ICO RoboAdvisorCoin

Robo-advisors generally use proprietary software to produce market forecasts and securities analyses. They first appeared in the U.S. in theaftermath of the financial crisis of 2008/2009. Following their successful implementation in the U.S., they have been adopted in Europe and Asia, where this new technology hasgrown rapidly to become an increasingly important part of the financial services industry.

At the time of writing, there are only a small number of fully-automatized Robo-Advisors which have been set-upand developed in Europe offering bespoke solutions for the prediction of market trends. The sector evolution can be outlined in four main steps:

Online platforms available to userswhich, to a degree, reduce the extent of human relationships. In this case the human component is still present and the use of the technology is aimed at limiting the interaction between the customer and the consultant. Passive allocation strategies such as ETFs are typically implemented

In addition to helping mitigate human interaction, the Robo-advisor is capable of providing the necessary information for customers to adjust their portfolios as required. Again, the technology involved is mostly, if not totally, concentrated around the provision of a user-friendly web platform that allows customers to follows their portfolios

A new generation of Robo-advisors provide customers (in addition to the above) with the possibility to make use of algorithms and optimise their portfolios and exploit pre-determined rules which are typically the result of the decisions of a human-based investment committee. The technological content in this case goes beyond the simple 11 design of the web platform and allows investors to complement their beliefs and intuitions with the input provided by Robo-based-advice

The last step in the evolution of Robo-advisors consists in the full automatisation of market analysis and financial advice. In this case, the human role is limited to the supervision, improvement and monitoring of the algorithmic predictive system. Unlike the first three steps, AI is heavily utilized for the design and implementation of large-scale ondemand predictive frameworks