AI Robo-Advisor with Big Data Analytics for Financial Services

Robo-Advisors has been growing attraction from the financial industry for offering financial services by using algorithms and acting as like human advisors to support investors making investment decisions. During the investment planning stage, portfolio optimization plays a crucial role, especially for the medium and long-term investors, in determining the allocation weight of assets to achieve the balance between investors expectation return and risk tolerance. The literature on the topic of portfolio optimization has been offering plenty of theoretical and practical guidance for implementing the theory; however, there is a paucity of studies focusing on the applications which are designed for Robo-Advisors. In this research, we proposed a modular system and focused on integrating big data analysis, deep learning method and the Black-Litterman model to generate asset allocation weight. We developed a portfolio optimization module which takes the information from a variety of sources, such as stocks prices, investor profile and the other alternative data, and used them as input to calculate optimal weights of assets in the portfolio. The module we developed could be used as a sub-system for Robe-Advisors, which offers a customized optimal portfolio based on investors preference.