Software Architecture for Machine Learning in Personal Financial Planning

Abstract

Trials in the automated investment management, or Robo-advisor, industry have increased with the introduction of newer data analysis tools and technologies. This has resulted in new methods, variables, and ideations being considered for optimal predictive analysis in the stock, bond, and cryptocurrency markets. Large data sets used in conjunction with machine learning are telling and predictive for different points in time. Our research attempts to define a model that can be utilized by financial advisors to theorize future asset predictability.