Asset Allocation with Predictive Full-Scale Optimization

Abstract

Full-Scale Optimization (FSO) is a framework of portfolio construction that directly maximizes expected utility over a sample of historical returns. The existing formulation of FSO is based merely on the empirical distribution of returns, which can lead to poor out-of-sample performance when the return distribution is time-varying. In this paper, we propose a framework of portfolio construction, Predictive Full-Scale Optimization (PFSO), which combines FSO and distributional prediction. PFSO is flexible enough to incorporate investors' risk appetite and perspectives on future return distribution. Also, we propose a novel continuous optimization algorithm for FSO that rapidly converges to optimal solutions under hierarchical budget constraints. We perform numerical experiments on real-world portfolio data and demonstrate the effectiveness of our proposed method.