IDEAS home Printed from https://ideas.repec.org/a/gam/jfinte/v3y2024i1p7-115d1332493.html
   My bibliography  Save this article

Robo Advising and Investor Profiling

Author

Listed:
  • Raquel M. Gaspar

    (ISEG, Universidade de Lisboa and REM/Cemapre Research Center, 1200-781 Lisboa, Portugal)

  • Madalena Oliveira

    (AXCO Consulting, 4150-174 Porto, Portugal)

Abstract

The rise of digital technology and artificial intelligence has led to a significant change in the way financial services are delivered. One such development is the emergence of robo advising, which is an automated investment advisory service that utilizes algorithms to provide investment advice and portfolio management to investors. Robo advisors gather information about clients’ preferences, financial situations, and future goals through questionnaires. Subsequently, they recommend ETF-based portfolios tailored to match the investor’s risk profile. However, these questionnaires often appear vague, and robo advisors seldom disclose the methodologies employed for investor profiling or asset allocation. This study aims to contribute by introducing an investor profiling method relying solely on investors’ relative risk aversion (RRA), which, in addition, allows for the determination of optimal allocations. We also show that, for the period under analysis and using the same ETF universe, our RRA portfolios consistently outperform those recommended by the Riskalyze platform, which may suffer from ultraconservadorism in terms of the proposed volatility.

Suggested Citation

  • Raquel M. Gaspar & Madalena Oliveira, 2024. "Robo Advising and Investor Profiling," FinTech, MDPI, vol. 3(1), pages 1-14, February.
    • Handle: RePEc:gam:jfinte:v:3:y:2024:i:1:p:7-115:d:1332493
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2674-1032/3/1/7/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2674-1032/3/1/7/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zakamouline, Valeri & Koekebakker, Steen, 2009. "Portfolio performance evaluation with generalized Sharpe ratios: Beyond the mean and variance," Journal of Banking & Finance, Elsevier, vol. 33(7), pages 1242-1254, July.
    2. 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.
    3. Agostino Capponi & Sveinn Ólafsson & Thaleia Zariphopoulou, 2022. "Personalized Robo-Advising: Enhancing Investment Through Client Interaction," Management Science, INFORMS, vol. 68(4), pages 2485-2512, April.
    4. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    5. Donald Meyer & Jack Meyer, 2005. "Relative Risk Aversion: What Do We Know?," Journal of Risk and Uncertainty, Springer, vol. 31(3), pages 243-262, December.
    6. Muhammad Anshari & Mohammad Nabil Almunawar & Masairol Masri, 2022. "Digital Twin: Financial Technology’s Next Frontier of Robo-Advisor," JRFM, MDPI, vol. 15(4), pages 1-9, April.
    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. Potì, Valerio & Wang, DengLi, 2010. "The coskewness puzzle," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1827-1838, August.
    2. Cette, Gilbert & Devillard, Aurélien & Spiezia, Vincenzo, 2021. "The contribution of robots to productivity growth in 30 OECD countries over 1975–2019," Economics Letters, Elsevier, vol. 200(C).
    3. Bloom, Nicholas & Hassan, Tarek Alexander & Kalyani, Aakash & Lerner, Josh & Tahoun, Ahmed, 2021. "The diffusion of disruptive technologies," LSE Research Online Documents on Economics 113870, London School of Economics and Political Science, LSE Library.
    4. Dennis C. Hutschenreiter & Tommaso Santini & Eugenia Vella, 2022. "Automation and sectoral reallocation," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 13(1), pages 335-362, May.
    5. Basso, Henrique S. & Jimeno, Juan F., 2021. "From secular stagnation to robocalypse? Implications of demographic and technological changes," Journal of Monetary Economics, Elsevier, vol. 117(C), pages 833-847.
    6. Piketty, Thomas & Bozio, Antoine & Garbinti, Bertrand & Goupille-Lebret, Jonathan & Guillot, Malka, 2020. "Predistribution vs. Redistribution: Evidence from France and the U.S," CEPR Discussion Papers 15415, C.E.P.R. Discussion Papers.
    7. Uwe JIRJAHN & Stephen C. SMITH, 2018. "Nonunion Employee Representation: Theory And The German Experience With Mandated Works Councils," Annals of Public and Cooperative Economics, Wiley Blackwell, vol. 89(1), pages 201-233, March.
    8. Federico Huneeus & Kory Kroft & Kevin Lim, 2021. "Earnings Inequality in Production Networks," NBER Working Papers 28424, National Bureau of Economic Research, Inc.
    9. Marco Manacorda & Guido Tabellini & Andrea Tesei, 2022. "Mobile Internet and the Rise of Political Tribalism in Europe," Working Papers 941, Queen Mary University of London, School of Economics and Finance.
    10. Ufuk Akcigit & Sina T. Ates, 2023. "What Happened to US Business Dynamism?," Journal of Political Economy, University of Chicago Press, vol. 131(8), pages 2059-2124.
    11. Lütkenhorst, Wilfried, 2018. "Creating wealth without labour? Emerging contours of a new techno-economic landscape," IDOS Discussion Papers 11/2018, German Institute of Development and Sustainability (IDOS).
    12. Carbonero, Francesco. & Ernst, Ekkehard & Weber, Enzo., 2018. "Robots worldwide the impact of automation on employment and trade," ILO Working Papers 995008793402676, International Labour Organization.
    13. Joshua Greenstein, 2020. "The Precariat Class Structure and Income Inequality among US Workers: 1980–2018," Review of Radical Political Economics, Union for Radical Political Economics, vol. 52(3), pages 447-469, September.
    14. Daniele Angelini, 2023. "Aging Population and Technology Adoption," Working Paper Series of the Department of Economics, University of Konstanz 2023-01, Department of Economics, University of Konstanz.
    15. Renaud Bourlès & Dominique Henriet, 2012. "Risk-sharing Contracts with Asymmetric Information," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 37(1), pages 27-56, March.
    16. Greg Howard & Carl Liebersohn, 2019. "What Explains U.S. House Prices? Regional Income Divergence," 2019 Meeting Papers 1054, Society for Economic Dynamics.
    17. Xuan Zhang, 2023. "The impact of digital finance on corporate labor productivity: evidence from Chinese-listed companies," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 50(3), pages 527-550, September.
    18. Erik Brynjolfsson & Catherine Buffington & Nathan Goldschlag & J. Frank Li & Javier Miranda & Robert Seamans, 2023. "The Characteristics and Geographic Distribution of Robot Hubs in U.S. Manufacturing Establishments," Working Papers 23-14, Center for Economic Studies, U.S. Census Bureau.
    19. Czarnitzki, Dirk & Fernández, Gastón P. & Rammer, Christian, 2023. "Artificial intelligence and firm-level productivity," Journal of Economic Behavior & Organization, Elsevier, vol. 211(C), pages 188-205.
    20. Siliang Tong & Nan Jia & Xueming Luo & Zheng Fang, 2021. "The Janus face of artificial intelligence feedback: Deployment versus disclosure effects on employee performance," Strategic Management Journal, Wiley Blackwell, vol. 42(9), pages 1600-1631, September.

    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:gam:jfinte:v:3:y:2024:i:1:p:7-115:d:1332493. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.