Asset Allocation via Machine Learning and Applications to Equity Portfolio Management

Abstract

In this paper, we document a novel machine learning based bottom-up approach for static and dynamic portfolio optimization on, potentially, a large number of assets. The methodology overcomes many major difficulties arising in current optimization schemes. For example, we no longer need to compute the covariance matrix and its inverse for mean-variance optimization, therefore the method is immune from the estimation error on this quantity. Moreover, no explicit calls of optimization routine are needed.

Applications to a bottom-up mean-variance-skewness-kurtosis or CRRA (Constant Relative Risk Aversion) optimization with short-sale portfolio constraints in both simulation and real market (China A-shares and U.S. equity markets) environments are studied and shown to perform very well.

Keywords: Portfolio Optimization, Machine Learning, Hierarchical Clustering, K-Means Clustering, Deep Learning Regression, Mean-Variance-Skewness-Kurtosis, Reinforcement Learning, Monte-Carlo Simulation, Bottom-up Approaches

JEL Classification: C61, C63