Risk profiling question investigation for robo-advisor
Abstract
Purpose – this study aims to thoroughly investigate by reviewing previous literature on risk assessment queries for robo-advisors, comparing it with three existing robo-advisors and proposing suitable risk assessment questions for robo-advisor.
Research methodology – utilize the deductive content analysis technique to examine the risk assessment issue for financial robo-advisors, which is influenced by previous study.
Findings – there are nine questions share a similar context both in previous literature and among existing robo-advisors, with income being the most commonly used question. Then, there are three questions that are only asked by the existing robo-advisors: emergency funds, home ownership, and the source of transaction. These findings suggest some additional questions to enhance the effectiveness of risk assessment in robo-advisory services for individuals.
Research limitations – only two previous research papers have focused on risk profiling, and three available applications used in this research.
Practical implications – the robo-advisor’s developer should take into account various factors such as local culture and economic conditions, financial product knowledge, etc. when crafting diverse risk profiles to provide more precise investment recommendations.
Originality/Value – the study is the first research which explore the risk profiling for financial robo-advisor, which used by existing robo-advisor then compared to other countries in the world.
Keyword : robo advisor, risk profiling, fintech, literature, content analysis
How to Cite
Hasanah, E. N., Wiryono, S. K., & Koesrindartoto, D. P. (2024). Risk profiling question investigation for robo-advisor. Business, Management and Economics Engineering, 22(2), 382–400. https://doi.org/10.3846/bmee.2024.21182
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Shanmuganathan, M. (2020). Behavioural finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions. Journal of Behavioral and Experimental Finance, 27, Article 100297. https://doi.org/10.1016/j.jbef.2020.100297
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Alemanni, B., Angelovski, A., di Cagno, D. T., Galliera, A., Linciano, N., Marazzi, F., & Soccorso, P. (2020). Do investors rely on robots? Evidence from an experimental study. CONSOB Fintech Series, 7, 1–61. https://doi.org/10.2139/ssrn.3697232
Bayón, P. S. (2018). A legal framework for robo-advisors. In E. Schweighofer, F. Kummer, A. Saarenpää, & B. Schafer (Eds.), Data protection/legaltech: Proceedings of the 21st international legal informatics symposium IRIS 2018 (pp. 311–318). Editions Weblaw, Bern, Switzerland. SSRN. https://ssrn.com/abstract=3226644
Beltramini, E. (2018). Human vulnerability and robo-advisory: An application of Coeckelbergh’s vulnerability to the machine-human interface. Baltic Journal of Management, 13(2), 250–263. https://doi.org/10.1108/BJM-10-2017-0315
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, Article 100281. https://doi.org/10.1016/j.jbef.2020.100281
Brenner, L., & Meyll, T. (2020). Robo-advisors: A substitute for human financial advice? Journal of Behavioral and Experimental Finance, 25, Article 100275. https://doi.org/10.1016/j.jbef.2020.100275
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
Chang, Y., & Wang, R. (2023). Conservatives endorse Fintech? Individual regulatory focus attenuates the algorithm aversion effects in automated wealth management. Computers in Human Behavior, 148, Article 107872. https://doi.org/10.1016/j.chb.2023.107872
Day, M.-Y., Cheng, T.-K., & Li, J.-G. (2018, August 28–31). AI robo-advisor with big data analytics for financial services. In Proceedings of International Conference on Advances in Social Networks Analysis and Mining, (ASONAM) (pp.1027–1031). Barselona, Spain. IEEE. https://doi.org/10.1109/ASONAM.2018.8508854
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
Efendić, E., Van de Calseyde, P. P. F. M., & Evans, A. M. (2020). Slow response times undermine trust in algorithmic (but not human) predictions. Organizational Behavior and Human Decision Processes, 157, 103–114. https://doi.org/10.1016/j.obhdp.2020.01.008
Elo, S., & Kyngäs, H. (2007). The qualitative content analysis process. Journal of Advanced Nursing, 62(1), 107–115. https://doi.org/10.1111/j.1365-2648.2007.04569.x
Filiz, I., Judek, J. R., Lorenz, M., & Spiwoks, M. (2021). Reducing algorithm aversion through experience. Journal of Behavioral and Experimental Finance, 31, Article 100524. https://doi.org/10.1016/j.jbef.2021.100524
Filiz, I., Judek, J. R., Lorenz, M., & Spiwoks, M. (2022). Algorithm aversion as an obstacle in the establishment of robo advisors. Journal of Risk and Financial Management, 15(8), Article 353. https://doi.org/10.3390/jrfm15080353
Financial Services Authority of Indonesia. (2020). Strategi Nasional Literasi Keuangan Indonesia (SNLKI) 2021–2025 [National Strategy on Indonesian financial literacy].
