragedy of Algorithm Aversion, WWP – Wolfsburg Working Papers, Nr. 21-02, Wolfsburg.
  30. Füllbrunn, S. & Luhan, W. J. (2015), Am I My Peer's Keeper? Social Responsibility in Financial Decision Making, Ruhr Economic Paper, No. 551, https://doi.org/10.2139/ssrn.2591379.
  31. Gilovich, T., Vallone, R. & Tversky, A. (1985), The hot hand in basketball: On the misperception of random sequences, Cognitive psychology, 17(3), 295-314, https://doi.org/10.1016/0010-0285(85)90010-6.
  32. Goetzmann, W. N. & Kumar, A. (2008), Equity Portfolio Diversification, Review of Finance, 12(3), 433-463, https://doi.org/10.1093/rof/rfn005.
  33. Gomes, F., Haliassos, M., & Ramadorai, T. (2021), Household finance, Journal of Economic Literature, 59 (3), 919-1000, https://doi.org/10.1257/jel.20201461.
  34. Gubaydullina, Z., Judek, J. R., Lorenz, M. & Spiwoks, M. (2021), Creative Drive and Algorithm Aversion – The Impact of Influence in the Process of Algorithmic Decision-making on Algorithm Aversion, WWP – Wolfsburg Working Papers, Nr. 21-04, Wolfsburg.
  35. Gubaydullina, Z. & Spiwoks, M. (2015), Correlation Neglect, Naïve Diversification, and Irrelevant Information as Stumbling Blocks for Optimal Diversification, Journal of Finance and Investment Analysis, 4(2), 1-19.
  36. Guiso, L., Haliassos, M. & Japelli, T. (2002), Household Portfolios, MIT Press, Cambridge, MA, https://doi.org/10.7551/mitpress/3568.001.0001.
  37. Hedesstrom, T. M., Svedsater, H. & Garling, T. (2006), Covariation Neglect among Novice Investors, Journal of Experimental Psychology-Applied, 12(3), 155-165, https://doi.org/10.1037/1076-898X.12.3.155.
  38. Hibbert, A. M., Lawrence, E. R. & Prakash, A. J. (2012), Can Diversification Be Learned? The Journal of Behavioral Finance, 13(1), 38-50, https://doi.org/10.1080/15427560.2012.654547.
  39. Hinsen, S., Hofmann, P., Jöhnk, J. & Urbach, N. (2022), How Can Organizations Design Purposeful Human-AI Interactions: A Practical Perspective From Existing Use Cases and Interviews, Proceedings of the 55th Hawaii International Conference on System Sciences (HICSS), Honolulu, HI, University of Hawai'i at Manoa, Hamilton Library, https://doi.org/10.24251/HICSS.2022.024.
  40. Huberman, G. & Sengmueller, P. (2004), Performance and Employer Stock in 401(k) Plans, Review of Finance, 8(3), 403-443, https://doi.org/10.1007/s10679-004-2544-y.
  41. Huberman, G., & Jiang, W. (2006), Offering versus choice in 401 (k) plans: Equity exposure and number of funds. The Journal of Finance, 61(2), 763-801, https://doi.org/10.1111/j.1540-6261.2006.00854.x.
  42. Jung, D., Dorner, V., Glaser, F. & Morana, S. (2018), Robo-Advisory - Digitalization and Automation of Financial Advisory, Business & Information Systems Engineering, 60(1), 81-86, https://doi.org/10.1007/s12599-018-0521-9.
  43. Jung, M. & Seiter, M. (2021), Towards a better understanding on mitigating algorithm aversion in forecasting: an experimental study, Journal of Management Control, 32, 495-516, https://doi.org/10.1007/s00187-021-00326-3.
  44. Kallir, I. & Sonsino, D. (2009), The Neglect of Correlation in Allocation Decisions, Southern Economic Journal, 75(4), 1045-1066, https://doi.org/10.1002/j.2325-8012.2009.tb00946.x.
  45. Kawaguchi, K. (2021), When will workers follow an algorithm? A field experiment with a retail business, Management Science, 67(3), 1670-1695, https://doi.org/10.1287/mnsc.2020.3599.
  46. Kim, J., Giroux, M. & Lee, J. C. (2021), When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations, Psychology & Marketing, 38(7), 1140-1155, https://doi.org/10.1002/mar.21498.
  47. Lease, R. C., Lewellen, W. G. & Schlarbaum, G. G. (1974), The Individual Investor: Attributes and Attitudes, The Journal of Finance, 29(2), 413-433, https://doi.org/10.1111/j.1540-6261.1974.tb03055.x.
