Strategic Bayesian Asset Allocation

TL;DR

This paper introduces a Bayesian regularization approach for strategic asset allocation, optimizing stock selection and portfolio weights to maximize risk-adjusted alpha returns relative to benchmarks.

Contribution

It extends existing methods by incorporating investor preferences and regularization penalties into Bayesian portfolio optimization, tailored with sparse MCMC algorithms.

Findings

Effective stock selection from SP100 and hedge fund holdings.

Improved risk-adjusted returns demonstrated in case studies.

Framework adaptable for future research directions.

Abstract

Strategic asset allocation requires an investor to select stocks from a given basket of assets. The perspective of our investor is to maximize risk-adjusted alpha returns relative to a benchmark index. Historical returns are used to provide inputs into an optimization algorithm. Our approach uses Bayesian regularization to not only provide stock selection but also optimal sequential portfolio weights. By incorporating investor preferences with a number of different regularization penalties we extend the approaches of Black (1992) and Puelz (2015). We tailor standard sparse MCMC algorithms to calculate portfolio weights and perform selection. We illustrate our methodology on stock selection from the SP100 stock index and from the top fifty holdings of two hedge funds Renaissance Technologies and Viking Global. Finally, we conclude with directions for future research.