Portfolio design and management through state-based analytics: A probabilistic approach

Abstract

This paper presents an innovative new approach to investment portfolio design, which applies a discrete, state-based methodology to defining market states and making asset allocation decisions with respect to both current and future state membership. State membership is based on attributes taken from traditional finance and portfolio theory namely expected growth, and covariance. The transitional dynamics of the derived states are modeled as a Markovian process. Asset weighting and portfolio allocation decisions are made through an optimization-based approach coupled with heuristics that account for the probability of state membership and the quality of the state in terms of information provided.