Copyright (c) 2023 Eneng Nur Hasanah, Sudarso Kaderi Wiryono, Deddy P. Koesrindartoto (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Financial Robo-Advisor: Learning from Academic Literature
Corresponding Author(s) : Eneng Nur Hasanah
Jurnal Minds: Manajemen Ide dan Inspirasi,
Vol. 10 No. 1 (2023): June
Financial Robo-Advisor is the technology that integrates machine learning and self-identification to determine investment decisions. This study explores the financial robo-advisor based on bibliometric analysis and a systematic literature review. The method used three steps: determining the keyword, bibliometric analysis of literature metadata using VOSviewer, then collecting and analysing the articles. The bibliometric analysis results show five cluster keywords defined with different colors. In the network visualization, the robo-advisor connects to other keywords: investment, fintech, and artificial intelligence. Furthermore, the systematic literature review shows that the articles are divided into seven research objectives: (1) Law, Regulation, and Policy; (2) Investment Literate and Education; (3) Offered Services; (4) Present Risk-Portfolio Matching Technology; (5) Optimal Portfolio Methods; (6) Human-Robo Interaction; (7) Theoretical Design and Gap. Furthermore, this study can be used by academicians and practitioners to find out about robo-advisors based on an academic perspective.
Adam, M., Toutaoui, J., Pfeuffer, N., & Hinz, O. (2020). Investment decisions with robo-advisors: The role of anthropomorphism and personalized anchors in recommendations. 27th European Conference on Information Systems - Information Systems for a Sharing Society, ECIS 2019, 1–18.
Agarwal, S., & Chua, Y. H. (2020). FinTech and household finance: a review of the empirical literature. China Finance Review International, 10(4), 361–376. https://doi.org/10.1108/CFRI-03-2020-0024
Ahn, W., Lee, H. S., Ryou, H., & Oh, K. J. (2020). Asset allocation model for a robo-advisor using the financial market instability index and genetic algorithms. Sustainability (Switzerland), 12(3). https://doi.org/10.3390/su12030849
Atwal, G., & Bryson, D. (2021). Antecedents of intention to adopt artificial intelligence services by consumers in personal financial investing. Strategic Change, 30(3), 293–298. https://doi.org/10.1002/jsc.2412
Au, C.-D., Klingenberger, L., Svoboda, M., & Frère, E. (2021). Business Model of Sustainable Robo-Advisors: Empirical Insights for Practical Implementation. Sustainability (Switzerland), 13(23), 1–12. https://doi.org/10.3390/su132313009
Baek, S., Lee, K. Y., Uctum, M., & Oh, S. H. (2020). Robo-advisors: Machine learning in trend-following ETF investments. Sustainability (Switzerland), 12(16), 1–15. https://doi.org/10.3390/SU12166399
Bataev, A. V., Dedyukhina, N., & Nasrutdinov, M. N. (2020). Innovations in the Financial Sphere: Performance Evaluation of Introducing Service Robots with Artificial Intelligence. ICITM 2020 - 2020 9th International Conference on Industrial Technology and Management, 256–260. https://doi.org/10.1109/ICITM48982.2020.9080379
Bayón, P. S. (2018). A legal framework for robo-advisors. Jusletter IT, (February), 1–14.
Belanche, D., Casaló, L. V., & Flavián, C. (2019). Artificial Intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management and Data Systems, 119(7), 1411–1430. https://doi.org/10.1108/IMDS-08-2018-0368
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., Atiq, R., Mehta, M., & Divekar, R. (2021). Artificial intelligence in financial services: a qualitative research to discover robo-advisory services. Qualitative Research in Financial Markets, 13(5), 632–654. https://doi.org/10.1108/QRFM-10-2020-0199
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, 100281. https://doi.org/10.1016/j.jbef.2020.100281
Bhatia, A., Chandani, A., Divekar, R., Mehta, M., & Vijay, N. (2021). Digital innovation in wealth management landscape: the moderating role of robo advisors in behavioural biases and investment decision-making. International Journal of Innovation Science. https://doi.org/10.1108/IJIS-10-2020-0245
Bi, Q., Tang, J., Van Fleet, B., Nelson, J., Beal, I., Ray, C., & Ossola, A. (2020). Software Architecture for Machine Learning in Personal Financial Planning. 2020 Intermountain Engineering, Technology and Computing, IETC 2020, 3–6. https://doi.org/10.1109/IETC47856.2020.9249171
Boreiko, D., & Massarotti, F. (2020). How Risk Profiles of Investors Affect Robo-Advised Portfolios. Frontiers in Artificial Intelligence, 3, 1–9. https://doi.org/10.3389/frai.2020.00060
Brandl, B., & Hornuf, L. (2020). Where Did FinTechs Come From, and Where Do They Go? The Transformation of the Financial Industry in Germany After Digitalization. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2020.00008
Brenner, L., & Meyll, T. (2020). Robo-advisors: A substitute for human financial advice? Journal of Behavioral and Experimental Finance, 25, 100275. https://doi.org/10.1016/j.jbef.2020.100275
Bruckes, M., Westmattelmann, D., Oldeweme, A., & Schewe, G. (2019). Determinants and barriers of adopting robo-advisory services. 40th International Conference on Information Systems, ICIS 2019.
