IDEAS home Printed from https://ideas.repec.org/a/eee/beexfi/v31y2021ics221463502100068x.html
   My bibliography  Save this article

Reducing algorithm aversion through experience

Author

Listed:
  • Filiz, Ibrahim
  • Judek, Jan René
  • Lorenz, Marco
  • Spiwoks, Markus

Abstract

In the context of an experiment, we examine the persistence of aversion towards algorithms in relation to learning processes. The subjects of the experiment are asked to make one share price forecast (rising or falling) in each of 40 rounds. A forecasting computer (algorithm) is available to them which has a success rate of 70%. Intuitive forecasts made by the subjects usually lead to a significantly poorer success rate. Feedback provided after each round of forecasts and a clear financial incentive lead to the subjects becoming better able to estimate their own forecasting abilities. At the same time, their aversion to algorithms also decreases significantly.

Suggested Citation

  • Filiz, Ibrahim & Judek, Jan René & Lorenz, Marco & Spiwoks, Markus, 2021. "Reducing algorithm aversion through experience," Journal of Behavioral and Experimental Finance, Elsevier, vol. 31(C).
    • Handle: RePEc:eee:beexfi:v:31:y:2021:i:c:s221463502100068x
    DOI: 10.1016/j.jbef.2021.100524
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S221463502100068X
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jbef.2021.100524?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. Dimmock, Stephen G. & Kouwenberg, Roy & Mitchell, Olivia S. & Peijnenburg, Kim, 2016. "Ambiguity aversion and household portfolio choice puzzles: Empirical evidence," Journal of Financial Economics, Elsevier, vol. 119(3), pages 559-577.
    2. Filiz, Ibrahim & Nahmer, Thomas & Spiwoks, Markus, 2019. "Herd behavior and mood: An experimental study on the forecasting of share prices," Journal of Behavioral and Experimental Finance, Elsevier, vol. 24(C).
    3. William N. Goetzmann & Alok Kumar, 2008. "Equity Portfolio Diversification," Review of Finance, European Finance Association, vol. 12(3), pages 433-463.
    4. 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.
    5. Efendić, Emir & Van de Calseyde, Philippe P.F.M. & Evans, Anthony M., 2020. "Slow response times undermine trust in algorithmic (but not human) predictions," Organizational Behavior and Human Decision Processes, Elsevier, vol. 157(C), pages 103-114.
    6. Andrey Kudryavtsev & Gil Cohen & Shlomit Hon-Snir, 2013. "“Rational” or “Intuitive”: Are Behavioral Biases Correlated Across Stock Market Investors?," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 7(2), June.
    7. Andrew Prahl & Lyn Van Swol, 2017. "Understanding algorithm aversion: When is advice from automation discounted?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(6), pages 691-702, September.
    8. Bhatia, Ankita & Chandani, Arti & Chhateja, Jagriti, 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, Elsevier, vol. 25(C).
    9. Brad M. Barber & Terrance Odean, 2000. "Trading Is Hazardous to Your Wealth: The Common Stock Investment Performance of Individual Investors," Journal of Finance, American Finance Association, vol. 55(2), pages 773-806, April.
    10. Proeger, Till & Meub, Lukas, 2014. "Overconfidence as a social bias: Experimental evidence," Economics Letters, Elsevier, vol. 122(2), pages 203-207.
    11. Meub, Lukas & Proeger, Till & Bizer, Kilian & Spiwoks, Markus, 2015. "Strategic coordination in forecasting – An experimental study," Finance Research Letters, Elsevier, vol. 13(C), pages 155-162.
    12. Anders Anderson, 2013. "Trading and Under-Diversification," Review of Finance, European Finance Association, vol. 17(5), pages 1699-1741.
    13. Brad M. Barber & Terrance Odean, 2001. "Boys will be Boys: Gender, Overconfidence, and Common Stock Investment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 116(1), pages 261-292.
    14. Berkeley J. Dietvorst & Joseph P. Simmons & Cade Massey, 2018. "Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them," Management Science, INFORMS, vol. 64(3), pages 1155-1170, March.
    15. Mikhail Beketov & Kevin Lehmann & Manuel Wittke, 2018. "Robo Advisors: quantitative methods inside the robots," Journal of Asset Management, Palgrave Macmillan, vol. 19(6), pages 363-370, October.
    16. Otwin Becker & Johannes Leitner & Ulrike Leopold-Wildburger, 2009. "Expectation formation and regime switches," Experimental Economics, Springer;Economic Science Association, vol. 12(3), pages 350-364, September.
    17. Harry V. Roberts, 1959. "Stock‐Market “Patterns” And Financial Analysis: Methodological Suggestions," Journal of Finance, American Finance Association, vol. 14(1), pages 1-10, March.
    18. Zielonka, Piotr, 2004. "Technical analysis as the representation of typical cognitive biases," International Review of Financial Analysis, Elsevier, vol. 13(2), pages 217-225.
    19. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
    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. Ivanova-Stenzel, Radosveta & Tolksdorf, Michel, 2023. "Measuring Preferences for Algorithms - Are people really algorithm averse after seeing the algorithm perform?," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage" 277692, Verein für Socialpolitik / German Economic Association.
    2. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    3. Chugunova, Marina & Sele, Daniela, 2022. "We and It: An interdisciplinary review of the experimental evidence on how humans interact with machines," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 99(C).
    4. Jan René Judek, 2024. "Willingness to Use Algorithms Varies with Social Information on Weak vs. Strong Adoption: An Experimental Study on Algorithm Aversion," FinTech, MDPI, vol. 3(1), pages 1-11, January.
    5. Te Bao & Elizaveta Nekrasova & Tibor Neugebauer & Yohanes E. Riyanto, 2022. "Algorithmic trading in experimental markets with human traders: A literature survey," Chapters, in: Sascha Füllbrunn & Ernan Haruvy (ed.), Handbook of Experimental Finance, chapter 23, pages 302-322, Edward Elgar Publishing.
    6. Alexia GAUDEUL & Caterina GIANNETTI, 2023. "Trade-offs in the design of financial algorithms," Discussion Papers 2023/288, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    7. Zulia Gubaydullina & Jan René Judek & Marco Lorenz & Markus Spiwoks, 2022. "Comparing Different Kinds of Influence on an Algorithm in Its Forecasting Process and Their Impact on Algorithm Aversion," Businesses, MDPI, vol. 2(4), pages 1-23, October.
    8. Sutton, Steve G. & Arnold, Vicky & Holt, Matthew, 2023. "An extension of the theory of technology dominance: Capturing the underlying causal complexity," International Journal of Accounting Information Systems, Elsevier, vol. 50(C).

