Implementation of Robo-Advisors Using Neural Networks for Different Risk Attitude Investment Decisions

Abstract

This paper is a sequel to our previous works related to Robo-advisors and cryptocurrencies. Our goal now is to build two application modules for a single Robo-advisor. The first module is a Long short-term memory (LSTM) neural network which forecasts cryptocurrencies prices daily. The second module uses Robo-advising approach to build an investment plan for novice cryptocurrencies investors with different risk attitude investment decisions. The third module does ETL (Extract-Transform-Load) for a statistics dataset and neural networks models. Results of the investigation show that investing in cryptocurrencies can give 23.7% per year for risk-averse, 31.8% per year for risk-seeking investors and 16.5% annually for riskneutral investors.