Brummer, C., & Yadav, Y. (2019). Fintech and the innovation trilemma. Georgetown Law Journal (Vol. 107). https://doi.org/10.2139/ssrn.3054770
Brunen, A. C., & Laubach, O. (2021). Do sustainable consumers prefer socially responsible investments? A study among the users of robo advisors. Journal of Banking and Finance, 136(2021), 106314. https://doi.org/10.1016/j.jbankfin.2021.106314
Buchanan, B. G., & Wright, D. (2021). The impact of machine learning on U.K. financial services. Oxford Review of Economic Policy, 37(3), 537–563. https://doi.org/10.1093/oxrep/grab016
Chen, X., Shujun, Y., & Huang, C. (2021). Cluster-based Mutual Fund Classification and Price Prediction Using Machine Learning for Robo-Advisors. Computational Intelligence and Neuroscience, 2021. https://doi.org/10.1155/2021/4984265
Cheng, Y. M. (2020). Will robo-advisors continue? Roles of task-technology fit, network externalities, gratifications and flow experience in facilitating continuance intention. Kybernetes, 50(6), 1751–1783. https://doi.org/10.1108/K-03-2020-0185
D’Hondt, C., De Winne, R., Ghysels, E., & Raymond, S. (2020). Artificial Intelligence Alter Egos: Who might benefit from robo-investing? Journal of Empirical Finance, 59(October), 278–299. https://doi.org/10.1016/j.jempfin.2020.10.002
Dai, W. (2021). Development and Supervision of Robo-Advisors under Digital Financial Inclusion in Complex Systems. Complexity, 2021, 1–12. https://doi.org/10.1155/2021/6666089
Darskuviene, V., & Lisauskiene, N. (2021). Linking the Robo-advisors Phenomenon and Behavioural Biases in Investment Management: An Interdisciplinary Literature Review and Research Agenda. Organizations and Markets in Emerging Economies. https://doi.org/10.15388/omee.2021.12.65
Das, S. R., Ostrov, D., Radhakrishnan, A., & Srivastav, D. (2020). Dynamic portfolio allocation in goals-based wealth management. Computational Management Science, 17(4), 613–640. https://doi.org/10.1007/s10287-019-00351-7
Day, M.-Y., Cheng, T.-K., & Li, J.-G. (2018). AI Robo-Advisor with Big Data Analytics for Financial Services. In International Conference on Advances in Social Networks Analysis and Mining, ASONAM (pp. 1027–1031). IEEE. https://doi.org/10.1109/ASONAM.2018.8508854
Day, M.-Y., & Lin, J.-T. (2019). Artificial intelligence for ETF market prediction and portfolio optimization. In Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019 (pp. 1026–1033). https://doi.org/10.1145/3341161.3344822
Day, M.-Y., Lin, J.-T., & Chen, Y.-C. (2018). Artificial intelligence for conversational robo-advisor. In International Conference on Advances in Social Networks Analysis and Mining, ASONAM (pp. 1057–1064). IEEE. https://doi.org/10.1109/ASONAM.2018.8508269
Deng, L., Lv, Y., Liu, Y., & Zhao, Y. (2021). Impact of Fintech on Bank Risk-Taking: Evidence from China. Risks, 9(5), 99. https://doi.org/10.3390/risks9050099
Deo, S., & Sontakke, N. (2021). Usability, User Comprehension, and Perceptions of Explanations for Complex Decision Support Systems in Finance: A Robo-Advisory Use Case. Computer, 54(10), 38–48. https://doi.org/10.1109/MC.2021.3076851
Fan, L., & Chatterjee, S. (2020). The Utilization of Robo-Advisors by Individual Investors: An Analysis Using Diffusion of Innovation and Information Search Frameworks. Journal of Financial Counseling and Planning, 31(1), 130–145. https://doi.org/10.1891/JFCP-18-00078
Flavián, C., Pérez-Rueda, A., Belanche, D., & Casaló, L. V. (2021). Intention to use analytical artificial intelligence (A.I.) in services – the effect of technology readiness and awareness. Journal of Service Management, 33(2), 293–320. https://doi.org/10.1108/JOSM-10-2020-0378
Garvia, L. (2018). Towards A Taxonomy Of Nudges, (March).
