Data Analysis of Personalized Investment Decision Making Using Robo-Advisers

TitleData Analysis of Personalized Investment Decision Making Using Robo-Advisers
Publication TypeJournal Article
Year of Publication2020
AuthorsKobets, VM, Yatsenko, VO, Mazur, AYu., Zubrii, MI
Short TitleSci. innov.
DOI10.15407/scine16.02.080
Volume16
Issue2
SectionThe World of Innovations
Pagination80-93
LanguageEnglish
Abstract
Introduction. Nowadays, the problem of the optimal balance between consumption and savings, transformed into investments is solved by using automated systems for making investment decisions, such as robo-advice services which have the mathematical algorithm based on the main principles of consumption-savings theories.
Problem Statement. The task assignment of developed IT service is to maintain a constant level of client’s consumption during life-long period through automated analysis of how much he/she has to consume and save each year. Results of consumption and savings proposals can be modified if initial financial data changes.
Purpose. To develop investment plan of investors’ profiles taking into account their risk preferences using data analysis of robo-adviser service.
Materials and Methods. SWOT-analysis of robo-advice (RA) services and comparative characteristics of robo-advisers explain advantage of RA services. Microservice for calculating stable consumption, finance consulting model of robo-advisor to ensure a constant level of consumption for the client are developed using the following technologies: Python 3.6, Django 2.0, Django Rest framework, AngularJs, HTML5, CSS 3, Bootstrap.
Results. We considered consumption-saving ratio in economics, emerging trends of robo-advice (RA) services for making investment decisions. A mathematical model of robo-advisor in long-run period was developed and the support of investment decision making was described using micro-service of robo-advisor.
Conclusions. The development RA is intended primarily for private persons (investors) who invest in longterm financial instruments in order to provide them with a permanent passive income based on their chosen savings period and the moment of retirement.
Keywordsannuity, data analysis, long life decision making, robo-advisor
References
1. The Rise of Robo-Advice. Changing the Concept of Wealth Management. 
2. Park, J. Y., Ryu, J. P., Shin, H. J. (2016). Robo Advisors for Portfolio Management. Adanced Science and Technology Letters, 141 (Green and Smart Technology II), 104-108.
3. Alós-Ferrer, C., Hügelschäfer, S., Li, J. (2016). Inertia and Decision Making. Front. Psychol., 7, 169.
 4. Park, J. H., Ryu, J. P., Shin, H. J. (2016). Predicting KOSPI Stock Index using Machine Learning Algorithms with Technical Indicators. Journal of Information Technology and Architecture, 13, 331–340.
5. The expansion of Robo-Advisory in Wealth Management - Deloitte. 
6. Jung, D., Dorner, V., Glaser, F., Morana, S. (2018). Robo-Advisory - Digitalization and Automation of Financial Advisory. Business & Information Systems Engineering, 60(1), 81-86.
7. Kohavi, R., Provost, F. (1998). Glossary of terms. Machine Learning - Special Issue on Applications of Machine Learning and the Knowledge Discovery Process. Machine Learning, 30, 271-274.
8. The implications of machine learning in finance. 
9. Lam, J. W. (2016). Robo-Advisers: A Portfolio Management Perspective. Senior Thesis, Yale College. 
10. Faggella, D. Machine Learning in Finance – Present and Future Applications.  
11. Kashner, E. Ghosts in the Robo Advisor Machine.  
12. Markowitz, H. M. (1952). Portfolio Selection. The Journal of Finance. 7(1), 77-91.
13. Fisher, I. (1977). The Theory of interest. Philadelphia: Porcupine Press.
14. Black, F., Litterman, R. (1992). Global Portfolio Optimization. Financial Analysts Journal, 48(5), 28-43.
15. Baker, T., Dellaert, B. (2018). Regulating Robo Advice Across the Financial Services Industry. Faculty Scholarship at Penn Law. 1740. 
16. Betterment Review. 
17. Future Advisor Review. 
18. Thangavelu, P. Motif Investing Broker Review: Easy Thematic Investing. 
19. Motif Investment Review.  
20. Fein, M. L. Robo-Advisors: a Closer Look. 
URL: http://dx.doi.org/10.2139/ssrn.2658701 (Last accessed: 16.12.2018).
21. Robo advising - KPMG.  
22. Kobets, V., Yatsenko, V. (2016). Adjusting business processes by the means of an autoregressive model using BPMN 2.0. CEUR Workshop Proceedings, 1614, 518–533.
URL: CEUR-WS.org/Vol-1614/ICTERI-2016-CEUR-WS-Volume.pdf (Last accessed: 16.12.2018).
23. Kobets, V., Poltoratskiy, M. (2016). Using an Evolutionary Algorithm to Improve Investment Strategies for Industries in an Economic System. CEUR Workshop Proceedings, 1614, 485–501.
URL: CEUR-WS.org/Vol-1614/ICTERI-2016-CEUR-WS-Volume.pdf (Last accessed: 16.12.2018).
24. Snihovyi, O., Ivanov, O., Kobets, V. (2018). Implementation of Robo-Advisors Using Neural Networks for Different Risk Attitude Investment Decisions. 9th International conference on intelligent systems, (25–27 September 2018, Funchal-Madeira, Portugal), Funchal-Madeira, 2018, 332–336.
25. Kobets, V., 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, 144–159.
URL: CEUR-WS.org/Vol-2104/ (Last accessed: 16.12.2018).