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Robo-Advising Risk Profiling through Content Analysis for Sustainable Development in the Hong Kong Financial Market

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  • Mike K. P. So

    (Department of Information Systems, Business Statistics and Operations Management, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China)

Abstract

Nowadays, we mainly depend on financial consultants or advisors to conduct risk assessments for individual investors before providing them with any investment advice or recommendations. Individual investors should understand the risk level of their investment choices and their investment decisions should match their risk profile. This process is usually conducted in face-to-face meetings. However, during the recent coronavirus disease 2019 pandemic, which has seriously impacted daily life with social distancing, in order to maintain sustainability, contact-free advising, such as robo-advising, becomes more important. The aim of this paper was to assess customers’ risk in regards to investment and identify important risk factors needed to profile individual risk preferences, in order to prepare for robo-advising. Inductive content analysis is applied to classify 180 questions from 20 risk assessment questionnaires, sourced from banks and investment service providers, into different types. Then, the number of types is reduced by collapsing similar areas into broader higher order categories (the important risk factors). This paper also makes specific recommendations for the implementation of risk profiling in robo-advising.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:3:p:1306-:d:487702
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    References listed on IDEAS

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    1. John B. Davis & D. W. Hands & Uskali Mäki (ed.), 1998. "The Handbook of Economic Methodology," Books, Edward Elgar Publishing, number 741.
    2. 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.
    3. Cocca, Teodoro, 2016. "Potential and Limitations of Virtual Advice in Wealth Management," Journal of Financial Transformation, Capco Institute, vol. 44, pages 45-57.
    4. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    5. Shanmuganathan, Manchuna, 2020. "Behavioural finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    6. 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.
    7. 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).
    8. Mark A Chen & Qinxi Wu & Baozhong Yang, 2019. "How Valuable Is FinTech Innovation?," The Review of Financial Studies, Society for Financial Studies, vol. 32(5), pages 2062-2106.
    9. Wonbin Ahn & Hee Soo Lee & Hosun Ryou & Kyong Joo Oh, 2020. "Asset Allocation Model for a Robo-Advisor Using the Financial Market Instability Index and Genetic Algorithms," Sustainability, MDPI, vol. 12(3), pages 1-15, January.
    10. 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.
    11. Dominik Jung & Verena Dorner & Christof Weinhardt & Hakan Pusmaz, 2018. "Designing a robo-advisor for risk-averse, low-budget consumers," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(3), pages 367-380, August.
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