Garvía, L. (2018). Towards a taxonomy of robo-advisors. Jusletter IT, (2017).
Gerlach, J. M., & Lutz, J. K. T. (2021). Digital financial advice solutions – Evidence on factors affecting the future usage intention and the moderating effect of experience. Journal of Economics and Business, 117(May), 106009. https://doi.org/10.1016/j.jeconbus.2021.106009
Giudici, P. (2018). Fintech risk management: A research challenge for artificial intelligence in finance. Frontiers in Artificial Intelligence, 1. https://doi.org/10.3389/frai.2018.00001
Godwin, A. (2017). Brave new world: Digital disclosure of financial products and services. Capital Markets Law Journal, 11(3), 442–457. https://doi.org/10.1093/cmlj/kmw012
Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the Fintech Revolution: Interpreting the Forces of Innovation, Disruption, and Transformation in Financial Services. Journal of Management Information Systems, 35(1), 220–265. https://doi.org/10.1080/07421222.2018.1440766
Guidici, P., Pagnottoni, P., & Polinesi, G. (2020). Network Models to Enhance Automated Cryptocurrency Portfolio Management. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/frai.2020.00022
Guo, L. (2020). Regulating Investment Robo-Advisors in China: Problems and Prospects. European Business Organization Law Review, 21(1), 69–99. https://doi.org/10.1007/s40804-020-00187-8
Haberly, D., MacDonald-Korth, D., Urban, M., & Wójcik, D. (2019). Asset Management as a Digital Platform Industry: A Global Financial Network Perspective. Geoforum, 106(March), 167–181. https://doi.org/10.1016/j.geoforum.2019.08.009
Hildebrand, C., & Bergner, A. (2020). Conversational robo advisors as surrogates of trust: onboarding experience, firm perception, and consumer financial decision making. Journal of the Academy of Marketing Science, 49(4), 659–676. https://doi.org/10.1007/s11747-020-00753-z
Hodge, F. D., Mendoza, K. I., & Sinha, R. K. (2020). The Effect of Humanizing Robo-Advisors on Investor Judgments*. Contemporary Accounting Research, 38(1), 770–792. https://doi.org/10.1111/1911-3846.12641
Horn, M., & Oehler, A. (2020). Automated portfolio rebalancing: Automatic erosion of investment performance? Journal of Asset Management, 21(6), 489–505. https://doi.org/10.1057/s41260-020-00183-0
Ivanov, O., Snihovyi, O., & Kobets, V. (2018). Implementation of Robo-Advisors Using Neural Networks for Different Risk Attitude Investment Decisions. 9th International Conference on Intelligent Systems 2018: Theory, Research and Innovation in Applications, I.S. 2018 - Proceedings, (May), 332–336. https://doi.org/10.1109/IS.2018.8710559
Jiang, T. (2021). Using machine learning to analyze merger activity. Frontiers in Applied Mathematics and Statistics, 7. https://doi.org/10.3389/fams.2021.649501
Jung, D., Dorner, V., Glaser, F., & Morana, S. (2018). Robo-Advisory: Digitalization and Automation of Financial Advisory. Business and Information Systems Engineering, 60(1), 81–86. https://doi.org/10.1007/s12599-018-0521-9
Jung, D., Erdfelder, E., & Glaser, F. (2018). Nudged to win: Designing robo-advisory to overcome decision inertia. In Twenty-Sixth European Conference on Information Systems (ECIS2018).
Jung, D., Glaser, F., & Köpplin, W. (2019). Robo-advisory: Opportunities and risks for the future of financial advisory. Contributions to Management Science, (January), 405–427. https://doi.org/10.1007/978-3-319-95999-3_20
Jung, D., & Weinhardt, C. (2018). Robo-advisors and financial decision inertia: How choice architecture helps to reduce inertia in financial planning tools. International Conference on Information Systems 2018, ICIS 2018, 1–17.