Graneheim, U. H., Lindgren, B. M., & Lundman, B. (2017). Methodological challenges in qualitative content analysis: A discussion paper. Nurse Education Today, 56, 29–34. https://doi.org/10.1016/j.nedt.2017.06.002
Harwood, T. G., & Garry, T. (2003). An overview of content analysis. The Marketing Review, 3(4), 479–498. https://doi.org/10.1362/146934703771910080
Hasanah, E. N., Wiryono, S. K., & Koesrindartoto, D. P. (2023). Financial robo-advisor: Learning from academic literature. Jurnal Minds: Manajemen Ide Dan Inspirasi, 10(1), 17–40. https://doi.org/10.24252/minds.v10i1.33428
Hodge, F. D., Mendoza, K. I., & Sinha, R. K. (2021). The effect of humanizing robo‐advisors on investor judgments*. Contemporary Accounting Research, 38(1), 770–792. https://doi.org/10.1111/1911-3846.12641
Jianakoplos, N. A., & Bernasek, A. (2006). Financial risk taking by age and birth cohort. Southern Economic Journal, 72(4), 981–1001. https://doi.org/10.1002/j.2325-8012.2006.tb00749.x
Jung, D., Dorner, V., Weinhardt, C., & Pusmaz, H. (2018). Designing a robo-advisor for risk-averse, low-budget consumers. Electronic Markets, 28(3), 367–380. https://doi.org/10.1007/s12525-017-0279-9
Jung, D., Glaser, F., & Köpplin, W. (2019). Robo-advisory: Opportunities and risks for the future of financial advisory. In V. Nissen (Ed.), Advances in consulting research. Contributions to Management Science (pp. 405–427). https://doi.org/10.1007/978-3-319-95999-3_20
Jung, M., & Seiter, M. (2021). Towards a better understanding on mitigating algorithm aversion in forecasting: An experimental study. Journal of Management Control, 32(4), 495–516. https://doi.org/10.1007/s00187-021-00326-3
Karataş, M., & Cutright, K. M. (2023). Thinking about God increases acceptance of artificial intelligence in decision-making. Proceedings of the National Academy of Sciences, 120(33), Article e2218961120. https://doi.org/10.1073/pnas.2218961120
Krippendorff, K. (2004). Content analysis an introduction to its methodology (2nd ed.). Sage.
Litterscheidt, R., & Streich, D. J. (2020). Financial education and digital asset management: What’s in the black box? Journal of Behavioral and Experimental Economics, 87, Article 101573. https://doi.org/10.1016/j.socec.2020.101573
Mandal, B., & Roe, B. E. (2007). Risk tolerance and its relation to important life events. SSRN. https://doi.org/10.2139/ssrn.985314
Morgan, D. L. (1993). Qualitative content analysis: A guide to paths not taken. Qualitative Health Research, 3(1), 112–121.
Neuendorf, K. A. (2017). The content analysis guidebook (2nd ed.). Sage.
Niszczota, P., & Kaszás, D. (2020). Robo-investment aversion. PLoS ONE, 15(9), Article e0239277. https://doi.org/10.1371/journal.pone.0239277
Rastogi, S., Sharma, A., Pinto, G., & Bhimavarapu, V. M. (2022). A literature review of risk, regulation, and profitability of banks using a scientometric study. Future Business Journal, 8(1), Article 28. https://doi.org/10.1186/s43093-022-00146-4
Rühr, A., Streich, D., Berger, B., & Hess, T. (2019, January 8–11). A classification of decision automation and delegation in digital investment management systems. In Proceedings of the 52nd Hawaii International Conference on System Sciences (pp. 1435–1444). Hawaii, USA. ScholarSpace. https://doi.org/10.24251/HICSS.2019.174
Schreier, M. (2012). Qualitative content analysis in practice. SAGE Publications. https://doi.org/10.4135/9781529682571
Shanmuganathan, M. (2020). Behavioural finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions. Journal of Behavioral and Experimental Finance, 27, Article 100297. https://doi.org/10.1016/j.jbef.2020.100297
So, M. K. P. (2021). Robo-advising risk profiling through content analysis for sustainable development in the Hong Kong financial market. Sustainability, 13(3), Article 1306. https://doi.org/10.3390/su13031306
Tertilt, M., & Scholz, P. (2018). To advise, or not to advise – How robo-advisors evaluate the risk preferences of private investors. Journal of Wealth Management, 21(2), 70–84. https://doi.org/10.3905/jwm.2018.21.2.070
United States General Accounting Office (GAO). (1996). Content analysis a methodology for structuring and analyzing written material. The Office.
Weber, R. P. (1990). Basic content analysis. Sage.
Xue, J., Liu, Q., Li, M., Liu, X., Ye, Y., Wang, S., & Yin, J. (2018). Incremental multiple kernel extreme learning machine and its application in robo-advisors. Soft Computing, 22(11), 3507–3517. https://doi.org/10.1007/s00500-018-3031-2