Development of Software Architecture and Machine Learning Modules of Robo-Advisor System for Personalized Investment Portfolio Generation

Abstract

We researched how to use financial technology in the finance industry on the example of robo-advisors; defined the basic functionality of a robo-advisor; got the robo-advisors implementation based on analysis of the most popular financial services. We compared their functions, composed a list of critical features and described the high-level architectural design of a general robo-advisor tool, scope of using robo-advisors, their key features, and a brief overview of existing solutions. Using Markowitz model, we set up a concept of using a robo-advisor by investors who have different attitudes towards risks. Our goal is to cover the main features of financial robo-advisor and to describe a high-level architecture for such applications using prediction of financial instruments rates to rebalance investment portfolio. We have defined the main modules that represent the architecture of a typical robo-advisor. We also described different techniques, which could be applied building a personalized investment portfolio. We considered ARIMA models to predict stock prices. The experimental part demonstrates how to use LSTM neural networks and multiple linear regression techniques in the scope of the Robo-Advisor profitability-forecasting module.