Kabulova, J., & Stankevičienė, J. (2020). Valuation of fintech innovation based on patent applications. Sustainability (Switzerland), 12(23), 1–14. https://doi.org/10.3390/su122310158
Karkkainen, T., Panos, G. A., Broby, D., & Bracciali, A. (2018). On the Educational Curriculum in Finance and Technology. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10750 LNCS(January), 7–20. https://doi.org/10.1007/978-3-319-77547-0_1
Kim, W. C., Kwon, D. G., Lee, Y., Kim, J. H., & Lin, C. (2019). Personalized goal-based investing via multi-stage stochastic goal programming. Quantitative Finance, 20(3), 515–526. https://doi.org/10.1080/14697688.2019.1662079
Kobets, V., Yatsenko, V. O., Mazur, A. Y., & Zubrii, M. I. (2020). Data Analysis of Personalized Investment Decision Making Using Robo-Advisers. Nauka Ta Innovacii, 16(2), 87–100. https://doi.org/10.15407/scin16.02.087
Kobets, Vitaliy, Yatsenko, V., Mazur, A., & Zubrii, M. (2018). Data analysis of private investment decision making using tools of Robo-advisers in long-run period. CEUR Workshop Proceedings, 2104(May), 144–159
Lee, J. (2020). Access to Finance for Artificial Intelligence Regulation in the Financial Services Industry. European Business Organization Law Review, 21(4), 731–757. https://doi.org/10.1007/s40804-020-00200-0
Lee, K. Y., Kwon, H. Y., & Lim, J. I. (2018). Legal Consideration on the Use of Artificial Intelligence Technology and Self-regulation in Financial Sector: Focused on Robo-Advisors. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10763 LNCS). Springer International Publishing. https://doi.org/10.1007/978-3-319-93563-8_27
Leow, E. K. W., Nguyen, B. P., & Chua, M. C. H. (2021). Robo-advisor using genetic algorithm and BERT sentiments from tweets for hybrid portfolio optimisation. Expert Systems with Applications, 179(April). https://doi.org/10.1016/j.eswa.2021.115060
Lewis, D. R. (2018a). Computers may not make mistakes but many consumers do. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10923 LNCS). Springer International Publishing. https://doi.org/10.1007/978-3-319-91716-0_28
Lewis, D. R. (2018b). The perils of overconfidence: Why many consumers fail to seek advice when they really should. Journal of Financial Services Marketing, 23(2), 104–111. https://doi.org/10.1057/s41264-018-0048-7
Li, J. Bin, & Chen, C. Y. (2019). Practice of a Two-Stage Model Using Support Vector Regression and Black-Litterman for ETF Portfolio Selection. Proceedings - 2019 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2019, 18–19. https://doi.org/10.1109/ISPACS48206.2019.8986236
Lightbourne, J. (2017). Algorithms & Fiduciaries: Existing and proposed regulatory approaches to artificially intelligent financial Planners. Duke Law Journal, 67(3), 651–679.
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(September 2019), 101573. https://doi.org/10.1016/j.socec.2020.101573
Liu, C. yong. (2018). Legal risks and the countermeasures of developing intelligent investment advisor in China. Advances in Intelligent Systems and Computing, 722, 76–82. https://doi.org/10.1007/978-3-319-73888-8_13
Liu, R. (2020). Research on Financial Risks of Robo-Advisor Platforms. E3S Web of Conferences, 218, 1–5. https://doi.org/10.1051/e3sconf/202021801035
Lourenço, C. J. S., Dellaert, B. G. C., & Donkers, B. (2020). Whose Algorithm Says So: The Relationships Between Type of Firm, Perceptions of Trust and Expertise, and the Acceptance of Financial Robo-Advice. Journal of Interactive Marketing, 49, 107–124. https://doi.org/10.1016/j.intmar.2019.10.003
Lu, B., Hao, S., Pinedo, M., & Xu, Y. (2021). Frontiers in Service Science: Fintech Operations-An Overview of Recent Developments and Future Research Directions. Service Science, 13(1). https://doi.org/10.1287/SERV.2021.0270
Łyczkowska-Hanćkowiak, A. (2020). On Application Oriented Fuzzy Numbers for Imprecise Investment Recommendations. Symmetry, 12(10), 1672. https://doi.org/10.3390/sym12101672
Mehrotra, A. (2019). Artificial Intelligence in Financial Services - Need to Blend Automation with Human Touch. In 2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019 (pp. 342–347). IEEE. https://doi.org/10.1109/ICACTM.2019.8776741
Mehrotra, A., & Menon, S. (2021). Second Round of FinTech - Trends and Chall. In Proceedings of 2nd International Conference on Computation, Automation and Knowledge Management, ICCAKM 2021 (pp. 243–248). https://doi.org/10.1109/ICCAKM50778.2021.9357759
Méndez-Suárez, M., García-Fernández, F., & Gallardo, F. (2019). Artificial intelligence modelling framework for financial automated advising in the copper market. Journal of Open Innovation: Technology, Market, and Complexity, 5(4), 1–13. https://doi.org/10.3390/joitmc5040081
Misina, S., & Latvia, R. (2019). Financial Web Calculators. In 2019 60th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS) (pp. 1–4). IEEE.
