Data Analysis of Personalized Investment Decision Making Using Robo-Advisers

1Kobets, VM
2Yatsenko, VO
1Mazur, AYu.
1Zubrii, MI
1Kherson State University
2Taras Shevchenko National University of Kyiv
Sci. innov. 2020, 16(2):80-93
Section: The World of Innovations
Language: English
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.
Keywords: annuity, data analysis, long life decision making, robo-advisor