    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. Zulia Gubaydullina & Jan René Judek & Marco Lorenz & Markus Spiwoks, 2022. "Comparing Different Kinds of Influence on an Algorithm in Its Forecasting Process and Their Impact on Algorithm Aversion," Businesses, MDPI, vol. 2(4), pages 1-23, October.
    2. Filiz, Ibrahim & Nahmer, Thomas & Spiwoks, Markus & Gubaydullina, Zulia, 2020. "Measurement of risk preference," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    3. Ibrahim Filiz & Thomas Nahmer & Markus Spiwoks & Kilian Bizer, 2018. "Portfolio diversification: the influence of herding, status-quo bias, and the gambler’s fallacy," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(2), pages 167-205, May.
    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. Alexia GAUDEUL & Caterina GIANNETTI, 2023. "Trade-offs in the design of financial algorithms," Discussion Papers 2023/288, Dipartimento di Economia e Management (DEM), University of Pisa, Pisa, Italy.
    6. Merkle, Christoph, 2017. "Financial overconfidence over time: Foresight, hindsight, and insight of investors," Journal of Banking & Finance, Elsevier, vol. 84(C), pages 68-87.
    7. Ibrahim Filiz & Jan René Judek & Marco Lorenz & Markus Spiwoks, 2022. "Algorithm Aversion as an Obstacle in the Establishment of Robo Advisors," JRFM, MDPI, vol. 15(8), pages 1-25, August.
    8. Daniel Barth, 2018. "The Costs and Beliefs Implied by Direct Stock Ownership," Management Science, INFORMS, vol. 64(11), pages 5263-5288, November.
    9. Xie, Yuxin & Tang, Ruohua & Pantelous, Athanasios A. & Lu, Xiaomeng, 2024. "Narrow framing and under-diversification: Empirical evidence from Chinese households," China Economic Review, Elsevier, vol. 83(C).
    10. Migliavacca, Milena & Goodell, John W. & Paltrinieri, Andrea, 2023. "A bibliometric review of portfolio diversification literature," International Review of Financial Analysis, Elsevier, vol. 90(C).
    11. Kristjan Liivamägi, 2015. "Investor Education and Portfolio Diversification on the Stock Market," Research in Economics and Business: Central and Eastern Europe, Tallinn School of Economics and Business Administration, Tallinn University of Technology, vol. 7(1).
    12. Camille Magron & Maxime Merli, 2012. "Stocks repurchase and sophistication of individual investors," Working Papers of LaRGE Research Center 2012-02, Laboratoire de Recherche en Gestion et Economie (LaRGE), Université de Strasbourg.
    13. 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).
    14. Mahmud, Hasan & Islam, A.K.M. Najmul & Ahmed, Syed Ishtiaque & Smolander, Kari, 2022. "What influences algorithmic decision-making? A systematic literature review on algorithm aversion," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    15. Wang, Albert Y. & Young, Michael, 2023. "Mood, attention, and household trading: Evidence from terrorist attacks," Journal of Financial Markets, Elsevier, vol. 66(C).
    16. Zoran Ivković & Scott Weisbenner, 2005. "Local Does as Local Is: Information Content of the Geography of Individual Investors' Common Stock Investments," Journal of Finance, American Finance Association, vol. 60(1), pages 267-306, February.
    17. Lu, Xiaomeng & Guo, Jiaojiao & Gan, Li, 2020. "International comparison of household asset allocation: Micro-evidence from cross-country comparisons," Emerging Markets Review, Elsevier, vol. 43(C).
    18. Juhani T. Linnainmaa, 2011. "Why Do (Some) Households Trade So Much?," The Review of Financial Studies, Society for Financial Studies, vol. 24(5), pages 1630-1666.
    19. Francisco Gomes & Michael Haliassos & Tarun Ramadorai, 2021. "Household Finance," Journal of Economic Literature, American Economic Association, vol. 59(3), pages 919-1000, September.
    20. Pikulina, Elena & Renneboog, Luc & Ter Horst, Jenke & Tobler, Philippe N., 2014. "Bonus schemes and trading activity," Journal of Corporate Finance, Elsevier, vol. 29(C), pages 369-389.

    More about this item

    Keywords

    Algorithm aversion; Overconfidence; Operating experience; Stock market forecasting; Behavioral finance; Experiments;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • D84 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Expectations; Speculations
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

    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:beexfi:v:31:y:2021:i:c:s221463502100068x. 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: https://www.journals.elsevier.com/journal-of-behavioral-and-experimental-finance .

    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.