North, R. (2020). Sensitivity validation of a fuzzy system for asset allocation. AIMS Electronics and Electrical Engineering, 4(2), 169–187. https://doi.org/10.3934/ElectrEng.2020.2.169
Oehler, A., Horn, M., & Wendt, S. (2021). Investor Characteristics and their Impact on the Decision to use a Robo-advisor. Journal of Financial Services Research. https://doi.org/10.1007/s10693-021-00367-8
Park, J. Y., Ryu, J. P., & Shin, H. J. (2017). How to manage portfolio by robo-advisor. Information, 20(5), 3463–3470.
Paul, L. R., & Sadath, L. (2021). A systematic analysis on fintech and its applications. Proceedings of International Conference on Innovative Practices in Technology and Management, ICIPTM 2021, 131–136. https://doi.org/10.1109/ICIPTM52218.2021.9388371
Potdar, A., & Pande, M. (2020). Comprehensive Analysis of Machine Learning Algorithms Used in Robo-Advisory Services. Journal of Physics: Conference Series, 1964(6), 062105. https://doi.org/10.1088/1742-6596/1964/6/062105
Rasiwala, F. S., & Kohli, B. (2021). Artificial Intelligence in Fintech: Understanding stakeholders perception on innovation, disruption, and transformation in finance. International Journal of Business Intelligence Research, 12(1), 48–65. https://doi.org/10.4018/IJBIR.20210101.oa3
Riasanow, T., Flötgen, R. J., Setzke, D. S., Böhm, M., & Krcmar, H. (2018). The generic ecosystem and innovation patterns of the digital transformation in the financial industry. Proceedings of the 22nd Pacific Asia Conference on Information Systems - Opportunities and Challenges for the Digitized Society: Are We Ready?, PACIS 2018, (June).
Ruishi, J., & Shujun, Y. (2020). The development status and legal supervision of robo-advisors under financial technology. Proceedings - 2020 International Conference on Big Data Economy and Information Management, BDEIM 2020, 70–73. https://doi.org/10.1109/BDEIM52318.2020.00025
Sa, J. H., Lee, K. B., Cho, S. I., Lee, S. H., & Gim, G. Y. (2018). A study on the influence of personality factors on intention to use of Robo-advisor. Journal of Engineering and Applied Sciences. https://doi.org/10.3923/jeasci.2018.7795.7802
Sabharwal, C. L., & Anjum, B. (2018). Robo-revolution in the financial sector. Proceedings - 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018, 1289–1292. https://doi.org/10.1109/CSCI46756.2018.00249
Salo, M., & Haapio, H. (2017). ROBO-advisors and investors: Enhancing human-robot interaction through information design. Jusletter IT, (February), 1–8. https://doi.org/10.2139/ssrn.2937821
Seiler, V., & Fanenbruck, K. M. (2021). Acceptance of digital investment solutions: The case of robo advisory in Germany. Research in International Business and Finance, 58(June), 101490. https://doi.org/10.1016/j.ribaf.2021.101490
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, 100297. https://doi.org/10.1016/j.jbef.2020.100297
Sironi, P. (2016). FinTech Innovation: From Robo-Advisors to Goal Based Investing and Gamification. John Wiley & Sons, Ltd.
Snihovyi, O., Ivanov, O., & Kobets, V. (2018). Implementation of Robo-Advisors Using Neural Networks for Different Risk Attitude Investment Decisions. In 2018 International Conference on Intelligent Systems (I.S.) (pp. 332–336).