  48. Mahmud, H., Islam, A. N., Ahmed, S. I., & Smolander, K. (2022), What influences algorithmic decision-making? A systematic literature review on algorithm aversion, Technological Forecasting and Social Change, 175, 121390, 1-26, https://doi.org/10.1016/j.techfore.2021.121390.
  49. Meulbroek, L. (2005), Company Stock in Pension Plans: how costly is it?, The Journal of Law and Economics, 48(2), 443-474, https://doi.org/10.1086/430807.
  50. Morrin, M., Inman, J. J., Broniarczyk, S. M., Nenkov, G. Y. & Reuter, J. (2012), Investing for Retirement: The Moderating Effect of Fund Assortment Size on the 1/N Heuristic, Journal of Marketing Research, 49(4), 537-550, https://doi.org/10.1509/jmr.08.0355.
  51. Pahlke, J., Strasser, S. & Vieider, F. M. (2015), Responsibility effects in decision making under risk, Journal of Risk and Uncertainty, 51(2), 125–146, https://doi.org/10.1007/s11166-015-9223-6.
  52. Pahlke, J., Strasser, S. & Vieider, F. M. (2012), Risk-taking for others under accountability, Economics Letters, 114(1), 102-105, https://doi.org/10.1016/j.econlet.2011.09.037.
  53. Polkovnichenko, V. (2005), Household Portfolio Diversification: a Case for Rankdependent Preferences, Review of Financial Studies, 18, 1467-1502, https://doi.org/10.1093/rfs/hhi033.
  54. Polman, E. (2012), Self–other decision making and loss aversion, Organizational Behavior and Human Decision Processes, 119(2), 141–150, https://doi.org/10.1016/j.obhdp.2012.06.005.
  55. Prahl, A., & Van Swol, L. (2017), Understanding algorithm aversion: When is advice from automation discounted? Journal of Forecasting, 36(6), 691-702, https://doi.org/10.1002/for.2464.
  56. Reynolds, D. B., Joseph, J. & Sherwood, R. (2009), Risky Shift Versus Cautious Shift: Determining Differences In Risk Taking Between Private And Public Management Decision-Making, International Journal of Economics and Business Research, 7(1), 63-78, https://doi.org/10.19030/jber.v7i1.2251.
  57. Roberts, H. V. (1959), Stock market ‘‘patterns’’ and financial analysis: Methodological suggestions, Journal of Finance, 1(14), 1–10, https://doi.org/10.1111/j.1540-6261.1959.tb00481.x.
  58. Rogers, P. (1998), The cognitive psychology of lottery gambling: A theoretical review, Journal of gambling studies , 14(2), 111-134, https://doi.org/10.1023/A:1023042708217.
  59. Rossi, A. G. & Utkus, S. P. (2020), Who Benefits from Robo-advising? Evidence from Machine Learning, SSRN Working Paper, https://ssrn.com/abstract=3552671 or https://doi.org/10.2139/ssrn.3552671  
  60. Rühr, A., Streich, D., Berger, B. & Hess, T. (2019), A Classification of Decision Automation and Delegation in Digital Investment Systems, in: Proceedings of the 52nd Hawaii International Conference on System Sciences, 1435-1444, https://doi.org/10.24251/HICSS.2019.174.
  61. Singh, I. & Kaur, N. (2017), Wealth Management Through Robo Advisory, International Journal of Research - Granthaalayah, 5(6), 33-43, https://doi.org/10.29121/granthaalayah.v5.i6.2017.1991.
  62. Tversky, A., & Kahneman, D. (1971), Belief in the law of small numbers, Psychological bulletin, 76(2), 105-110, https://doi.org/10.1037/h0031322.
  63. Uhl, M. W. & Rohner, P. (2018), Robo-advisors versus traditional investment advisors: An unequal game, The Journal of Wealth Management, 21(1), 44-50, https://doi.org/10.3905/jwm.2018.21.1.044.
  64. Ungeheuer, M., & Weber, M. (2021), The perception of dependence, investment decisions, and stock prices, The Journal of Finance, 76(2), 797-844, https://doi.org/10.1111/jofi.12993.
  65. Vieider, F., Villegas-Palacio, C., Martinsson, P. & Mejía, M. (2016), Risk taking for oneself and others: A structural model approach, Economic Inquiry, 2016, 54(2), 879-894, https://doi.org/10.1111/ecin.12290.
  66. Wärneryd, K.-E. (2001), Stock-market psychology, Cheltenham: Edward Elgar.
  67. Zielonka, P. (2004), Technical analysis as the representation of typical cognitive biases, International Review of Financial Analysis, 13, 217–225, https://doi.org/10.1016/j.irfa.2004.02.007.