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Robo Advisors: quantitative methods inside the robots

Author

Listed:
  • Mikhail Beketov

    (Deloitte GmbH)

  • Kevin Lehmann

    (Deloitte GmbH)

  • Manuel Wittke

    (Deloitte GmbH)

Abstract

Robo Advisors (RAs) are perhaps the most important disruptive trend in wealth and asset management today. There is an immense amount of information about RA systems, but still little is known about the core portfolio optimization and asset allocation methods applied within such systems. Thus, to date, there is no comprehensive analysis of the methods used in RAs, their occurrences in these systems, the respective volumes of assets under management (AuM), and the future methodological prospects of the RAs. We analyzed 219 existing RAs worldwide and showed that Modern Portfolio Theory remains the main framework used in RAs. The current trend is to improve and augment this framework rather than applying and developing entirely new approaches. However, we also revealed that the AuM volumes tend to be higher for the systems applying newer and more sophisticated methods. In general, there is a clear gap between the predominant methods applied in RAs and new methodological developments. In the future, as the RA services mature, we can expect that the RAs system will adopt many of the new approaches since they promise good performance and have certain marketing potential.

Suggested Citation

  • 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.
  • Handle: RePEc:pal:assmgt:v:19:y:2018:i:6:d:10.1057_s41260-018-0092-9
    DOI: 10.1057/s41260-018-0092-9
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    References listed on IDEAS

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    Cited by:

    1. Nourallah, Mustafa, 2023. "One size does not fit all: Young retail investors’ initial trust in financial robo-advisors," Journal of Business Research, Elsevier, vol. 156(C).
    2. Bartram, Söhnke & Branke, Jürgen & Motahari, Mehrshad, 2020. "Artificial Intelligence in Asset Management," CEPR Discussion Papers 14525, C.E.P.R. Discussion Papers.
    3. Agostino Capponi & Sveinn Olafsson & Thaleia Zariphopoulou, 2019. "Personalized Robo-Advising: Enhancing Investment through Client Interaction," Papers 1911.01391, arXiv.org, revised Nov 2020.
    4. 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).
    5. Yongjae Lee & Woo Chang Kim & Jang Ho Kim, 2020. "Achieving Portfolio Diversification for Individuals with Low Financial Sustainability," Sustainability, MDPI, vol. 12(17), pages 1-16, August.
    6. Cardillo, Giovanni & Chiappini, Helen, 2024. "Robo-advisors: A systematic literature review," Finance Research Letters, Elsevier, vol. 62(PA).
    7. Bernd Scherer & Sebastian Lehner, 2023. "Trust me, I am a Robo-advisor," Journal of Asset Management, Palgrave Macmillan, vol. 24(2), pages 85-96, March.
    8. Ko, Hyungjin & Byun, Junyoung & Lee, Jaewook, 2023. "A privacy-preserving robo-advisory system with the Black-Litterman portfolio model: A new framework and insights into investor behavior," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 89(C).
    9. 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.
    10. Xusen Cheng & Fei Guo & Jin Chen & Kejiang Li & Yihui Zhang & Peng Gao, 2019. "Exploring the Trust Influencing Mechanism of Robo-Advisor Service: A Mixed Method Approach," Sustainability, MDPI, vol. 11(18), pages 1-20, September.
    11. Haoran Wang & Shi Yu, 2021. "Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning," Papers 2105.09264, arXiv.org.
    12. Tao, Ran & Su, Chi-Wei & Xiao, Yidong & Dai, Ke & Khalid, Fahad, 2021. "Robo advisors, algorithmic trading and investment management: Wonders of fourth industrial revolution in financial markets," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    13. 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.
    14. 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).
    15. Bai, Zefeng, 2021. "Does robo-advisory help reduce the likelihood of carrying a credit card debt? Evidence from an instrumental variable approach," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    16. Krzysztof Waliszewski & Anna Warchlewska, 2020. "Socio-Demographic Factors Determining Expectation Experienced while Using Modern Technologies in Personal Financial Management (PFM and robo-advice): A Polish Case," European Research Studies Journal, European Research Studies Journal, vol. 0(Special 2), pages 893-904.
    17. Gechun Liang & Moris S. Strub & Yuwei Wang, 2021. "Predictable Forward Performance Processes: Infrequent Evaluation and Applications to Human-Machine Interactions," Papers 2110.08900, arXiv.org, revised Dec 2023.
    18. Keffert, Henk, 2024. "Robo-advising: Optimal investment with mismeasured and unstable risk preferences," European Journal of Operational Research, Elsevier, vol. 315(1), pages 378-392.
    19. Mike K. P. So, 2021. "Robo-Advising Risk Profiling through Content Analysis for Sustainable Development in the Hong Kong Financial Market," Sustainability, MDPI, vol. 13(3), pages 1-15, January.
    20. Tan Zi Yi & Noor Ashikin Mohd Rom & Nurbani Md. Hassan & Mohamad Shaharudin Samsurijan & Andrew Ebekozien, 2023. "The Adoption of Robo-Advisory among Millennials in the 21st Century: Trust, Usability and Knowledge Perception," Sustainability, MDPI, vol. 15(7), pages 1-16, March.

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