Snihovyi, O., Kobets, V., & Ivanov, O. (2019). Implementation of Robo-Advisor Services for Different Risk Attitude Investment Decisions Using Machine Learning Techniques. Communications in Computer and Information Science (Vol. 1007). Springer International Publishing. https://doi.org/10.1007/978-3-030-13929-2_15
So, M. K. P. (2021). Robo-advising risk profiling through content analysis for sustainable development in the Hong Kong financial market. Sustainability (Switzerland), 13(3), 1–15. https://doi.org/10.3390/su13031306
Tan, G. K. S. (2020). Robo-advisors and the financialization of lay investors. Geoforum, 117(August), 46–60. https://doi.org/10.1016/j.geoforum.2020.09.004
Tao, R., Su, C.-W., Xiao, Y., Dai, K., & Khalid, F. (2021). Robo advisors, algorithmic trading and investment management: Wonders of fourth industrial revolution in financial markets. Technological Forecasting and Social Change, 163, 120421. https://doi.org/10.1016/j.techfore.2020.120421
Tokic, D. (2018). BlackRock Robo-Advisor 4.0: When artificial intelligence replaces human discretion. Strategic Change, 27(4), 285–290. https://doi.org/10.1002/jsc.2201
Torno, A., & Schildmann, S. (2020). What Do Robo-Advisors Recommend? - An Analysis of Portfolio Structure, Performance and Risk. Lecture Notes in Business Information Processing (Vol. 401). Springer International Publishing. https://doi.org/10.1007/978-3-030-64466-6_6
Tubadji, A., Denney, T., & Webber, D. J. (2021). Cultural relativity in consumers’ rates of adoption of artificial intelligence. Economic Inquiry, 59(3), 1234–1251. https://doi.org/10.1111/ecin.12978
Turner, J. A., & Klein, B. W. (2021). Turner & Klein (2021) Improving on Defaults_Helping Pension Participants Manage Financial Market Risk in Target Date Funds.pdf. Risks, 9(4), 1–14. https://doi.org/10.3390/risks9040079
Tyukhova, E., & Sizykh, D. (2019). The cluster analysis method as an instrument for selection of securities in the construction of an investment portfolio. In Proceedings of 2019 12th International Conference &quot;Management of Large-Scale System Development&quot;, MLSD 2019. IEEE. https://doi.org/10.1109/MLSD.2019.8910991
Waliszewski, K., & Warchlewska, A. (2020). Attitudes towards artificial intelligence in the area of personal financial planning: A case study of selected countries. Entrepreneurship and Sustainability Issues, 8(2), 399–420. https://doi.org/10.9770/jesi.2020.8.2(24)
Wang, P. Y., Liu, C. S., Yang, Y. C., & Huang, S. H. (2019). A Robo-Advisor Design using Multiobjective RankNets with Gated Neural Network Structure. In Proceedings - 2019 IEEE International Conference on Agents, ICA 2019 (pp. 77–78). IEEE. https://doi.org/10.1109/AGENTS.2019.8929188
Wexler, M. N., & Oberlander, J. (2020). Robo-advisors (R.A.s): the programmed self-service market for professional advice. Journal of Service Theory and Practice, 31(3), 351–365. https://doi.org/10.1108/JSTP-07-2020-0153
Xiang, Y., Li, Z., Lee, T. H., Tang, D., Wu, K., Lei, Z., & Wang, Y. (2019). Smart Wealth Management System for Robo-Advisory. In CIFEr 2019 - IEEE Conference on Computational Intelligence for Financial Engineering and Economics. IEEE. https://doi.org/10.1109/CIFEr.2019.8759063
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
Xue, J., Zhu, E., Liu, Q., Wang, C., & Yin, J. (2018). A Joint Approach to Data Clustering and Robo-Advisor. In International Conference on Cloud Computing and Security (Vol. 1, pp. 97–109). Springer International Publishing. https://doi.org/10.1007/978-3-030-00006-6_9
Xue, J., Zhu, E., Liu, Q., & Yin, J. (2018). Group recommendation based on financial social network for robo-advisor. In IEEE Access (Vol. 6, pp. 54527–54535). IEEE. https://doi.org/10.1109/ACCESS.2018.2871131
Xue, R., Gepp, A., O’Neill, T. J., Stern, S., & Vanstone, B. J. (2020). Financial literacy and financial strategies: The mediating role of financial concerns. Australian Journal of Management, 46(3), 437–465. https://doi.org/10.1177/031289622094076
Zhang, L., Pentina, I., & Fan, Y. (2021). Who do you choose? Comparing perceptions of human vs robo-advisor in the context of financial services. Journal of Services Marketing, 35(5), 634–646. https://doi.org/10.1108/JSM-05-2020-0162